Redalyc.Metavirómica en masas de agua: pasado, presente y perspectivas futurasMetavirómica en masas de agua: pasado, presente y perspectivas futuras. Hidrobiológica López-Pérez, Marcos; Fernández, Francisco Josévol. 23, núm. 3, 2013, pp. 287-302Universidad Autónoma Metropolitana Unidad Iztapalapa Distrito Federal, MéxicoLópez-Pérez, Marcos; Fernández, Francisco Joséal209304657@correo.azx.uam.mx202019-01-18T20:03:00Z2019-01-18T20:03:00Z16121266669655515778665162015-03-24T00:00:00ZRedalyc2019-01-18T00:00:00ZHidrobiológica 2013, 23 (3): 287-302Metavirómica en masas de agua287Metavirómica en masas de agua: pasado, presente y perspectivas futuras Metaviromics in water bodies: past, present and future perspectivesMarcos López-Pérez 1 y Francisco José Fernández21Departamento de Ciencias Ambientales. Universidad Autónoma Metropolitana-Lerma2Departamento de Biotecnología. Universidad Autónoma Metropolitana-Iztapalapa. Apdo. Postal 55-535. Col. Vicentina. México, D. F., 09340. Méxicoe-mail fjfp@xanum.uam.mxLópez-Pérez M. y F. J. Fernández. 2013. Metavirómica en masas de agua: pasado, presente y perspectivas futuras. Hidrobiológica 23 (3): 287-302.RESUMENLos virus son los microorganismos más abundantes en la biosfera. Debido a su gran versatilidad para infectar a todo tipo de organismos y a su condición de parásitos intracelulares obligados, tienen efectos muy importantes en la di- námica de los ecosistemas. La identificación y análisis de las interacciones en las comunidades víricas es un trabajo complicado, debido a la dificultad intrínseca en el cultivo de estos microorganismos. Además, la carencia de secuen- cias de ADN conservadas entre grupos imposibilita su identificación con una tecnología del tipo ADNr, la común en la actualidad para la identificación de los organismos vivos. La metagenómica surge como una alternativa para la caracterización molecular de los virus en sus ambientes naturales, una alternativa que arroja información relevante en cuanto a la composición y estructura en dicho ambiente. En este artículo de revisión se hace un repaso del papel de los virus en los ecosistemas acuáticos, además de enfatizar en la importancia de una adecuada implementación de las técnicas de metagenómica. Finalmente, se revisan las principales herramientas informáticas para el tratamiento de los datos metagenómicos obtenidos.Palabras clave: Acervo genético, metagenomas víricos, papel ecológico, virus acuáticos.ABSTRACTViruses are the most abundant organisms in the biosphere. Because of their versatility to infect all types of organisms and their status as obligate intracellular parasites, they have important effects on ecosystem dynamics. Identification and analysis of interactions in viral communities is a hard work, due to the inherent difficulty in culturing these micro- organisms. Furthermore, the lack of conserved DNA sequences among groups precludes identification of the viruses by using rDNA technology, as the current molecular standard for the identification of living organisms. Metagenomics arises as an alternative to the molecular characterization of the virus in its natural environments, which yields informa- tion relevant to its composition and structure. In this article, we review the role of viruses in aquatic ecosystems, in addition to emphasizing the importance of proper implementation of these metagenomic techniques. Finally, we review the main tools for the treatment of the obtained metagenomic data.Key words: Genetic stock, metaviromics, ecological role, aquatic viruses.HidrobiológicaVol. 23 No. 3 • 2013INTRODUCCIÓNLos virus son partículas pequeñas, normalmente con tamaños de entre 20 y 200 nm. Básicamente están constituidos de materialgenético (ADN o ARN, de cadena sencilla o doble), rodeado por una envoltura de proteínas. Algunos virus cuentan, además, con una capa externa de naturaleza lipídica. Se caracterizan funda- mentalmente por carecer de metabolismo propio, lo que les obli- PAGE 288López-Pérez M. y F. J. Fernández PAGE 289Metavirómica en masas de aguaga a interaccionar con otros organismos y utilizar su maquinaria bioquímica y metabólica; de tal manera que son parásitos intra- celulares obligados.Tradicionalmente, los organismos procariotas han sido con- siderados la parte más relevante de los ecosistemas acuáticos, en cuanto a su impacto en la dinámica de estos ambientes. Sin embargo, en los últimos 15 años, y con base en las características generales de los ciclos de vida víricos, las investigaciones han demostrado que los virus influyen de forma muy importante en los procesos bioquímicos, ciclos de nutrientes, distribución de tama- ños de partículas, biodiversidad relativa y distribución de algas y bacterias (Fuhrman, 1999). Además, existen reportes que