UNI PR GENÉ D VERSID POS EPARA TICA C IRECTO ASES AD N GRADO INST CIÓN I UANTI MIGU R DE TE OR DE TE ACION EN C ITUTO NMUNO TATIVA AEDE EL ÁNG SIS: DR. SIS: DR AL AU IENCIA DE E LÓGIC DE LA S AEGY EL MOR ALEJAND . HUMBE TÓNO S BIO COLOG A Y AS RESPU PTI ENO GA RO CÓR RTO LAN MA DE MÉDIC ÍA PECTO ESTA RCÍA DOBA A Z MEND MÉXI AS S DE L INMUN GUILAR OZA CO A E DE UNAM – Dirección General de Bibliotecas Tesis Digitales Restricciones de uso DERECHOS RESERVADOS © PROHIBIDA SU REPRODUCCIÓN TOTAL O PARCIAL Todo el material contenido en esta tesis esta protegido por la Ley Federal del Derecho de Autor (LFDA) de los Estados Unidos Mexicanos (México). El uso de imágenes, fragmentos de videos, y demás material que sea objeto de protección de los derechos de autor, será exclusivamente para fines educativos e informativos y deberá citar la fuente donde la obtuvo mencionando el autor o autores. Cualquier uso distinto como el lucro, reproducción, edición o modificación, será perseguido y sancionado por el respectivo titular de los Derechos de Autor. A mis padres Miguel Moreno y María Esther García A la Universidad Nacional Autónoma de México                                 AGRADECIMIENTOS Al Dr. Alejandro Córdoba por la dirección, consejos y todo el apoyo durante el tiempo que duro el desarrollo de la tesis. Muchas gracias por la amistad. Al Dr. Humberto Lanz por permitirme ser parte integral de su grupo de trabajo en el Instituto Nacional de Salud Pública, y dejarme desarrollar libremente las ideas en su modelo de estudio. Por todas las correcciones y consejos en los experimentos y las interpretaciones de resultados. Por el apoyo académico, económico y sobre todo personal. Muchas gracias por la confianza y amistad. Al Dr. Juan Núñez y Dra. Robyn Hudson por haber aceptado ser parte del comité tutoral. Muchas gracias por todas las sugerencias que hicieron crecer enormemente este trabajo. A los miembros del Jurado de Examen, Dr. Mario H. Rodríguez, Dr. Raúl Cueva, Dra. Ingeborg Becker, y Dr. Juan Fornoni muchas gracias por el tiempo invertido en la revisión de la tesis, y por las acertadas sugerencias que mejoraron el escrito de la tesis. Al Dr. Salvador Hernández por los protocolos. Al grupo de Malaria-Dengue por las sugerencias y buenos ratos. A los amigos y compañeros del INSP: Martha, Javier, Renaud, Lupita, Raúl, Toñita, Didier, Rebeca, Avigail, Priscila, Marisol, Raquel, Benito, Luis, Inci. A Dolores Méndez por todo el apoyo en trámites y por la amistad. A los amigos y compañeros del Instituto de Ecología, UNAM: Dr. Carlos Cordero, Rafa, Nubia, Víctor, Lizeth, Ceci, Jorge, Isaac, Raúl, Memo, Daniel, Daniela, Adriana, Ángela, Jesús, Robert, Ana Lesher, Karla, Allari. A los grandes amigos (y colegas) que hice en Cuernavaca: Alex “Veracruz”, Alex “El Jefe”, Fabi, Chalio, El Vera, Valeria, Jimena, Che y Brenda, Karlita, Bernardo, a Cassandra y toda su familia, Uli, Brenda y Richard, Yadira y Richard, More y su familia, muchas gracias por su amistad y abrirme las puertas de sus casas. A Peter, Axa, Magali, Emmanuel, Carmen, Horacio, Ana, Adriana, Manuel, Adrian, gracias. A CONACYT (Beca No. 172947), Instituto de Ecología, Posgrado en Ciencias Biomédicas, UNAM.   ÍNDICE CAPÍTULO I. INTRODUCCIÓN GENERAL I. TEORÍA INMUNOECOLÓGICA II. LA ESPECIE DE ESTUDIO: AEDES AEGYPTI III. AEDES AEGYPTI Y EL VIRUS DENGUE IV. REFERENCIAS CAPÍTULO II. EVOLUTIONARY ECOLOGY OF INSECT IMMUNITY I. INTRODUCTION II. LIFE HISTORY THEORY AND IMMUNITY III. IMMUNITY AND SEXUAL SELECTION IV. PATHOGEN VIRULENCE, MULTIPLE INFECTIONS AND WITHIN-HOST COMPETITION V. MECHANISMS OF INSECT IMMUNE RESPONSE VI. QUANTITATIVE GENETICS OF THE IMMUNE RESPONSE VII. CONCLUSIONS VIII. REFERENCES CAPÍTULO III. ANTICIPATORY RESPONSE IN AEDES AEGYPTI AGAINST BACTERIAL CHALLENGES I. INTRODUCTION II. MATERIALS AND METHODS III. RESULTS IV. DISCUSSION V. REFERENCES VI. TABLES VII. FIGURES CAPÍTULO IV. GENETIC VARIANCE AND GENOTYPE-BY-ENVIRONMENT INTERACTION OF IMMUNE RESPONSE IN AEDES AEGYPTI (DIPTERA: CULICIDAE) I. INTRODUCTION II. MATERIALS AND METHODS III. RESULTS IV. DISCUSSION V. REFERENCES VI. TABLES VII. FIGURES CAPÍTULO V. DISCUSIÓN Y CONCLUSIÓN GENERAL APÉNDICE 1. CURRENT IMMUNITY MARKERS IN INSECT ECOLOGICAL IMMUNOLOGY: ASSUMED TRADE-OFFS AND METHODOLOGICAL ISSUES I. INTRODUCTION II. RATIONALE BEHIND THE IMMUNE MARKERS USED IN EVOLUTIONARY ECOLOGY RESEARCH III. SOME CONCLUDING REMARKS IV. REFERENCES V. TABLE 1 CAPÍTULO I. INTRODUCCIÓN GENERAL Teoría Inmunoecológica El sistema inmune de los animales es un medio de defensa que ha evolucionado para proteger al organismo de los efectos ocasionados por agentes infecciosos. Estudios recientes (ver Schmid- Hempel 2005) han revelado las células y moléculas que participan en el reconocimiento, eliminación y/o tolerancia a los agentes patógenos y sus efectos. Actualmente se sabe que estas células y moléculas actúan de manera conjunta entre sí, y con otros sistemas (ej. nervioso, reproductivo; Tu, Flat & Tatat 2005). Por esta razón se ha propuesto que la actividad inmune está íntimamente relacionada con las estrategias de ciclo de vida de los organismos involucradas con la supervivencia y reproducción diferencial (i.e. adecuación) en un ambiente dado. A este conjunto de estrategias se le conoce como la historia de vida de un organismo, y en conjunto maximizan la probabilidad de supervivencia y reproducción (Stearns, 1992). Estas estrategias están asociadas a características como el tamaño, edad a la maduración, tasa de crecimiento, desarrollo y reproducción. Existen también características asociadas a las características de historia de vida, como la coloración corporal, comportamientos de apareamiento, producción hormonal, y que se clasifican comúnmente como características morfológicas, fisiológicas y conductuales que también están relacionadas con la supervivencia y reproduccón diferencial del organismo. Hamilton y Zuk (1982) y Folstad y Karter (1992), fueron los primeros en proponer que la capacidad inmune de los organismos podía estar relacionada con las características de historias de vida (e intermediarias) antes mencionadas. Estos autores propusieron que la resistencia a parásitos en vertebrados está relacionada con la expresión de características sexuales secundarias relacionadas con la elección de pareja. A partir de entonces surge formalmente una nueva rama de la biología conocida como inmunoecología (“ecological immunity”). La inmunoecología trata de explicar cuáles son los procesos microevolutivos (presiones selectivas, flujo de genes entre poblaciones, efectos de deriva génica y mutaciones) y cómo, en combinación con factores ambientales (ej. cantidad y calidad de recursos, humedad, temperatura) y de interacción con otros organismos (ej. competencia, depredación, parasitismo), ha evolucionado la forma en que los organismos montan una respuesta inmune en contra de patógenos. La evidencia encontrada en un amplio número de insectos, muestra que existe un costo evolutivo de la habilidad de los organismos de montar y mantener una respuesta inmune para minimizar los efectos de una infección, a esta habilidad se le conoce como inmunocompetencia (Owens & Wilson 1999). Este costo es debido a que las características dirigidas a lidiar con los patógenos puedan estar relacionadas negativamente con características de historias de vida. La respuesta inmune al covariar negativamente con otras características de historia de vida puede estar generando la aparición de disyuntivas (o trade-offs en inglés) (la disyuntiva más común es supervivencia vs. reproducción). Las disyuntivas ocurren cuando dos o más características no pueden ser favorecidas por el mecanismo de la selcción natural (Núñez-Farfán 1993), esto a pesar de que las características en disyuntiva puedan estar genéticamente correlacionadas (Cheveraud et al, 1983) y contribuyendo a incrementar el éxito reproductivo del organismo. De forma tal que las disyuntivas podrán limitar la evolución de las características que se encuentran en disyuntiva. La base de las disyuntivas radica en que los recursos requeridos para la generación de la respuesta inmune pueden ser recursos que también son requeridos para la expresión de otras características involucradas en la supervivencia y reproducción (Zuk y Stoehr 2002). A pesar del amplio conocimiento que se ha generado en los últimos años dentro de la rama de la inmunoecología y que demuestra la existencia de disyuntivas, y su repercusión en la evolución de la respuesta inmune, existen tópicos poco examinados. Dentro de estos se encuentran los efectos que tiene la constante presencia de un patógeno en la respuesta inmune y componentes de adecuación del hospedero; y la variación fenotípica de origen genético y no genético y a la sensibilidad de la respuesta inmune ante condiciones ambientales heterogéneas. El objetivo general de esta tesis es incrementar el conocimiento de estos dos puntos antes mencionados. Esto mediante (1) la evaluación del efecto que tienen encuentros consecutivos con un patógeno sobre la respuesta inmune, supervivencia y reproducción del mosquito Aedes aegypti; y (2) cómo se afecta la respuesta inmune ante la heterogeneidad en los recursos alimenticios. Este último punto, abordando las diferencias que existen entre la hembra y macho del mosquito, y las determinantes genéticas y ambientales de la respuesta inmune. El conocimiento generado a partir de esta tesis podrá ser utilizado para aumentar la comprensión de la biología de esta especie de mosquito vector del virus Dengue y del de la Fiebre amarilla, y en un futuro proponer estrategias para su control. Esto con la finalidad de reducir el impacto que tiene sobre la salud de las poblaciones humanas. La tesis comienza con un apartado (capítulo II) en el cual se revisan las disyuntivas generadas entre efectores de la respuesta inmune y otras características (morfológicas y conductuales). Se exponen las teorías de las razones de dichas disyuntivas. Se abordan temas que han sido poco estudiados como interacciones patógeno-hospedero y de competencia patógeno-patógeno y su influencia en la evolución del hospedero y del patógeno. Se contemplan las diferencias entre sexos, y su consecuencia evolutiva, que sería el dimorfismo sexual en caracteres morfológicos y de respuesta inmune. Brevemente, y para contemplar su relación con factores ecológicos, se mencionan los mecanismos de generación de la respuesta inmune, y se exploran algunas ideas sobre la capacidad que tiene el sistema inmune de insectos para responder de una manera eficaz a los ataques de patógenos. Por último, se exponen hipótesis que podrían explicar la evolución de la respuesta inmune, esto considerando la coevolución entre el patógeno y su hospedero; a través de la correlación entre componentes del sistema inmune; y limitaciones generadas por estas asociaciones. Se contempla al estudio de la variación fenotípica de características de respuesta inmune para determinar cómo influye el ambiente sobre la expresión de caracteres cuantitativos (controlados por más de un gen). Se examinan los posibles efectos de la estocasticidad ambiental y efectos maternos sobre dichas características. El grado de especificidad del sistema inmune de insectos no cuenta con un reconocimiento y respuesta contra agentes infecciosos después de un contacto inicial (como ocurre en vertebrados), es posible que tengan la capacidad de mostrar una mejora su respuesta inmune a lo largo de lo ontogenia e inclusive que pueda ser heredable. Existe evidencia (ver Elliot et al, 2003; Kurtz y Franz, 2003; Moret y Siva-Jothy, 2003) que insectos, presentan un fenómeno análogo a la memoria de vertebrados, denominado prevención inmunológica (“immunological priming”; Little y Kraaijeveld, 2004; Little et al, 2005). Los organismos generan una posterior respuesta inmune más eficaz después de haber tenido una experiencia previa con agentes infecciosos. Sin embargo cabe aclarar que para invertebrados el uso del término memoria se limita a una mejora en la respuesta inmune en posteriores retos inmunes. En el capítulo III se evalúa la capacidad que tiene el sistema inmune del mosquito para incrementar su supervivencia al ser infectado con patógenos con los que previamente ya ha tenido contacto. De igual forma, se evalúan dos parámetros inmunes (actividad de fenoloxidasa y producción de óxido nítrico) que posiblemente están relacionados con la capacidad de desarrollar un tipo de memoria inmunológica. Los organismos tienen que enfrentar el problema de cómo maximizar su adecuación (en términos de supervivencia y reproducción) en ambientes heterogéneos y/o estresantes, los individuos con una constitución genética que permita variaciones fenotípicas para ajustarse a los diferentes cambios ambientales podrían tener una ventaja selectiva en dicho ambiente (Zhivotovsky et al, 1996). En algunas ocasiones un solo genotipo puede tener la capacidad de producir varios fenotipos alternativos como resultado de su interacción (y sensibilidad) con diferentes ambientes. A esto se le conoce como plasticidad fenotípica (Roff, 1997; Nylin y Gotthard, 1998). Existe evidencia de que la limitación de alimento afecta negativamente la respuesta inmune en insectos (Fellowes, 1998; Siva-Jothy y Thompson, 2002). Dado que los organismos tienen que enfrentar el problema de cómo maximizar su supervivencia y reproducción en ambientes que están en constante cambio, los individuos con una constitución genética que permita variaciones fenotípicas para ajustarse a los diferentes cambios ambientales podrían tener una ventaja selectiva en un ambiente cambiante (Via et al, 1995). Bajo este contexto, en el capítulo IV se estimó el componente genético y ambiental de la respuesta inmune, si existe plasticidad fenotípica y si existe variación genética para la plasticidad (componente ambiental e interacción GxA) de la respuesta inmune de mosquitos en condiciones de alimentación limitadas. Asociado a esto, se evalúa si existen diferencias entre los sexos en la forma de responder al ambiente. Es probable que, en caso de existir, las diferencias inmunológicas entre sexos sean consecuencia de la variación en la disponibilidad y tipo de recursos utilizados distintamente por hembras y machos. En el capítulo IV se expone una breve discusión resaltando los principales resultados obtenidos en esta tesis. Además se proponen perspectivas de estudio y el potencial de aplicación en un futuro del conocimiento generado. Adicionalmente en el apéndice I se explican las metodologías comunmente usadas para la cuantificación de respuesta a parámetros inmunológicos. Se describe el contexto ecológico y evolutivo de cada uno de estos parámetros y se ofrecen hipótesis que explican la posible relación entre efectores de la respuesta, recurso necesarios para esto y la consecuencia ecológica (disyuntivas) y evolutiva. Se ofrecen sugerencias para lograr una óptima medición de la respuesta inmune. La especie de estudio: AEDES AEGYPTI Aedes aegypti (Diptera, Culicidae) tiene una amplia distribución entre los trópicos y zonas subtropicales llegando hasta los 40o Sur y 45o Norte, (Nelson, 1986; Badii et al., 2007). Por lo general habita en áreas geográficas con una temperatura media anual entre 17-30ºC (Ibáñez- Bernal & Gómez-Dantés 1995). Su rango de distribución altitudinal llega a los 2400 m.s.n.m. (Badii et al 2007). En México el rango de distribución estimado abarca 29 estados (Ibáñez- Bernal y Gómez-Dantés 1995). A. aegypti tiene cuatro estadios en su desarrollo postembrionario: huevo, larva, pupa y mosquito adulto (Fig. 1.). Los huevos son puestos en sustratos sólidos ubicados en la interface agua-tierra. Después de que ha ocurrido la oviposición, se da la melanización, lo que les confiere resistencia a la desecación, entrando en diapausa durante periodos secos, permaneciendo viables hasta 2 años (Christophers 1960). Cada hembra produce de 100 a 120 huevos por puesta (Apóstol et al 1994), con un éxito de eclosión muy alto. La larva eclosionada es acuática y pasa por cuatro fases. Las tres primeras fases tienen un desarrollo rápido, mientras que la última es más prolongada que es cuando la larva aumenta de tamaño y peso adquiriendo recursos para el periodo de metamorfosis (Nasci 1986). Por lo general, el tiempo de desarrollo de la etapa larval es de una semana (Christophers 1960), pero en condiciones de baja temperatura o escasez de alimento, la cuarta fase larval puede prolongarse por varias semanas antes de transformarse en pupa (Tun-Lin et al 2000). La mortalidad más alta ocurre durante las dos primeras fases larvales. Su fuente de alimentación se compone de microorganismos, particularmente hongos, algas, protozoarios y otros insectos (Merrit et al., 1992). La pupa es la fase de metamorfosis de larva a adulto, la cual tiene la cualidad de desplazarse activamente en el medio acuático en respuesta a estímulos externos como vibraciones o cambios en intensidad lumínica. Esta etapa del ciclo de vida dura aproximadamente de dos a tres días (Christophers, 1960).Las larvas y las pupas de los machos se desarrollan más rápido que las de las hembras. El mosquito adulto recién emergido pasa sus primeras 24 horas en reposo, periodo durante el cual se completa el desarrollo (Clements, 1999). El mosquito presenta dimorfismo sexual, las hembras son más grandes que los machos, sus antenas tienen vellos cortos y escasos, y los palpos son de un tercio o menos de longitud que la proboscis (Busvine, 1975), mientras que el macho tiene antenas plumosas con pelos largos y abundantes y palpos con un tamaño similar a la proboscis (Busvine, 1975). Los machos son poligínicos, y las hembras presentan un patrón de apareamiento monándrico. Un apareamiento es suficiente para fecundar sus huevos. La conducta de reproductiva y de alimentación de sangre (en hembras) aparece entre las 24 y 72 horas post- emergencia adulta (Clements, 1999). Los machos se alimentan del néctar de las flores, las hembras de azúcares y de sangre; esta última le confiere nutrientes para la maduración de los huevos (Clements, 1999). Después de cada alimentación sanguínea se desarrolla un lote de huevos, aunque una alimentación reducida no conlleva a la producción de huevos. Después de 48 a 72 horas post alimentación, se realiza la oviposición (Carrada et al., 1984). Terminada la oviposición, la hembra reanuda la conducta de búsqueda de una nueve fuente de sangre para la producción del siguiente grupo de huevos. El adulto, en condiciones naturales sobrevive en promedio de 15 a 30 días (Badii et al., 2007). Figura 1. Ciclo de desarrollo de Aedes aegypti. AEDES AEGYPTI y el virus Dengue El mosquito Aedes aegypti es el principal vector del virus dengue, ocasionando fiebre del dengue y fiebre hemorrágica del dengue en humanos (Clarke, 2002). La invasión del virus al mosquito solo ocurre en los casos en que la hembra, al momento de la alimentación sanguínea, pica a un hospedero que porta al virus. Esta hembra ya infectada podrá transmitir el virus a hospederos sanos. La capacidad de diapausa que tienen los huevos del mosquito, rápido ciclo de vida y la preferencia de la hembra por ovipositar en cuerpos de agua relativamente pequeños, son factores que han contribuido en la expansión del rango de distribución del mosquito (Fig. 2). Unido a esto, la preferencia del mosquito a ambientes domésticos en su ciclo de vida y una aclimatación muy rápida a áreas urbanas rurales, y probablemente el cambio climático global, contribuye a que el virus dengue también pueda expandir su rango de distribución (Gubler, 2002), dado que la presencia del vector es indispensable para la transmisión del virus. El control de esta enfermedad Adulto Huevo Larva Pupa req pa Fi RE Ap Ba Bu Ca Ch Ch uiere del c ra el manej gura 2. Exp FERENCIA óstol BL, DNA a families dii MH, La (Ecolog svine, J.R. rrada BT, Investig everud JM correlat 895-905 ristophers Bionom onocimient o racional d ansión esti S Black WC mplified by at oviposit nderos L, C y and histo 1975. Arth Vázquez V ación Preli , Rutledge ions among . SR. 1960. ics and Stru o integral d e este vecto mada del ra ( , Reiter P, polimeras ion sites in erna E, Ab ry of dengu ropod Vect L, García minar. Terc JJ, Atchley age-specif Aedes aeg cture. Cam el mosquit r. ngo de dist tomado de Miller BR a chain re San Juan, P reu JL. 200 e in Americ ors of Disea IL. 1984 era parte. S WR. 198 ic trait valu ypti (L): bridge Uni o con una v ribución de Gubler, 200 . 1994. Use action to e uerto Rico 7. Ecología as). Int. J. se. Edward . La ecolo al. Púb. Me 3. Quantita es and the the Yellow versity Pre isión holíst Aedes aegy 2). of random stimate the . Am. J. Tro e historia Good Cons Arnold & gía del de x. 26: 297- tive geneti evolution e Fever M ss, Cambrid ica que gen pti en Am ly amplifi number o p. Med. Hy del dengue cience 2: 3 Company, ngue y el 311. cs of devel of ontogen osquito: i ge. ere inform érica hasta ed polymo f Aedes ae g. 51: 89-9 en las Amé 09-333. London. 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Handbook of the biology of aging. 6th edition. San Diego: Academic Press (Elsevier), 415-448. Tun-Lin W, Burkot TR, Kay BH. 2000. Effects of temperature and larla diet on development rates and survival of the dengue vector Aedes aegypti in north Queensland, Australia. Med. Vet. Entomol. 14: 31-37. Via S, Gomulkiewicz R, DeJong G, Scheiner SM, Schlichting CD, Van Tienderen PH. 1995. Adaptative phenotypic plasticity: consensus and controversy. TREE 10: 212-217 Zhivotovsky LA, Feldman MW, Bergman A. 1996. On the evolution of phenotypic plasticity in a spatially heterogeneous environment. Evolution 50: 547-558. Zuk M, Stoehr AM. 2002. Immune defences and host life history. Am. Nat. 160: S9-S22. CAPÍTULO II EVOLUTIONARY ECOLOGY OF INSECT IMMUNITY Miguel Moreno-García1,2 Humberto Lanz-Mendoza2 Alex Córdoba-Aguilar1 1Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Apdo. P. 70-275, Circuito Exterior, Ciudad Universitaria, 04510, Coyoacán, Distrito Federal, México. Tel: ++ 52 (55) 56 22 90 03; fax: ++ 52(55) 56 16 19 76; e-mail: miguelmoga2000@yahoo.com.mx 2Centro de Investigación Sobre Enfermedades Infecciosas, Instituto Nacional de Salud Pública, Avda. Universidad 655. Col. Sta. María Ahuacatitlán, 62508 Cuernavaca, Morelos, Mexico. ABSTRACT A large number of recent ecological studies have reported interactions between immunity and traits related to survival and reproduction (i.e. life history traits). In addition, recent molecular/physiological immune mechanisms studies have revealed a wide array of efficient immune responses, which can be enhanced through ontogeny and transferred to the offspring (immunological priming). Here, we review and identify areas where more investigation and integration of information from these different biological fields is needed. We also discuss possible explanations for variation in insect immune response highlighting the ecological and physiological contexts of trade-offs between immunity and other life history traits. We emphasize the analysis of the genetical and environmental bases of variation in immune response, as a way to expand our knowledge of insect immunity evolution and its mechanisms. An interdisciplinary approach will be a key to the advancement of the emerging discipline of immunoecology. KEYWORDS Immune response, insects, immunological priming, immunoecology, life history theory, quantitative genetics INTRODUCTION The understanding of insect immunity has considerably expanded in recent years. Ecological evidence demonstrates that it is a trait deeply involved in the evolutionary ecology of the reproductive and survival strategies of the organisms. The vast wealth of information on the area, demands integration in order to fully conceptualize the participation and consequences of immune response, and its genetic and phenotypic variation within and among individuals, populations and species. The primary goal of this review is to analyse the current knowledge and ideas related to the adaptive functionality and variation of insect immune response, considering: (1) the ecological context in which immune response is expressed in terms of required resources and related with other fitness-related traits, as well as the evolutionary consequences on life history strategies and the effect of pathogen virulence on the host; (2) the mechanisms responsible for immune response operation with a focus on the possible evolutionary outcome of maladaptive immune responses and individual-primed immune responses through ontogeny and between generations; and (3) the evolutionary features via individual phenotyíc variation (quantitative genetics) of immune response, genetic correlations, and the production of different phenotypes according to distinct environmental conditions, as well as the causes and possible adaptive value of this variation. All these ideas are linked using a standpoint that integrates ecological, physiological and population genetics perspectives. It is hoped that this review will facilitate the reflection on encourage a more unified approach to the study of insect immune responses LIFE HISTORY THEORY AND IMMUNITY The majority of empirical research in the field of ecological immunity has been under the life history and trade-offs theory perspective. Life history theory seeks to explain the evolution of traits (e.g. size at birth, growth and mortality rates, size and age at maturity, clutch size and reproductive effort) (modified from Stearns, 1976; Roff, 1992; Stearns, 1992) all related with the life history of an individual, and the changing environmental conditions to which organisms are exposed. These traits are commonly shaped by intrinsec (genetic, or physiological) restricition called “trade-offs” (Stearns, 2000), which affect both survival and reproduction. Trade-offs occur when natural and/or sexual selection is unable to maximize the expression of two or more traits at the same time (Núñez-Farfán, 1993), due to possible genetic or phenotypic correlation with other traits, and despite their potential contribution to fitness (Cheverud, Rutledge & Atchley, 1983) In insects there is growing empirical evidence of trade-offs between these traits and immune defense. Examples include the reduction in larval competitive ability for dietary resources (Kraaijeveld & Godfray, 1997), an increase in predation vulnerability (Rigby & Jokela, 2000), longer developmental time, reduction in egg viability, lower survival rates (Fellowes, Kraaijeveld & Godfray, 1998; Boots & Begon, 1993; Moret & Schmid-Hempel, 2000; Hoang, 2001), and a decrease in reproductive success (Sandland & Minchella, 2003a; Schmid-Hempel & Schmid- Hempel, 1998). Thus, the ability of an insect to minimize an infection (i.e. immunocompetence, Owens & Wilson, 1999) represents an evolutionary cost given its negative covariation with other fitness components (Schmid-Hempel, 2003). Ecological basis of trade-offs Infections affects the optimal allocation of host resources, changing the host’s “internal state” (Agnew, Koella & Michalakis, 2000),in terms of energy reserves and viability (Iwasa, Pomiankowsli & Nee, 1991; Iwasa & Pomiankowski, 1994). In addition, the required resources used for the immune response are also demanded for the expression of other traits linked with survival and reproduction (Sheldon & Verhulst, 1996), resulting in a trade-off. Organisms have limited resources; as a result, they cannot maximize investment on all life history and/or intermediate traits (e.g. body size, colored traits, behavioral traits, etc.) or immunity. Therefore, organisms must physiologically “decide” how to allocate available resources to maximize fitness. Optimal allocation depends on the strategies used to increase fitness in the particular habitat of the organism (Schlichting & Pigluicci, 1998). Trade-offs between the immune response and other traits are usually detected in stressing environments (Sandland & Minchela 2003b), particularly with variables involved in the organism’s internal state, or condition such as parasite load or prevalence/intensity of infection (McNamara & Houston, 1996), and external variables (e.g. food abundance, predators, population density, etc). Under favorable environmental conditions, trade-offs can be masked, since the value of life history traits are high (de Jong & Van Noordwijk, 1992) or the costs imposed by immune system activation and the consequent trade-offs are not evident (Moret & Schmid-Hempel, 2000). The occurrence of trade-offs may therefore depend on the amount of resources available, acquisition efficiency and their optimal allocation (Van Noordwijk & de Jong, 1986). Survival, reproduction and immunity Life history theory predicts that survival and fecundity are not usually maximized simultaneously (Roff, 1992). For example, current reproduction can negatively affect future reproduction due to the prevailing trade-off between reproduction (which uses resources for the expression of sexually selected traits, quantity and quality of eggs produced, etc.) and survival (i.e. resources used to escape from predators, competence or immunity; Reznick, Nunney & Tessier, 2000). The organism can also allocate its resources in relation to chances of future reproduction which is strongly related to life expectancy. Thus, when there is a high probability of future reproduction, an organism must allocate enough resources to current reproduction in such a way that this investment will not affect subsequent reproductive events (Williams, 1966). On the other hand, when the probability of future reproduction is low ( i.e. lack of