UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO
PROGRAMA DE DOCTORADO EN CIENCIAS BIOMÉDICAS
CENTRO DE CIENCIAS GENÓMICAS
ANÁLISIS DE LA REGULACIÓN DE LA EXPRESIÓN GÉNICA DE PHASEOLUS
VULGARIS A NIVEL TRANSCRIPCIONAL Y POST-TRANSCRIPCIONAL,
ENFOCADO EN EL SPLICING ALTERNATIVO
TESIS
PARA OPTAR POR EL GRADO DE:
DOCTOR EN CIENCIAS
PRESENTA:
LUIS PEDRO IÑIGUEZ RÁBAGO
DRA. GEORGINA HERNÁNDEZ DELGADO
CENTRO DE CIENCIAS GENÓMICAS, UNAM
DR. ENRIQUE MERINO PÉREZ
INSTITUTO DE BIOTECNOLOGÍA, UNAM
DR. CEI ABREU GOODGER
LABORATORIO NACIONAL DE GENÓMICA PARA LA BIODIVERSIDAD,
CINVESTAV
CUERNAVACA, MORELOS, MÉXICO, NOVIEMBRE 2017
 
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Quiero dedicar esta tesis
a mi familia, a mis amigos
y todos lo que hicieron esto posible
III
Agradecimientos
Primero que nada quisiera agradecerle a Gina, la Dra. Georgina Hernández, por haberme
recibido en su laboratorio, por confiar en mı́ y mandarme a Minnesota a colaborar en un
proyecto del cual tenı́amos poca idea de cómo abordarlo. Gracias también por ser mi tutora
en la tesis de licenciatura y aceptarme para realizar mis estudios doctorales en un proyecto
bioinformático, del cual ambos sabı́amos muy poco pero querı́amos aprender mucho. Pero
sobre todo muchı́simas gracias por permitirme conocer en ti a una mujer tan admirable y
aprender tanto de ti.
Muchas gracias a todos los miembros del laboratorio: Vı́ctor, Maru, Sara y Alfonso; por
recibirme y permitirme formar parte del grupo. Quisiera agradecerle también a Marlene por
ser mi confidente y escucharme tanto en mis buenos como en mis malos momentos y a Mario
Ramı́rez porque siempre que a lo lejos se escuchaba una voz ronca cantando una canción
yo sabı́a que se avecinaba una buena plática sobre las Chivas, la Selección Mexicana o el
Real Madrid. Gracias también a los miembros que formaron parte del laboratorio y que aho-
ra forjan su camino en otros lados; Bárbara, Ana Belén, Sole y Sujay. Y también quisiera
agradecerles a los nuevos miembros del grupo; Mario Serrano, Marta, Damien, Alexandre
Tromas y Jose. Ustedes llegaron en el momento justo para reencender la curiosidad en mı́.
De Mario y Marta aprendı́ a no caer en el conformismo y buscar siempre trascender basándo-
se en un grupo sólido y confiable. Gracias también a Damien por las pláticas cientı́ficas y las
discusiones filosóficas dentro y fuera del laboratorio.
Estos logros también se deben a los diversos colaboradores de este proyecto. Scott Jack-
son y Brad Barbazuk que fomentaron en mi las ideas de evolución y conservación de distintos
aspectos, esto cambió en mı́ la forma de ver la ciencia. Por otro lado Mario Ramı́rez, con
quien hice una buena mancuerna entre la parte experimental, de su parte, y la parte bioin-
formática, de la mı́a, para concretar diversos proyectos. En este apartado no puedo olvidar
al Dr. Federico Sánchez, quien fue un gran profesor para mı́ y fue el primero en confiar
en mis conocimientos y enseñarme el amor por la ciencia. Quisiera prometerle que seguiré
intentando transmitir mi pasión por la ciencia como él lo hizo conmigo.
IV
Un punto fundamental ha sido mi familia y mi esposa principalmente. A Dany quisiera
agradecerle el aguantarme de tantos años de estudio, alejado de ella, y los sermones moti-
vacionales cuando los necesitaba para seguir adelante. Gracias también por mantenerse a mi
lado, querer formar una familia conmigo y seguirme en mis aventuras, ya sea hacia el post-
doc o hacia la vacación. De ti he aprendido muchı́simas cosas que he aplicado en esta etapa
de mi vida como el no darme por vencido, luchar por lo que uno desea y que siempre habrá
pretextos para darse por vencidos pero queda en nosotros sobrepasarlos.
Gracias a mi mamá por ser comprensiva conmigo, por ayudarme y apoyarme en todo
momento. Sé que sin tu ayuda nunca lo hubiera logrado. Gracias por darme una gran familia,
ser la columna de ella, por enseñarme a adaptarme a todos los entornos y resolver todo aquello
que tiene solución, que tú llamas problema. Papá mil gracias por impulsarme a trabajar con
algo que aunque tenı́a poco experiencia tenı́a gran potencial. Gracias también por inculcar
en mı́ tu “orden? de las cosas y por la perseverancia para no dejar inconcluso un proyecto.
Gracias también a mi hermano por siempre cubrirme las espaldas y por las pláticas eternas y
necias sobre temas que los demás consideran irrelevantes. Agus: tú me has plantado muchas
más dudas que posibles respuestas y no me deja de sorprender tu valentı́a.
Mi familia va más allá de mis padres y hermano y no quisiera dejarlos afuera de estos
agradecimientos ya que gracias a ellos hice esto posible. En especial quisiera agradecer a
mi abuela adoptiva, mi tı́a Alicia, ya que ella no me dejó rendirme y siempre me impulsó
a terminar esta etapa. Tı́a siempre has estado a mi lado y hemos compartido nuestra pasión
por la ciencia. Me has enseñado a defenderme y que la familia es lo más importante. Gracias
a toda mi familia que hacen que cada semana santa o navidad sea un motivo de alegrı́a y
diversión.
No quisiera dejar de agradecerles a mis amigos de toda la vida; Alain, Raúl, Alex, Elı́as
y Marco. Gracias por aguantarme, mantenerme como su amigo y siempre lograr sacar de mı́
las risas, incluso en los momentos más tristes. Gracias también a mis compañeras y amigas
del alma; Heid, Karen, Majo y Mafe porque con su cariño y su linda forma de ser me han
apoyado en todo momento.
V
Gracias también a todo el equipo de los Bioperros; Checho, Caborca, Braulio, Chuy, Rafa
y Ricardo, por motivarme a la competencia y generar una distracción fuera de los laboratorios.
Ahora más que nunca vamos a la ?palapa? por una y ya, para festejar.
A Mitzy, Victor, Jazmin, Querétaro, Agus y Mario quisiera agradecerles por las pláticas
exhaustivas sobre cualquier tema. Es increı́ble poder platicar con ustedes sobre lo que sea
y discutirlo a fondo. Nunca olvidaré las maratónicas noches de dominó, papelitos o UNO.
Orlando, gracias por llegar a ser parte del grupo, por adaptarte a lo que ya era la quinta
y por compartir tantos gustos conmigo. Chiapas, sabes que te considero un gran amigo y
te agradezco por todos esos momentos reflexivos donde tu forma de pensar cambiaba la
perspectiva del tema, sólo tengo 2 palabras que decirte. Lauri y Daniela, muchas gracias por
ser mis compañeras no sólo en la licenciatura, también en el doctorado. Gracias por todas
esas tardes de Iker con pláticas que se extendı́an hasta la madrugada. Gracias porque juntos
formamos un muy buen equipo y nos logramos comprender juntos. Estoy seguro de que esto
no hubiera sido posible sin que estuvieran a mi lado.
Por último quisiera agradecerles a los demás que han hecho esto posible. Sra. Carolina;
muchı́simas gracias por permitirme hacer de su casa mi hogar los últimos 10 años. A Ian y
Val por ser mis coaches en el crossfit y además de darme un hobby, enseñarme a no rendirme,
a todos los que me faltaron y al Centro de Ciencias Genómicas y a la UNAM por darme la
formación necesaria tanto en la licenciatura como en el doctorado para, espero, trascender en
la vida y en la ciencia.
México, Pumas, Universidad
Goya, goya, cachun cachun ra ra, cachun cahun ra ra, goya, UNIVERSIDAD.
VI
Abstract
The adequate transcription of the genes leads an organism to adapt to its environment and
to develop itself in an appropriate way. This process is known as gene expression. Gene ex-
pression is regulated by different factors at different levels such as transcriptional regulation,
which depends, for example, on the binding sites of transcription factors in the promoter re-
gions of the genes. Another example may be post-transcriptional regulation that affects gene
expression at the RNA level. The post-transcriptional regulation considers the interaction of
other molecules independent of the messenger RNA as miRNA’s to inhibit its translation or
the proper messenger RNA can undergo several changes in its structure to generate multiple
proteins. The latter process is known as Alternative Splicing. The different regulatory me-
chanisms orchestrate a series of processes that direct the cell to establish different patterns
of gene expression dependent on the cellular environment. In this work different aspects of
the regulation of gene expression, especially Alternative Splicing, are addressed, all this was
studied using the legume Phaseolus vulgaris, better known as common bean, as a model
specie.
VII
Resumen
La adecuada transcripción de los genes conlleva a un organismo a adaptarse a su entorno
y desarrollarse de forma apropiada. A este proceso se le conoce como expresión génica. La
expresión génica está regulada por distintos factores a distintos niveles como puede ser una
regulación transcripcional, que depende por ejemplo de los sitios de unión de factores de
transcripción en las regiones promotoras de los genes. Otro ejemplo puede ser la regula-
ción post-transcripcional que afecta la expresión génica a nivel de RNA. La regulación post-
transcripcional considera la interacción de otras moléculas independientes del RNA mensa-
jero como miRNA’s para inhibir su traducción o el mensajero de RNA puede sufrir diversos
cambios en su estructura para generar múltiples proteı́nas. A este último proceso se le conoce
como Splicing Alternativo. Los diferentes mecanismos de regulación orquestan una serie de
procesos que dirigen a la célula a establecer diferentes patrones de expresión génica depen-
dientes del entorno celular. En este trabajo se abordan distintos aspectos de la regulación de
la expresión génica en especial el Splicing Alternativo, todo esto se estudió utilizando a la
leguminosa Phaseolus vulgaris, mejor conocida como frijol, como modelo.
VIII
Índice
Agradecimientos IV
Abstract VII
Resumen VIII
Índice IX
1. Introducción 1
2. Objetivos 10
3. Perfiles Transcripcionales 11
3.1. Perfil transcripcional de nódulos en estrés oxidativo . . . . . . . . . . . . . . 11
3.1.1. Introducción . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1.2. Resultados y Discusión . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2. Perfil transcripcional de nódulos de frijol en simbiosis con R. etli expresando
la hemoglobina de Vitroescilla, en condiciones de estrés oxidativo . . . . . . 15
3.2.1. Introducción . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.2. Resultados y Discusión . . . . . . . . . . . . . . . . . . . . . . . . . 17
4. Control Transcripcional y Post-Transcripcional 20
4.1. Identificación de pequeños RNAs de Phaseolus vulgaris . . . . . . . . . . . 20
4.1.1. Introducción . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.1.2. Resultados y Discusión . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2. El nodo miRNA172c-AP2 como regulador de la fijación de nitrógeno sim-
biótica en la relación P. vulgaris-Rhizobium etli . . . . . . . . . . . . . . . . 23
4.2.1. Introducción . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2.2. Resultados y Discusión . . . . . . . . . . . . . . . . . . . . . . . . . 24
5. Splicing Alternativo 32
5.1. AS en Phaseolus vulgaris y Glycine max . . . . . . . . . . . . . . . . . . . . 32
5.1.1. Introducción . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
5.1.2. Resultados y Discusión . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2. El Papel del Splicing Alternativo en la Duplicación Génica . . . . . . . . . . 53
5.2.1. Introducción . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2.2. Número de eventos en parálogos recientes de Glycine max . . . . . . 61
5.2.3. Conservación de eventos entre parálogos . . . . . . . . . . . . . . . 64
6. Discusión General y Perspectivas 67
Referencias 69
IX
A. Anexo 76
B. Anexo 95
C. Anexo 107
D. Anexo 126
X
1. Introducción
Phaseolus vulgaris, o frijol, es una especie sumamente importante en la dieta humana
en paı́ses en vı́as de desarrollo debido a su alto contenido de proteı́nas y micronutrientes
(Broughton et al., 2003). Dos independientes domesticaciones, una en Mesoamérica y otra en
los Andes, han permitido el cultivo y cosecha de este alimento (Kwak et al., 2012). En México
el frijol es el segundo producto más sembrado, después del maı́z. P. vulgaris pertenece a la
familia de las leguminosas y el estudio cientı́fico de esta familia ha sido considerablemente
alto ya que cuenta con especies como la soya, la alfalfa, el cacahuate, entre otras, que son de
suma importancia en la agricultura e industria.
A parte de su importancia comercial las leguminosas se ha estudiado de forma particular
ya que éstas tienen la capacidad de establecer una relación simbiótica con bacterias fijadoras
de nitrógeno (rhizobia) (Ferguson et al., 2010), proceso conocido como fijación simbiótica de
nitrógeno (FSN). El nitrógeno es un elemento fundamental para la vida ya que es un elemento
esencial de diversas moléculas, como aminoácidos o ácidos nucleicos, y su disposición en
los suelos es escasa. Debido a esta relación las leguminosas pueden crecer en condiciones
de estrés de nitrógeno. La simbiosis entre las leguminosas y las rhizobia se da en la parte
subterránea de la planta y empieza con un diálogo molecular entre la planta y las bacterias.
Una vez establecida la comunicación entre ambos organismos la planta forma un órgano
especializado para establecer la simbiosis. Este órgano se llama nódulo y es ahı́ donde las
bacterias se transforman en bacteroides y fijan nitrógeno atmosférico en forma de nitratos
que son procesados por la planta y a cambio reciben productos fotosintéticos. A este proceso
se le conoce como fijación de nitrógeno simbiótica (SNF, por sus siglas en inglés)(Ferguson
et al., 2010).
P. vulgaris es un organismo diploide que cuenta con 11 cromosomas y aproximadamente
28,000 genes. La secuenciación masiva del ácido desoxirribonucleico (DNA) ha permitido
obtener la secuencia genómica de dos cultivares de frijol, uno proveniente de la domestica-
ción mesoamericana (Vlasova et al., 2016) y otro de la andina (Schmutz et al., 2014). La
secuencia genómica nos permite conocer ciertos aspectos de la especie en estudio, como su
1
evolución, número de genes, vı́as metabólicas, identificar especies relacionadas evolutiva-
mente, entre otras. El genoma de P. vulgaris ha permitido la realización de diversos estudios
genómicos como la re-secuenciación del genoma de diversas accesiones de frijol que ha lleva-
do a entender mejor el proceso de domesticación y evolución de esta especie (Rendon-Anaya
et al., 2017). Otro de los estudios realizados a partir de la secuenciación masiva del DNA fue
identificar las regiones deletadas de mutantes de P. vulgaris generadas a partir de la técnica de
Fast Neutron (Li and Zhang, 2002),la cual consiste en radiar las células con neutrones de tal
forma que se produzcan deleciones de doble cadena en el DNA. Estas mutantes fueron selec-
cionadas por un determinado fenotipo y a partir de la secuenciación se identificaron posibles
blancos de estudio (O’Rourke et al., 2013).
El estudio del DNA no es suficiente para entender la biologı́a de un organismo debido a
que la información de los genes debe de ser transcrita para generar moléculas funcionales. La
transcripción génica es el proceso por el cual la secuencia nucleotı́dica de un gene es copiada
a una molécula de ácido ribonucleico (RNA) y esto constituye el primer paso en la expresión
génica. La mayorı́a de los transcritos que son traducidos a proteı́nas son sintetizados por la
RNA polimerasa II. Para iniciar la transcripción la polimerasa requiere de factores de trans-
cripción, proteı́nas que reconocen secuencias de DNA (motivos) en la región rı́o arriba de los
genes. Los factores de transcripción pueden ser activadores o represores transcripcionales. La
presencia, ausencia y diferencias de concentraciones de los factores de transcripción orques-
tan distintos perfiles de transcripción. Los perfiles de transcripción varı́an por tiempo, tejido,
estadio de desarrollo y condiciones de estrés lo que conlleva al organismo a desarrollarse y
adaptarse. El estudio de la transcripción ha permitido grandes avances en la biologı́a, el desa-
rrollo actual de la genómica permite conocer la totalidad de los transcritos producidos en una
célula/tejido en cierta condición, a lo que se le ha denominado transcriptómica. Esta cien-
cia hace posible el estudio de diversos fenómenos biológicos de manera integral (Velculescu
et al., 1997).
Existen diversas técnicas para estudiar la transcriptómica, uno de los primeros estudios
que se utilizó fueron los microarreglos. Estos son chips que contienen secuencias de DNA y
2
miden la expresión génica a partir de la hibridación de cadenas sintetizadas de determinada
muestra. Estas cadenas sintetizadas son marcadas con fluoróforos para que éstos sean libera-
dos al hibridar con la cadena complementaria. Esta metodologı́a fue de gran utilidad antes de
la secuenciación masiva, pero cuenta con el problema de necesitar la secuencia nucleotı́dica
a priori, es decir que solo se puede medir la expresión génica de secuencias conocidas. Sin
embargo las técnicas de secuenciación masiva han permitido identificar transcritos nuevos y
medir la expresión de éstas (RNA-seq)(Wilhelm and Landry, 2009).
La secuenciación del genoma del frijol en conjunto con RNA-seq han permitido estudiar
diferentes aspectos de la transcriptómica de frijol. Recientemente se realizó un estudio sobre
el atlas de expresión génica en frijol. En este trabajo se analizaron 24 muestras de RNA-seq
de distintos tejidos de la planta en diferentes estadios de desarrollo, como raı́z, nódulo, tallo,
hoja, flor, vaina o semilla y además se tomaron muestras de diferentes condiciones nutriciona-
les; plantas tratadas con fertilizante nitrogenado, plantas que fueron inoculadas con rhizobias
fijadoras de nitrógeno y plantas que fueron inoculadas con una cepa de rhizobia que forma
nódulos, pero no fija nitrógeno (Rhizobium giardini). Los diferentes patrones de expresión en
esta leguminosa arrojaron resultados importantes para lograr, en un futuro, entender procesos
de diferenciación, desarrollo y nutricionales (O’Rourke et al., 2014).
Los genes en organismos eucariotes están interrumpidos por secuencias no codificantes,
llamadas intrones. Los intrones tiene que ser removidos del transcrito inicial (pre-mRNA)
de forma que las secuencias codificantes, exones, formen una cadena madura (mRNA) que
después será traducida en los ribosomas (Reddy, 2007). El origen de los intrones es incierto,
aunque se cree que el ancestro común de los eucariotes haya tenido una densidad similar de
intrones a los organismos actuales. A lo largo de la evolución han habido grandes pérdidas
y ganancias de exones. Se cree que a corto plazo la pérdida de intrones se ve favorecida sin
embargo en las mayores transiciones evolutivas se observa una gran ganancia de intrones
(Rogozin et al., 2012). La pérdida de intrones puede suceder mediante una transcripción re-
versa del mRNA, es decir que el RNA sea leı́do por una transcriptasa reversa que codifica la
información en DNA, y su producto sea insertado en el genoma. En cambio, la ganancia de
3
intrones sucede por una intronización o mediante elementos móviles del DNA. La introniza-
ción se da cuando se genera un codón de paro, donde no deberı́a, y para evitar que se sintetice
una proteı́na trunca se forma un intrón de forma que el mRNA no contenga el codón de paro
(Catania and Lynch, 2008). Los elementos móviles del DNA que dan origen a los intrones
son comúnmente llamados “introners”, estos tienen caracterı́sticas de elementos transponi-
bles que se insertan en diversos sitios del genoma, tienen una rápida evolución y pierden las
capacidades de transponerse (van der Burgt et al., 2012; Worden et al., 2009).
Por qué se mantienen los intrones es una pregunta que aún no se contesta, sin embargo,
existen diversas teorı́as que explicarı́an la conservación de estos. Una de ellas tiene que ver
con recombinación, ya que al tener intrones los genes se vuelven más grandes y hay una
mayor posibilidad de recombinar exones (Patthy, 1999) lo cual generarı́a una mayor variabi-
lidad genética. Por otro lado, se ha visto que los intrones pueden regular la expresión génica al
alargar la secuencia génica en el DNA. Se ha visto que genes que están expresados altamente
tienden a tener intrones pequeños mientras que genes con una expresión menor presentan in-
trones más largos (Castillo-Davis et al., 2002). Mediante la genética de poblaciones se explica
la conservación de los intrones, ya que los organismos multicelulares eucariotes no llegan a
tener una población efectiva grande y por lo tanto no existe una selección negativa hacia los
intrones (Irimia and Roy, 2014). Otra explicación es que la presencia de intrones permite
llevar a cabo splicing alternativo, lo cual amplı́a enormemente la variabilidad de mRNA y en
consecuencia la aumenta la diversidad proteica.
Existen cuatro tipos diferentes de intrones en organismos eucariotes, el tipo I y tipo II no
necesitan de una maquinaria y sufren auto-splicing, los intrones de RNA de transferencia y los
intrones que requieren un complejo proteico llamado spliceosoma. Los intrones tipo I tienen
una estructura terciaria especı́fica y necesitan, comúnmente, una guanina libre que ataque el
fosfato del sitio 5’ del intrón para cortar la cadena de RNA. Luego el exón libera el intrón en
su región 3’ mediante un ataque nucleofı́lico y se une al otro exón. Los intrones tipo II tienen
una estructura terciaria que permite trans-esterificación entre una adenina interna del intrón
(branching point) y el sitio 5’ del intrón que corta el RNA y forma una estructura de lazo.
4
Luego al igual que los intrones tipo I el exón 5’ identifica el sitio 3’ del intrón y mediante una
esterificación libera el lazo y se da el splicing. La reacción de los intrones tipo II es la misma
que sucede en los intrones que requieren spliceosoma, sin embargo, el spliceosoma tiene
ribonucleoproteı́nas nucleares pequeñas (snRNP’s, por sus siglas en inglés) que identifican
los sitios 5’, branching point y 3’ de los intrones. Estos sitios están conservados en la mayorı́a
de los intrones spliceosomales: donador o sitio de splicing 5’(GU) y aceptor o sitio de splicing
3’ (AG). El otro tipo de intrones son de RNA de transferencia (tRNA) y estos dependen de
endonucleasas, que corten el mRNA, y ligasas, que unen los exones (Irimia and Roy, 2014).
En organismos eucariotes la mayorı́a de los intrones son dependientes del spliceosoma
aunque existe una gran diferencia entre el tamaño de estos entre animales y plantas. Los
animales presentan intrones mucho más grandes y por lo tanto el splicing se da a partir del
reconocimiento de exones, es decir la maquinaria reconoce el sitio 3’ del intrón rio arriba y el
5’ del intrón rio abajo. En cambio, en plantas la maquinaria reconoce al intrón (McGuire et al.,
2008). Debido a que los sitios de splicing son motivos muy pequeños y muy comunes un gene
puede presentar diversos patrones de splicing, conocidos como splicing alternativo (AS). Se
piensa que el splicing alternativo evolucionó para evitar mutaciones deletéreas de forma que
en un principio dos isoformas de un gene se transcribieran y se seleccionara la que no contara
con la mutación en el mRNA (Rogozin et al., 2012). Sin embargo, con el transcurso de la
evolución las diferentes isoformas, producto del AS, tienen funciones relevantes como la
regulación de la velocidad de traducción, la regulación de las concentraciones de mRNA o el
incremento de la diversidad proteica (Liu et al., 2017).
La relevancia evolutiva del AS ha sido relacionada con la complejidad de los organismo,
respecto al número de diferentes tipos celulares. El número de genes en nematodos es muy
similar al de humanos, sin embargo los diferentes tipos de células que forman a cada orga-
nismo varı́an significativamente (Hedges et al., 2004). El 98 % de los genes multi-exónicos
presentan AS en humanos (Pan et al., 2008) mientras que en nematodos sólo el 20 % (Ramani
et al., 2011). Chen et al. (2014) analizaron diversos organismos que varı́an en el número de
diferentes tipos de células, como aproximación a complejidad de organismos, y encontraron
5
una fuerte correlación positiva entre el número de células diferentes y los niveles de AS. Es
decir que los organismos con una mayor complejidad tienden a presentar niveles más altos
de AS.
Se conoce que el splicing y por lo tanto el AS son regulados por diferentes factores. Las
proteı́nas SR (proteı́nas ricas en serina y arginina) se unen al pre-mRNA mediante recono-
cimiento de motivos y promueven la identificación de sitios de splicing (Jeong, 2017). Por
otro lado, están las ribonucleoproteı́nas nucleares (hnRNP) que son complejos formados por
proteı́nas y otros RNA no codificantes (ncRNA) que inhiben la identificación de los sitios
de splicing (Jean-Philippe et al., 2013). La expresión de estos diferentes factores establecen
los patrones de splicing de los genes (Filichkin et al., 2015). Estos patrones de splicing se ha
observado que cambian en condiciones de estrés, estadios de desarrollo y por tejido (Reddy
et al., 2013).
Existen diversos eventos de AS que se generan por el reconocimiento de diferentes sitios
de splicing. Puede ser que haya variación en el sitio 5’, donador alternativo (AD, alternative
donor), en el 3’, aceptor alternativo (AA, alternative acceptor), o ambos pueden variar, sitios
de splicing alternativo (ASS, alternative splicing sites). Otros eventos de AS es la omisión de
un exón (ES, exon skipping) en comparación con el transcrito principal o la aparición de un
exón dentro de un intrón (RE, retained exon). Por otro lado, los intrones pueden ser retenidos
en (IR, intron retention) o generar uno nuevo dentro de un exón (NI, new intron). Los eventos
de AA, AD, ES e IR son los más estudiados en los organismos ecuariotes. Los efectos de
algunos eventos de AS pueden afectar la localización del mRNA al igual que la eficiencia de
traducción (Reddy, 2007; Reddy et al., 2013). Una de las consecuencias más estudiadas es la
generación de codones de paro debido a un cambio de fase, esto está asociado al decaimiento
mediado por incoherencia (NMD, non-sense mediated decay) (Kalyna et al., 2012). Otros
procesos relacionados con AS son el almacenaje de mRNA (Boothby2013), reconocimiento
de blancos de miRNA (Yang et al., 2012) y la traducción de distintas proteı́nas a partir de un
gene que se localizan en diferentes estructuras o presentan distintas funciones (Remy et al.,
2013).
6
Los estudios a escala genómica de identificación de AS comenzaron a partir de la se-
cuenciación de transcritos expresados (EST, expression sequence tags). En un principio se
estimaba que alrededor del 20 % de los genes en humanos presentaban AS, sin embargo,
a partir de la secuenciación masiva se ha descubierto que alrededor del 98 % de los genes
multiexónicos en humanos presentan AS (Pan et al., 2008). En plantas se han observado re-
sultados similares, ya que los primeros estudios arrojaban que el ˜15 % de los genes de A.
thaliana presentaban AS (Iida et al., 2004) y en estudios recientes, utilizando RNA-seq, se
estima que más del 60 % de los genes sufre distintos patrones de splicing (Marquez et al.,
2012). Las diferencias entre estos dos organismos en cuanto a los porcentajes de genes afec-
tados por el AS pudieran explicarse debido a la disparidad de estudios sobre el tema, ya que
en humanos existen muchos análisis de identificación de AS y en plantas muy pocos, o de-
bido a la complejidad antes mencionada. Sin embargo, los resultados de plantas y animales
difieren en aspectos importantes. El evento más común en animales es ES mientras que en
plantas es IR. Esto se correlaciona con el tamaño de los intrones y la forma de identificación
de estos en cada especie (McGuire et al., 2008).
Se han realizado diversos estudios de identificación del AS en diversas plantas (Ner-
Gaon et al., 2007; Zhou et al., 2011; Marquez et al., 2012; Li et al., 2014; Panahi et al., 2014;
Shen et al., 2014; Thatcher et al., 2014; Vitulo et al., 2014; Xu et al., 2014; Mandadi and
Scholthof, 2015), sin embargo, no se ha estudiado a detalle la conservación de eventos de AS
entre especies. La conservación de eventos de AS es importante para identificar eventos que
pudieran definir funciones relevantes. Un ejemplo claro es el TFIIIA que presenta un evento
de ES, el exón omitido está altamente conservado en plantas y su función en generar un
codón de paro para regular su expresiónHammond2009,Barbazuk2010. Hasta el momento se
ha reportado un análisis de conservación de AS entre especies, donde se identifican eventos
en 9 especies de angiospermas a partir de muestras de RNA-seq (Chamala et al., 2015). En
este estudio se reportan eventos de AS en 8 y 10 muestras de RNA-seq de P. vulgaris y G.
max, respectivamente (Chamala et al., 2015). Este es el único reporte, que conozco, donde se
identifican eventos de AS a partir de RNA-seq y se analiza su conservación en leguminosas.
7
La identificación, caracterización y conservación de eventos de AS son temas de relevancia
para entender mejor la complejidad transcipcional.
Existen otros reguladores post-transcripcionales como los microRNAs (miRNAs). Los
miRNAs se han descrito como importantes reguladores negativos de la expresión génica en
plantas y se les han atribuido funciones en el desarrollo, metabolismo, respuesta a estrés,
defensa ante patógenos, integridad del genoma y simbiosis, entre otras cosas(Combier et al.,
2006; Chiou et al., 2006; Navarro et al., 2006; Meng et al., 2010; Staiger et al., 2013). Es-
tos miRNAs son fragmentos procesados de RNA con una longitud entre 19-23 nucleótidos
(Voinnet, 2009). Los miRNAs se transcriben del genoma mediante la polimerasa II dando ori-
gen al pri-miRNA. Los pri-miRNA presentan una estructura terciaria de tallo-asa la cual es
reconocida por una proteı́na RNAasa tipo III llamada DICER-LIKE1 (DCL1) que identifica
esta estructura y forma al precursor de los miRNAs conocido como pre-miRNA en el núcleo
de la célula. En plantas, a diferencia de animales, los pre-miRNA son procesados dentro
del núcleo formando una estructura de doble cadena compuesta por el miRNA y su secuencia
complementaria (miRNA∗) mientras que en animales el procesamiento sucede en citoplasma.
El duplex miRNA:miRNA∗ es identificado por un complejo protéico llamado RISC (RNA-
Induced Silencign Complex) que facilita la identificación de los blancos del miRNA mediante
el apareamiento casi perfecto de secuencias entre el miRNA y su mRNA blanco. Existen di-
versas formas en las que los miRNAs inhiben la expresión génica; pueden degradar al mRNA
blanco, secuestrarlo o bloquear su traducción. En plantas, a diferencia de animales, es más
frecuente que la inhibición suceda a partir de degradar el blanco mediante una proteı́na llama-
da ARGONAUTE (AGO1), un componente esencial de RISC (Jones-Rhoades et al., 2006).
La identificación de los miRNAs y sus respectivos blancos en P. vulgaris ha sido del
interés cientı́fico y el primer análisis para identificarlos se realizó de manera in silico. En
este trabajo se buscaron posibles precursores en los EST’s reportados para frijol y a partir de
estos se lograron identificar 24 miRNAs (Arenas-Huertero et al., 2009). Otra aproximación
se realizó a partir de secuenciar pequeños RNAs de forma masiva (small RNA-seq) de hojas,
raı́ces, plántulas y flores. Este análisis se realizó cuando aún no se contaba con el genoma
8
de P. vulgaris y por lo tanto el objetivo fue identificar cuáles miRNAs reportados en otras
plantas estaban presentes en estos tejidos de frijol (Pelaez et al., 2012).
La secuenciación del genoma de P. vulgaris ha sido el comienzo de muchos otros análisis
que han permitido analizar diferentes aspectos de su biologı́a. El término de transcriptómica
abarca distintos aspectos relacionados al RNA y es un estudio fundamental para entender
cómo los organismos se adaptan a su entorno, se desarrollan y se diferencian. Por estos mo-
tivos mis estudios doctorales se han enfocado en analizar la expresión génica a partir de la
regulación transcripcional y post-transcripcional y he ahondado más en el splicing alternativo
de P. vulgaris.
9
2. Objetivos
• Objetivo General:
Análizar la regulación de la expresión génica de Phaseolus vulgaris (frjiol) a distintos
niveles como el transcripcional y el post-transcripcional, en especı́fico sobre el AS y
miRNAs.
• Objetivos Especı́ficos:
◦ Analizar los perfiles transcripcionales de Phaseolus vulgaris:
− En nódulos bajo estrés oxidativo inducido por paraquat.
− En nódulos de plantas inoculadas con una cepa modificada de R. etli que
expresa la globina Vhb en condiciones control y bajo estrés oxidativo.
◦ Analizar procesos del control transcripcional y post-transcripcional de la expre-
sión génica de Phaseolus vulgaris a través de:
− La identificación a nivel genómico de miRNAs y sus respectivos blancos.
− La identificación de otros RNAs pequeños no codificantes conocidos como
phasiRNAs.
− El análisis de genes coexpresados con el factor de transcripción AP2-1, que
es blanco de miRNA172c.
− Identificar los sitios de pegado de factores de transcripción en las regiones
promotoras de los genes coexpresados ası́ como de los loci de miRNA172.
− La anotación de la familia de factores de transcripción AGL y el análisis de
su expresión en nódulos y raı́ces.
◦ Analizar el Splicing Alternativo (AS) a través de:
− La identificación de diferentes eventos de AS en las especies de leguminosas
relacionadas; P. vulgaris y G. max.
− La caracterización de los diferentes tipos de eventos de AS respecto a la
región génica afectada por el evento, motivos de reconocimiento de splicing
en regiones 5’ y 3’ de los intrones.
− La identificación de eventos conservados en genes homólogos de P. vulgaris
y G. max.
− La caracterización de eventos de AS presentes en genes duplicados.
10
3. Perfiles Transcripcionales
La transcriptómica, como se menciona en la introducción, es el estudio de aquellas se-
cuencias de RNA que fueron transcritas a partir del DNA. Uno de los principales objetivos de
la transcriptómica es averiguar cuáles genes se están expresando en determinada condición y
en que concentraciones. De esta forma al conocer el transcriptoma de dos o más condiciones
contrastantes se podrı́an comparar las concentraciones y ası́ determinar los genes inducidos
o reprimidos en determinado entorno y se puede llegar a inferir su función.
3.1. Perfil transcripcional de nódulos en estrés oxidativo
3.1.1. Introducción
Las plantas, como organismos sésiles, tienen que contender contra los diferentes tipos
de estrés de formas diferentes a otros organismos. Diferentes tipos de estrés como la sequı́a,
salinidad, bajas temperaturas, alta luminosidad y toxicidad por metales, han demostrado que
generan un estrés de tipo oxidativo en la planta. El estrés oxidativo sucede cuando hay un
desequilibrio entre las reacciones de oxidación y reducción y se generan las llamadas especies
reactivas de oxı́geno (ROS, por sus siglas en inglés). Las ROS son moléculas como el anión
superóxido (·O2-), el peróxido de hidrógeno (H2O2 ) o el radical hidroxilo (·OH), entre otros,
que se generan en todas las células con metabolismo aeróbico. Sin embargo, se ha visto
una acumulación de ROS en organismos bajo diferentes estreses lo cual provoca un daño
directo o indirecto a los ácidos nucleicos, lı́pidos, aminoácidos, o co-factores de proteı́nas y
generalmente conlleva a una muerte celular.
Las ROS tienen un papel fundamental en los inicios de la relación simbiótica entre las le-
guminosas y Rhizobium, ya que son esenciales para un desarrollo óptimo del nódulo(Montiel
et al., 2016). Sin embargo éstas, en exceso, pueden afectar la formación del nódulo y por
lo tanto la SNF. La SNF se ve afectada ya que varias proteı́nas importantes para la fija-
ción son sensibles a ROS, como nitrogenasas, ferrodoxinas, hidrogenasas y leghemoglobi-
na(Matamoros et al., 2003). La alta tasa respiratoria de los nódulos incrementa la producción
11
de ROS, especialmente H2O2 y ·O2-, y se necesita de un conjunto de enzimas tanto de la plan-
ta como del bacteroide para combatir las ROS sobre-acumuladas y contender contra el estrés
oxidativo. Además, diferentes estreses abióticos como los ya mencionados pueden llevar a
una sobre-acumulación de ROS y generan estrés oxidativo en los nódulos. Se conoce que en
esas condiciones se producen cambios en el perfil transcripcional tanto del bacteroide como
del nódulo, lo cual es un tema de interés en el grupo de investigación donde desarrollé mi
tesis. Esto ha llevado al planteamiento y realización de proyectos orientados a identificar los
cambios en la expresión génica de manera global (transcriptómica) de los nódulos de frijol en
condiciones de estrés oxidativo generado por condiciones ambientales adversas. Durante mi
doctorado yo participé en uno de estos proyectos que se reportó en el artı́culo: Ramı́rez M,
Guillén G, Fuentes SI, Iñiguez LP, Aparicio-Fabre R, Zamorano-Sánchez D, Encarnación-
Guevara S, Panzeri D, Castiglioni B, Cremonesi P, Strozzi F, Stella A, Girard L, Sparvoli F,
Hernández G. (2013) TRANSCRIPT PROFILING OF THE COMMON BEAN NODULES
UNDER OXIDATIVE STRESS. Physiologia Plantarum. 149: 389-407, que se muestra en el
Anexo A. A continuación resumiré mi participación en este trabajo.
3.1.2. Resultados y Discusión
Para estudiar el estrés oxidativo en plantas de frijol en simbiosis con rhizobia se usó un
herbicida llamado paraquat (PQ) (dicloruro de 1,1’-Dimetil-4,4’-bipiridilo o metil vilógeno)
que induce la producción de ROS y genera estrés oxidativo en la planta de manera similar a
estreses abióticos como sequı́a, salinidad o toxicidad por metales (Donahue et al., 1997). Este
se aplicó a plantas inoculadas con Rhizobium tropici CIAT 899 a los 16 dı́as post inoculación
(dpi) y los nódulos fueron recolectados a 18 dpi, cuando su desarrollo se considera maduro
y presentan una tasa alta de fijación de nitrógeno. Se extrajo el RNA tanto de nódulos bajo
los efectos de PQ como su control, sin el herbicida, y se analizó el perfil transcripcional de
ambas muestras.
El transcritoma de los nódulos se analizó a través de la hibridación de microarreglos.
Se diseñó y preparó el microarreglo denominado ”Bean Custom Array 90K”. Para el diseño
12
de este microarreglo se utilizaron EST’s reportadas para Phaseolus vulgaris y Glycine max
(soya), una leguminosa mucho más estudiada y relacionada con el frijol. El chip contiene
30,150 sondas (secuencias) que incluyen un conjunto de 18,867 secuencia únicas de frijol y
11,205 de soya.
Al comparar los perfiles transcripcionales de los nódulos bajo el estrés y su control se
encontraron 4280 EST’s expresadas diferencialmente. De estas 2,218 EST’s se reprimieron
y 2,062 fueron inducidas en nódulos bajo el estrés oxidativo. Estos genes fueron analizados
de diferentes maneras para entender el significado biológico de su expresión diferencial en
los nódulos tratados con PQ. Una de los aspectos estudiados fueron las rutas metabólicas en
las que pudieran estar participando. Se identificaron aquellos genes que participaran en las
rutas metabólicas presentes en el microarreglo, de estos se identificaron los expresados dife-
rencialmente y mediante una prueba de Fisher se identificaron aquellas vı́as que presentaran
una significancia estadı́stica. 5 vı́as metabólicas presentaron una disminución de expresión y
4 un aumento de expresión en nódulos bajo estrés oxidativo (Tabla 1).
Tabla 1: Vı́as metabólicas
Reprimidos Inducidos
Fijación de carbono Biosı́ntesis de isoflavonoides
Metabolismo de sulfatos Metabolismo de metano
Metabolismo de purinas Metabolismo de fármacos - citocromo P450
Metabolismo de almidón y sacarosa Metabolismo de glicerolı́pidos
Fijación de CO2
Además de este análisis se realizó la identificación de ontologı́as génicas (GO, por sus si-
glas en inglés) sobre-representadas en las listas de EST’s con expresión inducida o reprimida.
Los resultados se muestran en la Figura 1.
Los resultados de ambos análisis muestran aspectos biológicos relevantes para entender
lo que sucede en los nódulos sometidos a un estrés oxidativo. Los resultados de GO muestran
de forma más general en qué están involucrados los EST’s inducidos o reprimidos mientras
que el análisis de vı́as metabólicas es un poco más especı́fico.
A manera general los análisis reflejan que en los nódulos tratados con PQ se inducen pro-
13
Figura 1: GO de Procesos Biológicos en EST’s diferencialmente expresados
Se muestran los porcentajes de cada uno de los GO pertenecientes a procesos biológicos que se encontraron en
las listas de EST’s diferencialmente expresados. Las barras negras corresponden al total de cada categorı́a de
los EST’s probando en el microarreglo, las barras gris oscuras representan el porcentaje de cada proceso entre
los EST’s reprimidos y las barras gris claro el porcentaje entre los EST’s inducidos bajo el estrés de PQ. Los
asteriscos representan categorı́as que mostraron una sobre-representación en los EST’s reprimdios mientras que
los puntos negros una sobre-representación en los EST’s inducidos.
cesos de muerte celular o respuestas a estrés. De forma más especı́fica las vı́as metabólicas
que se inducen incluyen el metabolismo de metano, de fármacos y la biosı́ntesis de isofla-
vonoides, éstas consideradas parte del metabolismo secundario. Dentro de la biosı́ntesis de
isoflavonoides se encuentran enzimas involucradas en la biosı́ntesis de fitoalexinas, moléculas
que tienen una función de antimicrobiana y son consideradas importantes para las defensas
vegetales. De forma similar sucede con la biosı́ntesis de fármacos. Estos resultados mues-
tran un estrés generalizado de los nódulos y una activación de los mecanismos de defensa
vegetales.
El análisis general de las EST’s que se encuentran reprimidas ante el estrés oxidativo in-
dica que como resultado del estrés se inhiben procesos generalmente fundamentales como
el crecimiento, organización de componentes celulares y la traducción. De manera más es-
pecı́fica a través de las vı́as metabólicas sobre-representadas en los EST’s inhibidos (Tabla
1) se observan vı́as esenciales para el funcionamiento del nódulo como las relacionadas con
14
el metabolismo de carbono (fijación de CO2, metabolismo de almidón y sacarosa), ya que a
partir de éstas la planta le brinda al bacteroide la energı́a (ATP’s) necesarios para la fijación
de nitrógeno. Además, se ven reprimidas vı́as del metabolismo de nitrógeno como la sı́nte-
sis de purinas que interviene en la sı́ntesis de uréidos. En frijol, el amonio aportado por el
bacteroide es asimilado en las células del nódulo y ahı́ la planta utiliza el amonio para sin-
tetizar uréidos. Los uréidos son los principales compuestos nitrogenados que se transportan
desde el nódulo hasta las hojas como fuente de nitrógeno. La represión de estas vı́as afecta
drásticamente la eficiencia de la fijación y asimilación del nitrógeno.
Estos resultados se discuten a más detalle en el Anexo A.
3.2. Perfil transcripcional de nódulos de frijol en simbiosis con
R. etli expresando la hemoglobina de Vitroescilla, en condi-
ciones de estrés oxidativo
3.2.1. Introducción
Aunque los rhizobia son organismos aeróbicos, la fijación SNF por los bacteroides en
los nódulos de leguminosas ocurre en un ambiente microaeróbico. La nitrogenasa, comple-
jo enzimático que cataliza la reducción de nitrógeno atmosférico a amonio, es sumamente
sensible al O2. La leghemoglobina producida por la planta en los nódulos es fundamental
para proveerle oxı́geno a la oxidasa terminal del bacteroide. Esta oxidasa opera de manera
óptima a presiones bajas de oxı́geno y genera el ATP requerido para el funcionamiento de la
nitrogenasa.
En la naturaleza existen diversas proteı́nas con funciones de transporte y almacenamiento
del oxı́geno similares a las de la leghemoglobina. Una de éstas es la globina de la bacteria
gram-negativa aeróbica: Vitroescilla sp. que se denomina Vhb. Esta proteı́na tiene una efi-
ciencia más alta que sus homólogos en organismos multicelulares y se ha asociado a diversas
funciones como catalizador de reacciones redox y protección celular ante estrés oxidativo
(Hardison, 1998; Kaur et al., 2002; Geckil et al., 2003).
La alta afinidad de la VHb de Vitroescilla hacia el oxı́geno ha generado un gran interés
15
de la comunidad y se ha utilizado para la biotecnologı́a. Se ha estudiado el crecimiento de
Escherichia coli que expresan esta hemoglobina y se ha visto que existe un mayor crecimiento
y una mayor densidad celular en comparación con la cepa silvestre (Geckil et al., 2001;
Khosla and Bailey, 1988). También se ha visto que la expresión de VHb en E. coli le confiere
resistencia al estrés oxidativo ya que se correlaciona con una alta expresión de antioxidantes
(Geckil et al., 2003; Anand et al., 2010). Estos hallazgos han permito plantear la hipótesis
sobre si la expresión de Vhb en rhizobia durante la simbiosis con leguminosas (bacteroides)
pudiera conferirle a la planta y al nódulo cierta resistencia al estrés oxidativo y de esta forma
mejorar la SNF.
En trabajos pasados del laboratorio se estudiaron los efectos de Vhb, expresada bajo su
promotor nativo, en R. etli tanto en vida libre como en simbiosis con P. vulgaris (Ramirez
et al., 1999). Los resultados muestran que en condiciones bajas de oxı́geno las bacterias de
vida libre tienen una mayor tasa respiratoria, contenido de energı́a quı́mica y expresión de
genes fijadores de nitrógeno comparado con R. etli silvestres que no expresan esta hemoglo-
bina heteróloga. Además, las plantas inoculadas con las bacterias que VHb mostraron mayor
biomasa, una temprana floración y formación de vainas, una mayor tasa de actividad de la
nitrogenasa y un mayor contenido de nitrógeno en comparación con plantas inoculadas con
R. etli silvestre (Ramirez et al., 1999).
Estos resultados fueron los antecedentes de un trabajo que se publicó en: Ramı́rez M,
Iñiguez LP, Guerrero G, Sparvoli F, Hernández G. (2016) Rhizobium etli BACTEROIDS
ENGINEERED FOR Vitreoscilla HEMOGLOBIN EXPRESSION ALLEVIATE OXIDATI-
VE STRESS IN COMMON BEAN NODULES THAT REPROGRAMME GLOBAL GENE
EXPRESSION. Plant Biotechnology Reports, 10: 463-474. El objetivo general de este tra-
bajo fue analizar el efecto de la cepa de R.etli expresando la VHb en simbiosis con plantas
de frijol creciendo en condiciones de estrés oxidativo, generado por el herbicida PQ. Como
se mencionó antes, la hipótesis era que la VHb en el bacteroide puede tener ventajas que
resulten en un mejor desempeño de las plantas en SNF ante este estrés. El enfoque de este
trabajo fue de corte genómico ya que se analizó de manera comparativa el transcriptoma de
16
los nódulos de frijol en cuatro diferentes simbiosis: frijol inoculado con R. etli silvestre en
condición control (1) y en estrés por adición de PQ (2); R. etli / VHb en condición control
(3) y en estrés por PQ (4). Para el análisis del transcriptoma se utilizaron los microarreglos
descritos en la sección anterior. A continuación resumo este artı́culo que se presenta en el
Anexo B.
3.2.2. Resultados y Discusión
Las plantas inoculadas con R. etli / Vhb mostraron una menor sensibilidad al tratamiento
con PQ, ya que presentaron un menor decaimiento de la actividad de nitrogenasa y contenido
de ureidos en comparación con las plantas en condiciones control. Estos resultados sugieren
que VHb protege a los nódulos del estrés oxidativo generado por PQ, lo que resulta en una
mejora en la eficiencia de la SNF.
Para analizar la expresión génica en los nódulos de las cuatro diferentes condiciones de
simbiosis, se extrajo RNA de nódulos de 18 dı́as y se analizaron utilizando el microarreglo
descrito en la sección anterior. Se identificaron los EST’s expresados diferencialmente entre
las muestras (Anexo B), sin embargo, las 2 comparaciones en las que se enfocó el análisis en
este trabajo fueron entre las condiciones +PQ vs control en los nódulos formados por la cepa
de R. etli que expresa la Vhb y la cepa silvestre, es decir 1 vs 3 y 2 vs 4. Sólo 8 % de los EST’s
expresados diferencialmente (reprimidos o inducidos) en la condición de estrés oxidativo se
compartı́an independientemente del inóculo. Esto indica que los patrones de expresión génica
se ven afectados por la modificación de R. etil / VHb. Para analizar esto de manera más pre-
cisa se identificaron los GO (procesos biológicos) de los EST’s diferencialmente expresados
(Figura 2a). Entre las categorı́as sobre-representadas de los EST’s inducidos en nódulos con
la cepa silvestre de R. etli tratados con PQ se encuentran proceso involucrados en defensa
e inducción a muerte celular y en los EST’s reprimidos procesos derivados de la fijación de
nitrógeno (Figura 2a). Estos resultados son similares a los discutidos en la sección anterior
(Anexo A). A diferencia de los nódulos inoculados con la cepa silvestre, los nódulos for-
mados por la cepa modificada que expresa la Vhb muestran una mayor cantidad de EST’s
17
inducidos que reprimidos. Las categorı́as de GO que aparecen sobre-representadas entre los
EST’s altamente expresados en nódulos con PQ se refieren a estreses abióticos (Figura 2b).
Esto muestra que la presencia de VHb está induciendo una respuesta para contender contra
el estrés y por lo tanto le está confiriendo cierta resistencia a la planta.
Figura 2: GO de Procesos Biológicos en EST’s diferencialmente expresados
Se muestran los GO de procesos biológicos significantemente enriquecidos en el set de genes reprimidos (barras
grises) y aumentados (barras negras). Procesos biológicos enriquecidos en EST’s diferencialmente expresados
de las comparaciones entre control vs PQ inoculados con la cepa silvestre (a) y con la cepa de R. etli que
expresa Vhb (b) y los procesos biológicos de los EST’s que presentan el mismo patrón de expresión en ambas
comparaciones (c).
18
A pesar de las diferencias en los patrones de expresión entre ambas comparaciones, existe
un grupo de EST’s que mantiene la tendencia de estar inducidos o reprimidos ante la presen-
cia de PQ. Estos EST’s presentan una clara tendencia a ser respuestas comunes del estrés
oxidativo. Entre los EST’s inducidos en los nódulos tratados con PQ se encuentran procesos
como respuesta a estrés oxidativo y procesos de oxidación-reducción entre otros (Figura 2c).
Los resultados de este trabajo muestran que la inclusión de VHb a R. etli beneficia a
la planta no solo en condiciones control (Ramirez et al., 1999) sino que también en condi-
ciones de estrés oxidativo, lo que sugiere explorar el uso de esta bacteria modificada como
biofertilizante para fines biotecnológicos.
19
4. Control Transcripcional y Post-Transcripcional
El control transcripcional constituye uno de los modos más importantes de la expresión
génica. Esta regulación está orquestada por factores de transcripción, regiones promotoras,
interacciones proteicas que inhiben o estimulan la transcripción, entre otras. Tanto las di-
ferentes concentraciones de factores de transcripción como la accesibilidad a las regiones
promotoras y la presencia de distintas proteı́nas provocan distintos perfiles de transcripción,
analizados y discutidos en la sección anterior. Sin embargo, para lograr entender la comple-
jidad de los perfiles transcripcionales es fundamental entender las piezas que lo conforman,
es decir entender cuáles factores de transcripción está presentes y estos a cuáles genes regu-
lan. Los factores de transcripción están catalogados en familias, dependiendo de los motivos
proteicos conservados/compartidos entre los miembros.
Además del control transcripcional existe la regulación post-transcripcional de la expre-
sión. Este tipo de regulación ocurre posterior a la transcripción y sucede en los mRNA. La
regulación post-transcripcional abarca,el splicing, el transporte de los mRNA producidos en
el núcleo hacia el citoplasma y a los ribosomas, donde serı́a traducido, y la eficiencia de
asociación entre el mRNA y los ribosomas para el inicio de la traducción. Los miRNAs,
mencionados en la introducción, son parte de la regulación post-transcripcional en las plan-
tas, estos son reguladores negativos que inhiben la asociación del mRNA con los ribosomas
ya sea secuestrándolo, o degradándolo para ası́ impedir su traducción.
4.1. Identificación de pequeños RNAs de Phaseolus vulgaris
4.1.1. Introducción
La regulación post-transcripcional a partir de los miRNAs descrita en la introducción no
es la única función que tienen estos ya que también son encargados de la producción de otro
tipo de RNAs pequeños no codificantes llamados phasiRNA (phased small interfering RNA).
Ciertos transcritos largos, blancos de miRNA, reclutan polimerasas de RNA que generan una
doble cadena de RNA. Una vez formada la doble cadena de RNA es procesada por DCL4 o
20
DCL3b que corta ambas cadenas produciendo pequeños RNAs de 21 o 24 nucleótidos. La
caracterı́stica más importante de estos sncRNA se da debido al procesamiento de las DCL4 o
DCL3b ya que corta las cadenas a manera de fase, es decir en ventanas de 21 o 24 nucleótidos.