indican que son los organismos más abundantes en ambientes acuáti- cos y están implicados en el control de las poblaciones, tanto de fitoplancton como de bacterias (Sieburth et al., 1988; Bratbak et al., 1990). El estudio del ADN de comunidades completas (meta- genómica) aplicada a los virus, lo que también es conocido como metavirómica, inició en el año 2002, con la caracterización de co- munidades víricas marinas (Breitbart et al., 2002). La mayor parte de las publicaciones sobre metagenómica vírica en cuerpos y masas de agua se refiere a zonas oceánicas, sin embargo existen reportes donde este análisis se extiende a masas de agua dulce, como aguas residuales (Cantalupo et al., 2011), lagos (López-Bue- no et al., 2009) y humedales (Jackson & Jackson, 2008). En la tabla 1 se muestran varias familias de virus de ADN y ARN que han sido identificados en algunos de los principales estudios de metaviró- mica en ecosistemas acuáticos.LA TAXONOMÍA DE VIRUS EN EL CONTEXTO DE LA METAGENÓMICALa taxonomía de los virus tradicionalmente ha resultado ser pro- blemática cuando se equipara con la taxonomía del resto de los organismos que componen la biosfera. El problema fundamental deriva de que estos organismos se encuentran en la interfase conceptual entre la materia viva y la inerte, siendo estructural y metabólicamente mucho menos complejos, lo que dificulta la búsqueda de características especiales que diferencien a nivel específico una estirpe de otras. A pesar de estos inconvenientes, el comité internacional para la taxonomía de virus (International Committee Taxonomy of Viruses, ICTV), ha definido una serie de criterios para clasificar taxonómicamente a estos organismos. Los tres criterios que se tienen en cuenta son: el tipo de organis- mo al que parasitan (es decir, si éste pertenece al dominio Eukar- ya, Bacteria o Archaea), la morfología del virión o partícula vírica (si son virus envueltos o desnudos y consideraciones acerca de la forma de sus cápsidas) y la constitución de su genoma (es de- cir, si son virus de ADN de doble cadena o cadena sencilla, o bien si son virus de ARN de una o dos cadenas y otras características, como si las moléculas de ácidos nucleicos están fraccionadas o no). Para virus de especial interés también se tienen en cuentaotros criterios, como los síntomas que genera su patogénesis, antigenicidad, perfil de proteínas y rango de hospedadores (Me- dina & García-Sastre, 2011). En metavirómica es importante re- saltar que por una parte las muestras ambientales contienen una enorme diversidad viral y, por otra, la mayoría de los virus en el planeta son aún desconocidos (Kuzmin et al., 2006). En los análisis metavirómicos frecuentemente se analiza a través de microsco- pía de epifluorescencia la fracción vírica de la muestra (Suttle et al., 1990; Marie et al., 1999), identificando la morfología de los virus de un modo superficial, debido a la gran diversidad viral de las muestras. Adicionalmente, es posible inferir el tipo de hospe- dadores que tienen los virus de una muestra haciendo una explo- ración de la diversidad de los organismos presentes en la misma. Sin embargo, el análisis metavirómico suele perder información referente a la morfología y los huéspedes a los que parasitan, al centrarse fundamentalmente en el análisis de las secuencias de ácidos nucleicos recuperadas. Por ello, es frecuente que en los análisis de metavirómica la clasificación se quede en niveles altos en la jerarquía taxonómica, como las familias (Middelboe et al., 2003), y no se busque como objetivo principal establecer criterios de diversidad viral, sino profundizar en el análisis de la dinámica de estas comunidades virales y su interacción con el ecosistema donde viven. Un ejemplo novedoso del análisis me- tavirómico, centrado en elementos funcionales y de dinámica de interacción entre la comunidad viral y el ecosistema, es el que se ha basado en el papel de los genes implicados en fotosíntesis aislados de genomas de los virus (Lindell et al., 2005).VIRUS EN ECOSISTEMAS ACUÁTICOS; IMPORTANCIA, ABUNDANCIA Y ACTIVIDADLa abundancia relativa de estos microorganismos en el agua ha sido medida, como ya se comentó, en masas de agua oceánica y de agua dulce. Con respecto a las masas de agua oceánicas, algunas investigaciones indican que los virus son las entidades biológicas más abundantes en el mar, tanto en zonas cercanas a la costa como en zonas alejadas, en aguas boreales y tropica- les (Wommack et al., 1992; Boehme et al., 1993; Børsheim, 1993; Cochlan et al., 1993; Paul et al., 1993; Maranger et al., 1994; Hara et al., 1996; Noble & Fuhrman, 1998), llegando a superar entre 5 y 25 veces en número de partículas-individuos a las poblacio- nes bacterianas y constituyendo el factor más