resources, environmental heterogeneity, incidence of infectious agents), the organism must allocate a large portion of its resources to current reproduction (Fessler et al., 2005). Although this terminal investment theory was originally laid out for iteroparous organisms, it may be applied to semelparous organisms, as long as reproduction occurs in multiple egg laying events (as with insects) where there is sufficient temporal separation in which the organism is expected to vary in investment. This can be expected in social insects, given their generally long life expectancies and high risk of infection due to living in large colonies compared to non-social insects. Survival and reproductive strategies adopted by an individual also depend on the force and course of infection. Van Baalen (1998) proposed a theoretical host recovery threshold, thus when the intensityof infection is below this threshold (i.e. no significant damage to the host), the organism should not make any investment to immune response at all. Above, but still close to the threshold, it then pays to allocate more into recovery and survival. However, when infection is high, without recovery possibilities, organism must allocate all resources to reproduction because a new infection may occur almost immediately leaving the animal without survival probabilities. Heterogeneity in food availability and kind and burden (and their interaction) of pathogens will have an effect on the evolution of the survival and reproductive strategies. Social insects seem very adequate to test these ideas as the risk of infection is higher in these animals compared to non-social insects. IMMUNITY AND SEXUAL SELECTION Sexually selected traits and immune response The evolution of the immune response has been also examined under the context of sexual selection (competition to leave more offspring; Darwin, 1871), Recent evidence suggests that trade-offs between immunity and sexually selected traits (SST) favored during competition for mates, are common in nature (e.g. Siva-Jothy, 2000; Rantala et al., 2003; Contreras-Garduño, Canales-Lazcano & Córdoba-Aguilar, 2006; Hosken, 2001; McKean & Nunney, 2001). Hamilton and Zuk (1982) were the first to suggest that in vertebrates pathogen resistance is correlated with the expression of SST, presumably because, androgenic hormones necessary for the expression of sexual traits and behavior can act as immunosuppressors, giving rise to a physiological trade-off (Folstad & Karter, 1992), and rendering males more prone to infection. Thus, SST only could be expressed if males are able to fight-off infections in spite of the immunosuppressive action of hormones, so that the expression of SST becomes an honest indicator of male immunocompetence both to females (during intersexual selection) and/or males (during intrasexual selection) (Folstad & Karter, 1992). Insects have fundamentally different hormones to those used by vertebrates (Sheridan et al., 2000), but the rationale would be the same as vertebrates. Insects produce juvenile hormone, which has a physiological effect in several traits, for example: sexual maturation, pheromone production, ovaries development, courtship behavior, morphological polyphenisms (Flatt, Tu & Tatar, 2005) and immune function (Tu, Flatt & Tatar, 2005, Rolff & Siva-Jothy, 2002). This suggests that probable, JH induces or prevents allocation of resources to different functions. In beetles the trade- off between immune function and pheromone production seems physiologically mediated by JH (Rantala, Vainikka & Kortet, 2003). As in vertebrates, only males in good condition (i.e. males that can deal successfully with infections,) will be able to produce and maintain SST. These traits could be assessed by females when basing their mating decisions with the presumable indirect benefit of giving birth to resistant offspring. One essential piece in the immunocompetence hypothesis is that female offspring would obtain disease resistance genes as signaled by SST. However, the relation between SST and the acquisition of genes related to disease resistance is not necessarily direct. Adamo and Spiteri (2005) recently proposed that females may accrue the direct benefit of avoiding infections when mating with males of current good health status (Able, 1996). Furthermore, selection for healthy males may not only be maintained via female choice; intrasexual competition can also contribute to the maintenance of honest traits, as long as male-male competition implies energetically costly endurance. If males use signals to indicate their fighting ability, it is then likely that these signals may indirectly reveal immune condition. Contreras-Garduño et al. (2006) and Serrano-Meneses et al (2007) found that in the damselfly Hetaerina americana, male wing coloration seemed to indicate territorial fighting ability, in terms of energy reserves. However, unlike other calopterygids of more recent origin (e.g. those of the genus Calopteryx), H. americana males do no court females but simply grab them and invariably mate with them. Wing coloration areas in species where males court females (e.g. Calopteryx) and Hetaerina males also positively correlate with immune ability. Given these relationships, in the Hetaerina case, it makes more sense to admit that SST indicates energetic condition, rather than immune ability to potential fighting contestants. However, even if courtship does not seem to occur in Hetaerina it does not reject the hypothesis that females still obtaining direct benefits for their offspring if they matemales with high fighting abilities (if it has a heritable basis) (Adamo & Spiteri, 2005; Contreras-Garduño, Lanz-Mendoza & Córdoba-Aguilar, 2007). In some fly and cricket species, male seminal products bear an immunosuppressive effect on the female (McGraw, Gibson & Clark, 2004; Fedorka, Zuk & Mousseau, 2004) diminishing her fitness (Fedorka & Zuk, 2005). Males could increase their fertilization success by reducing female immunocompetence, increasing sperm survival, preventing sperm to be recognized as foreign invading bodies while avoiding female immune response (Fedorka & Zuk, 2005).This sexual conflict perspective for immune response evolution is unique and gives support to this hypothesis, and it should be considered as an alternative to female choice. Mating systems and differences between the sexes Sexual selection intensity differs among species depending on their mating system (monogamy vs. polygamy) (Andersson, 1994). This varying intensity should promote differences in the pattern of investment to immune defense by each sex. In polygynous species, males are expected to increase their fitness by reducing their investment on immune defense while investing resources on reproductive effort (for example SST) (Zuk & McKean, 1996; Sadd et al., 2006). On the other hand, natural selection would favor an increase in resource investment to immunity in females, under the assumption that increased longevity could enhance fitness via egg production (Rolff, 2002). In Acheta domesticus, females increase egg production when a bacterial infection occurs, compensating the reduced life expectancy and future reproduction due to infection (Adamo 1999). Whereas in another cricket;Gryllus texensis, male immunocompetence decreased in the face of an infection, supporting the hypothesis that males trade-off immune response for reproduction (Adamo et al 2001). Sex biases in immunity may be due to the different cost for SST compared to egg production and laying, and the presumed selection for longer life expectancy for females compared to males (Stoehr & Kokko 2006; Forbes, 2007). One missing piece in the presumed sex bias in immunity is the difference in kinds of pathogens attacking each sex. For example, in mosquitoes, adult females require carbohydrates (sugar) and proteins for vitellogenesis which usually comes in the blood meal. While males, feed on sugar solutions (nectar) (Clements, 1999). This difference does not mean that females are more prone to infections than males, but divergent host-pathogen interactions for each sex are possible and can be reflected in the course of action of individual immune responses. This disparity could lead to wrong conclusions when testing for sex immunity differences, where the same artificial infection (e.g. inoculation of bacteria, yeast, etc.) is employed equally in males as in females. Future studies must consider if males and females share the same pathogens before testing and assuming other reasons for sex immunity biases. PATHOGEN VIRULENCE, MULTIPLE INFECTIONS AND WITHIN-HOST COMPETITION In order to explain immunological survival or reproductive differences in the kind of pathogens and its virulence (i.e. harm imposed on a host measured in terms of a reduction in survival or fecundity due to pathogen growth or reproduction) must be considered. Pathogen fitness depends on transmission success to new hosts (May & Anderson, 1983). The transmission increases through enhanced growth or replication rates (Ebert, 1998), but with an associate cost: host fitness. Since this can lead to pathogen dead too, there will be a trade-off between fecundity and longevity for the pathogen (Frank, 1996). Therefore it is expected that pathogens change their virulence depending on the host’s natural history. For example, if reproductive adults have a decreased immune response, pathogens should reduce their virulence, although high levels of virulence can be advantageous when infecting non-immune suppressed females (see Pfennig, 2001). Selection could be then favoring the maintenance of sex-or taxon-specific strategies, based on an optimal virulence level understood as that strategy that increases fecundity and longevity of pathogens with low host damage. Different immunological degree responses among hosts are also expected. In order to prove differential effects of pathogens on host, we need measures of differential virulence damage since, currently, variation in virulence is ignored. An individual can be infected by more than one pathogen lineage (Read & Taylor, 2001). Higher levels of virulence could arise because within-host competition (Frank, 1996). In a mixed infection, low lethal pathogens can be eliminated through competitive exclusion by more virulent pathogens or strains, so selection will favor pathogens or strains less likely to be competitively suppressed (de Roode et al., 2005). Different spatial and temporarily virulence heterogeneity in within-host competition becomes a fluctuating selective pressure altering the expression, variation and evolution of immune mechanisms of insects through generations. How the host’s immune ability varies, according to both virulence and within-host competition, in the context of studies of the evolution of immune response is still an open niche for further investigation. MECHANISMS OF INSECT IMMUNE RESPONSE The immune system seeks to maintain a relative homeostatic state under a wide variety of internal and external conditions (e.g. development, infectious agents). The immune responses in insects include cells and products which are interconnected (Hoffmann et al., 1999) (Fig. 1). Here we briefly review general mechanism of insect immunity to build up frameworks of physiological trade-offs, linking immunopathology and immunological priming. The first lines of defense include the exoskeleton cuticle as a physical barrier, and epidermis, gut epithelium, and male and female reproductive accessory glands (Gillespie et al, 1997; Casteels, 1998). These tissues can secrete cytotoxic molecules like lysozymes, reactive oxygen species (ROS, e.g. superoxide anions, peroxides, hydroxyl radicals) (Schmid-Hempel, 2005a), which are transported to the wound or where infection take place (Nappi & Ottovianni, 2000). Recongnition of pathogens is needed. The membrane of haemocytes comprises proteins called Pattern Recognition Receptors (PRRs). PRRs recognize conserved molecular features of pathogens called Pathogen-AssociatedMolecular Patterns (PAMPs). Lipopolysaccharides (LPS), mannoses, β- 1, 3 glucans and peptidoglycans are considered the most common PAMPs (Gillespie et al., 1997). Lectins are also involved in the recognition of oligosaccharides and polysaccharides present on the pathogen cell membrane (Wilson, Chen & Ratcliffe, 1999). PRRs include the gram-negative bacteria-binding protein (GNPB) and the peptidoglycan recognition proteins (PGRP); the latter includes molecules of long transmembranal form that are secreted into the haemolymph (Leclerc & Reichhart, 2004). Figure 1. Components of insect immunity. After recognition, coordinated responses of haemocytes begin. Phagocytosis is a response by which infectious agents become engulfed and destroyed (Gillespie et al., 1997). It may also occur that a large number of haemocytes bind to bacterial aggregations to form a nodule (Gillespie et al., 1997). When infectious agents are large in size (for example, parasitoid larvae), the process of encapsulation, which is similar to nodulation (the difference between both process depends on the size of infectious agents), is used. Occasionally, during encapsulation, a melanin layer is produced to cover parasites which die by anoxia, host generation of free radicals or starvation (Nappi et al, • Systemic AMPs synthesis • TEPs • Regulatory pathways/products • Lysozyme Hemocyte Secreted PRR NO NO NO NO O2 O2 O2 O2 H2O2 H2O2 H2O2 H2O2 • Recognition • Phagocytosis • Nodulation • Encapsulation/melanotic encapsulation • Hemocyte PO cascade (melanization, cytotoxic molecules) • NO (intra and extracellular) • ROS • Gut PO cascade • NO • ROS • Lysozyme • Epidermis/Cuticle PO cascade (quinones) Cuticle Fat body Hemocoel Epidermis Basal lamin Epithelium gut PATHOGENS AND PARASITOIDS PATHOGENS AND PARASITOIDS PATHOGENS AND PARASITOIDS PATHOGENS AND PARASITOIDS NO H2O2 2000; Narayanan, 2004). Haemocytes also contribute to clot formation by aggregating at wound sites (Gregoire, 1974). Other immune responses are activated, which includes antimicrobial molecules produced within the reproductive accessory glands, gut cells, fat body and haemocytes (Manetti, Rosetto & Marchini, 1998 1998; Schmid-Hempel, 2005a) which are commonly referred as humoral response. Most of these molecules can be secreted into the haemolymph, epitheliums, Malpighian tubes or near cuticle. These molecules include lysozymes with depolymerizing bacteria (mainly Gram¯) cell wall action (Gillespie et al, 1997); Thoiester-containing proteins (TEPs), with opsonizination activity that enables phagocytosis (Tzou, De Gregorio & Lemaitre, 2002); nitric oxide (NO), a highly reactive and unstable free radical gas produced during the oxidation of L- arginine to L-citrulline by the nitric oxide synthase (NOS) (Müller, 1997) that crosses cell membranes to act in nearby targets (Müller, 1997), inhibits protein catalytic activity and has protein and pathogen DNA harming effects (Colasanti et al., 2001; Rivero, 2006); ROS, which damages pathogen nucleic acids, proteins and cell membrane (Nappi et al, 2000); and antimicrobial peptides (AMPs; e.g. cecropins, atticins, diptericins, drosomycins, metchnikowins, and defensins, all with isoforms; Lemaitre, Reichart & Hoffmann, 1997; Narayanan, 2004) which induce a collapse of the pathogen membrane and/or prevent the synthesis of molecules within the pathogen (Otvos Jr, 2000; Bulet, Charlet & Hetru, 2003). The proteolytic pro-phenoloxidase (proPO) cascade is a key immune component for the synthesis of ROS, cytotoxic molecules and melanin (Iwanaga & Lee, 2005). After pathogen recognition, proPO system activation starts. As a first step, phenylalanine is hydroxilated and converted into tyrosine (Christensen et al, 2005). Then proPO is activated by a serin protease to its active form PO (an oxidoreductase enzyme) which catalyzes the reaction where tyrosine is transformed into dopa and then to dopaquinone (Söderhäll & Cerenius, 1998). After non-enzymatic polymerizations, dopaquinone is finally converted to melanin which will be used for wrapping up pathogens and wound clotting (Nappi & Christensen, 2005). Opsonic factors, ROS, and citotoxins such as quinones and semiquinones are important intermediate molecules, which are highly reactive and toxic to pathogens that greatly amplify immune response (Cerenius & Söderhäll, 2004; Nappi & Ottovianni, 2000). In Drosophila, the immune system is regulated by the products of three signaling pathways: TOLL, Immune Deficiency (Imd) and the Janus kinase/Signal Transducers and Activators of Transcription (JAK/STAT). Regulation is ensured by transcriptional factors produced in the fat body, preventing self tissue damage and keeping haemocyte proliferation and differentiation controlled (Agaisse & Perriomon, 2004). The TOLL pathway bears a key function during antimicrobial peptide production after being activated by fungal and Gram+ bacterial infections (Janeway & Medzithov, 2002; Hoffmann & Ligoxygakis, 2004). The Cactus protein, a TOLL transduction pathway product, maintains haemocyte proliferation regulated (Qiu, Pan & Govind, 1998). The Imd pathway gives rise to antimicrobial peptides when activated by Gram¯ bacteria (Khush, Leulier & Lemaitre, 2002; Hoffmann, 2003). Zaidman-Rémy et al. (2006) found that the Imd pathway can be regulated by PGRP-L, preventing host tissue damage as a consequence of extended immune activity. The JAK/STAT pathway regulates the secretion of humoral factors like TEPs (Tzou, De Gregorio & Lemaitre, 2002).This pathway also regulates haemocyte proliferation and differentiation (Agaisse & Perriomon, 2004). Interestingly, not all insects use this differential activation and regulation pathways. For example, in the mosquito Aedes aegypti phagocytic and melanization responses are independent of bacterial Gram type (see Hillyer, Schmidt & Christensen, 2004). Physiology of immune responses and trade-offs In ecological studies, trade-offs between immune responses and other traits are usually treated as a black box, with very little understanding of their underlying physiological mechanism.This kind of knowledge must be used as a starting point to understand some general aspects about the basis of the trade-offs generated and those that should be looked for. Tyrosine is responsible for coloring the egg chorion in some insects (Li & Christensen, 1993) and other metabolic pathways. Arginine, another essential amino acid, its necessary for NO generation used in immune response (Rivero, 2006), but it is also important for sperm maturation (Osanai & Chen, 1993), egg production (Uchida, 1993), long term memory, chemosensory (antennal lobes, olfaction) and visual information processing (Müller, 1997). Other efectors are the AMPs, which contains fewer than 150–200 amino acids (Bulet et al., 1999). The overall AMPs concentration, in Drosophila haemolymph, can reach up to 200 µM (Otvos Jr, 2000), at this concentration, antimicrobial peptides seem costly to produce. Resources (proteins) gathered through ontogeny are indispensable for antimicrobial peptide generation as well as for the synthesis of other molecules. In addition most of the AMPs are cationic, due to their higher content in arginine (Bulet et al., 1999). Consequently arginine could be a limited resource for NO production and AMPs synthesis leading to negative correlations between immune responses. During the PO cascade, tyrosine is used as substrate for the formation of melanin and highly reactive and toxic intermediate molecules. For these reasons, it is likely that tyrosine and arginine are restrictive resources in insects and may lead to trade-offs between immune response and other traits. Experimental manipulation of doses of these amino acids could be used to investigate the insect’s response to these changes and trade offs. Immunopathology: evolutionary and ecological implications As mentioned before, several molecules (quinones, reactive oxygen and nitrogen molecules, peroxides, etc.) are produced during insect immune response which can be harmful to their own tissues and cells, a phenomenon referred as immunopathology or autoreactivity. This process has been frequently ignored as a possible constraint for the insect immune system (Schmid-Hempel, 2005a). It can be argued that natural selection may favor high responsiveness to ensure control of pathogens despite the risk of immunopathology (Graham, Allen & Read, 2005). Nonetheless, activation and upholding of immune response depend on the kind and burden of pathogens, and a novel pathogen could elicit responses beyond immunological control with potentially harmful effects. Mechanisms and molecules produced to kill pathogens could be over expressed or overproduced if monitoring, regulatory, and recognizing systems are not synchronized. On the other hand, if virulence of pathogens elicits autoreactive host responses, low virulence could be favored. Only in cases where competition among pathogens occurs, natural selection will favor high levels of virulence. In both cases there must be fitness advantages to the pathogen regardless of autoreactivity and decrease in host survival. In some occasions hosts could kill pathogens, but bears an extreme survival cost. Meanwhile other species may suffer the damage generated by pathogens but, nevertheless they could be able to survive and reproduce, so a large immunological response could not be the best strategy. The strategy adopted via immune mechanisms and their regulation will depend on pathogens (probably not all novel pathogens will elicit the same degree of autoreactivity), differences in host immune response of the hosts (i.e. unstressed individuals with much to invest in immunity) may be more prone to immunopathological costs (Graham et al., 2005), and cost and benefits between autoreactivity and fitness of pathogens and hosts. Furthermore, immune response suppression during stressful and energetically demanding situations (e.g. molting or during display of sexual traits) can be another strategy to avoid autoreactivity (Kotiaho, 2001 and references therein). In this sense, it is not resource limitation the factor that produces the phenomenon, the assumed trade-off, but a type of “on-off” switch regulation of the immune system during ontogeny. Immunological Priming It has been argued that invertebrates are not capable of developing any kind of immunological memory (as vertebrates do). Nevertheless, recent experiments (Faulhaber & Karp, 1992; Moret & Schmid-Hempel, 2001; Kurtz & Franz, 2003; Moret & Siva-Jothy, 2003) have provided evidence of a phenomenon similar to that in with vertebrate acquired immune response which has been called immunological priming (Little & Kraaijeveld, 2004; Little, Hultmark & Read, 2005; Kurtz, 2005; Schmid-Hempel, 2005a, b). This process assumes that previous experience with infectious agents might enhance individual immunity which can be transmitted to subsequent generations (transgenerational priming) (Kurtz & Franz, 2003; Little et al., 2003; Rahman et al., 2004; Sadd et al., 2005; Moret, 2006; Sadd & Schimd-Hempel, 2006; Pham et al., 2007). This novel idea is nowadays being explored in detail by physiologists and immunoecologists, and, given its key importance, has been coined as prompting a new era in the evolutionary and ecological studies of the immune system (for experimental designs see Little & Kraaijeveld, 2004). The immune invertebrate memory argument has been criticized (Klein, 1997). The basis of this is the lack of mechanisms for specific Ig production, rearrangement of somatical genes and clonal expansion. Then, if an immune response is not specific it makes no sense to refer it as “memory”. So, the term “anticipatory response” could be more suitable in a system with an enhanced but unspecific response after secondary immune challenge (Kurtz, 2004). The specificity of molecules with potential memory function, which enable the host to adapt to infectious agents during its lifetime, needs further investigation. Until then, the term memory must be used with caution. That is why we consider adequate to use the term “immunological priming” to refer to any anticipatory response with or without specific memory in insects (and invertebrates) (see also Schmid-Hempel, 2005b). Below we put forward some general mechanisms proposed as for how this priming could operate, however, these possible mechanisms need further investigation. Lectin bindings (Kurtz & Franz, 2003), AMPs (Little et al., 2003, Schmid-Hempel, 2005b) and PRRs (Kurtz, 2005) could be the molecules involved in the priming process. Lectins, through their high structural diversity, highly specific microorganism recognition, agglutination action and opsonization role in phagocytosis (Marques & Barraco, 2000), could be mediators for the enhancing of secondary immune responses. AMPs show great structural diversity (more than 170 isoforms have been found in insects; Bulet et al., 1999) and are produced soon after foreigner recognition (1-4 hours) with a pathogen-specific and efficient killing action (Bulet et al., 1999, Otvos Jr., 2000, Schmid-Hempel, 2005b). Although transcription of AMPs became turned off, some AMPs may remain in haemolymph for up to three weeks (Schmid-Hempel, 2005b), which is convenient during subsequent pathogen encounters. PRRs are differentially induced depending on the infectious agents, and particularly the recognition proteins secreted into the haemolymph could be molecules conferring specificity to invertebrate immune response. It cannot be excluded the possibility of somatic gene alterations of immune molecules to enhance the response to infectious agents (Flajnik, Miller & Du Pasquier, 2003). In fact, current knowledge shows that some invertebrate IgSF proteins called Down syndrome cell adhesion molecule (Dscam; Watson et al., 2005). Dscam proteins are used as opsonization factors that enhance the phagocytic efficiency of haemocytes, increasing host survival (Dong et al. 2006). Also, the challenge with different pathogens induces specific Dscam repertoires with different affinity, showing specificity. In snails, fibrinogen related proteins (Freps) somatically diversify (like Ig of vertebrates) and are produced in the haemolymph in the presence of pathogens (Zhang et al., 2004). The genetic and molecular mechanism underlying these rearrangements also needs further investigations. However, given the diversity of insect species, it cannot be exclude the presence of a greater diversity of mechanisms behind immunological memory. Enhanced immunity of offspring from mothers that were immune stimulated, has been also reported. Mother AMPs (or lectins and PRRs), and also mRNA (Huang & Song, 1999), in a transcription factor-like mode, could be promoting transcriptional initiation of immune response genes. So, there can be heritable changes in immune response among generations that cannot be due to DNA sequences changes or genetic variation, i.e. inherited epigenetic variation (Richards, 2006). DNA methylation can modify histones. These modifications alter affinity of proteins that mediate transcription and affect interaction between nucelosomes and chromatin. As a consequence gene expression and phenotype could be affected (Richards, 2006). As a result heritable changes in gene expression could affect immune response outcome.Therefore, these molecules could be working as elicitors that could interact with the offspring embryonic cells inducing defense; so when immature stages start fighting against infectious agents, they are already primed, increasing survival chances (Rahman et al., 2004). This idea is not so radical, in distant organisms such as plants. Molinier et al. (2006) found that immune elicitors increased epigenetic somatic homologous recombination of a transgenic reporter which can persist in subsequent generations, which could potentially enhance offspring defenses. Although we are not proposing homologous recombination epigenetic change as the general mechanism of immune transgenerational priming, the fact is that transgenerational memory is not an exclusive property of vertebrates and therefore, can take place in different plant and animal groups. Other relevant issue of priming is the high energetic cost of prolonged activation or synthesis of immune molecules at a higer level. Since pathogens can act as a factor that changes the host’s internal environment, an optimal resource allocation is likely to change over time, partly because infections are unpredictable and their impact is not usually immediate. One can expect that the first contact with a pathogen not only enhances immune response, but changes the host’s life history strategies. For example, reproductive events, before a secondary infection occurs. It has been widely documented that survival is impaired when hosts devote more resources to immune defense (Schmid-Hempel, 2005a). However, the direction of change in reproductive strategies under priming is unclear. If immune priming occurs, not only survival or immune response need to be measured, but other important trait intimately linked with reproduction (e.g. egg production). QUANTITATIVE GENETICS OF THE IMMUNE RESPONSE Traits closely related to fitness are commonly under natural or sexual selective pressures. Traits in ecological and evolutionary analyses are continuous variables and their variation is thought to be polygenic and intimately affected by the biotic and abiotic environment. These traits must be analyzed using quantitative genetics, by partitioning and