Los phasiRNA resultantes de este proceso pueden actuar como miRNAs e inhibir de la misma
forma ciertos genes a través del complejo RISC.(Jones-Rhoades et al., 2006; Voinnet, 2009)
La identificación de los miRNAs en P. vulgaris ha sido fundamental en los estudios que
se han realizado en el laboratorio y en cuanto se hizo pública la secuencia genómica de
P. vulgaris (Schmutz et al., 2014) realizamos un proyecto de identificación de miRNAs y
sus respectivos blancos, ası́ como la identificación de phasiRNAs. Para esto se utilizaron
las muestras de small RNA-seq publicadas anteriormente (Pelaez et al., 2012) en conjunto
con otras de nódulos y además secuencias de ”degradoma”. Estas últimas provienen de una
técnica para identificar los blancos de miRNAs que son degradados por AGO1. Los miRNAs
en el complejo RISC al identificar a su blanco y cortarlo mediante AGO1 dejan un fosfa-
to libre en el extremo 5’ que es único para los blancos digeridos por miRNAs/AGO1. Para
secuenciar estos productos el protocolo establece que hay que seleccionar transcriptos con
poly-adenilación en el 3’ terminal, ligar un adaptador al 5’ que contenga el fosfato libre y
utilizar una enzima de restricción para seleccionar a aquellas secuencias que tengan el adap-
tador (German et al., 2008). En este proyecto participé especı́ficamente en la identificación
y análisis de phasiRNA, los cuales nunca habı́an sido descritos en P. vulgaris. Los resulta-
dos se publicaron en: Formey D, Iñiguez LP, Peláez P, Li Y-F, Sunkar R, Sánchez F, Reyes
J, Hernández G. (2015) GENOME-WIDE IDENTIFICATION OF THE Phaseolus vulgaris
sRNAome USING SMALL RNA AND DEGRADOME SEQUENCING. BMC Genomics,
16: 423. A continuación resumo este artı́culo que se presenta en el Anexo D.
4.1.2. Resultados y Discusión
En este análisis se identificaron 185 nuevos miRNAs provenientes de 307 pre-miRNAs
distribuidos en 98 familias diferentes. De estos, 64 ya habı́an sido identificados en otras es-
pecies, 57 son nuevas isoformas y el resto (64) son especı́ficos de frijol. Debido a la gran
21
cantidad de información que se obtiene de small RNA-seq se pudo asignar una expresión
para cada miRNA en los diferentes tejidos analizados. Los miRNAs que ya habı́an sido iden-
tificados en otras especies presentaban una expresión en varios tejidos mientras que los es-
pecı́ficos de P. vulgaris preferencialmente se expresaban en tejidos únicos. Estos resultados
sugieren una funcionalidad ancestral y global de los miRNAs conservados y una regulación
post-transcripcional especie-especı́fica para los miRNAs nuevos.
Parte de estos análisis se enfocaron en miRNAs de nódulos. Para ello se infirieron redes
de coexpresión en nódulos en las que se encontraron miRNAs conservados ya previamente
identificados en procesos de simbiosis junto con miRNAs o isoformas nuevas. Esto da la
pauta para caracterizar estos miRNAs no conservados e identificar su función en los nódulos.
Los datos del degradoma permitieron identificar 181 blancos de miRNAs, siendo la ma-
yorı́a blancos de miRNAs conservados. Estos resultados sugieren, al igual que los de la ex-
presión, una regulación a post-transcipcional de mRNAs expresados en diversos órganos por
parte de los miRNAs conservados que los especie-especı́ficos. Cabe resaltar que el degra-
doma sólo muestra aquellos blancos que están siendo degradados por miRNAs pero no se
identifican a los blancos secuestrados o que su traducción está siendo bloqueada debido a la
especificidad de la metodologı́a del degradoma.
Para identificar los phasiRNAs se utilizaron los mapeos de las small RNA-seq al genoma y
se calculó un score de fase (Zhai et al., 2011) para cada fragmento de 21 nucleótidos alineados
al genoma. Para eliminar el ruido producido por fragmentos degradados se tomaron en cuenta
las lecturas mapeadas en fase y las no en fase y se utilizó una prueba de Chi (p<0.01). En total
se identificaron 125 loci capaces de producir phasiRNAs, 13 de ellos en todas las muestras
(Figura 3a). Para identificar si estos provenı́an de los miRNAs se buscaron cuáles pudieran ser
blancos de los miRNAs. 47 loci de phasiRNAs se identificaron como posibles blancos para
31 miRNAs. Un aspecto interesante de los phasiRNAs es que sólo dos de ellos se encontraron
conservados en otras leguminosas (Figura 3b). Esto pudiera deberse a dos situaciones, una
serı́a la falta de estudio de este tipo de sncRNA o que los mecanismos biológicos de acción
de estos sean especı́ficos para cada especie (Apéndice D).
22
Figura 3: Diagramas de Venn de phasiRNA
(a) Distribución de los phasiRNA genes en los 5 órganos de la planta. Los números en cada área del diagrama
de Venn corresponden al número de loci de phasiRNA expresados en los distintos órganos. (b) Conservación de
genes phasiRNA presentes en 3 especies de leguminosas
4.2. El nodo miRNA172c-AP2 como regulador de la fijación de
nitrógeno simbiótica en la relación P. vulgaris-Rhizobium etli
4.2.1. Introducción
Dado su papel fundamental como reguladores centrales, los miRNAs han sido de gran in-
terés para el laboratorio. Además de los proyectos generales sobre identificación de miRNAs,
que se mencionaron antes, se han analizado miRNAs especı́ficos y se ha demostrado su papel
en procesos relevantes para las plantas de frijol. Ası́ se ha estudiado el papel de los miRNAs
en P. vulgaris en condiciones como la deficiencia de fósforo (Valdes-Lopez et al., 2008; Ra-
mirez et al., 2013), la deficiencia de nutrientes (Valdes-Lopez et al., 2010) y la toxicidad por
metales (Mendoza-Soto et al., 2012; Naya et al., 2014; Mendoza-Soto et al., 2015). Uno de
estos proyectos del grupo, en los que participé, fue sobre el nodo del miRNA172 y su blanco
AP2. Los resultados de este proyecto se publicaron en: Nova-Franco B, Iñiguez LP, Valdés-
López O, Alvarado-Affantranger X, Leija A, Fuentes SI, Ramı́rez M, Paul S, Reyes JL, Girard
L, Hernández G (2015) THE microRNA-172c-APETALA2-1 NODE AS A KEY REGULA-
TOR OF THE COMMON BEAN RHIZOBIA NITROGEN FIXATION SYMBIOSIS. Plant
Physiology, 168: 273-290. Yo colaboré con análisis de tipo transcriptómicos relacionados a
23
a 
63 
Flower 
Nodule b 
vulgaris 
Medicago 
truncatula 
Glycinemax 
este nodo y su participación en la simbiosis de frijol con Rhizobium etli.
El miR172 se identificó como promotor de la floración en A. thaliana (Aukerman and
Sakai, 2003), ya que inhibe la traducción de un inhibidor de la floración llamado APETALA2.
Sin embrago en los trabajos de identificación de miRNAs en P. vulgaris se detectó una isofor-
ma del miRNA172 inducida en nódulos de frijol (Pelaez et al., 2012; Formey et al., 2015), lo
que permitió plantear la hipótesis de que este miRNA y su gene blanco tenı́an alguna función
biológica relevante en la simbiosis con rhizobia.
4.2.2. Resultados y Discusión
En este trabajo se identificaron 6 loci en el genoma que codifican para isoformas del miR-
NA172, sin embargo, sólo tres diferentes isoformas (a-c) del miRNA maduro son detectables,
ya que la isoforma a es idéntica a la e y f, y la isoforma c es igual a la d. Se identificó que el
miRNA172c es el más expresado en los nódulos mientras que la isoforma miRNA172a está
expresada mayoritariamente en flores. Otro aspecto importante en los estudios de miRNA es
la identificación de su respectivo blanco. Arenas-Huertero et al. (2009) ya habı́an identificado
y validado experimentalmente al factor de transcripción AP2-1, que muestra una expresión
opuesta (correlación negativa) al nivel del miRNA172 en diferentes órganos y etapas de la
simbiosis con rhizobia. Los resultados indican que la expresión del miRNA172c se da en
los nódulos y regula post-transcripcionalmente a AP2-1. En ese trabajo se observó que el
miR172c maduro se incrementa ante la infección con rhizobia y continúa incrementando
hasta alcanzar su máximo nivel en nódulos maduros y disminuye en nódulos senescentes.
La expresión del miRNA y por lo tanto su función como regulador post-transcripcional es
dependiente del estadio de desarrollo del nódulo.
La caracterización funcional del miRNA172c continuó al analizar el fenotipo simbiótico
de mutantes transitorias de frijol. Las mutantes transitorias, o plantas compuestas, se logran
a partir transformar raı́ces y nódulos mediante Agrobacterium rhizogenes (Estrada-Navarrete
et al., 2007) que sobre-expresan el miRNA172c o el AP2-1 mutado en el sitio que reconoce
el miRNA, esto le confiere insensibilidad a la presencia del miRNA172c. Las raı́ces sobre-
24
expresantes del miRNA172c muestran un incremento en su crecimiento, una mayor infección
rhizobial, una mejor nodulación, una mayor tasa de fijación de nitrógeno y un aumento en la
expresión de genes relacionados con los primeros pasos de la nodulación y la autoregulación
de la misma. La sobre-expresión de AP2-1 causó los efectos contrarios.
Para entender mejor los efectos de esta regulación se consideró que el factor de transcip-
ción AP2-1 tuviera un rol de activador transcripcional y se buscaron, en el atlas de expresión
de P. vulgaris, los genes que tuvieran el mismo patrón de expresión que AP2-1 (O’Rourke
et al., 2014) en las bibliotecas de nódulos y raı́z. Se identificaron 114 genes que presentaban
un patrón similar al del blanco de miRNA172c (Figura 4). Se analizaron las regiones promo-
toras de estos genes para identificar los motivos de pegado de los factores de transcripción y
se encontró una sobre-representación de sitios ERF2 y DREB1B, ambos pertenecientes a la
familia de AP2. Estos resultados sugieren que los genes que están coexpresados con AP2-1
pueden ser regulados de manera positiva por este factor de transcripción. Para confirmar esta
hipótesis se verificó la expresión de algunos de los genes coexpresados en las raı́ces transgéni-
cas que sobre-expresan AP2-1 y los resultados muestran un incremento en su expresión. Tam-
bién se analizaron las categorı́as de GO sobre-representadas en los genes coexpresados y se
encontró una categorı́a de proteı́nas cinasas que se han relacionado con la senescencia de los
nódulos en Medicago truncatula (Van de Velde et al., 2006; Perez Guerra et al., 2010). En
general, los resultados de este trabajo indican que la regulación post-transcripcional del AP2-
1 por el miRNA172c es relevante para etapas iniciales de la simbiosis con rhizobia ası́ como
para la senescencia de los nódulos, por lo que el miRNA172c debe regular negativamente la
expresión de AP2-1 en los nódulos maduros. El artı́culo publicado en el Anexo C presenta
todos los resultados de este trabajo, ası́ como una discusión más amplia de los mismos.
Otro aspecto importante dentro de la regulación post-transcripcional del AP2-1 es conocer
cómo se regula el miRNA172c a nivel transcripcional. En la compleja red de regulación de la
floración en A. thaliana la transcripción del miRNA se activa por un factor de transcripción
llamado SPL (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE). Para identificar si
esto mismo sucede en frijol se buscaron los sitios de unión de factores de transcripción en las
25
Figura 4: Perfil de Expresión de Genes Coexpresados con AP2-1
Se muestra el perfil de expresión de los 144 genes coexpresados con AP2-1 (Z-scores: rojo=mayor expresión,
blanco= menor expresión). Las muestras que aparecen en el mapa de calor son: YR=raı́z joven, RI=raı́z ino-
culada con nódulos que no fijan nitrógeno a 21 dpi, RE=raı́z con nódulos fijadores de nitrógeno a 21 dpi, RF=
raı́z fertilizada, NE= nódulos fijadores de nitrógeno (21 dpi), N5= nódulos pre-fijadores (5 dpi), NI= nódulos
no fijadores (21dpi)
regiones promotoras de las seis isoformas de miRNA172. Los resultados no muestran motivos
para SPL, sin embargo se encontró una sobre-representación de motivos de AGL. Resultados
similares se obtuvieron en genes altamente expresados en nódulos maduros (O’Rourke et al.,
2014). Esto sugiere que los factores de transcripción de la familia AGAMOUS-LIKE PRO-
TEIN (AGL) pueden estar involucrados en la regulación de la nodulación, no sólo a través de
la expresión del miRNA172c sino que de otros genes importantes para este proceso.
El papel de ciertos factores de transcripción de la familia AGL se ha estudiado a partir
de la regulación de la organogénesis de la flor (Jofuku et al., 1994). Algunos miembros de
esta familia son indispensables para este proceso, el cual se regula por una compleja red en la
que interaccionan los AGL con otros factores de transcripción, como los AP2 y con miRNAs
como el miRNA172 (Azpeitia et al., 2014; Causier et al., 2010).
26
Por los resultados ya comentados en el Anexo C decidimos continuar el análisis de la
familia AGL en frijol. Este trabajo fue publicado en el artı́culo: Iñiguez LP, Nova-Franco
B, Hernández G (2015) NOVEL PLAYERS IN THE AP2-miR172 REGULATORY NET-
WORK FOR COMMON BEAN NODULATION. Plant Signaling & Behavior, 10 (10),
e1062957, que se muestra a continuación. Encontramos que en el genoma de P. vulgaris
hay 91 genes anotados como AGLs. Al analizar sus patrones de expresión en diferentes órga-
nos encontramos que 21 AGLs muestran expresión en nódulos o raı́ces de frijol (O’Rourke
et al., 2014) y 8 de ellos muestran una expresión diferencial entre las raı́ces o nódulos en
comparación con otros tejidos. Los AGLs que mostraron una expresión en los tejidos sub-
terráneos fueron anotados con mayor detenimiento utilizando a A. thaliana como referencia
y buscando los genes ortólgos de soya.
27
Novel players in the AP2-miR172 regulatory network for common
bean nodulation
Luis P I~niguez, Barbara Nova-Franco, and Georgina Hernandez*
Centro de Ciencias Genomicas; Universidad Nacional Autonoma de Mexico; Cuernavaca, Mexico
The intricate regulatory network for
floral organogenesis in plants that
includes AP2/ERF, SPL and AGL tran-
scription factors, miR172 and miR156
along with other components is well
documented, though its complexity and
size keep increasing. The miR172/AP2
node was recently proposed as essential
regulator in the legume-rhizobia nitro-
gen-fixing symbiosis. Research from our
group contributed to demonstrate the
control of common bean (Phaseolus vul-
garis) nodulation by miR172c/AP2-1,
however no other components of such
regulatory network have been reported.
Here we propose AGLs as new protago-
nists in the regulation of common bean
nodulation and discuss the relevance of
future deeper analysis of the complex
AP2 regulatory network for nodule
organogenesis in legumes.
The transcription factor (TF) super-
family APETALA2/Ethylene Responsive
Factor (AP2/ERF) is conserved among the
plant kingdom. Members of this large TF
family, that regulate plant development
and response to stress, have the character-
istic AP2/ERF domain (IPR001471,
www.ebi.ac.uk/interpro/) that consists of
60-70 amino acids and is involved in
DNA binding.1,2 Since 1994, Jofuku
et al.3 reported the relevant role of AP2 in
the control of flower development in Ara-
bidopsis. The AP2 gene is part of a com-
plex regulatory network that includes the
microRNA172 (miR172) which promotes
flowering by repressing AP2 through
translation inhibition or mRNA cleav-
age.4-6 Transcription of MIR172 genes is
activated by SPL (SQUAMOSA PRO-
MOTER BINDING PROTEIN-LIKE)
TF, that are targets miR156.7 In addition,
both MIR156 and MIR172 promoters
have binding sites for AP2 that acts as a
dual transcriptional regulator activating
MIR156 and repressing MIR172, thus
constituting a negative feedback loop in
the complex and robust floral organogene-
sis networks.8
The key regulatory role of miR172/
AP2 in legume nodulation during the rhi-
zobia nitrogen-fixation symbiosis has been
reported for soybean (Glycine max) and
common bean (Phaseolus vulgaris).9-11 We
have recently demonstrated that in com-
mon bean miR172c and its target gene
AP2-1 (Phvul.005G138300) are essential
regulators for rhizobial infection and nod-
ulation through the regulation of the
expression of early nodulation and autore-
gulation-of-nodulation (AON) genes. In
addition we proposed that AP2-1 is a posi-
tive regulator of nodule senescence genes
and thus it is silenced, through miR172c-
induced target cleavage, in mature nod-
ules.11 Though these studies,9-11 suggest
that miR172 and their corresponding AP2
targets are part of complex regulatory net-
works in legume nodules, it is essential to
decipher other yet unknown network
components. We have analyzed SPLs as
possible transcription regulators of com-
mon bean MIR172 genes, but only nega-
tive results were obtained.11 In this work
we propose other regulators involved in
miR172/AP2-1 regulatory network for
common bean nodulation and discuss the
importance for their deeper future analysis
We used CLOVER,12 to analyze the
promoter sequence of each of the 6 P. vul-
garis MIR172 genes,11 in order to identify
additional transcriptional regulators. We
found that AGL (AGAMOUS-LIKE
PROTEIN) TF binding sites were statisti-
cally overrepresented in these promoters.
Keywords: AGL, legume-rhizobia symbi-
osis, MADS, microRNAs, nodulation,
regulatory networks, transcription factors
*Correspondence to: Georgina Hernandez; Email:
gina@ccg.unam.mx
Submitted: 05/13/2015
Revised: 06/09/2015
Accepted: 06/10/2015
http://dx.doi.org/10.1080/15592324.2015.1062957
Addendum to: Nova-Franco B, I~niguez LP,
Valdes-Lopez O, Alvarado-Affantranger X, Leija A,
Fuentes SI, Ramírez M, Paul S, Reyes JL, Girard L,
Hernandez G. The microRNA172c-APETALA2-1
node as a key regulator of the common bean-
Rhizobium etli nitrogen fixation symbiosis. Plant
Physiol 2015; 168:273-291
www.tandfonline.com e1062957-1Plant Signaling & Behavior
Plant Signaling & Behavior 10:10, e1062957; October 2015; © 2015 Taylor & Francis Group, LLC
ARTICLE ADDENDUM
28
In addition, a similar analysis was per-
formed to the promoters of genes previ-
ously identified as highly expressed in
P. vulgaris mature nodules, these genes
co-express with those from the purine and
ureide biosynthetic pathways.13 Ureides
are the main product from fixed-nitrogen
assimilation in warm season legumes and
the main nitrogen supply for plant nutri-
tion. Notably, among other TF binding
sites, AGL sites were statistically over-rep-
resented in the promoters of common
bean nodule-enhanced genes.
The AGL TF family, also known as
MADS (MINICHROMOSOME MAIN-
TENANCE1, AGAMOUS, DEFICIENS
and SERUM RESPONSE FACTOR) in
other eukaryotes, has been widely studied
for the regulation of flower organogene-
sis.14 Some members of this family, such
as AP1, AP3, SOC, AG and FUL, are
indispensable for the correct floral forma-
tion and may interact with members from
the AP2/ERF superfamily, such as AP2 or
TOE.15,16 AGLs, such as AGL15 and
SOC1, have been included as part of the
complex AP2 network for floral transition
and floral development in Arabidopsis.8
On this basis, we consider interesting
to study AGLs from common bean as a
new players of nodule organogenesis net-
works, something that, to our knowledge,
has not been yet analyzed for legumes.
Ninety-one genes were annotated as AGL
or MADS TF genes in the P. vulgaris
genome (www.phytozome.net/common-
bean.php, v1.0).17 According to the
P. vulgaris Gene Expression Atlas (Pv
GEA),13 only 21 (out of 91) AGL genes
were expressed in at least one root or nod-
ule tissue sample; these are shown in
Figure. 1. According to the common
bean expression analysis among different
tissues reported by O’Rourke et al.,13 8 of
the AGL genes shown in Figure. 1 were
significantly up regulated in nodules and/
or roots as compared to aerial tissues
This subset of 21 common bean AGL
genes were carefully annotated based in
their sequence similarity to Arabidopsis
AGL genes, following the annotation from
The Arabidopsis Information Resource
(TAIR, www.arabidopsis.org) as shown in
Table 1. In addition, Table 1 includes
information from TAIR about the expres-
sion in roots tissues of the Arabidopsis
AGL genes that are similar to the root/
nodule common bean AGL genes. Nota-
bly, most of these genes showed expression
in one or more root-tissue samples from
Arabidopsis (Table1), something that cor-
relates with their tissue expression in com-
mon bean (Fig. 1). In order to gain
insight into the potential roles of AGL TF
in the control of the symbiotic interaction
between legumes and nitrogen fixing bac-
teria we searched for the orthologous
genes, to those analyzed from common
bean, in soybean (Glycine max), a legume
closely related to P. vulgaris.17 As shown
in Table 1, we identified 17 soybean genes
orthologous to common bean AGL genes
expressed in root or nodule. The data
from the RNA-seq Atlas of G. max.18
revealed that 13 ortholog AGL genes
expressed highest in underground tissues
(Table 1). Thus, the data on soybean
gene expression, shown in Table 1, sup-
port the hypothesis of AGL genes as regu-
lators of the legume /rhizobia symbiosis
AGL21 is expressed in several different
Arabidopsis root tissues (Table1). This
gene is crucial for lateral root
development; its expression is affected by
phytohormones and in response to
stresses. Arabidopsis plants overexpressing
AGL21 produce more and longer lateral
roots. AGL21 has been implicated in
auxin accumulation in lateral root primor-
dia.19 The common bean AGL21
(Phvul.008G183700) is upregulated in
roots vs pods, seeds and stems and it is
also expressed in nodules (Fig.1). The soy-
bean AGL21 (Glyma.14G183800) shows
its highest expression in roots (Table 1).
It is known that the auxin/cytokinin ratio
is strictly controlled and plays an impor-
tant role in nodule development during
the legume-rhizobia symbiosis. Auxins
plays (at least) a dual role during nodula-
tion: in the early stages auxin transport
inhibition might result in a reduced
auxin/cytokinin ratio to allow cell division
to start and later divisions are inhibited by
super optimal auxin levels.20 Thus, it is
conceivable that the root/nodule expres-
sion of common bean AGL21 is related to
auxin regulation of the symbiotic process
XAL1 and XAL2 are other AGL’s
important for Arabidopsis root
Figure 1. Heatmap of the P. vulgaris AGL genes that are expressed in root/nodule tissues. Values of
expression were extracted from the Pv GEA, tissues were analyzed as reported in O’Rourke et al. 13.
Gene IDs are from www.phytozome.net/commonbean.php. Left: Expression patterns as Z-score.
The color scale represents the expression distribution, red indicates high expression, and black indi-
cates the mean of expression and green indicates low expression. Right: Differential expression (up
regulation) in root and/or nodule as compared to other tissues, based in data from the Pv GEA.13
e1062957-2 Volume 10 Issue 10Plant Signaling & Behavior
29
_2 _1 O 1 2 
Row Z-Soore 
development. Arabidopsis mutants
affected in any of these genes present
shorter roots. XAL1 is important for the
regulation of cell cycle.21 XAL2 controls
auxin transport in the root and is impor-
tant in the formation and maintenance of
the root stem-cell niche.22 In agreement,
the soybean XAL1 (Glyma.12G005000)
gene shows its highest expression in
roots.18
ANR1 (ARABIDOPSIS NITRATE
REGULATED1) is expressed in Arabi-
dopsis roots (Table 1), it is up-regulated
upon nitrate starvation and in nitrate rich
media it controls local proliferation of
lateral roots.23 Arabidopsis ANR1 is part
of a nitrogen sensing network that include
NLP7 (NIN-LIKE PROTEIN 7).23,24
The NIN (NODULE INCEPTION) TF
family was first identified in Lotus japoni-
cus,25 now it is known that NIN is crucial
for the initiation of nodule formation in
different legumes. Recently it was shown
that L. japonicus NIN directly transcribes
the CLE root signal genes, involved in the
AON.26 We identified 3 copies of P.
vulgaris ANR1 genes that are expressed in
roots and nodules and one soybean ortho-
log (Glyma.08G065400) with highest
expression in roots (Table 1, Fig. 1). Our
previous report indicates a positive regula-
tion of common bean NIN and AP2-1 to
RIC1 (RHIZOBIUM-INDUCED CLE
PEPTIDE1) and suggested the involve-
ment of these regulators in the AON in
common bean.11 On this basis and con-
sidering the relevance of nitrogen metabo-
lism in legume nodules we propose that
ANR1 as new player in the regulation of
legume/rhizobia symbiosis
The regulatory network for flower
organogenesis, composed by AP2/ERF,
AGL and miRNA members, is still grow-
ing in size and complexity. However, only
few players have been described in
Table 1. Annotation of P. vulgaris root/nodule AGL genes and their expression in A. thaliana and G. max (soybean) tissues. The P. vulgaris gene annotation
was based in that for Arabidopsis (TAIR, www.arabidopsis.org). The expression (TAIR) in one or more root tissue samples of the Arabidopsis orthologues of
each AGL common bean gene, is indicated. The soybean AGL genes identified as orthologues of the common bean genes are indicated as well as the plant
tissue where each gene presents its highest expression value, according to data from the RNA-seq Atlas of G. max.18
Phaseolus vulgaris Arabidopsis thaliana Glycine max
Gene Gene Expression in root tissues Gene Highest expression in
Phvul.002G143800 AGL17 root epidermis, root,
root cap, root
elongation zone
Glyma.09G201700 root
Phvul.002G143900 ANR1 root — —
Phvul.002G147600 SVP root Glyma.02G041500 root
Phvul.002G212400 TT16 — — —
Phvul.002G212500 SVP root Glyma.08G068200 young leaf
Phvul.002G215500 ANR1 root Glyma.08G065400 root
Phvul.003G182700 FUL — Glyma.05G018800 root
Phvul.003G213600 AGL19 root, lateral root cap Glyma.17G132700 root
Phvul.004G123500 AGL13 root Glyma.16G200700 nodule
Phvul.005G036700 AGL26 root Glyma.08G152700 young leaf
Phvul.006G200300 AGL17 root epidermis, root,
root cap, root
elongation zone
Glyma.13G255200 root
Phvul.006G200400 ANR1 root — —
Phvul.007G065100 AGL13 root Glyma.10G240900 root
Phvul.008G027800 CAL root Glyma.08G250800 root
Phvul.008G073800 SOC1 root Glyma.09G266200 root
Phvul.008G183700 AGL21 lateral root
primordium, primary
root tip, central
root cap
of primary root,
root endodermis,
primary root
apical meristem,
root procambium,
root, root stele
Glyma.14G183800 root
Phvul.009G037300 AGL24 — Glyma.06G095700 root
Phvul.009G203400 FUL — Glyma.06G205800 pod shell 10DAF
Phvul.010G088000 SOC1 root Glyma.07G080900 young leaf
Phvul.010G088100 XAL2 root — —
Phvul.011G005800 XAL1 non-hair root epidermal
cell, root vascular
system, primary
root differentiation
zone, root, root stele
Glyma.12G005000 root
www.tandfonline.com e1062957-3Plant Signaling & Behavior
30
networks for nodule development.
Recently, AP2 genes were described as
protagonists in nodulation,9-11 it seems
most probable that these do not play
alone. Here we propose AGL transcription
regulators as another component of nodu-
lation networks. Future work is required
to demonstrate our proposal and to deci-
pher the complete set of players from net-
works controlling the intricate nodule
organogenesis during the symbiotic nitro-
gen fixation, a most relevant process for
sustainable agriculture.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were
disclosed.
Funding
This work was supported by Direccion
General de Asuntos del Personal
Academico / UNAM (grants no. PAPIIT:
IN209710 and IN210814). LPI and BNF
received a PhD studentship from Consejo
Nacional de Ciencia y Tecnologıa, Mexico
(nos. 340334 and 351615, respectively).
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5. Splicing Alternativo
El AS como se explicó en la introducción es un proceso post-transcripcional que se refiere
a la transcripción de diversas isoformas de un solo gen. El AS además de generar diversidad
protéica funciona como un regulador de la expresión génica ya que a partir de éste se pueden
generar proteı́nas truncas que se regulen por NMD, puede interferir en los sitios de reconoci-
miento de miRNAs o puede provocar marcos de lectura abiertos rı́o arriba del inicio canónico
de la traducción. Las diferentes funciones del AS apenas están siendo exploradas en algunos
genes, sin embargo las caracterı́sticas globales del AS pueden ayudar a esclarecer algunos
aspectos del AS.
Por otra parte el AS tiene un aspecto importante en la evolución ya que algunas isoformas
pudieran ser consideradas como parálogos internos. Esto quiere decir que se contarı́a con dos
o más mRNAs de un mismo gen. Por lo tanto una de las preguntas evolutivas del AS es qué
sucede con el AS cuando un gen se duplica.
5.1. AS en Phaseolus vulgaris y Glycine max
5.1.1. Introducción
El estudio del AS en plantas se ha centrado más en plantas modelo como A. thaliana u
O. sativa (arroz). En leguminosas este aspecto se ha dejado a un lado ya que existen muy
pocos reportes sobre AS en esta familia. A nivel de leguminosas se reportó que el evento de
AS que más sucede es IR sin embargo este estudio se basó en secuencias de EST’s por lo que
la incidencia del AS fue bastante baja (˜20 % de los genes con AS) (Wang et al., 2008). A
pesar de esos resultados se lograron identificar eventos de AS conservados en las leguminosas
analizadas. Los eventos de AS conservados, como se mencionó en la introducción, sugieren
una funcionalidad y por ende una relevancia biológica.
La utilización de EST’s para identificar AS es importante ya que mediante esta técnica se
obtienen isoformas completas, pero debido a las limitaciones de la técnica sólo se alcanza a
32
apreciar una pequeña porción del AS. A partir de las metodologı́as de secuenciación masiva
(RNA-seq) se pueden distinguir muchos más eventos de AS, pero no se cuenta con la infor-
mación necesaria para obtener isoformas completas. A partir de RNA-seq se ha estudiado
el AS en las leguminosas Medicago truncatula y en Glycine max (Mandadi and Scholthof,
2015; Shen et al., 2014; Wang et al., 2014). En estos estudios de ha establecido que el evento
más común es el IR, consistente con lo reportado en A. thaliana (Marquez et al., 2012) sin
embargo sólo se han enfocado en 4 diferentes tipos de eventos de AS; AA, AD, ES e IR.
Las recientes publicaciones del genoma (Schmutz et al., 2014; Vlasova et al., 2016) de
P. vulgaris en conjunto con las bibliotecas públicas de RNA-seq de esta especie permitie-
ron plantear el proyecto sobre la identificación de eventos de AS en frijol. Para identificar
aquellos eventos biológicamente relevantes se utilizaron bibliotecas públicas de G. max, una
especie relacionada filogenéticamente a frijol. Estos datos conllevan al mayor estudio de
identificación, caracterización y conservación de eventos de AS con el que se cuenta hasta la
fecha.
5.1.2. Resultados y Discusión
Para identificar los eventos de AS en P. vulgaris se utilizaron todas las muestras dis-
ponibles en los archivos SRA de RNA-seq de esta leguminosa. En total se utilizaron 157
bibliotecas pertenecientes a 107 muestras biológicas diferentes, la longitud de las secuencias
estuvo en un rango de 36-101 nucleótidos en bibliotecas tanto single-end como pair-end. Se
analizaron más de 11 x 109 secuencias. Además, se utilizaron 176,782 secuencias más lar-
gas reportadas como EST’s. En cambio para G. max se utilizaron menos bibliotecas (84 de
77 muestras) de RNA-seq (3.5 x 109 secuencias) pero más EST’s (1,461,723). La cantidad
de muestras analizadas para cada especie y en total supera por mucho cualquier análisis de
identificación de AS en alguna otra planta.
Para la identificación de los eventos a partir de las bibliotecas de RNA-seq se utilizaron
dos diferentes algoritmos, cuyas estrategias difieren en la forma de identificar los eventos
de AS. A partir de estos resultados se logró identificar que alrededor del 70 % de los genes
33
expresados presentan algún tipo de AS. Los eventos se catalogaron en los 7 diferentes tipos
de eventos de AS e IR fue el evento más común en ambas leguminosas, seguido de AA y
AD. La concordancia en los resultados de ambas plantas muestra la importancia del AS en
las leguminosas y sobre todo sugiere mecanismos para seleccionar eventos de tipo IR, AA y
AD.
Los diferentes tipos de eventos también fueron caracterizados dependiendo de la región
génica que afectaban. Sorprendentemente los resultados de este análisis muestran que existe
una sobre-representación de regiones no codificantes para eventos de tipo AA, AD, ASS y
RE, mientras que IR sucede más en regiones CDS en ambas plantas. Otra caracterización de
los diferentes tipos de eventos que se realizó fue respecto a su posición relativa dentro de un
gen. Los eventos tanto de AA como AD tienden a estar en regiones 5’ del gen mientras que IR
tiende a presentarse hacia el extremo 3’. Al igual que los resultados de regiones codificantes
ambas leguminosas muestran el mismo patrón.
Para identificar los eventos de AS que se conservaran entre especies se identificaron los
genes homólogos. La conservación de eventos de AS de frijol en soya va desde el 22 % hasta
el 37 % dependiendo de cuáles eventos se tomen en cuenta. Se observa una conservación del
22 % cuando se toma la totalidad de eventos de AS identificados en P. vulgaris mientras que el
37 % se refiere a los eventos conservados únicamente en genes homólogos. Mientras tanto los
eventos conservados de G. max en P. vulgaris van desde el 18 % hasta el 35 %. Otro resultado
interesante de la conservación surge a partir de analizar cada tipo de evento por separado ya
que ambas leguminosas presentan el mismo patrón. IR es el evento más conservado seguido
de AA y AD, los eventos más comunes y le siguen NI y RE. Esto muestra que estos últimos
tipos de eventos pudieran tener una relevancia mayor que ASS y ES.
Dado el supuesto de que los eventos conservados tienen una función biológica relevante
se realizó un análisis de regiones génicas (5’UTR, CDS y 3’UTR) donde se encontró un en-
riquecimiento de los eventos conservados en regiones 5’UTR mientras que una disminución
notable en regiones 3’UTR. Esto pudiera significar que los eventos en las regiones 3’UTR se
comportan de forma aleatoria y tienen una función poco relevante o nula.
34
Otro aspecto importante dentro de este trabajo fue la validación de eventos de AS. Se
tomaron muestras de raı́z y hojas a tempranas edades de crecimiento de frijol y soya y se
probaron distintos eventos de AS en 9 genes. La metodologı́a que se utilizó para la validación
fue RT-PCR en punto final a partir oligonucléotidos diseñados para identificar el splicing
constitutivo y el alternativo en una misma reacción.
El análisis detallado, los resultados y discusión de este trabajo fueron publicados en el
artı́culo: Iñiguez LP, Ramı́rez M, Barbazuk WB, Hernández G (2017) IDENTIFICATION
AND ANALYSIS OF ALTERNATIVE SPLICING EVENTS IN PHASEOLUS VULGARIS
AND GLYCINE MAX. BMC Genomics 18:650 . El artı́culo se muestra a continuación:
35
RESEARCH ARTICLE Open Access
Identification and analysis of alternative
splicing events in Phaseolus vulgaris and
Glycine max
Luis P. Iñiguez1*, Mario Ramírez1, William B. Barbazuk2 and Georgina Hernández1*
Abstract
Background: The vast diversification of proteins in eukaryotic cells has been related with multiple transcript
isoforms from a single gene that result in alternative splicing (AS) of primary transcripts. Analysis of RNA sequencing
data from expressed sequence tags and next generation RNA sequencing has been crucial for AS identification and
genome-wide AS studies. For the identification of AS events from the related legume species Phaseolus vulgaris and
Glycine max, 157 and 88 publicly available RNA-seq libraries, respectively, were analyzed.
Results: We identified 85,570 AS events from P. vulgaris in 72% of expressed genes and 134,316 AS events in 70%
of expressed genes from G. max. These were categorized in seven AS event types with intron retention being the
most abundant followed by alternative acceptor and alternative donor, representing ~75% of all AS events in both
plants. Conservation of AS events in homologous genes between the two species was analyzed where an
overrepresentation of AS affecting 5’UTR regions was observed for certain types of AS events. The conservation of
AS events was experimentally validated for 8 selected genes, through RT-PCR analysis. The different types of AS
events also varied by relative position in the genes. The results were consistent in both species.
Conclusions: The identification and analysis of AS events are first steps to understand their biological relevance.
The results presented here from two related legume species reveal high conservation, over ~15–20 MY of
divergence, and may point to the biological relevance of AS.
Keywords: Alternative splicing, Conservation of alternative splicing, RNA-seq, Legumes, Common bean, Soybean
Background
The majority of protein-coding genes from eukaryotic
organisms contain introns, non-coding sequences that
need to be spliced from the primary transcript to gener-
ate mature functional mRNAs. Although some introns
can be self-spliced, most require a spliceosome, special-
ized splicing machinery. Spliceosomes are large ribonu-
cleoprotein complexes that include small nuclear RNAs
(snRNA) [1–3]. The spliceosome recognizes signals from
common introns that allow their removal from the pre-
mRNA. The U1 snRNA recognizes signals from the 5′
splice site, a GT dinucleotide. The U2 snRNA recognizes
the 3′ splice site that includes an AG dinucleotide, an
adenine which functions as a branching point upstream
of the 3′ splicing site and a polypyrimidine tract between
the branching point and the 3′ splicing site [4]. Other
snRNAs, such as U11 and U12, recognize different splice
sites, although spliceosomal introns of this class repre-
sent a minority [3, 4]. Different proteins that facilitate
the recognition of the motifs by the spliceosome also
mediate splicing. Serine/arginine-rich (SR) proteins fa-
cilitate the splicing of the intron while heterogeneous
nuclear ribonucleoproteins inhibit the recognition of
splicing sites [5]. The intron motifs as well as splicing
enhancers and inhibitors are commonly found at differ-
ent sites within the intron and these juxtaposed signals
can give rise to variation in splicing an intron from the
pre-mRNA; this phenomenon is known as alterative
splicing (AS) [6–8].
AS is a post-transcriptional regulatory process that
affects the fate of the mRNA; it has been found in
several tissues, stress conditions and developmental
* Correspondence: liniguez@lcg.unam.mx; gina@ccg.unam.mx
1Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México
(UNAM), Cuernavaca, Morelos, Mexico
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Iñiguez et al. BMC Genomics  (2017) 18:650 
DOI 10.1186/s12864-017-4054-2
36
BMC Genomics 
o BioMed Central 
stages of eukaryotic organisms [9]. AS can affect the
localization of the mature mRNA and their translation ef-
ficiency [8]. Also, AS may produce alternative stop codons
due to frame shifts in the mature mRNA sequence, thus
regulating mRNA abundance by nonsense-mediated decay
(NMD) [10]. Some other process linked to AS, such as
mRNA storage or the target recognition of micro RNAs,
have been reported [11, 12]. AS may also result in differ-
ent protein isoforms derived from a single gene thus
affecting protein localization or function [7, 13].
The evolutionary significance of AS has been related
to organismal complexity. The number of genes in nem-
atodes is very similar to that in humans although these
organisms develop strikingly different cell types [14].
However, 98% of human multiple-exon genes undergo
AS [15] in contrast to only 20% of nematode genes [16].
Chen et al. [17] analyzed several organisms that vary in
their amount of different cell types –a proxy for organis-
mal complexity- and found a strong positive correlation
between the number of cell types and the level of AS.
Organisms with higher complexity tend to have higher
levels of AS.
AS genome-wide analyses, based on expression se-
quence tags (ESTs) and next generation RNA sequences
(RNA-seq), have been reported for several plant species
[18–27]. The first available resources for such analysis
were EST databases that included sequences of large
mRNA fragments often representing complete mRNA
isoforms, but due to sampling the number of AS iso-
forms were likely underestimated. Next generation
RNA-seq technologies produce a huge amount of
sequences; however, these sequences are too short for
complete isoform identification but can be used for
characterization of AS events.
The recognition of splicing sites and the frequencies of
AS types, vary between plants and animals. Animals
tend to have very large introns and therefore the splicing
machinery recognizes exons (exon definition) while in
plants the spliceosome recognizes introns (intron defin-
ition) [28]. It has been proposed that a failure in the
exon definition can lead to skipping an exon during spli-
cing, while failure in intron definition results in intron
retention [29]. The intron and exon definition models
could explain the differences in the most common AS
processes observed between plants and humans. Specif-
ically, intron retention is the most abundant AS type
observed in plants [30] while exon skipping is most
common in animals [15].
Our research has focused on genome-wide analyses of
transcriptional and post-transcriptional regulation in
legume plants. Legumes are second only to Gramineae
in their importance as crops; they are rich in protein
content and have long been used for humans and animal
consumption. Legumes are important contributors to
biological nitrogen due to their ability to establish sym-
biosis with nitrogen-fixing soil bacteria (rhizobia). This
relationship allows the legumes to grow under low or
non-nitrogen fertilized media. Symbiotic nitrogen fix-
ation has been a focus of research due to its economic
and environmental importance [31]. Common bean
(Phaseolus vulgaris) and soybean (Glycine max) are the
most important legume crops worldwide. Common bean
is the most important legume for human consumption
as a source of proteins and micronutrients for millions
of people, especially in Latin America and Africa where
beans are important components of traditional diets
[32]. Soybean is important worldwide as the dominant
source of protein for animal feed and cooking oil [33].
These legumes are closely related and their evolutionary
history makes them ideal models for genomic studies.
Both the P. vulgaris and G. max genomes have been se-
quenced [33–35]. These legume species are evolutionary
closely related, having diverged only ~19.2 million years
ago (MYA), and share a whole-genome duplication
(WGD) event ~56.5 MYA. G. max experienced an inde-
pendent WGD ~10 MYA [34]. Thus, they are also good
models to analyze features related to polyploidization.
This work analyzes AS in P. vulgaris and G. max by
identifying and characterizing seven different AS events
types genome-wide. This includes the identification of
the introns/exons affected by AS and their relative pos-
ition in the gene. AS event conservation between these
legumes helps to elucidate some important aspects of
the different types of AS. This work increases our know-
ledge of AS in legumes and provides a platform for
further investigation.
Results and discussion
AS identification
The characterization of AS events is a first step to
understand the importance and prevalence of AS in
plants. Four different types of AS events are the most
frequently described in the literature: exon skipping
(ES), where a whole exon is missed in comparison to
the primary transcript; intron retention (IR), an intron
is not spliced and is part of the mature mRNA; alterna-
tive donor (AD), the donor site, also known as 5′ spli-
cing site, change in the mRNA isoform; and alternative
acceptor (AA), where the 3′ splicing site is different.
Based on a primary transcript three additional AS
events can be described; alternative splicing sites (ASS),
where both donor and acceptor sites change; new
intron (NI), when a splicing site appears in a reported
exon; and retained exon (RE), a new exon replaces a
previously annotated intron in the mature mRNA.
Schematic representations of these seven different type
of AS events are presented in Fig. 1.
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 2 of 17
37
AS events were called when an exon-exon junction of
a gene showed evidence for two or more splicing alter-
natives. The short length of RNA-seq sequences used for
analysis allows the identification of AS events, but ex-
cludes the possibility of identifying whole isoforms; thus,
alternative transcription start site, alternative polyadeny-
lation site, translation start site and trans-splicing were
not analysed.
Both the P. vulgaris and the G. max genome anno-
tations [33, 34] include gene isoforms produced by
AS in ~10 and ~23% of their genes, respectively. However,
this is not consistent with reports that show higher pro-
portions of AS genes in other plants [18–27, 36, 37]. The
most frequent AS event reported for both legume
genomes is AA, followed by AD and ES. In P. vulgaris the
order of frequency following the first three events is IR,
RE and NI and in G. max it is RE, NI and IR. The least
frequent AS event reported for both plants is ASS
(Additional file 1). Again, these results are not con-
sistent with the AS profiles reported for other plants
where IR the most common AS event, followed by AA
and AD, and ES being the least common [7, 8, 36, 37].
These results suggest that the current annotation lacks a
comprehensive identification of AS isoforms in these two
legume species.
Here, RNA-seq and EST’s were analysed based on the
methodology illustrated in Fig. 2, where two different
mapping algorithms were used. The qualitative workflow
allowed to identify AS events, based on the reported
primary transcript. Thus AS events could be identified
in any sample independently of the existence of the pri-
mary transcript in the same sample. This methodology
(Fig. 2) led to the identification of AS events from P.
vulgaris and G. max with a coverage of 88 and 72% of
previously reported AS events, respectively. IR events
were the most frequent for both species and ES the
least frequent (Table 1). A total of 82,343 and 115,881
new AS events were identified for P. vulgaris and G.
max, respectively (Table 1 and Additional file 2). The
complete list of events covered 65% of P. vulgaris anno-
tated genes and considering only the expressed genes
included in the analysed RNA-seq samples (24,862), it
Fig. 1 AS events. The types of AS events identified in this work are show, all of these are based on a primary transcript. Transcripts affected by AS
are highlighted with an asterisk on the right side of the drawing. Drawing by Illustrator for Biological Sequences [66]
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 3 of 17
38
Alternative Acceptor (AA) < * 
Alternative Donar (AD) < * 
Alternative Spllcing Sites (ASS) < * 
Exon Skipping (ES) --- -- < * ----------
Intron Retention (IR) < * 
New Intron (NI) < * 
Retained Exon (RE) 
~ < V V * 
was 72% of genes (Table 2). For G. max, 55% of anno-
tated genes were affected by AS, or 70% of genes in-
cluded in analysed RNA-seq libraries (43,712) (Table 2).
Almost all (~98%) reported junctions in both legumes
present the canonical intron motifs for the spliceosome
recognition: the 5′ splicing site GT and the 3′ splicing
site AG [4]. Most of the new junctions identified in this
work, 78% for P. vulgaris and 85% for G. max, present
the exact same motifs thus being considered substrates
of the spliceosomal machinery (Additional file 3). The
rest of the identified introns that presented non-
canonical splicing sites, were also considered in our
analysis based in previous knowledge about relevant
regulatory roles of spliceosome-independent (self-splicing)
introns from other organisms [38]. Nevertheless, the
proportion of canonical splicing sites considered in this
analysis –including reported and newly identified junc-
tions- remains >93% for both legumes (Additional file 3).
The proportion of genes affected by AS are similar to
those reported in other plants [18–27, 36, 37]. However,
the distribution among types of AS events for both spe-
cies differ from that reported in the genome annotations
[33, 34] (Table 1 and Additional file 1). The three most
frequent AS events identified for both plants were IR, AA,
AD; corresponding to ~75% of all AS events (Table 1).
Despite the different samples used for both species, the
proportion of events as well as the number of genes af-
fected by AS were similar (Tables 1 and 2). In mammals,
the most common AS event is ES, ~50% of all events, this
contrasts with plants where ES is generally less than 10%
(Table 1) [9, 39]. Key processes have been implicated in
the functionality of AS in plants for AA, AD and IR
events. IR has been implicated in the process of NMD
[10] due to the incorporation of stop codons. IR also plays
an important role in Mariselea vestita in mRNA storage
during it embryo development [12]. AA and AD are
Fig. 2 Method for AS event identification. Workflow for the identification of AS events in a single species. RNA-seq reads were mapped to their
respective genome by two different algorithms, each mapping file was processed with Cufflinks to obtain the gene models. All gene models were
compared to the primary transcripts to identify AS events. EST’s were also mapped and with both mapping files as well as the replicates all events
were filtered. An expression value for each gene was calculated based on the mapping files
Table 1 Genome-wide AS identification
Phaseolus vulgaris Glycine max
AS event Identified from genome
annotationa [34]
New Total % Identified from genome
annotationa [33]
New Total %
AA 1076 16,997 18,073 21 5209 26,901 32,110 24
AD 814 12,464 13,278 15 4126 20,113 24,239 18
ASS 59 6001 6060 7 343 5300 5643 4
ES 579 4285 4864 6 2543 8351 10,894 8
IR 387 33,091 33,478 39 1980 41,016 42,996 32
NI 115 3819 3934 5 2325 5267 7592 6
RE 377 5686 6063 7 1909 8933 10,842 8
aTotal AS events reported in the annotated genomes are shown in Additional file 1
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 4 of 17
39
Gene 
Model 
Expressio'!. # • 
,-_.\ .: .. .,: 
, Gene \ 
, expression I 
..... _---"" 
reported as consequences of small duplications in the
splicing sites, allowing the incorporation or exclusion of
one or several amino acids [40–43].
Experimental validation of identified AS events
The RT-PCR approach was used to experimentally valid-
ate AS events selected from those identified in ~70%
expressed genes from P. vulgaris and G. max genomes
(Tables 1 and 2). Nine P. vulgaris genes and their corre-
sponding G. max homolog, expressed in the plant
tissues analyzed (roots and leaves from seedings), were
selected for RT-PCR analysis. The selected genes pre-
sented conserved AS within both species (see subse-
quent sections). The two RT-PCR reactions performed
for each gene, with RNA samples from root and from
leaf, showed similar results in every case; Fig. 3 shows
the results from leaves samples except for panels a and f
that show results from root samples. In every gene
analyzed the amplified products (ranging from 166 to
1599 bp) corresponded to expected fragments from the
primary transcript or from transcript isoforms derived
from AS events, according to each gene model (Fig. 3).
The different types of AS events (Fig.1) validated for the
selected genes included: AA (Fig. 3a–g), ASS (Fig. 3f ),
IR (Fig. 3b–h) and RE (Fig. 3b, c). As expected, only one
amplified product corresponding to the primary tran-
script, could be observed in the control gene selected
(Fig. 3i). In Fig. 3g, h additional amplification products,
from those predicted from the gene models, could be
observed; we cannot rule out that these correspond to
AS events not identified in our analysis something that
could be related to restrictions in the methodology we
used. Taken together, the experimental results (Fig. 3) do
validate and increase the reliability of the bionformatic
data from this work.
AS in CDS-UTR regions
The seven event types analysed here can be divided into
two classes depending on how the AS event modifies the
reference transcript. AA, AD, ASS, IR and RE events
modify reference exon-exon junctions, while NI and ES
alter reference exons, either excluding it or introducing
a new intron (Fig. 1). There are three main types of
introns defined by the untranslated regions (UTR) and
coding DNA sequences (CDS) of the primary transcript,
and six types of exons. The intron classification is based
on the types of exon they are delimiting: 5’UTR-5’UTR,
CDS-CDS and 3’UTR-3’UTR. Exons, on the other hand,
can be classified in seven different types, 5’UTR, CDS,
3’UTR, 5’UTR-CDS, when the translation start site is in
that exon, CDS-3’UTR, when the stop codon is in this
exon, and 5’UTR-3’UTR which are genes without introns.
In both P. vulgaris and G. max, most introns of the pri-
mary transcript are CDS-CDS (~94%) and the rest are
5’UTR-5’UTR (~4%) and 3’UTR-3’UTR (~2%) (Fig. 4a-c).
In the case of exons, also in both genomes, the
majority (69%) were CDS (Fig. 4e), while the other
exon types, 5’UTR-CDS and CDS-3’UTR, correspond
to 12% (Fig. 4g, h). The other 7% of exons were di-
vided into 3% single exon genes (Fig. 4i), 3% 5’UTR
and 1% 3’UTR exons (Fig. 4d, f ).
The introns, or exon junctions, affected by the AS
were analysed to determine if they were randomly
distributed. The percentage of each type of intron/exon
in the genome was compared to the percentage affected
by AS. Both legumes showed an enrichment of AS
events in UTR introns (Additional file 4), whereas, AS
events in CDS junctions were under-represented, despite
being the majority of affected junctions. Similar data
were observed for each individual AS event (Fig. 4a-c).
In the case of the exons affected by AS a decrease of
CDS exons and an over-representation of every other
type of exon were observed (Additional file 4). Interestingly
in the case of exons, the under- and over-representation of
each type of AS event varied among types of exons. CDS
exons were underrepresented in NI events while overrep-
resented in ES events (Fig. 4e). In contrast, 5’UTR-CDS
and CDS-3’UTR were enriched in NI and decreased in ES
events (Fig. 4g, h). Common bean and soybean showed
similar results (Fig. 4).
While the main effect of ES is to skip CDS exons, NI
introduces introns in combined exons such as 5’UTR-
CDS or CDS-3’UTR. Marquez et al. [44] reported the
presence of NI in single exon genes and called them
“exitrons”. Together, these results point to a non-ran-
dom distribution of AS and to the selection of AS in spe-
cific regions of the genes. The similar results obtained for
both legumes suggest important aspects of specific AS
events in these species.
An interesting example of AS in CDS-CDS regions
was identified in the CSN7 gene, this protein is one of
the eight subunits of the COP9 signalosome (CSN) that
is a key player in the DNA-damage response, cell-cycle
Table 2 Genes affected by AS
Phaseolus vulgaris Glycine max
AS event Number Percentage Number Percentage
All Expressed All Expressed
AA 8820 32% 35% 16,092 29% 37%
AD 7339 27% 30% 13,872 25% 32%
ASS 4117 15% 17% 3893 7% 9%
ES 3396 12% 14% 7431 13% 17%
IR 14,162 52% 57% 22,058 39% 50%
NI 3650 13% 15% 6990 12% 16%
RE 4940 18% 20% 8967 16% 21%
Total 17,789 65% 72% 30,677 55% 70%
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 5 of 17
40
Fig. 3 (See legend on next page.)
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 6 of 17
41
d 
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control and gene expression. CSN7, as well as other 5 sub-
units, contains a N-terminal PCI domain that is important
for subunits interactions. In addition, CSN7 C-terminal
tail is responsible for interactions with the non-PCI
protein CSN6 as well as with other proteins such as the
ribonucleotide reductase RNR2. The different protein-
protein interactions of CSN7 C-terminal regulate the CSN
complex assembly as well as the function of RNR2 [45].
As shown in Fig. 3a, the CSN7 gene from P. vulgaris
(Phvul.011G190600) and G. max (Glyma.13G187200)
presents a conserved a AS event, of the AA type, in a
CDS-CDS junction (intron 7) of the C-terminal region.
The reported primary transcript differs among these
species, being the P. vulgaris primary transcript similar to
an alternative transcript isoform of G. max and viceversa
(Fig. 3a). The AA transcript isoform from P. vulgaris pre-
sents a modification of the reading frame from exon 8 that
shifts the stop codon to exon 9 (Fig. 3a). In G. max CDN7
the stop codon of the primary transcript is located in exon
9 and a similar AA event is present on intron 7 with the
(See figure on previous page.)