importante para el control de dichas poblaciones (Paul et al., 1993; Peduzzi & Schiemer, 2004), lo que repercute directamente en la dinámica del ecosistema. De este mismo efecto sobre las bacterias se de- riva el impacto de su actividad en el ciclo de varios nutrientes, como en el caso del carbono (Fig. 1). Para profundizar en este sentido, es importante aclarar que en los ciclos víricos se pue- den describir tres tipos básicos de reproducción: a) infección o ciclo lítico, en el que el virus ataca a la célula hospedadora; a continuación se produce una abundante progenie en el interior de la célula que termina lisándose y consecuentemente murien-TABLA 1: Familias de virus ADN y ARN identificados mediante análisis metavirómicos en ecosistemas acuáticos. TABLA 1: Principales estudios y familias de virus ADN y ARN identificados mediante análisis metavirómicos en ecosistemas acuáticos. Las iniciales P y S se refieren a la tecnología de secuenciación (pirosecuenciación y shotgun, respectivamente) utilizada en cada referencia. Las iniciales que aparecen en esta columna se refieren a la técnica utilizada en la referencia. El orden se corresponde respectivamente con el orden de las referencias.Tipo de ecosistema/organismoFamilia víricaHospederoTipo de técnicade secuenciaciónReferenciasLagos antárticos, agua reciclada, aguas termales, océano ÁrticoVirus de ADNVirus de ADN doble hebraHerpesviridaeMamíferosP, S, P, PAngly et al. (2006); Schoenfeld et al.(2008); Vega Thurber et al. (2008); López-Bueno et al. (2009)Aguas termalesFuselloviridaeArqueasSSchoenfeld et al. (2008) Lagos antárticosPhycodnaviridaeAlgasPLópez-Bueno et al. (2009) Lagos antárticosMimiviridaeAlgasPLópez-Bueno et al. 2009 Arrecifes de coralAdenoviridaeMamíferosSMarhaver et al. (2008)Lagos antárticos, aguas termalesRudiviridaeArchaeaS, PSchoenfeld et al. (2008); López-Buenoet al. (2009)Océano ÁrticoPapillomaviridaeMamíferosPAngly et al. (2006) Agua recicladaSiphoviridaeBacteriasPRosario et al. (2009)Virus de ADN de cadena sencillaLagos antárticos, arrecifes de coralNanoviridaePlantasP, PMcDaniel et al. (2008); López-Bueno etal. (2009)Tortugas de marGyrovirus genusAvesSFei Fan et al. (2009b) Lagos antárticosCircoviridaeMamíferos/AvesPLópez-Bueno et al. (2009)Lagos antárticos, océano Ártico InoviridaeBacteriaP, PAngly et al. (2006); López-Bueno et al.(2009)Lagos antárticos, agua reciclada, arrecifes de coralPhoca vitulina, Eumetopias jubatusGeminiviridaePlantasS, S, P, P, SBreitbart et al. (2004); Kim et al. (2008);Vega Thurber et al. (2008); López-Buenoet al. (2009); Blinkova et al. (2010)AnelloviridaeMamíferosS, SFei Fan et al. (2009a); Fei Fan et al.(2011b)Lagos antárticosMicroviridaeBacteriaPLópez-Bueno et al. (2009)Virus de ARNVirus ARN de cadena sencilla, polaridad positiva o negativaAgua reciclada, lagos artificialesPicornaviridaeAves/MamíferosP, P, PDjikeng et al. (2009); Kapoor et al.(2008); Li et al. (2010)Océano ÁrticoRetroviridaeAves/MamíferosP, PAngly et al. (2006); Park et al. (2011) Lagos artificialesFlaviviridaeMamíferosPDjikeng et al. (2009)Lagos artificialesFlexiviridaePlantasPDjikeng et al. (2009) Océano ÁrticoCoronaviridaeAves/MamíferosPAngly et al. (2006)Virus de ARN cadena dobleLagos artificialesPicobirnavirusMamíferosPDjikeng et al. (2009)Lagos artificialesReoviridaeAves/Mamíferos/ Insectos/ Plantas/HongosPDjikeng et al. (2009)Lagos artificialesPartiviridaeHongosPDjikeng et al. 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Modelo del flujo de carbono en ecosistemas marinos, en el que se teoriza la ausencia y presencia de virus. Este modelo presupone que la lisis debida a actividad lítica supone un 50% de la mortalidad bacteriana y un 7% de la mortalidad del fitoplanc- ton. Los números asociados a las flechas se refieren a unidades de carbono transferidas entre los diferentes macro-componen- tes del ecosistema. Modificado de Fuhrman, 1992 (ver dicho trabajo para profundizar en el análisis y en las cifras).do. Posteriormente, los viriones son liberados al ambiente donde comienzan un nuevo ciclo. b) infección crónica, donde, a dife- rencia de la infección lítica, la célula hospedadora no muere al ser liberada la progenie, sirviendo así como célula hospedadora durante sucesivas generaciones. c) ciclo lisogénico, en donde el ácido nucleico del virus, una vez integrado en el ADN genómico de la célula hospedadora, se mantiene latente, reproduciendo su material genético con el de la célula que lo contiene (en lo que se denomina estado de provirus) (Fuhrman, 1999). En este último caso, cambios en la fisiología celular del hospedador, como por ejemplo condiciones de estrés, pueden derivar en la activación de una infección o ciclo lítico (Fuhrman, 1999). Es importante entender, por lo tanto, cómo esta