estimation of variances and covariances into causal components (Falconer & Mackay, 1996) to understand how they respond to selection. Immune response occurs via a set of physiological traits and is supposed to show low additive variance and hence low heritability values (like other physiological traits, see Mousseau & Roff, 1987). Nevertheless, recent studies have shown that different immune components such as haemocyte load (Ryder & Siva-Jothy, 2001; Cotter, Kruuk & Wilson, 2004a; Rolff, Armitage & Coltman, 2005; Simmons & Roberts, 2005), antibacterial activity (Kurtz & Sauer, 1999; Cotter et al., 2004; Simmons & Roberts, 2005), PO activity (Hosken, 2001; Cotter & Wilson, 2002; Rolff et al., 2005; Schwarzenbach, Hosken & Ward, 2005) and melanization (Fellowes et al., 1998; Simmons & Roberts, 2005) tend to have high heritability (from 0.24 to 0.91), indicating high genetic variance. There are at least three non-mutually excluding hypotheses to explain variance of immune response: host-pathogen co-adaptation cycles, genetic basis of trade-offs (genetic correlations), and phenotypic plasticity. Host-pathogen co-adaptation cycles Genes involved in immune resistance always show significant variance because of host-pathogen co-adaptation cycles (Hamilton & Zuk, 1982). The pathogen’s short generational cycles may provide enough time to adapt to the host’s immune response and given the number of infectious agents, there will be grounds for high genetic variability in immune responses. Variation will allow the host fighting against all varieties of infections produced by different pathogens. This has been used as the explanation for the maintenance of variation in SSTs which are under intense sexual selection (for example female choice; Kirkpatrick & Ryan, 1991). According to a perspective of sexual selection, if genetic variation of immune response is maintained, females (if female choice is the selective filter, but the same may apply for male-male competition and sexual conflict) will be able to continue exerting mate choice over SSTs (as these covariate with immune ability, reflecting male genetic quality), which will passed on to offspring (Rolff et al., 2005). Correlation among immune effectors In an individual, different traits are frequently found to be genetic or phenotypically correlated. Genetic correlation arises because a single gene can influence multiple traits in a positive and negative fashion (pleiotropy and antagonistic pleiotropy, respectively) or because of linkage disequilibrium between genes affecting different characters (Falconer & Mackay, 1996). Meanwhile phenotypic correlations include the genetic causes and the positive or negative influences of environmental factors between traits (Roff, 1992). Negative correlations between immune responses and other life history traits (or intermediated traits) have been found and are clearly detected under stressful conditions (usually resource limitation). However, trade-offs within immune system parameters are also possible. A number of studies have examined genetic and phenotypic correlations among encapsulation, lytic activity, cuticular darkness, PO activity and haemocyte load, showing an unclear pattern with positive and negative correlations (see Rantala & Kortet, 2003; Cotter et al., 2004b; Fedorka et al., 2004; Ryder & Siva-Jothy, 2004; Rantala & Roff, 2005; Roff et al., 2005). It must be considered that not all pathogens will induce the same reaction, or at least the magnitude of response can differ depending on the host-pathogen interaction type (coevolutionary history). So, genetic correlations, type and kind of pathogens could be closely related with the type and the intensity of correlations. The evolutionary trajectories of different immune responses can be difficult to predict when hosts are exposed to changing pathogens. Pathogens could differ spatially and temporarily in occurrence, burden and virulence so genetic/phenotypic correlations could change among generations. Therefore, pathogens to which different organisms have been exposed to may have played a role in the evolution of immune responses. Natural or sexual selection will be acting simultaneously on the different immune responses constraining their independent evolution, but probably with a fitness advantage for the host. Phenotypic plasticity As long as the organism has to deal with the problem of maximizing fitness in changing or stressful environments, a genetic background which can express changes in phenotype expression could have a selective advantage in that environment (Zhivotovsky, Feldman & Bergman, 1996). Occasionally, a single genotype can have the ability to produce distinct phenotypes when exposed to different environments, a phenomenon known as phenotypic plasticity (Schlichting, 1986; Roff, 1997; Nylin & Gotthard, 1998). Phenotypic plasticity could explain the maintenance of genetic variance because plasticity uncouples the phenotype from genotype, buffering the impact of natural or sexual selection in the gene pool (Stearns, 1992), leading to a slow depletion of genetic variance. Phenotypic plasticity in quantitative traits therefore represents a genetic response to environmental heterogeneity. The quantitative and qualitative differences in immune response could be used as an indicator of the strategies followed by an organism in relation to environment stochasticity, and this is one reason why the evolution of immune response must be intimately related to the adaptive evolution to environmental heterogeneity. Despite the evidence that immune response can be strongly affected by the environment, really only a handful of studies have approached immune response using a phenotypic plasticity perspective. Related to this, Barnes & Siva-Jothy (2000) in beetles, Cotter et al. (2004b) in butterflies (reared at different population densities) and Mucklow & Ebert (2003) in water fleas, found phenotypic plasticity for mounting an immune response, concluding that investment in immunity depends on the infection probability based on the number of conspecifics, and that re-distribution of resources is adaptive. However, at the same time that some immune effectors are elevated at high population densities, while others are not. Since immune response is costly to express, this flexibility to cope with fluctuating environments could give fitness advantages. The adaptive value of phenotypic plasticity depends on the ecological context in which it is expressed. Other ecological studies arguing the adaptive value of phenotypic plasticity (see Stirling, Roff & Fairbairn, 1999; Gebhardt & Stearns, 1993; David, Capy & Gauthier, 1990) have concluded that alternative strategies can be equally adaptive in some environments, but occasionally may not render advantages for the organism in other environments. Even the parental environment could have effects in the offspring behavioral phenotype (e.g. gregarious and solitary), related to immune response enhancing (Elliot, Blandford & Horton, 2003). It is therefore necessary to evaluate if different environments produce different phenotypes and if genotypes respond differently to these environments (genotype-by-environment interaction, which represents genetic variation for phenotypic plasticity) and if these alternative phenotypes have fitness advantages. Due to these reactions, phenotypic plasticity should be carefully considered in immunological studies, For example, it may explain biases in immune response between sexes (Joop & Rolff, 2004; McKean & Nunney, 2005), which may represent different male and female reproductive strategies. Sexual differences may not be genetic but simply a differential phenotypic expression under varying environment regimes. Phenotypic plasticity should be carefully analyzed in current immunocompetence hypotheses of male quality. It can be argued that a possible sexual selection cost of the expression of phenotypic plasticity is that the phenotypic traits may not be good indicators of male immunocompetence ability, because there is not a direct concordance between the phenotype, immune response and genotype (for analogous discussions see Adamo & Spiteri, 2005). Nevertheless in a broad sense immunocompetence could not only include the capacity of an individual to resist an infection but also the ability to produce distinct immune response phenotypes when exposed to different environments. This line of thought will enrich our comprehension of what male quality, in immunity terms, may mean and its interpretation in sexual selection studies. Maternal Effects and Immunological Priming Maternal effects arise when a mother’s phenotype, which is intimately correlated with the environment she experiences, has a phenotypic effect on her offspring (Mousseau and Fox 1998). The local maternal environment might influence offspring phenotype, as maternal experiences may provide an indicator of the environmental conditions that offspring will face (Fox & Mousseau, 1998; Rossiter; 1996). if mothers have been exposed to infectious agents, information could be inherited (via maternal lectins, AMPs, PRRs ormRNA; already discussed previously), inducing offspring phenotypic variation in immune defense (a kind of transgenerational phenotypic plasticity). Threre are four ecological conditions for transgenerational phenotypic plasticity in immune response to evolve: (1) infectious agents are variable and unpredictable on time, (2) a direct relation between cue and response exists, (3) induced defense is effective and (4) immune response is costly to express (adapted from Harvell, 1990; Harvell & Tollrain, 1999). For these reasons, maternally inherited effects have been proposed as the main explanation for transgenerational priming (Little et al., 2003; Grindstaff, Brodie III & Ketterson, 2003; Sadd et al., 2005; Moret, 2006). Again little evidence has been forwarded to support this. Little & Kraaijeveld (2004) have proposed immunological priming is expected to evolve under high longevity and clonal reproduction. However, nearly any organism (clonal or sexual, short or long-lived) may be attacked repeatedly by a disease as this is temporally and spatially unpredictable. If, an infectious agent appears in the population, there is a high probability of being reinfected by the same agent and it is the same for the offspring; this is because as prevalence increases, the infectious agent also can increase in frequency (Moret & Siva-Jothy, 2003). Furthermore, immunological (transgenerational) priming can evolve by natural selection in gregarious or social organisms with low mobility and overlapping generations. Additionally, maternal inheritance can produce time lags in the response to selection, hence low depletion of genetic variance. The response to selection will depend on the kind of pathogens (and their virulence) and the evolutionary response for those in the previous generation, as well as the co- occurrence of the same pathogens in the current generation. Heritability measures and artificial selection experiments (see Kraaijeveld & Godfray, 1997; Fellowes et al., 1998; Rahman et al., 2004), have shown that immune response can evolve. The phenotypic differences between individuals are related with differences in survival and reproductive success. Natural or sexual selection will act on phenotypic variation, favoring some phenotypes. As long as the phenotypic variation has a genetic background, the population can evolve. The fact that the immune response is enhanced across generations necessarily implies that transgenerational immunological priming not only depends on the offspring’s phenotype but on the mother’s phenotype too (Kirkpatrick & Lande, 1989). So, variation among mothers in the molecule transmission capacity can also be under selective pressure. Future studies are essential to understand the mechanisms of intra and intergenerational priming. Until these results are available and similar to what we discussed before with phenotypic plasticity, it is necessary to be careful when interpreting enhanced immunity ability during ontogeny and its heritability basis. As mentioned before, organisms and their offspring are under constant attack by infectious agents, which occasionally lead to infections. However, at other times, the host cannot totally eliminate the pathogen but, just keeps it “under control”. This control can be due to the organism’s ability to express different survival and reproductive strategies in response to stressful environmental conditions to which is exposed during its lifetime. Previous experience with infectious agents could also generate the expression of behaviors aimed to avoid negative effects of infections (see Loehle, 1995; Moore, 2002). For example behaviors to avoid areas with parasites (Hart, 1990), foraging behaviors for increase resource intake, and also physiological changes for the optimization of resource allocation. CONCLUSIONS This review illustrates some evolutionary and ecological principles that can direct the study and understand how the immune system fights infectious agents and how far we are from merging the evolutionary, ecological and physiological disciplines to understand the evolution of immune response. Evolutionary biologists have focused on quantifying phenotypic variation rather than on understanding its nature this by means, the immune mechanisms, the underlying trait architecture and trade-offs. Ecological insights can be very relevant to understanding immune system dynamics and vice versa. Since immune system is highly complex, several aspects of immune response must be therefore integrated (e.g. immunopathology, immunological priming) to understand the evolutionary ecology of insect immunity. We have attempted to look at the available data on insect immune response pointing out some research avenues and careful considerations. The starting line in insect immunoecology must incorporate the great range of environmental factors involved using a trade-off framework. 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Moreno-García et al 1 Running head: Anticipatory immune response in Aedes aegypti 1 2 3 4 5 6 7 Anticipatory immune response in Aedes aegypti against bacterial challenges and 8 its effects on female reproduction 9 10 Miguel Moreno-García1,2, Priscila Bascuñan-García1, Guadalupe Hernández1, Javier 11 Izquierdo1, Alex Córdoba-Aguilar2 and Humberto Lanz-Mendoza1* 12 13 14 15 1 Centro de Investigaciones Sobre Enfermedades Infecciosas, Instituto Nacional de Salud 16 Pública, Avda. Universidad 655. Col. Sta. María Ahuacatitlán, 62100 Cuernavaca, Morelos, 17 México. 18 19 2 Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma 20 de México, Circuito Exterior s/n, Apdo. Postal 70-275, México, D. F. 04510, México. 21 22 *Corresponding author: humberto@correo.insp.mx 23 24 25 Moreno-García et al 2 Abstract 26 In insects exposure, to pathogens induces an anticipatory immune response, a phenomenon 27 called immune priming. However, little is known of whether immune effectors improve their 28 efficiency with time; the temporal dynamics of immune enhancement; and, its reproduction 29 costs. In this study, we demonstrate that priming (using live bacteria) protects the mosquito 30 Aedes aegypti against a lethal second challenge. We experimentally reveal the temporal 31 dynamics of phenoloxidase (a key immune effector) measurement which suggests a 32 deactivation rather than activation; this may be due to host use of the enzyme to control 33 recurrent infection with live bacteria. Meanwhile, nitric oxide (another key immune effector) 34 increased to some extent after second challenge. Finally, that egg production was not affected 35 during the priming dose and second challenge; however, the number of egg laying females 36 with two recurrent infections (i.e. priming dose+second challenge) was lower compared to the 37 unprimed groups. Our study not only corroborates the presence of insect immune priming, but 38 also documents the point that not all immune parameters can be enhanced when hosts have had 39 previous contact with pathogens. Immune priming may be considered not only as an enhanced 40 response, but an optimal response to protect the organisms after a previous experience with an 41 elicitor. 42 43 Keywords: immunological priming, phenoloxidase, nitric oxide, Aedes aegypti 44 45 46 47 48 49 50 51 Moreno-García et al 3 Introduction 52 It is widely acknowledged that, unlike vertebrates, insects do not have mechanisms for specific 53 Ig production, rearrangement of somatic genes and clonal expansion (Janeway, 2005). Also, 54 there are no molecules with potential specific immune memory function (but see Zhang et al., 55 2004; Watson et al., 2005), which enable the host to adapt to infectious agents during its 56 lifetime. However, it can be safely assumed that a vast majority of insects are attacked 57 repeatedly by the same pathogen because the incidence of the latter is temporally and spatially 58 unpredictable. If, for example, an infectious agent appears in the population, there is a high 59 probability of a host being re-infected by the same agent. This is because prevalence of the 60 infectious agent increases in the host population (Moret & Siva-Jothy, 2003). Thus, the ability 61 of an anticipatory immune response in non-vertebrate organisms could represent an adaptive 62 characteristic to such environmental selective pressure. 63 64 Despite the lack of the physiological and biochemical machinery that might allow for an 65 adaptive immune response in insects, it has been long recognized that immunization with 66 killed or living bacteria can induce an anticipatory immune response in insects (Boman et al, 67 1972). This fact has been recently supported by experimental observations (Faulhaber & Karp, 68 1992; Moret & Schmid-Hempel, 2001; Kurtz & Franz, 2003; Moret & Siva-Jothy, 2003). The 69 phenomenon has been coined as immune priming (Little & Kraaijeveld, 2004; Little, Hultmark 70 & Read, 2005; Kurtz, 2004; Schmid-Hempel, 2005a, b) and assumes that a previous 71 experience with an elicitor might raise an enhance immune response when re-exposed to the 72 same agent (Pham & Schneider, 2008). 73 74 Protective enhancement of immune response due to priming (i.e. prolonged activation or 75 increased levels of previously immune stimulated organisms) has been assessed using different 76 response variable such as survival, behavior, reproductive capacity, and different immune 77 Moreno-García et al 4 effectors (phagocytosis, antimicrobial peptide load and synthesis, and antimicrobial and 78 phenoloxidase activity) (Deverno et al 1983, Adamo, 1998, Rosengaus et al 1999, Brown et al 79 2003, Little et al 2003, Sadd & Schmid-Hempel 2006, Pham et al 2007). In these studies, 80 immune response became more effective upon repeated exposure to the elicitor, an effect that 81 probably persists during the host’s lifetime (see model in Fig. 1). However, there is 82 controversy with respect to whether such efficiency applies to all immune effectors. For 83 example, Pham et al (2007) found that hemocytes, through phagocytosis, are responsible for 84 an enhancement effect, which was not the case for antimicrobial peptides or the 85 prophenoloxidase (proPO) system. One potential reason as for why immune effectors 86 apparently vary in effectiveness is that they may be better explained by methodological rather 87 than physiological factors. In particular, the moment of quantification of immune parameters 88 should be controlled, while characterizing the dynamics of immune response after a priming 89 dose. This will detect how fast improvement (if it occurs) is acquired. To our knowledge this 90 information has not been gathered by previous studies. 91 92 Priming has been referred to as either as “short” (hours, or few days) or “long” (weeks or 93 lifetime) in terms of immune protection persistence (Pham & Schneider, 2008). However these 94 definitions have yet to be concretely established, as several studies have reported different 95 results. One explanation for these inconsistencies is the variable period of time elapsed 96 between challenges, as well as the nature of the immune response being evaluated in these 97 cases. For example, in the lepidopteran Heliothis virescens, a second re-infection was 98 performed at 48 hrs after a priming dose, but immune enhancement reached only 3% after 12 99 hrs (Ourth & Parker, 2006); thus, these authors concluded that in this species priming is not 100 elicited. In this specific case, however, it is likely that the second challenge overlapped with 101 the response generated by the priming dose, avoiding the results of the prime effect. Related to 102 this explanation, in the moth Galleria mellonella, humoral antibacterial activity was noted 5 hr 103 Moreno-García et al 5 after infection, reaching a maximum level at 18-48 hrs post infection, and decreasing over a 104 few days (Jarosz, 1993; Andrejko et al, 2009). Conversely, in other species, immune 105 parameters such as hemocytes and the PO system are very rapidly activated but quickly turned 106 down (Schmid-Hempel & Ebert 2003). Therefore, if priming occurs in these other species, the 107 second challenge and quantification of immune parameters should be performed after the 108 overall immune recovery period (Fig. 1). In general, all these results mean that the nature of 109 immune response time dynamics needs to be taken into account when looking for immune 110 priming effects. 111 112 Another important, yet unexplored, aspect relevant to the time of inducible protection is the 113 high energetic cost of prolonged activation or synthesis of immune molecules. A high 114 energetic cost is expected, as the required resources used for immune response can also be 115 demanded for the expression of other traits linked with survival and reproduction (Sheldon & 116 Verhulst, 1996, Roff, 1992), as predicted by the resource allocation theory (Stearns, 1992). 117 Modulation of the immune response can be expected after previous challenge in order to an 118 optimal adjustment of resources used in immunity and other traits (see Pham & Schneider 119 2008 for a similar claim) (Fig. 1). Then, a recovery period can be expected for resource 120 acquisition and a suitable enhanced immune response. 121 122 Organisms must physiologically “decide” how to allocate available resources to maximize 123 fitness. An optimal allocation depends on the strategies used to increase fitness in the habitat 124 of the organism (Schlichting & Pigluicci, 1998). Since pathogens can act as a factor that 125 changes the host’s internal environment, an optimal resource allocation is likely to change 126 over time, partly because infections are unpredictable and their impact is not usually 127 immediate. One can expect that the first contact with a pathogen not only activates immune 128 response, but changes the host’s life history strategies, for example, reproductive events, 129 Moreno-García et al 6 before a secondary infection occurs. It has been widely documented that survival is impaired 130 when hosts devote more resources to immune defense (reviewed by Schmid-Hempel, 2005a). 131 However, the direction of changes in reproductive strategies under recurrent infections is 132 unclear. 133 134 Using Aedes aegypti Linnaeus (Diptera: Culicidae), the principal vector of Dengue and Yellow 135 Fever virus, as our study subject, we have first documented its survival following a challenge 136 with a lethal dose of the bacterium 7 days after being previously primed with a non lethal dose. 137 We consider this period of time long enough to detect an effect (if it exists) between the 138 priming dose and the second challenge. This period was based on information about the 139 activation and synthesis of immune molecules in insects (for example, antimicrobial peptide 140 transcription has a range from 6 hrs to 72 hrs after challenge; PO activation ; (Jarosz, 1993; 141 Lemaitre et al 1997; Uttenweiler-Joseph et al 1998; Schmid-Hempel & Ebert 2003; Andrejko 142 et al, 2009). Second, we have documented the temporal dynamics of two immune parameters 143 during the priming dose period and also after a second challenge. The immune parameters 144 used were phenoloxidase activity (PO) and nitric oxide production (NO), two key components 145 during insect immune defense. In insects, PO (an oxidoreductase enzyme), catalyzes the 146 transformation of tyrosine into dopa and then to dopaquinone (Söderhäll & Cerenius, 1998). 147 After a number of non-enzymatic polymerizations, dopaquinone is finally converted to 148 melanin which is used for wrapping up pathogens and wound clotting (Nappi & Christensen, 149 2005). Also, during the PO cascade highly reactive and toxic molecules are produced (opsonic 150 factors, reactive oxygen species, quinones and semiquinones) (Cerenius & Söderhäll, 2004; 151 Nappi & Ottovianni, 2000). NO is a highly reactive and unstable free radical gas produced 152 during the oxidation of L-arginine to L-citrulline by the nitric oxide synthase (NOS) (Müller, 153 1997). NO crosses cell membranes to act on nearby targets (Müller, 1997), inhibit protein 154 catalytic activity and harms protein and pathogen DNA (Colasanti et al., 2001; Rivero, 2006). 155 Moreno-García et al 7 Finally, and in terms of changes in reproductive strategies and resource allocation theory, we 156 have documented the number of eggs produced and the proportion of egg laying females with 157 and without a priming dose and with recurrent immune challenges 158 159 160 Materials and Methods 161 Mosquitoes were reared under insectary conditions at the Instituto Nacional de Salud 162 Pública(INSP), Cuernavaca, Mexico (the colony stock has been maintained at over 2000 163 individuals per generation with random mating). Three day-old adult female mosquitoes were 164 used at the beginning of each experiment. We used live or heat killed Serratia marcescens and 165 Escherichia coli (both Gram-negative bacteria). Both bacteria were kindly donated by Lilia 166 González Cerón (Centro de Investigaciones en Paludismo, INSP) and Jesús Silva (Centro de 167 Investigaciones Sobre Enfermedades Infecciosas, INSP) respectively. Bacteria were grown 168 overnight in LB-broth at 37°C while shaking at 200 RPM until they reached stationary phase. 169 To determine bacterial concentrations, 100 μl of a 1/1000, 1/10,000 or 1/100,000 bacterial 170 culture was spread on LB agar plates. Plates were grown overnight at 37°C and the colony 171 forming units (CFU) were counted. LD doses were previously determined by inoculating 172 separate groups of mosquitoes with serial bacterial dilutions and selecting the dose closest to 173 killing 100%, 50% , <10% of the animals. We recorded mosquito mortality during the next 24 174 hours after bacterial injection. 175 176 Survival experiment 177 For the survival analyses, two experiments were carried out. In the first experiment, three days 178 old adult female mosquitoes were randomly divided into four groups of 100 individuals each: 179 control and three experimental groups. For the priming dose, mosquitoes of group one (RPMI-180 G) were refrigerated and then inoculated with RPMI directly into the hemocele. The second 181 Moreno-García et al 8 group (Live-B) was inoculated with live S. marcescens (LD<10; 74x104 CFU) suspended in 182 RPMI. The third group (Dead-B) was inoculated with dead S. marcescens (at 74x104 CFU) 183 suspended in RPMI. The Control group (C) was only refrigerated at 4° C for about 10 minutes. 184 At day 7 post priming dose, mosquitoes were re-infected via inoculation in the abdomen (see 185 below). For the second challenge RPMI-G, Live-B and Dead-B, were inoculated with live S. 186 marcescens at a higher concentration (LD>50; 12.3x105 CFU). C group was again refrigerated. 187 Survival was quantified until the live mosquito proportion ceased to change. 188 189 For the second experiment we used E. coli. Three groups (Live-B, RPMI-G and C) of 100 190 females each were used. Dead-B was not included given that previous observations showed 191 that dead bacteria do not elicit priming condition (unpublished data). For the priming dose, 192 Live-B was inoculated with bacteria at 92.5x103 CFU (LD<10), RPMI-G was inoculated with 193 RPMI medium, and C (negative control) group was only refrigerated. At day 7 post priming 194 dose, mosquitoes were re-infected via inoculation in the abdomen (see below). RPMI-G and 195 Live-B were inoculated with a higher dose of bacteria (LD>50; 10.6x104CFU). For this 196 experiment we included a primed RPMI-G challenged again with RPMI medium as positive 197 control. Survival was quantified until live mosquitoes proportion ceased to change. 198 199 All inoculations were made using a pulled glass needle attached to a Drumond microinjector. 200 For manipulation, mosquitoes were previously refrigerated at 4oC for 10 min. Organisms were 201 always injected in the abdomen close to the junction between the ventral and dorsal cuticles. 202 The volume of inoculate was ~1µl. After inoculation, all groups were transferred to an 203 insectary and maintained on a 12:12h light:dark cycle at 25-26oC and allowed to feed ad 204 libitum on cotton soaked with sugar solution. 