Fig. 3 Experimental validation of AS events in Phaseolus vulgaris and Glycine max genes. Each selected gene, with conserved AS events in both
species, is shown in a different panel: a) Phvul.011G190600, Glyma.13G187200; b) Phvul.008G270400, Glyma.02G293300; c) Phvul.007G262600,
Glyma.09G104200; d) Phvul.005G127700, Glyma.12G181300; e) Phvul.003G077300, Glyma.07G259200; f) Phvul.002G298304, Glyma.08G023000; g)
Phvul.001G014900, Glyma.14G075500; h) Phvul.006G191200; Glyma.13G245600; i) Phvul.008G013100, Glyma.18G289000. From top to bottom each
panel includes: drawing of the gene model or of a different gene model for each species (not drawn to scale) with arrows indicating the position
of the primers used for RT-PCR reactions and dotted lines indicating the splicing resulting in the primary transcript (above the gene model line)
and the AS (below the line), the color code for different types of AS events is the same used in Fig. 1; drawings representing the amplification
products expected for each transcript isoform, with its size (bp) indicated at the left; RT-PCR products resolved in 3% agarose gels, arrows indicate
size (bp) of predicted fragments, the GeneRuler 1 kb Plus DNA ladder (Thermo Scientific, USA) was included for reference (third lane)
Fig. 4 Percentage of introns and exons from UTR or CDS regions affected by AS events. AS events are dissected in terms of the percentage of
exons or introns they affect. a, b and c show the introns affected by AA, AD, ASS, IR and RE (top to bottom, colored bars) and the percentage of
introns in the genome (black bar, bottom). Each AS event includes two bar plots: the upper bar (dark tone color) shows the percentage of the
type of junction is affected by AS event and the bottom bar (light tone color) shows the percentage of the type of junction affected in
conserved AS events. The left side of each graph corresponds to Phaseolus vulgaris and the right side to Glycine max. d, e, f, g, h) and i) follow
the same structure as the above, showing the percentage of exons affected by ES and NI
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 7 of 17
42
a b e 
'" ". ""' .. .. '"" 15" ~ 1(l(N lIMIi 6MI..t0'4:21:)% '" ~ CO'!i. 6Cl'4 ilI:'),¡, l 00'r0 ,. 
'" .. "" 
P vulgaris G. max P vulgaris G. max P vulgaris G. max 
d e f 
- ES - ES .. _. 
- NI - NI 
100'lI0 iIO'JC, 6O'fo 40% 201\. '" ,.,""~ 60'4 9O!C. 100'4 
P vulgaris G. max P vulgaris G. max P vulgaris G. max 
9 h 
P vulgaris G. max P vulgaris G. max 
P vulgaris G. max 
stop codon in exon 8 (Fig. 3a). This AA event on both
species modifies the C-terminal of CSN7, we propose this
could regulate the interactions with different proteins thus
affecting the functionality of CNS in these legume species.
Conservation of AS between P. vulgaris and G. max
Different approaches may be used to analyse the conser-
vation of AS between homologous genes. Here, we used
a junction conservation approach rather than the overly
strict position conservation approach. The position con-
servation approach is based on the conservation of the
event in exact positions while for junction conservation
only the event and intron must be conserved [46]. An
AS event was considered conserved if both homologous
genes from P. vulgaris and G. max showed the same AS
event at a specific junction. Redundant AS events were
removed from identified AS events in both plants.
Redundant AS events imply that the same type of event
occurred in the same intron or exon but at a different
position. Since a junction conservation approach was
used for the AS event conservation analysis the exact
position of the AS event was, for this study, irrelevant.
The first step to identify the conservation of AS events
was to define P. vulgaris and G. max homologous genes
with the same gene model. P. vulgaris and G. max have
a relative short evolutionary distance of ~19.2 MY [34]
and soybean experienced a recent WGD, ~10 MYA;
therefore, a high proportion of common bean genes
(51%) have two homologous genes in soybean (repre-
senting 50% of the total gene set), resulting in a 1:2 rela-
tionship. As shown in Fig. 5a, 13,962 P. vulgaris genes
with 2 G. max homologs were identified. Of these 55%
(7693) were expressed and had the same gene model in
both G. max homologs. There were 7039 P. vulgaris genes
with only one identifiable homolog in G. max (Fig. 5b) (in-
cluding genes with a 1:2 relationship but where one did
have the same gene structure or only 1 G. max homolog
was expressed, plus those genes with only one expressed
homolog with the same gene structure). In total 14,712 P.
vulgaris genes and 22,405 G. max genes, representing
more than 50% of expressed genes in both plants, were
selected for analysis of AS conservation.
The junction conservation approach considers the type
of AS event and the affected intron/exon. Therefore, if
two events from the same type that coincide in a single
intron/exon, even although they differ in positions, were
considered redundant and were collapsed into non-
redundant events. 61 and 51% of all non-redundant
events belong to the genes described in Fig. 5 in Phaseolus
vulgaris and Glycine max, respectively. The proportions of
the types of AS in the non-redundant events is
similar in genes with homology in the other species
relative to the proportions of AS types in all expressed
genes (Additional file 5).
Considering only homologous genes with the same
model between P. vulgaris and G. max the rate of con-
servation of P. vulgaris AS events within G. max was
37%, here assumed as the maximum conservation rate.
Since not all the homologous genes were considered, the
minimum rate of AS conservation was 22% based on all
non-redundant events in common bean (78,027) (Fig. 6a).
On the other hand, the rate of AS conservation of G. max
in P. vulgaris homologs with the same gene model was 35
and 18% of AS events for all soybean non-redundant
events (121,133) (Fig. 6b). Interestingly, conservation dif-
fered depending on the type of AS event. The proportion
of IR events from P. vulgaris conserved in G. max was 45
(Fig. 6a) and 53% of the soybean IR events were conserved
in common bean homologous genes (Fig. 6b). Following
the order of conservations, AA and AD stand after IR
(Fig. 6a, b), coinciding with the order of abundance of AS
types (Table 1).
These results support the suitability of using the junc-
tion approach for identifying AS conservation among
homologous genes. Chamala et al. [46] reported that the
proportion of conservation of AS events varies among
the types of AS events. They analysed four types of
events (AA, AD, ES and IR) and found IR to be the most
conserved type, consistent with our data. The results of
Chamala et al. [46] and this work indicate that IR, AA
and AD are not only the most common events in both
legume plants but these present the highest AS event
conservation rate across angiosperms. Their evolutionary
conservation indicates potential function. Although ASS
and ES as well as NI and RE have similar proportions in
both species, the first two are less conserved. This could
be interpreted as ASS and ES being more species spe-
cific than NI and RE or their function is not conserved.
The data used for these analyses also enabled the iden-
tification of AS events conserved between soybean para-
logs that arose during the last WGD. The paralogous
genes set must have identical gene models in order to
identify the conserved events by junction. A total of 40%
AS events were conserved among G. max paralogous
genes (Fig. 6c). Similar as seen for AS conservation be-
tween species, IR was the type of AS event with the
highest conservation rate followed by AA and AD; while
AS and ES showed the lowest percentage of conserva-
tion although this was higher than 20% (Fig. 6c). New
AS events could arise after the WGD within either one
or both paralogs, isoforms could have predated the
WGD and still remain in both paralogs, or only one of
the paralogs could have lost its isoform subsequent to
the WGD event. To address this question further ana-
lyses on conserved AS within genes duplicated through
WGD in another species need to be examined.
The conservation of AS events between these two le-
gumes may be the result of their performing an essential
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 8 of 17
43
function, particularly since these events have been con-
served over ~20 MY. The isoforms produced due to AS
could be important for a specific tissue, condition or
developmental stage. To understand the function, and/
or temporal and spatial conditions under which these
AS isoforms are expressed will require additional inves-
tigation. However, since the majority of the AS events
are not conserved, the lack of conservation of AS
events may not only be due to their biological function,
but may reflect the divergence time between these
species. Paralogous genes in G. max with shorter diver-
sification time accounted for a majority of the gene
specific AS events. A combination of functionality and
diversification time could lead to the percentage of AS
conservation observed here.
Two examples of AS events conserved among plant
species that indicate their important biological functions,
are the following. The transcription factor TFIIIA is re-
quired for the synthesis of 5S–rRNA by RNA polymer-
ase III. The third exon of this gene, with a structural
element that mimics 5S rRNA, presents an ER event
[47–49]. This TFIIIA exon, that is highly conserved in
land plants [50], is referred to as a suicide exon because
its absence produces a functional transcript whereas its
retention results in a non-functional transcript contain-
ing premature termination codons (PTC) thus targeted
Fig. 5 Homologous Phaseolus vulgaris and Glycine max genes. The left side of each panel is a schematic representation of the evolutionary
history of a gene, the cross represents the WGD that occurred in G. max after the speciation of both plants. a Number of P. vulgaris genes with
two copies in G. max (1:2) b the number of P. vulgaris genes with one copy in G. max (1:1). Different color-coded ellipses indicate the numbers of
expressed genes, genes with the same gene model between both species and genes sharing these the two characteristics. The number of P.
vulgaris genes for AS conservation analysis are underlined
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 9 of 17
44
· aPi re\atíonship 1:2 
0.\\0'\\ 
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a 
Expressed Same Model 
b 
.(:-.. 705 "-
.... ~vo!. . €>7.-bc \ 
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ary relationshlP \ . 
to NMD. The L5 ribosomal protein binds to the 5S
rRNA mimic and controls the synthesis of 5S RNA by
regulating AS of TFIIIA [48]. Fig. 3b shows that the ER
event identified in the third exon of Arabidopsis TFIIIA
is conserved in P. vulgaris (Phvul.008G270400) and G.
max (Glyma.02G293300). The absence or presence of
the exon is clearly observed in both species and G. max
presents additional transcript isoforms varying in the
length of the RE (Fig. 3b). As in Arabidopsis, PTC were
identified in the RE in both legume species, thus indicat-
ing the conservation of AS and of regulation/function of
TFIIIA in the legume species analysed in this work.
Another example is the SCL33, a protein from the SR
family that regulate splicing by binding to splicing regu-
latory elements -found in exons or introns-, facilitating
spliceosome assembly and enhancing splicing [51]. The
extensive AS of the third intron of Arabidopsis SCL33,
that includes a RE with PTC, results in potential targets
of NMD and these have been implicated in auto-
regulation of AS by SCL33 [52]. This AS event is con-
served in other plants such as Brachypodium distachyon
[27]. Fig. 3c shows that an RE event in the third intron
is also conserved in P. vulgaris (Phvul.007G262600) and
G.max (Glyma.09G104200) SCL33; the RE presents PTC
in both legume species. We propose that the AS-related
function of the legumes’ SCL33 gene is similar to that
known for Arabidopsis, regarding its targeting to NMD
and the auto-regulation of SCL33 protein content.
Conservation of AS in UTR-CDS regions
Conserved AS events between P. vulgaris and G. max
constitute a small proportion relative to all AS events in
each species (Fig. 6). However, an assumption we made
was that conserved AS events may have a biological
function. The percentage of intron/exons of UTR and
CDS regions affected in conserved AS events from hom-
ologous genes as compared to all AS events were ana-
lyzed to explore potential function (Fig. 4).
Regarding AS events in 5’UTR-5’UTR junctions (Fig. 4a),
AD, ASS and RE showed a higher percentage of conserved
events as compared to all AS events in both plants. These
data indicate that AD, ASS and RE events are preferentially
conserved in 5’UTR introns, which could imply a con-
served function, though such function is yet unknown. AS
occurring in 5’UTR regions has been implicated in up-
stream open reading frames (uORF) of small proteins,
that in turn have been implicated in the mRNA stability
through NMD and in translation efficiency [53]. Never-
theless, the amino acid conservation among uORF from
different organisms suggests a possible translation of
small proteins and a possible function of these [53].
Proteomic studies would be required for further studies
on the existence and function of such small proteins in
legumes.
IR, however, exhibits a different pattern since the per-
centage of conserved events in 5’UTR-5’UTR junctions
was lower than the percentage of all IR events (Fig. 4a).
Nevertheless, in both species the percentage of IR was
higher than the percentage of all events from CDS-CDS
junctions (Fig. 4b), indicating a possible role for this type
of event in CDS introns. In contrast, a reduction in the
percentage of conservation of AD, ASS and RE in CDS
introns was observed in both plants (Fig. 4b). For AA
the percentage of conserved events in the 5’UTR and
CDS introns followed a different pattern between P. vul-
garis and G. max (Fig. 4a, b), showing that 5’UTR or
CDS regions do not affect clearly the conservation of
Fig. 6 Conservation of AS among Phaseolus vulgaris and Glycine max. Cumulative bar plots represent total AS events (black bars) and each AS
event type (colored bars). In every bar plot the stripped bar (top) corresponds to AS events in non-homologous genes, the dark tone color bar
(middle or top) to conserved AS events and the light tone color bar (bottom) to non-conserved AS events in homologous genes. The numbers
over the bar indicate the percentage of conserved events over all AS events (top) and over AS events in homologous genes (bottom). AS event
conservation of P. vulgaris in G. max (a), of G. max in P. vulgaris (b) and in paralogous G. max genes, originated by its recent WGD (c)
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 10 of 17
45
a 
1.0 
0.5 
0.0 
22% 23% 22% 
37% 39% 36% 
4% 
7% 
11 % 28% 
19% 45% 
Aa ASS ES IR 
16% 17% b 
26% 30% 
1.0 
0.5 
0.0 
RE 
18% 16% 
35% 31% 
27% 10% 12% e 
53% ,.20%= " 1",24 .. %;,. 
40% 43% 39% 23% 27% 46% 30% 32% 
1.0 
15% 5% 6% 
27% 12% 13% 
0.5 
0.0 
AD ASS ES IR RE 
AA events. Interestingly these five types of AS events
that affect introns showed a reduction in percentages of
3’UTR-3’UTR junctions in conserved AS events as
compared to all events in both species (Fig. 4c).
A similar analysis was done for ES and NI events
that affect exons (Fig. 4d-i). Despite the differences in
conserved NI event proportions between these two
species, this AS was enriched in conserved AS events,
for 5’UTR-CDS, CDS-3’UTR exons and single exon
genes (Fig. 4g–i). This indicates potential functional
relevance in those regions. Notably, the 3’UTR exons
showed a reduction in the percentage of conserved
events for ES and NI compared to all events as well
as for the five AS events affecting introns (Fig. 4f ).
Taken together the results of conserved AS events in
CDS and UTR regions suggest that the potential of AS
to affect either introns or exons is greater on regions
upstream from the 3’UTR region.
An example of conserved AS in the 3’UTR region was
identified for U2AF35, that is a component of the U2AF
(U2 snRNP auxiliary factor) heterodimer, an essential
pre-mRNA splicing factor. U2AF35 plays critical roles in
the recognition of the 3′-splicing [54]. In addition
human U2AF35 is implicated in the determination of
mRNAs 3’UTR-length; mutated U2AF35 results in lon-
ger 3’UTR of certain genes [55]. AS in 3’UTR has been
associated with the regulation of protein expression, by yet
unidentified mechanisms [56]. Figure 3d shows the con-
served 3’UTR AA event in P. vulgaris (Phvul.005G127700)
and G. max (Glyma.12G181300) resulting in mature
U2AF35 mRNAs varying in their 3’UTR length (Fig. 3d).
This is another example of a gene with different primary
transcript in both species; the primary transcript of one is
similar to the alternative transcript isoform of the other
(Fig. 3a, d). Additionally, a conserved IR event was
validated in both species (Fig. 3d). Based in previous know-
ledge [55], we hypothesize that protein expression of
U2AF35a in both legume species could be self-regulated
through AS.
AS simulation
An important question in this work was if the 22 and
18% of AS conservation (Fig. 6) is a significant result
from the P. vulgaris and G. max comparison, or if such
values are just been obtained by chance. To answer this
question four different simulations for AS event conser-
vation were performed (Additional file 6). In the first
simulation (“random events”) all the non-redundant AS
events in the expressed genes were shuffled, thus any
intron/exon could be alternative spliced and the real
homologous genes were maintained. In the second
simulation (“random genes”) the genes were shuffled to
consider different homologs with their AS events main-
tained, the distribution of the number of exons per gene
(Additional file 7) was not considered. The third simula-
tion (“random genes same model”) was like the second,
maintaining real AS events, but the exon distribution
was that of the real homologs (Additional file 7). The
objective of “random genes” and “random genes same
model” simulations was to explore if the homology be-
tween P. vulgaris and G. max genes was important for
the AS event conservation. The last simulation (“random
genes + random event”) where homologous genes and
AS events were shuffled was a combination of the first
and second simulations (Additional file 6).
The data on percentages of AS conservation (Fig. 6)
were compared to the data obtained for each of the sim-
ulations; data of P. vulgaris in G. max are represented in
Fig. 7 and G. max in P. vulgaris in Additional file 8. All
simulations resulted in a value for the percentage of AS
conservation that is lower than observed in our analysis
for both overall and individual AS events, thus indicat-
ing that these results are neither random or artefacts
(Fig. 6, Additional file 8).
The analysis of conservation of each type of AS de-
rived from the simulations revealed interesting features
such as a correlation of exon number with the conserva-
tion of AS events. For every type of AS event, except NI,
the “random genes + random event” simulation showed
the highest percentage of conserved AS (Fig. 7). One in-
terpretation is that having more exons increases the
probability of having AS and therefore the AS event
conservation is also more likely. NI does not follow this
rule as gene structure plays a major role for this type of
AS event. This is consistent with the results of NI per-
centage in the UTR-CDS regions, where a high percent-
age was observed in the single exon genes (Fig. 4i),
similar to a report by Marquez et al. [44]. For NI the
highest percentage of conservation was observed in “ran-
dom genes” and “random genes same model” simula-
tions (Fig. 7). This suggests that higher NI occurrence is
also related to specific genes.
The simulations also provided insights into the conserva-
tion of AS events. The position of the AS events within the
gene tends to influence AS event conservation. If the posi-
tions of the AS conserved events within the gene were ran-
dom, the percentage of conservation from “random events”
and “random genes same model” would be similar. How-
ever, “random events” presented higher percentage of con-
servation in AA, AD and IR events (Fig. 7), something that
could indicate that some homologous genes tend to present
AS event conservation in specific introns or exons. The
most common AS event types (AA, AD and IR) showed a
bias for conserving AS events relative to position within the
gene, as well as the number of exons, as described above.
This tendency added to the exon number tendency were
observed in the “random genes same model” simulation
with the lowest conservation of AS events in AA, AD and
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 11 of 17
46
IR (Fig. 7). A similar phenomena was observed for “random
genes” in comparison with “random genes + random
events” simulations, where besides having more exons the
percentage of AS event conservation was lower in “random
gene” simulation than when randomizing events (“random
events + random genes”) (Fig. 7, Additional file 7). This in-
dicates that there is a bias for certain genes to conserve
these types of events in certain introns. With these re-
sults, it is not possible to determine if RE, ASS and ES
types have a bias in the position, as the percentages of
conserved AS events in the simulations were similar
due to the low number of non-redundant AS events
(Fig. 7 and Additional file 8).
AS position within the gene
For an in-depth analysis of the position of AS event
within a gene the affected introns/exons were catalogued
with respect to their relative position in the gene, with
the first or single exon/intron designated as 0, and the
terminal exon designated as 1. The percentages of
affected exons/introns for each relative position were
calculated (Fig. 8). A linear regression was calculated for
each type of event looking for a bias in the positions of
AS events within the genes. In agreement with the
results from the simulations, AA, AD and IR presented
a positional bias (Figs. 7, 8). AA and AD were enriched
in initial introns, in contrast to IR where there was a bias
for the terminal introns (Fig. 8a, b). RE and ES did not
show a clearly position bias in the conservation studies,
while this analysis uncovers a tendency of these events
to affect initial introns in both plants (Fig. 8c, d). This pos-
ition preference was similar to that seen for AA and AD.
These results were consistent in both species (Fig. 8) and
highlight the importance of the position of the AS event.
The position bias of AS events within the gene observed
for different AS events provide insights into how and in
which gene positions each AS event is regulated.
It has been observed that the splicing occurs co-
transcriptionally and sometimes it depends on the rate
of the RNA-polymerase reaction [57]. The transcription
rate also plays an important role in the formation of the
secondary structure of the nascent RNA. The secondary
RNA structure has been implicated in AS, there are
some structures that prevent splicing site recognition
and others that facilitate it [58]. The inhibition of spli-
cing due to secondary RNA structure has been related
Fig. 7 Comparison of conservation percentages of AS events in Phaseolus vulgaris. Four different simulations were performed; “random events”,
“random genes”, “random genes same model” and “random genes + random events”, each one is represented with a different color as indicated.
The blue line corresponds to the values of percentages of AS conservation over all AS events of P. vulgaris in G. max (shown in Fig. 5a).
Conservation of all AS events and of each type of AS events was plotted individually, as indicated. For each type of simulations, 1000
randomizations were preformed and the percentage of conservations for every simulation was plotted
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 12 of 17
47
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with a competition between the structure and the site
recognition of splicing factors. Therefore the splicing
efficiency is directly correlated with the stability of the
secondary RNA structure. On the other hand RNA
structures could bring important splicing signals into
closer proximity enhancing the splicing [59]. Based on
this information we hypothesize that at transcription ini-
tiation, the nascent RNA still lacks a stable secondary
structure thus facilitating the splicing site recognition
and producing AS events such as AA, AD, ES or RE. IR
events presented a tendency for terminal introns from
already transcribed RNA with defined secondary RNA
structures that could inhibit the recognition of splicing
sites thus resulting in an IR. Nevertheless, these hypoth-
eses need to be tested in order to better understand the
regulation of the different AS events.
Fig. 8 Linear modeling of AS events relative to the position of affected exon/intron. Each event was characterized to their relative exon/intron of
the gene affected, being 0 the first or only intron/exon and 1 the last. Each type of AS event is presented separately. A simple linear model was
fitted to the data. a distribution of AA (r2 = 0.63 p-value = 1.418e-05), AD (r2 = 0.6 p-value = 3.358e-05) and IR (r2 = 0.67 p-value = 5.656e-06) for P. vulgaris the
left axis represent the percentage of AA and AD and the right one of IR; c Distribution of ASS (r2 = 0.05 p-value = 0.1769), RE (r2 = 0.64 p-value = 1.418e-05),
ES (r2 = 0.19 p-value = 0.02895) and NI (r2 = 0.05 p-value = 0.7848), the left axis is for the intron events (ASS and RE) and the right axis for the exons affected
(ES and NI) for P. vulgaris. b and d are same as (a) and (c) but for G. max. (AA: r2 = 0.71 p-value = 2.216e-06, AD: r2 = 0.49 p-value = 0.0003575, IR: r2 = 0.45
p-value = 0.0006914, ASS: r2 = 0.02 p-value = 0.256, RE: r2 = 0.29 p-value = 0.008569, ES: r2 = 0.24 p-value = 0.01595, NI: r2 = 0.05 p-value = 0.7779)
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 13 of 17
48
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Conclusions
The algorithms and methodology used in this work
allowed the identification and analysis of seven different
types of AS events from two agronomically important
legume species: P. vulgaris (common bean) and G. max
(soybean). While the number of AS events was highly
underestimated in their respective genome sequence
annotations [33, 34], here it was shown that ~60% of all
genes and ~70% of expressed genes from both species
may undergo AS. Each type of AS event affected a differ-
ent proportion of genes, with IR being the most frequent
AS event followed by AA and AD in both plants.
The AS events were characterized in terms of the region
they affected, and their relative position within the gene.
The results of this characterization exposed different
patterns for each of the AS events, such as preference for
single exon genes from NI events or the contrasting result
for position preferences between AA, AD and IR. These
results were similar for both species, highlighting global
aspects of these AS events in these legumes.
The conservation of AS events in two evolutionary
related legume species was analysed considering those P.
vulgaris genes with two (evolutionary relationship 1:2) or
one (1:1) homologous gene in G. max. A significant
proportion (ranging from 18 to 37%) of AS events were
conserved between species. The conserved AS events are
key to further research since they have been conserved for
~20 MY and they may provide insights into the functional
role of these AS events in legumes. The conservation of AS
events was experimentally validated for 8 selected genes,
through RT-PCR analysis, something that enhances the re-
liability of bioinfomatic data from this work. The proposed
function and biological significance of some of the validated
conserved AS events was discussed, nevertheless they need
to be further studied. The percentage of AS event conserva-
tion varies among the different AS types, with IR, AA and
AD the most highly conserved in both species. Conserved
events were also characterized in terms of the gene region
they affect. The results threw a significant tendency to
conserve events upstream 3’UTR regions, which indicates
that events in 3’UTR are preferentially specie specific.
This work increases the knowledge of the yet almost
unexplored process of AS. Tissue specific analysis, as
well as isoform analyses, need to be performed to
understand this relevant process for genome expression/
function in eukaryotes.
Methods
RNA-seq libraries and expressed sequenced tags (ESTs)
All RNA-seq from Phaseolus vulgaris available (February
2016) in the Sequence Read Archive were downloaded
and small RNA-seq were filtered. A total of 157 libraries
belonging to 105 different samples were selected for the
AS analysis in common bean. Eighty four libraries
belonging to 77 different samples from Glycine max
were selected based on project or tissue similarity with
those from P. vulgaris. (Additional file 9). All available
EST from NCBI and TGI database [60] from common
bean and soybean were analysed. EST’s from both data-
bases were sequenced form multiple tissues and condi-
tions. In total 176,782 and 1,461,723 EST sequences
from common bean and soybean, respectively, were
analysed.
Mapping and AS annotation
All RNA-seq libraries were mapped to their respective
genome without gene annotation information, align-
ments must be unique and perfect. For the mapping two
different approaches were used; a seed and extend
approach, where reads are sliced into short seeds, which
are mapped to the genome, allowing the identification of
splicing sites; and an exon first approach, where
complete reads are mapped at first and based on that
mapping an exon-exon junctions database is created in
order to align the unmapped reads later [61]. TopHat2
[62] was used for the exon first approach and gsnap [63]
for the seed extended (Fig. 2). Each mapping result, one
for gsnap and one for TopHat2, was the input for a gene
prediction modelling performed with Cufflinks [64], this
was carried out also without any gene annotation infor-
mation as well (Fig. 2). EST’s were mapped to their
respective genome with Gmap [63] (Fig. 2). The gene
models were filtered to avoid chimeras based on the
coordinates of the primary transcript of each gene in the
genome annotation. Models in zones where genes over-
lap or models that are part of two or more genes were
removed. Each gene model from each mapping algo-
rithm of each RNA-seq library was compared to their
corresponding annotated primary transcript in the gen-
ome with an in-house perl script (Fig. 2). This algorithm
identifies the seven different AS events (AA, AD, ASS,
ES, IR, NI and RE) by comparing the genome coordi-
nates of the out coming gene models to all primary tran-
scripts (Fig. 1). AS events present in EST’s, or in both
mapping results for a particular library, or in at least half
of the sample replicates from a type of mapping were
selected for further analysis (Fig. 2 and Additional file 2).
Gene expression
The expression value for each gene was calculated using
all FPKM values from each model that belong to each
gene (Fig. 2). The FPKM from each model was multiplied
by the length of the model, the sum of all products from
each gene was then divided by the length of the primary
transcript resulting in a normalized value of expression
per mapping algorithm. The mean of both normalized
expression value, one for gsnap and other for TopHat2,
was the library expression. The sample expression was
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 14 of 17
49
calculated by the median of the replicates libraries expres-
sions values. A gene was considered expressed in a tissue
if the sample expression was above one.
RT-PCR analysis
For RNA isolation surface-sterilized seeds from P. vulgaris
and G. max were germinated over moist paper, in sterile
conditions, for 2 days. The root and cotyledonary leaves
from germinated seedlings were cut, frozen in liquid nitro-
gen and stored separately at −80 C until used. Total RNA
was isolated from 200 to 400 mg frozen tissue using Trizol
reagent (Life Technologies, California, USA), as reported
[65]. Absence of genomic DNA contamination was subse-
quently confirmed for each sample by PCR amplification
using primers for the ACR9 (ACT-domain containing
protein) gene (Phvul.008G013100, Glyma.18G289000). To
validate the presence of different transcript isoforms iden-
tified through bioinformatics analysis, two-step RT-PCR
was performed following the manufacturer’s directions
(Thermo Scientific, USA) using poly-thymine deoxynu-
cleotide primer. Eight genes with from P. vulgaris and
their corresponding G. max homologs were selected for
AS events validation and the ACR9 gene that did not
present AS was included as a control. For each selected
gene, a pair of oligonucleotide primers was designed to
amplify products specific for the primary transcript or for
transcript isoforms derived from AS events; primer
sequences as well as genes IDs and annotation are shown
in Additional file 10. For RT-PCR reactions the thermocy-
cler was set to: 60 / 68 °C for annealing / extension and
35–40 cycles and a High Fidelity DNA Polymerase (Jena
Bioscience, Germany) was used. Amplification products
were resolved in a 3% agarose gel in 1xTAE and EtBr
stained for visualization.
AS simulation
Simulation of AS were performed based on number of
exons/introns of expressed genes. Four different types of
simulations were performed: “random events”, “random
genes”, “random genes same model” and “random genes +
random event”. One thousand independent simulations
were performed for each type of simulation.
AS event conservation
Phaseolus vulgaris and Glycine max orthologous genes
were pulled out from Schmutz et al. [34]. There were
13,962 common bean genes with two orthologous genes
in soybean, resulting from the recent whole genome du-
plication in this legume, and 5624 orthologs with only
one copy in soybean. The AS conservation was based on
junction conservation and not in position conservation.
Due to this fact, genes should have the same gene model
(same number of exons) and been expressed in at least
one sample (7′692 common bean genes with two ortho-
logous genes in soybean and 7′039 with one gene).
Additional files
Additional file 1: AS events reported in the Phaseolus vulgaris [34] and
Glycine max [33] annotated genomes. (XLSX 11 kb)
Additional file 2: AS event. (XLSX 8631 kb)
Additional file 3: Splicing sites. Percentage of splicing sites motifs
reported in the genome (inner circle), in the new junctions (middle
cirlce) and the genome with the new junctions (outer circle). U2 motifs
(gray), U12 motifs (black) and non-canonical splicing sites (striped). Panel
a show the results from P. vulgaris while b from G. max. (TIFF 816 kb)
Additional file 4: Introns and exons from CDS and UTR regions affected
by AS events. Percentage of P. vulgaris and G. max introns (a) and exons
(b) affected by AS compared to their total proportion in each genome.
Proportions of common bean as well as soybean are plotted. (TIFF 159 kb)
Additional file 5: Percentage of non-redundant AS event types in
homologous genes. (XLSX 11 kb)
Additional file 6: Four AS conservation simulations. Four different
simulations for AS event conservation percentage testing were
performed. “random events”: randomize the AS events in the expressed
genes maintaining homologous genes; “random genes”: randomize
homologous genes, the gene model was not taken into account but the
events remained as the real data; “random genes same model”: same as
“random genes” but the gene model stays equal and “random events +
random genes”: AS events as well as homologous genes, ignoring real
gene models, were randomized. (TIFF 232 kb)
Additional file 7: Exon distribution. Proportions of number of exons per
gene in the annotated P. vulgaris and G. max genomes, homologous
genes with an evolutionary relationship of with evolutionary relationship
1:2 and 1:1 and pseudo-homologous genes resulted from “random
genes” simulation. (TIFF 115 kb)
Additional file 8: Comparison of conservation percentages of AS events
in Glycine max. Data from each performed simulation are plotted with a
different color while the blue line corresponds to the values of
percentage of AS conservation shown in Fig. 6b. The percentage of AS
conservation of G. max in P. vulgaris considered over all AS events in
soybean were analyzed. For description of each plot see legend to Fig. 7.
(TIFF 136 kb)
Additional file 9: Sample used for the AS event identification.
(XLSX 41 kb)
Additional file 10: P. vulgaris and homologous G. max genes selected
for RT-PCR analysis. (XLSX 9 kb)
Abbreviations
AA: Alternative acceptor; AD: Alternative donor; AS: Alternative splicing;
ASS: Alternative splicing sites; CDS: Coding DNA sequences;
DN: Nonsynonymous mutations; DS: Synonymous mutations; ES: Exon
skipping; EST: Expressed sequence tags; IR: Intron retention; MYA: Millions
years ago; NI: New intron; NMD: Nonsense-mediated decay; RE: Retained
exon; RNA-seq: Next generation RNA-sequencing; snRNA: Small nuclear
RNAs; SR: Serine/arginine-rich proteins; uORF: Upstream open reading frame;
UTR: Untranslated region; WGD: Whole genome duplication
Acknowledgments
The authors are grateful to the University of Florida Research Computing
HiPerGator where the mapping for the AS identification were performed and
to Drs. Cei Abreu, Scott A. Jackson and Enrique Merino for their advice and
discussions of this work. LPI is a PhD student of Doctorado en Ciencias
Biomédicas – UNAM and a recipient of a studentship from the Consejo
Nacional de Ciencia y Tecnología (CONACyT), Mexico (no. 340334).
Funding
This work was supported by funding from the Dirección General de Asuntos del
Personal Académico - UNAM [grant number IN200816] to GH. Funding for open
Iñiguez et al. BMC Genomics  (2017) 18:650 Page 15 of 17
50
access charge: Dirección General de Asuntos del Personal Académico – UNAM
(IN200816). The funding agency was not involved in the design of the study,
collection, analysis, and interpretation of data or in writing the manuscript.
Availability of data and materials
The datasets supporting the conclusions of this article are included within
the article and its additional files.
Authors’ contributions
LPI, WBB and GH designed the research and discussed the data. LPI
performed the bioinformatic research and analyzed the data. MR designed
and performed the experimental research. LPI and GH wrote the manuscript.
All authors have read and approved the manuscript for publication.
Ethics approval and consent to participate
Plant materials were developed by Mario Ramírez, plants were grown in
greenhouses from the Centro de Ciencias Genómicas, Universidad Nacional
Autónoma de México at Cuernavaca, México. The seed samples from
Phaseolus vulgaris cv. negro jamapa and from Glycine max cv. amsoy are
publicly available for research institutions from Mexico and abroad upon
reasonable request.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México
(UNAM), Cuernavaca, Morelos, Mexico. 2Department of Biology, University of
Florida, Gainesville, FL, USA.
Received: 6 February 2017 Accepted: 11 August 2017
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5.2. El Papel del Splicing Alternativo en la Duplicación Génica
El Splicing Alternativo es un tema en constante desarrollo, durante los últimos años se
ha incrementado la cantidad de información disponible sobre este tipo de regulación post-
transcripcional. Cada vez son más los trabajos que prueban la importancia de este tema y las
implicaciones funcionales de este proceso dentro de diversos organismos. Por otra parte la
duplicación génica, aunado a mutaciones en los genes duplicados, es uno de los procesos por
el cual los organismos adquieren nuevos genes. La evolución de estos dos aspectos es de mu-
cho interés ya que a partir de entender su historia evolutiva se podrá comprender un poco más
sobre los procesos y fuerzas que llevan hacia los distintos destinos de los genes duplicados.
La conservación y divergencia del Splicing Alternativo se ha estudiado en diferentes espe-
cies y esta sección se enfocará en el splicing alternativo y su relación con las duplicaciones
génicas.
5.2.1. Introducción
La diversidad proteica que existe en los organismos hoy en dı́a es el resultado de varios
procesos evolutivos. Se conoce que la duplicación génica en conjunto con la acumulación
de mutaciones son responsables, junto con otros factores, del incremento en el número de
diferentes proteı́nas. Otro de los factores que promueven la diversidad proteica es el AS, dis-
cutido en la sección anterior, por el cual se pueden transcribir diferentes isoformas de mRNA
a partir de un solo gene. Estos dos mecanismos por los cuales se incremente la diversidad de
proteı́nas varı́an enormemente y pueden influir en el otro. Una pregunta importante es cómo
se ven afectados los patrones de AS una vez que los genes se hayan duplicado. La relación
evolutiva entre la duplicación génica y el AS ha sido un tema de interés y discusión por lo
que decidimos publicar una revisión del tema en el siguiente artı́culo.
53
fgene-08-00014 February 11, 2017 Time: 14:47 # 1
MINI REVIEW
published: 14 February 2017
doi: 10.3389/fgene.2017.00014
Edited by:
Florent Hubé,
UMR7216 Epigenetics and Cell Fate,
France
Reviewed by:
Izabela Makałowska,
Adam Mickiewicz University
in Poznań, Poland
Mainá Bitar,
Universidade Federal de Minas
Gerais, Brazil
*Correspondence:
Luis P. Iñiguez
liniguez@lcg.unam.mx
Georgina Hernández
gina@ccg.unam.mx
Specialty section:
This article was submitted to
RNA,
a section of the journal
Frontiers in Genetics
Received: 23 November 2016
Accepted: 02 February 2017
Published: 14 February 2017
Citation:
Iñiguez LP and Hernández G (2017)
The Evolutionary Relationship
between Alternative Splicing
and Gene Duplication.
Front. Genet. 8:14.
doi: 10.3389/fgene.2017.00014
The Evolutionary Relationship
between Alternative Splicing and
Gene Duplication
Luis P. Iñiguez* and Georgina Hernández*
Programa de Genómica Funcional de Eucariotes, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de
México, Cuernavaca, México
The protein diversity that exists today has resulted from various evolutionary processes.
It is well known that gene duplication (GD) along with the accumulation of mutations are
responsible, among other factors, for an increase in the number of different proteins.
The gene structure in eukaryotes requires the removal of non-coding sequences,
introns, to produce mature mRNAs. This process, known as cis-splicing, referred to
here as splicing, is regulated by several factors which can lead to numerous splicing
arrangements, commonly designated as alternative splicing (AS). AS, producing several
transcripts isoforms form a single gene, also increases the protein diversity. However,
the evolution and manner for increasing protein variation differs between AS and GD.
An important question is how are patterns of AS affected after a GD event. Here, we
review the current knowledge of AS and GD, focusing on their evolutionary relationship.
These two processes are now considered the main contributors to the increasing
protein diversity and therefore their relationship is a relevant, yet understudied, area
of evolutionary study.
Keywords: alternative splicing, evolution, gene duplication, polyploidy, mRNA isoforms
INTRODUCTION
Organismal protein diversity has increased through evolution. This diversity has allowed
organisms to adapt to different environments, to develop and to differentiate specialized tissues.
GD, along with mutations, has been an important process to increase the protein diversity. The
genome sequence of a variety of organisms has allowed the identification of paralogous genes
produced by GD. Nevertheless, the vast amount of proteins cannot be solely explained by this
process. Before the human genome was sequenced it was widely believed that the human genome
would encode around 100,000 genes based on an estimate of the number of different proteins
existing in human cells (Fields et al., 1994). Surprisingly only∼21,000 coding genes were annotated
in the genome sequence (Auton et al., 2015). Almost all protein coding genes from eukaryotes
contain non-coding sequences, known as introns, flanked by coding sequences, or exons. During
transcription introns need to be removed in order to form a mature mRNA, this process is
Abbreviations: AA, alternative acceptor; AD, alternative donor; AS, alternative splicing; ES, exon skipping; GD, gene
duplication; IR, intron retention; MEE, mutually exclusive exon; SSD, short segmental duplication; WGD, whole genome
duplication.
Frontiers in Genetics | www.frontiersin.org 1 February 2017 | Volume 8 | Article 14
54
frontiers 
in Genetics 
OPENACCESS 
fgene-08-00014 February 11, 2017 Time: 14:47 # 2
Iñiguez and Hernández Alternative Splicing - Gene Duplication
called splicing. Gilbert (1978) proposed that alterations on the
splicing of a gene could form multiple isoforms from a single
gene. AS is the process through which a single gene can produce
different mRNA isoforms and those in turn, if translated, could
lead to multiple proteins. The AS process could explain the
discrepancy between the estimates of number of genes and
number of proteins.
GENE DUPLICATION
The vast number of genes in eukaryotic organisms is in
large part due to GD. Several processes can occur in
the cell that can lead to the duplication of genes. There
are two GD classifications; SSD and WGD. SSD duplicates
one or several genes while WGD increases significantly the
offspring’s gene count compared to the parent. SSDs events
may result from unequal cross-over (DNA-dependent) or
retrotransposition (RNA-dependent). Both processes give rise
to different gene structures. Retrotransposed genes become
single exon genes while DNA-dependent duplications inherit
gene structure and regulatory sequences (reviewed by Van
de Peer et al., 2009). A WGD is caused by autopolyploidy
or allopolyploidy (Van de Peer et al., 2009). It has been
hypothesized that WGDs provide organisms with certain
defenses against extinction, because individuals can accumulate
mutations in duplicated genes that may enhance their adaptation
to stress or environmental conditions (Innan and Kondrashov,
2010).
Four models have been proposed to describe the
evolution of duplicated, or paralogous genes. The model
of neofunctionalization establish that one copy of the gene
retains the ancestral function while the function of the other
diverges into a new one. Subfunctionalization, or duplication-
divergence-complementation propose that the ancestral gene
function is partitioned between paralogs. Subfunctionalization
was tested by Kito et al. (2016) in several yeast species,
some of which contained several SSD. Using proteomics,
they found that in species that experienced SSD, the sum
of paralogous proteins was similar to the amount of the
non-duplicated homologous proteins in other yeast species.
Another evolutionary model is that one paralog retains the
ancestral function while the other paralog devolves into a
pseudogene (Panchy et al., 2016). Finally, the function of both
paralogs can remain similar if an increased production of
the protein is advantageous or if a dosage balance occurs in
conjunction with other gene products (Innan and Kondrashov,
2010; Magadum et al., 2013). Wang et al. (2011, 2012)
studied the spatiotemporal expression profiles of duplicated
genes to identify the different evolutionary models based
on the gene expression profiles of paralogous genes. They
concluded that the divergence in expression depends on
the process of duplication. To complement these studies,
Lan and Pritchard (2016) analyzed SSD and found that the
genomic distance, the type of duplication and the time since
duplication all influence the fate of paralogous genes. These
evolutionary models have been studied and reviewed by
Cañestro et al. (2013) where examples of each model are
described.
ALTERNATIVE SPLICING
Alternative splicing is a post-transcriptional process that occurs
in different stresses, developmental conditions and in different
cell types (reviewed by Wang et al., 2008; Staiger and Brown,
2013; Li et al., 2016). AS affects the localization of the mature
mRNA and its translation efficiency (Zhiguo et al., 2013). It can
also produce alternative stop codons and can regulate protein
expression by non-sense-mediated decay (Kalyna et al., 2012).
AS may result in different protein isoforms derived from a
single gene and these isoforms alter their cellular localization
or function with respect to the primary transcript (Remy et al.,
2013). AS can also influence in protein–protein interactions.
These interactions have been associated with intrinsically
disordered protein domains, which are more susceptible to AS
(Niklas et al., 2015; Yang et al., 2016). Five main AS events lead
to the production of different isoform transcripts as shown in
Figure 1: ES, where a complete exon is absent from the primary
transcript; AA, where the 3′ end of an intron changes; AD,
where the 5′ splice site of the intron is different; IR, where a
reported intron is not spliced and is part of the mature mRNA
and MEE, where one of two exons is retained in a given isoform
but not both exons. These AS events vary in their frequency
among different eukaryotic organisms. In animals, ES is the
most common AS event, which represents around 50% of all AS
events, while in plants IR is the most frequent AS event (reviewed
Vélez-Bermúdez and Schmidt, 2014; Zhang et al., 2015).
Nematodes and humans show high variation in cell types and
both genomes code for a similar number of genes. However,
98% of human multiple-exon genes exhibit AS (Pan et al., 2008)
while AS is present in only 25% of nematode genes (Ramani
et al., 2011). Chen et al. (2014) analyzed several organisms which
vary in their number of different cell types –referred here as
organism complexity- and found a strong positive correlation
between the number of cell types and the number of AS events.
Organisms with more complexity tend to have higher AS. In
addition to AS, it has been found that non-coding RNA’s have a
correlation with organismal complexity (Liu et al., 2013). There
is evidence that proteome size, structural disorder of proteins,
protein–protein interactions and AS are all part of a fine tuning of
a complex network to ensure organism complexity (Schad et al.,
2011; Dunker et al., 2015).
THE EVOLUTION OF AS IS CLOSELY
LINKED TO GD EVENTS
Transcript isoforms resulting from AS events can be viewed as
having “internal-paralogs” in the same gene (Kopelman et al.,
2005). These “internal-paralogs” may have different functions,
similar to the neofunctionalization model of gene evolution. For
these reasons the comprehension and analysis of the relationship
between AS and GD is an interesting topic in the evolutionary
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Iñiguez and Hernández Alternative Splicing - Gene Duplication
FIGURE 1 | Alternative splicing events. Types of AS events, previously described and commented in this work, are based on a comparison between the
constitutive splicing and the alternative splicing events for a certain gene. Color boxes represent exons while black lines represent introns from a gene. Splicing sites
are depicted by connections with dashed lines.
field. It has been observed that mutating a single intronic
nucleotide can provoke changes in gene splicing patters (Hsiao
et al., 2016), which would facilitate a fast evolution on patterns
of AS in paralogus genes. Reddy et al. (2013) reviewed three
different models to explain this relationship, these models are
summarized in Figure 2. The independent model establishes
the lack of relationship between GD and AS and therefore
the number of isoforms in paralogs vs. non-duplicated genes
would be similar. The functional sharing model illustrates the
subfunctionalization of the paralogous genes, where one paralog
would adopt certain number of ancestral AS events and the other
paralog adopts the rest of the ancient isoforms. Therefore, in
the functional sharing model the number of AS events per gene
decreases in comparison with the non-duplicated genes. The last
model is the accelerated AS model, where an increase in the
number of AS events per gene results from a relaxed selection
pressure for each paralogous gene.
The most common AS event analyzed in paralogous genes is
MEE. This AS event accounts for the particularity where given
the possibility of two exons, one is maintained in one duplicated
gene and lost in the other, and vice versa in the paralog. The
first example found to have this pattern was the microphthalmia-
associated transcription factor in Danio rerio (Altschmied et al.,
2002). This is a single copy gene with and at least two mRNA
isoforms in mammals. The isoforms from this gene vary in the
3′-end of thematuremRNA and are expressed in different tissues.
Altschmied et al. (2002) analyzed this gene in zebrafish, a species
from the teleost which have presented several GD events in their
evolutionary history. They found two paralogs, one that had
one exon while the paralog had the other exon. Both paralogs
were expressed in different tissues, thus confirming that GD
had replaced AS. Several reports have confirmed the model of
function sharing for MEE events in a few genes (Wei-Ping et al.,
2003; Pacheco et al., 2004; Cusack and Wolfe, 2007; Hultman
et al., 2007; Marshall et al., 2013). To generalize this model,
Abascal et al. (2015) analyzed ∼90 human genes exhibiting MEE
events. They identified the duplicated orthologous genes in five
different fish species, including zebrafish. While several cases
were identified that fit the function sharing model, one paralog
containing one exon, while the other paralog contained the other
exon, not all orthologous genes fit the function sharing model. In
this report they also found duplicated genes with the same MEE
event observed in humans. This report suggested that a single
model could not be generalized, instead each gene possesses a
unique AS evolutionary model.
The first attempt to generalize an evolutionary model for
AS after GD was proposed by Kopelman et al. (2005). They
divided the human genes in families depending on the number
of paralogs and determined the proportion of genes affected
by AS. They reported that larger gene families tend to have
fewer genes affected by AS in comparison with single-copy genes
(singletons). They also searched for homologous genes in mouse
and classified them into duplicated and non-duplicated genes.
They found that duplicated genes were less affected by AS in
both organisms. These results were further confirmed by Su et al.
(2006), who proposed the function sharing model as the main
evolutionary model of AS after GD. In this study they report
that AS events are lost in recent gene duplicates but novel AS
events are gained in ancient paralogs. They also investigated the
symmetry of AS in paralogs, where the number of AS events in
the two paralogs are equal. They argued that 16 to 34% of the
paralogous genes analyzed exhibit patterns of asymmetric AS.
Jin et al. (2008) analyzed the proportion of genes affected by AS
and the average number of AS events per family. They found
that larger families have a smaller proportion of genes affected
by AS and that the number of AS events per gene is lower in
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56
Constitutive splicing 
Altentative Splicing 
events 
AIternative Donor (AD) 
Alternative Acceptor (AA) 
Exon Skipping (ES) 
Intron Retention (IR) 
Mutually Exclusive Exons (MEE) 
Primar y Transcript 
Altentative Transcripts 
(lsoforms) 
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Iñiguez and Hernández Alternative Splicing - Gene Duplication
FIGURE 2 | Evolutionary models of AS after GD. The three different proposed models of evolution of AS after GD are depicted. Color boxes represent exons
while black lines represent introns from a gene. The upper part represents a single-copy gene, before GD, that give rise to the different mRNA isoforms (connected
by a dash-line arrow) due to AS. The lower part represents the same gene after GD and each of the possible evolutionary models of AS. The isoforms highlighted in
a dashed line box represent those present before GD.
larger gene families. Another important aspect to consider in the
relationship between GD and AS is the expression patterns of
the paralogous genes and their isoforms. Talavera et al. (2007)
studied the correlation of expression of paralogs with and without
AS events in a set of tissues. They found that duplicated genes
without AS have more similar expression patterns compared to
the expression pattern between duplicated genes with AS. This
result suggests a relationship between gene expression patterns
and AS in duplicated genes. Nevertheless, it is not clear if AS
controls gene expression or vice versa.
Hughes and Friedman (2008) analysis of the AS and GD
relationship in Caenorhabditis elegans, confirmed findings from
previous analyses in mouse and human indicating that larger
gene families had fewer AS events. However, these authors
focused their work on an interaction network that consisted
of ∼900 genes; they found a negative correlation between AS
events and the connectivity in the network. This means that a
gene with multiple connections to other genes, also known as a
hub, had fewer AS events than genes connected to one or two
genes. Further, they argued that duplication of hub genes was
an uncommon phenomenon. Therefore AS, interpreted as an
internal paralog, was unlikely to occur in hub genes.
Plants are organisms with a tendency to have undergone
WGDs throughout their evolutionary history; and are therefore
particularly important in the study of AS andGD. Lin et al. (2008)
studied families of paralogous genes in Arabidopsis thaliana and
Oryza sativa. They analyzed several factors and characteristics of
singletons vs. duplicated genes and found that singletons were
less affected by AS than paralogs in both plant species; which
contrasts with findings from animal studies. In agreement with
these results, Roux and Robinson-Rechavi (2011) found that
paralogous families containing exactly two members had more
AS events and a higher proportion of these genes are affected
by AS than the rest of the other gene families in humans.
This is also in agreement with previous reports indicating that
larger families have fewer AS events and are less affected than
singletons genes. They classified the duplicated genes according
to their time of appearance in the evolutionary history, observing
a positive correlation between the number of AS events and the
time since duplication indicating that duplicated genes acquire
AS events over time. They also observed a negative correlation
between selective pressure and AS, meaning that paralogous
genes under strong positive selection tend to have fewer AS
events than paralogs under weak selection. They also searched
for orthologous genes and AS events in mouse and found that
genes duplicated in human but not in mouse had fewer AS
events than genes that have not undergone duplication. They
argue that the model best explains AS after GD and, based on
the comparison with mouse, genes with fewer AS events tend to
duplicate more frequently. Chen et al. (2011) analyzed duplicated
genes in human and mouse and observed, based on protein
similarity, that the time of duplication is positively correlated
with AS event acquisition. This accounts for why ancient paralogs
tend to have more AS than recently duplicated genes, which is
consistent with the findings described above for plants.
The two contrastingly AS evolutionary models, function
sharing and independent, were discussedmore recently by Su and
Gu (2012). They argued against the acquisition of AS through
time and a predisposition of duplication based on their AS events
mentioned by Roux and Robinson-Rechavi (2011). These authors
also argued the AS evolution after GD is most influenced by
the different types of GD. Thus, GD arising from SSD tend
to accumulate more mutations than WGD. Such mutations
could be a replacement of AS events and therefore AS events
are lost quickly. Tack et al. (2014) studied different WGD and
tandem duplications in A. thaliana looking for the qualitative
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Gene 
Isoforms 
Independent Model 
Gene 
lsoforms 
tG,n, DupHco';on (GD) 
Function Sharing Model 
.. 
Accelerated AS Model 
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Iñiguez and Hernández Alternative Splicing - Gene Duplication
and quantitative conservation of AS events in paralogs. Such
conservation was higher in paralogous genes resulting from
tandem duplications than from WGD-resulting paralogs. They
found that IR, the most frequent AS event in plants, was also
the event with highest conservation in A. thaliana. These results
suggest that the fate of AS events after GD depends on the type of
AS event and the type of GD.
Lambert et al. (2014) analyzed paralogous and singleton
genes from three important gene ontology categories in zebrafish
to better understand the relationship between AS and GD.
The authors investigated the conservation of exon structure in
paralogous genes by cataloging genes into paralogs with the
same exon structure and paralogs with different exon structures.
They found that paralogs with a change in its exon structure
tend to have fewer AS events than genes with the same exon
structure. They also looked for orthologous genes and their
isoforms in human. They found that the percentage of human
genes affected by AS and the number of AS events was less
in homologous genes with altered exon structure compared to
genes with the same exon structure. For this work authors pooled
together retrogenes with DNA-dependent SSD which could
influence the conclusions. These results refute the hypothesis
that genes lacking AS are predisposed to GD events (Roux
and Robinson-Rechavi, 2011). Rather, there is the possibility
that exon structure of paralogs is predisposed to change if
the ancient gene encoded only a low number of AS events.
There was no evidence of AS differences between duplicated
genes with the same exon structure and singletons. These
findings were confirmed by Lambert et al. (2015), where only
DNA-dependent SSD were analyzed. This study compared three
gene sets from human, mouse and zebrafish: duplicates with
same exon structure, duplicates with different exon structure
and singletons. They found that AS was less frequent in
paralogous genes with different exon structure and these
genes exhibited tissue-specific expression. They concluded that
paralogs with altered exon structure are subfunctionalized
because the expression of the two paralogs occurs in different
tissues.
The relationship between GD and AS is far from being
understood. Several models of this relationship have been
proposed and examples of each have been demonstrated.
The analysis of these processes is complex and therefore a
generalization of an evolutionary model is a difficult task. The
development and utilization of new technologies has allowed the
identification of gene isoforms expressed in a tissue-specific or
even a cell-specific manner in more model organisms (Conesa
et al., 2016). More studies in this field need to be performed to
more fully understand this relationship.