dinámica reproductiva puede repercutir en la redistribución de nutrientes en el ecosistema, ya que en función de ella será mayor o menor la mortalidad bacte- riana y, en consecuencia, la fracción de biomasa que está dis- ponible para la captación por otros estratos de organismos de la pirámide trófica en el ecosistema. En el momento en que las células se lisan, se liberan al medio los virus y toda la biomasa celular queda en forma de materia orgánica (MO) disuelta. Esta MO disuelta, en su mayoría, impacta de nuevo en la generación de nuevas células bacterianas (Bratbak et al., 1990; Proctor et al., 1990). De forma independiente, es interesante destacar que la lisis bacteriana y la subsiguiente liberación del contenido celular re- percute en la concentración y en las condiciones fisicoquímicas de esa zona de la columna de agua. Un ejemplo de este efecto es la capacidad de agregación de componentes celulares debida a la presencia de polímeros (Proctor et al., 1991). De forma alterna- tiva, es posible pensar en agregados celulares que tienden a des- cender a aguas más profundas, pero por efectos de la lisis vírica se disgregan permaneciendo en la zona eufótica (Proctor et al., 1991).Con respecto a los virus que afectan a algas y cianobacterias, éstos permiten que sus tasas declinen durante las explosiones de población de estos organismos y se restituya la distribución de nutrientes en el ecosistema (Yau et al., 2011). Adicionalmente, un aspecto interesante que debe ser resaltado es el papel que pue- den jugar los virus en la dinámica del clima. En este sentido, se ha publicado que la lisis celular (la cual es derivada en gran medida de la actividad lítica de los virus) libera DMS (dimetil sulfuro), un gas que se ha comprobado que interviene en los procesos de ge- neración de las nubes. Existen estudios que han demostrado que la infección vírica causa la liberación intracelular del precursor del DMS (el dimetildisulfopropionato, DMSP) en organismos del fitoplancton como Micromonas pusilla (R. W. Butcher) I. Manton et M. Parke. Este gas puede ser incorporado por otros microorga- nismos o bien puede ser liberado a la atmósfera (Kiene & Bates, 1990). Los virus, además, afectan a la diversidad y estructura de las comunidades microbianas, interviniendo en algunos casos (como ocurre con los bacteriófagos), en el intercambio de ma- terial genético entre diferentes miembros de las comunidades bacterianas, algo que también contribuye a la dinámica evoluti- va en estos ecosistemas (Fuhrman, 1992; Wilhelm & Suttle, 1999; Wommack & Colwell, 2000; Brussaard, 2004; Weinbauer, 2004;Weinbauer & Rassoulzadegan, 2004; Suttle, 2005, 2007; Munn,2006; Ogunseitan, 2008).DIVERSIDAD VÍRICA EN AGUA DULCELos estudios de diversidad vírica realizados en masas de agua dulce han sido establecidos con base en las características fun- damentales que diferencian cada uno de estos ambientes. En es- te sentido hay estudios realizados sobre humedales artificiales y naturales, ríos, lagos, ambientes extremos y aguas residuales.Lagos. Los estudios de metagenómica viral en lagos se han reali- zado en gran variedad de ecosistemas. Uno de los ejemplos que aporta datos más interesantes es el trabajo de López-Bueno y colaboradores (2009), realizado en lagos antárticos. En dicho tra- bajo, los análisis metagenómicos revelaron que las comunidades de virus son enormemente diversas y abundantes. A diferencia de lo hallado en otros viromas lacustres, donde normalmente hay una abundancia dominante de bacteriófagos, en este ambiente se encontró una gran presencia de virus de eucariotas, incluyen- do ficodnavirus y otros virus de ADN de cadena sencilla. Clasen y colaboradores (2008) realizaron investigaciones sobre la abun- dancia viral en 16 lagos. Los resultados indicaron que existe una enorme variabilidad entre las comunidades víricas en función del ecosistema. Adicionalmente, establecieron un interesante paralelismo entre las poblaciones de bacterias y cianobacterias y la abundancia relativa de los virus en diferentes ambientes cli- máticos, algo que ha sido reportado también en otros trabajos (Djikeng et al., 2009; Rodriguez-Brito et al., 2010). Debe destacarse la posible incidencia de los virus en el ambiente, por su reper- cusión en las concentraciones relativas de gases atmosféricos. Esto es debido a la fuerte presión de selección que ejercen sobre las cianobacterias y, por lo tanto, a las alteraciones del metabo- lismo fotosintético de estos microorganismos (López-Bueno et al., 2009).Humedales. Es interesante resaltar que los estudios morfológi- cos y moleculares en humedales indican que una gran mayoría de los virus encontrados en estos ambientes son bacteriófagos. Sin embargo, es común identificar también virus que parasitan