205 206 Kinetics of immune response, hemolymph collection and immune measurements 207 Moreno-García et al 9 Three groups were formed (Live-B, RPMI-G and C). Live-B group was inoculated with E. coli 208 in RPMI (87x103 CFU; non lethal dose) directly to hemocele. RPMI-G was inoculated with 209 RPMI medium only. At day 7 post priming dose, mosquitoes were re-infected. One half of the 210 individuals in RPMI-G was inoculated with RPMI, while the other RPMI-G half and Live-B 211 groups were inoculated with live E. coli at 99x103 CFU (LD<5), a higher but minimal lethal 212 dose after priming A minimal but lethal dose was used because mosquitoes needed to survive 213 in order to be enough individuals for daily hemolymph collection. C group was never 214 manipulated. Kinetics experiments were repeated twice. C group was included only in the 215 second repetition. Mosquitoes were inoculated as above. After inoculation, all groups were 216 transferred to an insectary and maintained as above. 217 218 Daily, after the priming dose, mosquitoes were macerated with a biovortexer in 130 µl of PBS 219 buffer and each sample was centrifuged for 10 min at 10,000 rpm (4oC). Supernatant was used 220 to record protein load concentration, PO activity and NO production. Prior to recording PO 221 activity, protein concentration was determinate to control for differences in protein content 222 among samples (see Contreras-Garduño et al. 2007). The BCATM (Pierce) assay kit was used 223 to determine protein concentration for each sample. Briefly, sample supernatant, 40µl of PBS 224 and 150µl of BCATM kit reagents mix were added to a 96 microwell plate and incubated for 10 225 min. at 37oC. A known concentration of albumin (5-60µgr) was used as a standard reference 226 curve. The absorbance was recorded at 562nm on a plate reader. After protein load adjustment 227 and to measure PO activity, the sample supernatant plus PBS gauged at 50µl was mixed on a 228 96 microwell plate with 50µl L-DOPA (L-dihydroxyphenylalanine; 4mg/ml) as substrate and 229 incubated for 10 min at room temperature (24oC) . 50µl of buffer mixed with 50µl of L-DOPA 230 was used as blank. Absorbance was recorded at 490nm on a plate reader. An increment in OD 231 after 30 minutes was defined as PO activity. Three mosquitoes were required for a single 232 Moreno-García et al 10 sample since one individual does not provide enough material for spectrophotometer readings. 233 Three samples per group were quantified every day. 234 235 The Griess reaction was used to determine NO concentration (Eckmann et al. 2000). 50 µl of 236 each sample supernatant were mixed with 50µl of 1% sulfanilamide and 50 µl of 0.1% 237 naphthylethylenediamine on a 96 microwell plate and incubated for 10 min at room 238 temperature (24oC). NO was quantified using a NaNO2 (1-100 µM) standard reference curve 239 for each assay. Absorbance was recorded at 540nm on a plate reader. The highest readings 240 obtained in an interval of 30 min (with measurements every 5 min.) were defined as NO 241 production (expressed as µM). 242 243 Maceration and plate filling (for PO and NO) were done in a cold room (4oC) to exclude room 244 temperature changes that could affect quantifications. 245 246 Egg production and proportion of egg laying females 247 To record the number of eggs produced and egg laying females, tree groups were used (C, 248 RPMI-G, and Live-B). Live-B was inoculated with E. coli in RPMI (87x103 CFU) directly to 249 hemocele. RPMI-G was inoculated with RPMI medium. After inoculation, all groups were 250 maintained on a 12:12h light: dark cycle at 25-26oC and allowed to feed ad libitum on cotton 251 soaked with sugar solution. At day 7 post priming dose, half of the individuals of each 252 treatment were allowed to artificially feed on male sheep blood. In order to ensure that only 253 blood-fed females were used, all mosquitoes were chilled on ice and non-fed-females were 254 removed after blood feeding. For egg laying, single females isolated in plastic glass containers 255 with humid filter paper as the egg laying substrate. Half of mosquitoes were re-infected for 256 which half of which were inoculated with RPMI, and the other half with RPMI-G and Live-B 257 groups were inoculated with live E. coli at 99x103 CFU. C group was never manipulated. After 258 Moreno-García et al 11 inoculation mosquitoes were maintained as above. At day 7 post second challenge, females of 259 each treatment were allowed to artificially feed and lay eggs as above. The number of eggs 260 produced by each female and the number of egg laying females were recorded. 261 262 Statistical analysis 263 We performed a survival analysis using a Log-rank x2 to detect differences in survival curves. 264 Analyses were performed using JMP 7.0 (SAS Institute, 2004). For the kinetics of PO and NO, 265 we performed repeated measures ANOVAs with time (time period elapsed since the priming 266 dose) and treatment (C, RPMI-G+RPMI-G, RPMI-G+ Live-B, and Live-B+Live-B) as fixed 267 factors. For kinetics, various transformations did not lead to normal distribution of data. 268 However we still used the repeated measures ANOVA since this test is still appropriate for 269 finding significance when they exists even with non-transformed data (Zar, 1999). Analyses 270 were performed using STATISTICA 7.0 (Statsoft, 2004). 271 272 A one-way ANOVA was conducted to test whether the number of eggs produced at day 7 273 (priming dose effect) was significantly different among treatments (C, RPMI-G, Live-B). To 274 achieve normal error distribution, data were transformed using a Box-Cox transformation (Fit 275 Model module of JMP Ver. 7.0). Since the absolute values of egg production at day 14 (second 276 challenge effect) were not normally distributed, a non-parametric Kruskal-Wallis test was 277 performed to compare among groups. A x2 test analysis was performed to detect differences in 278 the proportion of egg laying females among groups (C, RPMI+RPMI, RPMI+ Live-B, and 279 Live-B+Live-B) at day 7 (priming dose effect) and at day 14 (second challenge effect) 280 281 282 Results 283 Survival 284 Moreno-García et al 12 Previous exposure to a minimal lethal dose of S. marcescens provided protection to 285 mosquitoes against a lethal challenge administered 7 days later (Log-Rank x2= 13.72, df=3, P= 286 0.0033; notice that removal of C group from the survival model did not change the significant 287 differences, Log-Rank x2=12.30, df= 2, P= 0.0021) (Fig. 2). Thus, individuals primed with live 288 bacteria died at a slower rate than the RPMI-G+RPMI-G groups. Dead bacteria did not induce 289 the same magnitude of protection against the second lethal challenge (Fig. 2). At day 4 post 290 second challenge, survival curves between RPMI-G+Live-B and Dead-B+Live-B achieved the 291 same survival probability (Fig. 2). 292 293 In groups inoculated with E. coli, similar results were observed. Inoculation with a low dose of 294 bacteria affected mosquito survival. After the second inoculation with a high bacterial dose, 295 the Live-B+Live-B group died slower than the RPMI-G+Live-B group, meanwhile C and 296 RPMI-G+RPMI-G groups exhibited a reduced mortality rate (overall model Log-rank x2= 297 18.61, df= 3, P=0.0003). When C and RPMI-G+RPMI-G groups were excluded from the 298 survival model, the differences between the curves of RPMI-G+Live-B and Live-B+Live-B 299 groups were still statistically significant (Log-rank x2=11.16, df= 1, P=0.008) until day 300 13,which no longer occurred at day 14 (Log-rank x2=1.74, df =1, P= 0.18). 301 302 Kinetics of PO and NO 303 For PO activity, there were differences among treatments (Table 1a) and a significant 304 interaction Time by Treatment interaction (only for the second repetition; Table 1b) which 305 indicates that the PO activity changed over time but according to treatment. PO values, in 306 both repetitions, for the Live-B+Live-B group were always below the mean PO of the other 307 groups (Fig. 3a, b). However, on some days, mostly after the second challenge, the RPMI-308 G+Live-B group showed lower PO values (Fig. 3a: day 7 and 9; Fig. 3b: day 11 and 10). 309 Interestingly, the PO dynamics of this group appears less complex in comparison to those that 310 Moreno-García et al 13 occurred within the RPMI-G+RPMI-G and RPMI-G+Live-B groups. PO mean peaks and falls 311 for the Live-B+Live-B group were not as intense as compared to the other experimental 312 groups in different days. Bacteria induced differences in the dynamics among groups, this 313 because the groups inoculated with bacteria showed an overall mean PO activity lower than 314 the RPMI-G+RPMI-G group. 315 316 NO production, by contrast, showed a more complex dynamics than PO activity (Fig. 4a, b). 317 There were differences among treatments (Table 2a, b) and a significant interaction Time by 318 Treatment interaction (only for first the repetition; Table 2a) which indicates that NO 319 production changed over time but differential for each treatment. During the first repetition, 320 NO values of Live-B + Live-B were always below the mean NO values of the other 321 treatments. Interestingly, in both repetitions, after the second challenge, mean NO production 322 for priming mosquitoes was above that of the other groups (at day 9 and 10 for the first 323 repetition, and markedly after day 8 for the second repetition). For this immune parameter we 324 also noticed an effect of the inoculation with live bacteria: mean NO values of the bacteria 325 challenged groups were constantly below the mean values of the group inoculated only with 326 RPMI. Intriguingly, in the second repetition, C group after day 7 (without any manipulation) 327 showed lower values compared to the manipulated groups (Fig. 4b). 328 329 Effects on number of eggs produced and egg laying females 330 We found differences in eggs production among groups (F2,73= 3.58, P= 0.032): the Live-B 331 group fed at day seven (priming dose effect) was significantly larger in comparison to egg 332 production of females injected with RPMI-G (Fig. 5A). Egg production between Live-B and C 333 groups was similar. Females in the Live-B+Live-B group produced more eggs at day 14 334 (second challenge effect) compared with the RPMI-G+RPMI-G and RPMI-G+Live-B groups; 335 however, this difference was not significant (Fig. 5B). 336 Moreno-García et al 14 337 We did not find differences in the proportion of females (fed at day 7; priming dose effect) that 338 laid eggs among C group (96%), RPMI (80%) and Live-B (80%) (Fig. 6A). After the second 339 challenge, the proportion of egg laying females in the Live-B+Live-B group was smaller 340 (45%) compared with the other three groups (C= 66%; RPMI-G+RPMI-G= 61%; RPMI-341 G+Live-B=65%) (Fig. 6B), however, the overall model was not statistically significant 342 (x2=2.88; P=0.40). 343 344 Discussion 345 Our results demonstrate that Ae. aegypti mosquitoes that were previously exposed to live 346 bacteria are more likely to survive a re-exposure 7 days later to the same bacteria at a higher 347 doses. This supports the idea that insects are capable of developing resistance upon a 348 secondary exposure to a pathogen (Little & Kraaijeveld, 2004; Little, Hultmark & Read, 2005; 349 Kurtz, 2005; Schmid-Hempel, 2005a, b; Moret & Schmid-Hempel, 2001; Kurtz & Franz, 350 2003). However, a previous exposure to E. coli does not permanently alter the mosquito’ 351 response, as it does with S. marcescens. The total experimental time span was 23 days (3 days 352 given age of the mosquitoes + 7 days after priming dose +13 days after the second challenge), 353 which is a considerably long time taking into account the maximum lifespan of 21 days 354 reported in the wild for this animal (Clements, 1999). Despite the fact that E. coli does not 355 permanently change mosquito resistance, the time of protection was still long enough to 356 ameliorate the negative impact caused by a second infection. The crucial point in this case is 357 the absolute life span relative to time elapsed between exposures. 358 359 While inoculation with dead bacteria did not induce “long” protection (just a weak 3-4 days 360 potentiation), live bacteria S. marcescens did enhance protection. Immunization with E. coli 361 provided protection for 13 days. The reasons for these differences remain unclear. It is 362 Moreno-García et al 15 possible that dead bacteria, used as an elicitor, do not release molecules that act as the 363 inducing agents. A similar phenomenon has been observed in the tsetse fly, Glossina 364 morsitans (Kaaya and Darji, 1988): inoculation with dead bacteria did not stimulate 365 antibacterial activity; however lysozyme response was weaker in comparison to the response 366 of live bacteria. The use of non-living molecules (e.g. sephadex beads, LPS vs. bacteria) could 367 be useful to prove if soluble substances from the elicitors are responsible for induction, as 368 occurs in other insects (Söderhäll 1982; Lemaitre et al. 1997; Moret & Siva-Jothy, 2003; Pham 369 et al, 2007) (see Wiesner, 1991, for similar discussion). 370 371 After the priming dose, we found overall lower PO values for immune-stimulated mosquitoes 372 when compared to RPMI-G+RPMI-G and RPMI-G+Live-B groups. These results suggest a PO 373 deactivation rather than activation possibly because the organism is using it to control live 374 bacteria. Since bacteria used for the priming dose may cause the conversion of proPO to PO, 375 post- priming dose, it is possible that when we collect hemolymph, we are just detected the 376 remaining PO after being used to defend against bacteria. Groups RPMI-G+RPMI-G and C (in 377 second repetition) were always the groups with highest PO activity (Fig 3a, b). This result 378 indicates the natural dynamics of PO in the mosquito and the effect of the wound post 379 manipulation. Contrary to what it was expected with our model (Fig. 1), no enhancement of PO 380 activity was noticed after a second challenge. Immediately after the second challenge, a 381 decrement in the PO activity in the Live-B+Live-B group was observed, however, at day 10 a 382 slight recovery was detected. The effect of the bacteria used for the second challenge was also 383 detected. As mentioned, the dynamics of PO activity for Live-B+Live-B is not as complex as 384 those in RPMI-G+Live-B. This is probably due to previous contact with bacteria, which induced 385 long-term changes, not enhancing PO activity, but giving place to an efficient use of resources. 386 For example, tyrosine is a PO substrate, and is used for the expression and manufacture of other 387 traits. One of this is coloring the egg chorion in some insects (Li & Christensen 1993). This is 388 Moreno-García et al 16 why in mosquitoes, melanization responses against worms generate a delay in TYR 389 accumulation in the ovaries, and therefore a decrease in the number of eggs produced after an 390 immune challenge (Li & Christensen, 1993) and a delay in oviposition (Ferdig et al. 1993). 391 Examples like this are suggestive that the substrates of PO and melanin substrates - PHE and 392 TYR - are restrictive resources that may lead to trade-offs among immune response and other 393 key functions including molting, basal metabolic rates and protein synthesis. Also, the PO 394 enzyme is used for different physiological processes (wounding, clotting, cuticle composition, 395 melanotic encapsulation, and probably spermatheca formation (Ilango 2005). Due to these 396 different functions, PO is constantly synthesized; therefore it could be costly to produce for the 397 organism. Also, it must be considered that intermediate molecules produced during the PO 398 cascade serve to amplify immune response (Cerenius & Söderhäll 2004; Nappi & Ottovianni 399 2000).  400 401 We have considered two alternative explanations for the low levels of PO. One is based on 402 resource allocation theory. PO enzyme is used for different immune responses (wounding, 403 clotting, cuticle composition, melanotic encapsulation, production of cytotoxic molecules; 404 (Christensen et al. 2005; Nappi & Christensen 2005) and therefore seems costly for the 405 organisms to produce (Schmid-Hempel, 2005b). It is possible that proPO gets activated in the 406 hemocele, consequently leaving other target tissues with a proPO deficiency. This means that a 407 trade off may arise because of a proPO shortage. Other negative effects may arise when PO 408 cascade is continuously activated; this leads to our second explanation of the low PO levels 409 observed. Several molecules (e.g. quinones, reactive oxygen molecules) are produced during 410 the PO cascade (Nappi & Christensen, 2005). These molecules can be harmful to pathogens 411 but also to host tissues and cells, a phenomenon referred to as immunopathology (Graham, 412 Allen & Read, 2005). It is possible that a controlled activation of PO could be a strategy to 413 Moreno-García et al 17 avoid this negative effect. In this sense, low PO levels would not be the result of resource 414 limitation but a type of “on-off” switch regulation to avoid self harm. 415 416 NO production was similar to that of PO. During first repetition, after a priming dose, NO 417 levels for Live-B+Live-B were below the other groups. This reveal that the production of NO 418 after an injury induced by manipulation is not similar to the NO response to bacteria. 419 Additionally, an interesting NO production dynamics was observed after the second challenge. 420 NO production for Live-B+Live-B was higher than RPMI-G+Live-B, RPMI-B+RPMI-B and 421 C groups after the second challenge. This result was anticipated by our model (Fig. 1). In Ae. 422 aegypti NO participates in the control of the dengue virus load (Ramos-Castañeda et al. 2008). 423 It is known that during malaria parasite infection, Anopheles spp generate NO to an extent that 424 limits parasite development (Herrera-Ortiz et al., 2004; Peterson, Gow & Luckhart, 2007). In 425 Drosophila, NO is key for activating both the Immune Deficiency (Imd) (Foley & O’Farrel 426 2003) and upstream Imd pathways. It is also a signaling molecule, so a constant production of 427 NOS is necessary for homeostatic purposes, while inducible NOS is only synthesized after an 428 immune challenge (Nappi et al. 2000). Given the importance of NO for control pathogens, it is 429 therefore expected that production increases after an immune challenge. Despite this, 430 Krishnan, Hyršl & Šimek (2006) found that NO production was similar in non-stimulated and 431 stimulated hemocytes in lepidopteran larvae. In this particular case, the authors call attention 432 to the fact that NO synthesized by an inducible NOS, can persist for long periods of time 433 (hours to days). This means that efficacy of NO in killing pathogens may not only reside on 434 NO concentration, but on the duration of NO activity (see Laurent et al. 1996). However our 435 results show that NO production dynamics were affected differently by the presence of 436 bacteria compared to the effect of inoculation. The higher production of NO in the group Live-437 B+Live-B after the second challenge can be explained by the previous contact with bacteria 438 inducing priming, causing enhanced NO production. 439 Moreno-García et al 18 440 We detected differences in the enhancement levels of NO production between repetitions. NO 441 production is probably dependent on animal condition. In a previous study there was no 442 environmental effect on basal (without immune challenge) NO production and PO activity, 443 due to food quality and quantity limitation (Moreno-García et al, 2010). However, the 444 presence of recurrent infections could lead to different levels of immune parameters activation. 445 If there is a difference in nutrition among cohorts, possibly priming may be detected only 446 when the organisms had enough resources to mount an enhanced immune response after 447 continuous infections. Related to this, NO is produced during the oxidation of L-Arginine 448 (Müller 1997). Arginine is an amino acid that must be obtained from the diet (Rivero 2006). 449 Arginine is also important in sperm maturation (Osanai & Chen 1993), egg production 450 (Uchida 1993), long term memory, chemosensory (antennal lobes, olfaction), and visual 451 information processing (Müller 1997). 452 453 454 There is currently a gap in terms of the life history consequences of immune priming. Our data 455 demonstrate that a first immune stimulation results in an increase in egg production. It is 456 probable that when this first infection occurs, the organism is allocating resources to 457 reproduction. It is possible that the egg laying strategy depends on the force and course of 458 infection. After the first challenge, females could be allocating their resources to lay eggs, in 459 an effort to compensate for the reduced life expectancy that would diminish their chances of 460 future reproduction. Related to costs, in Ae. aegypti and D. melanogaster, up regulation of 461 immune genes reduce the life span of the organism (Libert et al, 2006; Kambris et al, 2009; 462 McMeniman et al, 2009), so, investment in early reproduction could be adaptive. After the 463 second challenge there was no difference in egg production among individuals in the different 464 treatments, however, survival and NO production for Live-B+Live-B was higher than the other 465 Moreno-García et al 19 groups. It can be argued that survival (and related immune traits) and fecundity are being 466 maximized simultaneously. On the other hand, our results also indicate that the proportion of 467 egg laying females in the Live-B+Live-B group was lower than the other groups, indicating 468 that not all females are disposed to produce eggs, increment survival and enhance immune 469 response. It is possible that the egg laying strategy depends on the force and course of 470 infection, and that the organisms that recover from infection and reduce immune activation 471 costs use its resources in relation to chances of future reproduction which is strongly related to 472 life expectancy. 473 474 In summary, we have provided evidence that PO activity and NO production do not show an 475 overall increased level. However, NO production may be enhanced when the host has had a 476 previous contact with live bacteria. Combined with survival results, the immune levels found 477 here can be explained as the efficacy of organisms in fighting bacteria. Depending on the 478 immune markers measured, immune priming can be considered not only an enhanced 479 response, but an optimal response (in terms of survival and reproduction) to protect the 480 organisms after a previous exposure to an elicitor. Reactions and products of immune response 481 are interconnected and the kind of response is related to the pathogens virulence. It is possible 482 that not all pathogens will induce the same reaction, or at least the magnitude of response can 483 differ depending on the host-pathogen interaction type. Therefore, it is recommended to use 484 more than one immune marker when possible.The mechanisms of immune response in insects 485 include reactions and molecules that are interconnected. It is also important to ascertain the 486 contribution of other immune parameters such as hemocyte activation or antimicrobial 487 synthesis whose activities are enhanced after a second pathogen exposure and have an 488 integrated system of dynamics within immune response. Finally, in terms of reproduction 489 effects, we did not detect an obvious negative result on egg production and number of egg 490 laying females. Reasons for this result granted further studies. 491 Moreno-García et al 20 492 493 Acknowledgements 494 We thank L. González-Cerón and J. Silva for kindly donating bacterial strains. We also thank 495 the Malaria-Dengue Group at the INSP for useful comments. We thank Gloria Tavera for 496 revising this paper in English. MM-G thanks the Posgrado en Ciencias Biomédicas 497 (CONACYT grant No. 172947), Instituto de Ecología, Universidad Nacional Autónoma de 498 México, and Centro de Investigaciones Sobre Enfermedades Infecciosas, INSP, México. AC-499 A was supported by a PAPIIT-UNAM grant (Project No. 211506). HL-M thanks the Bill and 500 Miranda Gates Foundation for the Grand Challenges Exploration Grant (Grant ID 51996). 501 502 References 503 Adamo, S. A. 1998. The specificity of behavioral fever in the cricket Acheta domesticus. 504 Journal of Parasitology 84: 529-533. 505 Ahmed, A.H., S.L. Baggott, R. Maingon, and H. Hurd. 2002. 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Results of repeated measures ANOVA of immune kinetics for (a) NO production and 670 (b) PO activity (first repetition). 671 NO production SS df MS F P Treatment 363.3 2 181.6 122.7 0.008 Error 2.9 2 1.4 Time 1846 10 184.6 5.4 0.000 Time*Treatment 1597.4 20 79.8 2.3 0.032 Error 683.2 20 34.1 672 673 674 PO activity SS df MS F P Treatment 0.205 2 0.102 44.6 0.021 Error 0.004 2 0.002 Time 0.309 10 0.030 3.5 0.007 Time*Treatment 0.225 20 0.011 1.2 0.289 Error 0.175 20 0.008 675 676 677 678 679 680 681 682 683 684 685 686 687 688 Moreno-García et al 28 Table 2. Results of repeated measures ANOVA of immune kinetics for (a) NO production and 689 (b) PO activity (second repetition). 690 691 NO production SS df MS F P Treatment 265.2 3 88.4 4.43 0.047 Error 139.5 7 19.9 Time 1811.2 11 164.6 5.48 0.000 Time*Treatment 1251.7 33 37.9 1.26 0.200 Error 2312.1 77 30 692 693 694 PO activity SS df MS F P Treatment 0.573 3 0.191 50.79 0.000 Error 0.022 6 0.003 Time 0.085 11 0.007 2.17 0.026 Time*Treatment 0.256 33 0.007 2.18 0.003 Error 0.234 66 0.003 695 696 697 698 699 700 701 702 703 704 705 706 707 Moreno-García et al 29 Figure 1. A hypothetical model of the temporal dynamics of the immune response after a 708 priming dose and a second challenge. Dashed line= Prolonged activation. 709 Figure 2. Survival curves of mosquitoes after a second exposure with A) S. marcescens, and 710 B) E. coli. 711 Figure 3. In vivo dynamics of (a) PO activity and (b) NO production (first repetition). 712 Figure 4. In vivo dynamics of (a) PO activity and (b) NO production (second repetition). 713 Figure 5. Egg production in females fed at day 7 after priming dose (a), and in females fed at 714 day 14, after second challenge (b). 715 Figure 6. Proportion of egg laying in females fed at day 7 after priming dose (a), and in 716 females fed at day 14, after second challenge (b). 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 Moreno-García et al 30 Figure 1. 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 Second challenge Recovery period Time (days, weeks) Im m un e re sp on se Priming dose Moreno-García et al 31 Figure 2. 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 B C Live - B+Live - B Dead - B+Live - B RPMI+Live- B 0 0.2 0.4 0.6 0.8 1 1.2 0 1 2 3 4 5 6 7 8 9 S ur vi va l Days after second challenge A Days after second challenge 0 0.2 0.4 0.6 0.8 1 1.2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 S ur vi va l C RPMI+RPMI RPMI+Live - B Live - b+Live - B Moreno-García et al 32 Figure 3. 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 P O a ct iv it y 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Priming Dose 1 2 3 4 5 6 Challenge Time (days) 8 9 10 11 RPMI - G+RPMI - G RPMI - G+Live - B Live-B+Live - B A B 0 5 10 15 20 25 30 35 40 Priming Dose 1 2 3 4 5 6 Challenge 8 9 10 11 RPMI-G+RPMI-G RPMI-G+Live-B Live-B+Live-B N O p ro du ct io n Time (days) Moreno-García et al 33 Figure 4. 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 0 5 10 15 20 25 Priming Dose 1 2 3 4 5 6 Challenge 8 9 10 11 12 C RPMI -G+RPMI - G RPMI -G+Live - B Live -B+Live - B Time (days) N O p ro du ct io n B 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Priming Dose 1 2 3 4 5 6 Challenge 8 9 10 11 12 C RPMI - G+RPMI - G RPMI - G+Live - B Live -B+Live - B A P O a ct iv it y Time (days) Moreno-García et al 34 Figure 5. 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 B C RPMI -G+RPMI-G RPMI-G+Live-B Live-B+Live-B 0 10 20 30 40 50 60 70 80 E gg p ro du ct io n A 0 10 20 30 40 50 60 70 80 90 E gg p ro du ct io n C RPMI-G Live-B Moreno-García et al 35 Figure 6. 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 C RPMI -G+RPMI-G RPMI-G+Live-B Live-B+Live-B 0.66 0.61 0.65 0.45 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 E gg la yi ng f em al es B C RPMI-G Live-B A 0.96 0.8 0.8 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 E gg la yi ng f em al es DEVELOPMENT, LIFE HISTORY Genetic Variance and Genotype-by-Environment Interaction of Immune Response in Aedes aegypti (Diptera: Culicidae) MIGUEL MORENO-GARCÍA, 1,2 HUMBERTO LANZ-MENDOZA, 2 AND ALEX CÓRDOBA-AGUILAR 1 Departamento de Ecologṍa Evolutiva, Instituto de Ecologṍa, Universidad Nacional Autónoma de México, Circuito Exterior s/n, Apdo. Postal 70-275, México, D. F. 04510, México J. Med. Entomol. 