PERSPECTIVES
The correct identification and classification of AS and GD is
fundamental to improve the understanding of the evolution
of both processes. AS events must be classified in terms of
how they modify the primary transcript and the expression of
unique isoforms in specific conditions, tissues and developmental
stages. Future studies should also consider the percentage of
affected genes and that the frequency of different AS event
types varies between plants and animals. The classification of
AS events should also be complemented by identifying and
classifying GD events. The time elapsed after a GD event is
an important factor in paralogous gene AS. In addition, this
could be complemented with the mutation rates for each gene.
These could give insights of the evolution of the paralogous
genes. Besides classifying GD events as either SSD or WGD,
researchers must also determine the way they were produced
and the time since the duplication is necessary. For this
classification, the comparison between species is important.
There are a variety of model species, each uniquely qualifies
as a study organism, for the different GD processes. Plants
and teleost species in the animal kingdom, for example,
are good models for the classification of WGD and SSD,
respectively. Several organisms and a variety of tissues and
conditions need to be analyzed with a characterization and
classification of type of AS and GD in order to identify an
evolutionary model of AS events after GD. These analyses could
be complemented with other genome-wide analysis including
isoforms quantification and proteomic studies (Payne, 2015).
A single evolutionary model of AS after GD may not be solely
responsible for AS events, instead a combination of multiple
models is more likely. Identifying the mechanisms governing
which models are utilized in specific genes will improve our
understanding of the evolutionary relationship between GD
and AS.
AUTHOR CONTRIBUTIONS
All authors listed, have made substantial, direct and intellectual
contribution to the work, and approved it for publication.
FUNDING
LI is a Ph.D. student of Doctorado en Ciencias Biomédicas –
UNAM and a recipient of a studentship from the Consejo
Nacional de Ciencia y Tecnología (CONACyT), Mexico (no.
340334).
ACKNOWLEDGMENTS
We are grateful to Professors José L. Reyes, Enrique Merino, and
Jamie A. O’Rourke for their critical reviews and comments of this
manuscript.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2017 Iñiguez and Hernández. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (CC BY). The use,
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author(s) or licensor are credited and that the original publication in this journal
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5.2.2. Número de eventos en parálogos recientes de Glycine max
Existen diversas teorı́as sobre lo que sucede con los eventos de AS una vez que los genes
se han duplicado que se discuten en la introducción de esta sección. Estas teorı́as se basan en
las observaciones donde encuentran un bajo número de eventos de AS en genes duplicados.
Para explorar este fenómeno las plantas son modelos ideales ya que cuentan con una gran
variedad de duplicaciones. Todas las leguminosas, como se comentó en el artı́culo de la sec-
ción anterior (Iniguez et al., 2017), cuentan con una duplicación genómica completa ancestral
(˜56 millones de años) y una fracción de los genes presentan un parálogo hasta hoy en dı́a.
Además de esta caracterı́stica de las leguminosas, G. max presenta una duplicación reciente
( 10 millones de años) y muchos de los genes presentan un parálogo. Esta duplicación re-
ciente conlleva a que para un gen de P. vulgaris hay dos genes homólogos en G. max. Esta
caracterı́stica de ambas especies permite el análisis evolutivo del AS en genes duplicados a
partir de una duplicación genómica completa.
En la sección anterior se analizó la conservación de eventos entre soya y frijol y para
determinar si la conservación se pudiera dar de manera aleatoria se realizaron diversas simu-
laciones. Estas simulaciones arrojaron resultados sobre el número de eventos de AS que se
esperarı́a en los parálogos recientes de soya donde el número de eventos esperados era mayor
al identificado en las muestras analizadas. Por otro lado para los genes de G.max que no pre-
sentaban un duplicado de acuerdo a los criterios de selección de parálogos (falta de expresión
de un parálogo o diferencias en el número de exones), el número de eventos de AS era simi-
lar en las simulaciones comparado con el número de eventos identificados (Figura 5). Estos
resultados fueron consistentes con los reportados en humanos y ratones donde el número de
eventos de AS en genes duplicados es menor al número de eventos de AS en genes sin copias
(Su et al., 2006; Chen et al., 2011).
Por otro lado los genes de P. vulgaris que presentaban homologı́a con parálogos recientes
de soya (“relación evolutiva 1:2”) también mostraron en general un menor número de eventos
de AS al esperado al azar, exceptuando los eventos de IR y NI (Figura 5). Y aquellos genes
de frijol con una “relación evolutivarespecto a soya de 1:1 mostraron un número similar de
61
eventosalosesperadosalazar.Parainvestigarestefenómenoseanalizarondiversoscompo-
nentesdelosgenespertenecientesaambas“relacionesevolutivas”(1:1y1:2),tantodefrijol
comodesoya.
Figura5:Númerodeeventosesperadosalazarparagenescon“relaciónevolutiva1:2y1:1”
Númerodetodosloseventos(All)ycadaunodelostiposdeeventosqueseobtuvierondelosdatosanalizados
(lı́neaazul)yladistribucióndeeventosobtenidaenlassimulaciones.(a)GenesdeP.vulgarisconunsolo
homólogoconlamismaestructuraenG.max(1:1),loshomólogosdesoyaestángraficadosen(b).(c)y(d)son
igualesa(a)y(b)peroparagenesconuna“relaciónevolutiva1:2”.
Engeneral,losgenesdeambasespeciesconuna“relaciónevolutiva1:2”presentaron
menoseventosdeAS,enespecialAA,AS,ASSESyREalcompararlosconlosgenesen
“relaciónevolutiva1:1”.LoseventosdeIRyNI,enP.vulgaris,nopresentarondiferencias
entreambaslistasgénicas.LoseventosdeESfueronlosquemásdiferenciasmostraronentre
ambasrelacionesenlasdosespecies,locualconcuerdaconlosresultadosdeChenetal.
(2011)enlosquevenquelosduplicadosdehumanosyratonestienenmenoseventosdeAS
ydadoqueESeseleventomáscomúnenestosorganismoselresultadosereflejademanera
globalenloseventosdeAS.
62
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Se analizaron tanto el número de exones como la longitud de los mismos y los tipos de
exones, ya fuera 5’UTR, CDS o 3’UTR, pertenecientes a ambas relaciones evolutivas. La
distribución del número de exones por gene y exones de tipo CDS resultaron similares entre
ambas relaciones evolutivas en las dos especies. El número de exones combinados (5’UTR-
CDS y CDS-3’UTR) fue menor en los genes cuya “relación evolutiva es de 1:1.a aquellos
con una “relación evolutiva 1:2”. Estos tipos de exones se vieron enriquecidos en los eventos
de NI (Iniguez et al., 2017) pero no influyeron de manera positiva en los genes con “relación
evolutiva 1:2”. La distribución del número de exones y longitud de exones no codificantes
también fue diferente entre ambas relaciones evolutivas; genes con una “relación evolutiva
1:1”presentaron más exones de estos tipos (3’UTR y 5’UTR) pero más pequeños que aque-
llos en una “relación evolutiva 1:2”. Esto quiere decir que los genes con “relación evolutiva
1:1”presentan más exones no codificantes pero de longitud pequeña. Estos resultados son
importantes porque pueden explicar el bajo número de algunos eventos de AS en genes con
“relación evolutiva 1:2 2a que como se explicó en el artı́culo de Iniguez et al. (2017) algunos
tipos de eventos de AS tienden a estar en intrones de exones no codificantes y por lo tan-
to al haber menos exones no codificantes en los genes con “relación evolutiva 1:2resulta en
una disminución de eventos de AA, AD, ASS y RE. Los eventos de IR no mostraron una
preferencia por intrones de exones no codificantes como los demás eventos (Iniguez et al.,
2017), y como el número de exones codificantes fue similar en ambas listas el número de
eventos de IR esperado en ambas listas serı́a similar. El último aspecto evaluado fue la ta-
sa evolutiva utilizando la tasa de sustituciones no sinónimas sobre sustituciones sinónimas
(dN/dS) de ambas listas génicas al ser comparadas con su homólogo en la otra especie. La
tasa evolutiva en general fue menor en genes con “relación evolutiva 1:2”, esto implica que
estos genes están bajo una selección más fuerte y esto puede influenciar a la adquisición de
nuevos eventos de AS restringiendo la producción de isoformas.
Es difı́cil identificar el mecanismo que da origen a las observaciones donde los genes con
una “relación evolutiva 1:2”presentan menos eventos que aquellos con 1:1 pero los resultados
que se muestran en esta sección guı́an hacia ciertos experimentos y datos que se necesitan
63
analizar. Los resultados mostrados aquı́ indican una tendencia a tener pocos eventos de AS en
genes duplicados, pero el bajo número de eventos de AS en genes recientemente duplicados
de G. max se ve también en los genes homólogos de P. vulgaris. Estos resultados concuerdan
con los de (Lambert et al., 2014, 2015) donde se sugiere que los eventos de AS no se pierden
en los duplicados recientes sino que los genes que tienden a mantener su estructura génica,
uno de los criterios utilizados para establecer ortólogos, tienen un menor número de eventos
de AS.
5.2.3. Conservación de eventos entre parálogos
Una de los modelos sobre el destino del AS al duplicarse los genes es la de función com-
partida, donde las isoformas que se producı́an antes de la duplicación se dividen en los parálo-
gos. Si esto sucediera la conservación se perderı́a y el número de isoformas se reducirı́a. Por
otro lado un modelo opuesto es el de independencia donde cada parálogo es independiente
y la transcripción de isoformas a partir del AS mantiene cierta libertad mientras las funcio-
nes ancestrales se conserven. Con el modelo de independencia el número de isoformas se
mantiene y puede llegar a conservar eventos de AS.
El número de eventos se ha visto que es bajo en los genes duplicados, discutido en la
sección anterior. Mientras que la conservación de eventos en genes parálogos es una pregunta
que queda pendiente para analizar. Debido a las dos duplicaciones que presenta G. max, la re-
ciente y la ancestral compartida con P. vulgaris es posible identificar los eventos conservados
entre parálogos recientes y ancestrales. Para analizar la conservación de parálogos recientes
se analizaron 9,058 parejas de parálogos de G. max y se observó una conservación general
del 40 % de los eventos, mientras que para cada evento en especı́fico la conservación varió
entre un 23 % para ASS a 46 % para IR (Figura 6. En P. vulgaris la conservación en parálo-
gos ancestrales (2114 pares de parálogos) se redujo a un 29 % de manera general y para cada
evento el porcentaje de conservación fue entre 10 % y 40 %. Para identificar la conservación
en parálogos ancestrales de soya se colapsaron los eventos en los pares de genes que presen-
taban duplicaciones recientes. Esto permitió comparar entre pares de parálogos ancestrales.
64
2,296 pares de genes colapsados presentaron una conservación ancestral general del 31 % y
el rango de conservación entre los eventos especı́ficos fue de 7 %-46 % (Figura 6).
Figura 6: Conservación de eventos entre parálogos
En la gráfica se muestran los porcentajes de conservación entre los parálogos de manera general (color negro) y
para cada evento en especı́fico en las duplicaciones ancestrales de P. vulgaris y G. max y en la reciente para soya.
Las barras con lı́neas diagonales hacia la izquierda corresponden a G. max y las barras con lı́neas diagonales
hacia la derecha a la duplicación ancestral. Los porcentajes de la parte superior corresponden a la conservación
de eventos de AS en duplicados recientes de soya, duplicados ancestrales de frijol y duplicados ancestrales de
soya, de manera descendente.
Los resultados de la conservación muestran que existe una mayor conservación entre
parálogos recientes que ancestrales para casi todos los eventos sin embargo la conservación
de IR es similar entre las duplicaciones ancestrales y recientes. Esto quiere decir que la fun-
cionalidad o regulación de estos eventos de AS no es dependiente del tiempo de divergencia
entre parálogos y por lo tanto el modelo de función compartida no aplica y se ajustarı́a mejor
el modelo de independencia. Para el resto de los eventos la conservación se reduce conside-
rablemente con el tiempo y es constante entre ambas especies pero se mantiene una conser-
vación significativa. Con estos resultados se puede concluir que un único modelo evolutivo
65
no se puede aplicar para todos los eventos, sino que depende del tipo de evento y puede ser
que cada gen presente algún modelo. Para investigar esto a más detalle se necesitan realizar
más experimentos.
66
6. Discusión General y Perspectivas
La transcriptómica es un enfoque fundamental para entender la biologı́a, no solo la vegetal
sino la de todos los organismos, ya que permite identificar los genes expresados ante situacio-
nes, condiciones especı́ficas o durante el desarrollo. El control de la transcripción regula los
mRNA que se expresan y en qué concentraciones pero no es la única regulación por la cual
se definen las concetraciones protéicas ya que existen diversos factores post-transcripcionales
que también la regulan. Entre éstos se encuentran los miRNAs y el AS.
Los distintos perfiles transcripcionales que se estudiaron a lo largo de la tesis muestran
distintos patrones de expresión en determinadas circunstancias y aunque los resultados mues-
tran a gran escala lo que sucede a nivel transcriptómico un estudio más a detalle sobre los
genes, las vı́as o las ontologı́as diferencialmente expresadas puede resultar en contribucio-
nes de mayor relevancia para lograr esclarecer la respuesta celular ante el estrés oxidativo.
Además de esto una mejor anotación genómica pudiera funcionar para identificar a mejor
detalle diversos aspectos relacionados con el estrés.
La identificación de miRNAs en P. vulgaris en conjunto con su expresión determinaron
una variedad de redes de coexpresión donde además de encontrarse miRNAs estudiados se
identificaron nuevos. Esto sugiere una posible funcionalidad de estos inéditos RNA’s no codi-
ficantes. La integración de datos experimentales junto con resultados bioinformáticos puede
dar origen a nuevos aspectos biológicos, incluyendo aspectos de interés sobre los nódulos.
Este fenómeno se demostró con el trabajo del miRNA172c y su blanco AP2 y actualmen-
te se están realizando análisis experimentales y bioinformáticos para determinar relaciones
funcionales entre ciertos miRNAs y sus respectivos blancos.
A partir del análisis de genes co-expresados con el factor de transcripción AP2 se logró
obtener genes candidatos relacionados con el desarrollo y muerte de los nódulos. De igual
forma al analizar los sitios de unión de factores de transcripción en las regiones promotoras
de los genes de MIRNA172 se identificó una familia de factores de transcripción (AGL). Esta
familia se ha caracterizado durante la organogénesis de flor y puede ser importante para el
67
desarrollo de nódulo. A partir de esto ya se están realizando diversos trabajos en el laboratorio
para caracterizar la función de algunos de estos factores de transcripción.
El estudio a escala genómica del AS en P. vulgaris y G. max arrojó una gran cantidad
de datos sobre AS. Una de las preguntas que quedan por resolver es la funcionalidad de los
eventos de AS conservados. Para esto ya se realizó una búsqueda de eventos presentes en
nódulos o raı́ces pero no en ambos tejidos y también aquellos presentes en diversas muestras
provenientes de plantas que fueron crecidas en diferentes fuentes de nitrógeno, como ferti-
lización vs SNF. Por el momento se cuenta con una lista de genes candidatos con eventos
de AS que indican una función biológica diferente al mRNA principal y los experimentos
pertinentes se están comenzado a realizar.
La relación que existe entre el AS y la duplicación génica es un aspecto importante para
la evolución y algunos análisis presentados en esta tesis muestran resultados interesantes. Sin
embargo estos no son del todo concluyentes y más análisis se necesitan realizar. La duplica-
ción génica puede provocar un desequilibrio en la cantidad de AS que presenten los parálogos
y esto pudiera tener alguna relación con la expresión génica o con la funcionalidad biológica.
Este tipo de preguntas quedan pendientes de resolverse.
Por último queda mencionar que la integración de todos los datos es fundamental para
la comprensión de los organismos. A partir de la secuenciación masiva se pueden plantear
una serie de experimentos a diferentes escalas que conlleven a un mejor entendimiento de la
biologı́a en general. Y con base en esos resultados se deben de plantear los experimentos y
análisis precisos que orienten a entender cada una de los mecanismos que conforman a los
organismos.
68
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Physiologia Plantarum 149: 389–407. 2013 © 2013 Scandinavian Plant Physiology Society, ISSN 0031-9317
Transcript profiling of common bean nodules subjected
to oxidative stress
Mario Ramı́reza,∗, Gabriel Guillénb, Sara I. Fuentesa, Luis P. Íñigueza, Rosaura Aparicio-Fabrea, David
Zamorano-Sáncheza, Sergio Encarnación-Guevaraa, Dario Panzeric, Bianca Castiglionic, Paola
Cremonesic, Francesco Strozzid, Alessandra Stellac,d, Lourdes Girarda, Francesca Sparvolic and
Georgina Hernándeza
aCentro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad 1001, Cuernavaca, Mor. 62209, Mexico
bInstituto de Biotecnologı́a, Universidad Nacional Autónoma de México, Av. Universidad 2001, Cuernavaca, Mor. 62209, Mexico
c Istituto di Biologia e Biotecnologia Agraria, CNR, Via Bassini 15, 20133, Milano, Italy
dParco Tecnologico Padano, Statistical Genomics and Bioinformatics Unit, Via Einstein Loc. Cascina Codazza, 26900, Lodi, Italy
Correspondence
*Corresponding author,
e-mail: mario@ccg.unam.mx
Received 27 September 2012;
revised 25 January 2013
doi:10.1111/ppl.12040
Several environmental stresses generate high amounts of reactive oxygen
species (ROS) in plant cells, resulting in oxidative stress. Symbiotic nitro-
gen fixation (SNF) in the legume–rhizobia symbiosis is sensitive to damage
from oxidative stress. Active nodules of the common bean (Phaseolus vulgaris)
exposed to the herbicide paraquat (1,1′-dimethyl-4,4′-bipyridinium dichloride
hydrate), which stimulates ROS accumulation, exhibited reduced nitrogenase
activity and ureide content. We analyzed the global gene response of nod-
ules subjected to oxidative stress using the Bean Custom Array 90K, which
includes probes from 30 000 expressed sequence tags (ESTs). A total of 4280
ESTs were differentially expressed in stressed bean nodules; of these, 2218
were repressed. Based on Gene Ontology analysis, these genes were grouped
into 42 different biological process categories. Analysis with the PathExpress
bioinformatic tool, adapted for bean, identified five significantly repressed
metabolic pathways related to carbon/nitrogen metabolism, which is crucial
for nodule function. Quantitative reverse transcription (qRT)-PCR analysis of
transcription factor (TF) gene expression showed that 67 TF genes were differ-
entially expressed in nodules exposed to oxidative stress. Putative cis-elements
recognized by highly responsive TF were detected in promoter regions of
oxidative stress regulated genes. The expression of oxidative stress responsive
genes and of genes important for SNF in bacteroids analyzed in stressed
nodules revealed that these conditions elicited a transcriptional response.
Introduction
Reactive oxygen species (ROS), such as singlet oxygen
(1O2), superoxide anion radical (O2
– ), hydroxyl radical
(OH·) and hydrogen peroxide (H2O2), are generated
as inevitable by-products of aerobic metabolism. The
dynamic balance between reactions that generate ROS
Abbreviations – APX, ascorbate peroxidase; EST, expressed sequence tag; MDA, malondialdehyde; ROS, reactive oxygen
species; SNF, symbiotic nitrogen fixation; SOD, superoxide dismutase; TF, transcription factor.
existing in every aerobic cell, stimulates nonspecific
oxidations and antioxidants. A change in this balance
in favor of oxidative reactions is known as oxidative
stress (Sies 1991). Because ROS react with a wide
range of biomolecules, such as nucleic acids, proteins
and lipids, that are essential for maintenance of
the integrity of cellular structures, they may cause
Physiol. Plant. 149, 2013 389
A. Anexo
76
irreversible damage that could subsequently lead
to tissue necrosis and eventually kill the organism
(Op den Camp et al. 2003, Apel and Hirt 2004). A
large variety of environmental stresses, such as drought,
salinity, chilling, high light intensity and metal toxicity,
can cause an increase in the generation of ROS. In
the majority of aerobic organisms, there is a need to
effectively remove the ROS generated as a result of
environmental stress. To control the amount of ROS
with the purpose of protecting the cells from oxidative
damage, plants have developed a complex antioxidant
defense system to scavenge ROS. This antioxidant
system includes various enzymes and nonenzymatic
metabolites that destroy ROS that are produced in excess
from those normally required for metabolism (Inze and
Van Montagu 1995, Vranova et al. 2002, Minchin
et al. 2008). Nonenzymatic antioxidants include mainly
ascorbate and glutathione (γ -glutamylcysteinylglycine),
as well as tocopherol, flavonoids, alkaloids and
carotenoids. Plant ROS scavenging enzymes include
superoxide dismutase (SOD), ascorbate peroxidase
(APX), glutathione peroxidase and catalase. Although
ROS were initially recognized as hazardous derivatives
of aerobic metabolism that need to be removed, it
subsequently became apparent that they are key actors in
regulating development of the plant, responses to stress
and programmed cell death. It is now generally accepted
that ROS plays an important signaling in diverse cellular
mechanisms (Neill et al. 2002).
Legumes can establish symbioses with bacteria
of the Rhizobiaceae family to capture atmospheric
nitrogen directly and thereby to support plant growth.
The symbiotic nitrogen fixation (SNF) occurring in
the legume–rhizobia symbiosis is the main source
of nitrogen in agro-ecosystems. SNF takes place in
specialized rhizobia-induced legume root nodules and
involves extensive recognition and a tight association
between the two symbionts. The involvement of ROS
in the initial stages of the Rhizobium–legume symbiosis
has been outlined; ROS is essential for the development
of an optimal symbiosis, pointing to a signaling role
for ROS (Mandon et al. 2009). On the other hand, the
root nodules of legumes are sites of strong biochemical
activity and therefore are at increased risk of harm
from ROS generation (Dalton et al. 2009, Becana et al.
2010). O2
– and H2O2 are generated in nodules by
high respiratory rates necessary to support SNF, the
autoxidation of the oxygenated form of leghemoglobin,
and the oxidation of various proteins with strong
reducing potential (e.g. nitrogenase, ferredoxin and
hydrogenase). To cope with oxidative stress during
the symbiotic interaction, both the host plant and
rhizobia have at their disposal a set of antioxidant tools
(Becana et al. 2000, Matamoros et al. 2003). However,
modification of redox homeostasis and a decline in
antioxidative defense, which are both triggered by ROS
accumulation, greatly influence the metabolism of the
nodule and SNF. Drought, salinity, high temperature and
metal toxicity are common abiotic stress conditions of
the tropical semiarid regions in which legume crops are
grown and stimulate changes in redox homeostasis and a
decline in antioxidative defense. In this work, we sought
to further our understanding of the global response
to oxidative stress in the common bean (Phaseolus
vulgaris)–Rhizobium tropici symbiosis.
The most important legume for direct human con-
sumption worldwide is common bean. SNF and bean
crop production are affected by environmental factors,
such as drought, salinity and nutrient stress that may
lead to oxidative stress (Broughton et al. 2003). Our
understanding of how common bean plants respond
to and cope with abiotic stresses, such as drought and
salinity, is improving due to the research of several
groups (Serraj et al. 1998, Verdoy et al. 2004). However,
much less is known about the response of common bean
to metal toxicity. Several areas in which bean is grown
are characterized by acidic soils and low drainage,
which favor the increased availability of certain metals,
including aluminum (Al), copper (Cu) and manganese
(Mn). Mn toxicity stimulates ROS production and gener-
ates oxidative stress; it negatively affects photosynthesis,
respiration and the homeostasis of Cu, iron (Fe) and zinc
(Zn), which are vital nutrients in the legume–rhizobia
symbiosis (González and Lynch 1997, Yang et al. 2009).
We recently identified microRNAs that are induced or
repressed in nodules of bean plants exposed to Mn stress
(Valdés-López et al. 2010). In this work, we proposed to
identify the transcription factors (TFs) that are regulated
in common bean nodules under Mn toxicity.
In this work, we analyzed the response of mature
common bean nodules exposed to the herbicide
paraquat (PQ), a ROS-inducing agent that mimics the
oxidative stress resulting from abiotic stresses. PQ is
a widely used herbicide generally applied to shoots; its
mode of action is strengthened by light during the process
of photosynthesis in plants (Donahue et al. 1997, Iturbe-
Ormaetxe et al. 1998). Inside leaf cells, PQ is reduced by
chloroplasts to its radical cation and upon auto-oxidation
H2O2 and several oxygen radicals are formed. These
radicals are phytotoxic, causing chlorophyll destruction
and lipid peroxidation. However, to analyze the in vivo
effect of oxidative stress on nodule metabolism, we
applied PQ directly to the nodulated roots of common
bean plants. This approach is similar to the one reported
by Dalton (1992) for Glycine max (soybean) nodules and
Marino et al. (2006) for Pisum sativum (pea) nodules.
390 Physiol. Plant. 149, 2013
77
Most of the antioxidants, both enzymatic and
nonenzymatic, in legume nodules are similar to those in
other plant tissues or organs; however, concentrations
are often higher, which suggests an important link
between SNF and antioxidants (Dalton 1995, Becana
et al. 2000, Matamoros et al. 2003, Minchin et al.
2008, Becana et al. 2010). On the other hand, rhizobia,
like other bacteria, possess certain ROS-scavenging
enzymes, including catalase, SOD, peroxidases and
peroxiredoxin (Crockford et al. 1995, Ohwada et al.
1999, Santos et al. 2000, Ampe et al. 2003, Djordjevic
et al. 2003, Vargas et al. 2003, Dombrecht et al. 2005).
There are few reports on the effect of oxidative stress on
rhizobia transcript profiles and these mainly correspond
to free-living conditions. These include the genome-wide
transcript profiles of B. japonicum exposed to H2O2 or
PQ (Donati et al. 2011, Jeon et al. 2011). Martinez-
Salazar et al. (2009) reported a transcriptional profile
analysis of R. etli that suggested that RpoE4, an ECF σ
factor, is an important general regulator involved in the
responses to several stresses.
Plants exhibit specific transcriptomic responses to
oxidative stress. Several studies have indicated that
according to the type of ROS or its subcellular site
of production (plastid, cytosol, peroxisome or apoplast),
a different physiological, biochemical and molecular
response is caused and consequently results in a clear
reprogramming of the plant’s transcriptome (Mittler
et al. 2004, Gadjev et al. 2006). In Arabidopsis
thaliana, genome-wide microarrays have provided the
means to analyze how expression profiling are affected
by rising ROS levels (Op den Camp et al. 2003,
Gechev et al. 2004, 2005, Vandarauwera et al. 2005).
Transcriptomic footprints have been produced for plants
with compromised levels of ROS-scavenging enzymes,
such as chloroplastic Cu/Zn SOD, APX cytosolic
and peroxisomal catalase mitochondrial alternative
oxidase (Davletova et al. 2005b, Umbach et al. 2005,
Vandarauwera et al. 2005), as well as for plants
treated with agents that generate ROS (Gechev et al.
2004, 2005). While transcriptomic approaches, through
macroarray and microarray analyses, have allowed the
identification of a great number of genes that are
differentially expressed in nodules of different legumes
such as Medicago truncatula, Lotus japonicus, soybean
and common bean (Colebatch et al. 2002, 2004, El
Yahyaoui et al. 2004, Kouchi et al. 2004, Lee et al.
2004, Asamizu et al. 2005, Ramı́rez et al. 2005, Starker
et al. 2006, Brechenmacher et al. 2008, Hernández et al.
2009), there is no information on the transcriptional
profile of legume nodules exposed to oxidative stress.
On this basis, we hypothesized that the global
response of common bean nodules to oxidative stress
occurs at the transcriptional level and aimed to decipher
the extent of transcript changes derived from ROS
accumulation in both symbionts. To this end, we carried
out a microarray-based transcript profiling of PQ-treated
common bean nodules, elicited by Rhizobium tropici,
using the Bean Custom Array 90K designed by our
group. In addition, we analyzed the expression of
rhizobial genes that had previously been reported to
be differentially expressed under stress conditions and
of transcriptional regulators and enzymes that control
nitrogen fixation.
The metabolomic and transcriptomic profile of
Arabidopsis cells exposed to oxidative stress revealed
that this treatment had a profound effect on central
metabolic pathways, such as the tricarboxylic acid
cycle and amino acid metabolism (Baxter et al.
2007). In the case of legumes, the decrease in SNF
caused by environmental stresses is intimately related
to cellular carbon/nitrogen metabolism, which is key
to nodule function and to efficient SNF. Therefore,
we conducted a transcriptomic analysis of PQ-stressed
nodules to identify metabolic pathways that might be
affected by oxidative stress. MapMan (Thimm et al.
2004), PathExpress (Goffard and Weiller 2007, Goffard
et al. 2009), and Gene Ontology (The Gene Ontology
Consortium et al. 2000) bioinformatics tools adapted
to common bean were used to interpret the microarray
gene expression data. PathExpress allowed us to identify
differentially expressed genes that were assigned EC
numbers and were thus associated with the relevant
metabolic pathways that operate in nodules under
oxidative stress. Furthermore, we hypothesized that TFs
function as global regulators of the nodule’s response
to ROS accumulation. Specific TFs and other regulatory
elements of the transcriptomic response of Arabidopsis
to oxidative stress are beginning to be uncovered
(Davletova et al. 2005a, Baxter et al. 2007). We have
identified TFs that respond to phosphorus deficiency in
common bean nodules (Hernández et al. 2009). In this
work, we used our qRT-PCR-based TF profile platform
(Hernández et al. 2007) to identify TFs that are involved
in the regulation of gene expression in bean nodules
subjected to oxidative stress. In addition we analyzed the
sequences of promoter regions of selected PQ responsive
genes to identify putative TF-binding cis-elements.
Materials and methods
Plant and bacterial growth conditions
Surface sterilization of Phaseolus vulgaris cv. Negro
Jamapa 81 seeds was performed with a sodium
hypochlorite solution (10%) for 10 min and seedlings
were germinated at 30◦C on moist sterile filter paper.
Physiol. Plant. 149, 2013 391
78
Three-days-old seedlings were planted in pots with
vermiculite, as substrate, and then inoculated with 1
ml bacterial suspension (approximately 107 cells) per
plant. The inoculum was prepared from an overnight
culture of Rhizobium tropici CIAT 899 grown in PY
medium at 30◦C. Pots were watered 3 days per week
with a nitrogen-free nutrient plant solution (Summerfield
et al. 1977). Nodulated plants were grown in a naturally
lit greenhouse with a controlled temperature (26–28◦C)
for 18 days, at which point the nodules reached maturity.
Different stress treatments known to generate oxidative
stress were tested in mature common bean nodules.
For PQ treatment, at 16 days post-inoculation (dpi)
plants were watered with 1 mM 1,1′-dimethyl-4,4′-
bipyridinium dichloride hydrate, the herbicide PQ, for
48 h. For chilling stress, plants (at 18 dpi) were incubated
at 4◦C for 4 h and then the nodules were harvested. For
salinity stress, plants (16 dpi) were watered with nutrient
solution supplemented with 150 mM of NaCl at 0 and
12 h and were harvested at 12 and 24 h. Bean plants
were subjected to drought stress for 1 week by stopping
watering at 15 dpi. To generate Mn toxicity conditions,
plants were inoculated and watered with nutrient plant
solution supplemented with 400 µM MnCl for 18 days.
Mature nodules from stressed and control plants were
harvested and immediately frozen in liquid nitrogen and
stored at –80◦C until used.
Lipid peroxidation, ROS detection, nitrogenase
activity and ureide content
Lipid peroxidation, an indicator of cell membranes dam-
age by high concentrations of ROS, was assessed from
18-dpi nodules harvested from stressed or control bean
plants in three independent experiments (three replicates
per experiment) as reported (Heath and Packer 1968, Du
and Bramlage 1992). MDA equivalents were calculated
using an extinction coefficient of 155 M–1 cm–1. ROS
accumulation in PQ-treated nodules, as compared to
control nodules, was assessed by fluorescence activity
assays. Nodules (16 dpi) treated with PQ for 24 h and
nontreated nodules were further incubated in 10 µM
2′,7′-dichlorodihydro-fluorescein diacetate (DFCH-DA)
(Invitrogen, Carlsbad, CA) for 6 h. After a brief wash in
distilled water, the nodules were observed using a Carl
Zeiss AXIOSKOPZ fluorescence optical microscope. All
fluorescence images were captured using a Canon Power
Shot G5 digital camera. DFCH-ROS complexes present
in the nodules of bean plants were quantified based
on fluorescence intensity using the NIH IMAGEJ software
program (http://rsbweb.nih.gov/ij/). Student’s t-tests were
performed to detect significant differences in transcript
levels (P < 0.05).
Nitrogenase activity was determined by measuring the
acetylene reduction activity of 18-dpi-nodulated roots
from 15 plants per condition and three independent
experiments. Ethylene production was determined
by gas chromatography in a Variant model 3300
chromatograph as reported (Ramı́rez et al. 1999).
Specific activity is expressed as nmol ethylene h–1 g–1
nodule DW.
The ureide content was analyzed in nodules and in
xylem exudates from 18-dpi PQ-treated and control bean
plants. For nodule assays, 200 mg FW of tissue was used
and three independent experiments with three biological
replicates each were performed. Xylem exudates were
collected for 1 h from the cut stems of 10–15 plants
in three independent experiments. Stem collection was
done from 12:00 to 13:00 h, and plants were watered
1 h before samples were collected. After recording the
volume of the exudates, the collected sap was stored at
–20◦C until used for analysis. The ureide content was
determined using the differential analysis reported by
Vogels and Van Der Drift (1970).
Bean microarray design and hybridization
The bean microarray was printed using a platform with
a custom 90K array layout at the Plant Functional
Genomic Center of the University of Verona, Italy. To
define the layout, an in-house bioinformatics pipeline
was created to collect, compare and filter bean, and
soybean RNA sequences available from the DFCI Bean
Gene Index (http://compbio.dfci.harvard.edu/tgi, version
3.0) with 21 497 total unique sequences and 33 001
reference soybean genes from the NCBI UniGene build
38.0 (http://www.ncbi.nlm.nih.gov/unigene). Because
the bean sequences could not be considered as a
complete set in terms of representation of genes and
transcripts, these were used as references and the
soybean UniGenes were added to generate a larger
dataset. Duplicated sequences were then removed using
a homology search pipeline with BlastN, to define
minimally redundant datasets of transcripts. After this
step, all the sequences in the dataset were processed
to design probes of 35–40 nucleotides, according to
the OligoArray 2.0 parameters (Rouillard et al. 2003).
These probes were subsequently filtered to avoid cross-
hybridizations. The final layout accounted for 18 867
unique bean sequence probes and 11 205 soybean
UniGene probes, along with positive and negative
controls to yield a total of 30 150 different probes on the
microarray. Each probe was printed in triplicate to ensure
the presence of internal replicates and to provide a good
statistical representation of each transcript on the array.
The microarray was named Bean Custom Array 90K.
392 Physiol. Plant. 149, 2013
79
Total RNA from 18-dpi PQ-treated and control bean
nodules was isolated using Trizol reagent (Life Tech-
nologies, Carlsbad, CA) as reported (Hernández et al.
2007). Total RNA (1 µg) was used as template to syn-
thesize antisense RNA (aRNA) with Cy5-ULS, using
the RNA Amplification and Labelling Kit from Com-
biMatrix (ampULSe, Kreatech Biotechnology, Amster-
dam, the Netherlands), according to the manufacturer’s
instructions. Prehybridization was made by incubat-
ing the arrays with prehybridization solution (6X SSPE,
0.05% Tween-20, 20 mM EDTA, 5× Denhardt’s solu-
tion, 100 ng µl–1 salmon sperm DNA, 0.05% SDS) for
30 min at 45◦C. Labeled aRNA (4 µg) was fragmented
by incubation with 5.2 µl of fragmentation solution
(200 mM Tris-acetate pH 8.1, 500 mM KOAc, 150 mM
MgOAc) for 20 min at 95◦C.
Hybridization was done at 45◦C for 16 h in
hybridization solution (6X SSPE, 0.05% Tween-20,
20 mM EDTA, 25% diformamide, 100 ng µl–1 salmon
sperm DNA, 0.04% SDS). After hybridization and
washing, the microarray was dipped in imaging
solution, covered with LifterSlip™, and then scanned
using a GenePix 4000B microarray scanner (Axon,
Toronto, Canada) and the accompanying acquisition
software (CombiMatrix Microarray Imager Software).
Each hybridization was scanned multiple times at
different photomultiplier (PMT) settings. Arrays were
stripped and re-hybridized using the CustomArray
Stripping Kit for 90K CombiMatrix (ampULSe, Kreatech
Biotechnology, Amsterdam, the Netherlands), following
the protocols of the manufacturer. Each array was used
up to four times without showing a deterioration in signal
or increase in background.
Bean custom array 90K data analysis
The raw intensity data were first processed using
COMBIMATRIX MICROARRAY IMAGER software. This program
allows the visual inspection of the entire microarray
slide and is used to check the quality of each spot
and, if needed, to perform possible corrections (i.e.
for dust or scratches on the surface). Intensity data
were then exported into the Feature and Probe format
of CombiMatrix, where the actual raw intensity per
probe and per spot is stored. Data were loaded into R
(http://www.r-project.org) and analyzed using the Limma
package (Smyth 2004). The median value of each spot on
the array was determined and the probes were filtered to
remove quality and factory controls. Within-array probe
replicates were defined as technical replicates, and the
mean intensity across different probes was calculated.
The probe intensity values of each biological condition
were then normalized using the quantile function of
Limma. The values present in the expression matrix were
transformed in log2 and a design matrix was defined to
describe the biological samples. The expression matrix
was then used to fit a linear model using the design
matrix and the functions of Limma. A set of contrast
matrices was defined to describe the comparisons among
samples in the experiment and a second linear fit
was performed for each contrast. Errors were corrected
using the Bayesian functions of Limma and the list of
differentially expressed genes was generated for each
contrast after correcting for multiple testing using the
Benjamin-Hochberg method and setting 0.05 as the
adjusted P-value cutoff.
For the purpose of interpreting the biological signif-
icance of gene expression data three bioinformatics-
based approaches were used for analyses. First, the sta-
tistically significant array data (Table S1) were organized
in functional categories according to Gene Ontology
(GO) guidelines (The Gene Ontology Consortium et al.
2000). Fisher’s exact test (Routledge 1998) was applied
to determine which GO categories were statistically
over-represented in each set of differentially expressed
ESTs that was induced or repressed under oxidative
stress. Second, an expression data analysis was per-
formed using MAPMAN software version 3.5.1 (Thimm
et al. 2004, http://gabi.rzpd.de/projects/MapMan/). To
extend MAPMAN to common bean, a Phaseolus vulgaris
map was developed and uploaded to MAPMAN. The fold
change PQ/control of the induced or repressed common
bean genes, expressed in log2, was used to visualize
the expression data. We used the PathExpress web-
based tool (Goffard and Weiller 2007, Goffard et al.
2009), to identify the most relevant metabolic path-
ways related to the subsets of differentially expressed
bean genes. To adapt PathExpress to common bean,
we defined the orthologous gene from soybean and
its Affymetrix ID for each differentially expressed com-
mon bean EST (Table S1) and used the Affymetrix
Soybean Genome Array (Glycine max) data set from
the PathExpress web-based tool. ESTs associated with
metabolic pathways or subpathways that were statisti-
cally over-represented (P ≤ 0.05) were detected amongst
the differentially expressed sets of ESTs.
Quantitative RT-PCR (qRT-PCR) analysis
A qRT-PCR approach was used to validate the microarray
data and to generate a transcript profile of previously
reported bean TF genes (Hernández et al. 2007). Total
RNA was isolated from 200 mg of frozen nodules
as described above, for qRT-PCR. Three biological
replicates were carried out for each condition, using PQ-
treated and control nodules, and RNA was extracted from
Physiol. Plant. 149, 2013 393
80
differing sets of plants grown under similar conditions.
A NanoDrop ND-1000 spectrophotometer (NanoDrop
Technologies, Wilmington, DE) was used to measure
RNA concentration. Genomic DNA degradation, cDNA
synthesis and quality verification for qRT-PCR were
done as reported (Hernández et al. 2007). Reactions
were done in a 96-well format with the 7300 Real-
Time PCR System and 7300 System Software (Applied
Biosystems, Foster City, CA). In each qRT-PCR run, the
APX (TC32379) coding sequence was included as a
marker gene of the PQ response in bean nodules. APX
is an integral component of the glutathione-ascorbate
cycle, and its transcript level is remarkably increased in
response to the presence of ROS (Yoshimura et al. 2000).
The gene encoding APX was induced in nodules exposed
to oxidative stress (ca. three average expression ratio),
thus confirming the oxidative stress status of the nodules
under study. UBC9 (TC34057), which was constant in
the tested conditions, was included for normalization
in every qRT-PCR run. Average expression ratios (PQ/C)
were calculated with the CT method as reported
(Hernández et al. 2007) and the fold change value (log2)
was calculated. Student’s t-test was performed with a
P-value cutoff of 0.05.
To validate the microarray data, 30 genes were
selected that had been identified by microarray analysis
as being induced or repressed in nodules exposed to
oxidative stress. For each qRT-PCR reaction, 100 ng of
RNA free of genomic DNA were used as template.
Table S5 shows the sequences of the qRT-PCR primer
pairs used. Relative transcript levels of selected genes
were quantified using the Power SYBR Green RNA – to
CT 1 Step Kit (Applied Biosystems Foster City, CA).
TF profiling, based on qRT-PCR, was performed
to determine the differential expression of TF genes
in nodules exposed to oxidative stress. The profile
included a set of 372 bean TF genes previously
identified (Hernández et al. 2007). Primer pairs and qRT-
PCR conditions used have been reported (Hernández
et al. 2007).
Gene expression of bacteroids from PQ-stressed and
control nodules was analyzed by qRT-PCR. Total RNA
isolated from bean nodules was used to synthesize
bacteroid cDNA using the RevertAid™ H Minus First
Strand cDNA Synthesis Kit and random hexamer primers
(Fermentas, Copenhagen, Denmark), following the
manufacturer’s instructions. Specific primers of selected
genes known to be related to the bacterial response
to oxidative stress, such as peroxidases, superoxide
dismutase and glutathione peroxidase, and of some key
genes involved in SNF were designed using the genome
sequence of R. etli CFN42 (González et al. 2006; Table
S6). The equipment and conditions used for bacteroid
qRT-PCR analysis were similar to those used for plants,
as described above.
In silico analysis of cis-elements in the promoter
regions of responsive genes
The sequences of 63 genes corresponding to the pro-
moter regions, which were over-represented in different
metabolic pathways, were obtained by performing a
BLAST search against the bean genome deposited in
Phytozome (www.phytozome.net, Goodstein et al.
2012). Putative cis-elements present in the promoter
sequences, defined as the 2-kb upstream region, of
selected genes were examined using the Plant Pro-
moter Analysis Navigator (PlantPAN), which identifies
transcription factor binding sites in a group of gene
promoters (Chang et al. 2008).
Results
Symbiotic and oxidative stress response phenotype
In this work, we analyzed the response of common
bean nodules to oxidative stress after exposure to the
herbicide known as PQ. PQ has been widely used
to evaluate the effects of ROS in plants as it mimics
the action of environmental stresses. The selected PQ
concentration (1 mM) is similar to that reported by
Dalton (1992) used to generate oxidative stress in
soybean nodules. We measured lipid peroxidation by
monitoring malondialdehyde (MDA) content, which is
among the most widely accepted assay for oxidative
damage in plants (Shulaev and Oliver 2006). Lipid
peroxidation in effective common bean nodules (18 dpi)
exposed to PQ for 6–48 h increased over time
and peaked at 48 h, when the MDA content was
threefold that in nodules of plants grown under
control conditions (Fig. 1A). This result was confirmed
by analyzing ROS content in nodules incubated
with 2′,7′-dichlorodihydro-fluorescein diacetate and
subsequently observed by fluorescence microscopy. A
twofold increase (P ≤ 0.05) in fluorescence intensity
was observed in nodules exposed to PQ for 48 h
as compared to control nodules (data not shown)
(Fig. 1B). We tested if PQ treatment had the same effect
as actual oxidative stress arising from environmental
stresses by comparing the extent of ROS production in
bean nodules under chilling, salinity, drought and Mn
toxicity stresses. The levels of MDA observed in nodules
exposed to environmental stresses ranged from 2.7- to
3.5-fold with respect to those in the control nodules
(Fig. 1A). These values were similar to those obtained in
nodules exposed to PQ for 48 h (Fig. 1A). Therefore, we
reasoned that a 48-h PQ (1 mM) treatment mimics the
394 Physiol. Plant. 149, 2013
81
A
B
Fig. 1. Lipid peroxidation and ROS detection in common bean nodules.
(A) Lipid peroxidation, in terms of malondialdehyde (MDA) content, in
mature common bean nodules exposed to various stress treatments
known to induce high levels of ROS production, with respect to that in
control nodules. PQ (6, 12, 24 and 48 h), Low T (4◦C, 4 h), NaCl (150 mM
of NaCl, 12 and 24 h), D (drought, water withheld for 1 week), and Mn
(400 µM MnCl for 18 days). Values are mean ± SE of three independent
experiments. (B) ROS detection in PQ-treated bean nodules (24 h) using
2′,7′ dichlorodihydrofluorescein-diacetate (DCFH-DA).
actual oxidative stress achieved in common bean plants
growing under environmental stress conditions similar to
those occurring in nature and so this treatment regimen
was chosen for this work.
The symbiotic phenotype was assessed in 18-dpi
bean plants under PQ treatment, by determining the
nitrogenase activity and ureide content of the plants.
The specific nitrogenase activity of PQ-treated nodules
was 48% lower than that of control nodules (Fig. 2A).
Ureides are the main N-compounds transported from
bean nodules to the aerial parts of the plant (Schubert
1981, Boldt and Zrenner 2003). In the xylem sap of
control plants, the ureide content was 2.5-fold more
than in the xylem of plants grown under conditions
of oxidative stress (Fig. 2B). In the nodules of plants
with increased ROS production, the ureide content was
around 1.5-fold lower (Fig. 2C). Overall, these results
indicate that SNF is negatively affected in PQ-exposed
Fig. 2. The effect of oxidative stress (PQ treatment) on bean plants
in symbiosis with Rhizobium tropici. Nitrogenase activity (A) and ureide
content (B, C) were determined in 16-dpi plants treated with PQ for 48 h
(gray bars) and in 18-dpi control (nontreated) plants (white bars). (A)
Specific nitrogenase activity, as determined by an acetylene reduction
assay. Ureide content in xylem exudates (B) and mature nodules (C).
Values are mean ± SE of three independent experiments.
nodules and this directly prevents optimum ureide
synthesis and transport within the plant.
Microarray analysis of the nodule’s response to
PQ-induced oxidative stress
In contrast to our previous work using macroarrays
printed with ca. 2000–3000 bean ESTs (Ramı́rez et al.
2005, Hernández et al. 2009), we now used, for the
first time, a microarray approach for bean nodule
transcriptome analysis to obtain global insight into the
response of the expression of genes of nodules subjected
to oxidative stress as compared to control nodules. For
this analysis, we designed a Bean Custom Array 90K,
which includes a 30K unigene set from common bean
(ca. 18 000 ESTs) and nonredundant soybean (Glycine
max) genes (ca. 11 000 ESTs). The data reported in
this paper were depositeds in NCBI’s Gene Expression
Omnibus (Edgar et al. 2002) and are available through
GEO Series accession number GSE37638 (http://www.
ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37638). To
verify that the microarray data were reliable, the nor-
malized median signal intensities resulting from control
samples were compared through all the repetitions
(i.e. two biological replicates and three technical
replicates of each biological sample). A high correlation
coefficient, ranging from 0.94 to 0.96, was observed
between control samples that indicates a low technical
and biological variation among repetitions. Following
statistical analysis of the data, a total of 4280 ESTs were
defined as being differentially expressed (corrected P
values < 0.05; signal intensity ≥ 1.5-fold) in response to
ROS accumulation induced by PQ. From among these
genes, 34% corresponded to G. max ESTs included in
Physiol. Plant. 149, 2013 395
82
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• 
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-
Fig. 3. Biological processes significantly over-represented in the set of
induced (•) or repressed (*) ESTs (Fisher exact test P < 0.05), according
to Gene Ontology analysis (The Gene Ontology Consortium et al. 2000).
Black bars, percentage of ESTs corresponding to each biological process
within the microarray.
the microarray (see Table S1). More ESTs were repressed
(2218) than induced (2062).
The induced or repressed ESTs as determined
by microarray analysis were organized in functional
categories according to Gene Ontology (GO) guidelines
(The Gene Ontology Consortium et al. 2000). Of
the 4280 probe sets differentially expressed upon
PQ treatment, we assigned at least one GO term
to 3822 probe sets based on sequence similarity.
Differentially expressed ESTs were classified into 42
different biological processes (see Fig. S1). Fig. 3 shows
the six GO categories statistically over-represented
within the induced EST data set and the six GO categories
that were over-represented in the repressed EST data set.
To further analyze the gene expression data, we also
used the PathExpress (Goffard and Weiller 2007, Goffard
et al. 2009) bioinformatic tool adapted to common bean
to identify the differentially expressed genes associated
with the relevant metabolic pathways operating in bean
nodules under oxidative stress. Table 1 shows the path-
ways significantly induced or repressed in PQ-treated
bean nodules. Several of significantly induced pathways
and their corresponding enzymes have been implicated
in defense processes for different types of abiotic
stresses. Most of the significantly repressed pathways
were related to carbon/nitrogen metabolism, which are
relevant pathways for the optimal functioning of legume
nodules (see Table S4). Fig. 4 illustrates three relevant
pathways that were significantly repressed in bean
nodules under oxidative stress: carbon fixation, purine
metabolism and starch and sucrose metabolism. Our
microarray analysis showed that several enzymes from
each of these pathways were significantly repressed.
Validation of microarray results by
real-time qRT-PCR
To confirm the results obtained from microarray anal-
ysis, we performed quantitative reverse transcription
(qRT)-PCR on three biological replicates and three tech-
nical replicates of each biological sample. Thirty genes
showing various degrees of repression or induction
in the microarray analysis were chosen for validation
(Table 2; see Table S5). We found a good correlation
(r2 = 0.86) and a similar pattern between the microarray
and qRT-PCR fold-change values for each gene, indicat-
ing the reliability of our microarray data. The variation
in the fold-change values between the microarray and
qRT-PCR gene expression data is probably due to the
different sensitivities of the two methods.
Some of the genes selected for the validation
correspond to enzymes belonging to metabolic pathways
that were repressed in nodules exposed to oxidative
stress, as revealed by PathExpress analysis. For all of these
genes, the repressed fold-change value from microarrays
was confirmed by qRT-PCR (Table 2, Fig. 4).
qRT-PCR-based TF transcript profiling
We used the reported qRT-PCR platform for common
bean TF expression profiling (Hernández et al. 2007) to
identify the differential expression of 372 bean TF genes
in nodules subjected to oxidative stress. Table 3 shows
that 67 TF genes from 26 TF gene families (Riechmann
2002) were differentially expressed, twofold or more
(P ≤ 0.05), in nodules treated with PQ. Among these,
18 TF genes from 13 families were repressed and 49
TFs corresponding to 22 families were induced. The
qRT-PCR expression analysis validated the microarray
data of 10 TF genes (see Table S2); the remaining 57
responsive TF genes (Table 3) showed no significant
differential expression upon PQ treatment after our
statistical analysis of microarray data. The latter finding
may be related to the higher sensitivity and accuracy
of the qRT-PCR platform for TF expression profiles. The
TF gene families that had more members that were
differentially expressed in nodules exposed to oxidative
stressed are as follows: the MYB superfamily (seven
members); the AP2/EREBP family (seven members);
and the GRAS, C2C2(Zn), and C2H2(Zn) families
(four members each). In addition, we performed a TF
expression profile analysis in common bean nodules
subjected to Mn toxicity, as this condition also generates
ROS accumulation (Fig. 1). We found that 28 of 372
TF genes were differentially expressed in bean nodules
exposed to Mn toxicity, and 21 of these were induced
(see Table S3).
396 Physiol. Plant. 149, 2013
83
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Table 1. Metabolic pathways significantly over-represented in common bean nodules after PQ treatment. The analysis was performed using the
PathExpress bioinfomatic tool (Goffard et al. 2009) adapted to common bean. Pathways shown are those significantly associated with the list of
submitted sequence identifiers with a P-value threshold of 0.05 (see Table S4).
Pathway/subpathway
No. of enzymes included
in bean EST sequences
No. of differentially
expressed enzymes
(TC or Gene Bank Accession) P
Repressed
Carbon fixation 22 10 3.69e-03
(BQ481899, NP7938839, TC14212,
TC16003, TC8682, TC10742, CB541583,
FG232722, BQ481541, BQ481565)
Sulfur metabolism 9 5 3.12e-02
(20TC8860, FD789961, TC18230, TC16782,
TC13963)
Purine metabolism 46 16 3.39e-02
(TC13266, TC10993, TC12224, CV535717,
FG988543, TC12194, BI970858,
TC12218, TC8904, TC13149, CV538017,
TC8860, NP7927042, TC16782,
TC14212, TC15930)
Starch and sucrose metabolism 29 11 4.09e-02
(BQ481932, TC15905, TC9852, TC13149,
FG987269, TC15649, TC10682, TC15408,
FE676015, FD797285, CX129651)
Reductive carboxylate cycle (CO2 fixation) 7 4 4.92e-02
(NP7938839, EG948449, TC8682, TC19131)
Induced
Isoflavonoid biosynthesis 3 3 5.58e-03
(TC24029,CV542400, GW908631)
Methane metabolism 8 4 3.70e-02
(NP7938634, TC14371, TC14097, TC13495)
Drug metabolism-cytochrome P450 5 4 4.22e-02
(TC9537, TC12978, TC9580, FG229649)
Glycerolipid metabolism 16 6 4.88e-02
(TC9580, TC8317, TC10848, FD796300,
TC8984, TC15381)
In silico analysis of cis-elements in the promoter
regions of responsive genes
To determine whether there is a link between oxidative
stress-regulated genes and TF that respond to this type
of stress, we did an in silico search for cis-elements
corresponding to putative for TF binding sites in the
promoter regions of the genes over-represented in
metabolic pathways (Table 3). The analysis revealed
that the group of genes analyzed contained cis-
motifs matching those of the MYB superfamily,
AP2/EREBP, ARF, WRKY, bZIP, CCAAT-box, ZF-HD or
bHLH motifs (Table 4). These represent TF families
reported in the present work as being differentially
expressed in bean nodules exposed to oxidative
stress (Table 3). As expected, given that this analysis
corresponds to genes expressed in nodules, most of the
analyzed genes contained cis-elements that correspond
to TF binding sites that regulate the nodule gene
expression.