algas, protozoos e incluso plantas vasculares (Wommack & Co- lwell, 2000). En cuanto a los principales trabajos que analizan la diversidad y dinámica de las poblaciones víricas en humedales, destacan los realizados en humedales naturales, aunque también hay estudios sobre la diversidad viral en humedales artificiales (Gersberg et al., 1989; Nokes et al., 2003; Vidales et al., 2003; Vy- mazal, 2005). En los naturales, las investigaciones se han enfoca- do recurrentemente en el análisis de las condiciones ambientales para explicar las fluctuaciones en la diversidad vírica (Fischer et al., 2003; Filippini et al., 2006; Filippini & Middelboe, 2007). Un ejem- plo de dichos estudios fue el realizado en el humedal Talladega, en Alabama, EUA (Farnell-Jackson & Ward, 2003), en el que se evaluó el patrón de diversidad vírica asociado con las diferentes estaciones del año. Los autores hallaron que las diferencias esta- cionales estaban asociadas con variaciones en la abundancia de las poblaciones bacterianas, pero también del carbono orgánico disuelto y de otros nutrientes de naturaleza inorgánica (Farnell- Jackson & Ward, 2003). La diversidad en los resultados obtenidos fue explicada por los autores debido a la alta heterogeneidad de estos ecosistemas. Es posible argumentar, por ejemplo, que la profundidad será un factor muy relevante, de manera que si hay poca profundidad, la comunidad que habita el bentos o los virus asociados a plantas en el sedimento podrán constituir una frac-ción muy importante de la muestra sometida a análisis. De igual forma, la profundidad afectará directamente a la cantidad de luz que penetra en el agua, lo que afecta a los microorganismos con metabolismo fotosintético y a su vez repercute en la dinámica de las poblaciones bacterianas (y consecuentemente, en los virus que hospedan).Ríos. El análisis de diversidad vírica en ríos es el menos reportado en la literatura científica. Uno de los primeros trabajos fue reali- zado en el río Danubio, con la finalidad de evaluar el efecto de las comunidades víricas sobre las poblaciones de bacterias (Mathias et al., 1995). En otro estudio, realizado en una zona de estuario de Francia, se evaluó la distribución del viroplancton en función de las variables ambientales. La abundancia viral fue relaciona- da con la abundancia y actividad del bacterioplancton, con los pigmentos fotosintéticos, la concentración de nutrientes y otros parámetros físico-químicos, variables que a su vez condicionan la dinámica de la poblaciones bacterianas (Auguet et al., 2005). Uno de los trabajos más recientes en el que se evaluó la diversi- dad del viroplancton, se realizó en dos ríos de la República Checa (Slováčková & Maršálek, 2008), pudiéndose determinar una co- rrelación entre la abundancia de virus y bacterias con la cantidad de clorofila total y la concentración de ortofosfatos. Finalmente, otro de los estudios en esta misma línea de investigación se reali- zó en el sedimento de un estuario, evaluándose la distribución de virus y bacterias asociadas a la actividad diagenética (Middelboe et al., 2003).Ambientes extremos. La presencia de virus en ambientes extre- mos se debe fundamentalmente a que son capaces de invadir células de organismos de los tres dominios presentes en estos ecosistemas (Eukarya, Archaea y Bacteria), a pesar de que los organismos predominantes en dichos ambientes sean arqueas. En la literatura, el análisis metagenómico de estas comunidades se ha hecho en función del ambiente objeto de estudio, como es el caso del análisis que se realizó sobre fuentes geotérmicas en Yellowstone (Schoenfeld et al., 2008). En este estudio se encontró que la diversidad vírica fue similar a la hallada en ambientes no extremos. Además, y a diferencia de lo que se pensaba anterior- mente (que los virus sometidos a condiciones extremas de tempe- ratura tenderían a permanecer en el interior celular, manteniendo así un ciclo de vida no lítico), las secuencias halladas en los virus aislados en este trabajo mostraron secuencias que codificaban en su mayoría para un ciclo lítico (Schoenfeld et al., 2008).Otro de los ambientes extremos donde se han realizado es- tos estudios son los lagos hipersalinos. En estos ecosistemas, a pesar de que son ambientes con una gran carga de partículas vi- rales (109 partículas/ml), la cantidad de halovirus aislados es muy baja (Santos et al., 2010). Para explicar la gran abundancia de partículas virales en estos ambientes se proponen dos hipótesis,la alta estabilidad física de las partículas víricas en estos am- bientes y b) una alta producción de partículas virales, propiciadapor las condiciones de este entorno (Santos et al., 2007; Schapira et al., 2009; Bettarel et al., 2010). Es muy llamativa la alta