47(2): 111Ð120 (2010); DOI: 10.1603/ME08267 ABSTRACT Immune response can be negatively affected by resource limitation, so it is expected that organisms evolve strategies to minimize the impact of this environmental outcome. Phenotypic plasticity in immune response could represent a genetic response to face such situations. We inves- tigated the effects of high and low quality and quantity of food at the larval stage on two important immune components, phenoloxidase activity (PO) and nitric oxide production (NO) measured in adults of the Dengue vector,Aedes aegypti. We reared families to determine the magnitude and pattern of expression of genetic variance, environmental variance and genotype-by-environment interaction (GEI). In addition, we quantiÞed whether there were differences in plastic immune responses in both sexes. Our results indicated additive variance for PO and NO, but rearing environment did not produce differences among individuals. For NO and PO in males, there were large differences among families in plasticity, as indicated by the different slopes produced by each reaction norm. Therefore, there is additive genetic variation in plasticity for NO production and PO activity. One possible interpre- tation of these results is that different genotypes may be favored to Þght pathogens under the different food quality situations. Males and females showed similar overall GEI strategies but there were differences in PO and NO. Males showed a phenotypic correlation between PO and NO, but we did not Þnd genetic correlations between immune parameters in both sexes. KEY WORDS immune response, quantitative genetics, phenotypic plasticity, Aedes aegypti Immune response is a trait closely linked to survival and reproduction (Schmid-Hempel 2003, Schulen- berg et al. 2009). Despite the fact that investment to immunity is adaptive, immune response is strongly impacted by environmental conditions. This occurs in situations of environmental heterogeneity (such as variation in food abundance), in which an overall Þtness decrement for a given genotype is observed (e.g., Leclaire and Brandl 1994, Fellowes et al. 1998, Metcalf and Monagham 2001, Siva-Jothy and Thomp- son 2002). In mosquitoes, for example, larvae that have been reared in crowded or undernourished condi- tions, give rise to adults with a weak cellular encap- sulation (Suwanchaichinda and Paskewitz 1998). On the same line of research, it has been also found that when mosquito adults are stressed with food shortage, encapsulation immune response decreases (Chun et al. 1995, Schwartz and Koella 2002). Therefore, the organisms are expected to deal with the problem of maximizing Þtness in changing or stressful environ- ments. A genetic background that expresses changes in phenotype expression could have a selective advan- tage in a given environment (Zhivotovsky et al. 1996). In some cases, a single genotype can have the ability to produce distinct phenotypes when exposed to dif- ferent environments, a phenomenon known as phe- notypicplasticity(Roff 1997,NylinandGotthard1998, Schlichting and Pigliucci, 1998). Phenotypic plasticity is shown by many traits and immune response is not an exception (Fordyce 2006). In fact, such immune plasticity could be used as an indicator of the strategies that an organism has followed when dealing with en- vironmental heterogeneity. Paradoxically, given the recent explosion in ecological and evolutionary stud- ies of immunity, only a handful of studies have ap- proached immune response using a phenotypic plas- ticity perspective (but see Barnes and Siva-Jothy 2000, Mucklow and Ebert 2003, Cotter et al. 2004a, Lazzaro et al. 2008, McKean et al. 2008). To have a better understanding of the evolution of immune responses, more studies of phenotypic plasticity are therefore needed. To afford the problem of whether plastic immune responses could have an impact in population evolu- 1 Departamento de Ecologṍa Evolutiva, Instituto de Ecologṍa, Univer- sidad Nacional Autónoma de México, Circuito Exterior s/n, Apdo. Postal 70-275, México, D. F. 04510, México (e-mail: miguelmoga2000@ yahoo.com.mx). 2 Centro de Investigaciones Sobre Enfermedades Infecciosas, In- stituto Nacional de Salud Pública, Avda. Universidad 655. Col. Sta. Marṍa Ahuacatitlán, 62100 Cuernavaca, Morelos, México. 0022-2585/10/0111Ð0120$04.00/0  2010 Entomological Society of America tion, it is necessary to evaluate if different environ- ments produce different phenotypes and if genotypes respond differently to these environments (genotype- by-environment interaction GEI, which represents genetic variation for phenotypic plasticity). It has been proposed that GEI can allow populations to evolve to an optimum phenotypic mean in different environments, promoting adaptation to heteroge- neous environments (Via and Lande 1985). As a result, the amount of genetic variance, as assessed by the slope of reaction norms (i.e., a graphical description of the GEI), may be a strong determinant of the popu- lation Þtness (Fry 1996). Notwithstanding, a lack of variation may lead to a failure to respond adaptively to environmental changes. There are theoretical advan- tages of using this approach. For example, phenotypic plasticity could explain the maintenance of genetic variance recently found in different immune compo- nents (e.g., see Ryder and Siva-Jothy 2001, Simmons and Roberts 2005, Cotter et al. 2004b, Rolff et al. 2005, Schwarzenbach et al. 2005, Fellowes et al. 1998). The fact that plasticity uncouples the phenotype from ge- notype and thus releases the gene pool from the im- mediate impact of natural or sexual selection (Stearns 1992), will lead to slow depletion of genetic variance. However, some reaction norms will not produce the optimal mean trait value in a given environment (Schlichting and Pigliucci 1998, Roff 1997), slowing down the rate of adaptation for immediate genera- tions. The conventional approach to see how a trait re- sponds to selective pressures is to analyze the trait in question using quantitative genetics by partitioning and estimating variances and covariances into causal components (Falconer and Mackay 1996). In this study we have explored the environmental heteroge- neity affecting immune components in the mosquito Aedes aegyptiLinnaeus (Diptera: Culicidae), the prin- cipal vector of Dengue and Yellow Fever virus. We varied levels of food quality and quantity in the larval stage, and as response variables two immune markers were measured in the adult: the basal levels of phe- noloxidase (PO) activity and nitric oxide (NO) pro- duction. PO is an oxidoreductase enzyme used in in- sect cellular and humoral response such as cuticle melanization, wound repair, cytotoxin production, and melanotic encapsulation (Söderhäll and Cerenius 1998). NO is a highly reactive and unstable free radical gas that inhibits protein catalytic activity and produces protein and harming effects on pathogensÕ DNA (Riv- ero 2006). Recent studies have shown that PO activity is costly to produce (reviewed by Kanost and Gorman 2008). Although there is no direct evidence for such costs for NO, their physiological pathways strongly indicate that it is potentially costly (Rivero 2006, Car- ton et al. 2008). We evaluated genetic variation, phe- notypic plasticity and tested for GEIs of PO and NO using a split family design. We predicted the existence of genetic differences and environmentally sensitive production of alternative phenotypes by given geno- types on these immune markers and a negative effect because of limitation in food quality: mosquito adults whose larval stage had poor quality food would show decreased basal PO activity and NO production while the opposite would be found for mosquito adults whose larval stage had access to high quality food. Reproductive strategies could promote differences in the pattern of investment to immune defense by each sex. Males are expected to increase their Þtness by reducing their investment to immune defense while investing resources to reproductive effort (Zuk and McKean 1996, Sadd et al. 2006). Meanwhile, in females natural selection would favor an increase in resource investment to immunity, under the assump- tion that increased longevity could enhance Þtness via egg production (Rolff 2002). How this presumable sexual dimorphism translates into plastic response dif- ferences in both sexes has been little explored. Mc- Kean and Nunney (2005) found sex speciÞc plastic responses in relation to the availability of limiting resources. Given this background information, we thus evaluated if males and females express different strategies in their plastic responses in PO activity, NO production, and GEIs strategies. Materials and Methods Mosquitoes were obtained from an insectary at the Instituto Nacional de Salud Pública, Cuernavaca, Mex- ico. The stock population had been held in this place for at least 130 generations. The colony was main- tained with a protocol that minimizes inbreeding: fairly high number of individuals per generation (over 2,000 individuals per generation) and random mating. During the study, the colony was kept on a 12L:12D cycle at 25Ð28C. Experimental Design. For PO activity and NO pro- duction, we had 44 families by randomly choosing 44 mated and blood fed females. We then split their F1 hatched larvae into one of two rearing environments that differed in food quality and quantity: the high quality food (HQF) environment was based on rat chow, yeast extract and lactoalbumin hydrolisate (1: 1:1 mix; 25 g/200 ml), while the low quality food (LQF) environment was based on rat chow only (25 g/200 ml). Preliminary observations have shown these dietary requirements are effective enough to change the individual condition, as reßected by adult size (S. Hernandez-Martinez personal communication), life- span, and egg clutch and size (see Nasci 1986, Packer and Corbet 1989). Larvae were fed according to the schedule shown in Table 1. Each family of larvae was reared in plastic glasses containing 100 ml of water. Adult females and males of 3 d postemergence from these larvae were used to obtain PO and NO readings. To minimize degradation of molecules, individuals were collected and frozen at 70C until they were processed. Given the Þnal large number of individuals and with the aim to avoid sample degradation, mac- eration, supernatantcollectionandreadingswereonly done for the exact number of tests supported in the microwell plate (80 wells for test samples plus 16 for standard reference curves, see below) that can be processed and quantiÞed in a single day. A group of 112 JOURNAL OF MEDICAL ENTOMOLOGY Vol. 47, no. 2 three mosquitoes was required for a single sample because one individual cannot provide enough sample for spectrophotometer readings (MM-G, unpublished data). All organisms were macerated with a biovortexer in 120 l of PBS buffer (4C) and each sample was cen- trifuged for 10 min at 10,000 rpm (4C). Supernatant was used to record protein load concentration and PO activity. Before recording PO activity, we determined protein load concentration to control for individual differences in protein content among samples that may bias PO readings (see Contreras-Garduño et al. 2007). The BCA (Pierce) assay kit was used to deter- mine protein concentration for each sample. There were 10 l of sample supernatant, 40 l of PBS, and 150 l of BCA kit reagents mix added to a 96 microwell plate and incubated for 10 min at 37C. A known concentration of albumin (5Ð60 g) was used as a standard reference curve. The absorbance was re- corded at 562 nm in a plate reader. For PO activity, the sample supernatant plus PBS gauged at 50 l (after protein load adjustment) was mixed on a 96 microwell plate with 50 l L-DOPA (L-dihydroxyphenylalanine; 4 mg/ml) as substrate and incubated for 10 min at room temperature, 50 l of buffer mixed with 50 l of L-DOPA was used as blank. The absorbance was re- corded at 490 nm in a plate reader. An increment in OD after 30 min was deÞned as PO activity. PO read- ings of each microwell plate were obtained as pro- portional values (highest reading  100%; lowest read- ing  0%). The Griess reaction was used to determine NO concentration (Eckmann et al. 2000). There were 50 l of each sample supernatant mixed with 50 l of 1% sulfanilamide and 50 l of 0.1% naphthylethylenedia- mine on a 96 microwell plate and incubated for 10 min at room temperature. NO was quantiÞed using a NaNO2 (1Ð100 M) standard reference curve for each assay. Absorbance was recorded at 540 nm in a plate reader. The highest readings obtained in an interval of 30 min (with readings every 5 min) were deÞned as NO production (expresses as micrometers). NO read- ings of each microwell plate were obtained as pro- portional values (highest reading  100%; lowest read- ing  0%). Plasticity and Genotype-by-Environment Interac- tion Data Analysis. We Þrst investigated whether there were differences in wing length according to food treatments. For this, a t-test was used to compare wing length between adult females raised when larvae in LQF and HQF. We investigated the effects of fam- ily, food quality and quantity environment (HQF- LQF) and the interaction (GEI) of PO activity and NOproductionusingamixed-modelANOVA(type III SS) for unbalanced data using the Variance Compo- nents module of STATISTICA 7.0 (StafSoft 2004). Family was entered as a random effect while environ- ment was entered as a Þxed effect. This method cor- responds to the Scheffé model of FryÕs (1992), in which the effect of family is tested using the formula FMSfam/MSerror.The MS of the interaction was used as denominator for the Þxed effect. To estimate GEIs we Þrst tested for family by rearing environment in- teractions using the mixed model ANOVA. If a signif- icant interaction was detected, we then evaluated GEI by calculating the cross-environment genetic corre- lation (rg; see Fry 1992). rg is deÞned as the correlation between the mean of a trait of a genetic group in one environment and the groupÕs mean in another envi- ronment. Thus, rg assumes that a single trait expressed in different environments represents two separate traits (Falconer and Mackay 1996). We used the SAS model of Fry (1992) where rg  Cov(M1j, M2j)/ [Var(M1j) x Var(M2j)], where M1j and M2j are the mean trait values of genetic group j under environ- mental conditions 1(HQF) and 2(LQF) where Cov(M1j, M2j) is the covariance of the mean trait values between conditions HQF and LQF, and where Var(M1j) and Var(M2j) are the variances of the mean trait values under conditions HQF and LQF. Cross- over interactions in reaction norms are more feasible when rg values are 1 (Fry 1992). We used families with at least one sample in both rearing environments. ANOVA were performed on untransformed data when non-normal distribution was present. Various transformations did not lead to a normal distribution of data. However, we still performed the ANOVA using the nontransformed as this test is still appropri- ate as it gives a better chance of Þnding signiÞcances when they exist (Zar 1999). Also, it was necessary to perform the parametric ANOVA to obtain theMS given by STATISTICA 7.0 Variance Component module. Sexual Dimorphism in PO Activity and NO Pro- duction. For PO a t-test was used to compare the average reaction norm of each sex, using the GEI resultant least-square means of each family reared at HQF and LQF environment. For NO, data has non- normal distribution, so a MannÐWhitney U test was used to compare the average reaction norm of each sex, using the GEI resultant least-square means of each family reared at HQF and LQF environment. We also evaluated if rearing environment induced different plastic responses for PO activity and NO production between the sexes using a MannÐWhitney U test for which we used nontransformed data. Genetic and Phenotypic Correlations. We exam- ined phenotypic and genetic correlations separately for each rearing environment and sex. We estimated genetic correlations between PO activity and NO pro- duction by calculating Spearman correlation coefÞ- Table 1. Rearing schedule according to food regimes Day posteclosion HQF LQF 1 30 l 15 l 2 0 l 0 l 3 30 l 15 l 4 70 l 35 l 5 110 l 55 l 6 50 l 25 l 7 50 l 25 l 8 50 l 25 l 9 50 l 25 l HQF, high quality food; LQF, low quality food. March 2010 MORENO-GARCṍA ET AL.: QUANTITATIVE GENETICS OF MOSQUITO IMMUNE RESPONSE 113 cient for mean trait values for each family. We esti- mated phenotypic correlations between traits with Spearman correlation coefÞcient for individual trait values for which we used nontransformed data. Results Plasticity and Genotype-by-Environment Interac- tion. No differences in wing length were observed between diet regimes (P  0.05). The ANOVA re- vealed a strong family effect on female (Table 2; Fig. 1) and male (Table 3; Fig. 2A) PO activity, which means that there was genetic variation (Family source) for this immune response. There was no sig- niÞcant effect for rearing environment for males and females and no interaction between the effects of family and rearing environment in females. However, our anal- ysis revealed strong effects of family-by-rearing environ- ment on PO activity in males (Table 3; Fig. 2B). NO production in females had a signiÞcant family effect (Table 3A; Fig. 3A). Females reared in the LQF regime produced more NO than females reared in the HQF regime (Fig. 3B) although such differences were not statistically signiÞcant (Table 3A.). For males, rearing environment did not have a signiÞcant effect, family revealed a strong effect, and the family-by- rearing environment was marginally nonsigniÞcant (Table 3B; Fig. 4A and B). It is possible that genetic variation exists in NO production and genotypes differ in the level or direction of plasticity in this immune component. The family-by-rearing environment interaction de- tected for PO activity and NO production in males suggested GEIs. We thus calculated rg from the vari- ance components of the mixed-model ANOVA (Table 2B; 3B): for PO there was a rg  0.641; for NO there was a rg 0.535. These values are consistent with the crossover interactions in reaction norms. Visual in- spection of Fig. 2B revealed 19 families (out of 44) that showed higher PO values for individuals reared at LQF compared with his brothers reared at HQF. For NO (Fig. 4B), 16 families (out of 43) showed higher NO production when individuals were reared at LQF contrasting with their brothers reared at HQF. These cross-environmental genetic correlations showed that the PO production and NO activity can be viewed as a different trait whose production depends on the environment. Sexual Dimorphism in PO Activity and NO Pro- duction. PO GEI least-square average means (the av- erage slopes for the reaction norms of each family) produced a sexual difference (t  8.862; df  1, 172; P  0.0001; Fig. 5A): males showed higher average reaction norm than females. Meanwhile, NO average reaction norm was higher in females (U  327; P  0.0001; Fig. 5B). Concordant with the average reaction norms, males of both rearing environments had higher PO activity than females (U 55146; P 0.0001; Fig. 6A). Females, compared with males, showed higher NO production in both rearing environments (U  29392; P  0.0001; Fig. 6B). Genetic and Phenotypic Correlations.We only de- tected a phenotypic correlation between PO and NO in males in both environments (Table 4). This result indicates that variance in PO is related to variance of NO, variation in this immune parameters is not inde- pendent. We did not detect any correlation in females. Discussion We explored whether there was variance in PO activity and NO production that can be ascribed to Family 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 P h en o lo x id as e ac ti v it y Fig. 1. Genetic differences (means  SE) of 44 half-sib families for PO activity in adult female mosquitoes. Table 2. Mixed model ANOVA testing for the effect of family, rearing environment, and GEI on PO activity in adult female and male mosquitoes Adult mosquitos df SS MS F P A. Female Family 43 2.8752 0.0668 2.0651 0.0005 Rearing environment (HQF-LQF) 1 0.0093 0.0093 0.2405 0.25 Family environment 43 1.666 0.0387 1.1970 0.19 Error 337 10.911 0.0323 B. Male Family 43 3.704 0.0861 3.3089 0.0005 Rearing environment (HQF-LQF) 1 0.065 0.0657 1.3674 0.25 Family environment 43 2.066 0.0480 1.8459 0.0014 Error 396 10.309 0.0260 HQF, high quality food; LQF, low quality food. Table 3. Mixed model ANOVA testing for the effect of family, rearing environment, and GEI on NO production activity in adult female and male mosquitoes Adult mosquitos df SS MS F P A. Female Family 42 2.4284 0.0578 1.6114 0.025 Rearing environment (HQF-LQF) 1 0.0098 0.0098 0.2751 0.25 Family environment 42 1.5019 0.0357 0.9966 0.48 Error 337 12.0920 0.0358 B. Male Family 43 1.0886 0.0253 1.6180 0.025 Rearing environment (HQF-LQF) 1 0.0024 0.0024 0.1120 0.25 Family environment 43 0.9446 0.0219 1.4039 0.052 Error 396 6.1963 0.0156 HQF, high quality food; LQF, low quality food. 114 JOURNAL OF MEDICAL ENTOMOLOGY Vol. 47, no. 2 genetic differences between individuals, as well as the presence of additive genetic variation for plasticity (GEI). PO activity and NO production show genetic variation for both sexes, this fact has been already documented in other insects (e.g., Cotter et al. 2003b; Schwarzenbach and Ward 2006). In these cases, ge- netic variation has been interpreted as an adaptive response to face pathogen attack: given the large num- ber of infectious agents, there will be ground for high genetic variability in immune response. In the case of our study subject, such variation could allow the male and female mosquitoes to Þght against all varieties of infections produced by different pathogens (e.g., the coexistence of different dengue serotypes; Thavaral et al. 2006). Interestingly, no environmental variance was de- tected for both overall PO activity and NO production despite contrary evidence suggesting that immunity is affected by, for example, dietary restrictions (e.g., Siva-Jothy and Thompson 2002) among other envi- ronmental factors (reviewed by Schmid-Hempel 2003). It may be that the speciÞc dietary components that we varied may not be directly involved in PO activity. One component ascribed to be key as PO substrate is tyrosine, which is derived from phenylal- anine hydroxylation (Christensen et al. 2005). Phe- nylalanine (thus tyrosine) is gathered through larval development in many insects (Kramer and Hopkins 1987). Furthermore, melanin, the Þnal product of the phenoloxidase cascade, is a nitrogen-rich compound. Nitrogen or protein investment is expected to be re- quired for its production (Blois 1978). Poor diet could reduce these compounds destined for melanin pro- duction, consequently the production of PO could be synthesized in low quantities. Meanwhile, NO is pro- duced during the oxidation of L-arginine to L-citrulline (Müller 1997); arginine is an essential amino acid which must be obtained from diet (Rivero 2002) mainly through ontogeny. Therefore, suboptimal food quality conditions for larvae could result in a high genetic variability in adult immunocompetence. A sit- uation like this can be found in the wild. It was ex- pected that the beneÞcial effect of supplementing lactoalbumin and yeast extract (as amino acids dona- tors; see Davis, 1975) on larvae diet may render dif- ferences in PO activity and NO production between HQF and LQF. However, rearing effects were not detected in the Mixed ANOVA. It may be that the concentration of proteins mixture in the LQF in this study may not be suboptimal for these immune pa- rameters. However, such explanation does not seem to be entirely correct as the presence of phenotypic correlations in males indicate a degree to which PO Family 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 P h en o lo x id as e ac ti v it y A HQF LQF 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 P h en o lo x id as e ac ti v it y B Fig. 2. (A) Genetic differences (means  SE), and (B) reaction norms of PO according to different food treatments for males of 44 half-sib families. HQF, high quality food; LQF, low quality food. Family 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 N it ri c o x id e p ro d u ct io n A HQF LQF 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.50 0.51 N it ri c p x id e p ro d u ct io n B Fig. 3. (A) Genetic differences (means  SE), and (B) rearing environment effects in NO for adult female mosquitoes (43 families). HQF, high quality food; LQF, low quality food. March 2010 MORENO-GARCṍA ET AL.: QUANTITATIVE GENETICS OF MOSQUITO IMMUNE RESPONSE 115 and NO respond to variation in the same environmen- tal factors (HQF or LQF). GEI was not present in females. However, in males there exists some genetic variation in the sensitivity to environmental effects among different mosquito fam- ilies. The presumed sex-biased genetic-by-environ- ment difference in immunity could be a consequence of the difference in pathogens, quantity of resource acquired during ontogeny, and differences in survival and reproductive strategies. It is possible that females are under the selective pressures of a large variety of pathogens for which, consequently, genetic variance should be expected. However, a female strategy could be to buffer the variation caused by stressing envi- ronmental factors (i.e., limited resources), ensuring phenotypic expression within individuals given a spe- ciÞc genotype and environment. The absence of GEI in females for NO and PO could be the result of the ability of females to withstand environmental pertur- bations and, in combination with the presence of ge- netic variance, to respond adaptively to changes in the environment and pathogen challenges as a result of stabilizing selection. However, the intrinsic physio- logical differences between males and females (e.g., fat body reserves, life span and reproductive effort) might also preclude the GEI in females (see below for discussion about sexual dimorphism). For males, the cross-environmental genetic corre- lations (rg) for both immune parameters suggest dif- ferences in genetic architecture across the environ- mental food conditions. It is possible that differences in the amount of reserves accumulated during the larval life affect the immune response of adult mos- quitoes that would depend on their genotype. It has been suggested that the presence of GEI may allow adjustment of development that maximizes Þtness in a particular environment (Stearns 1992, Fry 1996). For example, under poor food conditions, it may be ad- vantageous to cease growth and allocate the available energy to immunity. However a similar phenotypic response may also occur as consequence of stress, lacking any Þtness beneÞts (Kearsey and Pooni 1996). Although we cannot address the possible adaptive signiÞcance of the observed GEI in this study, the genotypes differing in their reaction norms have the potential to ensure immunocompetence during peri- ods of food quality stress and inßuence the course and rate of evolution in heterogeneous environments (e.g., Gillespie and Turelli 1989). Nevertheless, to see how realistic this situation is in periods of food stress and test that our results are adaptive, further experi- mental approachesneed tobedone(fora similar claim see Siva-Jothy et al. 2005). Family 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 N it ri c o x id e p ro d u ct io n A HQF LQF 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 N it ri c o x id e p ro d u ct io n B Fig. 4. (A) Genetic differences (means  SE), and (B) rearing environment effects in NO for adult male mosquitoes (44 families). HQF, high quality food; LQF, low quality food. HQF LQF 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 L S M p h en o lo x id as e ac ti v it y Male Female A HQF LQF 0.20 0.25 0.30 0.35 0.40 0.45 0.50 L S M N it ri c o x id e p ro d u ct io n Female Male B Fig. 5. Means of slopes of each sexÕs reaction norm for (A) PO activity and (B) NO production based on least-square means. HQF, high quality food; LQF, low quality food. 116 JOURNAL OF MEDICAL ENTOMOLOGY Vol. 47, no. 2 There were differences between male and female overall values of GEI (Fig. 4A and B), and PO activity and NO production (Fig. 5A and B), which suggests that the two sexes exhibit dissimilar immune strate- gies. This difference is consistent with the idea that traits involved in immune response do not have the same importance for the adult Þtness of the two sexes in terms of condition (McKean and Nunney 2005). PO activity was larger in males than females that can be explained as a consequence of resources being allo- cated to reproductive activity (host search and oo- genesis) rather than to the maintenance of PO cas- cade. During this cascade, phenylalanine and tyrosine are used as substrates for the formation of melanin and highly reactive and toxic intermediate molecules. Phe- nylalanine is also involved in cuticle formation and pigmentation, relevant in natural selection functions such as aposematism and crypsis (to avoid predation), thermoregulation, resistance to UV radiation and col- ored traits that can be under sexual selection (True 2003). Meanwhile, tyrosine is also responsible for col- oring the egg chorion in some insects (Li and Chris- tensen, 1993) and other metabolic pathways. It has been shown that mosquitoes undergoing melanization responses against Þlarial worms after blood-feeding exhibit a delay in tyrosine accumulation in the ovaries, and therefore a delay in egg production and oviposi- tion (Ferdig et al. 1993). For these reasons, it is likely that phenylalanine and tyrosine are restrictive re- sources in females mosquitoes leading to trade-offs between immune response and other life history traits. In the absence of an immune challenge, females would favor an increase in resource investment to longevity and egg production rather to immunity (see also Rolff 2002). NO was produced by females in a twofold relation compared with males. It could be argued that a trade- off is also present. Arginine is an important immune molecule, but it is also essential for sperm maturation (Osanai and Chen 1993), egg production (Uchida 1993), long-term memory, chemosensory (antennal lobes, olfaction) and visual information processing (Müller, 1997). However, in Anopheles stephensi, NO limits parasite development (Tina et al. 2007), and in Ae. aegypti NO participates in the control of the den- gue virus load (Ramos-Castañeda et al. 2008). This presumed sex bias in NO production could result from different kinds of pathogens attacking each sex. For example in mosquitoes, adult females require carbo- hydrates (sugar) and proteins for vitellogenesis that are usually present in the blood meal. Meanwhile, males need sugar solutions (nectar) (Clements 1999). If some pathogens are acquired when feeding (as it is the case of the dengue virus) when each sex occupies or uses different habitats, then both sexes should show extremely different pathogens and, therefore, distinct immune adaptations against pathogens. Also, if adult female mosquitoes that have inherited pathogens from their parents, or become contaminated when mating with infected males (males that have inherited patho- gens too) (e.g., Diallo et al. 2000) are expected to enhance survival. Thus, not only there are differences 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 P h en o lo x id as e ac ti v it y Female Male HQF LQF A Female Male 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 N it ri c o x id e p ro d u ct io n HQF LQF B Fig. 6. Sexual dimorphism in (A) PO activity and (B) NO production for adult mosquitoes reared in varying regimes of food quality and quantity. HQF, high quality food; LQF, low quality food. Table 4. Genetic and phenotypic correlations between PO activity and NO production in adult male and female mosquitoes reared in two different food regimes during larval development Males Females At HQF PO activity At LQF PO activity At HQF PO activity At LQF PO activity Phenotypic correlations NO production 0.191 (**) 0.144 (*) 0.082 (NS) 0.004 (NS) N 244 N 242 N 221 N 207 Genetic correlations NO production 0.252 (NS) 0.031 (NS) 0.172 (NS) 0.218 (NS) N 44 N 44 N 44 N 44 Spearman correlation coefÞcients calculated with untransformed data.Pvalues are indicated between parentheses (*,P 0.05; **,P 0.005). HQF, high quality food; LQF, low quality food; NS, nonsigniÞcant. March 2010 MORENO-GARCṍA ET AL.: QUANTITATIVE GENETICS OF MOSQUITO IMMUNE RESPONSE 117 in the incidence of different pathogens affecting males and females that explain the sexual dimorphism ob- served for NO and PO, but different reproductive strategies followed by each sex that could be impor- tant. This fact could be reßected in the course of action of individual immune response. Immune response occurs via a set of physiological traits and is supposed to show low additive variance and hence low heritability values (like other physio- logical traits, see Mousseau and Roff 1987). Our breed- ing results of PO activity and NO production con- Þrmed the (additive) genetic variance previously reported for other immune parameters in other insects (e.g., Fellowes et al. 1998, Kurtz and Sauer 1999, Hosken 2001, Rolff et al. 2005, Simmons and Roberts 2005). In male mosquitoes, genetic variation can be maintained if environmental conditions vary and if distinct alleles maximize Þtness under each environ- ment. GEIs may contribute to genetic variability in immunocompetence although this consideration has been rarely investigated. Natural or sexual selection will act on phenotypic individual variance, favoring some phenotypes, and as long as the phenotypic vari- ance has a genetic background, the population can evolve. The lack of genetic correlations (pleiotropy) (in males and females) and rg (for males) indicate that if some genotypes are favored under different envi- ronmental conditions, there should be no strong ge- netic constraints for adaptation to each of the envi- ronments for independent PO and NO responses. However, the presence of additive genetic variation could be the result not only of direct selection on the mosquitoÕs immune response, but also because of dif- ferences in immune strategies between the sexes, and indirect selection acting on genetically correlated traits (see Koella and Boëte 2002). It has to be mentioned that our study was conducted using basal immune components (i.e., in the absence of an immune challenge). Moreover, this basal im- mune response could be adaptive in cases where fe- males have inherited pathogens from their parents, or progeny that do not carry the virus at their emergence but become contaminated while mating with infected partners. Despite our methodological approach, it is likely that the interaction between Ae. aegypti geno- types and abiotic environment can affect the evolu- tion of resistance to infection because the perfor- mance of different genotypes changed across food quality and quantity environments. In view of this, the effects of environmental variation must not be ignored in future studies of immunocompetence of mosquitoes and other insects. These results may also be important to understand the colonizing success of Ae. aegypti to new habitats (in natural or urbanized areas) and/or habitats where resources and pathogens vary season- ally and geographically (Kittayapong et al. 1999, Wearing and Rohani 2006). Acknowledgments We thank Salvador Hernández-Martṍnez for allowing ac- cess to protocols and helpful suggestions. We are deeply grateful with Priscila Bascuñan, Guadalupe Hernández, Ji- mena Patiño, and Valeria Vargas for their help with larval maintenance and laboratory work. To Betsabé Ruiz for help- ful suggestions on an early version of the manuscript. To Kim Van Ryzin for invaluable help. M.M-G. thanks the Posgrado en Ciencias Biomédicas (CONACYT Grant No. 172947), Instituto de Ecologṍa, Universidad Nacional Autónoma de México, and Centro de Investigaciones Sobre Enfermedades Infecciosas, Instituto Nacional de Salud Pública, México. AC-A was supported from a PAPIIT-UNAM grant (Project No. 211506). Two anonymous reviewers provided key com- ments that greatly enriched this work. References Cited Barnes, A. I., andM.T. Siva-Jothy. 2000. Density-dependent prophylaxis in the mealworm beetle Tenebrio molitor L. 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Sex differences in parasite infections: patterns and processes. Int. J. Parasitol. 26: 1009Ð1024. Received 6 November 2008; accepted 3 November 2009. 120 JOURNAL OF MEDICAL ENTOMOLOGY Vol. 47, no. 2 CAPÍTULO V. DISCUSIÓN Y CONCLUSIÓN GENERAL En este trabajo se contribuyó a entender cómo la respuesta inmune afecta y se ve afectada por estrategias y características de historia de vida. En específico, cómo se ve afectada la respuesta inmune ante factores bióticos y abióticos como lo son la presencia recurrente de patógenos, y la heterogeneidad en los recursos alimenticios. Para esto se evaluaron dos aspectos: (1) el efecto de la presencia recurrente de un mismo patógeno en hembras del mosquito Ae. aegypti, la forma en que responde inmunológicamente y su consecuencia en la supervivencia y reproducción. (2) El efecto que tiene la variación en la calidad y cantidad de la dieta a lo largo del desarrollo larval en la actividad de fenoloxidasa y producción de óxido nítrico de hembras y machos adultos de Aedes aegypti. A continuación se discuten los puntos esenciales de cada una de estas observaciones, y se plantean perspectivas de estudio. Factores que moldean la respuesta inmune de insectos A pesar de que los insectos cuentan con un sistema inmune que consta de células especializadas, moléculas efectoras, y mecanismos de reconocimiento y regulación muy eficaces, constantemente sufren infecciones. Además la respuesta inmune varía entre especies. La amplia evidencia generada en diversos estudios de insectos indica que fisiológicamente el individuo tiene que decidir, en base a los recursos energéticos disponibles, la asignación de estos hacia diversas actividades, unas involucradas con la supervivencia y otras con la reproducción (Moret & Schmid-Hempel 2002). La inversión dirigida a montar y mantener la respuesta inmune para combatir patógenos infecciosos en muchas ocasiones limita la reproducción de los organismos (ej. Adamo et al, 2002). El individuo se verá favorecido con la inversión que al final le genere el dejar la mayor descendencia viable. La inversión diferencial de los recursos hacia distintas estrategias afectará directamente la evolución de de la respuesta inmune y otras características (ej. caracteres sexuales secundarios). Además hay que considerar la patogenicidad y virulencia del agente infeccioso (Pfennig, 2001). El daño que un patógeno puede infringir a su hospedero está en función de estos dos aspectos. El hospedero también tendrá que “decidir” la inversión en energía en función del potencial daño. Otro aspecto que se considera es que el sistema inmune no es simple, distintos parámetros que lo comprenden pueden estar correlacionados positiva o negativamente. El sistema inmune se activa cuando el organismo reconoce moléculas no propias, sin embargo, se sabe que en ocasiones el tipo de patógeno determinará la activación diferencial de los parámetros inmunes (Janeway & Medzhitov, 2002). Las correlaciones negativas podrán verse favorecidas en el caso en esta correlación tenga como consecuencia la eficaz eliminación del patógeno. Aunque puede darse el caso en que la correlación negativa entre parámetros inmunes sea consecuencia de la limitante en los recursos (de Jong & Van Noordjwik, 1992). Se debe tener siempre en consideración que la disponibilidad de los recursos y la presencia (y recurrencia) de patógenos son temporal y espacialmente estocásticos. Ambos factores son causas próximas que actúan sobre el sistema inmune de insectos. La expresión de estrategias inmunes estará íntimamente relacionada con estos dos factores ecológicos. Encuentros recurrentes con patógenos y su efecto en Aedes aegypti En esta tesis (Capítulo III) se comprobó que el mosquito puede resistir dosis letales de bacterias después de haber tenido un contacto previo con la misma bacteria (viva y a bajas concentraciones). Los datos de supevivencia a constantes retos con patógenos encontrados en este trabajo se une a la evidencia previa que hace constar que mosquitos tengan la capacidad de mostrar un tipo de memoria lo largo de la vida del individuo (i.e. priming inmunológico) (Little & Kraaijeveld, 2004; Little, Hultmark & Read, 2005, Pham & Schneider, 2009). Por primera vez se realizó una cinética midiendo dos parámetros en los que se esperaba que mostraran una mejora en su producción y activación. Sin embargo, ninguno de los efectores (fenoloxidasa y óxido nítrico) cuentificados mostró incremento significativo durante el segundo encuentro con el mismo patógeno. La actividad de fenoloxidasa muestra una continua deactivación. Es posible que el que la continua activación de la fenoloxidasa evite daño (debido a las moléculas tóxicas producidas durante la cascada) a tejidos propios del mosquito. Lo que indica que el incremento en la supervivencia de los individuos esta relacionada con una regulación de los efectores inmunes, lo que sería vetntajos en caso que se incremente la eficiencia para controlar patógenos. Esposible que la actividad de fenoloxidasa, se vea comprometida con otras características, por ejemplo la producción de huevos. Se necesitan más estudios para poner en prueba esta idea. Así, se tendría un excelente ejemplo de la disyuntiva entre supervivencia y reproducción, y que la memoria inmunológica en el mosquito, a pesar de ser una estrategia adaptativa, también ha sido y es moldeada por factores ecológicos como es la limitante de recursos energéticos. Como ocurre con frecuencia una pregunta biológica, a parte de responderse, genera más preguntas. En el caso del priming inmune encontrada en Ae. aegypti ocurre lo mismo y generan perspectivas de estudio. Por ejemplo, el priming inmune en otros organismos muestra especificidad hacia el patógeno. En insectos es probable que también exista este fenómeno, lo cual sería ventajoso si es que existen vías moleculares inmunes especificas para los distintos tipos de patógenos (bacterias Gram-, Gram+, hongos, protistas, parasitoides o virus). En el mosquito podrían hacerse infecciones cruzadas (ej. Gram- vs Gram+ o viceversa) para establecer si la especificidad también esta presente. Otro aspecto sería evaluar la presencia de un efecto transgeneracional del fenómeno de priming inmune. Este aspecto fue contemplado teóricamente en el Capítulo II. Se presentó evidencia, a partir de diversos estudios llevados a cabo por distintos grupos de investigación, de que padres inmunizados pueden heredar la información a sus hijos, haciéndolos menos susceptibles al ataque de patógenos. Aunque temporalmente es difícil que un mosquito adulto tenga contacto con sus hijos, el ambiente en el que viven sí puede ser compartido. La existencia de priming transgeneracional podría ser una estrategia adaptativa en este grupo de insectos. Por último, no hay que dejar el lado el necesario estudio de los mecanismos genéticos, moleculares y celulares que se dan para que los insectos puedan presentar el fenómeno de priming inmune. En esta tesis se observó la existencia del priming en el mosquito, sin embargo se desconoce el mecanismos de cómo es que esto ocurre. Actualmente el estudio de los mecanismos inmunes es llevado a cabo por un gran numero de grupos de investigación, diversas herramientas están siendo utilizadas para investigar cuales son los parámetros inmunes que se activan y cómo es que están regulados. Efecto de la variación en la calidad y cantidad de la dieta en la respuesta inmune del mosquito Los resultados obtenidos en esta tesis (Capítulo IV) muestran que los individuos tienen una constitución genética que permite variaciones fenotípicas para ajustarse a la heterogeneidad ambiental. En específico, en ocasiones un solo genotipo puede tener la capacidad de producir dos fenotipos alternativos como resultado de su interacción con la limitación de alimento durante el desarrollo (Schlichting 1986). La respuesta inmune se puede ver como la suma de los efectos genéticos y los efectos ambientales que se dan durante el desarrollo larval, dando como resultado variación en la respuesta inmune del adulto. Es posible que la plasticidad pueda estar manteniendo la variación en la población. Las diferencias fenotípicas entre los individuos son, generalmente, las responsables de las diferencias en la adecuación, por lo tanto el cambio evolutivo en la respuesta inmune, debido a selección natural, estará determinado por dicha variación. Relacionado con lo anterior, las diferencias en la respuesta inmune entre hembras y machos pueden ser utilizadas como un indicador de las estrategias que cada sexo sigue dependiendo del ambiente y la forma de respuesta ante éste. Lo que podría estar cambiando entre generaciones no son los genes sino las normas de reacción de los genotipos, los beneficios o costos que la plasticidad traiga va a dar como resultado la evolución de las normas de reacción del fenotipo inmune que se esta expresando. Al igual que para el priming, los mecanismos moleculares de plasticidad fenotípica son desconocidos. Sin embargo, es un hecho que las señalaes ambientales son moduladores de la actividad transcripcional de genes, alterando su expresión (Kent et al. 2009). La base molecular se desconoce, sin embargo, se ha propuesto que genes con estructura TATA box, son capcaces de de respuesta rápidas y variables (Richards et al. 2006; Liefting et al. 2009). Procesos epigenéticos, como la metilación de ADN, puede modificar el nivel de expresión de caracteres (Bender 2004). Es posible que estos mecanismos sean los responsbles de la diferencias fenotipicas observadas dentro de los genotipos. Estas diferencias estarían dadas como respuesta a la variación ambiental. El priming inmune también podría estar siendo modulado por fenómenos epigenéticos. La presencia de patógenos puede ser considerada como una señal tanto de ausencia y presencia, como de intensidad (virulencia). La modulación transcripcional a través de epigenesis necesita ser evaluada para lograr entender los mecanismos detrás de la expresión de la respuesta inmune en insectos. Implicaciones del estudio de la ecología evolutiva del mosquito en salud pública El mosquito actualmente en zonas en las cuales comúnmente no se encontraba (ver Fig. 2, Capítulo I). Este hecho no solo se debe al incremento de temperatura, que posiblemente contribuya ampliar el rango de distribución del mosquito, sino a la capacidad del mosquito de colonizar y mantenerse en nuevos nichos ecológicos. Esta capacidad inherente del mosquito muy posiblemente este relacionado a la plasticidad del organismo para adaptarse al ambiente. El estudio de la existencia de variación genética, efecto del ambiente y la interacción Genotipo x Ambiente en diversas poblaciones de mosquitos de campo sería valioso para poder concluir si en realidad la plasticidad fenotípica es realmente una característica esencial para el éxito del mosquito. Todos los organismos están bajo en continuo contacto con agentes infecciosos. Debido a esto es necesario un adecuado y eficiente sistema de defensa para proteger la integridad, y asegurar la supervivencia y reproducción, del organismo. Barreras físicas, células y sobre todo moléculas son comunes entre los distintos taxones (incluyendo plantas y probablemente hongos) (Heine, 2008). En los últimos años la concepción del sistema innato de defensa ha dejado de considerase “sencillo” para ahora tener el lugar que desde un principio le correspondía que es el de “esencial e imprescindible”. El sistema inmune de insectos difiere con mamíferos por la ausencia del llamado sistema adaptativo (linfocitos y anticuerpos). Sin embargo cuentan con un sistema eficaz de defensa. Este sistema, tiene como finalidad conservar un estado de homeostasis entre el organismo y el medio que lo rodea. El estudio de fenómenos como la memoria inmunológica y aspectos genéticos cuantitativos de la respuesta inmune de los mosquitos son útiles para el entendimiento de los procesos evolutivos yecológicos que la están moldeando. Este conocimiento puede y debe ser aplicado, por ejemplo la memoria inmune de mosquitos puede ser útil para limitar la transmisión del virus Dengue. El esparcir virus Dengue inactivo (inclusive mezclado con insecticida) podría hacer que los mosquitos que sobreviven puedan generar memoria, para que cuando estén en contacto con el virus activo, este tenga una nula o menor tasa de replicación dentro del mosquito y en consecuencia disminuir la incidencia de casos de dengue clásico o hemorrágico en las poblaciones humanas. Referencias Adamo SA, Jensen M, Younger M. 2001. Changes in lifetime immunocompetence in male and female Gryllus texensis (formerly G. Integer): trade-offs between immunity and reproduction. Animal Behaviour 62: 417-425. Bender J. 2004. DNA methylation and epigenetics. Ann Rev Plant Biol 55:41–68 De Jong G, Van Noordwijk AJ. 1992. Acquisition and allocation of resources: genetic (co)variances, selection, and life histories. American Naturalist 139: 749-770. Janeway Jr. CA, Medzhitov R. 2002. Innate immune recognition. Annual Review of Immunology 20: 197-216. Heine H. 2008. Innate immunity of plants, animals and humans. Series: nucleic acids and molecular biology, vol. 21, Springer-Verlag Kent CF, Daskalchuk T, Cook L, Sokolowski MB, Greenspan RJ. 2009. The Drosophila foraging gene mediates adult plasticity and gene-environment interactions in behaviour, metabolites, and gene expression in response to food deprivation. Plos Genetics 5 Liefting M, Hoffmann AA, Ellers J. 2009. Plasticity versus environmental canalization: population differences in thermal responses along a latitudinal gradient in Drosophila serrata. Evolution 63:1954– 1963 Little TJ, Kraaijeveld AR. 2004. Ecological and evolutionary implications of immunological priming in invertebrates. Trends in Ecology and Evolution 19: 58-60. Little TJ, Hultmark D, Read AF. 2005. Invertebrate immunity and the limits of mechanistic immunology. Nature Immunology 6: 651-654. Moret Y, Schmid-Hempel P. 2000. Survival for immunity: the price of immune system activation for bumblebee workers. Science 290: 1166-1168. Pfennig HS. 2001. Evolution of pathogen virulence: the role of variation in host phenotype. Proceedings of the Royal Society ser b 268: 755-760. Pham LN, Schneider DS. 2008. Evidence for specificity and semory in the insect innate immune response. Pages 97-128 in N. Beckage editor. Insect mmunology. Academic Press, San Diego,USA. Richards CL, Bossdorf O, Muth NZ et al (2006) jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions. Ecology Letters 9:981–993 Schlichting CD. 1986. The evolution of phenotypic plasticity in plants. Annual Review of Ecology and Systematics 17: 667-693. For R eview O nly                                                  !" #$ %&  ' () $   %  *    &+   ' , -. $  * %# ,!% $.&+  ' , -. $ * % *  /! /$0&  ' () $   %  *     1  * 2 %$( "      3  %  %    %    4 53 4      % ! 6          7$% %  4     50$ 4 8        %     4 %$   $   $%       /      $ .     $  .   $ /       59   %       %!   4  5  $  %  %%     4   5 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 1 1 2 Current immunity markers in insect ecological immunology: assumed 3 trade-offs and methodological issues 4 5 Running title: Insect immuno-ecology markers, Moreno-García et al. 6 7 8 MIGUEL MORENO-GARCÍA1, 2, ALEX CÓRDOBA-AGUILAR1 & HUMBERTO 9 LANZ-MENDOZA2* 10 11 12 1Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional 13 Autónoma de México, Apdo. P. 70-275, Circuito Exterior, Ciudad Universitaria, 04510, 14 Coyoacán, Distrito Federal, México. 15 16 2*Centro de Investigaciones Sobre Enfermedades Infecciosas, Instituto Nacional de Salud 17 Pública, Avda. Universidad 655. Col. Sta. María Ahuacatitlán, 62100 Cuernavaca, 18 Morelos, México. e-mail: humberto@correo.insp.mx 19 Page 1 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 2 Abstract 20 The field of ecological immunology currently relies on using a number of immune 21 effectors or markers. These markers are usually used to infer ecological trade-offs (via 22 conflicts in resource allocation), though physiological nature of these markers remains 23 elusive. Here, we review markers frequently used in insect evolutionary ecology 24 research: cuticle darkening, haemocyte density, nodule/capsule formation, phagocytosis 25 and encapsulation/melanization via use of nylon filaments and beads, phenoloxidase 26 activity, nitric oxide production, lysozyme and antimicrobial peptide production. We also 27 provide physiologically based information that may shed light on the probable trade-offs 28 inferred when these markers are used. In addition, we provide a number of 29 methodological suggestions to improve immune marker assessment. 30 31 Keywords: evolutionary ecology, insect immunity, immune markers32 Page 2 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 3 Introduction 33 34 Ecological immunology is a rapidly expanding young discipline (Rolff & Reynolds 35 2009). Within this field, immune response is seen as a set of traits that involve costs 36 (Sheldon & Verhulst 1996). Resources must be allocated between an organism’s 37 maintenance and proper functioning and the expression of other traits linked with 38 survival and reproduction, resulting in ecological trade-offs among trait types. At the 39 moment, there is growing empirical evidence of trade-offs associated between life-history 40 traits (size at birth, growth and mortality rates, size and age at maturity, longevity and 41 survival, clutch size and reproductive effort; reviewed in Stearns 1992) and immune 42 defense (e.g. Kraaijeveld & Godfray 1997, Rigby & Jokela 2000, Fellowes et al. 1998; 43 Boots & Begon 1993, Moret & Schmid-Hempel 2000, Hoang 2001). 44 45 The ecological immunity framework has been applied to a number of disciplines, 46 including sexual selection (i.e. differential mating and fertilization success; Darwin 47 1871) (e.g. Siva-Jothy 2000; McKean & Nunney 2001, Roberts et al. 2004) and, to a 48 lesser extent, social evolution (e.g. Calleri et al. 2007; Bocher et al. 2008), predator-prey 49 relationships (e.g. Packer et al. 2003; Roy & Holt 2008) and parental investment (e.g. 50 Hoi-Leitner et al. 2001; Brzek & Konarzewski 2007). The influence of ecological 51 immunity has also extended to broader fields such as life-history theory (Schmid-Hempel 52 2005a), parasite-host coevolution (e.g. Day et al. 2007, Duffy et al. 2007), conservation 53 biology (e.g. Stevenson 2006, Tarlow & Blumstein 2007) and learning (e.g. Barnard et 54 al. 2006), among others. 55 56 Page 3 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 4 One animal group where ecological immunology has been extensively investigated is the 57 insects (see, for example, Lawniczak et al. 2006). Insects have properties (e.g. small size, 58 large sample sizes, short development times) that allow a wide range of experiments. 59 Insect studies have measured a number of immune effectors related to both cellular and 60 humoral defence reactions (hereon referred to as immune markers), in which a cost of 61 such immune response, measured through these markers, is assumed (see also Adamo 62 2004). The logic of choosing such markers has been based on the assumption that they 63 protect the insect, but their production negatively correlates with other life history or 64 intermediate traits (e.g. colored traits, behavioral traits, etc.). Many feasible and highly 65 sensitive techniques in insect immunology (e.g. proteomic, transcriptomic, liquid 66 chromatography /mass spectrometry) have been employed at the individual-level, but 67 costs confine these approaches to exploratory or qualitative studies at the population -68 level. This turns into a restriction when the objects of study are primarily numerous 69 individuals in natural populations; because differences among organisms in immune 70 responses (and their trade-offs among other behavioural or reproductive traits) could be 71 an important cause of adaptive change of the population. 72 73 Here we review each of the most common immune markers used in ecological and 74 evolutionary studies that can be utilized across large sample sizes. Our first aim is to 75 identify, when possible, the physiological processes behind each marker. This may allow 76 the inference of trade-offs with other functions through individual data. Second, we 77 briefly outline the methodological inconveniences in the assessment of such markers and 78 suggest solutions. Our aim is to provide a set of more robust methodological practices. 79 An outline of these two aims appears in Table 1, we present each in greater detail in text 80 Page 4 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 5 below. First, however, we present a basic short review of how insect immunity works, 81 which serves as the framework for discussing the trade-offs and methodological issues. 82 83 Insect Immune Mechanisms 84 The insect immune system is formed by a set of cells, molecules and reactions. All of 85 these features are continuously evolving to resist (attack and eliminate) pathogen 86 invasion and to limit the negative consequences of the infection (Hoffman & Reichhart 87 2002, Schmid-Hempel 2005a, Schneider 2009). The first lines of defence include the 88 exoskeleton cuticle, physical and chemical properties (e.g. pH) of the epidermis, gut 89 epithelium, and male and female reproductive accessory glands (Gillespie et al. 1997; 90 Casteels 1998). These tissues also secrete cytotoxic molecules like lysozymes and 91 reactive oxygen species (ROS: e.g. superoxide anions, peroxides, hydroxyl radicals) 92 (Schmid-Hempel 2005a). Pathogens are mainly recognized by the membrane of 93 haemocytes (free in the haemolymph) or by the membranes of epithelial cells. These 94 membranes bear proteins called Pattern Recognition Receptors (PRRs) that recognize 95 conserved molecular features of pathogens called Pathogen-Associated Molecular 96 Patterns (PAMPs: LPS, mannoses, β-1, 3 glucans and peptidoglycans) (Gillespie et al. 97 1997). Once pathogens are recognized as non-self, cellular and humoral immune 98 mechanisms are activated. Cellular responses include phagocytosis, nodulation, and 99 encapsulation. During encapsulation, small and large pathogens are surrounded and 100 bound by haemocytes (Gillespie et al. 1997). During nodulation and encapsulation, a 101 melanin layer is constructed (frequently referred to as melanotic 102 nodulation/encapsulation) to cover foreign agents, which ultimately die by anoxia, toxic 103 ROS or starvation (Nappi & Ottoviani 2000, Narayanan 2004). 104 105 Page 5 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 6 Melanin production is a consequence of the phenoloxidase (PO) cascade (Söderhäll & 106 Cerenius 1998). During the cascade, opsonic factors, ROS, and cytotoxins such as 107 quinones and semiquinones are produced. These important intermediate molecules are 108 highly reactive and toxic to pathogens, and serve to amplify the immune response 109 (Cerenius & Söderhäll 2004, Nappi & Ottovianni 2000). PO is involved in various 110 physiological processes including: cuticular sclerotization, immune defense (such as 111 melanotic encapsulation and wound healing). Three kinds of PO exist (Sugumaran & 112 Kanost 1993): monophenol monooxigenase (also referred to as tyrosinase-type PO; 113 Ashida & Brey 1997), ο-diphenoloxidase (also referred to as cathecoloxidase-type PO; 114 Decker & Jaenicke 2004) and ρ-diphenoloxidase (also referred to as laccase-type PO; 115 Sugumaran & Kanost 1993). These three kinds of PO and their activating systems are 116 structurally almost indistinguishable; hence, the term phenoloxidase is often used in the 117 literature without distinguishing among them. PO associated with sclerotization and 118 pigmentation of cuticle (monophenol monooxigenase and ρ-diphenoloxidase) appears to 119 be under hormonal control (Ashida & Brey 1997), while ο-diphenoloxidase is triggered 120 by recognition of non-self particles (Ashida & Brey 1997). Haemolymph PO is mainly 121 kept inside or near haemocytes; nevertheless, it can be a feature of the humoral response. 