Gene expression analysis of the bacteroid
response to oxidative stress
We aimed to determine if the expression of transcrip-
tional regulators of genes essential for SNF and of
selected genes known to respond in oxidative stress
in other rhizobia was significantly affected in R. tropici
bacteroids from PQ stressed bean nodules (Table 4).
Our analysis revealed that a putative sodB gene (super-
oxide dismutase) was significantly overexpressed (with
a PQ/control ratio of 95) after PQ treatment. On the
other hand, the expression of katG and oxyR and a
putative hydroperoxide reductase, which participate in
the response to hydrogen peroxide in different bacteria,
was only moderately induced (Table 4; Table S6). Inter-
estingly, an fnrN-like gene was overexpressed after PQ
treatment. These transcriptional regulators activate the
expression of the symbiotic terminal oxidase (fixNOQP
operon) in rhizobia (Schlüter et al. 1997, Lopez et al.
2001). We evaluated the transcription of a fixN-like
Physiol. Plant. 149, 2013 397
84
Fig. 4. Selected metabolic pathways identified by PathExpress
(Goffard et al. 2009) as being repressed in nodules under oxidative
stress. Graphical representations were produced using MapMan. The
metabolites are shown in boxes. Those enzymes significantly repressed in
each pathway are indicated by their name or abbreviation corresponding
EC according to the International Union of Biochemistry Molecular
Biology. Circles highlight enzymes with repressed expression ratio
values confirmed by qRT-PCR (Table 2). (A) Carbon fixation. (B) Purine
metabolism. (C) Starch sucrose metabolism.
gene of R. tropici and found that it was only moderately
induced. In R. etli, it was reported that stoRd is a nega-
tive regulator of fixN and of SNF (Granados-Baeza et al.
2007). Our results revealed the presence of a similar
stoR gene in R. tropici, the expression of which was
induced after PQ treatment. Overexpression of this regu-
lator might at least partially account for the drop in SNF
and the reduction in fixN expression under these condi-
tions. We found that the transcription of nifH, encoding
for the iron–protein subunit of the nitrogenase reductase
complex, was negatively affected in PQ-treated bac-
teroids, it was only 6% that in untreated bacteroids.
Under our experimental conditions, the expression of
NifA, the activator of nifH in nitrogen fixing bacteria,
was only moderately reduced. We propose that the
reduced transcription level of nifH might be the result of
post-translational modifications in NifA that render the
regulator inactive.
Discussion
In this work, we analyzed the response at the
transcriptomic level of common bean nodules to
oxidative stress, generated by the addition of PQ. The
PQ treatment used resulted in a similar degree of ROS
generation as that achieved in common bean plants
growing under environmental stresses such as salinity,
drought, low temperature and Mn toxicity (Fig. 1). The
effect on SNF, nodule cell metabolism and whole plant
physiology depends on the concentration and time of
exposure to PQ (Marino et al. 2006). When applied to
the root, PQ translocation to the shoot depends on the
capacity of root cells to sequester PQ by accumulating it
in the vacuole and then expelling it across the tonoplast
and plasma membrane back into the external solution
(Lasat et al. 1997). Marino et al. (2006) reported that
only a high dose of PQ applied to SNF pea roots affected
photosynthesis and reduced chlorophyll content after
48–72 h, suggesting that PQ was translocated to the
shoot. Similar as reported by Dalton (1992) for soybean,
we applied a low dose of PQ for a short period of
time to SNF common bean roots and observed that
the chlorophyll content in leaves was similar in stress
and control conditions (data not shown). Therefore, we
assumed that the translocation of PQ to the shoots
was minimal, if occurring at all, and so the treatment
used allowed us to characterize the response to ROS
production directly in root nodules before any PQ could
travel to the shoots, deplete the level of photoassimilates
and disrupt nodule function. In addition, the treatment
used mimics the action of environmental stresses, but
avoids the complex effects that these might have, such
as disrupting long distance transport or resulting in a
deficiency of nutrients. The deleterious effects detected
in PQ treated common bean nodules are in agreement
with those reported for other rhizobia–legume symbiosis
(Dalton 1992, Minchin et al. 2008, Becana et al. 2010).
Bacteroids from PQ-treated common bean nodules
clearly showed a repression of the nitrogenase reductase
(nifH) gene as well as an induction of a negative regulator
398 Physiol. Plant. 149, 2013
85
[ - ~J - 11 - 1 
Table 2. Validation of microarray data by qRT-PCR analysis. Selected genes identified as being induced or repressed by microarray analysis in nodules
under oxidative stress. Fold change (log2) values are the average of three biological replicates. Normalized values from qRT-PCR and microarray
analyses of each gene are shown. Enzymes of significantly over-represented metabolic pathways identified by PathExpress analysis are in bold.
GenBank Fold change
Accession/TC Annotation qRT-PCR Microarray
Repressed
BQ481932 Fructokinase −1.95190115 −0.49457439
CV535717 Formylglycinamide ribonucleotide amidotransferase −2.46443725 −0.83182409
TC39206 Phosphoenolpyruvate Carboxylase −0.85218044 −0.68288539
TC14212 Pyruvate kinase −1.31417487 −0.4300321
TC32699 Malate dehydrogenase −1.68215314 −0.62119766
FG232722 Triosephosphate isomerase −2.6916894 −0.69059881
TC34973 Aspartate aminotransferase −0.91494788 −0.5883288
BQ481565 Fructose 1,6-bisphosphatase −3.1841602 −1.88680387
EG948449 ATP-citrate synthase −1.63412326 −0.86573177
TC32974 Guanine deaminase −1.25120767 −0.90464125
TC35238 Malic enzyme −1.63218785 −0.77583495
CB541583 Alanine amino transferase −1.19974818 −0.79701952
TC36935 Inosine monophosphate dehydrogenase −0.85273768 −0.74852143
TC35171 1,4 alpha glucan-branching Enzyme −0.81570607 −1.19458157
TC41168 Glucose phosphate isomerase −0.43660897 −0.57051891
Induced
TC33445 Glutathione S-transferase 3.25481869 1.83184673
TC40887 Isoflavone 7-O-methyltransferase 1.12824712 0.93360918
TC39388 Ferredoxin hydrogenase 1.01432508 0.60352121
TC33776 Trehalose 6-phosphate synthase 1.49464108 0.5152012
FD788437 Heat shock protein 2.541844 1.17823886
TC44951 Heme oxygenase 1.22478938 1.3024448
TC41871 No symbiotic hemoglobin 1.8394217 0.92409108
TC39083 Methylenetetrahydrofolate reductase 1.098865 0.61120015
TC39733 Mannitol dehydrogenase 3.47421458 1.98544248
TC33800 Alcohol dehydrogenase 1.02455883 1.15918073
TC40120 Dihydroflavanol 4-reductase 0.92110651 1.04529227
TC43445 Cys peroxiredoxin 1.80433923 3.98401971
NP7938634 Peroxidase 1.64124339 0.59225671
CB543559 Chalcone synthase 1.85203812 1.0767921
TC38753 Oxophytodienoate reductase 3.23363093 2.58904155
of SNF (stoR) (Table 5). The activator of nifH in nitrogen
fixing bacteria is NifA, we observed that the expression of
this regulator was only moderately reduced. It has been
suggested that, in rhizobia, this regulator might need
to interact with a metal cofactor in order to activate
transcription, and that the resulting cluster functions as
an oxygen sensor (Fischer et al. 1989, Souza et al.
1999). Thus, we suggest that the reduced transcription
of nifH might be due to the metal-catalyzed oxidation of
NifA, which renders the regulator inactive. Furthermore,
the ATP pool required for nitrogenase activity might be
affected by an impairment of carbon supply from the
shoot, resulting from the downregulation of enzymes
involved in carbon metabolic pathways (Table 1, Fig. 4)
and/or from the inhibitory effect of PQ on photosynthesis.
Carbon/nitrogen metabolism are important processes
in effective legume nodules, because they provide
bacteroids with the carbon compounds used to generate
ATP and to assimilate ammonium from SNF into
organic N-compounds. Environmental stress conditions
commonly affect carbon/nitrogen metabolism in the
nodules (Vance and Heichel 1991). In PQ treated
nodules showed a profound inhibitory effect on the
expression of genes involved in the central metabolic
pathways, such as tricarboxylic acid metabolism,
nitrogen assimilation, glycolysis, sucrose metabolism,
amino acid metabolism and purine biosynthesis, such as
sucrose synthase (CX12965), malic enzyme (TC10742),
pyruvate kinase (TC14212), alanine aminotransferase
(CB541583), cysteine synthase (TC18230), serine acetyl-
transferase (TC13963) and aspartate aminotransferase
(AAT, TC16003) genes (Table 1, Fig. 4). These and other
enzymes that participate in the tricarboxylic acid cycle
they are known to be susceptible indicators of oxidative
Physiol. Plant. 149, 2013 399
86
Table 3. TF families significantly expressed in bean nodules under oxidative stress identified by real-time RT-PCR (see Table S2). TFs that were also
found to be significantly induced or repressed in the microarray analysis are underlined. TFs that were also induced or repressed in nodules subjected
to Mn toxicity are in bold (see Table S3).
TF family or domain Induced Repressed Total
MYB superfamily 7 0 7
(TC45841,BQ481567,TC37047,TC32577,CV537484,TC43761,TC34453)
AP2/EREBP 5 (TC32865,TC38814,TC43751,CV536700,CV537333) 2 (TC34846,TC35571) 7
C2H2(Zn) 4 (TC44148,CV534889,CV535367,TC36863) 2 6
(TC41414,TC43607 )
C2C2(Zn) 4 1 (TC36074) 5
(TC32961,BQ481590,BQ481651,TC40362)
GRAS 2 2 4
(TC42487,TC36190) (TC43723,TC43983)
Aux/IAA 2 1 (TC39030) 3
( TC33102,TC42047)
WRKY(Zn) 0 3 3
(TC33590,TC36992,TC43110)
CCAAT 2 1 3
(TC35458,CV533732) (TC39639)
C3H-type 1(Zn) 3 (CV537094,TC33834,CV536281) 0 3
ARF 3 (BQ481892,CV534368,CV539480) 0 3
ZIM 2 0 2
(TC37352,TC45159)
YABBY C2C2(Zn) 2 0 2
(TC38230,TC33137)
TAZ 1 1 (TC45225) 2
(CV538139)
Response regulator receiver 1 1 2
(CV542253) (TC38359)
Dof-type C2C2(Zn) 2 0 2
(TC38448,TC34332)
bZIP 1 1 2
(TC35580) (TC36609)
bHLH 2 0 2
(CV535953,TC32288)
Znf_CCHC 1 0 1
(CV537577)
ZF-HD 0 1 (CB543438) 1
TUB 0 1 (TC34864) 1
K-box 1 0 1
(CV541709)
HSF 1 0 1
(TC32970)
CBF/NF-Y 1 0 1
(TC38784)
AS2 0 1 (TC41522) 1
SR-rich splicing factor 1 0 1
(TC32718)
Alfin-like 1 0 1
(CB542396)
stress (Verniquet et al. 1991, Baxter et al. 2007).
Interestingly, the negative effect of oxidative stress on
carbon metabolic pathways is not exclusive to legume
nodules. Arabidopsis plants subjected to oxidative
stress showed a rearrangement in carbon metabolism
that reflects a short-term survival mechanism; genes
encoding enzymes involved in starch and sucrose
biosynthetic pathways were downregulated (Scarpeci
and Valle 2008). Similar results showing that oxidative
stress had a negative impact on carbon metabolism in
alfalfa nodules were reported by Naya et al. (2007);
enzymes involved in carbon metabolism, mainly
sucrose synthase, which is essential for SNF in legume
nodules, were downregulated.
400 Physiol. Plant. 149, 2013
87
Table 4. Enriched putative cis-elements for binding of PQ-responsive TFs (Table 3) identified in the promoter regions of genes from over-represented
metabolic pathways (Table 1). Carbon fixation (CF); sulfur metabolism (SM); purine metabolism (PM); starch and sucrose metabolism (SSM);
reductive carboxylate cycle (RCC); isoflavonoid biosynthesis (IB); methane metabolism (MM); drug metabolism-cytochrome P450 (DM) and glycerolipid
metabolism (GM). Bullets indicate the pathways in which the putative cis-elements are present.
Genes over-represented in metabolic pathways
Putative cis-elements Responsive TF CM SM PM SSM RCC IB MM DM GM
WBOXATNPR1 WRKY • • • • • • • • •
RAV1AAT AP2/ERBF • • • • • • • •
SURECOREATSULTR11 ARF • • • •
CCAATBOX1 CCAAT • • • •
Core consensus binding sequence ZF-HD • •
MYBCORE MYB • • • •
MYB B4 MYB •
MYB1AT bHLH • • • •
MYB2CONSENSUSAT bHLH •
PREATPRODH bZIP •
GBF5 bZIP • • •
DPBFCOREDCDC3 bZIP • •
Table 5. Rhizobium tropici bacteroids gene expression after PQ
treatment. Fold change (log2) values are the average of three biological
replicates (Table S6).
Gene Annotation Fold change (log2) P
sodB Superoxide dismutase protein 6.572 0.012
fnrN Transcriptional regulator protein,
Fnr/CRP family
2.231 0.002
hpr Hydroperoxide reductase protein 1.056 0.000
fixN cbb3-type cytochrome c oxidase 0.578 0.027
oxyR Hydrogen peroxide sensing
transcriptional regulator protein
0.686 0.014
katG Catalase protein 0.614 0.020
stoR Probable transcriptional regulator
(activator) protein, CRP family
3.356 0.003
nifH Nitrogenase, iron protein −1.435 0.000
nifA Transcriptional regulator NifA protein −0.285 0.009
Ureides, the main nitrogen compound product of SNF,
are exported from the nodule to the shoot of common
bean plants (Schubert 1981, Boldt and Zrenner 2003).
The reduction in ureide content of nodule and xylem
sap form PQ-stressed common bean plants (Fig. 2B, C),
indicates that the observed reduction in SNF decreased
the assimilation of ammonium into nitrogen compounds
in the nodule cells, which led to a decrease in ureide
transport through the xylem (Alamillo et al. 2010). Genes
(Kim et al. 1995) were repressed in PQ-treated bean nod-
ules. The repression of genes encoding key enzymes that
regulate the biosynthesis of purines, precursors of ureides
(Table 1, Fig. 4), would reduce the activity of enzymes
from relevant metabolic pathways thus significantly
decreasing the ureide concentration in the nodules.
On the other hand, isoflavone biosynthesis (related
to secondary metabolism), methane metabolism,
drug metabolism-cytochrome P450 and glycerolipid
metabolism pathways were significantly induced in
bean nodules treated with PQ (Table 1). Genes induced
in the isoflavonoid biosynthesis pathway include those
encoding enzymes that are involved in the biosynthesis
of phytoalexines, low molecular mass secondary
metabolites with antimicrobial activity that are induced
in the stress response and are important components
of the plant defense repertoire (Ahuja et al. 2012).
Representative phytoalexins of Phaseolus spp. are
isoflavones, isoflavans, pterocarpans (e.g. phaseolin),
coumestans and isoflavanones (e.g. kievitone) (Durango
et al. 2002). Induced genes from the methane and
drug metabolism-cytochrome P450 pathways included:
glutathione-S-transferases GST (TC9373, TC12978,
TC9537), peroxidase (NP7938634), ferredoxin hydro-
genase (TC14371), and a S-(hydroxymethyl) glutathione
dehydrogenase (TC13495), which are involved in the
antioxidant response and are as closely related to the
oxidative stress response as to that occurring in PQ-
treated nodules. GSTs, which form a large gene family in
plants are involved in xenobiotic detoxification and ROS
scavenging and may remove peroxidized lipids (Becana
et al. 2010). The importance of GST in nitrogen fixing
nodules was observed in soybean nodules which contain
at least 14 isoforms of GST with variable but significant
expression levels, moreover silencing the most predom-
inant form (GST9) results in a substantial reduction of
nitrogenase activity (Dalton et al. 2009). The induction
of glycerolipid metabolism in PQ-stressed nodules
(Table 1) might be related to the general response of
plants to stress, which involves remodeling of membrane
fluidity and the release of α-linolenic acid (18:3) from
membrane lipids. The fluidity in membranes is regulated
Physiol. Plant. 149, 2013 401
88
by changes in unsaturated fatty acid levels, partly by the
controlled activity of fatty acid denaturizing enzymes.
Adjusting membrane fluidity, through the biosynthesis
of glycerolipids and the production of polyunsaturated
fatty acids, maintains an appropriate setting for the
function of essential integral proteins during stressful
conditions (Upchurch 2008). Interestingly, common
bean genes of the glycerolipid pathway that are induced
upon exposure to PQ (Table 1) include those encoding
enzymes responsible for the synthesis of chloroplast
membrane lipids, i.e. monogalactosyldiacylglycerol
(MGDG) and digalactosyldiacylglycerol (DGDG). In
Arabidopsis, an increase in the DGDG:MGDG ratio
and fatty acid unsaturation led to an uncharacterized
cellular adaptation capacity to respond to drought stress
(Gigon et al. 2004). Furthermore, DGDG biosynthesis
is stimulated by phosphorus deficiency revealing a
biochemical machinery by which plants conserve phos-
phate by replacing phospholipids with nonphosphorous
galactolipid, a process required for survival under
phosphate deprivation (Härtel et al. 2000).
Little is known about the involvement of TFs in
legume nodule development and/or in the response
to environmental stress (Libault et al. 2009). Because
the level of variation in the expression of TF genes
in response to environmental stress is low, the use of
qRT-PCR is a more sensitive and reliable approach,
as compared to microarray analysis, to analyze global
TF expression profiles in plants (Czechowski et al.
2004, Hernández et al. 2007). We report 67 TF genes
differentially expressed in bean nodules exposed to
PQ or to Mn toxicity (Table 3, see Table S2). The
transcriptomic responses are known to partially overlap
among different kinds of stress (Kreps et al. 2002,
Fujita et al. 2006). In agreement we found that the
expression pattern of 10 TFs overlapped in nodules
subjected to Mn toxicity and PQ, probably because both
treatments resulted in ROS production. The TF families
that exhibited the strongest responses to oxidative stress
were MYB, AP2/EREBP, C2H2(Zn), C2C2(Zn), GRAS,
CCAAT, ARF and ZnF-CCHC (Table 3, see Tables S2
and S3). The stress-modulated expression of these TFs
suggests that they may be important in the oxidative stress
response and that they may constitute a core group of TFs
relevant for the response of bean nodules to oxidative
stress. Three members of the WRKY TF gene family were
repressed in PQ-treated bean nodules (Table 3); TFs from
this family respond to oxidative stress in Arabidopsis
(Scarpeci et al. 2008). Four members of the GRAS
family responded to oxidative stress in bean nodules,
including TC42487, which was induced by up to 13-fold
(Table 3). Members of the GRAS TF family are involved
in development and other processes, such as rhizobial
Nod-factor induction and drought stress response;
however, little is known about their physiological role
under oxidative stress (Ge et al. 2010). Seven members of
the MYB family, which is involved in biotic and abiotic
stress responses as well as in developmental processes
(Yanhui et al. 2006), were differentially expressed (from
three- to sevenfold) in PQ-treated nodules. TFs from the
AP2-EREBP family were both induced and repressed in
bean nodules subjected to oxidative stress; members
of this family are known have important roles in the
development and growth of the plant and particularly
in response to different stresses and hormonal regulation
(Riechmann and Meyerowitz 1998).
A mechanism for the regulation of gene expression
is the interaction among TFs and cis-regulatory DNA
sequences, it is an essential functional connection
between gene regulatory networks. The different types
of cis-elements detected in our in silico analysis have
been reported to be involved in responses to abiotic and
biotic stresses. Most of the genes that were regulated by
oxidative stress, according to our PathExpress analysis,
contained at least three of these cis-elements (Table 4).
The W-box and W-box-like motifs, which correspond
to the binding sites of TF-type WRKY, were present
in all groups of genes analyzed, and motifs were also
found in the cis-elements of these genes that facilitate
binding to the AP2/ERBF TF family (Table 4). A more
detailed analysis of the regulatory elements present
in the promoter regions of genes that are responsive
to oxidative stress can be undertaken to further our
understanding of gene regulatory networks in beans
exposed to oxidative stress conditions.
Our current knowledge of bean oxidative stress
responsive genes is limited. Here, we performed a
comprehensive transcriptome profiling of common
bean nodules exposed to oxidative stress. Nearly 15%
of the tested genes, which participate in key biological
processes, such as secondary metabolism, response
to stress, translation, cell death and carbon/nitrogen
metabolism, responded to oxidative stress, thus indi-
cating that the accumulation of ROS within the nodule
induced transcriptome reprogramming in this organ.
It would be of great interest to pinpoint differences
between the specific response to oxidative stress in
nodules and the response to reduced SNF and hence
diminished nitrogen assimilation. The identification
of highly oxidative stress-responsive TFs is of crucial
importance, especially in the case of legumes, as less
that 1% of the total TFs present in the genome have been
characterized (Udvardi et al. 2007). Several of the TFs
identified here may represent interesting candidates for
the genetic engineering of bean plants with enhanced
resistance to oxidative stress. The results of our study
402 Physiol. Plant. 149, 2013
89
may provide a basis for future research that aims to
improve the common bean germplasm through genetic
or biotechnological approaches.
Acknowledgements – We thank Dr Rafael Palacios and his
group from Centro de Ciencias Genómicas (CCG), UNAM
for their support in the use of qRT-PCR equipment and
Vı́ctor Bustos, Waldo Dı́az, Patricia Rivera and Emmanuel
Salazar for technical assistance. This work was supported
by grants PAPIIT IN214308 and IN209710 from Dirección
General de Asuntos del Personal Académico – UNAM
and partially supported by grants from Consejo Nacional
de Ciencia y Tecnologı́a (CONACyT) (grants 83206 and
152776), a collaborative grant from CONACyT (CCG-
UNAM, México – IBBA-CNR, Italy, J010/0091/10).
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Fig. S1. Gene ontology (GO) biological processes of bean
nodule ESTs differentially expressed under oxidative
stress.
Table S1. Gene expression significantly affected by PQ in
common bean nodules identified by microarray analysis.
Table S2. TF genes with significant changes in expression
in common bean nodules under PQ treatment, as
identified by qRT-PCR analysis.
Table S3. TF genes significantly expressed in common
bean nodules exposed to Mn toxicity, as identified by
qRT-PCR analysis.
Table S4. Metabolic pathways and enzymes significantly
over-represented in common bean nodules under PQ
treatment.
Table S5. Primers used for qRT-PCR expression analysis
to validate microarray gene expression data.
Table S6. Primers used for qRT-PCR expression analysis
of bacteroids subjected to PQ treatment.
Edited by K.-J. Dietz
Physiol. Plant. 149, 2013 407
94
ORIGINAL ARTICLE
Rhizobium etli bacteroids engineered for Vitreoscilla hemoglobin
expression alleviate oxidative stress in common bean nodules
that reprogramme global gene expression
Mario Ramı́rez1 • Luis P. Íñiguez1 • Gabriela Guerrero1 • Francesca Sparvoli2 •
Georgina Hernández1
Received: 29 July 2016 / Accepted: 4 December 2016 / Published online: 17 December 2016
 Korean Society for Plant Biotechnology and Springer Japan 2016
Abstract The common bean (Phaseolus vulgaris L.)–
Rhizobium etli symbiosis and crop productivity are highly
affected by adverse environmental conditions that cause
oxidative stress. Based on the improved symbiosis of
common bean inoculated with engineered R. etli expressing
the Vitreoscilla hemoglobin (VHb) (Ramı́rez et al., Mol
Plant Microbe Interact 12:1008–1015, 1999), in this work
we analyzed the effect of this strain in plants exposed to the
herbicide paraquat (PQ) which generates oxidative stress.
PQ-treated plants inoculated with the engineered (VHb) R.
etli strain showed higher nitrogenase activity and ureide
content than plants inoculated with the wild type strain. We
performed microarray transcriptomic analysis to identify
PQ-responsive genes in nodules elicited by engineered vs
wild type strains. An evident reprogramming of the tran-
scriptional profile was observed in PQ-treated nodules, and
the global changes in gene expression were different
between nodules elicited with each strain. The most rele-
vant difference was the increased number of up-regulated
PQ-responsive genes in wild type strain nodules as com-
pared to VHb-expressing nodules. The majority of these
genes were classified into biological processes/functional
categories related to defense, response to abiotic stress or
signaling, as revealed by Gene Ontology and MapMan
analysis. Taken together our analysis suggests that the
expression of VHb in R. etli bacteroids contributes to
buffering the damage caused by increased reactive oxygen
species, and this is reflected in nodule cells that showed
decreased sensitivity to oxidative stress and response of
stress-related genes. Biotechnological applications of VHb-
expressing rhizobia inoculants could be further explored.
Keywords Symbiotic nitrogen fixation  Phaseolus
vulgaris–Rhizobium etli symbiosis  Oxidative stress 
Vitreoscilla hemoglobin  Rhizobia engineered strain 
Nodule transcriptomics
Introduction
Legumes are the second most important crop for humans,
accounting for 27% of crop production worldwide (Graham
and Vance 2003). A hallmark trait of legumes is their
ability to establish symbiotic relations with N2-fixing soil
bacteria collectively known as rhizobia. Infection of
legume roots by compatible rhizobia results in the forma-
tion of a new organ, the root nodule, where bacteroids carry
out the symbiotic nitrogen fixation (SNF) in a microaerobic
environment. Leghemoglobin is synthesized in high
amounts by nodule cells and plays a key role in delivering
oxygen to the bacteroid terminal oxidase system, which
operates optimally at very low oxygen pressure and gen-
erates ATP required for nitrogenase (Wittenberg 2012).
SNF occurring in the legume-rhizobia symbiosis is the
main source of nitrogen in agro-systems. The advantages of
legume crop production using SNF, or bio-fertilization, is
the reduced costs for producers and the prevention of soil
and water pollution by excessive use of chemical
Electronic supplementary material The online version of this
article (doi:10.1007/s11816-016-0422-7) contains supplementary
material, which is available to authorized users.
& Mario Ramı́rez
mario@ccg.unam.mx
1 Centro de Ciencias Genómicas, Universidad Nacional
Autónoma de México, Av. Universidad 1001,
62209 Cuernavaca, Morelos, Mexico
2 Istituto di Biologia e Biotecnologia Agraria, CNR, Via
Bassini 15, 20133 Milan, Italy
123
Plant Biotechnol Rep (2016) 10:463–474 Online ISSN 1863-5474
DOI 10.1007/s11816-016-0422-7 Print ISSN 1863-5466
B. Anexo
95
<1> CmssMark 
fertilizers. Thus, research aiming to improve rhizobia-
based bio-fertilizers has been going on for several years,
for example, through the discovery of rhizobia strains with
increased SNF capacity to be used as better inoculant. For
this, approaches based in rhizobial genetic modification/
engineering are in progress.
Common bean (Phaseolus vulgaris L.) is the main food
protein crop for direct human consumption worldwide. It is
a staple food in developing countries, such as Mexico,
where beans are the main dietary supply of plant proteins
(Broughton et al. 2003; Graham and Vance 2003). Its N2-
fixing capacity is one of the main advantages of this crop
because it reduces the nitrogen fertilizer demand, thus
having a positive impact on the economy of developing
countries. Research from the National University of Mex-
ico has led to modified Rhizobium etli strains, the promi-
nent rhizobial species for common bean nodulation
(Segovia et al. 1993), with better SNF capacity. Examples
of modifications in improved R. etli strains are: the
expression of the Bradyrhizobium japonicum fixNOQP
operon (Soberón et al. 1999) or the mutation in StoRd, a
negative regulator of ccb3 (Granados-Baeza et al. 2007),
both resulting in the overproduction of the bacteroid ccb3
symbiotic high oxygen affinity terminal oxidase; the
expression of the heterologous Vitreoscilla hemoglobin
VHb as described below (Ramı́rez et al. 1999); and
increased nitrogenase production obtained through the
expression of a chimeric nifHDK operon regulated by the
strong nifHc promoter. The latter engineered strain was
produced by the Mexican bio-fertilizer industry and suc-
cessfully used as an inoculant for common bean (Peralta
et al. 2004).
Common bean SNF and crop productivity is highly
affected by disease and insect pressure and also by
edaphic constraints. SNF in the legume-rhizobia sym-
biosis is sensitive to oxidative stress resulting from the
excessive production of reactive oxygen species
(ROS)—superoxide (O
2 ), hydrogen peroxide (H2O2),
and hydroxyl radicals (OH*)—that are generated by
abiotic stresses, such as high and low temperatures,
particularly in combination with high light intensities,
drought, exposure to air pollutants, ultraviolet light,
toxic metals in the soil, and exposure to herbicides such
as paraquat (PQ) (Vadez et al. 2012). Using ‘omics and
other approaches, our group has analyzed the response of
common bean plants to some of these abiotic stresses,
including aluminum and copper toxicity as well as PQ
treatment (Ramı́rez et al. 2013; Naya et al. 2014; Men-
doza-Soto et al. 2015). Ramı́rez et al. (2013) showed
that the effect of PQ (1,10-dimethyl-4,40-bipyridinium
dichloride hydrate) treatment in SNF common bean
plants was similar to that resulting from oxidative stress
generated by environmental constraints, such as low
temperature, manganese toxicity, salinity and drought.
Rhizobium tropici-nodulated common bean plants
exposed to PQ for short periods were drastically affec-
ted, showing reduced nitrogenase activity and ureide
content that correlated with nodule transcriptome
reprogramming. The main transcriptomic response of
PQ-exposed nodules included the repression of genes for
carbon/nitrogen metabolism that are crucial for nodule
function (Ramı́rez et al. 2013).
Amajor goal for agriculture is to improve stress tolerance
and crop yield in plants. Research in this area with legumes
includes, among others, obtaining stress-tolerant rhizobia
strains that when used as inoculants would reduce the sen-
sitivity of SNF-legumes to abiotic stress. Interestingly, there
are reports showing that the inoculation of common bean
plants with modified R. etli strains alleviate the negative
effects of abiotic stresses to SNF and plant yield. For
example, both a R. etli strain that over-expresses the tre-
halose-6-phosphate synthase gene (Suárez et al. 2008) as
well as a strain that over-expresses the bacteroid oxidase
cbb3 (Talbi et al. 2012) decrease drought sensitivity of SNF
common bean plants inoculated with either of these engi-
neered bacteria. To this end, in this work we investigated the
effect of an engineered R. etli strain expressing VHb on the
performance of SNF common bean plants under oxidative
stress generated by PQ addition.
VHb is a hemoprotein produced by Vitreoscilla sp., a
gram-negative strictly aerobic bacteria found in hypoxic
environments (Joshi et al. 1998; Wakabayashi et al. 1986).
VHb has higher chemical reactivity than its hemoglobin
homologues from multicellular organisms and is associated
with diverse functions, such as catalyzing redox reactions
and protecting cells against oxidative stress, besides its
function of oxygen storage and transport (Geckil et al. 2003;
Hardison 1998; Kaur et al. 2002). There are numerous
reports indicating that different microorganisms expressing
VHb show improved growth or metabolite production.
Escherichia coli expressing VHb showed higher oxygen
uptake rates as well as better growth and higher cell density
as compared to control strains (Geckil et al. 2001;Khosla and
Bailey 1988). Interestingly, the expression of VHb both in
E. coli and in Enterobacter aerogenes confers significant
protection against oxidative stress that is correlated with a
higher expression of antioxidant enzymes (Anand et al.
2010; Geckil et al. 2003).
We have reported the effect VHb expression, driven by
its native promoter, in R. etli, both under free-living con-
dition and in symbiosis with common bean (Ramı́rez et al.
1999). The presence of VHb transcript and protein in the
engineered R. etli strain was demonstrated. Free-living R.
etli expressing VHb grown under limiting oxygen con-
centrations showed increased respiratory activity, chemical
464 Plant Biotechnol Rep (2016) 10:463–474
123
96
energy content, and expression of the nitrogen fixation
gene nifHc. Phenotypic analysis of common bean plants
inoculated with the engineered R. etli (VHb) strain as
compared plants inoculated with the wild type strain
showed increased foliage dry weight, earlier pod formation
and flowering, higher nitrogenase activity and total nitro-
gen content. Our results led us to propose that VHb pro-
duction in R. etli could improve oxygen delivery to the
symbiotic terminal oxidase, thus increasing the respiratory
efficiency and nitrogen fixation in bacteroids that result in
increased nitrogen assimilation by the plant and overall
growth improvement.
In this work, we explore if the presence of VHb in
engineered R. etli in symbiosis with common bean plants
would decrease sensitivity to oxidative stress generated by
the addition of PQ. Our data show that PQ treatment plants
inoculated with the engineered R. etli strain (CE3/VHb)
showed higher nitrogenase activity and ureide content than
plants inoculated with the wild type strain (CE3). Com-
parative transcriptomic analyses were performed to assess
nodule global gene expression in plants inoculated with
CE3/VHb or CE3 strains grown in control or PQ treat-
ments. Beside the transcriptome reprogramming in oxida-
tive stressed nodules (Ramı́rez et al. 2013), a clear
difference in transcript profile was observed in nodules
with engineered bacteroids. Nodule differentially expres-
sed genes (DEG) in PQ treatment were analyzed using the
Gene Ontology and MapMan bioinformatics tools (Ash-
burner et al. 2000; Thimm et al. 2004) adapted to common
bean. The DEG analysis led to interpretations about their
role in the better performance of CE3/VHb-inoculated
common bean plants subjected to oxidative stress. Our
work sets the basis for exploring bio-fertilizers made up of
VHb-engineered rhizobial strains with the aim of con-
tributing to improvement of common bean yield, especially
when grown in deleterious environmental conditions
resulting in oxidative stress.
Materials and Methods
Plant material and growth conditions
The common bean (Phaseolus vulgaris) Mesoamerican cv.
Negro Jamapa 81 was used in this study. The growth
conditions and tissues collected were similar to those
reported by Ramı́rez et al. (2013). Briefly, surface-steril-
ized seeds were germinated at 30 C on sterile, wet filter
paper in darkness for 3 days. Germinated seedlings were
planted in pots with vermiculite and inoculated with 1 ml
of saturated Rhizobium etli liquid culture. The R. etli strains
used in this study were the wild type CE3 strain and CE3
bearing the stable plasmid pMR4 (Ramı́rez et al. 1999)
containing the Vitreoscilla sp. vgb gene (the gene encoding
VHb), hereby denominated strain CE3/VHb. The expres-
sion of VHb, driven by its natural promoter, as well as the
presence of VHb protein in the CE3/Vhb strain was
demonstrated previously (Ramı́rez et al. 1999). Pots were
watered 3 days per week with N-free Summerfield plant
nutrient solution (Summerfield et al. 1977). Plants were
grown under controlled environmental conditions
(26–28 C, 70% humidity, 16-h photoperiod) for 18 days.
To induce oxidative stress, at 16 days post-inoculation
(dpi) plants with active nodules were watered with nutrient
solution supplemented with 1 mM PQ (1,10-dimethyl-4,40-
bipyridinium dichloride) for 48 h. At 18 dpi, plants from
control condition (C) or PQ treatment were harvested for
analysis; mature nodules were detached, frozen in liquid
nitrogen and stored at -80 C until used.
Phenotypic analysis
Nitrogenase activity was determined by the acetylene
reduction assay (Hardy et al. 1968) in detached nodulated
roots from ten plants from each treatment (C or PQ) from
three independent experiments. Ethylene production was
determined by gas chromatography in a Variant model 3300
chromatograph as reported (Ramı́rez et al. 1999). Specific
activity is expressed as nmol ethylene h-1 g-1 nodule DW.
The ureide content was analyzed in xylem exudates
from 18-dpi PQ-treated and control bean plants as descri-
bed previously (Ramı́rez et al. 2013). In brief, xylem
exudates were collected from the cut stems of 10–15 plants
from three independent experiments. After recording the
volume of the exudates, the collected sap was stored at
-20 C until used for analysis. The ureide content was
determined using the differential analysis reported by
Vogels and Van Der Drift (1970).
Transcriptomic analysis by microarray
hybridization
The global gene responses of nodules elicited by CE3 or
CE3/Vhb strains from plants grown under C and PQ
treatments were analyzed using the Bean Custom Array
90 K described previously (Aparicio-Fabre et al. 2013;
Ramı́rez et al. 2013). A total of 30,150 different features
were available on the microarray, including 18,867 unique
common bean sequence probes (based in the EST
sequences available at The Gene Index Project. http://
compbio.dfci.harvard.edu/tgi/plant.html, v3.0) and 11,205
soybean UniGene probes (based in NCBI UniGene build
38.0) along with positive and negative controls. Each probe
was printed in triplicate to ensure the presence of internal
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replicates and to have a good statistical representation of
each transcript on the array.
Total RNA from 18-dpi PQ-treated and C nodules from
common bean plants inoculated with the different R. etli
strains was isolated using Trizol reagent (Life Technologies,
Carlsbad, CA) as reported (Hernández et al. 2007). Total
RNA (1 lg) was used as template to synthesize antisense
RNA (aRNA) with Cy5-ULS, using the RNA Amplification
and Labelling Kit from CombiMatrix (ampULSe, Kreatech
Biotechnology, Amsterdam, The Netherlands), according to
the manufacturer’s instructions. Prehybridization and
hybridization conditions were described previously
(Ramı́rez et al. 2013). After hybridization and washing, the
microarray was dipped in imaging solution, covered with
LifterSlipTM, and then scanned using a GenePix 4000B
microarray scanner (Axon) and the accompanying acquisi-
tion software (CombiMatrix Microarray Imager Software).
Multiple time scans at different photomultiplier (PMTs)
were provided for each hybridization.
Microarray data analysis
The data discussed in this publication have been deposited
in NCBI’s Gene Expression Omnibus (Edgar et al. 2002)
and are accessible through GEO Series accession number
GSE 83324 (http://www.ncbi.nlm.nih.gov/geo/query/acc.
cgi?acc=GSE83324).
Statistically significant signal intensities from the
microarray probes were obtained as reported previously
(Aparicio-Fabre et al. 2013, Ramı́rez et al. 2013). Briefly,
raw intensity data were first processed using the Com-
biMatrixMicroarray Imager software and then exported into
the Feature and Probe format of CombiMatrix, where the
actual raw intensity per probe and per spot is stored. Data
were loaded into R and analyzed using the Limma package
(Smyth 2004).Within-array probe replicates were defined as
technical replicates, by calculating the mean intensity across
the different probes. The values in the expression matrix
were transformed into log2 and a design matrix was defined
to describe the biological samples. The expression matrix
was used to fit a linear model using the design matrix and the
functions of Limma.A set of contrastmatriceswas defined to
describe the comparisons among samples and a second linear
fitting was performed for each contrast. The data were then
error corrected using the Bayesian functions of Limma and a
list of DEG was generated for each contrast, after correcting
for multiple testing using the Benjamin–Hochberg method
and setting 0.05 as the adjusted P value cutoff.
We used Gene Ontology (GO) (Ashburner et al. 2000)
and MapMan (Thimm et al. 2004) bioinformatics-based
approaches for analyses aimed at interpreting the biological
significance of gene expression data. The ESTs corre-
sponding to the microarray probe sets were organized in
functional categories according to GO guidelines (Ash-
burner et al. 2000). The Fisher’s exact test (Routledge 1998)
was applied to determine which GO categories were statis-
tically overrepresented within each set of differentially
expressed ESTs analyzed (P B 0.01, corrected by Bonfer-
roni adjustment). A second approach for expression data
analysis was based on the MapMan software version 3.5.1
(Thimm et al. 2004; http://gabi.rzpd.de/projects/MapMan/).
The P. vulgaris map previously developed (Aparicio-Fabre
et al. 2013; Ramı́rez et al. 2013) was used to extendMapMan
to common bean. The change in expression ratio of each gene
was calculated as the log2-fold change to generate the
MapMan experimental file. TheMapMan software was used
to visualize the amplitudes of the changes in expression of
individual genes in diagrams of metabolic pathways or cel-
lular processes.
qRT-PCR analysis
A quantitative reverse transcriptase-polymerase chain reac-
tion (qRT-PCR) approachwas used to validate themicroarray
data of selected genes. Total RNA was isolated from 200 mg
of frozen nodules as described. Nodules were detached from
different sets of plants growing in conditions similar to those
described for C and PQ treatments. Genomic DNA removal,
cDNA synthesis, and quality verification for qRT-PCR were
performed as reported (Hernández et al. 2007; Ramı́rez et al.
2013). Three biological replicates were carried out for each
condition. Reactions were done in a 96-well format with the
7300 Real-Time PCR System and 7300 System Software
(AppliedBiosystems, FosterCity,CA). In eachqRT-PCRrun,
the ascorbate peroxidase gene (APX, Phvul.009G126500)
was included as amarker gene (Ramı́rez et al. 2013); in all the
experiments this gene was induced (ca. 3-fold) in PQ-treated
nodules, thus confirming the oxidative nature of the stress.
Ubiquitin Conjugating Enzyme 9 (UBC9,
Phvul.006G110100), which was constant in the tested con-
ditions, was included for normalization in every qRT-PCR
run. The sequences of specific oligonucleotide primers used
for qRT-PCR amplification for each gene have been reported
(Hernández et al. 2007; Ramı́rez et al. 2013). Relative
expression for each sample was calculated with the compar-
ative Ct method as reported (Hernández et al. 2007) and the
fold change value (log2) was calculated. Student’s t test was
performed with a P value cutoff of 0.05.
Results
Symbiosis and oxidative stress response phenotype
We characterized the oxidative stress response of SNF
common bean plants inoculated with the VHb-expressing
466 Plant Biotechnol Rep (2016) 10:463–474
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R. etli strain CE3/VHb as compared to plants inoculated
with the CE3 wild type strain. At 16 dpi plants with active
nodules were watered with nutrient solution (control con-
dition) or with solution supplemented with PQ for 2 days.
In agreement with Ramı́rez et al. (1999), when grown in
the control condition common bean plants inoculated with
CE3/VHb showed increased length (20–25%) and foliage
dry weight (ca. 40%), as well as higher nitrogenase activity
(28%) as compared to CE3-inoculated plants (Table 1). In
common bean nodule cells, ammonia from N2-fixation
provided by the bacteorids is assimilated to generate urei-
des that are the main N-compounds transported from the
nodules to aerial parts of the plant (Boldt and Zrenner
2003). As shown in Table 1, CE3/VHb-inoculated plants
showed 33% higher ureide content in xylem sap than CE3
inoculated plants. Similar to the results obtained with R.
tropici-inoculated common bean plants (Ramı́rez et al.
2013), the PQ treatment had a negative effect on R. etli
CE3-inoculated plants, which showed decreased nitroge-
nase activity (66%) and ureide content (71%) (Table 1).
However, CE3/VHb-inoculated plants showed less sensi-
tivity to PQ as their nitrogenase activity and ureide content
decreased only 46 and 48%, respectively, as compared to
plants inoculated with the same strain under control con-
ditions (Table 1). The comparison between control CE3-
inoculated plants and CE3/VHb-inoculated plants under
PQ treatment revealed a lower decrease in nitrogenase
activity (32%) and uriede content (23%) (Table 1). Our
results (Table 1) indicate that the presence of VHb in the
nodule bacteroids has a protective role against the oxida-
tive stress generated by PQ that results in a more efficient
SNF and plant growth under PQ treatment.
Transcriptomics: experimental design
and validation
We performed transcriptomic analysis to gain insight into
global gene expression in nodules elicited by CE3 or CE3/
VHb strains grown under control and PQ-stressed condi-
tions, as described above. Our approach was based on
hybridization using the Bean Custom Array 90 K (Apari-
cio-Fabre et al. 2013; Ramı́rez et al. 2013). Since the P.
vulgaris genome has been sequenced, we mapped the
30,150 common bean EST printed on the microarray to the
genome (Schmutz et al. 2014; http://www.phytozome.net/
commonbean v1.0) and could assign a gene ID and anno-
tation to 25,442 of the printed ESTs.
The experimental design, based on circular hybridiza-
tions, included four conditions as shown in Fig. 1a, with two
independent biological replicates and three technical repli-
cates for each conditions that allowed five possible com-
parisons (I–V, Fig. 1a, b). A total of 2418 DEG were
identified among the different combinations shown in
Fig. 1a. The lists of DEG (log2-fold changeC1.0, P B 0.01)
from each comparison (I–V) are presented in Supplemental
Table s1 and their numbers in Fig. 1b. Though the number of
DEG fromcomparison I (CE3_PQvsCE3_C) is very similar
to that of comparison II (CE3/VHb_PQvsCE3/VHb_C), the
majority of genes from comparison I are up-regulated while
in comparison IImost were down-regulated (Fig. 1b). On the
other hand, comparison III that includes nodules elicited by
different strains from control plants (CE3/VHb_C vs
CE3_C) showed the lowest DEG number, while comparison
IV that includes nodules from stressed plants (CE3/VHb_PQ
vs CE3_PQ) showed the highest DEG number (Fig. 1b).
The results from microarray analysis were validated by
qRT-PCR expression analysis. In our previous work
(Ramı́rez et al. 2013), we identified 67 transcription factor
(TF) genes that responded to PQ treatment in common bean
nodules belonging to TF families known to participate in
plant stress responses. On this basis, in this work we selected
13 TF genes that were differentially expressed in CE3/
VHb_PQ vs CE3_ PQ nodules according to the microarray
data for qRT-PCR expression analysis. We found a good
correlation (r2 = 0.89) and a similar pattern (up- or down-
regulation) between the microarray and the qRT-PCR fold
change values, indicating the reliability of the microarray
data (Table 2). The variation between the microarray and
qRT-PCR expression data is probably due to different sen-
sitivities of the twomethods. Table 2 shows thatWRKY and
bZIP were the most abundant TF families showing response
to PQ: members from these families are known to respond to
oxidative and drought/salinity stress in Arabidopsis (Mal-
hotra and Sowdhamini 2014). In common bean, an enrich-
ment of putative cis-elements for WRKY and bZIP TF was
identified in the promoter regions of genes differentially
expressed in PQ-treated nodules (Ramı́rez et al. 2013). Our
data indicate that beside the previously observed response of
Table 1 The effect of VHb
expression in Rhizobium etli
during symbiosis with bean
plants under paraquat treatment
CE3 CE3/VHb
C PQ C PQ
Nitrogenase activity (nmol C2H2/min-1/g-1 DW) 628 (±48) 277 (±43) 800 (±95) 426 (±32)
Ureide content in xylem sap (lmol/ml/h) 235 (±44) 68 (±13) 355 (±43) 182 (±38)
C control, PQ paraquat
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TF genes to PQ treatment in nodules elicited by a wild type
strain (Ramı́rez et al. 2013), an additional response of some
of these genes to VHb expression occurs in nodules of PQ-
treated plants (Table 2).
Gene Ontology and MapMan analysis of DEG
from PQ-stressed nodules elicited by VHb-
expressing or wild type R. etli
Gene Ontology (GO) analysis was performed to interpret
the biological significance of DEG from nodules under PQ
treatment. Our main interest was to compare the PQ-re-
sponsive genes in nodules elicited by VHb-expressing
strain with those from nodules elicited by the wild type
strain lacking this protein, therefore we focused on com-
parisons I and II (Fig. 1a). From the total DEG from
comparisons I and II (Figs. 1b, Supplemental Table s1),
1397 could be organized in biological processes according
to GO guidelines (Ashburner et al. 2000): these are shown
in Supplemental Table s2. Fisher’s exact test (Routledge
1998) was applied to determine the statistically overrep-
resented GO categories, down- or up-regulated in the
comparisons. Besides GO enrichment analysis from the
DEG exclusive to comparisons I and II (Fig. 2a, b), we also
analyzed DEG that are commonly down- or up-regulated
between these two comparisons, which we refer to as
second level comparison (Fig. 2c).
The GO enrichment analysis of DEG exclusive to CE3-
elicited nodules in PQ treatment, comparison I, revealed 11
down-regulated and 23 up-regulated biological processes
(Fig. 2a). The majority of up-regulated DEG were classi-
fied in processes related to defense (GO:0080167 response
to karrikin, GO:0009408 response to heat, GO:0009611
response to wounding, GO:0009819 drought recovery,
GO:0009651 response to salt stress) and to central meta-
bolism (GO:0010325 raffinose family oligosaccharide
biosynthetic process, GO:0001676 long-chain fatty acid
metabolic process, GO:0009807 lignan biosynthetic pro-
cess, GO:0046274 lignin catabolic process) (Fig. 2a).
Likewise, processes related to secondary metabolism
(GO:0051555 flavonol biosynthetic process, GO:0009699
phenylpropanoid biosynthetic process, GO:0046865 ter-
penoid transport), known to participate in cell protection
against biotic and abiotic stresses, were overrepresented in
the up-regulated DEG of these nodules (Fig. 2a).
Similar analysis for DEG exclusive to VHb-expressing
nodules in PQ treatment, comparison II, showed less
overrepresented GO biological processes; 8 were down-
regulated and 10 were up-regulated (Fig. 2b). In contrast to
results from comparison I (Fig. 2a), fewer enriched up-
regulated processes were related to defense against abiotic
stress (GO:0009615 response to virus, GO:0043967 histone
H4 acetylation, GO:0010043 response to zinc ion,
GO:0046686 response to cadmium ion). Other processes
up-regulated in CE3/VHb PQ-treated nodules were related
to nitrogen fixation and carbon metabolism (GO: 0009399
nitrogen fixation, GO:0044262 cellular carbohydrate
metabolic process) that are basic metabolic pathways for
adequate nodule function (Fig. 2b).
The second level comparison between I and II allowed
identifying DEG that commonly respond to PQ treatment
in nodules elicited by different strains (Fig. 2c). All the
commonly enriched up-regulated GO biological processes
(11) indicate their general roles in nodule responses to
CE3_ PQ vs CE3_C (I)
DEG 734
Down 269
Up 465
CE3/VHb_PQ vs CE3/VHb_C (II)
DEG 761
Down 494
Up 267
CE3/VHb_C vs CE3_C (III)
DEG 398
Down 146
Up 252
CE3/VHb_PQ vs CE3_ PQ (IV)
DEG 938
Down 546
Up 392
CE3/VHb_PQ vs CE3_C (V)
DEG 789
Down 365
Up 424
a
b
Fig. 1 Experimental design based on circular hybridization of Bean
Custom Array 90 K used for transcriptomic analysis to identify
differentially expressed genes (DEG) from nodules elicited by R. etli
strains CE3 or CE3/VHb on plants under PQ treatment or control
condition. a Scheme representing the circular hybridization and
possible comparisons among analyzed conditions. For each condition,
two biological replicates and three technical replicates of each
biological sample were analyzed. b Number of DEG (log2-fold
change C1.0, P B 0.01) identified among different comparisons
numbered as shown in a
468 Plant Biotechnol Rep (2016) 10:463–474
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III 
CE3IVHb_C 
11 1 U 
CE3_PQ E ) CE3IVHb_PQ 
IV 
stress; these were related to defense (GO:0071369 cellular
response to ethylene stimulus, GO:0071493 cellular
response to UV-B, GO:0046686 response to cadmium ion)
and to oxidation–reduction processes (GO:0055114 oxi-
dation–reduction process) that participate in oxidative and
nitric stresses (GO:0006979 response to oxidative stress,
GO: 0071732 cellular response to nitric oxide) (Fig. 2c).
Interestingly, the genes included in these processes showed
higher induction in PQ-treated nodules elicited by strain
CE3 as compared to nodules expressing VHb (Supple-
mental Table s2).
MapMan and GO analyses are counterpart for com-
parative gene expression analyses (Klie and Nikoloski,
2012). Therefore, to further analyze our gene expression
data we used MapMan software (Thimm et al. 2004;
http://gabi.rzpd.de/projects/MapMan/ v.3.5.1) to validate
GO enrichment data and to classify DEG in more detail.
As for GO analysis, our MapMan analysis focused on
comparisons I and II (Fig. 1A). Using the P. vulgaris map
previously developed and up-loaded to MapMan (Apari-
cio-Fabre et al. 2013; Ramı́rez et al. 2013), 1140 from the
total DEG from comparison I and II (Figs. 1b, Supple-
mental Table s3), could be assigned to a MapMan func-
tional category and were considered for analysis. These
DEG were classified into 32 tree-structured Bins from
MapMan, the DEG classified as unknown (Bin 35) was
not further analyzed.
The majority (77%) of analyzed DEG for comparison I
were up-regulated while 48% were up-regulated for com-
parison II. Figure 3 shows the five most abundant func-
tional categories (Bins) from both comparisons: protein,
transport, RNA, miscellaneous enzyme families and stress.
A higher up-regulation in CE3 nodules under PQ treatment
was evident (Fig. 3). Additionally, Fig. 4 depicts repre-
sentative maps related to stress response and signaling,
obtained with MapMan visualization tool, showing differ-
ent response to PQ in nodules with CE3 bacteroids as
compared to nodules with CE3/VHb bacteroids. In contrast
to CE/VHb-elicited nodules, nodules with CE3 bacteroids
showed a higher number of PQ-responsive genes with
increased expression level (Fig. 4a, b). This was evident
for stress response related maps (abiotic stress, secondary
metabolism, heat shock proteins, glutathione-S-transferase)
and for signaling related maps (transcription factors, hor-
mone signaling). These results suggest that nodules with
bacteroids lacking VHb are facing a more aggressive
oxidative stress condition, something that induces higher
gene expression of processes that help cope with the stress.
A comparative analysis on the number of DEG up- or
down-regulated in CE3- and CE3/VHb-elicited nodules
under PQ stress is presented as a Venn diagram in Fig. 5.
Few DEG were common in both comparisons; from these
10% were up-regulated and 3% were down-regulated
genes. Notably, 13 genes overlapped between CE3 and
CE3/VHb PQ-treated nodules albeit with a different
response, being up-regulated in CE_PQ nodules but down-
regulated in CE/VHb_PQ nodules (Fig. 5).