relación partícula viral por bacteria encontrada en ambientes hipersali- nos (alrededor de 100), comparada con la relación en ambientes acuáticos no extremos (de 5 a 10 partículas virales) (Wommack & Colwell, 2000; Sime-Ngando et al., 2010). De forma general, la cantidad de virus en estos ambientes puede ser correlacionada con la cantidad de células en una muestra (Santos et al., 2010). Esta tendencia ha sido confirmada en varios estudios adicionales (Guixa-Boixareu et al., 1996; Bettarel et al., 2010).DIVERSIDAD VÍRICA EN AGUA SALADALos estudios de metavirómica en ecosistemas marinos diferen- cian, en general, los ambientes costeros de los no costeros (tam- bién llamado océano profundo), así como la localización geográ- fica del océano objeto del estudio.Ambientes costeros. Uno de los primeros trabajos que se enfocó al análisis de la dinámica viral en este ambiente estableció que no hay cambios apreciables en las poblaciones virales muestreadas en ambientes costeros, a pesar de que se aislaron en recipien- tes y se sometieron a cambios en la concentración de nutrientes, irradiación con fuertes intensidades de luz o drásticos cambios de temperatura. Además, comprobaron fuertes cambios en la abundancia viral del ambiente en función de parámetros tempo- rales y de la profundidad (Bratbak et al., 1996).Otros estudios se han enfocado al estudio de la diversidad viral utilizando el análisis de genes relacionados con la actividad fotosintética. En este sentido, es destacable la presencia de ge- nes fotosintéticos en poblaciones virales de diferentes tamaños (Sandaa et al., 2008). Este resultado es indicativo de cómo los vi- rus pueden estimular el metabolismo de sus hospedadores con el objetivo de desviar energía hacia la síntesis de nuevos viriones.Finalmente, uno de los trabajos que más repercusión ha teni- do en este tipo de ambientes describió el análisis metagenómico de virus de ARN (Culley et al., 2006). Se encontró que los virus de ARN de cadena sencilla (ya fueran de polaridad positiva o nega- tiva) y los de ARN de doble cadena afectan a todos los niveles tróficos (lo que generaría un fuerte impacto, tanto económico co- mo ecológico). El análisis también reveló que contienen genomas de pequeño tamaño, una variable que facilitó el posterior análisis metagenómico. La mayoría de los virus de ARN aislados en es- tos ambientes marinos son evolutivamente distantes de los virus conocidos, y su diversidad genética ha sido muy poco explorada con respecto a los que afectan a la comunidad procariota. Adi- cionalmente, no se encontraron bacteriófagos de ARN, lo cual so- porta la teoría de que la mayoría de bacteriófagos marinos tienen genomas de ADN (Weinbauer, 2004).Ambientes no costeros. Los análisis metagenómicos y de diver- sidad viral realizados en océanos profundos son más escasos.Uno de los más recientes enfatiza que la mayoría de las secuen- cias obtenidas no han podido ser identificadas por comparación con las bases de datos (Steward & Preston, 2011), lo cual es una señal de lo poco que se conoce de la diversidad vírica de estos ambientes. Además, en este trabajo se pone de manifiesto que la mayoría de los virus encontrados (94%), fueron bacteriófagos y sólo el 6% correspondió a virus que afectan a organismos eu- cariotas. En otro estudio se indica que en la zona fótica predomi- nan los bacteriófagos que infectan cianobacterias, mientras que conforme se incrementa la profundidad desciende la abundancia relativa de estos virus (Williamson et al., 2008). Por otra parte, al- gunos autores han propuesto que en estos ambientes los virus son uno de los elementos fundamentales para la generación de diversidad microbiana, gracias al intercambio genético que favo- recen en sus ciclos de infección (Hendrix et al., 2000). Finalmente, uno de los trabajos que más trascendencia ha tenido en este tipo de ambiente, se realizó en cuatro regiones oceánicas diferentes (Océano Ártico, la costa de British Columbia, el Golfo de México y el Mar de los Sargazos; Angly et al., 2006). Los análisis meta- virómicos se caracterizaron porque los procesos de ensamblaje de las secuencias de los virus aislados fueron diferentes, lo que probablemente reflejaría la existencia de diferentes presiones de selección, indicando de forma adicional que el grado de diver- sidad viral también se ve afectado por procesos de migración. Asimismo, este estudio reflejó la variabilidad existente en función de la latitud y la longitud, dejando claro que a pesar de que hay especies endémicas, la gran mayoría de la especies de virus están repartidas en diferentes regiones oceánicas (Angly et al., 2006).MUESTREO EN ESTUDIOS DE METAGENÓMICA VIRAL EN MASAS DE AGUACon el objetivo de evaluar la variabilidad de la comunidad víri- ca en