122 The precursor of PO, pro-phenoloxidase (proPO) is primarily released from haemocytes 123 into the haemolymph after contact with non-self particles (Ling & Yu 2006). It also 124 contributes to humoral melanization of pathogens. The general PO activation pathway is 125 initiated when phenylalanine is hydroxylated and converted into tyrosine. Then, proPO is 126 induced into active form PO. PO catalyzes both the hydroxylation of tyrosine to dopa and 127 the oxidation of dopa to dopaquinone. Finally, dopaquinone is converted to melanin, 128 which is used for wrapping pathogens and wound clotting (Christensen et al. 2005, 129 Söderhäll & Cerenius 1998, Nappi & Christensen 2005). 130 Page 6 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 7 131 Other humoral responses include antimicrobial systemic molecules, which are also 132 synthesized mainly in the fat body and reproductive accessory glands, gut cells, and 133 haemocytes (Manetti et al. 1998, Schmid-Hempel 2005a). Most of these molecules are 134 secreted close to the cuticle or into the haemolymph, gut tract, or malpighian tubes. 135 Antimicrobial peptide (AMP) molecules induce a number of negative effects on pathogen 136 membranes including membrane collapse, prevention of cell division, and permeability 137 disruption (Otvos Jr 2000, Bulet et al. 2003). Nitric oxide (NO) is another immune-138 relevant molecule: it is a highly reactive and unstable free radical gas that crosses cell 139 membranes to act on nearby targets (Müller 1997). NO also inhibits protein catalytic 140 activity and has damaging effects on pathogen protein and DNA (reviewed in Rivero 141 2006). Molecules such as ROS can damage pathogen nucleic acids, proteins and cell 142 membrane (Nappi et al. 2000, Herrera-Ortiz et al. 2004). Lysozymes are hydrolytic 143 enzymes that cleave the glycosidic bond between N-acetylmuramic acid and N-144 acetylglucosamine in peptidoglycan, a major component of the Gram- wall polymer 145 (Jollès 1996). 146 147 Trade-offs among immune responses 148 Trade-offs within immune system effectors are also possible. In an individual, different 149 traits are frequently found to be genetically or phenotypically correlated. Genetic 150 correlation arises because a single gene can influence multiple traits in positive or 151 negative fashions (pleiotropy and antagonistic pleiotropy, respectively) or because of 152 linkage disequilibrium between genes affecting different characters (Falconer & Mackay 153 1996). Phenotypic correlations include the positive or negative influences of 154 environmental factors on traits (Roff 1992). Reactions and products of immune response 155 Page 7 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 8 are interconnected and the kind of response could be related to the type of attacking 156 pathogen. A number of studies have examined genetic and phenotypic correlations 157 among encapsulation, lytic activity, cuticular darkness, PO activity and haemocyte load. 158 In summary, these studies report positive and negative correlations resulting in a lack of a 159 clear pattern across species or even populations (see Rantala & Kortet 2003, Cotter et al 160 2004, Fedorka et al. 2004, Ryder & Siva-Jothy 2001, Rantala & Roff 2005, Roff et al. 161 2005, Moreno-García et al. 2010). This point will be reconsidered later. 162 163 164 Immune Markers Used in Evolutionary Ecology Research 165 166 Cuticle darkness 167 The main components of insect cuticle –quinones and melanin (see Phenoloxidase 168 activity) – are also used for other functions, thus strongly suggesting a trade-off (e.g. 169 Barnes & Siva-Jothy 2000. Armitage & Siva-Jothy 2005). Quinone compounds 170 determine cuticle pigmentation (via sclerotization) by making covalent links with 171 proteins, resulting in coloured products, a process called quinone tanning (Riddiford & 172 Hiruma 1988). Quinones also form methide derivatives that crosslink cuticle proteins 173 through the quinone side chain (β-sclerotization reaction) and are responsible for cuticle 174 hardening (Saul & Sugumaran 1988). Cuticle darkness is thus related to how much 175 quinone is deposited in every shed or wound repair site. 176 177 Methodology 178 179 Page 8 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 9 Cuticular colour is frequently assessed under a fluorescent white light and the emerging 180 colour classes are analyzed using a greyscale value (e.g. Barnes & Siva-Jothy 2000, 181 Thompson et al. 2002, Rolff et al. 2005). However, since the cuticle structure is 182 composed of hundreds of proteins (Andersen et al. 1995), chitin and lipids (Chapman 183 1998), and such protein composition greatly influences cuticle mechanical properties 184 (e.g. flexibility; Gosline et al. 2002, Haas et al. 2000), there could be a possible 185 overestimation of quinine and melanin tanning. Furthermore, since cuticles among insect 186 taxa vary considerably in stiffness, hardness, and pigmentation, it is possible that the dark 187 aspect of a particular tissue may not necessarily reflect or be related to a reduced or 188 heightened immune ability. In combination with cuticular colour analysis, we recommend 189 the use of transverse section preparations (using a microtome) from different 190 representative regions of the insect body and cuticle width measurement. This would 191 reflect how much the insect has invested in producing a thick cuticle and, presumably, 192 quinines and melanin. Note, however, that cuticular melanization is not always associated 193 with greater immunogenic response (see Robb et al. 2003). 194 195 196 Cellular Responses 197 198 Total number of haemocytes (haemocyte density) 199 200 Haemocyte counting and/or activity is a straightforward way of assessing cellular 201 immune ability (e.g. Rantala et al. 2000, Kraaijeveld et al. 2001; Cotter et al. 2004). 202 Haemocytes are generally responsible for immune activities such as engulfing, 203 surrounding and destroying infectious agents. After bacteria have penetrated the 204 Page 9 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 10 haemocoel, haemocytes are the first and most efficient bacteria-clearing mechanism, 205 (Hillyer et al. 2003). However, there are different haemocyte types, each playing a 206 different function (recognition, phagocytosis, nodulation, encapsulation or melanization) 207 (see reviews by Lavine & Strand 2002, Ribeiro & Brehelín 2006). Differences in 208 pathogen type (e.g. bacteria, virus, nematodes, etc.) among individuals, sexes, or species 209 must be considered. For example, in Aedes aegypti, Escherichia coli is mainly 210 phagocytosed, meanwhile Micrococcus luteus is melanized (Hillyer et al. 2003). 211 However, the factors eliciting phagocytic vs. melanization responses against bacteria are 212 independent of Gram type (Hillyer et al. 2004). 213 214 Methodology 215 216 For total and viable counts, haemolymph is collected and incubated in humidity chamber; 217 then counted using an hemocytometer using (phase contrast) microscopy (e.g. Rantala et 218 al. 2000; Kraaijeveld et al. 2001). For collection and incubation of haemolymph, we 219 suggest the use of cell culture media such as RPMI, Schneider or Grace rather than PBS 220 buffers. This media may allow a better viability of cells. Trypan blue can be used to 221 exclude between dead and viable cells. Haemolymph extraction may be accomplished by 222 perfusion-bleed; a droplet can be collected from a puncture/cut on the thorax or by a 223 removed leg. Although this may appear an easy task, haemocytes can adhere to tissues 224 such as the fat body. One solution to this problem is to use anticoagulant buffers of low 225 pH (Pech et al. 1994). An accurate extraction of these cells can also be carried out by 226 injecting a protease inhibitor blend (e.g. PMSF, TLCK, leupeptine, EDTA). However, 227 optimal conditions for obtaining insect haemocytes cannot be generalized, and in each 228 procedure of extraction the use (or non-use) of anticoagulant must be adjusted. 229 Page 10 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 11 230 231 Phagocytosis 232 233 Phagocytic activities require the internalization of the surface membrane and action of 234 several organelles, including endosomes and lysosomes, which fuse with the plasma 235 membrane and provide the membrane for phagosome formation (Desjardins et al. 2005). 236 In mosquitoes, the synthesis of lipids for the membranes of the phagosome, plasma 237 membrane and for the production of cytotoxic proteins for the degradation of 238 phagocytised material decreases their reproductive output (Ferdig et al. 1993, Hogg & 239 Hurd 1995, Hillyer et al. 2003). For these reasons, trade-offs are expected among 240 phagocytic activities and fitness traits. Furthermore, it is also probable that haemocytes 241 undergo apoptosis after phagocytosis, which reduces the number of circulating 242 haemocytes (as occurs in Ae. aegypti; Hillyer et al. 2005). This would lead to another 243 trade-off, as it would generate a decrease in the homeostasis and remodelling of tissues, 244 as well as the capacity for fighting pathogens. Phagocytosis is an important mechanism to 245 remove dead or damaged cells, as well as to remodel organs during metamorphosis 246 (Abrams et al. 1993, Franc et al, 1996). Thus, a delay in growth seems necessary for 247 pathogen clearing, via releasing haemocytes from their immune activity and facilitating 248 the wound repairing process before cuticle shedding (Lavine & Strand 2002). 249 250 Methodology 251 252 Phagocytic activity has been measured by exposing the host to microbial pathogens such 253 as bacteria and yeast cells or polystyrene beads, and assessing in vitro phagocytosis 254 Page 11 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 12 (Kurtz & Sauer 1999, Kurtz et al. 2000, Kurtz 2002). One way to assess this process is by 255 inoculating fluorescent, labelled beads or bacteria in vivo (Kurtz 2002). In either process, 256 the phagocytised intruders are counted manually or by using image analysis software. 257 Care must be taken to ensure the beads do not become obstructed by melanin. Recovery 258 of remaining beads (if possible) from the haemolymph is a useful method to estimate 259 phagocytosis activity. Trypan blue can also further assess the viability of labelled cells or 260 molecules. The effect of this colorant is that only the ingested particles retain their 261 fluorescence, while non-ingested particles become coated with trypan blue dye (see Kurtz 262 et al. 2000). Another method is to use pHrodo dye conjugated bacteria. Bacteria become 263 fluorescent only in the acidic environment of the haemocyte phagosome (see Luce-264 Fedrow et al. 2009); while extracellular non-phagocytosed bacteria do not fluoresce. 265 When using live bacteria to evaluate the efficiency of phagocytosis, or any of the immune 266 parameters referred to in this paper, we recommend first to assess the bacterial clearance 267 of the infected host. To do this, supernatant of haemolymph or homogenized individuals 268 can be spotted in appropriate plates and colony forming units counted (see Pham et al. 269 2007). 270 271 272 Capacity to attach to non-self surfaces (Nodule/Capsule Formation) 273 274 Quite frequently, researchers have introduced pathogens or microbial cell wall 275 components to insect hosts to assess cellular immune response (e.g Howard et al. 1998). 276 This induces the production of nodules/capsules, which are then counted when 277 haemolymph is extracted after the challenge or directly in the haemocoel (e.g. Miller et 278 al. 1996, Goldsworthy et al. 2003). Nodules can persist in insects for long periods of time 279 Page 12 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 13 (Carton & Nappi 1997), which means that the haemocytes used for nodule formation 280 remain attached, thereby decreasing haemocyte density during insect lifetime. This 281 reduction in haemocyte number could in turn limit the availability of haemocytes that 282 become relevant during moulting and/or metamorphosis. Not all adult insects produce 283 haemocytes since haematopoietic organs are not always present at this stage (Chapman 284 1998). In lepidopterans and dipterans, hematopoietic organs have been identified 285 exclusively in the embryonic, larval and pupal stages (Gardiner & Strand 2000; Nardi et 286 al. 2003, Holz et al. 2003, Williams 2007). This means that any haemocytes present in 287 the adult stage were produced during embryogenesis, larval stages and metamorphosis. 288 However, the presence of hematopoietic organs in adult grasshoppers has been reported 289 (Hoffman 1973, Hoffman et al. 1974). Natural history differences among insect groups 290 (e.g. the presence of metamorphosis) likely leads to variation in haemocyte production 291 (consequently cell number). 292 293 Methodology 294 Pathogens such as bacteria, yeast cells are inoculated directly to haemocoel (Brookman et 295 al. 1989). Also, injected parasites can be used to assess nodule/encapsulation formation 296 (see Eleftherianos et al. 2008). Haemolymph is collected and incubated in humidity 297 chamber, nodules are observed using a microscope and counted. Care must be taken since 298 pathogens can be destroyed before nodulation takes place, for example, if pathogens enter 299 the midgut (Luckhart et al. 1998). Furthermore, nodules must be counted even when their 300 size varies, which can occur even within the same individual (see Howard et al. 1998). 301 Enumeration of different sizes can provide further information on differential immune 302 ability among hosts. We recommend to directly assess the size of nodules either 303 Page 13 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 14 qualitatively (by categorizing the different sizes and counting how many of these fall into 304 the distinct categories) or quantitatively with the use of image analysis software. 305 306 307 Melanotic nodulation/encapsulation (Nylon Monofilament and Beads Insertion) 308 309 For encapsulation/melanotic encapsulation assessment, inoculation with sephadex or 310 polystyrene beads or insertion of a nylon monofilament into the haemocoel is frequently 311 used. In this method, nylon and beads are thought to mimic natural infection (like that of 312 a parasite/parasitoid) while avoiding the damaging effects of pathogens (e.g. Siva-Jothy 313 2000, Koskimäki et al. 2004, Simmons et al. 2005, Rantala & Roff 2007). Cellular and 314 humoral responses are activated, which could lead to trade-offs similar to when nodule 315 formation and phagocytosis are elicited. In the melanin layer – which is frequently 316 produced during nodulation/encapsulation -- synthesis of melanin is required. Melanin is 317 costly to produce, not only because of the resources used (see Phenoloxidase Activity 318 below), but because melanin is required for cuticle formation, humoral toxic molecule 319 production, and colouring and hardening of other structural traits (e.g. wings, eggs, 320 spermatheca; Li & Christensen 1993, Ilango 2005). 321 322 Methodology 323 324 After insertion, filament/beads are then retrieved and the volume of melanized and non-325 melanized cell masses that encapsulate the filament are measured (Siva-Jothy 2000). In 326 vitro encapsulation assays can be also be performed. For this, haemolymph is collected 327 from each individual and mixed with beads. Encapsulation and melanization are observed 328 Page 14 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 15 after incubation by microscopy. Three methods have been developed for measuring such 329 masses. One method observes the mean gray scale of the covered area using image 330 analysis software (Simmons et al. 2005), while a second uses images software filters to 331 measure the density of the covered area (Koskimäki et al. 2004). Another approach 332 measures the encapsulated/melanized area (Contreras-Garduño et al. 2006). All 333 measures, however, are unable to differentiate the functional aspects of haemocytes, as 334 cellular nodulation/encapsulation and humoral melanization are two separate but related 335 processes (Ling & Yu 2006). Haemocytes release or contain surface proPO that when 336 activated to PO, causes melanization. This cellular encapsulation also enhances 337 interactions between haemolymph and PO. Ling & Yu (2006) noticed that melanization 338 did not occur when isolated haemocytes were used, it occurred only in the presence of 339 plasma. This suggests that factors in haemolymph are required for PO cascade activation 340 and hence melanization. Therefore, cellular encapsulation could be implicated in both 341 cellular and humoral immune responses. 342 343 Trade-offs among immunity and other traits (e.g. developmental time) using implants 344 have been found (see Rantala & Rolff 2005). The use of non-natural infections seems to 345 be a suitable index of host immune activation-recognition and its ecological costs (e.g. 346 Rantala & Roff 2007). For the methods above discussed, unfortunately, the distribution 347 of melanized/encapsulated areas is often so irregular that it impedes thorough assessment 348 (i.e. it depends on the position of the artificial object during area measurement). We 349 recommend several measures of the same implant. Additionally, inserting a nylon piece is 350 a difficult task for small insects. In this case, we recommend the use of inoculated beads. 351 We also recommend using live pathogens that induce melanotic encapsulation or other 352 immune responses. This can be useful, for example, when testing for immune tolerance 353 Page 15 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 16 and behaviour (see Ayres & Schneider 2009). Tolerance theory predicts a possibly 354 increase in the pathogen burden, but the immune response will limit the health or fitness 355 consequences of this pathogen load (sensu Schneider & Ayres 2008, Read et al. 2008). 356 Only live pathogens can replicate and continuously damage a host. It is also possible that 357 live pathogens activate more immune pathways (compared with inert implants) and 358 therefore give a more complete picture of an immune response. However, when using 359 live pathogens, it is important to first estimate the dosage of pathogen needed for accurate 360 measurement of a particular immune response. It has been observed that in some insects, 361 low to medium doses elicit phagocytosis, moderate to high doses elicit nodulation, and 362 overdoses can promote septic shock (Salvador Hernández-Martínez, personal 363 communication). Before a challenge, we recommend to discover the immune response 364 that is elicited in relation to the type and dose of infectious agent (natural or artificial) 365 model. 366 367 368 Humoral responses 369 370 Phenoloxidase Activity 371 372 PO is the most widely used immune marker in ecological immunology studies (e.g. 373 Goldsworthy et al. 2003, Wilson et al. 2003, Jacot et al. 2005). The PO enzyme is used 374 for different physiological processes (wounding, clotting, cuticle composition, melanotic 375 encapsulation, production of cytotoxic molecules), and probably spermatheca formation 376 (Ilango 2005). Due to these different functions, PO is constantly synthesized, therefore it 377 could be costly to produce for the organism. Recall that proPO molecules are produced 378 Page 16 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 17 mainly by haemocytes (other tissues, such as fat body and midgut epithelium, may also 379 produce proPO in smaller quantities), and that some of these molecules remain attached 380 to haemocytes, while some proPO might end up in several other tissues (i.e. cuticle, 381 midgut, salivary glands, eggs; Ashida & Brey 1997). If pathogens are detected during 382 proPO transportation from haemocytes to other tissues, it is possible that this proPO gets 383 activated in the haemocele for clearing pathogens, consequently leaving target tissues 384 with a proPO deficiency. This means that a trade-off may arise because of a proPO 385 deficiency (but not of other components, such as PO substrates; see below), as has been 386 assumed in general studies where PO and melanin have been involved (Siva-Jothy 2000). 387 This different perspective of a possible trade-off when measuring PO activity has not 388 been put forward nor examined previously and needs further empirical study. 389 390 The PO substrate tyrosine (TYR) and tyrosine subproducts are used for the expression 391 and manufacture of other traits. TYR is obtained through food and is naturally 392 synthesized by organisms or by the hydroxylation of phenylalanine (PHE) (Christensen et 393 al. 2005). Since PHE is an essential amino acid for protein synthesis, its importance lies 394 in the cost of acquiring this resource. TYR is also involved in a variety of functions. One 395 of these, for example, is the colouring of the egg chorion in some insects (Li & 396 Christensen 1993). In mosquitoes, melanization responses against worms generate a 397 delay in TYR accumulation in the ovaries, and therefore a decrease in the number of eggs 398 produced after an immune challenge (Li & Christensen, 1993) and a delay in oviposition 399 (Ferdig et al. 1993). Examples like this are suggestive of substrates of PO and melanin - 400 PHE and TYR - being restrictive resources that may lead to trade-offs between the 401 immune response and other key functions including moulting, basal metabolic rates and 402 protein synthesis. Although TYR can be stored (as tyrosine-O-phosphate, beta-alanyl-L-403 Page 17 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 18 tyrosine, tyrosine glucoside; Mitchell & Lunan 1964, Levenbook et al. 1969, Chen et al. 404 1978), trade-offs could be detected if not enough TYR is gathered in order to provide this 405 amino acid to all the metabolic requirements through the insect lifetime. However, not all 406 insects necessarily use TYR or PHE in the same way, PO measures need to be carefully 407 interpreted in every insect model. Melanin, the final product of the PO cascade, is also 408 involved in other morphological traits such as cuticle formation and pigmentation which 409 may affect functions related to aposematism and crypsis, thermoregulation, resistance to 410 UV radiation and coloured sexual traits. Many of these functions and their supposed 411 trade-offs have not been adequately investigated. 412 413 Methodology 414 415 PO activity is assayed by thawing haemolymph and o-diphenols (dopa or dopamine) as 416 substrate into a microplate well. This assay will not distinguish the three different PO 417 activities, since it is inferred that the ο-diphenoloxidase activity of all three enzymes will 418 be detected (Sugumaran & Kanost 1993). Care must be taken when using o-diphenols, 419 since there could also be a peroxidase-mediated melanin formation (Christensen et al. 420 2005), which can result in an overestimation of PO activity. We recommend the use of a 421 peroxidase suppressor (e.g. hydrogen peroxide in methanol or commercially available). 422 Another current problem with assessing PO is that protein load standardization is needed 423 before measurement, using a standard protein assay (Contreras-Garduño et al. 2007), a 424 practice that is rarely carried out and without which may result in disparate and unreliable 425 PO readings. Since occasionally real protein load can be masked if differences among 426 individuals, (e.g. bigger size of females than males; inaccurate extract of total amount of 427 haemolymph); we recommend the protein load practice mentioned above. Furthermore, 428 Page 18 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 19 there might be a rapid degradation of PO, if stored for relatively long time. This issue can 429 be resolved by using a protease inhibitor cocktail (e.g. PMSF, Leupeptin). Another 430 problem is that small-sized insects may not provide PO readings, since PO quantities are 431 so small. This problem can be solved by using more than one individual for a single 432 sample to obtain PO readings. As well, the total PO activity can be estimated using the 433 enzyme α-chymotrypsin to activate all pro-PO present in the haemolymph (see Bailey & 434 Zuk 2010). 435 436 437 Nitric Oxide 438 439 NO quantification is a relatively new immune marker in ecological immunity studies (see 440 for example, Moreno-García et al. 2010). NO damages pathogens and is also used as a 441 signalling molecule. In Drosophila, NO is indispensable for activation of the Immune 442 Deficiency (Imd) pathway (Foley & O’Farrel 2003). This is one of the three pathways 443 (the other two being Toll and JAK/STAT see Ferrandon et al. 2007), that produce 444 transcriptional factors in the fat body, leading to the synthesis of antimicrobial peptides 445 and other factors that prevent self tissue damage and control haemocyte proliferation and 446 differentiation (Foley & O’Farrell 2003; Agaisse & Perriomon 2004). Another interesting 447 point is the fact that the NO synthase (NOS) (an enzyme that converts L-Arginine to L-448 Citrulline and generates NO) is constitutive and inducible (Müller 1997). NO is also a 449 signalling molecule, so a constant production of NOS is necessary for homeostatic 450 purposes, while inducible NOS is only synthesized after an immune challenge (Nappi et 451 al. 2000). It is therefore expected that NO production increases after an immune 452 challenge. Nevertheless, Krishnana, Hyršl & Šimek (2006) found that NO production was 453 Page 19 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 20 similar in both non-stimulated and stimulated haemocytes in lepidopteran larvae. In this 454 particular case, the authors indicate that NO can be continuously synthesized by the 455 induced NOS for long periods of time (hours to days). This means that the effectiveness 456 for killing pathogens with NO may not only reside on NO concentration, but in the 457 duration of NO synthesis (see Laurent et al. 1996). 458 459 NO is produced during the oxidation of L-Arginine (Müller 1997). Arginine is an amino 460 acid, which must be obtained from the diet (Rivero 2006). Arginine is also important for 461 sperm maturation (Osanai & Chen 1993), egg production (Uchida 1993), long term 462 memory, chemosensory (antennal lobes, olfaction), and visual information processing 463 (Müller 1997). Trade-offs are thus expected among these traits and the immune response. 464 465 Methodology 466 467 Basal levels of NO are normally used, which provides an incomplete picture of host 468 immune ability. A better alternative is to assess NO production after host injection or 469 ingestion of pathogens. The standard method to measure NO is to use the Griess reaction 470 that measures nitrite production – an indirect measure of NO (Bredt & Snyder 1994). The 471 use of controls such as an L-NAME (a NOS-inhibitory arginine analogue; see Rivero 472 2006) and its inactive enantiomer D-NAME is recommended. Controls are needed 473 because nitrites can also be sub-products of other reactions not related with immunity. 474 For this method, however, it is unknown whether haemolymph protein load 475 standardization is needed before measurement. This is a technical problem that must be 476 resolved in the near future. Similar to PO, more than a single individual may be needed if 477 an individual host sample is too small. 478 Page 20 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 21 479 480 Lysozyme Activity and Antimicrobial Peptides 481 482 Lysozyme is an enzyme with hydrolytic action mainly against the peptidoglycan of 483 Gram+ cell walls. It can be induced, or constitutively expressed in the gut tract (Hetru et 484 al. 1997), haemocytes and fat body (Gillispie et al. 1997). AMPs show great structural 485 diversity (more than 170 isoforms have been found in insects; Bulet et al., 1999) and are 486 produced soon after foreign recognition (1-4 hours) with an efficient pathogen-specific 487 killing action (Schmid-Hempel 2005b). Some AMPs may remain in haemolymph for up 488 to three weeks (Schmid-Hempel 2005b), which is convenient during subsequent pathogen 489 encounters. AMPs are generally short peptides, containing fewer than 150–200 amino 490 acids (Bulet et al. 2004), so they are considered energy efficient, quick and economical to 491 produce (Otvos Jr 2000). However, the overall AMPs concentration in Drosophila 492 haemolymph can reach 200 µM (Otvos Jr 2000). At this concentration, antimicrobial 493 peptides do seem costly to produce. It is possible that resources (proteins) gathered 494 through ontogeny are indispensable for antimicrobial peptide generation as well as for the 495 synthesis of other molecules. This fact could potentially lead to trade-offs between 496 immunity and synthesis of other products (e.g. spermatophalyx peptides) and tissue 497 conformation. 