In general, our results showed good correspondence
among both the GO term and the MapMan enrichment
analyses, highlighting functional categories/biological
processes commonly observed in the response of plant cells
to abiotic stress.
Table 2 Validation of the microarray data of selected transcription factor genes identified as induced or repressed in nodules elicited by CE3_C
vs CE3/VHb under PQ treatment (comparison IV, Fig. 1b)
Gene ID (EST) Annotation Fold change
Microarray qRT-PCR
Phvul.005G097800 (BQ253715) bZIP transcription factor family protein -0.44737305 -2.84215718
Phvul.004G053600 (CV540001) Myb domain protein 52 -0.43988486 -0.63550938
Phvul.006G101700 (TC18231) bZIP transcription factor family protein -1.3695091 -2.00631975
Phvul.001G119300 (CV523295) bZIP transcription factor family protein 0.70484463 1.95048633
Phvul.005G153900 (GD956937) bZIP transcription factor family protein 1.42611818 2.107039974
Phvul.006G021900 (BM094171) bZIP transcription factor family protein 0.99095394 2.108264949
Phvul.006G078500 (TC11890) bZIP transcription factor family protein 0.83543646 0.753812
Phvul.009G094200 (TC11318) bZIP transcription factor family protein 1.1649796 1.48192373
Phvul.007G241600 (TC8812) AP2/ERF transcription factor family protein 0.56769841 1.6488426
Phvul.008G286100 (TC20065) WRKY family transcription factor 1.05326109 1.69716774
Phvul.002G297100 (TC11713) WRKY family transcription factor 0.772599831 1.785165967
Phvul.010G111900 (TC12249) WRKY family transcription factor 0.501022063 2.213934243
Fold change (log2) values are the average of three biological replicates. Normalized values from qRT-PCR and microarray analyses of each gene
are shown
Plant Biotechnol Rep (2016) 10:463–474 469
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ba
response to carbohydrate stimulus
oligopeptide transport
syncytium formation
plant-type cell wall loosening
nucleosome assembly 
Golgi organization
water transport
metal ion  transport
lateral root morphogenesis
regulation of epidermal cell differentiation
cellular response to iron ion starvation
floral organ morphogenesis
gibberellin mediated signaling pathway
raffinose family oligosaccharide
regulation of gibberellic acid signaling
response to karrikin
response to arsenic-containing sustance
toxin catabolic process
response to heat
response to wounding
lignin catabolic process
response to cadmium ion
exocytosis
drought recovery
salicylic acid catabolic process
response to salt stress
phenylpropanoid biosynthetic process
long-chain fatty acid metabolic process
lignan biosynthetic process
multicellular organismal development
flavonol biosynthetic process
terpenoid transport
vesicle docking involved in exocytosis
process involved in reproduction
-0.02 0 0.02 0.04
Anaerobic respiration
response to zinc ion 
positive regulation of seed germination 
methylammonium transport
histone H4 acetylation
response to virus
leucine catabolic process
response to cadmium ion 
cellular carbohydrate metabolic process
nitrogen fixation
induction of programmed cell death 
regulation of apoptosis 
transport
response to hydrogen peroxide
response to high light intensity 
transmembrane transport
defense response to fungus
response to hypoxia
-0.04 -0.02 0 0.02 0.04
-0.04 -0.02 0 0.02 0.04 0.06
response to manganese ion
regulation of systemic acquired resistance 
L-serine biosynthetic process
salicylic acid biosynthetic process 
nucleosome assembly
developmental programmed cell death
water transport
cellular response to ethylene stimulus
oxidation-reduction process
high-affinity iron ion transport
response to cadmium ion 
multicellular organism growth
cellular response to UV-B 
nicotianamine biosynthetic process
toxin catabolic process
cellular response to nitric oxide
glycogen catabolic process
response to oxidative stress
CE3/VHb_PQ vs CE3 /VHbCE3_PQ vs CE3_C
244 down
392 up
25 down
73 up
469 down
194 up
c
470 Plant Biotechnol Rep (2016) 10:463–474
123
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... ,... ....--
;::: • '" I 
~ -I " • '" I 
;::: • I .. • ¡:-- I -I '" • '" :....-....-.-- I ,.....- • '" I 
~ 
I 
Discussion
This work shows the decreased sensitivity to oxidative
stress, generated by PQ treatment, of SNF common bean
plants inoculated with an engineered R. etli strain
expressing the heterologous Vitreoscilla hemoglobin. After
PQ treatment plants inoculated with CE3/VHb showed a
decrease in nitrogenase activity and xylem sap ureide
content of ca. 47% while the decrease observed in CE3
inoculated plants was ca. 68%. These results are in
agreement with and add information relevant to the
improvement of SNF in common bean inoculated with
VHb-engineered R. etli grown in control condition, as
shown here and in a previous report (Ramı́rez et al. 1999).
While positive effects of VHb expression in various non-
symbiotic bacterial species are well documented (Stark
et al. 2015), to our knowledge there are no other studies on
VHb-engineered rhizobia species in symbiosis with their
host legume, besides our previous report (Ramı́rez et al.
1999). Positive effects of VHb expression in transgenic
plants, i.e., Arabidopsis, tobacco or cabbage, growing in
control or in stress conditions have also been reported
(Holmberg et al. 1997; Li et al. 2005; Wang et al. 2009).
The expression of vgb gene as well as the presence of
VHb protein was demonstrated for the engineered R. etli
bFig. 2 Biological processes significantly enriched in the set of down-
regulated (gray bars) and up-regulated (black bars) DEG according to
Gene Ontology analysis. Biological processes enriched exclusively in
nodules from comparisons I: CE3-PQ vs CE3_C (a), from comparison
II:CE3/VHb_PQ vs CE3/VHb_C (b) or those commonly enriched in a
second level comparison of I vs II (c). Fisher’s exact test P B 0.01,
FDR B 0.05
60
40
20
0
20
40
G
en
e 
n
u
m
b
er
U
p
 r
eg
u
la
te
d
D
o
w
n
 
re
g
u
la
te
d
CE3
CE3/VHb
Fig. 3 MapMan functional categories of DEG from comparison I
(light bars) and II (dark bars). The histograms represent the number
of up- or down-regulated genes assigned to each category or Bin,
designated as indicated below
Fig. 4 Selected MapMan maps
depicting DEG profiles from
a comparison I (CE3_PQ vs
CE3_C) and b comparison II
(CE3/VHb_PQ vs CE3/
VHb_C). Up- or down-
regulated genes are false color-
coded with increasing blue or
red, respectively, saturating at
amplitude of 0.2 (log2 value) as
indicated in the bar in the
middle
CE3/VHb_PQvs CE3 / VHb
Down  regulated(II)
CE3/VHb_PQ vs CE3 / VHb
Up regulated (II)
CE3_PQ vs CE3_C
Up regulated (I)
CE3_PQ vs CE3_C
Down  regulated (I)
279
147
60
181
351
18
13
0
Fig. 5 Venn diagram representing DEG analyzed by Mapman in PQ-
treated nodules elicited by CE3 or CE3/VHb. Number of up- and
down-regulated DEG from comparison I or II are shown in different
circles as indicated
Plant Biotechnol Rep (2016) 10:463–474 471
123
103
~ &mmst . 
• • 6"", •• i r 
o SA 
I • 
JA 
UDPGlyco.yI1r ... rera ... 
Ju. L 
Prdeolysis 
' ·2 
' ·1 I Heelshookprdens 
. o 
' 2 
UDP G~cosyl1 r .. r.,,~ 
(. I 
Transcription Fadors 
111 1 1 
• • 
strain used in this work (Ramı́rez et al. 1999). The
improvement in respiration, chemical energy content and
nitrogen fixation gene expression in R. etli CE3/VHb
grown in limited oxygen concentration indicates that VHb
expression in bacteroids contributes to better oxygen
delivery to the symbiotic terminal oxidase resulting in
increased respiratory capacity and N2 fixation that in turn
provides more nitrogen for a better growth of common
bean (Ramı́rez et al. 1999). This is in agreement with the
reports on improved SNF common bean performance
resulting form inoculation with modified R. etli strains
showing increased expression of the symbiotic terminal
oxidase (Granados-Baeza et al. 2007; Soberón et al. 1999).
The control of redox homeostasis in the legume-rhizobia
symbiosis is a key feature for efficient SNF, taking into
account the essential signaling role of ROS in the initial
steps of the symbiosis as opposed to the increased risk of
harm from ROS generation because of the strong bio-
chemical activity in nodules (Becana et al. 2010; Dalton
et al. 2009; Mandon et al. 2009). The PQ treatment used in
this work increases ROS production and generates oxida-
tive stress at a similar level as that generated by abiotic
stresses, such as metal toxicity, salinity or drought
(Ramı́rez et al. 2013). Expression of VHb in E. coli and
Enterobacter aerogenes protects against oxidative stress
(Anand et al. 2010; Geckil et al. 2003). We interpret that
VHb expression in R. etli bacteroids from nodules of plants
under oxidative stress, besides improving respiration and
N2 fixation, contributes to buffering the damage caused by
increased ROS and controlling redox homeostasis, and this
is reflected in nodule cells that showed reprogramming of
global gene expression.
Aiming to understand the molecular response to oxida-
tive stress of nodule cells derived from the expression of
VHb in the bacteroids, we performed comparative tran-
scriptomic analyses based in hybridization of the Bean
Custom Array 90K. This has proven as a reliable tool for
analyzing global gene expression in common bean organs,
such as nodules under oxidative stress (Ramı́rez et al.
2013) and roots under nutritional stress (Aparicio-Fabre
et al. 2013). The DEG analysis conducted to identify bio-
logical processes or functional categories was based in the
GO (Ashburner et al. 2000) and MapMan (Thimm et al.
2004) bioinformatics tools adapted to common bean
(Ramı́rez et al. 2013). For this we focused in comparisons I
and II that include DEG in oxidative stressed vs control
nodules with the engineered or wild type bacteroids.
It is known that nodules from common bean plants
under oxidative stress, after PQ exposure, reprogram their
transcript profile (Ramı́rez et al. 2013); data from this work
are in agreement with this finding. However, the tran-
scriptomic PQ-response from nodules elicited with CE3
was notably different from that of nodules with VHb-
expressing bacteroids. Only ca. 8% of DEG were com-
monly responsive in both type of nodules under similar
oxidative stress. Enriched down-regulated biological pro-
cesses in CE3_PQ nodules were related to carbon/nitrogen
primary metabolism, which are key to nodule function
(Ramı́rez et al. 2013), while these processes were less
affected or even up-regulated in CE3/VHb_PQ nodules.
Nevertheless, the most evident difference derived from our
transcriptome analysis of comparisons I and II was that the
response to PQ of CE3-elicited nodules included an
increased number of up-regulated DEG as compared to
CE3/VHb-elicited nodules. The majority of up-regulated
DEG from CE3_PQ nodules were classified into biological
processes/functional categories related to defense, response
to abiotic stress or signaling.
Examples of up-regulated GO biological processes
enriched in CE3_PQ nodules include synthesis of raffinose
oligosaccharide and of secondary metabolites. Raffinose
oligosaccharides accumulate under drought stress, func-
tioning as osmolytes to maintain cell turgor and to stabilize
cell proteins (ElSayed et al. 2014). They may also act as
antioxidants to counteract the accumulation of reactive
oxygen species (ROS) under stress conditions (Peshev
et al. 2013). Phenylpropanoids and flavonoids are widely
known to participate in abiotic stress responses in plants;
flavonoids have the capacity to absorb the most energetic
solar wavelengths (i.e., UV-B and UV-A), to inhibit the
generation of ROS and to quench these once they are
formed (Ferrer et al. 2008; Brunetti et al. 2013).
The MapMan analysis showed that the Transport (Bin
27) functional category included a large number of DEG;
again CE3_PQ nodules showed more up-regulated genes
from this category. DEG assigned to this category encode
protein, sugar and cation transporters and also transporters
from the large ABC (ATP-binding cassette) family, which
participate in export or import of various substrates across
biological membranes and have an important role in
response to abiotic stress (Moon and Jung 2014). The
abundant Signaling (Bin 30) functional category included
oxidative stress responsive kinases from several families,
such as the Mitogen-activated protein kinase (MAPK)
family that has been identified as important components in
stress signal transduction in response to ABA and antiox-
idant defense (Danquah et al. 2014). The Hormone meta-
bolism (Bin 17) also showed high number of DEG in PQ-
treated nodules, including genes from signaling pathways
known to be involved in environmental stresses, such as
ethylene, abscisic acid and jasmonic acid (Verma et al.
2016). Hormone signaling involves TF from different
families, such as the AP2 family (that includes ERF),
WRKY, and bZIP known to be relevant for plant response
to abiotic stress (Pieterse et al. 2012). We observed that
CE3_PQ nodules highly up-regulate different TF genes, in
472 Plant Biotechnol Rep (2016) 10:463–474
123
104
agreement with previous report (Ramı́rez et al. 2013). The
TF response in CE3/VHb_PQ nodules was the opposite,
showing mostly down-regulation of these genes.
Taken as a whole, data from this work indicate that VHb-
expressing R. etli bacteroids alleviate the oxidative stress
level in nodules of common bean plants exposed to PQ by a
major transcriptomic reprogramming. Our transcriptomic
datasets provide a basis for deeper analysis, based on
biotechnological approaches, of common bean genes that
could play key roles in response to oxidative stress. From
previous analyses (Ramı́rez et al. 2013), we predict that
similar positive effects would be obtained in CE3/VHb-
inoculated common bean plants subjected to drought,
salinity or metal toxicity, which are serious constraints in
acidic soils from Latin America where this crop is widely
grown. This work provides a foundation for exploring
inoculants based in VHb-engineered rhizobia that would be
relevant for increasing growth and crop production of
legumes grown under oxidative stress condition.
Acknowledgements We thank Dr. Michael Dunn for critical review
of this manuscript and Biol.Vı́ctor Bustos for technical assistance.
This work was supported partially by PAPIIT grant IN214308 from
Dirección General de Asuntos del Personal Académico – UNAM. LP
Íñiguez is a student from Doctorado en Ciencias Biomédicas –
UNAM and a recipient of a studentship from CONACyT, México
(No. 340334).
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The Micro-RNA172c-APETALA2-1 Node as a Key
Regulator of the Common Bean-Rhizobium etli
Nitrogen Fixation Symbiosis1[OPEN]
Bárbara Nova-Franco, Luis P. Íñiguez, Oswaldo Valdés-López, Xochitl Alvarado-Affantranger,
Alfonso Leija, Sara I. Fuentes, Mario Ramírez, Sujay Paul, José L. Reyes,
Lourdes Girard, and Georgina Hernández*
Centro de Ciencias Genómicas (B.N.-F., L.P.I., A.L., S.I.F., M.R., S.P., L.G., G.H.), Laboratorio Nacional de
Microscopía Avanzada (X.A.-A.), and Departamento de Biología Molecular de Plantas (J.L.R.), Instituto de
Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, Mexico; and
Laboratorio de Genómica Funcional de Leguminosas, Facultad de Estudios Superiores Iztacala, Universidad
Nacional Autónoma de México, Tlalnepantla, Edo de Mexico 54090, Mexico (O.V.-L.)
ORCID IDs: 0000-0002-2481-7715 (X.A.-A.); 0000-0001-5129-9741 (J.L.R.); 0000-0003-0004-9106 (L.G.); 0000-0001-6214-311X (G.H.).
Micro-RNAs are recognized as important posttranscriptional regulators in plants. The relevance of micro-RNAs as regulators of
the legume-rhizobia nitrogen-fixing symbiosis is emerging. The objective of this work was to functionally characterize the role of
micro-RNA172 (miR172) and its conserved target APETALA2 (AP2) transcription factor in the common bean (Phaseolus vulgaris)-
Rhizobium etli symbiosis. Our expression analysis revealed that mature miR172c increased upon rhizobial infection and continued
increasing during nodule development, reaching its maximum in mature nodules and decaying in senescent nodules. The expression
of AP2-1 target showed a negative correlation with miR172c expression. A drastic decrease in miR172c and high AP2-1 mRNA levels
were observed in ineffective nodules. Phenotypic analysis of composite bean plants with transgenic roots overexpressing miR172c or a
mutated AP2-1 insensitive to miR172c cleavage demonstrated the pivotal regulatory role of the miR172 node in the common bean-
rhizobia symbiosis. Increased miR172 resulted in improved root growth, increased rhizobial infection, increased expression of early
nodulation and autoregulation of nodulation genes, and improved nodulation and nitrogen fixation. In addition, these plants showed
decreased sensitivity to nitrate inhibition of nodulation. Through transcriptome analysis, we identified 114 common bean genes that
coexpressed with AP2-1 and proposed these as being targets for transcriptional activation by AP2-1. Several of these genes are related
to nodule senescence, and we propose that they have to be silenced, through miR172c-induced AP2-1 cleavage, in active mature
nodules. Our work sets the basis for exploring the miR172-mediated improvement of symbiotic nitrogen fixation in common bean, the
most important grain legume for human consumption.
The symbiotic nitrogen fixation (SNF) occurring in the
legume-rhizobia symbiosis takes place in root-developed
specialized organs called nodules. Nodulation is a com-
plex process that involves communication between rhi-
zobia and legumes through molecular signals, including
rhizobial lipochitin-oligosaccharide symbiotic signals
known as nodulation factors (NFs), that triggers a root-
signaling cascade essential for rhizobia infection (for re-
view, see Crespi and Frugier, 2008; Oldroyd and Downie,
2008; Kouchi et al., 2010; Murray, 2011; Oldroyd, 2013).
Nuclear Ca2+ oscillations, or calcium spiking, is one of the
earliest NF-induced responses in legume root hairs. Per-
ception and transduction of the calcium-spiking signal
involves Ca2+/CALMODULIN-DEPENDENT PROTEIN
KINASE (CCaMK), which interacts with the nuclear
protein CYCLOPS, and other downstream components,
such as the transcriptional regulators NODULATION
SIGNALING PATHWAY (NSP1)/NSP2, NUCLEAR
FACTOR YA1 (NF-YA1)/YA2, ETHYLENE-RESPONSIVE
FACTOR REQUIRED FOR NODULATION1, and
NODULE INCEPTION (NIN), which, in turn, control
the expression of early nodulation genes.
Legumes strictly regulate the number of developing
nodules in response to internal and external cues. An
important internal cue is the systemic feedback regula-
tory mechanism called autoregulation of nodulation
(AON), which consists of root-derived and shoot-derived
long-distance signals. AON is initiated in response to
rhizobial NF during nodule primordium formation by
the root production of CLAVATA3/Embryo-Surrounding
Region Protein-related (CLE) peptides (Reid et al., 2011a).
Some CLE peptides are predicted, although not proven,
to act as the ligand for a shoot CLAVATA1-like Leu-rich
1 This work was supported by the Dirección General de Asuntos
del Personal Académico/Universidad Nacional Autónoma de
México (grant nos. PAPIIT: IN209710 and IN210814) and the Consejo
Nacional de Ciencia y Tecnología, México (studentship nos. 351615
and 340334 to B.N.-F. and L.P.I., respectively).
* Address correspondence to gina@ccg.unam.mx.
The author responsible for distribution of materials integral to the
findings presented in this article in accordance with the policy de-
scribed in the Instructions for Authors (www.plantphysiol.org) is:
Georgina Hernández (gina@ccg.unam.mx).
[OPEN] Articles can be viewed without a subscription.
www.plantphysiol.org/cgi/doi/10.1104/pp.114.255547
Plant Physiology, May 2015, Vol. 168, pp. 273–291, www.plantphysiol.org  2014 American Society of Plant Biologists. All Rights Reserved. 273
C. Anexo
107
repeat receptor kinase (Okamoto et al., 2009). Activation of
this receptor is proposed to initiate the production of a
shoot-derived inhibitor that is transported to the root,
where it inhibits further nodule formation (for review, see
Magori and Kawaguchi, 2009; Ferguson et al., 2010;
Kouchi et al., 2010; Reid et al., 2011b). Soil nitrogen is
an important external cue for the control of nodulation
(Streeter and Wong, 1988). Recent work indicates that
nitrate inhibition of nodulation may function via an up-
regulation of a nitrate-induced CLE peptide that is per-
ceived by a Leu-rich repeat receptor kinase in the root
(Okamoto et al., 2009; Reid et al., 2011a).
In recent years, microRNAs (miRNAs), a class of
noncoding RNA 21 to 24 nucleotides in length, have been
identified as central regulators of gene expression in
plants, controlling fundamental processes such as stress
response, phytohormone regulation, organ morphogen-
esis, and development (Rogers and Chen, 2013). The
plant miRNA precursors, generally transcribed by RNA
polymerase II, adopt stem-loop structures that are pro-
cessed by several enzymes and generate mature miRNAs
that are exported to the cytosol. The role of miRNAs in
posttranscriptional regulation is mediated by the almost
perfect complementarity with their target mRNAs,
thereby causing their degradation or their translational
inhibition (Zhang et al., 2006; Rogers and Chen, 2013).
Progress in high-throughput sequencing technologies
has facilitated the genome-wide identification of large
miRNA populations and their target mRNAs in different
legumes (for review, see Simon et al., 2009; Bazin et al.,
2012; Bustos-Sanmamed et al., 2013). Conserved and
legume-specific miRNA families differentially expressed
during nodule organogenesis have been reported for
Medicago truncatula, soybean (Glycine max), and Lotus
japonicus (Subramanian et al., 2008; Lelandais-Brière et al.,
2009; De Luis et al., 2012; Turner et al., 2012; Dong et al.,
2013). Recently, Formey et al. (2014) identified miRNAs
from M. truncatula roots that respond to treatments with
purified NF. However, evidence for the functional in-
volvement of miRNAs in rhizobial infection and the
functionality of nodules has only been obtained for a
small number of candidates. The involvement of
M. truncatula microRNA166 (miR166), miR169, and
miR164 in nodule development has been reported. miR169
controls nodule meristem maintenance through the re-
pression of NF-YA1 (previously called HAEM ACTI-
VATOR PROTEIN2-1), a nodule-responsive transcription
factor (TF; Combier et al., 2006), while miR166 and its
target gene, HOMEODOMAIN-LEUCINE ZIPPER
protein of class III TF, regulate meristem activity and
vascular differentiation in roots and nodules (Boualem
et al., 2008). The overexpression of miR164, a conserved
miRNA targeting NAC1 (for no apical meristem [NAM],
Arabidopsis transcription activation factor [ATAF1-2],
and cup-shaped cotyledon [CUC2] domain1) TF in roots,
affected nodule organogenesis presumably through the
deregulation of auxin responses (D’haeseleer et al., 2011).
In soybean, the overexpression of miR482, miR1512, and
miR1515 results in increased nodule numbers without
affecting root development or the number of nodule
primordia (Li et al., 2010). Recently, Turner et al. (2013)
reported that the overexpression of soybean miR160,
which targets a set of repressor auxin response factors,
resulted in an enhanced sensitivity to auxin and inhibi-
tion of nodule development, apparently through a reduc-
tion in cytokinin sensitivity. Likewise, the overexpression
of M. truncatula miR160 affected root gravitropism and
nodule number (Bustos-Sanmamed et al., 2013). Specific
variants of L. japonicus andM. truncatulamiR171 target the
GRAS-family NSP2 TF, a key regulator of the common
symbiotic pathway for rhizobial and arbuscular mychor-
rizal symbioses (Ariel et al., 2012; De Luis et al., 2012;
Lauressergues at al., 2012). M. truncatula roots over-
expressing miR171h showed deceased arbuscular
mychorrizal colonization (Lauressergues at al., 2012),
while in L. japonicus, miR171c regulates the maintenance
and establishment of the nodule but not the bacterial
infection (De Luis et al., 2012). In addition, the role of
L. japonicus miR397 in nodule copper homeostasis,
through the regulation of a member of the laccase copper
protein family, has been documented (De Luis et al., 2012).
Common bean (Phaseolus vulgaris) is the most impor-
tant crop legume for human consumption and the main
source of proteins for people in African and Central/
South American countries (Broughton et al., 2003). Our
research is focused on identifying and functionally char-
acterizing common bean miRNAs. High-throughput se-
quencing of small RNAs generated from different organs
of common bean let us identify more than 100 conserved
miRNAs and to predict novel miRNAs (Peláez et al.,
2012). Common bean miRNAs that respond to drought,
salinity, nutrient deficiencies, or metal toxicity stresses
have been identified, and their target genes have been
predicted or validated (Arenas-Huertero et al., 2009;
Valdés-López et al., 2010; Contreras-Cubas et al., 2012).
The roles of miR399 in the common bean root response to
phosphorus deficiency (Valdés-López et al., 2008) and of
miR398 in the regulation of copper homeostasis and re-
sponse to biotic interactions (Naya et al., 2014) have been
demonstrated. In this work, we analyzed the role of
miR172 in common bean roots and nodules.
miR172 is conserved in all angiosperm lineages; its
conserved targets are TFs from the APETALA2 (AP2)
family. The miR172 node that involves the miR156
node is one of the best-understood networks that regu-
late developmental timing in Arabidopsis (Arabidopsis
thaliana) and other plants (Rubio-Somoza and Weigel,
2011). Aukerman and Sakai (2003) first described that
miR172 promotes flowering by repressing AP2 genes,
primarily through translation inhibition (Chen, 2004) but
also throughmRNA cleavage (Kasschau et al., 2003; Jung
et al., 2007). In addition, the miR172 node regulates the
juvenile-to-adult phase transition during shoot develop-
ment (Wu et al., 2009; Huijser and Schmid, 2011). Such
developmental transitions are coordinated by the an-
tagonistic activities of the miR156 and miR172 nodes.
miR156 targets a subset of SQUAMOSA PROMOTER-
BINDING PROTEIN-LIKE (SPL) TFs that bind to the
miR712 promoter and directly promote its transcription,
resulting in AP2 silencing (Wu et al., 2009).
274 Plant Physiol. Vol. 168, 2015
Nova-Franco et al.
108
miR172 has been identified in the legumesM. truncatula,
L. japonicus, soybean, and common bean; it is highly ac-
cumulated inmature nodules relative to other plant tissues
(Lelandais-Brière et al., 2009; Wang et al., 2009; Valdes-
López et al., 2010; De Luis et al., 2012). Yan et al. (2013)
reported the regulation of soybean nodulation by miR172
that involves a complex regulatory circuit in which
miR156 regulates miR172 expression, which, in turn,
controls the level of its AP2 target gene. They propose that
AP2, directly or indirectly, controls the expression of
nonsymbiotic hemoglobin, which is essential for regulat-
ing the levels of nodulation and nitrogenase activity (Yan
et al., 2013). Very recently, Wang et al. (2014) demon-
strated that soybean miR172c modulates rhizobial in-
fection and nodule organogenesis. They showed that
miR172c regulates nodule formation by repressing its
target geneNODULE NUMBER CONTROL1 (NNC1), an
AP2 TF, which directly targets and represses the early
nodulin gene ENOD40 that plays a key role in nodula-
tion. However, it is not clear whether miR172c controls
early responses that are critical to establish a functional
symbiosis between legumes and rhizobia.
The aim of this work was to analyze the role of the
miR172 node in the common bean-rhizobia symbiosis.
We determined an increased expression of miR172c upon
rhizobial infection and during nodulation, showing a
negative correlation with AP2-1 expression. We achieved
the overexpression of miR172c and of a mutagenized
AP2-1 insensitive to miR172 cleavage in composite com-
mon bean plants. Common bean plants with increased
miR172c levels showed an improved symbiotic phenotype
as well as lower sensitivity to nitrate inhibition of nodu-
lation. We explored the possible role of AP2-1 as a tran-
scriptional activator and/or repressor. Candidate target
genes for downstream transcriptional activation by AP2-1
were identified; these could be relevant in the nodule se-
nescence process. Our work extends the knowledge of
miR172 function in the nodulation of common bean, an
agronomically important legume.
RESULTS
Common Bean miR172 Isoforms and Target Genes
The Arabidopsis genome contains five loci that generate
miR172 isoforms miR172a to miR172e, while 12 miR172
isoforms are reported for soybean (www.mirbase.org,
version 20). The high-throughput small RNA sequencing
analysis by Peláez et al. (2012) led us to identify four iso-
forms of common bean miR172.
In this work, we analyzed the recently published
(Schmutz et al., 2014; www.phytozome.net/commonbean.
php, v1.0) common bean genome sequence and identified
six MIR172 loci that map in different common bean
chromosomes. The most stable secondary structure of the
miR172 precursors was predicted, and these showed the
expected stem-loop structure. The six isoforms of mature
common bean miR172, 20 or 21 nucleotides long, were
designated miR172a to miR172f (Supplemental Fig. S1).
The nucleotide sequences of miR172a, miR172b, and
miR172c isoforms differ. However, although encoded by
different loci, the sequences of miR172e and miR172f are
identical to miR172a, while miR172d is identical to
miR172c (Supplemental Fig. S1).
The conserved targets for miR172 in different plants
are transcripts that encode TFs from the AP2 superfamily.
In Arabidopsis, six AP2 TF genes, AP2, TARGET OF
EARLY ACTIVATION TAGGED1 (TOE1), TOE2, TOE3,
SCHLAFMUTZE, and SCHNARCHZAPFEN, which act as
floral repressors, are targeted by miR172 and silenced
through translation inhibition or cleavage (Aukerman and
Sakai, 2003; Schmid et al., 2003; Chen, 2004; Jung et al.,
2007). In soybean, 10 AP2 TF genes have been proposed as
miR172 targets (Song et al., 2011). A recent analysis of the
common bean transcriptome by RNA sequencing (RNA-
seq) combined with available gene calls (O’Rourke et al.,
2014) identified 202 transcripts encoding AP2-type TFs.
The phylogenetic tree generated using sequence align-
ments of the common bean AP2 proteins is depicted in
Figure 1A. From the whole set (202), we identified eight
AP2 transcripts, encoded by six loci, with putative miR172
binding sites within their coding regions. The six predicted
AP2 target genes showed base pairing with the three dif-
ferent miR172 isoforms (Fig. 1B). In each case, the penalty
score for miRNA:mRNA (Jones-Rhoades and Bartel,
2004) was low; the highest score was observed in
Phvul.007G240200, with the three miR172 isoforms. From
the predicted AP2 targets, Phvul.005G138300, hereafter
denominated as AP2-1, has been experimentally validated
as a target of common bean miR172 (Arenas-Huertero
et al., 2009). In addition, Phvul.011G071100 was identi-
fied as a target in a common bean degradome analysis
(D. Formey, L.P. Íñiguez, P. Peláez, Y.F. Li, R. Sunkar,
F. Sánchez, J.L. Reyes, and G. Hernández, unpublished
data). Interestingly, the transcripts of AP2 predicted tar-
gets were organized in a single clade of the phylogenetic
tree (Fig. 1A). However, this clade also includes the
Phvul.008G185400.1 transcript, which has an AP2 domain
(http://www.phytozome.net/commonbean.php) but
lacks a detectable miR172 binding site and, therefore,
is not proposed as a target (Fig. 1A). The recently
published Phaseolus vulgaris Gene Expression Atlas
(Pv GEA; O’Rourke et al., 2014) showed a very low
expression of this AP2 gene in all the tissues reported
(reads per kilobase per million = 6, highest values in
leaves and pods). Therefore, Phvul.008G185400 AP2
is perhaps highly expressed in tissue-, development-,
or environment-specific conditions not yet analyzed
and its transcript level could be regulated by factors
other than miR172. In addition, we could not detect a
Phvul.008G185400.1 ortholog in the soybean genome se-
quence, perhaps indicating that it could be a pseudogene.
Differential Expression of miR172, Predicted AP2 Target
Genes, miR156, and SPL6 in Plant Tissues
The differential expression of miR172 isoforms in plant
organs/tissues at different developmental stages has
been reported for Arabidopsis and soybean (Aukerman
Plant Physiol. Vol. 168, 2015 275
miR172/AP2 in Common Bean-R. elti Symbiosis
109
and Sakai, 2003; Wu et al., 2009; Yan et al., 2013; Wang
et al., 2014). In this work, we performed expression
analyses of the miR172 isoforms and their putative AP2
target genes (Fig. 1) in different tissues of SNF common
bean plants 18 d post inoculation (dpi) with Rhizobium
etli (Fig. 2).
Northern-blot analysis revealed that mature miR172
transcripts were most abundant in nodules followed
by flowers (Fig. 2A). The miR172a probe was used for
blot hybridization, but the quantified signals might
reflect the combined levels of miR172 isoforms whose
sequences differ only in two nucleotides (Supplemental
Fig. S1). For real-time quantitative reverse transcription
(qRT)-PCR expression analysis, specific primers were
synthesized for each of the miR172 isoforms (a, b, and c;
Supplemental Fig. S1; Supplemental Table S1). The data
obtained by northern-blot and qRT-PCR expression
analyses showed similar trends regarding the highest
levels in nodules and flowers and the lowest levels in
roots and leaves (Fig. 2, A and B). The observed variation
in the levels of cumulative miR172 expression for each
tissue may be attributable to the different sensitivities of
the two methods. In addition, qRT-PCR analysis
revealed differential expression of the miR172 isoforms
among tissues, especially in those tissues with higher
cumulative levels. Nodules showed the highest level
of miR172c and very low amounts of miR172a and
miR172b, while flowers showed the highest level of
miR172b, followed by miR172c and a low amount
of miR172a (Fig. 2B).
The transcript levels of each of the AP2 TF genes
proposed as miR172 targets (Fig. 1) were determined
by qRT-PCR (Fig. 2C) in tissues from SNF bean plants
(Fig. 2C). Cumulative AP2 transcript levels were very
high in roots and very low in nodules, thus showing a
negative correlationwith cumulativemiR172 levels in these
tissues (Fig. 2, B and C). In roots, the most highly expressed
of the AP2 genes were Phvul.005G138300 (AP2-1) and
Phvul.011G071100; AP2-1was also highly expressed in
embryonic leaves (Fig. 2C). However, a distinct pattern
was observed in flowers, where the expression of
these two genes was negligible and Phvul.001G174400,
Phvul.003G241900, and Phvul.002G16900 were highly
expressed.
The miR156 node has been implicated in upstream
negative regulation of the miR172 node (Rubio-Somoza
and Weigel, 2011). Arabidopsis miR156 represses miR172
expression by targeting members of the SPL family of TFs
that directly bind to the MIR172 promoter and posi-
tively regulate its expression (Wu et al., 2009). Here,
Figure 1. Common bean AP2 tran-
scripts with predicted miR172 binding
sites. A, Neighbor-joining tree of AP2
proteins retrieved from the common
bean genome sequence (http://www.
phytozome.net/commonbean.php, v1.0).
The clade including AP2 transcripts
with miR172 binding sites (underlined)
is shown in the inset. B, Pairing of the
three different miR172 isoforms (a–c)
with the predicted binding sites of the
six different AP2 transcripts highlighted
in A. Watson-Crick base pairing is in-
dicated by lines, G:U base pairing is
indicated by circles, and dashes indi-
cate mismatches. Penalty scores, shown
in parentheses, were calculated as de-
scribed by Jones-Rhoades and Bartel
(2004).
276 Plant Physiol. Vol. 168, 2015
Nova-Franco et al.
110
B 
Phvul.005G138300 
Phvul.011G071100 
Phvul.001 G174400 
Phvul.002G016900 
Phvul.003G241900 
Phvul.007G240200 
miR172a miR172b miR172c 
miRNA:mRNA pairing (penalty score) 
5'1111111111111'1111113'(0.5) 5'11111111111111'1111113' (1.0) 5'111111111111'1111111'3' (1.0) 
5'1111111111111'1111113'(0.5) 5'11111111111111'1111113' (1.0) 5'11111111111111'11111'3' (1.0) 
5'1111111111111'11111-3'(1.5) 5'11111111111111'11111-3' (1.5) 5'11111111111111'1111113' (0.5) 
5'1111111111111'11111-3'(1.5) 5'11111111111111'11111-3' (1.5) 5'11111111111111'1111113' (0.5) 
5'1111111111111'11111-3'(1.5) 5'11111111111111'11111-3' (1.5) 5'11111111111111'1111113' (0.5) 
5'-111111111111'11111-3' (2.5) 5'-1111111111111'11111-3' (2.5) 5'-1111111111111'11111-3' (2.5) 
we determined the levels of mature miR156a (Peláez
et al., 2012) in common bean tissues. The expression of
miR156a was elevated in roots and leaves but low in
nodules and flowers, showing an opposite trend of the
cumulative expression of miR172 (Fig. 2D). We identified
32 SPL genes in the common bean genome, and 14 of
these showed putative miR156 binding sites, including
Phvul.009G165100, which was validated as a common
bean miR156a target (Arenas-Huertero et al., 2009).
Comparative sequence analysis with the Arabidopsis
SPL gene family indicated that the common bean
Phvul.009G165100 SPL gene is an ortholog to Arabi-
dopsis SPL6. We analyzed the expression of the vali-
dated miR156a target SPL6 gene in different common
bean organs. As shown in Figure 2D, common bean
SPL6 was highly expressed in nodules and flowers
while it was decreased in roots and leaves, thus show-
ing a negative correlation with miR156 expression. We
also searched for SPL transcription factor binding sites
(TFBS) in the 59 (promoter) region of each of the six
MIR172 loci mapped in the genome, but we could not
identify any, while 35 other TFBS were present in one or
more of these loci (Supplemental Table S2).
Our data (Fig. 2) showed miR172c as the isoform
with the highest expression in nodules and low expres-
sion in roots. Its expression pattern is opposite that of
AP2-1 (Phvul.005G138300), the experimentally validated
target (Arenas-Huertero et al., 2009) that showed the
highest expression in roots. Therefore, we then focused
our analysis on miR172c and AP2-1 in common bean
plants interacting with R. etli.
Expression Analysis of miR172 and AP2-1
during Symbiosis
To assess a possible role of miR172c/AP2-1 in the
common bean-R. etli symbiosis, we determined their
expression in inoculated roots (Fig. 3) and in effective
nodules at different developmental stages (Fig. 4A).
To analyze miR172c/AP2-1 regulation at early stages
of the symbiosis, common bean plantlets were grown in
plastic square bioassay dishes and R. etli inoculum was
applied directly to the roots. For gene expression analy-
sis, only the responsive root zone, where initial bacteria-
host recognition takes place, was collected at the initial
time (0 h) and at 3, 6, 12, 24, and 48 h post inoculation
(hpi). The mature miR172c level increased significantly
after 6 h, whereas the high expression level of the AP2-1
target gene decreased significantly after 12 h; both tran-
script levels persisted until 48 h (Fig. 3A).
To investigate if miR172c up-regulation correlated
with relevant events in the rhizobial infection process,
we determined the expression of early nodulation
genes in inoculated roots (Fig. 3, B–G). O’Rourke et al.
(2014) identified common bean nodulation genes that
were highly expressed in young and/or mature nodules
and that are homologous to cognate nodulation genes
previously identified in other legume species. From these,
we selected six early nodulation genes for expression
analysis: the TF genes NF-YA1 (Phvul.001G196800.1),
NSP2 (Phvul.009G122700.1), andNIN (Phvul.009G115800);
CYCLOPS (Phvul.002G128600.1), coding for a nuclear
protein that interacts with CCaMK; FLOTILLIN-LIKE2
(FLOT2; Phvul.009G090700.1), coding for a lipid raft
component; and ENOD40 (Phvul.008G291800), which
lacks an open reading frame but encodes two small pep-
tides and may function as a cell-cell signaling molecule for
nodulation (Crespi and Frugier, 2008; Oldroyd and
Figure 2. Expression analysis of miR172, AP2 target genes, miR156a,
and its SPL transcription factor target gene in different tissues of R. etli
CE3-inoculated common bean plants (18 dpi). A, Mature miR172 levels
detected by northern-blot analysis using U6 small nuclear RNA (snRNA)
as a loading control for normalization. Signal intensities of the miR172
and U6 hybridization bands for each tissue were determined to calculate
normalized expression levels. Numbers in each lane indicate normalized
values of miR172 signal intensity. N, Nodules; R, roots; EL, embryonic
leaves; L, leaves; F, flowers; P, pods. B to D, Transcript levels of mature
miR172 isoforms (B; Supplemental Fig. S1), predicted AP2 target genes (C;
Fig. 1), and mature miR156a and its target gene SPL6 (D) determined by
qRT-PCR. Expression level refers to gene expression, based on threshold
cycle (Ct) value, normalized with the expression of the housekeeping
miR159 or UBIQUITIN CONJUGATING ENZYME9 (UBC9) gene.
Plant Physiol. Vol. 168, 2015 277
miR172/AP2 in Common Bean-R. elti Symbiosis
111
A 
miR172 
4 ~===~~ °mIRl 72a 
_ mI Rl72tl 
3 _ mIRl72c 
2 
e o 
Ci PtwuLOO7G240200 
4 
. PtwuL002GO I6900 
· PtwuLOO3G241900 • > - F't1vul.OO1GI74400 
" 3 
· Plwu1.Ol1G071100 e 
.Q ü PtwuI.005G1J8300 (AP2-1) • • 2 
~ 
~ 
W 
O O . SPL6 
DmiR156a 
0.5 
O 
N R EL L F P 
Downie, 2008; Kouchi et al., 2010; Murray, 2011; Oldroyd,
2013). Figure 3, B to G, shows the expression levels of the
early nodulation genes in the responsive zone of R. etli-
inoculated roots. The expression of all the genes tested
increased significantly after the initial time (3 h). Highest
levels ofNF-YA1 andNSP2 decreased gradually after 3 h.
Increased NIN expression persisted, whereas CYCLOPS
and FLOT2 transcripts increased further after 6 h and
persisted until 48 h. The ENOD40 transcript level in-
creased significantly after 12 h and persisted until 48 h.
We then analyzed the regulation of the miR172 node
during the development of effective nodules elicited by the
R. etli CE3 wild-type strain (Fig. 4A). Nodules from ino-
culated common bean plants were harvested at different
developmental stages, as defined by the differential ex-
pression of nodule development marker genes (Ramírez
et al., 2005; Van de Velde et al., 2006; Supplemental Table
S3). Immature, prefixing, 13-dpi nodules showed the
highest ENOD55 expression and low (19.6%) nitrogenase
activity. At 18 dpi, the nodules were fully developed and
showed the highest nitrogenase activity and expression of
the PHOSPHOENOLPYRUVATE CARBOXYLASE (PEPc)
gene, essential for carbon assimilation in mature nodules
(Ramírez et al., 2005). By 35 dpi, nodules had low nitro-
genase activity (11%) and high CYSTEINE PROTEINASE
(CP) gene expression, described as being specific for nodule
senescence (Van de Velde et al., 2006; Supplemental Table
S3). As shown in Figure 4A, the increased expression level
of miR172c observed at 2 dpi (or 48 h in Fig. 3A) persisted
in immature, prefixing nodules (13 dpi). In contrast,
miR172c increased significantly to its highest level in ma-
ture, fully active nodules (18 dpi). Afterward, a drastic
decrease in miR172c level was observed; it remained barely
detectable until nodule senescence (35 dpi). Slightly de-
creased levels of AP2-1 transcripts persisted in immature
nodules (13 dpi), a further decrease was observed in ma-
ture 18-dpi nodules, and afterward, the level of AP2-1
transcripts gradually increased. The lowest level of AP2-1
correlated with the highest level of miR172c in mature
nodules (18 dpi).
Figure 3. Increased expression of
miR172c and of early nodulation
genes upon rhizobial infection. Ex-
pression levels of mature miR172c,
AP2-1 (Phvul.005G138300; A), and
early nodulation genes (B–G) were de-
termined in roots inoculated with R.
etli CE3 at the initial time (0) and after
the indicated hpi. The common bean
early nodulation genes were identified
in the Pv GEA (O’Rourke et al.,
2014): NF-YA1, Phvul.001G196800
(B); NSP2, Phvul.009G122700 (C);
CYCLOPS, Phvul.002G128600 (D);
NIN, Phvul.009G115800 (E); FLOT2,
Phvul.009G090700 (F); and ENOD40,
Phvul.002G064200 (G). Values repre-
sent averages 6 SD from three biologi-
cal replicates and two technical
replicates each. Expression level refers
to gene expression, based on Ct value,
normalized with the expression of the
housekeeping miR159 or UBC9 gene.
Different lowercase letters indicate
statistically different groups (ANOVA,
P , 0.001); in A, lowercase and up-
percase letters were used for AP2-1 and
miR172c values, respectively.
278 Plant Physiol. Vol. 168, 2015
Nova-Franco et al.
112
3 
A . miR172 
2.5 
~ DAP2- ' (Phvul ,Q05G 138300) .. 2 
o o 
. ~ 1.5 ± 
• 
~ 
,¡j 
0.5 
A A A 
O 
Ohpi 3hpi 6hpi 12hpi 24hpi 48hpi 
1.5 1.5 
B NF-YA1 (HAP2-1) e NSP2 
0.5 0.5 
o o 
o CYCLOPS E NIN 
o 0,4 m 
> 
, 
j1 0.8 ~ 
o ' O , 3~ 
. ~ 0.6 O· 
~ DA 
0 .2::' 
W 
" 0. 1 ~ w 0.2 
o 
O 
FLOT2 
O 
F G ENOO40 
0.015 3 
0 .01 
0.005 
O O 
O 3 6 12 24 48 O 3 6 12 24 4S 
hpi hpi 
Taken together, these data suggest that miR172c is
involved in rhizobial infection and nodule development/
function.
Altered Expression of miR172c and AP2-1 in
Ineffective Symbioses
Because the maximum level of miR172c expression
and AP2-1 silencing correlated with the peak of SNF
(Fig. 4A; Supplemental Table S2), we assessed the regu-
lation of the miR172 node in ineffective, nonfixing com-
mon bean-R. etli symbioses.
The nitrogen fixation genes regulator A (NifA)/RNA
polymerase sigma factor complex is a master regulator of
the N2 fixation genes in rhizobia. Transcriptional analysis
of the R. etli nifA2 (CFNX247) mutant strain demon-
strated the nifA dependency of symbiotic genes on the
symbiotic plasmid (Girard et al., 1996). The symbiotic
phenotype of common bean plants inoculated with the
R. etli nifA2 mutant strain was drastically altered, as evi-
denced by a diminished amount of early-senescent nod-
ules with few infected cells having bacteroids and devoid
of nitrogenase activity and with symptoms characteristic
of nitrogen deprivation in the leaves (Supplemental Fig.
S2; Supplemental Table S3). As shown in Figure 4B, the
ineffective nodules elicited by R. etli nifA2 had nearly
undetectable levels of miR172c, although a minor, but
significant, increase was observed in 18-dpi ineffective
nodules. Meanwhile, the AP2-1 target gene was highly
induced at the different developmental stages of ineffective
nodules; these values were even higher (approximately
2-fold) than those observed during effective symbiosis
(Fig. 4, A and B). A slight but significant decrease inAP2-1
transcript was observed in 18-dpi ineffective nodules,
when the miR172c level increased (Fig. 4B).
A similar effect was observed when the abolishment
of SNF was achieved by adding nitrate to effective
R. etli-elicited nodules (Fig. 4C), a well-known phenom-
enon in the legume-rhizobia symbiosis (Streeter and
Wong, 1988). A short time (1 and 3 d) after nitrate ad-
dition, nitrogenase activity decreased drastically and
nodules senesced (Fig. 4C; Supplemental Table S3). The
latter correlated with the drastic decrease in mature
miR172c and a concomitant increase of AP2-1 transcript
level in the ineffective nodules (Fig. 4C).
Taken together, these data indicate a contrasting
regulation of miR172c/AP2-1 expression in effective
versus ineffective symbioses.
Effect of miR172c Overexpression on Root Development
and Rhizobial SNF
To further investigate the role of miR172c and its
target gene AP2-1 in SNF, we aimed to modulate their
expression in common bean composite plants with trans-
genic roots and untransformed aerial organs, generated
through Agrobacterium rhizogenes-mediated genetic trans-
formation. This protocol has been used as an alternative
method for stable transformation in common bean and
other recalcitrant species (Estrada-Navarrete et al., 2007).
The construct for miR172 overexpression (OE172) con-
tained the 35S cauliflower mosaic virus promoter fused to
the miR172c precursor. The OEAP2m plasmid contained a
mutagenized AP2-1 gene that is insensitive to miR172
cleavage due to nucleotide substitutions in the miR172
binding site. Both constructs as well as the control empty
vector (EV) contain the tdTomato (red fluorescent protein)
reporter gene (Supplemental Fig. S3). We obtained several
composite plants and determined the level of transgene
expression for each plant (Supplemental Fig. S4). The
OE172 composite plants showed very high levels of ma-
ture miR172c in both nodules and roots as well as a de-
creased level of AP2-1 transcript. Roots and nodules of
OEAP2m plants showed very high levels of AP2-1. The
Figure 4. miR172c and AP2-1 are differentially regulated in effective versus ineffective R. etli symbioses. Transcript levels were
determined by qRT-PCR in inoculated roots or nodules harvested at the indicated dpi. A, Plants inoculated with the CE3 wild-type
strain; determinations at 0 and 2 dpi were done in inoculated roots. B, Plants inoculated with the fix2 R. etli nifA2 mutant. C, Plants
inoculated with the CE3 wild-type strain were grown for 18 d and watered with nitrogen-free nutrient solution. Subsequently, these
plants were watered with nutrient solution supplemented with 10 mM KNO3 (black arrow). Nitrogenase (Nase) activity and transcript
levels were determined at 18 dpi and at 1 or 3 d after nitrate addition (19 or 21 dpi). Values represent averages 6 SD from three
biological replicates and two technical replicates each. Expression level refers to gene expression, based on Ct value, normalized with
the expression of the housekeeping miR159 orUBC9 gene. Different letters indicate statistically different groups (ANOVA, P, 0.001);
lowercase and uppercase letters were used for AP2-1 and miR172c values, respectively.
Plant Physiol. Vol. 168, 2015 279
miR172/AP2 in Common Bean-R. elti Symbiosis
113
5 , 200 :; 
A B i e _ miRl 72c ~ 
j 4 '--------' AP2· t (Plwul.OCI5G138300¡ 
150l z • --+- Nas. A~ IM ~ 
~ •• c 3 
100 ;~ o .¡¡; 
w 2 
~ 
'9 c. 
~< 
x 50 .~ 
w g ~ 
• o o o 
o 2 13 18 25 35 13 18 25 35 18 19 21 E 
dpi dpi dpI 
variation in the degree of overexpression between indi-
vidual transgenic roots is because each results from an
independent transformation event.
We first assessed if miR172 overexpression affected
the root phenotype of fertilized (noninoculated) common
bean plants as compared with those inoculated with
R. etli. As shown in Figure 5, roots with high miR172c
showed increased biomass and density of secondary
roots, both in fertilized and SNF composite plants. The
opposite phenotype was observed in OEAP2m com-
posite plants. These data indicate that miR172 had a
positive effect on root biomass/architecture independent
of the presence of rhizobia.
To analyze if the positive effect of miR172 on root
development (Fig. 5) also affects rhizobial infection and
SNF, we investigated the response of composite plants
altered in miR172 content to R. etli infection, early sym-
biotic stages, and nodule development/function.
Figure 6 shows data for the analysis of rhizobial in-
fection and early nodulation gene expression. For these
experiments, the plastic square bioassay dish system was
used for the inoculation and growth of OE172, EV, or
OEAP2m composite plants. Notably, the amount of
deformed root hairs was significantly higher in 48-hpi
inoculated roots that overexpress miR172, while the
opposite effect was observed in OEAP2m roots (Fig.
6A; Supplemental Fig. S5). A correlation of altered root
hair deformation and the expression of early nodulation
genes essential for rhizobial infection was observed af-
ter determining the transcript level of selected genes
(NF-YA1, NSP2, CYCLOPS, ENOD40, FLOT2, and NIN)
in the responsive root zone from 0 to 48 hpi (Fig. 6,
B–G). All the genes tested showed increased expres-
sion in OE172 inoculated roots; NIN expression was
increased only as compared with OEAP2m inocu-
lated roots (Fig. 6, B–G).
Nodule number and nitrogenase activity as well as
histological analysis of nodules stained with SYTO13 (a
fluorescent dye binding nucleic acids) from composite
plants overexpressing miR172 or AP2m and control (EV)
plants are presented in Figures 7 and 8. At 14 and 21 dpi,
OE172 plants showed increased nodule number and ni-
trogenase activity that correlated with higher PEPc and
reduced CP expression (Fig. 7; Supplemental Table S4).
In addition, OE172 plants showed accelerated nodule
development: nodule primordia and well-formed nod-
ules (approximately 50 per root) were easily observed at
5 and 7 dpi, respectively, while only some unorganized
primordia and very few tiny nodules were seen in EV
plants. In addition, young (7 and 14 dpi) nodules from
OE172 plants were larger (increased perimeter; Fig. 8, A
and B). In contrast, OEAP2m plants at 21 dpi had fewer
nodules with diminished nitrogenase activity and
higher CP expression (Fig. 7; Supplemental Table S4).
At all time points analyzed, the OEAP2m nodules had
significantly reduced perimeters (Fig. 8, A and B).
Similar values for SYTO13 intensity per nodule area
were obtained for EV, OE172, and OEAP2m nodules,
indicating similar bacteroid densities in infected cells
(Fig. 8C).
We assessed if increased nodulation in OE172 common
bean plants could be related to alterations in the AON. In
soybean, AON involves long-distance signaling requiring
the interaction of RHIZOBIA-INDUCED CLE peptides
(RIC1/RIC2), with NODULE AUTOREGULATION RE-
CEPTOR KINASE (NARK) in the leaf and the subsequent
inhibition of nodulation via the production of a shoot-
derived inhibitor. For local nitrate inhibition, the nitrate-
induced CLE peptide (NIC1) interacts with NARK in the
root, leading to a nitrate-induced inhibitor (Reid et al.,
2011a). The homologous common bean RIC and NIC
genes RIC1 (Phvul.005G096900),RIC2 (Phvul.011G135900),
and NIC1 (Phvul.005G097000) were identified from the Pv
GEA (O’Rourke et al., 2014). As in soybean (Reid et al.,
2011a), the common bean RIC1 genes were expressed in
inoculated common roots at early stages of rhizobial in-
fection, while RIC2 was expressed at later time points in
prefixing and mature nodules. Figure 6, H and I, shows
the expression levels of RIC1 and NIC1 genes in 48-hpi
inoculated roots from OE172, EV, and OEAP2m plants.