función de las condiciones ambientales, en la mayoría de los estudios de metavirómica publicados se realiza una toma de muestras distribuida tanto espacial como temporalmente (Djikeng et al., 2009; Oh et al., 2011). Este tipo de muestreo debe aplicarse en contexto con las particularidades del ecosistema objeto de estudio, como por ejemplo la zona más oxigenada o donde la ac- tividad fotosintética sea mayor en la columna de agua. Otro de los procedimientos que sistemáticamente ha sido utilizado en la toma de muestras para estudios de metagenómica viral es el filtrado (Figura 2), y se ha demostrado que el tamaño de las partículas ví- ricas varía en función de la época de año en la que se produzca la recolecta de las muestras (López-Bueno et al., 2009). Eso implica la necesidad de variar el tamaño de poro de los filtros utilizados en el muestreo para evaluar si existen diferencias en la abundan- cia de las especies, en cuanto a su tamaño, a lo largo del año. 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Ejemplo de metodología utilizada en la filtración para la obtención de la fracción vírica en masas de agua.tren en el análisis ácidos nucleicos celulares. Además, también es habitual utilizar enzimas degradadoras de ácidos nucleicos, con el objetivo de evitar que se analicen ácidos nucleicos libres, en solución (Rebekah et al., 2009). Esta manera de proceder asegura que se capten únicamente los viriones. Finalmente, el proceso de aislamiento de un virus debe ser confirmado visualmente me- diante microscopía electrónica (Marie et al., 1999). Como ejemplo, en el trabajo de Djikeng y colaboradores, antes de procederse al aislamiento de la fracción vírica hicieron un prelavado con filtra- ción tangencial a 2.0 y 1.0 µm. Seguidamente, se aplicó ultracen- trifugación o filtración tangencial para partículas por encima deDa (Djikeng et al., 2009). Otros trabajos hacen un filtrado preliminar de la muestra con papel Wattman de 1.6 µm de tamaño de poro y posteriormente aplican una filtración tangencial con un tamaño de poro de 0.22 µm, aplicando presión con una bomba peristáltica.PROBLEMAS FRECUENTESEN LA METAGENÓMICA DE VIRUSA pesar de que los virus son agentes biológicos presentes prác- ticamente en toda diversidad de ambientes en el planeta, la com- prensión de la dinámica de sus poblaciones en los diferentes ecosistemas está muy limitada, debido fundamentalmente a la imposibilidad de cultivarlos y trabajar con ellos en el laboratorio. De manera adicional, la carencia de secuencias genéticas con- servadas entre las especies, como el ADN ribosómico utilizado para eucariotas y procariotas, incrementa todavía más la dificul-tad del estudio de estos microorganismos (Breitbart et al., 2002). Además, una vez que se han obtenido los datos metagenómicos de muestras ambientales, con mucha frecuencia se encuentran fragmentos truncados de material genético, elementos de ADN libre y marcos abiertos de lectura incompletos. Finalmente, otra de las dificultades que se plantea en la mayoría de los estudios de metagenómica viral es que la gran mayoría de los virus aisla- dos en muestras ambientales son desconocidos y, consecuente- mente, la mayoría de los genes secuenciados no tienen similitud suficiente como para poder ser comparados con las bases de datos (Lorenzi et al., 2011). Debido a estos factores, es necesaria la implementación de tecnologías más sofisticadas en el análisis de secuencia, como la secuenciación shotgun (Venter et al., 2004; Edwards & Rohwer, 2005; Delwart, 2007) o la pirosecuenciación (Eriksson et al., 2008). Estas nuevas tecnologías han servido para incrementar considerablemente la información contenida en las bases de datos.IMPORTANCIA DE LAS TECNOLOGÍAS DE SECUENCIACIÓN EN EL CONTEXTO DE LA METAVIRÓMICAEn el contexto de la metavirómica, es importante conocer las ca- racterísticas de las tecnologías de secuenciación, con el propó- sito de comprender el análisis bioinformático que se requiere y a partir de ahí establecer inferencias sobre el ambiente objeto de estudio. Es necesario hacer énfasis en que los metaviromas son muestras enormemente diversas, a lo que se suma el hecho de que la mayoría de los virus no han sido descritos (lo cual hace de las bases de datos herramientas menos eficientes en el análisis de los mismos). Como ya se mencionó anteriormente, en estudios de metavirómica se han utilizado mayoritariamente dos tipos de metodología para la secuenciación y obtención de los metaviro- mas: la pirosecuenciación y la secuenciación shotgun. La pirose- cuenciación es una técnica reciente enfocada a la determinación de un número elevado de secuencias de ADN (Fakhrai-Rad et al., 2002; Margulies et al., 2005). Algunas de las ventajas de este mé- todo son la economía y el