498 499 Methodology 500 501 Lysozyme activity is commonly assayed via the clearance rate of bacterial suspension 502 using haemolymph. The method is relatively simple: after the haemolymph is extracted 503 Page 21 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 22 and mixed with a bacterial solution, a turbidity assay is then performed (e.g. Adamo 504 2004). Small absorbance values indicate high lytic activity. Another way is to add small 505 drops of haemolymph to bacterial culture, measuring the area of lytic activity after some 506 time (12 hours). The lytic activity appears as clear circular zones in the culture, which 507 indicates that bacteria have been cleared; the diameter of the hole is measured and taken 508 as the indicator of lytic effectiveness. 509 510 AMPs have been measured by injection or ingestion of complete bacteria, fungi, parasites 511 or their fractions (e.g. LPS, peptidoglycane). AMPs activity is assessed in three ways: 1) 512 measurement of the clearance rate of a bacterial suspension (via a turbidity assay, similar 513 to the lysozyme protocol described above); 2) measurement of the rate of bacterial 514 growth on a plate, after the same bacteria have been injected into the host and the 515 haemolymph has been extracted after some time; and, 3) the antibacterial zone assay, via 516 measurement of the hole area, similar to the lysozyme protocol described above. A 517 problem with these different assays is that antimicrobial activity can be due to other 518 molecules and not necessarily AMPs. Since lysozyme needs a pH 8 to 6.5 for optimal 519 activity this can be used to distinguish between other AMP activity (Rantala & Kortet 520 2004, Ahtianen et al 2005, but see Da Silva et al 2000, Adam & Parsosn 2006). 521 522 523 Some concluding remarks 524 525 One intention of this review is to highlight the ecological and physiological context of 526 insect immune markers and their value for estimating trade-offs between immunity and 527 other traits. Thus, current (and new) knowledge of immune physiological mechanisms 528 Page 22 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 23 must be incorporated into ecological data to allow for a deeper understanding of the 529 reasons for evolutionary correlations and constraints. The use of each method mentioned 530 here must be determined by each researcher in terms of its overall cost (in terms of time-531 consumption or price), differences in the kind of pathogens or elicitors used to induce 532 immune response, and the immune marker that can address questions via an appropriate 533 experimental design. Another important consideration for each researcher is the potential 534 to make repeated immune measurements on the same sample. Unfortunately some of the 535 measurement methods referred here are not suitable to be repeated more than once. For 536 example, PO and NO contained in haemolymph extraction gradually degrade even stored 537 in an ultrafreezer (-70oC). Also, unfreezing and re-freezing samples accelerate 538 degradation. Occasionally, haemocyte membranes can become disrupted even in buffer 539 solution or culture medium if stored for long periods of time (e.g. weeks to months). We 540 recommend extracting enough sample to do all measurements required for the 541 experimental design. If a large number of individuals are required, they (or extraction) 542 can be stored and measurements should be only done for the exact number of tests that 543 can be processed and quantified in a single day. 544 545 Finally, there are two related points that we would like to highlight: (1) that some 546 immune markers mentioned here are usually seen as responses to attack and kill 547 pathogens; and (2) the correlations among immune responses. As mentioned before, 548 negative and positive correlations have been found among immune traits, but correlations 549 seem to be host-specific. Commonly, resources limitation has been used to explain the 550 negative correlations, however, the non-expression of an immune effector those not 551 necessarily means that there is no response. Reactions and products of immune response 552 are interconnected and the kind of response is related to the pathogens virulence. As 553 Page 23 of 50 Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 24 mentioned before, not all pathogens will induce the same reaction, or at least the 554 magnitude of response can differ depending on the host-pathogen interaction type. 555 Occasionally tolerance could be the best strategy to cope with infections. The resistance 556 or tolerance strategies adopted by an individual will depend on the force and course of 557 infection. There could be a critical host damage threshold, thus when the intensity of 558 infection is below this threshold (i.e. no significant damage to the host), the insect could 559 tolerate. Above the threshold, it then pays to allocate more into resist the pathogen. For 560 example, phagocytic activity could be turned on, meanwhile PO activity could remain 561 down regulated; in this case probably the host is avoiding autoreactivity molecules 562 (generated through the PO cascade). In this example, the immune strategy is enough in 563 order to limit pathogen and self damage, nevertheless the pathogen is not eliminated. 564 Since immune system is highly complex, several aspects of immune response must be 565 therefore measured to comprehend the insect immunity strategies. Also, occasionally 566 some immune responses are not suitable to test some hypothesis, for example, pro-PO 567 cascade and PO activity occasionally is not affected by the nutritional condition, but other 568 responses, such as lysozyme, are affected (see Jacot et al. 2005, Moreno-García et al. 569 2010). Therefore, it is recommended to use more than one immune marker when 570 possible. Finally, the starting line in insect immunoecology is that it must incorporate the 571 great range of environmental factors involved using a trade-off framework. Resource 572 allocation and investment strategies should be studied by taking into account evidence of 573 the real cost in natural condition and their effects in the expression of immune response 574 and generational changes. 575 576 Ideally, an ecological study of insect immunity should start with thoughtful design 577 regarding appropriate immune markers. Regarding methodological issues, in many cases 578 Page 24 of 50Methods in Ecology and Evolution F o r R eview O n ly Insect immuno-ecology markers, Moreno-García et al. 25 methodological information is available but it is possible that the status quo of how other 579 evolutionary ecology researchers have used such markers leads others not to think twice 580 about the best way to proceed. Lastly, we encourage ecological and evolutionary 581 biologists alike to incorporate useful molecular techniques (e.g. microassays, proteomics, 582 quantitative Reverse Transcriptase-PCR, etc.) to identify the possible mechanisms and 583 the genetic basis underlying the trade-offs between immunity and life history traits. It is 584 hoped that this information proves constructive in paving the way to a more robust field 585 of ecological immunology. 586 587 588 ACKNOWLEDGEMENTS 589 We thank Salvador Hernández-Martínez for providing assistance with protocols and for 590 helpful suggestions. We thank Gloria Tavera and Chris Anderson for revising this paper 591 in English, and providing thoughtful comments. MM-G was supported by the Posgrado 592 en Ciencias Biomédicas (CONACYT: grant no. 172947), Instituto de Ecología, 593 (Universidad Nacional Autónoma de México), and the Centro de Investigación Sobre 594 Enfermedades Infecciosas, Instituto Nacional de Salud Pública, México. 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Immune markers used in insect ecological immunity studies (see text for full discussion) Immune marker Immune function Methodology Recommendations Example references Cuticle One of the first lines of defense against pathogens; it senses injury and the presence of bacteria and fungi. Used for structural external barrier quantification. Cuticular color is assessed under a fluorescent white light. Color classes are analyzed through digital images using a grayscale value. Cuticular transverse section preparations (using microtome techniques) from legs, thorax or abdomen could determine cuticle resistance. Barnes & Siva-Jothy 2000 Thompson et al. 2002 Rolff et al. 2005 Haemocyte density Haemocytes mediate phagocytosis, nodulation, encapsulation/melanization, clotting and recognition of foreigners. Used for cellular immune response quantification. Haemolymph extraction for cell counting. Haemocytes (sometimes stained) are counted using a Neubauer chamber. The adhesive properties of haemocytes are reduced by using low pH, anticoagulant buffers. For accurate extraction of haemocytes (as most of them remain attached to tissues), an injected protease inhibitor blend (e.g. PMSF, TLCK, Leupeptine, EDTA) could be used. Rantala et al. 2000 Kraaijeveld et al. 2001 Pech et al. 1994 Phagocytosis The haemocyte process utilized to ingest and kill microbial pathogens. Used for cellular immune response quantification. In vitro phagocytosis. Fluorescent, labeled beads, bacteria or yeast cells are attached to haemocytes in vitro. Labeled cells or beads are then quenched with trypan blue and phagocyte cells or beads remain fluorescent. Ingested cells or beads are counted manually or using image analysis software. Phagocytic activity is given as the number/proportion of cells or beads ingested per µl haemolymph The use of small pathogens (bacteria) is also recommended. Pathogens intermediate in size may induce highest nodule formation and melanization. Bacterial aggregations may also induce some nodule formation. Assessment of cell viability using trypan blue is also recommended. Miller et al. 1996 Howard et al. 1998 Mavrouli et al. 2005 Nodule/capsule formation This results when multiple haemocytes attach to aggregations of pathogens. Used for cellular immune response quantification. An injection of pathogens* or microbial cell wall components (e.g. LPS, peptidoglycan) is needed. After some time, hemolymph is extracted or haemocele exposed and the number of nodules is counted. Categorical measures (small, medium, large), or quantitative measures (with stage and ocular microscope micrometers), in addition to the number of nodules, could be suitable for nodule quantification. Brookman et al. 1989 Kurtz & Sauer 1999 Eleftherianos et al. 2008 Page 42 of 50Methods in Ecology and Evolution For Review Only Insect immuno-ecology markers, Moreno-García et al. 43 Nylon monofilaments Mimic parasite/parasitoid infection. Used for cellular encapsulation and melanization quantification. Nylon is manually inserted, usually through abdominal pleura or soft body part. Qualitative measure: presence/absence of visible melanization Quantitative measure: encapsulated-melanized area; melanin coverage or intensity percentages; mean gray scales using image analysis software; densitometry (using red+green+blue filters). The use of dead pathogens (e.g. yeast) may be useful since they also induce melanotic encapsulation and are naturally recognized by the immune system. Nylon implants is not recommended for small insects. Siva-Jothy 2000 Koskimäki et al. 2004 Simmons et al. 2005 Nylon/silica beads Mimic small pathogen infection. Used for cellular nodulation, encapsulation and melanization quantification. Inoculation with microsyringe. Measurement methodology similar to that of nylon monofilaments. The use of dead, medium-sized pathogens (e.g. yeast) could be useful since they also induce melanotic encapsulation and are naturally recognized by the immune system. Adamo 1999 Kumar et al. 2003 Schwartz & Koella 2004 Phenoloxidase (PO) A constitutive oxidoreductase enzyme used during the early steps of melanin formation; highly reactive and toxic intermediate molecules are produced. Used for humoral response quantification. Haemolymph extraction. PO activity is assayed by thawing haemolymph and L-Dopa or Dopamine (as substrate) into a microplate well. Several readings are taken at ≈ 490nm in a microplate reader for varying periods (20-120min). Hemolymph protein load must be standardized before measurement (e.g. standard protein assay). α-chymotrypsin can be use to activate all pro-PO present in the haemolymph The use of a protease inhibitor cocktail (e.g. PMSF, Leupeptin) can inhibit pro-PO spontaneous activation of stored hemolymph. The use of peroxide suppressors may inhibit peroxidases that oxidize catecohls. PO will exhibit degradation/spontaneous activation when stored for a long period of time. Goldsworthy et al. 2003 Wilson et al. 2003 Jacot et al. 2005 Page 43 of 50 Methods in Ecology and Evolution For Review Only Insect immuno-ecology markers, Moreno-García et al. 44 Nitric oxide (NO) An inducible reactive oxygen molecule with an important role in destroying infectious agents. Used for humoral response quantification. In some cases, NO generation is activated by ingestion/injection of bacteria (or cell wall components, e.g. LPS, peptidoglycan), virus, fungi or parasite inoculation. Indirect quantification using nitrite production via the Griess reaction. Haemolymph, sulfanilamide and naphthylethylenediamine are thawed into a microplate well. Absorbance is recorded at 540nm. Commercial kits are available for nitrate and nitrite measurement. The use of L-NAME NOS inhibitor as a control verifies that quantified nitrites are a sub product of arginine degradation by NOS. Inoculation with dead pathogens activates the immune system without host damage. Nappi et al. 2000 Herrera-Ortiz et al. 2004 Faraldo et al. 2005 Lysozyme activity A constitutive and inducible non- specific enzyme with hydrolytic action against the peptidoglycan in Gram+ cell walls and antifungal activity. Used for humoral response quantification. Assayed by the clearance rate of bacterial suspension using haemolymph. Extracted haemolymph is mixed with a bacterial solution and continuously measured at ≈ 492nm for a period of time. Small absorbance values indicate high lytic activity. In the lytic zone assay, in which a bacterial culture in solid agar media is pinched, holes are filled with haemolymph sample. Diameter of clear lytic zones is then measured. Occasionally, the pH of buffer solutions is an important factor. 6.4 pH is commonly used to discriminate among lytic activity with other antimicrobial (peptide) activity. Kurtz et al. 2000 Rantala & Kortet 2003 Adamo 2004 Page 44 of 50Methods in Ecology and Evolution For Review Only Insect immuno-ecology markers, Moreno-García et al. 45 Antimicrobial peptides Inducible small peptides. They destabilize bacteria (Gram, Gram+) and fungi membranes. Used for humoral response quantification. Activity is induced with ingestion/injections of bacteria, fungi or parasites (or cell wall components e.g. LPS, peptidoglycan). (1) Assayed via the clearance rate of bacterial suspension, using haemolymph (as used in lyzozyme activity protocol). (2) Assayed via the clearance rate of an injection of bacteria. A standard bacterial dose is injected into the host. Extracted haemolymph is plated in media agar and cultivated; plates are then scored for the number of bacterial colonies formed by using a colony counter. This may also be done via an antibacterial zone assay (as used in lyzozyme activity protocol). Lysozyme generic inhibitor (e.g. tri- N-acetylgluycosamine) can be used to distinguish among antimicrobial molecules. Moret & Schmid-Hempel 2000 McKean & Nunney 2001 * Before inoculation, minimal lethal and/or sub-lethal doses must be determined. Overdoses can promote septic shock, rather than a measurable immunological response. ** Because antimicrobial activity can be due to lysozymes, reactive oxygen species and/or antimicrobial peptides, immune response measurement is commonly referred to as hemolytic, lyzozyme-like or antibacterial activity. 970 971 972 Page 45 of 50 Methods in Ecology and Evolution For R eview O nly Dr. Robert Freckleton Editor in Chief Methods in Ecology and Evolution Dear Dr. Freckleton: Thanks very much for your communication regarding the reviewers’ comments to our ms: Current immunity markers in insect ecological immunology: assumed trade-offs and methodological issues. We are happy to know that such comments were fairly positive. We are grateful with the reviewers as their comments have been very enriching. We have prepared a new version according to these comments. You will find in detail how we have addressed such changes. Such changes can be tracked directly to the line numbers that are also indicated below. Reviewer 1 1- One first general comment is related to immunity evolution. We agree that immune resistance is evolving. However, the insect immune system is a set of cells, molecules and reactions. These features not only resist pathogen invasion, but also limits the damage consequence of the infection (this idea is now mentioned in line 85-88). All this features characterize the immune system of insects and can change among generations. In consequence, we can state that the immune system is continuously evolving. Reviewer is also concerned about the how the immune markers are related with resistance against pathogens. At the Insect Immune Mechanism section we mentioned the effect over the pathogen of every immune response (which are related with the immune markers) mentioned here. Also, along the text we mentioned the effects of each marker on the pathogen (e.g. see lines 104-105, 109-110, 136-146) or its relation with host defence (please see lines 175-176, 203-206). 2- A second general comment is related to the emphasis of the ecology and the rationale of trade- offs. The occurrence of this trade-offs have been previously and accurately proven in numerous researches. Our intention is not to give a resume of these papers, which is the reason we do not give details of each one of this studies. Our main intention in this review is to propose theories (to be tested) about the physiological relationship between immunity and other life history or intermediate characters, that arise trade-offs. However, we completely agree with the reviewer that is necessarily to distinguish the level of the trade-offs detected. For this reason, a new paragraph entitle Trade-offs among immune responses has been included in the Insect Immune Mechanisms section (see lines 148-162). 3- We delete these lines in order to avoid confusion. 4- The reviewer is concerned about the methods repeatability. For this reason a new paragraph was included in the Conclusion (see lines 534-544). We appreciate this comment. 5- The reviewer notice that some methods (e.g. genetics, proteomics) are not discussed. An explanation for this has been included in the Introduction (see lines 65-72). Page 46 of 50Methods in Ecology and Evolution For R eview O nly Details 6- Sexual selection is now defined as the “differential mating and fertilization success” (line 47) 7- We refer as short developmental times now (line 59). 8- Parenthesis was checked 9- We differ from the reviewer’s opinion resolving that melanin is a polymer non-toxic per se; molecules produced through the PO cascade are (lines 108-110). Reviewer 2 1- We have changed the lines of the abstract, in order to avoid confusion when we refer to the use of immune markers. See now lines 27-30. 2- We agree that the immune marker does not have a cost, but the immune function related to the marker has. This paragraph was re-written to avoid confusion (please see lines 60-65). 3- We now give examples and reasons of the immune assays that can not be suitable for use. See now see lines 65-72. 4- The reviewer is completely correct. We rewrite this line (see lines 80-81). 5- Insect PO pathway was checked. We agree that catecholamines are better PO substrates, however PO also hydroxilate tyrosine, which is a precursor of the PO cascade in invertebrates. Some more details of this cascade are given in lines 125-130. 6- We refer as Cuticle Darkness now. See line 167. 7- Robb et al (2003) reference has been included to explain that in some species cuticle darkness is not related to disease resistance (see line 193-194) 8- The reviewer is entirely correct. Wound repair considered as an immune response now. Please see line 111-112. 9- Wes how now examples and references about non-immune functions of haemocytes (see lines 206-208). This section was also re-written to avoid confusion (see lines 203-213). 10- We explain methods to obtain haemocytes. However, we state that the method used for this and the use of anticoagulants must be considered properly by each researcher (please see lines 226-229). The reviewer is concerned about the problems generated by the use of anticoagulants and the dilution factor. Before extraction each researcher commonly Page 47 of 50 Methods in Ecology and Evolution For R eview O nly establishes the amount of medium in which sample (with or without anticoagulants) will be obtained and dilution factor is determined before extraction. As we consider this a common practice we did not include this issue in the text. 11- As perfectly suggested by the reviewer, we refer now the existence of haematopoietic organs in some insects (please see lines 289-290). 12- We detailed the enhancing interaction between haemolymph and PO activity (see lines 337- 342). 13- We have re-written this line in order to make it consistent with the whole sentence (see lines 344- 347). 14- We state that the use of implants are a suitable method to test for trade-offs (please see lines 345-347). As accurately suggested by the reviewer, we discuss now the probable difference when using dead or live pathogens. Also we give an example to illustrate this difference (see lines 352-359). 15- The reviewer was completely right. We were wrong when we state that there can be a PO deficiency without a melanin deficiency. This line is corrected now. See lines 385-387. 16- We agree with the reviewer that PO can be produced in other tissues, and this idea has been written in the text (see lines 378-382). Nevertheless, we have attempted to make a hypothesis related to the PO transportation to other tissues and the possible base of a trade-off, and we do not have any evidence if this idea is species-specific. However, we are aware that this hypothesis needs further exploration; this has been stated in the text (see lines 388-389). 17- The reviewer is correct; tyrosine (TYR) can be stored as multiple intermediate molecules, this is now stated in the text (see lines 403-405). However, trade-offs can be detected if not enough TYR (and its subproducts) is gathered through ontogeny (please see lines 404-406). As suggested by the reviewer, now we state that PO measures need to be interpreted in the context of the specie’s tyrosine metabolism, please see lines 406-407. 18- The peroxide suppressor has been stated in the text (see line 422). 19- Details of why controlling total protein load is now explained. Please see lines 426-428. 20- Reviewer is completely correct. We now state that NO can be continuously synthesized by the induced NOS for long periods of time (see lines 454-456). 21- The reviewer is concerned about the use of modern methods (e.g. gene expression). An explanation for this has been included in the Introduction (see lines 65-72). Also, a recommendation was also included in the Conclusion section (see lines 581-586). 22- We appreciate the reviewer advice. However, in the Introduction we point out that all the methods mentioned in the manuscript are summarized in Table 1. We consider redundant to refer Table 1 in every immune marker mentioned in the text. Page 48 of 50Methods in Ecology and Evolution For R eview O nly 23- We agree that the JH can be a complicate model to exemplify the handicap hypothesis and for demonstrate the correct use of an immune assay. As properly suggested by the reviewer, the whole section was removed. Reviewer asked for an example where an immune marker was not useful to answer a research question. We did not refer to any paper since our intention was not underestimate the current research in insect ecological immunity. The scope of this paper is to highlight immune markers that have been carefully and properly used in previous works. For these reasons we deleted or re-wrote lines where this confusing or negative tone was used (see lines 566-572). Needless to say, we are very grateful with these thoughtful comments. 24- The Schneider´s work has now been used to explain not only the use of immune markers to resist pathogens, but also to tolerate and avoid damage (please see lines: 354-359). Related to this, we also mention the need of multiple measures of immune response not only to test trade-offs with other traits, but trade-offs among immune responses (see lines: 556-575). We thank the reviewer for this excellent suggestion. Reviewer 3 1- The reviewer is concerned about the negative tone of the article. Our aim is to not underestimate the current methodologies in insect ecological immunity. The scope of this paper is to highlight immune markers that have been carefully and properly used in previous works. For these reasons we deleted or re-wrote lines where this negative tone was used (for example, see lines: 310-314, 344-347, 526-530). We agree with the reviewer that the immune marker is no the object of study itself. Commonly the ecological and evolutionary consequence of the trade-offs are discussed, but the possible physiological link among immunity and other traits are not considered. We believe that this link can be useful in future discussions in ecological immunity researches. For these reasons, we have attempted to make (with the available information but immunological and ecological) hypotheses related to the possible base of a trade-off among immunity and other traits. However, we are aware that these hypotheses need further exploration; this has been stated in the text (see lines 388-389). 2- As recommended by the reviewer, we carefully revised the immunology literature referred here. We were wrong when we stated that using the pHrodo assay is better than quenching assay, this line was re-written to avoid confusions (see lines 263-265). We thank the reviewer for pointed out this mistake. However, it seems that the reviewer found other places in which further improvement can be made. We are open to solve these issues if they are outlined by the reviewer 3- We agree that is not relevant to test AMP specificity, for this reasons we deleted this lines in the text as well in table 1. We have done some recommendation based in our own experience. For example, in one of our insect models, cuticular thickness, but not colour, was related with Page 49 of 50 Methods in Ecology and Evolution For R eview O nly an immune challenge (unpublished data). Nonetheless, measurements could be not an easy task, it is possible to gather a great number of individual measurements, and its cost is not that elevated. Of course, the use of every method mentioned here must be determined by each researcher not only in terms of its overall cost, but also in the question to be answered (this idea has been included in text, please see lines 530-534). 4- One of our aims in this review is to give an overview of the most frequently-used immunological markers applied in the field of evolutionary ecology of insects. As mentioned before, we have done some recommendation not only trying to improve techniques, but also gave some possible solutions; all based in the existent literature, and our own experience. We consider that we have proposed the use of new and feasible techniques, for example cuticular thickness, pHrodo assays, NO measurements.We agree with the reviewer in the sense that kind of pathogen must be considered, this idea has been included in the text (please see lines 208- 213, 364-366, 553-555). Also, the reviewer is completely correct, maximal immune response is not necessarily related with maximum fitness, even sometimes, the absence of immune response can be advantageous. This idea is mentioned in the Conclusion (lines 560-566). 5- The reviewer is concerned about the use of non pathogens to induce an immune response. Our intention was to make some recommendations not to consider them as inappropriate for studies in the field of ecological immunology. We deleted words or re-wrote lines to avoid confusing connotation, for example please see now lines 253-260, 310-314, 344-347. We completely agree with the reviewer that the use or not use of non living particles is an excellent issue to discuss. However, our principal aim in this paper was to discuss methods and proposed theories (some of them to be tested) about the physiological link with immunity (measured with the markers mentioned in the text) and other life history traits. For this reason we do not include this subject in the ms. We do not want to make feel the reviewer disappointed to see that the new manuscript does not include this suggestion, but we consider that this issue could be properly for an extend discussion (even a review for publication). 6- An additional column with references has been included in table 1. Some shortening of the text was done when possible. We appreciate this useful suggestion. The paper has been revised by two English speaking persons Finally, some new references have been added. Please, do not hesitate to contact me if there are further issues to discuss. Sincerely, Miguel Moreno-García Page 50 of 50Methods in Ecology and Evolution