RIC1 expression in control (EV) transgenic roots indicates
the rhizobial induction of CLE-derived peptides for AON.
Interestingly, the level of RIC1 was decreased significantly
in OE172 inoculated roots that showed increased nodula-
tion, while it was increased significantly in OEAP2m roots
with diminished nodulation (Fig. 6H). As expected, the
expression of NIC1 was low in the transgenic inoculated
roots (Fig. 6I) under nitrogen-free conditions. These data
point to the involvement of a common bean AON mech-
anism in the miR172 control of nodulation.
Figure 5. miR172c and AP2-1 control the root development of fertil-
ized and R. etli-inoculated common bean plants. Root fresh weight
(FW; A and B) and density of secondary roots (C and D) were deter-
mined in composite bean plants with transgenic roots overexpressing
miR172c (OE172) or a mutated insensitive AP2-1 target gene (OEAP2m)
as compared with control EV transformed roots. A set of composite
plants was fertilized with full-nutrient solution for 10 d (A and C), and
another set was inoculated with R. etli and watered with nitrogen-free
nutrient solution for 21 dpi (B and D). Values represent averages 6 SD
from roots of eight independent composite plants each. Different low-
ercase letters indicate statistically different groups (ANOVA, P , 0.001).
280 Plant Physiol. Vol. 168, 2015
Nova-Franco et al.
114
Fertilized R. etfi - inoculated 
2.5 r;----'-'== =----,=---'-''''''--'==='---, 
§ ' .5 • . , 
0 5 
OC112 'él 0E172 EV OEAP2m 
miR172c Overexpression Decreases the Sensitivity to
Nitrate Inhibition of Rhizobial Symbiosis
Nitrogen (nitrate or ammonia) in the soil perceived
by legume plants is an important external stimulus that
inhibits nodulation as part of the AON mechanism. Con-
sidering the improved rhizobial infection and nodule
development/function in common bean plants with
increased miR172c (Figs. 6–8), we assessed if these al-
terations could be related to a decreased sensitivity to
external nitrate inhibition of R. etli nodulation (Fig. 9).
For this experiment, we applied a low nitrate concen-
tration (1 mM) to R. etli-inoculated OE172, EV, and
OEAP2m composite plants. The nodulation of inoculated
OEAP2m plants, with low miR172c, was completely
abolished when low nitrate was added. For this reason,
we analyzed plants overexpressing miR172c as com-
pared with control (EV) plants (Fig. 9). As expected for
nitrate inhibition of the rhizobial infection process, the
expression level of most early nodulation genes was re-
duced in EV-inoculated plants in the presence of nitrate
(Fig. 9, A–F) as compared with the nitrogen-free condi-
tion (Fig. 6, B–G). Notably, in the presence of nitrate,
the early nodulation genes NF-YA1, NSP2, CYCLOPS,
ENOD40, and FLOT2 showed significantly increased
expression in OE172 inoculated roots (Fig. 9, A–F). In
fact, the expression level of early nodulation genes in
OE172 inoculated roots was similar in the absence (Fig. 6,
B–G) or presence (Fig. 9, A–F) of nitrate. As expected,
while the RIC1 gene was barely detectable, NIC1 was
expressed in the responsive root zone of EV plants in-
oculated in the presence of nitrate, and it was increased
in OE172 roots (Fig. 9, G–H).
Nitrate inhibition of nodulation was evident in control
(EV) plants that presented delayed and diminished nod-
ulation (Fig. 9I). These plants also showed decreased ni-
trogenase activity and PEPc expression and increased
ENOD55 (Fig. 9J; Supplemental Table S4) as compared
with nitrogen-free inoculated plants (Fig. 7B). Notably,
inoculated OE172 plants in the presence of nitrate
showed few active nodules at 14 dpi, while at 21 dpi they
had a similar number of mature nodules with slightly
higher nitrogenase activity as compared with plants
inoculated without nitrogen (Figs. 7 and 9, I and J;
Supplemental Table S4). In addition, we observed that
a higher nitrate concentration (3 mM) totally blocked the
nodulation of EV plants, while OE172 plants were able to
form active nodules (approximately 100 per root) at 21 dpi.Figure 6. miR172c and AP2-1 control the rhizobial infection of common
bean roots. Roots from OE172, EV, or OEAP2m composite plants were
inoculated with R. etli CE3, and at 48 hpi, the root responsive zones were
harvested for analysis. A, Quantification of the number of deformed root
hairs (branched and swollen root hair) per 0.5 cm; each box plot indicates
the number of the transgenic roots analyzed for each construct. B to I,
Expression analysis of selected early nodulation genes. Most gene identi-
fiers are indicated in the legend to Figure 3; RIC1, Phvul.005G096900;
and NIC1, Phvul005G097000. Values represent averages 6 SD from three
biological replicates and two technical replicates each. Expression level
refers to gene expression, based on Ct value, normalized with the ex-
pression of the housekeeping UBC9 gene. Different lowercase letters in-
dicate statistically different groups (ANOVA, P , 0.001).
Figure 7. miR172c and AP2-1 control nodule number (A) and nitro-
genase (Nase) activity (B) of SNF common bean. Plants were inocu-
lated with R. etli CE3 for the indicated dpi. Values represent
averages 6 SD from five replicate samples per time point. Different
lowercase letters from each set of values at different dpi indicate sta-
tistically different groups (ANOVA, P , 0.001).
Plant Physiol. Vol. 168, 2015 281
miR172/AP2 in Common Bean-R. elti Symbiosis
115
60 A 
l!.' 50 
2 
~ 40 
<: 
E 30 
~ 
u 
'O 20 
1: 
§ 10 
z 
o 
n=1 8 
------:--0=1 6 
(> -- ~ - -< .:~) 
L ____ ~~ ------ ~~ ---- OO lEAP2m 
EV 
OE 172~ _ ---;;;:;;:;¡ C __ ,'" 
B NSP2 NF·YA1 e 
~O 
A 
'00 
i 300 
¡ 200 
~ 
"0 
Exploring the Downstream AP2-1 Regulation in
SNF Plants
TFs from the AP2 superfamily are widespread in
plants and control diverse developmental programs and
stress responses. Different AP2 family members have
been classified as activators or repressors of specific tar-
get genes (Licausi et al., 2013). In this work, we aimed to
predict target genes for AP2-1 transcriptional activation
or repression by analyzing data from the root and nodule
libraries reported in the Pv GEA (O’Rourke et al., 2014),
especially those from young roots and mature effective
nodules that were derived from common bean tissues
similar to those analyzed in this work. However, a caveat
of this analysis is that the Pv GEA does not include li-
braries from roots inoculated with rhizobia for short pe-
riods, so we could not predict AP2-1 targets that would
be regulated during the rhizobial infection process.
As shown in Figures 2 and 4, AP2-1 showed high
expression in common bean roots as opposed to mature
nodules. We hypothesized that genes with an expression
pattern similar to AP2-1 are likely to be involved in root
function/development and to be positively regulated by
AP2-1, thus providing information on a possible mecha-
nism of action of miR172/AP2-1. We identified 114 genes
that had an expression pattern similar to AP2-1, desig-
nated AP2-1 coexpressed genes (Supplemental Fig. S6). In
order to support the latter contention, we searched for TFBS
in the 59 promoter region of AP2-1 coexpressed genes. Be-
sides WRKY, the most statistically overrepresented (P = 0
and 0.001) TFBS were ETHYLENE-RESPONSIVE FAC-
TOR2 (ERF2) and DEHYDRATION-RESPONSIVE
ELEMENT BINDING 1B (DREB1B), which belong
to the AP2 superfamily. These TFBS were identified
in 82% of AP2-1 coexpressed genes (Supplemental
Table S5).
Sixty-seven of the 114 AP2-1 coexpressed genes
could be assigned to a Gene Ontology (GO) category
(Table I; Supplemental Table S5). The most statistically
significant (P = 0.006) assigned GO category, which in-
cluded 19 coexpressed genes, is GO:0004672, associated
with protein kinase activity (Table I). We validated by
qRT-PCR the expression of protein kinase activity genes in
roots and nodules of control (EV) plants and also of plants
overexpressing AP2-1. We hypothesized that those genes
positively targeted byAP2-1, with high expression in roots
and low expression in nodules from wild-type or control
EV plants (Supplemental Fig. S6), would show a different
expression pattern, higher and/or similar in roots and
nodules, in OEAP2m plants that have constitutively en-
hanced expression of the AP2-1 transcriptional regulator.
Table II shows six AP2-1 coexpressed genes assigned to
the protein kinase activity category (GO:0004672) whose
expression levels agree with our hypothesis. In control
Figure 8. Alterations in the nodule
development of OE172 and OEAP2m
R. etli-inoculated composite bean plants.
A, Fluorescent (F; left) and corresponding
dark-field (DF; right) micrographs of
central sections of nodules harvested at
the indicated dpi. Red fluorescence from
the tdTomato reporter gene expressed in
A. rhizogenes transformed roots and green
fluorescence from SYTO13 staining were
observed. Magnification = 53. B and
C, Nodule perimeter (B) and SYTO13
fluorescence intensity per infection
area (C) were calculated using the
ImageJ program. Values represent av-
erages 6 SD from 10 replicate nodule
images per condition. Different lower-
case letters in B indicate statistically
different groups (ANOVA, P , 0.001);
values from C were not statistically
different.
282 Plant Physiol. Vol. 168, 2015
Nova-Franco et al.
116
A OE172 EV OEAP2m 
F DF F DF F DF 
7dpi 1\ ~ .fJ 
.. 
, "-
14dpi '. ~ . ~ .... 
, Q .. 
21dpi 
r 
. ~ . C .. ' 
B 
_ OEH2 
e 
5000 · 0E172 
DEI! 
JO CF.V 
COEAP2m 
~ 4000 • 
t~ 
coeJl.P2m 
~ 
~ 3000 ~~ 2Q , ." " 
o. 
.§ 2000 
;;l!1
-'~ ., 0-
z ~ " 1000 00 
7dpi 14dpi 21dpi 7dpi 14d;¡i 21dpi 
(EV) plants, these genes were highly expressed in roots as
compared with nodules, similar to AP2-1, thus validating
the Pv GEA data (O’Rourke et al., 2014; Supplemental Fig.
S6), while they showed an increased and/or similar ex-
pression in both tissues from OEAP2m plants (Table II).
Interestingly, these genes shared the DREB, ERF, or both
TFBS in their promoter regions (Table II; Supplemental
Table S5). Transcriptomic analyses of M. truncatula nodule
senescence have shown that protein kinases genes are one
of the highly induced gene classes at different stages
of this process (Van de Velde et al., 2006; Pérez
Guerra et al., 2010). On this basis, we asked if the
some of the AP2-1 coexpressed genes from the pro-
tein kinase activity GO category might be related to
nodule senescence in common bean. As shown in Table II,
four of the protein kinase activity genes analyzed
(Phvul.002G326600, Phvul.007G049500, Phvul.007G049000,
and Phvul.006G174500) were induced in senescent (35 dpi)
as compared with mature (18 dpi) nodules, thus indicating
their possible relation to common bean nodule senescence.
The root/nodule expression pattern of AP2-1 was
opposite that of miR172c, which showed highest ex-
pression in mature effective nodules and very low ex-
pression in young roots (Figs. 2 and 4). O’Rourke et al.
(2014) have described a set of 402 common bean genes
highly expressed in mature effective nodules as com-
pared with all other tissues analyzed in the Pv GEA.
These genes are likely involved in the establishment of
symbiosis and SNF as supported by their assigned GO
categories (O’Rourke et al., 2014). Here, we hypothesized
Figure 9. miR172c controls the sensitivity to ni-
trate inhibition of rhizobial symbiosis. To analyze
the effect of nitrate in the symbiosis of R. etli-
inoculated OE172 or EV composite plants, 1 mM
KNO3 was added to the nutrient solution used to
water each set of plants daily. A to H, Expression
analysis of selected early nodulation genes de-
termined at 48 hpi in the root responsive zone.
Gene identifiers are indicated in the legends to
Figures 3 and 6. Values represent averages 6 SD
from three biological replicates and two techni-
cal replicates each. Expression level refers to
gene expression, based on Ct value, normalized
with the expression of the housekeeping UBC9
gene. I and J, Nodules were counted (I) and ni-
trogenase (Nase) activity (J) was determined at
the indicated dpi. Values represent averages 6 SD
from five replicate samples per time point. As-
terisks indicate the level of statistically significant
difference, if any, among values from OE172 and
EV roots (Student’s t test, P , 0.01).
Plant Physiol. Vol. 168, 2015 283
miR172/AP2 in Common Bean-R. elti Symbiosis
117
sao 
~ 400 
E 
o 
c: 300 
'" o 
15 200 
z 
100 
o 
A 
o, 
o; 05 
> 
-" 
e 
º ~ • Q. ROB 
x 
W 
0.06 
0.04 
0.02 
" G 
0' 
OE172 
I 
• 14dpi 
NF·Y .... 1 8 NSP2 
EV OE1 72 EV 
J - OE172 
~ DEV 
~ .. ~ 
21dpi 14dpi 21dpi 
that these nodule-enhanced genes, lowly expressed in
young roots with increased AP2-1 (Figs. 2 and 4), are
candidates for AP2-1 negative regulation. To this end, we
searched for TFBS in the 59 promoter region of nodule-
enhanced genes, but we did not find a statistical over-
representation of AP2 TFBS.
DISCUSSION
The key role of the miR172 node in Arabidopsis
flowering time and phase transition is well known;
similar roles have also been documented in maize (Zea
mays), rice (Oryza sativa), and barley (Hordeum vulgare;
Zhu and Helliwell, 2011). Besides conserved roles,
specialized/particular species-specific functions of
miR172, such as the induction of tuberization in
potato (Solanum tuberosum), have been reported
(Martin et al., 2009). In legumes, conserved roles of the
miR172 node have been documented for L. japonicus
(control of flowering time; Yamashino et al., 2013) and
soybean (control of juvenile-to-adult phase transition;
Yoshikawa et al., 2013). In addition, the control of nod-
ulation during the rhizobia symbiosis has been proposed
as a family-specific acquired function of miR172 in
different legumes and has been demonstrated for soy-
bean (Yan et al., 2013; Wang et al., 2014). In this work,
we identified the miR172 node as a relevant regulator of
rhizobial infection and nodulation in common bean.
We propose that different miR172 isoforms regulate
different processes: miR172b is involved in flowering,
while miR172c mainly regulates nodulation. Our data
indicate that these miR172 isoforms exert their ef-
fects by silencing different target genes from the AP2
TF superfamily. Transcripts from two AP2 genes
(Phvul.005G138300 and Phvul.011G071100) are likely
to function in roots and are cleaved by miR172c in
nodules. Three other AP2 genes (Phvul.003G241900,
Phvul.002G16900, and Phvul.001G174400) are likely to
function in young flowers, which showed a high level of
these transcripts as well as of miR172, thus suggesting
that, in flowers, the AP2 target genes are silenced by
miR172-induced translation inhibition, similar to Arabi-
dopsis (Chen, 2004). Our work focused on the analysis of
the miR172c/AP2-1 (Phvul.005G138300) node in the
common bean-rhizobia nitrogen-fixing symbiosis, and our
proposed regulatory model is summarized in Figure 10.
In Arabidopsis, miR156 represses miR172 expression
by targeting SPL TFs that directly bind to the MIR172
Table I. GO categories statistically overrepresented for genes coexpressed with AP2-1 in roots
GO Identifier Description (Molecular Function) P
GO:0004672 Protein kinase activity 0.006
GO:0008703 5-Amino-6-(5-phosphoribosylamino)uracil reductase activity 0.016
GO:0005516 Calmodulin binding 0.018
GO:0005471 ATP:ADP antiporter activity 0.024
GO:0004674 Protein Ser/Thr kinase activity 0.036
GO:0004351 Glu decarboxylase activity 0.055
GO:0004435 Phosphatidylinositol phospholipase C activity 0.055
Table II. Selected genes coexpressed with AP2-1 from the statistically overrepresented GO:0004672 category: protein kinase activity
Expression level was determined by qRT-PCR from 21-dpi mature nodules (N) and roots (R) of EV and OEAP2m R. etli-inoculated composite plants
and from 18 dpi mature or 35 dpi senescent nodules (N) from R. etli-inoculated wild-type plants. Values represent averages 6 SD from three in-
dependent biological replicates and two technical replicates. TFBS for ERF and DREB (subfamilies of the AP2 TF family) were identified as statis-
tically overrepresented in the promoter sequence of each gene as indicated.
Gene Identifiera Annotationa TFBS
Expression Level
21 dpi Wild Type
EV OEAP2m 18 dpi 35 dpi
R N R N N N
Phvul.002G326600 Aminocyclopropane
carboxylate
oxidase
DREB 0.76 6 0.08 0.35 6 0.13 1.2 6 0.06 1.03 6 0.07 0.11 6 0.01 1.03 6 0.07
Phvul.007G049500 Ser/Thr protein
kinase
ERF 0.61 6 0.09 0.25 6 0.03 0.93 6 0.08 0.97 6 0.09 0.10 6 0.02 0.53 6 0.05
Phvul.007G049000 Ser/Thr protein
kinase
DREB 0.10 6 0.007 0.043 6 0.01 0.02 6 0.003 0.03 6 0.004 0.07 6 0.009 0.216 0.03
Phvul.006G174500 Glycogen synthase
kinase-3a
DREB 0.45 6 0.06 0.24 6 0.03 0.71 6 0.07 0.57 6 0.06 0.16 6 0.02 0.37 6 0.03
Phvul.011G169600 Ser/Thr protein
kinase
DREB 0.35 6 0.02 0.18 6 0.02 0.52 6 0.03 0.47 6 0.03 0.33 6 0.03 0.36 6 0.02
Phvul.008G263900 Ser/Thr protein
kinase
ERF and
DREB
1.2 6 0.06 0.15 6 0.04 0.57 6 0.02 0.50 6 0.09 0.0023 6 0.0003 0.0019 6 0.0002
aFrom http://www.phytozome.net/commonbean.php.
284 Plant Physiol. Vol. 168, 2015
Nova-Franco et al.
118
promoter and positively regulate its expression (Wu
et al., 2009). Transgenic soybean roots overexpressing
miR156 showed a reduction in nodulation, decreased
miR172 level, and decreased expression of two SPL
genes proposed as miR156 targets, although evidence for
the binding of SPL toMIR172 promoters for transcription
activation was not provided (Yan et al., 2013). Recently,
Wang et al. (2015) reported that the overexpression of
miR156 affects several aspects of plant architecture in
L. japonicus, including underdeveloped roots and re-
duced nodulation, which correlate with the repression of
several early symbiotic genes. However, the authors did
not analyze a possible regulation of miR172 by miR156,
which may be related to the miR156 effects in nodulation
that they showed (Wang et al., 2015). In common bean,
we observed opposite levels of mature miR156a as
compared with miR172 and also a negative correlation
between miR156a and its validated target SPL6 gene
(Phvul.009G165100). However, we could not identify
SPL TFBS in any of the promoter regions of the six
MIR172 loci from the common bean genome, so it is
difficult to propose SPL as a direct transcriptional regu-
lator of miR172. However, the binding of SPL proteins to
yet unknown sequence motifs present in MIR172 pro-
moters cannot be ruled out. Alternatively, miR156a may
exert its negative regulation over common bean miR172
through other target genes not yet identified. For ex-
ample, transcripts coding for tryptophan-aspartic acid
repeats protein domain proteins, which may be involved
in microtubule organization, protein-protein and protein-
DNA interactions, or chromatin conformation, have been
validated as miR156 targets in M. truncatula and L.
japonicus, but their specific regulatory function has not
been analyzed (Naya et al., 2010; Wang et al., 2015).
In this work, we showed that miR172c has a positive
effect on root development independent of rhizobium
infection. In addition, miR172c is relevant for the control
of rhizobial infection. This miRNA increased after 6 h in
R. etli-inoculated roots, when infection threads are
formed, and this is related to the increase in root hair
deformation observed in plants that overexpress
miR172c. Preliminary data indicate that the roots over-
expressing AP2m induce irregular infection threads
(B. Nova-Franco, O. Valdés-López, and G. Hernández,
unpublished data). Together, these data indicate a
regulatory/signaling role of miR172c in the rhizobial
infection of common bean (Fig. 10). Up-regulation of
miR172c was concomitant with that of early nodulation
genes, mainly expressed in the cortical cells, that are in-
volved in infection thread initiation/progression (i.e.
FLOT2 and ENOD40) and act downstream of NSP2,
NIN, and NF-YA1 (Murray, 2011; Oldroyd, 2013), whose
expression was highest after 3 h of rhizobial inoculation
of common bean roots. Therefore, we propose that
miR172c-mediated control of rhizobial infection is exer-
ted at the level of cortical cell division downstream of NF
perception, Ca2+ spiking, CCaMK, NSP2, and NIN (Fig.
10). In addition, our data on the repression of RIC1 in
roots overexpressing miR172c indicate the involvement
of this miRNA in the AON at early stages of the common
bean symbiosis. Soyano et al. (2014) reported that the
AON L. japonicus CLE root signal genes CLE-RS1 and
CLE-RS2, which are orthologous to soybean RIC1 and
RIC2, are directly transcribed by NIN, the essential in-
ducer for nodule primordium formation. This constitutes
a complex regulatory circuit with a long-distance feed-
back loop involved in the homeostatic regulation of
nodule organ production in L. japonicus (Soyano et al.,
2014). In soybean, Wang et al. (2014a) recently reported
that NARK negatively regulates miR172 transcription
during nodule primordium formation to prevent excess
nodulation. In this work, we showed that both NIN and
RIC1 are expressed at early stages of rhizobial infection
in common bean roots and that OEAP2 roots with de-
creased nodulation showed increased RIC1 levels. Taken
together, these data would indicate a positive regulation
of NIN and AP2-1 to RIC1, thus leading to reduced
nodulation through AON in common bean (Fig. 10).
Sequence analysis of the RIC1 promoter region led us to
identify NIN- and DREB/ERF-enriched regions, some-
thing that supports the latter contention. However, fur-
ther work is required to fully demonstrate which TFs
activate RIC1 expression in common bean.
The regulation of nodulation through AON signaling
is also relevant for the inhibition of nodulation that oc-
curs when nitrate is present in the rhizosphere. For local
nitrate inhibition, the nitrate-induced CLE peptide in
soybean (NIC) interacts with NARK in the root, leading
to a nitrate-induced inhibition of nodulation in soybean
(Reid et al., 2011a). Our data indicate that common bean
miR172c is a signaling component of the nitrate-induced
AON (Fig. 10). In the presence of nitrate, rhizobia-
inoculated roots that overexpress miR172c developed
more active nodules and showed very low expression of
NIC1 that correlates with the up-regulation of NF-YA1,
NSP2, CYCLOPS, ENOD40, and FLOT2 early symbiotic
genes. The expression of NIN was similar in EV and
OE172 roots inoculated in the absence or presence of
nitrate, which is in agreement with data from L. japonicus
reported by Soyano et al. (2015). The legume-rhizobia
symbiosis with increased resistance to soil nitrate is rel-
evant for improving plant growth and crop production.
Our better understanding of the elements involved in the
control of this phenomenon, such as miR172c in common
bean, opens the possibility to exploit it for the future
improvement of symbiosis.
The effect of miR172c in rhizobial infection and nod-
ulation of common bean is likely to be directly exerted by
its target gene, the AP2-1 transcriptional regulator. TFs
from the AP2 superfamily may function as repressors or
as activators of transcription (Licausi et al., 2013). Work
recently published by Yan et al. (2013) and Wang et al.
(2014) about the mechanism of action of AP2 in soybean
nodulation points to the repressor role of AP2. In this
work, we explored the possible role of AP2-1 as a tran-
scriptional activator and/or repressor of genes relevant
for common bean rhizobium infection and nodulation.
Recently, Soyano et al. (2015) reported that in L. japonicus,
the NIN TF could repress or activate transcription in
different scenarios of rhizobial infection in the presence or
Plant Physiol. Vol. 168, 2015 285
miR172/AP2 in Common Bean-R. elti Symbiosis
119
absence of nitrate. They postulated that such dual regu-
latory functions might depend on specific coactivator or
corepressor molecules that would interact with the same
NIN TF in different scenarios.
We identified genes that significantly coexpress with
AP2-1 and are candidates for transcriptional activation
by this TF (Fig. 10). Several of these genes were
assigned to the protein kinase activity GO category. The
best candidates are four protein kinases that are re-
pressed in mature nodules and induced in roots and in
senescent nodules of control plants, whereas they show
high and/or constitutive expression in roots and nod-
ules of AP2m overexpressing plants. This group in-
cludes the Phvul.007G049500 gene annotated (www.
phytozome.net/commonbean.php) as a Ser/Thr pro-
tein kinase with a Cys-rich receptor-like protein kinase,
Domain of Unknown Function26 (transmembrane), and
Ser/Thr protein kinase domains. We found that this
common bean kinase gene is similar (48%) to the Arab-
idopsis CYSTEINE-RICH RECEPTOR-LIKE PROTEIN
KINASE29 (CRK29) gene and to M. truncatula SymCRK
(52% similarity) and has domains characteristic of the
Cys-rich kinase family. Several members of this family
are induced duringM. truncatula nodule senescence (Van
de Velde et al., 2006; Pérez Guerra et al., 2010). Specifi-
cally, SymCRK is involved in senescence and defense-
like reactions during the M. truncatula-Sinorhizobium
meliloti symbiosis (Berrabah et al., 2014). Another gene
from this group encodes the aminocyclopropane car-
boxylase oxidase, the ethylene-forming enzyme. This
and other genes encoding enzymes from the ethylene
biosynthetic pathway are up-regulated during M. trun-
catula nodule senescence (Van de Velde et al., 2006).
Ethylene plays a positive role in nodule senescence and
also a significant inhibitory role in rhizobial infection and
nodule formation (Van de Velde et al., 2006; Murray,
2011). Our interpretation of these results is that AP2-1
transcriptional regulation is important in common bean
roots but that this TF needs to be silenced for an ade-
quate nodule function (SNF), something that is achieved
Figure 10. Model of miR172 node regulation in common bean-rhizobia symbiosis. Positive regulation is represented with
arrows and negative regulation with lines. The root signaling cascade, triggered by rhizobial NF, is essential for rhizobia in-
fection and nodule development in different legumes. Regulation of common bean rhizobial infection and nodulation by
miR172c and AP2-1 are represented by dashed arrows or lines. A high level of miR172c (thick line) induces AP2-1 degradation
(hatched circle), while active AP2-1 (circle) is present when the miR172c level is very low (thin line). miR172c positively
regulates early nodulation gene expression and rhizobial infection, silencing AP2-1 that may repress ENOD40 expression.
Nodule number is positively regulated by miR172c; AON decreases through low RIC1/NIC1 expression, perhaps regulated by
AP2-1. In mature nodules, abundant miR172c silences AP2-1, an activator of senescence-related genes that are further required
during nodule senescence when AP2-1 levels are recovered. ACCO, Aminocyclopropane carboxylase oxidase.
286 Plant Physiol. Vol. 168, 2015
Nova-Franco et al.
120
""..--o o 
"" , .. » o o ~ 
~ o o 
Ü R I, izol>ia 
CYCLOPS---+ NSP2 
r--- AP2-1')- ---------______ ---, 
T 
------T---¡ 
, ' ,, ' , ' , , , , , 
" : 
miR172c 
.lo 
...... NIN --+. NF-YA1 ~: FLOT2 : -+ Young 
: , 
! Li I AotI jI L 
1--lENOD40: 
RI~1_ t::::= === ~----ú ~ '------+ NIC1 
: ~ , , 
• 
, , 
i , 
Aceo, ___ J 
PK and other 
senescence-related 
genes 
by posttranscriptional target cleavage mediated by
miR172c. AP2-1 silencing in effective mature nodules
would maintain functionality by avoiding senescence
through the down-regulation of nodule senescence genes
activated by this TF (Fig. 10). Ineffective nodules where
miR172c is not induced and AP2-1 remains elevated, as
well as OEAP2 nodules, showed early senescence. Al-
ternatively, other protein kinases, proposed as AP2-1
targets, may participate in signaling pathways important
for root development or nodule senescence. Nodule-
specific protein kinases essential for signaling pathways
during initial stages of nodulation are known (Oldroyd
and Downie, 2008; Kouchi et al., 2010; Murray, 2011;
Oldroyd, 2013).
Regarding AP2 transcriptional repression, Wang et al.
(2014) recently reported that soybean NNC1, a miR172
target, represses ENOD40 expression, which results in
negative regulation of early stages of rhizobial symbiosis.
In this work, we showed that the expression of common
bean ENOD40 is decreased in rhizobia-inoculated roots
that overexpress AP2m. In addition, we identified ERF-
and DREB-enriched regions in the ENOD40 59 promoter
region. So it is conceivable that, as in soybean, common
bean ENOD40 expression is repressed by AP2-1 (Fig. 10).
However, it is important to consider that soybean NNC1
is not the ortholog of common bean AP2-1. We identified
NNC1 (glyma12g07800) as the ortholog (84% similarity)
of the Phvul.011G071100 AP2 gene, identified as a
miR172 target in a common bean degradome analysis
(D. Formey, L.P. Íñiguez, P. Peláez, Y.F. Li, R. Sunkar,
F. Sánchez, J.L. Reyes, and G. Hernández, unpublished
data) but not further analyzed in this work; while com-
mon bean AP2-1 was identified as the ortholog (93%
similarity) of the soybean AP2 gene glyma15g04930 that
was predicted but not validated as the miR172 target
(Wang et al., 2014). Therefore, different miR172 target
genes from the AP2 family analyzed in soybean and in
common bean may have different mechanisms for tran-
scriptional regulation.
In addition, Yan et al. (2013) postulated that the reg-
ulation of soybean nodulation by miR172 is explained by
the AP2 repression of nonsymbiotic hemoglobin (Hb)
gene expression that is essential for regulating the level
of nodulation; however, the authors did not provide
evidence demonstrating AP2 binding and direct tran-
scriptional repression of Hb genes. To explore if this
circuit is functioning in common bean, we first identified
Hb genes encoded by the common bean genome: five
symbiotic leg-hemoglobin (Lb) genes having greatly in-
creased expression in effective nodules and four non-
symbiotic Hb genes (O’Rourke et al., 2014; Supplemental
Fig. S7). From the latter, Phvul011G048600 and
Phvul.011G048700 were identified as orthologs of the
nonsymbiotic Hb-1 and Hb-2 genes in soybean, respec-
tively. In common bean, these Hb genes showed similar
low expression in roots and nodules of wild-type plants
and also in composite plants that overexpress AP2-1 or
that have very low AP2-1 resulting from miR172 over-
expression (Supplemental Fig. S7). Therefore, our data
differ from those of Yan et al. (2013) and lead us to
conclude that AP2-1 repression of Hb-1 genes is not
relevant for common bean nodulation. Our exploration
of other common bean symbiotic genes repressed by
AP2-1 included the identification of TFBS statistically
overrepresented in the promoter regions of 402 com-
mon bean genes reported by O’Rourke et al. (2014) as
nodule-enhanced genes. The expression pattern of these
genes is similar to that of miR172c and opposite to that
of AP2-1, which shows low expression in mature nod-
ules and high expression in roots, suggesting these
as candidates for transcriptional repression by AP2-1.
However, AP2 (ERF and DREB) TFBS were not over-
represented in these genes. The latter is different from
our data from AP2-1 coexpressed genes proposed as
being activated by this TF; these genes did show over-
representation of ERF/DREB TFBS in their promoter
regions. Further work is required to demonstrate the
direct transcription repression, if any, of the miR172c
target gene AP2-1 in common bean.
Legume crops with increased nodulation/decreased
nitrate inhibition of nodulation would be relevant for
sustainable agriculture. This work sets the basis for
further exploration, through genetic/genomic approaches,
for common bean cultivars with improved traits resulting
from increased miR172 in roots and nodules.
MATERIALS AND METHODS
Identification and Analysis of miR172 Precursor Genes,
Isoforms, and Target Genes
The common bean (Phaseolus vulgaris) genome sequence recently published
(Schmutz et al., 2014; http://www.phytozome.net/commonbean.php, v1.0)
was analyzed, and six miR172 isoforms (a–f) were identified. Of these, four
isoforms were described previously through RNA-seq analysis of common
bean small RNAs by Peláez et al. (2012). The secondary RNA structure of each
miR172 isoform was predicted using mfold software (Zuker, 2003) available at
http://mfold.ma.albany.edu, and only the lowest energy structure generated
for each sequence was chosen (Supplemental Fig. S1).
Since the only targets identified for miR172 from different plant species
belong to the AP2-type TF family, we focused our analysis on identifying
common bean miR172 targets within this gene family. We performed target
prediction analysis for all the common bean AP2 gene transcripts identified in
the Pv GEA (O’Rourke, et al., 2014) using the Web server psRNATarget
(http://plantgrn.noble.org/psRNATarget/; Dai and Zhao, 2011). Stringent
criteria were used to predict targets; that is, an alignment spanning at least 18
bp with maximum penalty score of 3. Score calculation considered 0.5 points
for each G:U wobble, one point for each non-G:U mismatch, and two points
for each bulged nucleotide in either RNA strand (Jones-Rhoades and Bartel.,
2004). In addition, we constructed a phylogenetic cladogram from the amino
acid sequences of common bean AP2 genes; these were aligned using ClustalX
version 2.1 (Larkin et al., 2007). The sequence-aligned file was used to con-
struct the bootstrapped neighbor-joining tree using the NJ clustering algo-
rithm and Phylip output format (.phb). The reliability of the phylogenetic
analysis was estimated from 1,000 bootstrap resamplings, and the tree was
viewed using the program MEGA version 5.2.1 (Tamura et al., 2011).
Plant Material and Growth Conditions
Common bean seeds from the Mesoamerican cv Negro Jamapa 81 were
surface sterilized and germinated for 2 d at 26°C to 28°C in darkness. Plants
were grown in a hydroponic system under controlled environmental condi-
tions as described previously (Valdés-López et al., 2010). The hydroponic trays
contained 8 L of Franco and Munns (1982) nutrient solution. The volume and
pH (6.5) of the trays were controlled throughout the experiment. For SNF
conditions, plantlets adapted by growth for 7 d in the hydroponic system with
Plant Physiol. Vol. 168, 2015 287
miR172/AP2 in Common Bean-R. elti Symbiosis
121
nitrogen-free nutrient solution were inoculated with 200 mL of a saturated
liquid culture of the Rhizobium etli CE3 wild-type strain or the R. etli fix2
mutant strain CFNX247 (DnifA::VSp/Sm; Girard et al., 1996). Plants were
harvested at different times (dpi) for analysis; tissues for RNA isolation were
collected directly into liquid nitrogen and stored at 280°C.
To analyze the initial events of rhizobial infection, 2-d-old seedlings were
placed in plastic square bioassay dishes (24 3 24 cm; Corning) with solidified
nitrogen-free Fähreus medium (Vincent, 1970). Plates containing common
bean seedlings were incubated in a growth chamber at 25°C with a 16-h
photoperiod. After 2 d, seedlings were inoculated by applying 1 mL of R. etli
CE3 saturated liquid culture directly to the root and were further incubated at
various times (3–48 h). After specific incubation times, the root responsive zones
were detached, frozen in liquid nitrogen, and stored at 280°C until used.
Plasmid Construction, Plant Transformation, and
Production of Composite Plants
To generate a plasmid to overexpress the pre-miR172 in common bean
transgenic roots, a 217-bp PCR product was obtained using common bean
nodule complementary DNA (cDNA) as template and the specific primers Fw-
pre172 (59-CACCCCAGTCACTGTTTGCCGGTGGAG-39) and R-pre172 (59-
AAAAACCTCCTTTGCTCTGAGCGT-39), based on the Phvul.001G233200
sequence. The PCR product was cloned by T-A annealing into pCR 2.1-TOPO
(Invitrogen) and sequenced. To construct the OE172 plasmid, the pre-miR172
region was excised using the XhoI and BamHI sites of the vector and cloned
into the pTDTO plasmid that carries the reporter tdTomato (red fluorescent
protein) gene (Aparicio-Fabre et al., 2013; Supplemental Fig. S3).
The complete cDNA clone of common bean AP2-1 was obtained by PCR
amplification using cDNA from roots and the specific primers Fw-AP2
(59-CAGCTACCTTTCCGCCAAATGC-39) and Rv-AP2 (59-TAGGCTGG-
GATGGTGTCTGCAG-39), based on the Phvul.005G138300 sequence. The
1,127-bp product was cloned by T-A annealing into pCR 2.1 TOPO (Invi-
trogen) and analyzed by sequencing. Mutations of the putative miR172
cleavage site of AP2-1 were introduced using the QuikChange Site-Directed
Mutagenesis Kit (Stratagene). The PstI site present in the wild-type miR172
cleavage site (59-CTGCAGCATCATCAGGATTCT-39) was eliminated by
changing the A to a C at the 39 position of the recognition site for this enzyme.
Additionally, the nucleotides at positions 9, 10, and 11 of the miR172 cleavage
site were modified by introducing a BglII site. The primers used were forward
(59-TTCTCTACTGCCGCCAGATCTGGATTCTCAATT-39) and reverse
(59-AATTGAGAATCCAGATCTGGCGGCAGTAGAGAA-39). The changes
were checked by sequencing, and the modified AP2 was cloned into plasmid
pTDTO to obtain plasmid OEAP2m. The nucleotide changes in AP2m intro-
duced an amino acid substitution (Arg for Ser), but this does not seem to affect
AP2-1 function (Supplemental Fig. S3).
Common bean composite plants with transformed root system and un-
transformed aerial system were generated as described (Estrada-Navarrete
et al., 2007; Aparicio-Fabre et al., 2013). For plant transformation, Agro-
bacterium rhizogenes K599 strains bearing the EV, OE172, or OEAP2m plasmids
were used. Selected composite plants were grown under controlled environ-
mental conditions in pots with vermiculite and watered daily with B&D nu-
trient solution (Broughton and Dilworth, 1971), either nitrogen free for the
symbiotic condition or with 10 mM potassium nitrate for the full-nutrient
condition. SNF plants were adapted by growing in pots for 7 d and then in-
oculated with R. etli CE3. For experiments designed to analyze the effect of
nitrate on symbiosis, B&D nutrient solution supplemented with 1 or 3 mM
KNO3 was used to water the inoculated plants daily from 1 dpi. Composite
plants were analyzed phenotypically at different dpi; transgenic roots and
nodules were collected in liquid nitrogen and stored at 280°C.
To analyze the initial events of rhizobial infection in transgenic roots, plastic
square bioassay dishes were used to grow selected composite plants under the
same conditions described above. Plates containing composite plants were
sealed with Parafilm, and the root zone was covered with aluminum foil. After
2 d, composite plants were inoculated by applying 1 mL of R. etli CE3 saturated
liquid culture directly to the root. At 48 hpi, the root responsive zones were
detached and stored at 280°C until used or collected into phosphate-buffered
saline (PBS) buffer for microscopic analyses.
Phenotypic Analysis
Nitrogenase activity was determined in detached nodulated roots by the
acetylene reduction assay essentially as described by Hardy et al. (1968).
Specific activity is expressed as nmol ethylene h–1 g–1 nodule dry weight.
The root fresh weight and the number of secondary roots per plant were
determined in composite plants grown under full-nutrient (10 d) or symbiotic
(21 dpi) conditions.
Microscopic analysis was performed on transgenic nodules at different
developmental stages from EV, OE172, and OEAP2m composite plants. The
protocol described by Haynes et al. (2004) was used for tissue staining with
the nucleic acid-binding dye SYTO13. Nodule sections were stained with
SYTO13 (1 mL mL–1) in 80 mM PIPES, pH 7, for 5 min, then mounted in
1% (v/v) PBS/50% (v/v) glycerol and analyzed. Images were obtained using
the Zeiss LSM 510 laser scanning microscope attached to an Axiovert 200 M.
SYTO13 excitation was obtained at 488 nm using an argon laser and an HFT
UV 488/543/633-nm dual dichroic excitation mirror with an LP 560 emission
filter for detection. Sequentially, red fluorescence from the reporter gene was
observed by exciting at 543 nm with a helium/neon laser, with the same dual
dichroic excitation mirror and a BP 500-530 IR emission filter. Images were
processed using the LSM 510 version 4.2 5P1 software (Carl Zeiss Micro-
Imaging). For the determination of nodule perimeter and SYTO13 intensity
per infected area, 10 images from individual nodule replicates from each
condition were analyzed using the ImageJ program.
Statistical analyses of symbiotic parameters (root biomass/architecture,
nodulation, and nitrogenase activity) were performed using one-way ANOVA
and multiple paired Student’s t tests (P , 0.001).
For analyses of root hair deformation and infection thread induction by
rhizobial infection, the root responsive zones from inoculated composite plants
grown in plastic square bioassay dishes, as described above, were collected at
48 hpi into PBS buffer. Responsive zone root samples were stained with
0.01% (w/v) Methylene Blue for 1 h and washed three times with double-
distilled water; infection events were observed in an optical microscope.
RNA Isolation and Analysis
Total RNA was isolated from 100 mg of frozen nodules, 250 mg of frozen
roots, or 200 mg of other frozen tissues from wild-type or composite plants
grown under similar conditions, using Trizol reagent (Life Technologies)
following the manufacturer’s instructions. These samples were preserved at
280°C until tested. Genomic DNA removal, cDNA synthesis, and quality
verification for qRT-PCR were performed as reported (Hernández et al., 2007).
RNA preparations were used to detect mature miR172 in different plant
tissues by low-molecular-weight RNA-gel hybridization using 15 mg of total
RNA, as reported (Naya et al., 2014). Synthetic DNA oligonucleotides with
antisense sequence corresponding to miR172 (59-ATGCAGCATCATCAA-
GATTCT-39) and to U6 snRNA (59-CCAATTTTATCGGATGTCCCCG-39)
were used as probes after radioactive labeling. Hybridization of U6 snRNA was
used as a loading control. The signal intensities of miR172 and U6 hybridization
bands were determined using ImageQuant 5.2 software (Molecular Dynamics).
Normalized miR172 expression levels were calculated related to U6 snRNA.
For the quantification of transcript levels of mature miRNAs, cDNA was
synthesized from 1 mg of total RNA using the NCode miRNA First-Strand
cDNA Synthesis Kit (Invitrogen) or the RevertAid H Minus First Strand cDNA
Synthesis Kit (Fermentas) for transcripts of selected genes. Resulting cDNAs
were then diluted and used to perform qRT-PCR assays using SYBR Green
PCR Master Mix (Applied Biosystems), following the manufacturer’s in-
structions. The sequences of oligonucleotide primers used for qRT-PCR am-
plification of each gene are provided in Supplemental Table S1. Reactions
were analyzed in a real-time thermocycler (Eco Illumina Real-Time PCR
System; Illumina) with settings of 50°C for 2 min, 95°C for 10 min, and 40
cycles of 95°C for 15 s and 57°C for 60 s. Relative expression for each sample
was calculated with the comparative Ct method. The Ct value obtained after
each reaction was normalized with the Ct value of miR159 for miRNA levels
or with the Ct value of UBC (Phvul.006G110100) for expression levels of
miRNAs and transcripts, respectively.
Statistical analyses of gene expression (miR172c, AP2-1, and early symbiotic
genes) from wild-type and composite SNF plants were performed using one-
way ANOVA and multiple paired Student’s t tests (P , 0.001).
Identification of Root-Enhanced Genes Coexpressed
with AP2-1
To identify common bean genes with an expression pattern similar to that of
the AP2-1 target gene, the Euclidian distance between Z scores for each gene
was determined in RNA-seq samples from the Pv GEA (O’Rourke et al., 2014).
The tissue samples analyzed were as follows: young roots, prefixing effective
288 Plant Physiol. Vol. 168, 2015
Nova-Franco et al.
122
(5 dpi) nodules, effective (21 dpi) nodules, ineffective (21 dpi) nodules, roots
from nonsymbiotic plants grown in full-nutrient solution, nodule-detached
roots (5 dpi), effective nodule-detached roots (21 dpi), and ineffective
nodule-detached roots (21 dpi). A threshold Euclidian distance of 0.9 was
established as significant. A total of 114 genes within the threshold were
identified as genes coexpressed with AP2-1 (Supplemental Table S5).
Gene Sequence Analysis for the Identification of TFBS
The CLOVER program (Frith et al., 2004) was used to identify TFBS in 59
promoter regions of AP2-1 coexpressed genes (Supplemental Table S5) and of
genes highly expressed in mature effective nodules described previously by
O’Rourke et al. (2014). For this analysis, a 2,000-bp sequence from the region
immediately upstream of the transcription start site of each gene was retrieved
from the common bean genome sequence (Schmutz et al., 2014; http://www.
phytozome.net/commonbean.php, v1.0).
Promoter regions (2,000-bp sequence) of selected early nodulation genes
were tested for DREB/ERF or NIN binding sites using http://plants.rsat.eu/.
A Markov order of 2 was used for predicting cis-regulatory element-enriched
regions with default parameters. The cis-regulatory element-enriched regions
were also searched in the promoter region of each locus encoding an miR172
isoform; 1,000 bp upstream of the transcription start site of isoforms a and c
and 1,500 bp upstream of the precursors of isoforms b, d, e, and f were ana-
lyzed (Supplemental Table S2).
Supplemental Data
The following supplemental materials are available.
Supplemental Figure S1. miR172 isoforms encoded in the common bean
genome and the most stable secondary structures predicted for their
precursors.
Supplemental Figure S2. Symbiotic phenotypes of common bean plants
inoculated with the R. etli nifA2 mutant strain as compared with the CE3
wild-type strain.
Supplemental Figure S3. Schematic representation of plasmids used for
miR172c or AP2-1m overexpression.
Supplemental Figure S4. Overexpression of miR172c and AP2m in trans-
genic roots and nodules of composite bean plants.
Supplemental Figure S5. miR172 and AP2-1 control rhizobial infection in
common bean roots.
Supplemental Figure S6. Expression pattern of AP2-1 coexpressed genes.
Supplemental Figure S7. Expression analysis of common bean symbiotic
(Lb) and nonsymbiotic (Hb) hemoglobin genes.
Supplemental Table S1. Primer sequences for qRT-PCR.
Supplemental Table S2. TFBS identified in the 59 promoter region of each
MIR172 gene.
Supplemental Table S3. Nitrogenase activity and expression analysis of
marker genes for nodule development.
Supplemental Table S4. Expression analysis of marker genes for nodule
development in transgenic nodules of OE172, EV, or OEAP2m plants.
Supplemental Table S5. AP2-1 coexpressed genes: assigned GO categories
and identified TFBS.
ACKNOWLEDGMENTS
We thank Dr. Jesús Arellano (Universidad Autónoma del Estado de Morelos
[UAEM]) and María del Socorro Sánchez Correa (Facultad de Estudios Superiores
Iztacala, Universidad Nacional Autónoma de México) for technical assistance in
plant transformation, Dr. Ramón Suárez (UAEM) for cooperation in qRT-PCR
determinations, Darla Boydston (Noble Foundation) for helping in the graphic
design of the figures, and Dr. Michael Dunn (Centro de Ciencias Genómicas-
Universidad Nacional Autónoma de México) for critically reviewing the article.
Received December 12, 2014; accepted March 2, 2015; published March 4,
2015.
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miR172/AP2 in Common Bean-R. elti Symbiosis
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RESEARCH ARTICLE Open Access
Genome-wide identification of the Phaseolus
vulgaris sRNAome using small RNA and
degradome sequencing
Damien Formey1, Luis Pedro Iñiguez1, Pablo Peláez2, Yong-Fang Li3, Ramanjulu Sunkar3, Federico Sánchez2,
José Luis Reyes2* and Georgina Hernández1*
Abstract
Background: MiRNAs and phasiRNAs are negative regulators of gene expression. These small RNAs have been
extensively studied in plant model species but only 10 mature microRNAs are present in miRBase version 21, the
most used miRNA database, and no phasiRNAs have been identified for the model legume Phaseolus vulgaris.
Thanks to the recent availability of the first version of the common bean genome, degradome data and small RNA
libraries, we are able to present here a catalog of the microRNAs and phasiRNAs for this organism and, particularly,
we suggest new protagonists in the symbiotic nodulation events.
Results: We identified a set of 185 mature miRNAs, including 121 previously unpublished sequences, encoded by
307 precursors and distributed in 98 families. Degradome data allowed us to identify a total of 181 targets for these
miRNAs. We reveal two regulatory networks involving conserved miRNAs: those known to play crucial roles in the
establishment of nodules, and novel miRNAs present only in common bean, suggesting a specific role for these
sequences. In addition, we identified 125 loci that potentially produce phased small RNAs, with 47 of them having
all the characteristics of being triggered by a total of 31 miRNAs, including 14 new miRNAs identified in this study.
Conclusions: We provide here a set of new small RNAs that contribute to the broader knowledge of the sRNAome
of Phaseolus vulgaris. Thanks to the identification of the miRNA targets from degradome analysis and the
construction of regulatory networks between the mature microRNAs, we present here the probable functional
regulation associated with the sRNAome and, particularly, in N2-fixing symbiotic nodules.
Keywords: Phaseolus vulgaris, microRNAs, phasiRNAs, Degradome, Nodules, Legumes, Common bean
Background
Phaseolus vulgaris, known as common bean, is the most
important legume for human consumption. This crop is
the principal source of protein for hundreds of millions
of people and more than 18 million tonnes of dry
common bean are produced annually [1]. As a legume,
P. vulgaris is also a model species for the study of symbi-
osis in association with nitrogen-fixing bacteria in the
genus Rhizobium. The recent release of the common
bean genome sequence [2] allows the research
community to extend their analyses and acquire needed
knowledge about this organism. In recent years, a number
of studies have focused on gene expression in common
bean and its role in a broad range of processes [3] includ-
ing the response to biotic [4, 5] or abiotic [6, 7] stresses.
Gene regulation has particularly been investigated at the
post-transcriptional level with the action of small regulat-
ing elements called microRNAs (miRNAs) [8–11].
The regulatory processes performed by miRNAs are
widely conserved in plants, animals, protists and fungi
[12–15], highlighting the significant influence that these
regulators can have on the evolution of gene expression.
The miRNAs are small non-coding RNA sequences of ~
22 nt that negatively regulate gene expression, usually,
post-transcriptionally by base-pairing to complementary
* Correspondence: jlreyes@ibt.unam.mx; gina@ccg.unam.mx
2Departamento de Biología Molecular de Plantas, Instituto de Biotecnología
(UNAM), Av. Universidad 2001, Cuernavaca 62210, Morelos, Mexico
1Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México
(UNAM), Av. Universidad 1001, Cuernavaca 62210, Morelos, Mexico
Full list of author information is available at the end of the article
© 2015 Formey et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Formey et al. BMC Genomics  (2015) 16:423 
DOI 10.1186/s12864-015-1639-5
D. Anexo
126
~ 
Genomics 
0 "'-" Central 
transcripts. In plants, these small RNAs are processed
from longer hairpin-shaped precursors encoded in the
genome and almost all of them are transcribed by RNA
polymerase II. This pri-miRNA, for primary miRNA
transcript, is first processed into a precursor, the pre-
miRNA, and then excised as a RNA duplex by the endo-
nuclease enzyme Dicer-like 1 (DCL1). The resulting
duplex sequence, composed by the guide miRNA and
the complementary miRNA* destined for degradation, is
then exported to the cytoplasm by diverse factors, in-
cluding the HASTY protein. Current knowledge is lack-
ing about the spatio-temporal separation of the two
strands but, once performed, the guide miRNA is loaded
on to a member of the AGO protein family to assemble
the RNA-induced silencing complex (RISC). The miRNA
can then play its regulatory role by specifically binding a
target transcript based on sequence complementarity.
To date, three types of action have been identified for
plant miRNAs: cleavage, leading to the degradation of
the corresponding transcripts; translational inhibition,
disrupting protein production; and in some cases DNA
methylation, preventing the transcription of the corre-
sponding genomic locus. In fine, the miRNA action leads
to the loss of function of the gene by inhibiting protein
production (see Voinnet [16] for review).
Plant miRNAs are involved in most, if not all, bio-
logical processes and have been found in all the organs
where they have been searched for [17]. These sequences
are key regulators in important processes such as hor-
mone regulation, nutrient homeostasis, development
and interaction with pathogens and symbionts [18–22].
As part of some of these mechanisms and processes,
miRNAs can act indirectly on gene regulation via trig-
gering the production of other small RNAs. These mole-
cules are called phasiRNAs, for phased small interfering
RNAs, including the tasiRNAs (trans-acting siRNAs)
and other phased small RNAs that require cleavage by
miRNAs [23]. Particular transcripts cleaved by micro-
RNAs are recruited by the RNA-dependent RNA poly-
merase RDR6 and SGS3 to generate double-stranded
RNA molecules [24] that are subsequently processed by
DCL4 or DCL5 into phasiRNAs of 21 or 24 nt, respect-
ively [25, 26]. Similar to what occurs with miRNAs,
these molecules can be loaded on to AGO protein-
containing complexes and can direct disruption of
protein production by transcript targets, including tran-
scripts distinct from their own production source, but
still exhibiting sequence complementarity [27]. Most of
the phasiRNAs are produced from protein-coding tran-
scripts and, if not, they are derived from long non-coding
mRNAs. Many phasiRNAs target, and are derived from,
large protein families such as NB-LRR, MYB and PPR pro-
teins. One recently hypothesized role of phasiRNAs is to
target NB-LRR proteins that broadly regulate plant defenses
and beneficial microbial interactions [27], which are im-
portant features for model legumes such as common bean.
To date, around 10000 miRNAs have been identified
in all the Viridiplantae organisms reported in miRBase
version 21 [28]. Despite several studies on this topic
[29–31], only 8 precursors of miRNAs, generating 10
mature sequences, are referenced for P. vulgaris in this
database while more than 500 are present for other
model legumes such as Medicago truncatula or Glycine
max. The phasiRNAs have not been studied in this or-
ganism. Here, we use the recently released genome of
common bean [2], 5 small RNA libraries obtained from
5 plant organs, and degradome sequencing to identify a
high confidence genome-wide common bean miRNA
dataset, the associated target transcripts, and the first P.
vulgaris phasiRNA catalog ever published.
Results and discussion
Overview of the sequencing data
In this study, we used four published high-throughput
sequencing libraries of small RNAs [29] obtained from
flowers, leaves, seedlings and roots of Phaseolus vulgaris
and a novel library acquired from symbiotic nodules of
the same organism obtained by infection with Rhizobium
tropici. To identify the whole set of miRNAs and phasiR-
NAs, we performed this analysis with the recently de-
scribed genome of Phaseolus vulgaris (Phaseolus vulgaris
v1.0, DOE-JGI and USDA-NIFA, http://www.phytozome.
net/commonbean) [2] as a reference. For the plant organ li-
braries and the symbiotic nodules, we obtained averages of
3,649,274 and 2,810,685 reads, respectively (Table 1). From
these sets, an average of 51 and 37 % of reads were matched
to the common bean genome, respectively (Table 1), and
8.3 % of the sequences from the nodule library matched
with the corresponding bacterial genome (Rhizobium tro-
pici CIAT899, [32]). The lower percentage of reads mapping
to the plant genome in the nodule library was partly due to
the presence of bacterial sequences and to an increased
abundance of ligated adaptor sequences in this particular
sample.
Identification and organ distribution of miRNAs
To identify the already referenced and novel miRNAs of
Phaseolus vulgaris, we used the miRDeep-P pipeline [33]
with each library as a set of small RNA candidates and
the P. vulgaris genome as the corresponding reference.
Table 1 Statistics of the 5 small RNA library genomic
mapping
Filower Leave Root Seedling Nodule
Raw reads 3356817 3321526 3963174 3955581 2810685
Mapped reads 1756842 1663979 1950627 2138258 1032364
% of mapped reads 52 % 5 % 49 % 54 % 37 %
Formey et al. BMC Genomics  (2015) 16:423 Page 2 of 17
127
We identified a total of 307 precursors that fulfilled our
criteria (see methods) producing 185 unique mature
miRNAs (Table 2). 64 of them are already referenced as
mature miRNAs in other plant species (miRBase data-
base ver. 21 [28]): these are produced by 111 precursors
and distributed throughout 27 families. In this work, we
refer to those sequences as “known” miRNAs. Additionally,
57 miRNAs are new isoforms (or family members) [34] of
already referenced miRNAs in the miRBase database: these
are generated by 98 precursors and distributed in 25 fam-
ilies (Additional file 1: Table S1). We refer to them as “new
isomiRs”. Finally, we identified 64 novel miRNAs that are
not members of previously described miRNA families.
These novel miRNAs are encoded by 98 precursors,
grouped in 59 families.
In summary, we identified a total of 185 mature miR-
NAs encoded by 307 precursors and distributed in 98
families. These microRNAs included 64 already known
miRNAs, 57 novel isoforms belonging to known miRNA
families and a last set of 64 novel miRNAs not identified
before. Among the 40 families already registered in
miRBase 21 that we have identified (Additional file 1:
Table S1), we retrieved all those conserved within the
angiosperm genomes, which are the miRNAs from
miR156 to miR408 [35]. As expected, the highly con-
served miR482, together with the more restricted
miR1512 and miR1515, which have been reported as
positively regulating nodule number [36], are also
present in our dataset. Part of the other identified
miRNA families belong to the so-called “legume” miR-
NAs [37] such as the miR2111, miR2118 and miR2119.
Other families retrieved in our libraries had only been
identified, up to now, in G. max (miR4415, miR4416 and
miR5786), M. truncatula (miR2597) or both (miR5037).
Thanks to the identification of these miRNAs in P. vul-
garis, we observe that they are now shared between at
least two legume species and can properly be called
“legume” miRNAs [37]. Interestingly, we also found 5
members of the miR1862 family and 1 member of the
miR6175 that so far have only been identified in rice
[38] and rubber tree [39], respectively. The members
of these two families are sparsely expressed in our li-
braries and we can imagine that these miRNAs, and
others characterized by exhibiting low expression, are
highly spatially- or temporally-specific sequences. It is
possible that this type of “specific” miRNA is, in fact,
present in several organisms and, with the increase in
the depth of high-throughput sequencing technolo-
gies, will begin to emerge in investigations focused on
the identification of miRNAs in less-studied organ-
isms. The miR4376 family may be an example of these.
It has been shown to be a super-family derived from
the miR390 and was probably present in the common
ancestor of Spermatophyta [40], suggesting that this
miRNA is conserved in most of the seed plants, but
currently only identified in five species: soybean,
tomato, ginseng, potato and, now, common bean
(miRBase 21).
By sequencing five small RNA libraries corresponding
to five different plant organs, i.e., flower, leaf, nodule,
root and seedling, we are able to indicate the distribu-
tion of the identified miRNAs in the whole plant based
on their expression (see methods). As expected, we
found about 61 % of the known miRNAs (39/64)
present in all of the organs while only about 14 % and
12 % of the new isomiRs (8/57) and the novel miRNAs
(8/64), respectively, are in this class (Fig. 1). In con-
trast, 22 novel miRNAs are organ-specific whereas
none of the known miRNA is. This suggests that the
known miRNAs, composed of a majority of conserved
miRNAs, play a role at the whole-plant level, as ex-
pected from their implication in regulatory networks
[41]. In contrast, the newly identified miRNAs, consid-
ered as recently emerged and, probably having a more
specific action (or no action at all) are characterized
by a specific spatial distribution [42]. Alternatively, the
characteristic lower expression of isomiRs and novel
miRNAs may preclude accurate detection in all organs
tested, thus limiting this analysis.
The analysis of miRNA expression and distribution in
different organs has been used to identify nodule-
specific novel miRNAs in G. max and M. truncatula [43,
44]. Similar approaches used in this work led us to iden-
tify three nodule-specific miRNAs in P. vulgaris. These
include the new isoform of a conserved miRNA family,
the miRNov627 from the mtr-miR399 family and two
newly identified miRNAs: miRNov153 and miRNov494.
Their putative targets are involved in the regulation of
plant-microorganism interactions, which will be dis-
cussed below. Other miRNAs with organ-specific ex-
pression patterns were detected in our samples and
could be also regulating specific processes in other plant
tissues (Additional file 2: Table S2).
Table 2 Statistics of the identified precursors and their
corresponding mature miRNAs
Known New isomiR Novel Total
Identified precursors 111 98 98 307
Identified miRNAs 64 57 64 185
Relative position
Intergenic 97 90 78 265
Intron 1 5 16 22
Exon 11 3 4 18
3’UTR 2 0 0 2
5’UTR 0 0 0 0
Formey et al. BMC Genomics  (2015) 16:423 Page 3 of 17
128
Validation of the predicted precursors
The recent release of the P. vulgaris gene atlas [45]
allowed us to verify that the predicted miRNAs have a
corresponding transcript in the 24 transcriptome data
samples collected from seven distinct tissues of common
bean at developmentally important time-points, includ-
ing plants inoculated with either effective or ineffective
Rhizobium. We found that about 65, 26 and 24 % of the
known miRNA, new isomiR and novel miRNA precur-
sors, respectively, have a complete transcript in at least
one of the 24 common bean expression libraries. These
validated precursors produce 84, 47 and 35 % of the
known, new isomiR and novel mature sequences. To dif-
ferentiate miRNAs from other small RNAs, we used
degradome data to identify the specific signatures of
DCL slicing during the miRNA precursor processing.
These signatures are characterized by the finding of a
significant processing event at the mature miRNA and/
or miRNA* boundaries within the precursor [46]. With
this method, we validated the correct processing of 41 %
of the known miRNA precursors, 30 % of the new iso-
miR precursors and 22 % of the novel miRNA precur-
sors, thus constituting the validation of 63 % of the
known mature miRNAs, 44 % of the new mature iso-
miRs and 33 % of the novel mature miRNAs (Fig. 2).
The failure to validate some miRNA precursors could be
due to undesirable RNA degradation producing random
degradome signatures, or the presence of degradome
signatures originating from other loci that mask the
expected significant signatures [47]. It is also possible
that some precursors are not functional and mature
miRNAs only originate from a subset of potential pre-
cursors within the same MIR family. Compared with the
precursors encoding well-conserved miRNAs, a lower
proportion of precursors for novel miRNAs allowed
degradome validation. A general problem encountered
with these sequences is their lower expression levels and
corresponding lower degradome signatures, the latter
being obscured by other gene degradation products.
Finally, if we combine the results of the expression
data with those of the degradome analysis, we obtain
supporting data for 78, 47 and 35 % of the known, new
isomiRs and novel miRNA precursors we encountered,
respectively, representing 92, 54 and 44 % of the corre-
sponding mature miRNAs originally identified. Overall,
64 % of the identified mature microRNAs show evidence
of expression for at least one of their proposed precur-
sors by at least one of the two methods examined.
miRNA identification aided by genomic sequence data
The present study profited from the use of the recently
released complete genomic sequence for P. vulgaris [2]
to identify miRNAs. Compared to the previous study of
Peláez et al. [29], this new approach allowed us to con-
firm the presence of 81 of the 113 known miRNAs iden-
tified by Peláez et al. in P. vulgaris. 25 out of the 32
miRNAs previously identified by Peláez et al. and not
encountered in our study are actually not present in the
Fig. 1 Distribution of the identified mature miRNAs in 5 plant organs.
Venn diagram of the distribution of a known miRNA, b new isomiR and
c novel miRNA in the different studied organs. Red: flower, green: leaf,
yellow: root, blue: seedling and black: nodule. The numbers in each Venn
diagram area correspond to the number of mature miRNAs encountered
in the corresponding overlapping organ area
Formey et al. BMC Genomics  (2015) 16:423 Page 4 of 17
129
A Seedling 
Flower 
B 
1 
Flower 
e Seedling 5 
6 
Flower 
Nodule 
1 
1 Nodule 
2 
Nodule 
current version of the genome and the remaining seven
were more precisely defined in our study. Four of the
seven non-identified miRNAs have been detected in the
libraries but we designated other more abundant iso-
forms or novel miRNAs as mature miRNAs in the corre-
sponding precursors. The 3 other miRNAs were not
identified by miRDeep-P because of potential splicing
events in the hairpin sequence or because of a non-
conventional folding of the stem-loop. In contrast, our
study revealed 4 already identified mature miRNAs in
the miRBase catalog of other species and 34 new iso-
miRs in addition to the set proposed by Peláez et al.
Concerning the novel miRNAs, none of those identified
by Peláez et al. fulfilled our selection criteria. We identi-
fied only one isomiR of a novel miRNA previously de-
scribed by Peláez et al., 21 could not be mapped to the
genome and 17 were present in more than 40 locations
in the genome and thus were not further considered (see
methods). The other 19 miRNAs were not selected be-
cause they do not fulfill our folding, splicing or expres-
sion criteria. Conversely, the availability of genomic
sequence data allowed us to identify 64 high-confidence
novel miRNAs that satisfy all our criteria and all those
currently accepted for microRNA annotation [48].
Means of 2.5 and 6.9 hits in genome for each known
and novel miRNA were found, respectively. We can
ascribe this difference to a lower selection pressure on
the novel miRNAs [49], compared to conserved ones,
and the fact that most of these newly identified miRNAs
are young miRNAs and, perhaps, their selection process
is still occurring.
In summary, the new whole-genome investigation of
the miRNA candidates allowed us to identify 307 genu-
ine precursors of already known and new miRNAs and
121 high confidence unpublished mature sequences.
MiRNA precursor genomic localization
In plants, most of the miRNA genes are encoded in
intergenic regions [16, 50] but some are present in in-
trons, exons, or UTRs [51]. In our study, we localized
the different precursors of miRNAs in the genome and
determined their position relative to annotated genes.
As expected, about 90 % of the known and new isomiR
are located in intergenic regions (Table 2). One of
the known miRNAs, pvu-miR1514a, has a precursor
encoded in an intron and two, bna-miR167d and gma-
miR167a, are located in 3’UTRs. There are 11 other
known miRNAs located in exons. The proportion of
known miRNAs located in exons of annotated genes is
high compared to that of miRNAs present in other
gene locations; however, due to the lack of curation in
the current annotation of the P. vulgaris genome this
could be an overestimation. These sequences are lo-
cated in exons of putative small proteins lacking
known domains or homologs in other organisms and
it is very likely that these precursors are, in fact, inter-
genic. For the novel miRNAs compared to known ones
and new isomiRs, we encountered fewer precursors in
Fig. 2 Distribution of degradome signatures on miRNA precursors. Distribution of degradome read abundance on a known miRNA and b novel
miRNA precursors. Vertical axes display the read abundance and the horizontal axes display the precursor position by base pair. The black
triangles show the read abundance for the corresponding precursor base. The plain and dotted arrows point to the first base of the mature
miRNA and the first base of the miR*, respectively. The schemes below the plots represent the corresponding precursors. The vertical bars show
the base pairing and the red and blue boxes represent the mature miRNAs and the miR*, respectively
Formey et al. BMC Genomics  (2015) 16:423 Page 5 of 17
130
A ~O ________________ _ 
300 
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intergenic regions (~80 %, Table 2) but more in in-
trons (~16 %). Since we found a higher proportion of
novel miRNAs in introns, we can imagine that some
of the younger miRNA genes could originate from in-
trons. There are different hypotheses on the origin of
the miRNAs: novel miRNA genes may originate from
duplication of other miRNAs, from inverted terminal
repeats of transposable elements or from random
stem-loop structures emerging from intergenic or in-
tronic regions (see Zhuo et al. for review [50]). In this
sense, the birth of a miRNA gene is less costly if it de-
rives from a ready-to-use transcription unit that an
intron can confer indirectly [52]. In our results, 15 of
the 16 precursors of novel miRNAs identified in in-
trons are located in the same orientation as the corre-
sponding gene, supporting this hypothesis. On the one
hand, the expression of various miRNAs located in in-
trons is dependent on the expression of the corre-
sponding host gene [53] and we can think that the
evolutionary selection of this type of transcription
mechanism is fixed. On the other hand, we can
hypothesize that some of the miRNAs present in in-
trons are young miRNAs and the use of the transcrip-
tional unit of the host gene is a first step in the
evolution and, thereafter, they may acquire their own
transcriptional unit and evolve as canonical independ-
ent miRNAs thanks to duplication events or exon
shuffling [54].
Conservation of the identified miRNAs in plants
We investigated the conservation of the identified miR-
NAs in 7 vascular plants and 1 moss. We chose two le-
gumes (Medicago truncatula and Glycine max), three
non-legume eudicots (Vitis vinifera, Populus trichocarpa
and Arabidopsis thaliana), two monocots (Oryza sativa
and Zea mays) and the moss Physcomitrella patens. A
miRNA is considered as conserved in a given species
when it is present in its full length without any mismatches.
As expected, in a global view, the known miRNAs are
the most conserved with 4 miRNAs (ath-miR156j, gma-
miR156a, gma-miR160a and pvu-miR166a) present in all
the selected genomes. We found 21 miRNAs, including 19
known and 2 new isomiRs (pvu-miR319c), conserved in all
the plants except the moss (Fig. 3). A total of 118 miRNAs
seem to be legume-specific: 22 % of the known miRNAs,
73 % of the new isomiRs and 100 % of the novel miRNAs.
Among them, 77 are specific to P. vulgaris: 1 known
miRNA (pvu-miR1514a), 20 new isomiRs and 56 novel
miRNAs.
The proportion of species-specific miRNAs is variable
in plants. For example, in Arabidopsis thaliana and A.
lyrata, 35 % of the identified miRNAs are species-
specific [55]. This is also the case of 23 % of the Medi-
cago truncatula miRNAs [44], 37 % in Populus species
[56], 41 % in wheat [57] and 56 % in apple [58]. In our
results we found that P. vulgaris is in the range of these
other plants with around 42 % of the miRNAs specific
to the common bean. Naturally, these numbers may
fluctuate depending on the depth of sequence obtained
and the methods used for the identification and the pre-
diction of the microRNAs. The lack of conservation for
the specific miRNAs suggests that they emerged recently
during evolution and can thus be considered as young
miRNAs.
Prediction and identification of miRNAs targets
Using degradome data and the prediction of putative tar-
gets, we are able to present a set of targets for the identi-
fied miRNAs. The degradome data reported here were
obtained using a method based on the 5'-rapid amplifi-
cation of cDNA ends (5’RACE) and further adapted for
high-throughput sequencing. It allows the experimental
identification of the target cleavage sites associated with
miRNA cleavage on a genomic scale [59]. The regulation
performed by miRNAs does not necessary include a
cleavage of the target transcript. To complete the degra-
dome data, we used a target prediction tool, called
psRNATarget [60], based on reverse complementary
matching between miRNA and target transcript. A mean
number of one degradome target and 6.5 predicted tar-
gets per miRNA candidate were found (Additional file 3:
Table S3). The known miRNAs possess significantly
more degradome targets (1.9/miRNA) compared to the
novel ones (0.3/miRNA) and also have more predicted
targets (8.8/known miRNA vs. 5.8/novel miRNA). Sev-
eral of the target transcripts identified here for known
miRNAs correspond to previously defined targets found
in other plant species, thus validating our analysis. As
discussed in previous studies, the lack of conventional
targets for the young miRNAs is not unusual. Most of
the young miRNAs, in contrast to the conserved ones,
are not involved in complex regulatory networks [41]
and their evolution has sometimes been considered as
neutral [49]. However, the experimental demonstration
of an interaction with its predicted target is considered
as “the most powerful method of validating a predicted
miRNA” [61]. Although 73 % of the conserved miRNAs
exhibit a degradome target in our data, only around
28 % of the novel miRNAs also have a target identified
by degradome analysis (Fig. 4). Here we found that a sig-
nificant portion of novel miRNAs has functional evi-
dence, as revealed by degradome data, for a role in
regulating gene expression, thus suggesting their rele-
vance in different biological processes. Their degradome
targets are for the most part involved in metabolic
processes, biosynthesis, binding processes and various
functions (Fig. 5). Compared to the conserved miRNAs
that preferentially target genes involved in complex
Formey et al. BMC Genomics  (2015) 16:423 Page 6 of 17
131
regulatory networks such as transcription factors [62],
young miRNAs tend to target more precise and diver-
sified functions. However, one of them, miRNov138,
seems to target a transcript coding for a Homeobox-
leucine zipper family protein. This transcription factor
family is known to play an important role during the
growth and development of plants by modulating
phytohormone-signaling networks [63] and also in the
interaction with microorganisms, especially during
nodulation [64]. This young miRNA is specific to P.
vulgaris and we hypothesize that it has been selected
to perform a more precise role in common bean, like
adaptation to a specific environment [63] or inter-
action with Rhizobium.
Analysis of microRNA co-expression networks in nodules
To understand the role of the newly identified miRNAs
in nodules, we constructed weighted correlation net-
works of miRNAs. These networks describe the pairwise
relationship among miRNAs that differentiate the nod-
ule library from other libraries based on the miRNA
expression patterns [65]. Using this approach, we identi-
fied 2 networks including pairwise relationships between
novel miRNAs, new isomiRs and conserved miRNAs.
One of these networks is composed by 4 novel miR-
NAs (miRNov153, miRNov155, miR156 and miR-
Nov494), two new members of the miR399 family, and
gma-miR319d suggesting that they could act jointly
(Fig. 6A). All these miRNAs have the same expression pat-
tern, with increased expression in nodules (Additional file
1: Table S1). As shown in Fig. 7A, increased expression of
pre-miR319d, pre-miRNov153/155 and pre-miRNov494 in
nodules was experimentally validated by qRT-PCR. More
precisely, the new isoform of the mtr-miR399j was specific-
ally identified in nodule (Additional file 2: Table S2). This
miRNA family is involved in phosphate homeostasis [66]
but, furthermore, has been identified as being repressed by
N-starvation [67]. Nodulation efficiency and functionality
Fig. 3 Conservation of the Phaseolus vulgaris mature miRNAs in 8 plant species. The different mature miRNAs studied are listed on the first row
of the tables. The first column indicates the organisms where the sequences have been searched for and the other columns correspond to
the different a known miRNAs, b new isomiRs and c novel miRNAs. The presence of a colored square at the intersections between rows and
columns indicates the 100 %-homology presence of the corresponding mature sequence in the genome of the corresponding species. The tree
on the left indicates the phylogenetic relationship between the studied species
Formey et al. BMC Genomics  (2015) 16:423 Page 7 of 17
132
A 
~ JitIMli_ 
r.Yinff-
z..,. 
aMJIiwI 
B 
~ 
G._ 
JI.11'fI1IaIIda I 
p.~1 
A.th1i_1 
Y.l'iIIif- I 
z..,. I 
O.uttwl I 
e 
~ 
P.Irlo::(,O<W!p_ 
Ji tlulNiDI. ,-
z...,. 
a,.,I/N 
are related to these two nutrients [68, 69] and we
hypothesize that this new isoform of miR399 could indir-
ectly regulate the normal establishment of nodulation in P.
vulgaris. Indeed, this miRNA family is known to target a
transcript encoding PHO2, an ubiquitin-conjugating en-
zyme crucial for acquisition and translocation of phosphate
[70]. In our data, we predicted another target for the
new isoform of miR399 which could regulate a NB-ARC
domain-containing protein, one of several factors known to
be involved in the plant resistance and activation of innate
immune responses [71]. The organ-specific accumulation
of this miRNA and the function of the corresponding target
suggest a role in the regulation of nodule-specific defense
mechanisms. Two of the novel miRNAs from this network;
miRNov153 and miRNov494, are specific to the nodule
library. The miRNov494 is predicted to target two tran-
scripts in our degradome data (Additional file 3: Table S3).
qRT-PCR expression analysis showed that in nodules the
expression of miRNov494 precursor increased while the
expression of one of the predicted targets, an aldehyde
dehydrogenase (Phvul.004G162200.1) decreased (Fig. 7A).
The members of this protein family are known to change
their expression in response to a wide variety of stresses
and are important in supporting environmental adaptability
Fig. 4 Target plots of miRNA-targeted transcripts using degradome data. Target plots of the distribution of degradome read abundance on
transcripts targeted by a. known miRNAs and b. novel miRNAs. Vertical axes display the read abundance and the horizontal axes display the
precursor position by base pair. The black vertical bars show the read abundance for the corresponding precursor base. The black stars indicate
signatures consistent with the miRNA-directed cleavage. The corresponding p-values are shown above the plots
Fig. 5 Distribution of the degradome target function. Radar plot
of the distribution of the degradome target functions. Each
branch corresponds to a different gene ontology category. The
proportion (%) of known miRNA (blue), new isomiR (yellow) and
novel miRNA (red) degradome targets that belong to each
functional category are indicated by the distance from the center
of the colored area intersection with the corresponding branch.
The “other” category represents all the functional categories
displaying less than 2 % of the targets
Formey et al. BMC Genomics  (2015) 16:423 Page 8 of 17
133
A 
~l • 
ama-miRl72f n. PhvuI.OllG07l100.2 
,...,..",.".,6 
Ta:g1I~ 17U ~ ,.,. 
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pa-miR319a \.\1'. Phvul,(X)10101000.2 
poO.003m29S6l1051SU 
B miRN0v42 w. Phvul.004G172700.1 
,.."" ...... """ 
miRNav39S '1.1. PhVDl.OO9Gl72100.1 
miRN0v446 vI. Ph11ll.OO4GOO2700.1 
" L "ª..I ~ll ----"--J ~I 11 I ~II h, I ~hll - - ~ - - - - -m:IlUIiI 30CC~1 .u.... 2D~1 
, """"n,,,,,,,, 
To."9O'~ u,. GlGfd¡.(!OCOCOO = .. 1503 
[72]. Our data suggest that the miRNov494 can regulate a
member of the aldehyde dehydrogenase family in nodules
and may help the plant to control the life cycle of this
symbiotic organ. MiRNov153 is ~18 times more highly
expressed than the miRNov494 and is encoded in an intron
of an F-box protein gene (Additional file 1: Table S1). Part
of this protein family plays important role in root symbioses
[73] and particularly in the auto-regulation of nodulation
[74]. This novel miRNA, miRNov153, is predicted to target
an uridine kinase (Phvul.003 g180800), for which no ex-
pression variation has been measured in nodule compared
to root tissue, and a transcript coding for a hypothetical
protein (Phvul.002 g255600), for which we have observed
an expression inversely correlated to its regulator miRNA
in nodule (Fig. 7A). Although this target does not guide us
to an obvious functionality, the fact that this miRNA is
encoded in an intron of an F-box protein gene, in the same
orientation, would permit us to envisage that this miRNA is
co-expressed with the host gene and could act in the regu-
lation of the systemic negative feedback control of nodula-
tion. Among the 8 species for which we searched for the
presence of the miRNov153, only the soybean genome en-
codes this miRNA and displays a bona fide hairpin-like pre-
cursor (Additional file 4: Figure S1A). To check if this locus
can produce a miRNA corresponding to the mature miR-
Nov153 in soybean, we mapped public nodule small RNA
sequences from Arikit et al. [75] to the miRNov153 soy-
bean precursor (Additional file 4: Figure S1B). Tens of
sequences mapped exactly to miRNov153 and the corre-
sponding miRNov153* and only three sequences mapped
to other coordinates, suggesting that nodules from soybean
also produce the miRNov153. The available public small
RNA libraries from nodule represent 5 time points from 10
dpi to 30 dpi. In these libraries, we observe an increased ex-
pression of the miRNov153 during nodule development.
Additionally, this miRNA is absent from Medicago
truncatula, which develops indeterminate nodules, and
is conserved in common bean and soybean, two species
developing determinate nodules. These results tend to
show a role of miRNov153, via its targets, in the loss of
meristematic activity or the senescence of determinate
nodules.
To our knowledge, no link has been published before
between miR319 and nodulation. This miRNA is a regu-
lator of the plant stress responses against salinity,
drought or cold [76, 77] but it is also implicated in the
regulation of cell proliferation via the control of its tar-
gets, members of the TCP transcription factor family
[78]. Here, we have validated the increase of miR319d
precursor expression in nodules and the decrease of one
of its predicted targets, a TCP transcription factor
family member (Phvul.011 g156900), in the same tissue
(Fig. 7A). Because this miRNA is present in a network
that distinguishes the nodule library from the others,
and because it is connected with miRNAs described
in previous studies as being related to nodules, we
hypothesize that miR319 and its targets are strong can-
didates for which a role in nodule development must be
investigated.
We identified a second network involving 7 miRNAs:
2 novel ones, 2 new isomiRs and 3 already known miR-
NAs (Fig. 6B). In this network, we retrieved members of
miRNA families already described as regulating nodule
development. Specific variants of L. japonicus and M.
truncatula miR171 target the GRAS-family NSP2 TF, a
key regulator of the common symbiotic pathway for
rhizobial and arbuscular mychorrizal symbioses [79–81].
MiR166 and its target gene, a HD-ZIP III transcription
factor, regulate meristem activity and vascular differenti-
ation in roots and nodules [64]. In soybean, the over-
expression of miR482, targeting a GSK-3-like protein
MsK4, resulted in an increase in nodule number without
affecting root development or the number of nodule
primordia [36]. The crucial role of miR172 and its target
gene, a transcription factor from the AP2 family, have
been described for soybean- and common bean-rhizobia
symbiosis [82–84]. For soybean, Yan et al. [82] postu-
lated that miR172 regulation of nodulation is explained
by the AP2 repression of non-symbiotic hemoglobin
(Hb) gene expression that regulates the level of nodu-
lation; while Wang et al. [84] recently reported that
soybean NNC1, a AP2 transcription factor target of
Fig. 6 Weighted correlation network analysis of microRNAs from
nodules versus other organs. Representation of two regulatory networks
a & b between the miRNAs. The nodes correspond to each miRNA
encountered in the corresponding network. The edges correspond to
the direct connection and relation between the nodes. Blue, yellow and
red tags indicate the known miRNAs, new isomiRs and novel miRNAs,
respectively
Formey et al. BMC Genomics  (2015) 16:423 Page 9 of 17
134
A 
mt:r-miR399j-fam 
miR172, represses ENOD40 expression that results in
negative regulation of early symbiotic stages. For
common bean, we recently demonstrated [83] that
miR172c, which has the transcription factor AP2-1 as
target gene, indirectly regulates the expression of the
transcription factors NF-YA1, NSP2 and CYCLOPS as
well as the gene FLOT2, all of which are essential reg-
ulators of early stages of the symbiosis. In addition, we
postulated that miR172-induced AP2-1 silencing in
mature common bean nodules is involved in down-
regulating expression of genes related to nodule senes-
cence postulated as targets of AP2-1 transcription ac-
tivation [83]. We also found a new isomiR of a family
already described as regulating the development of
nodules: the miR2111 targeting a Kelch-related pro-
teins [85]. Finally, the last two miRNAs encountered
in this network are new miRNAs: miRNov222 and
miRNov270. Although these miRNAs have low expres-
sion and present no processing evidences in degra-
dome data or transcription evidences in transcriptome
data (Additional file 1: Table S1), we have detected the
corresponding precursors by qRT-PCR in roots and
confirmed their expression decrease in nodules (Fig. 7B).
As elements in a network containing miRNAs previously
described as nodulation regulators, these novel miRNAs
are also probably involved in the normal establishment and
functioning of the nodulation process via regulation of their
targets. These two miRNAs putatively target a protein kin-
ase and a NB-ARC domain-containing disease resistance
protein, respectively (Additional file 3: Table S3). Although
the protein kinases belong to a large protein family, various
members are known to be involved in signalling during
pr
e-
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iR
31
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Phv
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04
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A
Root Nodule
pre-miRNov222
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B
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Fig. 7 Expression quantification of miRNA precursors and their targets involved in the weighted correlation networks. Relative quantification by
qRT-PCR of miRNA precursors and their corresponding targets. a Expression of miRNAs and corresponding targets in nodules compared to root
tissues from the Fig. 6A network. Histograms represent the Log2 fold change level and error bars represent the standard error of the mean of
the three expression ratios from three biological replicates. b Expression of novel miRNA precursors from Fig. 6B network. Histograms represent
the relative expression and error bars represent the standard error of the mean of the three relative expressions from three biological replicates.
Expression values have been standardized at 1 for the control roots and proportionally adjusted for nodule expression. The geometric mean of
the expression of three housekeeping genes was used for normalization of the expression levels
Formey et al. BMC Genomics  (2015) 16:423 Page 10 of 17
135
nodulation [86] and, as described above for the putative tar-
get of the new isoform of the miR399, the NB-ARC
domain-containing disease resistance proteins are known
to be involved in the plant resistance and activation of in-
nate immune responses [71] and could play a specific role
in the nodule and allow the proper functioning of the
nitrogen-fixing organ. These novel miRNAs are directly re-
lated to regulators of nodulation and it is conceivable that
these Phaseolus-specific miRNAs could act in the same way
as the connected conserved miRNAs and play a role during
nodule establishment in a more spatio-temporal specific
manner. Like the miR319 family, these novel miRNAs must
be considered as major candidates for deciphering the spe-
cific regulation of nodulation in P. vulgaris.
Identification of phasi-RNAs and their associated targets
In all the sequenced libraries, a large number of reads
do not correspond to miRNAs or other previously iden-
tified non-coding RNAs of common bean, suggesting
they could belong to another class of small RNAs. We
decided to focus on phasiRNAs and, based on the align-
ment of the reads with the genome and the phasing
score for each locus, we predicted 125 statistically
significant loci producing 21 nt-phased small RNAs
(Additional file 5: Table S4, see example in Fig. 8A).
Among the identified PHAS genes, only two are conserved
in the three legumes studied, i.e. M. truncatula, G. max
and P. vulgaris (Additional file 6: Figure S2). One of these
PHAS genes is known as the TAS3 gene targeted by the
miR390 and producing small RNAs targeting transcripts
that encode AUXIN RESPONSE FACTORs (ARF3 and
ARF4) [87]. In our data, we also identified miR390 and its
regulatory action on the TAS3 gene (Additional file 5: Table
S4), providing a positive control for our methodology. The
second PHAS gene codes for a member of the disease re-
sistance protein (TIR-NBS-LRR class) family known to be
targeted by miR2118 in soybean but not yet in M. trunca-
tula [71]. In our analysis, we encountered that miR2118 po-
tentially targets this PHAS gene but not in the expected
phase. However, for this gene, we have identified a potential
in-phase slicing by a novel miRNA, the miRNov212 (Add-
itional file 5: Table S4). Among the 125 loci identified, 47
are predicted to be targeted by 31 different miRNAs with a
cleavage site in phase with the initiation site of the corre-
sponding phasiRNAs. In recent years, loci producing 21 nt-
phasiRNAs (PHAS genes) have been identified in different
species and their number in each case varies widely: 50 in
Arabidopsis, 114 in M. truncatula, 157 in apple, 353 in
peach and 864 loci in rice [40, 71, 88–90]. Not all PHAS
genes are associated to a miRNA triggering phased small
RNA, and the proportion of PHAS genes targeted by a
miRNA also varies from species to species. For example, 26
Fig. 8 Identification of a phasiRNA loci and their triggering miRNAs. a Distribution of the phase score (vertical axis) as a function of the genomic
coordinates (horizontal axis) of the PHAS gene Chr08:3432925–3433198 coding for a pfkB-like carbohydrate kinase family protein. b Representation of the
miRNov582 triggering the 21 nt phasiRNA production (green and yellow alternation) of the PHAS gene Chr08:3432925–3433198. The G–U hydrogen bonds
are represented by a single dot and the A–U and G–C hydrogen bonds by two dots
Formey et al. BMC Genomics  (2015) 16:423 Page 11 of 17
136
A 
" ,--------------------------------------------
2' +-------------------,---,---,----------------
i 1' t-------------~-- ~-1---t--;-----------­
I 1' r-------;- -rl-.~~_.~~~~----
,:, .¡. "' = ' ~ ' ~ 43 C"" = l ~ ' · 43 ~ "" -':" 343 .. """=" 30432904 3432925 3432946 3432967 3432988 3433009 3433030 34330SI 
Cbr(J& Gcnomic coordinates 
B miRNov582 
22~~~1 
GGACAAGAGTGTCAAAAGATGACTCTTCTTATTTCTATTGTATAGTGTAGGGGTGCGATATTAAATAAATA 
GGTGTGAATGTATTAGAGAGAGTCACATTGTCCATGTGACTTAGGAGAGTAAGTGTAGGTGTAGTTTTTAG 
GATGTGTCATAGTAGAAAAAGGTCACACCTCCCATGTGACTTGTTTTTTGTGGGAATTTCAAATGAGTTTC 
ATTTGAATTTTCCCACCTATTTTTCAGCACCCTCACACTATAAATAGAGGTGTGGGCTCTTGTAAAATTCA 
GAATTGGAAATTGGAT 
Chr08:3432925-3433198 
of the 157 apple PHAS genes and 160 of the 864 rice PHAS
genes are potentially targeted by miRNAs that can trigger
phasiRNA production. Here, we identified 13 known miR-
NAs, 4 new isomiRs and 14 novel miRNAs that potentially
trigger the production of phasiRNAs (an example is shown
in Fig. 8B). Most of the 17 previously identified miRNAs
are known to be involved in the production of phasiR-
NAs in other species [27, 91]. The phasiRNAs produced
by these known miRNAs originate from transcripts
encoding proteins involved in a wide range of functions
like the MYB genes targeted by miR159, NB-LRR genes
targeted by miR482 and miR2118, Ca2+-ATPases tar-
geted by miR4376 or TIR/AFB targeted by miR393
[27]. Uncharacteristically, we identified one PHAS
gene (Chr11: 2402498–2402857) that possesses all the
features to produce two sets of phasiRNAs derived
from two distinct phases that are triggered by two
novel miRNAs (miRNov141 and miRNov316). The
resulting phasiRNAs are only expressed in the flower
and the corresponding miRNAs are almost uniquely
expressed in the same organ (Additional file 2: Table
S2). These phasiRNAs are produced from a transcript
coding for a putative serine carboxypeptidase-like 35,
a member of a large family involved in multiple
cellular processes. In rice, a member of this family has
been identified as playing a role in defense against
pathogens and oxidative stress [92]. The mechanism
of double-phasing by two different miRNAs allows
production of two overlapping sets of phasiRNAs from
the same sequence and we can imagine this confers an
additional layer of small RNA production allowing the
more precise regulation of defense reactions of the
organism. The complete set of PHAS genes targeted
by microRNAs must be larger than the number re-
ported here, since we may not have a complete list of
miRNAs yet. In addition, other miRNAs may generate
phasiRNAs by cleaving target transcripts at an out-
of-phase position by a phenomenon called phase-drift,
caused by a DCL slippage [93], and, identification of
these cleavage events becomes challenging.
All of the studied organs presented transcripts produ-
cing 21-nt phasiRNAs but only 13 of the 125 PHAS
genes (~10 %) were identified in the five different small
RNA libraries (Additional file 7: Figure S3). Moreover,
most of the phasiRNAs are organ-specific (~73 %) and
flowers present the largest set of identified phasiRNAs
expressed, with around 73 % of the loci, including ~69 %
of organ-specific expressed loci. Conversely, nodules had
lower expressed phasiRNAs, with ~13 % of the identified
loci. 86 of the 125 PHAS loci are localized in a predicted
transcript, including 46 with an associated putative func-
tion (Additional file 5: Table S4) suggesting that the cor-
responding phasiRNAs can also target these transcripts
or other members of the corresponding gene families.
Among the phasiRNA loci localized in transcripts with a
putative function, two are expressed in all the organs
and are derived from an Auxin signaling F-box 2 and a
NAC transcription factor-like 9. These two proteins are
related to auxin signaling and lateral root formation [94]
and the corresponding families are known to be com-
mon targets of phasiRNAs [40]. The presence of the cor-
responding phasiRNAs in all the studied organs reflects
the important role in regulation of these small RNAs at
the organism level. MiR2118 is predicted to target
two PHAS genes (Chr04:3128306–3129229 and Chr04:
9380782–9381299) coding for proteins involved in dis-
ease resistance: a NB-ARC domain-containing disease
resistance protein and a Disease resistance protein
(TIR-NBS-LRR class) family member. These loci are
expressed in all the organs except nodules (Additional
file 5: Table S4). Although miR2118 has been retrieved
in all the organs studied, including nodules, we can
hypothesize that miR2118 regulates nodule growth via
the control of phasiRNA production derived from the
NB-ARC and TIR-NBS-LRR transcripts. The investi-
gation of phasiRNAs is quite recent and we envisage
that, in coming years, the number of PHAS genes will
increase, as it has for miRNAs in recent years, in
terms of loci per species and number of studied plant
species.
Conclusions
Thanks to the genome-wide identification of miRNAs
and phasiRNAs from P. vulgaris, we provide a set of
sequences allowing the extension of the sRNAome of
common bean. The investigation of the miRNA degra-
dome targets, the miRNA correlation networks and
the PHAS gene function permitted us to propose a
role for the identified small RNAs and provide genuine
sequence candidates to be studied in plant-microorganism
interactions and specifically in the root-nodule symbiosis.
Methods
Plant materials and sequencing
Plant materials
Flower, leaf, root and seedling raw sequences were obtained
from the study of Peláez et al. [29]. Briefly, Phaseolus vul-
garis L. cv. Negro Jamapa and cv. Pinto Villa were grown
and roots (15 day old) and flower buds (35–40 day old)
were collected in liquid N2 and stored at - 80 °C. Whole 1–
4 days old seedlings were collected in liquid N2 and stored
at −80 °C. A pool of leaves from 10 and 20 day-old plants
was harvested for RNA purification.
Nodulation conditions
P. vulgaris L. cv. Negro Jamapa seeds were surface steril-
ized and germinated under sterile conditions for two
days and then planted in pots with sterile vermiculite. A
Formey et al. BMC Genomics  (2015) 16:423 Page 12 of 17
137
fresh inoculum of R. tropici CIAT899 grown on PY li-
quid medium supplemented with CaCl2 (7 μM), rifampi-
cin (50 μg/ml), and nalidixic acid (20 μg/ml) at 30 °C to
a cell density of 5 × 108 ml−1 was prepared. Immediately
after transferring bean plants into pots with fresh sterile
vermiculite, each plant was inoculated by adding 1 ml of
the R. tropici culture directly to the root. Plants were
grown in a greenhouse with a controlled environment
(25–28 °C, 16 h light/8 h dark) and were watered with
nitrogen-free B&D nutrient solution [95] every 2 days.
Root nodules were collected 18 and 27 days after
inoculation.
Library preparation and sequencing
Total RNA was isolated from frozen samples using the
Trizol reagent according to the manufacturer's instruc-
tions (Invitrogen, Carlsbad, CA) and 10 μg of each
sample was prepared for Deep Sequencing following
Illumina's Small RNA alternative sample preparation
protocol v1.5. Complementary DNA (cDNA) libraries
were separately prepared and Single Read-sequenced
using the Genome Analyzer IIx (GAIIx) (36 bp) and the
Illumina Cluster Station (Illumina Inc, USA) at the Insti-
tuto de Biotecnología (Universidad Nacional Autónoma
de México) and raw reads from the Illumina Pipeline 1.4
for the small RNA libraries were purged of sequence
adapters, low quality tags and small sequences (<16 nt
long) [29]. The contamination of the nodule library by
bacterial sequences was investigated by BLASTn of the
whole nodule small RNA set against the R. tropici
CIAT899 genome as a reference. Raw reads for the small
RNA sequencing are available in the Gene Expression
Omnibus database under accession number GSE67409.
Degradome library construction
Plant material
Seeds of P. vulgaris, var. Pinto Villa were surface steril-
ized in 50 % (v/v) sodium hypochlorite and 0.5 % (v/v)
Tween-20 for 3 min, rinsed with distilled water for
10 min and germinated in wet paper towels in the dark
at 24 °C for 3 days. At this point seedlings of similar size
were transferred to moist vermiculite watered to sub-
strate capacity and maintained at 24 °C for 48 h with
16 h/8 h light/dark day cycles. Seedlings were col-
lected, ground to a fine powder under liquid N2 and
preserved at −80 °C until the material was used for
RNA preparation.
Library construction for high throughput sequencing
The protocol used to obtain cDNA libraries for degra-
dome analysis was carried out essentially as previously
described [59, 96]. Total RNA from seedlings was ob-
tained using the Trizol reagent (Life Technologies) ac-
cording to the manufacturer’s directions. Subsequently,
poly-A+ RNA was ligated to a 5’adapter containing a
MmeI site at its 3’-end. The ligated products were used
for cDNA production and amplified by PCR for five
cycles. The PCR products were digested with MmeI and
the resulting fragments ligated to a second double-
stranded oligonucleotide, purified and amplified for
another ten PCR cycles. The final product was purified
and subjected to high throughput sequencing as de-
scribed below.
High Throughput sequencing
DNA libraries were subjected to Single Read-sequencing
(36 bp) using a Genome Analyzer IIx (GAIIx) and the
Illumina Cluster Station (Illumina Inc, USA) at the
UNAM Sequencing facility (Unidad Universitaria de
Secuenciación Masiva de DNA, Universidad Nacional
Autónoma de México). Raw reads for the degradome se-
quencing are available in the Gene Expression Omnibus
database under accession number GSE67432.
Bioinformatics analysis of sequencing data
miRNA identification
The identification of the miRNA precursors was per-
formed using the miRDeep-P pipeline [33] with a max-
imal identification window size of 250 nt, no mismatch
allowed and a number of hits in the genome lower than
40. The precursors with a small RNA overlapping an
ncRNA (tRNAs, rRNAs and other ncRNAs) were dis-
carded by the software. We recovered only the precur-
sors with a size of mature miRNA between 18 nt and
25 nt wherein the mature miRNA lies within the top
5 % of the most expressed sequences, in a given library.
The selected mature miRNAs were compared with all
the Viridiplantae miRNAs of the miRBase version 21
[28] using the NCBI BLASTn program [97], allowing no
mismatches to identify the already known miRNAs. To
identify the miRNAs families and the new isoforms of
already known miRNAs, we used CD-HIT (Cluster
Database at High Identity with Tolerance) [98] with at
least 84.2 % of identity. For the novel miRNAs, a more
stringent selection was performed and only those mature
sequences for which the best precursor miRDeep score
is greater than 2.2 (the threshold for the top 5 % of the
precursor miRDeep scores) have been kept. To list all
the precursors encoding for the final set of novel genu-
ine mature miRNAs, we recovered all the corresponding
precursors that have been identified by miRDeep-P and
passed all our filters without a selection on the attrib-
uted score.
Target identification
Two programs were used to identify the targets of the
selected miRNAs. CleaveLand ver.4 [99] was used to
identify the putative target sites from degradome data
Formey et al. BMC Genomics  (2015) 16:423 Page 13 of 17
138
(see Degradome library construction section). The tran-
scripts from the version 1 of the P. vulgaris genome [2]
were used as target templates, the total set of identified
miRNAs has been used as the small RNA candidates
and only targets with a p-value lower or equal to 0.05
were selected. psRNATarget [60] was used to predict
putative miRNA targets on the same transcript dataset.
Default parameters were used and only the targets with
an expectation value lower than 3 were retained.
To identify the functional distribution of these sets of
targets, we used the corresponding Gene Ontology
Annotation [100] and classified the corresponding GO
terms with CateGOrizer [101] using the GO_slim2 clas-
sification with the “accumulative all occurrences” count
method.
Organ distribution and conservation analyses
The organ distribution was performed manually: a
miRNA is considered as present in an organ when the
corresponding mature sequence is in the top 5 % of the
most expressed mapped sequences of the corresponding
organ library. The “five sets” Venn diagrams were in-
spired by Edwards’ Venn diagrams [102].
Conservation of mature miRNAs was investigated using
the NCBI BLASTn program [97] against the genomes of
8 species: two Fabaceae (Medicago truncatula 4.0 and
Glycine max Wm82.a2.v1), three non-legume eudicots
(Vitis vinifera Genoscope.12X, Populus trichocarpa 3.0
and Arabidopsis thaliana TAIR10), two monocots
(Oryza sativa 7.0 and Zea mays 6a) and the moss
Physcomitrella patens 3.0. No mismatches were allowed
in identifying the corresponding homologous sequences.
Weighted correlation network analysis of microRNAs
The weighted correlation networks were constructed
with the WGCNA package for R [65, 103] following the
automated one-step protocol and with the default pa-
rameters except the minimum module size of 3 miRNAs
in order to discover the smallest networks. We used
normalized expression data of the 185 mature miRNAs
according to the formula: (miRNA read number *
1.000.000)/total mapped reads per library. The eigengene
value was calculated for each module and networks in
order to test the significant association with the nodula-
tion trait (the difference between the nodule library and
the other libraries). Then, the networks were drawn
using Cytoscape [104].
PhasiRNA identification
Bowtie alignments [105] without mismatch were used to
predict the phasiRNAs. Phasing score [71] was calculated
for each 21 nt read mapped to the genome with a threshold
of 15 as mentioned in Zhai et al., [71]. To eliminate the
contribution of random fragments generated from RNA
degradation products a chi-test was performed per locus,
taking into account the number of reads in phase and the
reads not in phase (p < 0.01). Finally, a locus was predicted
as a phasiRNA if it passed the above filters and had 3 or
more 21 nt windows with a number of reads in the most
abundant 5 % and genome mapped reads in at least one
library. All loci located in transposable elements were re-
moved. Once the final phasiRNA-generating loci were
determined, they were tested for the identification of
phase-triggering microRNAs/phasiRNA-targeted tran-
scripts using psRNATarget [60] with default parameters
and a limit of expectation of 5.
Sample preparation and expression analysis
Total RNA was isolated from 100 mg of frozen roots
and nodules (21 dpi) from three biological replicates
using Trizol reagent (Life Technologies) following the
manufacturer’s instructions. cDNA was synthesized from
2 μg of total RNA using RevertAid™ H Minus First Strand
cDNA Synthesis Kit (Fermentas). Resulting cDNAs were
then diluted and used to perform qRT-PCR assays using
SYBR Green PCR Master Mix (Applied Biosystems), fol-
lowing the manufacturer’s instructions. The sequences of
oligonucleotide primers used are provided in Additional file
8: Table S5. Reactions were analyzed in a real-time thermo-
cycler Applied Biosystem 7300. Two technical replicates
were performed for each reaction. Relative expression was
calculated with the “comparative Ct method” and normal-
ized with the geometrical mean of three housekeeping
genes (HSP, MDH & Ubiquitin9) [106].
Availability of supporting data
The RNA-seq data sets supporting the results of this
study are available in the Gene Expression Omnibus
(GEO) database, under accession GSE67433.
Additional files
Additional file 1: Table S1. List of the identified microRNA precursors
and their corresponding mature forms. The blue, yellow and red lines
represent the known miRNAs, the new isomiRs and the novel miRNAs,
respectively. The “All precursors” sheet lists all the precursors identified
that fulfill our criteria. The “All matures nr” sheet lists all the non-redundant
mature miRNAs resulting from the “All precursors” list. The “Families” sheet lists
all the miRNA families identified in our study and the corresponding number
of members found.
Additional file 2: Table S2. List of the miRNAs and the corresponding
number of reads present in the different organs. The blue, yellow and
red lines represent the known miRNAs, the new isomiRs and the novel
miRNAs, respectively. The absence or presence of a “1” in the presence
columns indicate the absence or the presence of the corresponding
miRNA (lines) in the corresponding library (rows).
Additional file 3: Table S3. List of miRNA degradome and
psRNATarget-predicted targets. The blue, yellow and red lines represent
the targets of the known miRNAs, the new isomiRs and the novel miRNAs,
respectively. The “Degradome targets”, “Degradome targets rejected miRs”,
“psRNATarget-predicted targets” and “psRNATarget target reject” sheets list the
Formey et al. BMC Genomics  (2015) 16:423 Page 14 of 17
139
targets predicted by degradome data for the selected and rejected miRNAs,
and the targets predicted by psRNATarget for the selected and rejected
miRNAs, respectively.
Additional file 4: Figure S1. Distribution of sequencing reads from
soybean nodule libraries mapped on the soybean precursor of miRNov153.
(A) Minimum free energy structure prediction of miRNov153 precursor
of soybean. Heat colors represent the base-pair probabilities for each
nucleotide (Blue = 0; Red = 1). The brackets show the position of miRNov153
mature and star. (B) Visualization of the mapped read distribution on the
miRNov153 soybean precursor. miRNA reference sequence is at the top.
The oriented grey bars represent the mapped reds. The brackets show the
position of miRNov153 mature and star.
Additional file 5: Table S4. List of the identified PHAS genes and their
distribution in 5 plant organs. The presence of a “0” or “1” in the libraries
columns indicates the absence or presence of the corresponding PHAS
locus (lines) in the corresponding library (rows), respectively. The conservation
column indicates the initials of the legume species in which the PHAS locus is
conserved (Gma = Glycine max, Mtr =Medicago truncatula).
Additional file 6: Figure S2. Venn diagram of the conservation of the
PHAS genes in 3 legume species. Venn diagram of the distribution of
PHAS genes based on their presence in three legumes: Medicago
truncatula (red), Glycine max (yellow) and Phaseolus vulgaris (blue). The
numbers in each Venn diagram area correspond to the numbers of PHAS
genes encountered in the corresponding overlapping species area.
Additional file 7: Figure S3. Distribution of the identified PHAS genes
in 5 plant organs. Venn diagram of the distribution of PHAS gene
expression in the different studied organs. Red: flower, green: leaf, yellow:
root, blue: seedling and black: nodule. The numbers in each Venn
diagram area correspond to the numbers of expressed phasiRNA loci
encountered in the corresponding overlapping organ area.
Additional file 8: Table S5. List of oligonucleotide sequences used in
quantification experiments.
Abbreviations
5’RACE: 5'-rapid amplification of cDNA ends; AGO: Argonaute; bp: Base pair;
Cv: Cultivar; dsRNA: Double-strand RNA; GO: Gene Ontology; kcal: Kilocalorie;
miRNA: microRNA; mol: Molar; MYB: Myeloblastosis; NB-LRR: Nucleotide
Binding domain (NB) and a Leucine Rich Repeat (LRR) domain; ncRNA:
Non-coding RNA; NSP: Nodulation Signaling Pathway; nt: Nucleotide;
PPR: Pentatrico peptide repeat; RISC: RNA-induced silencing complex;
rRNA: Ribosomal RNA; TasiRNA: Trans-acting siRNAs; tRNA: Transfer RNA;
UTR: Untranslated region.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
DF performed the miRNA analyses, supervised all the analyses, the data
interpretation, and wrote the manuscript. LPI performed the phasiRNA
analyses, participated in data interpretation and contributed to the drafting
of the manuscript. FS and PP produced the sRNA libraries data. JLR, YFL and
RS produced the degradome data and JLR contributed to the drafting of the
manuscript. GH conceived and designed the whole project, contributed to
the drafting of the manuscript and gave final approval of the version to be
published. All authors read and approved the final manuscript.
Acknowledgements
JLR: Dirección General de Asuntos del Personal Académico (DGAPA),
Universidad Nacional Autónoma de México (UNAM), (PAPIIT: IN205112)
and Consejo Nacional de Ciencia y Tecnología (CONACyT) (no. 151571).
GH: DGAPA-UNAM (PAPIIT: IN202213). DF: Postdoctoral fellowship from
DGAPA-UNAM. LPI is a student in the Doctorado en Ciencias Biomédicas-UNAM
and a recipient of a studentship from CONACyT (No.340334). We are grateful
to Georgina Navarrete-Estrada from the Instituto de Biotecnología, UNAM for
technical assistance in preparation of sRNA libraries, to Zamri Ramírez Carbajal for
technical assistance in preparation of nodulation experiments and to Dr. Michael
Dunn for critically reviewing the manuscript.
Author details
1Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México
(UNAM), Av. Universidad 1001, Cuernavaca 62210, Morelos, Mexico.
2Departamento de Biología Molecular de Plantas, Instituto de Biotecnología
(UNAM), Av. Universidad 2001, Cuernavaca 62210, Morelos, Mexico.
3Department of Biochemistry and Molecular Biology, Oklahoma State
University, Stillwater, OK 74078, USA.
Received: 7 January 2015 Accepted: 18 May 2015
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