ser procesos menos laboriosos y más rápidos que otras tecnologías existentes. Sin embargo, esta téc- nica también conlleva algunas desventajas, como que la longitud de las secuencias obtenidas es mucho más corta (del orden de 100-250 pb o incluso fragmentos más cortos, de 40-60 pb (Fakhrai- Rad et al., 2002), con una tasa de error de entre 5 y 10 errores por kpb). Esta tasa de error tiene efecto en el proceso de ensamblaje, por lo que es preciso implementar metodologías para distinguir estos errores de los producidos por la tasa de variación natural debida a la mutación. En este punto es necesario hacer referen- cia a dos términos que, de forma general, se tienen en cuenta en análisis posteriores. Por una parte, el término haplotipo, que se utiliza en estudios de metavirómica para referirse a un conjunto de polimorfismos de un solo nucleótido (SNPs) en la secuencia (Purcell et al., 2007). Y por otra, el concepto de cuasiespecie: enlos virus de ARN este concepto es relevante y puede entenderse como distribuciones dinámicas de genomas víricos no idénticos, pero estrechamente relacionados entre sí, sometidos a un pro- ceso continuo de variación genética, competición y selección, que actúan como una unidad de selección. En estos casos, no se podría hablar sólo de un genoma definido y la secuencia con- senso aceptada sería un promedio de variantes (Domingo et al., 2000).El ensamblaje de las secuencias obtenidas por pirosecuen- ciación requiere un trabajo mayor, dado que los metaviromas están en estos casos constituidos por un número de secuencias (reads) muy elevado. A pesar de que la longitud de las secuen- cias es corta, esta metodología ha sido utilizada de forma recu- rrente para el análisis de metaviromas (Angly et al., 2006; Kapoor et al., 2008; Djikeng et al., 2009; Culley et al., 2010; Li et al., 2010; Rodriguez-Brito et al., 2010; Boujelben et al., 2012). Es necesario recalcar, en cualquier forma, que reconstruir genomas virales no es el objetivo principal en los análisis de metavirómica, sino más bien enfocar el análisis a un tipo de secuencia concreta de la comunidad vírica y ponerla en contexto con fenómenos de inte- racción con el ecosistema. Para ejemplificar esta situación, se puede mencionar el trabajo de Sharon y colaboradores, quienes identificaron genes implicados en la fotosíntesis de cianobacte- rias en genomas virales (Sharon et al., 2009).La otra metodología ampliamente usada en este tipo de es- tudios es la secuenciación shotgun, también conocida como se- cuenciación de novo (Fei Fan et al., 2009b), una modificación del clásico proceso de secuenciación basado en el método de San- ger (Sanger & Coulson, 1975; Sanger et al., 1977). En referencia a este método de secuenciación, y comparando con la tecnología de pirosecuenciación, es preciso aclarar que en este caso la lon- gitud de los fragmentos obtenida es de entre 800 y 1000 pb, con una tasa de error de 0.01/kpb (Malet et al., 2003; Huse et al., 2007). Hay numerosos ejemplos de estudios donde se utilizó este mé- todo para el análisis metavirómico (Djikeng et al., 2009; Oh et al., 2011; Steward & Preston, 2011). La tecnología shotgun se desa- rrolló para lograr secuenciación de genomas; para ello se requie- re un trabajo previo de generación de clones, donde cada uno de estos clones lleva un fragmento de la metagenoteca. Este método es más laborioso y más caro, sin embargo provoca la aparición de menos errores. En referencia al ensamblaje, esta tecnología presenta el problema de que cuando existen fragmentos de ADN de secuencia repetida es difícil ensamblar los fragmentos para generar los contigs (Schatz et al., 2010).Una nueva tecnología de secuenciación está adquiriendo una importancia creciente y tiene como objetivo leer grandes fragmentos de ácidos nucleicos (del orden de miles de nucleóti- dos), la denominada Illumina Solexa sequencing, con una capaci- dad de procesamiento similar a las tecnologías de secuenciación tradicional que producen fragmentos de longitud más corta, co-mo la secuenciación shotgun o la pirosecuenciación (Korlach et al., 2008).BIOINFORMÁTICA UTILIZADA EN METAGENÓMICA VIRALUna de las razones fundamentales por las que la metagenómica se está utilizando es su gran utilidad en el análisis ecológico de los diferentes ambientes, debido a que permite establecer hipóte- sis referentes a las relaciones entre los miembros de una comu- nidad. Estas inferencias derivan de diferentes parámetros, siendo algunos de los más importantes la información depositada en las bases de datos y el análisis bioinformático de dichos datos, lo que será objeto de revisión del presente apartado.Ensamblaje de secuencias. El proceso de ensamblaje en meta- genómica se refiere a la unión de pequeños fragmentos de ADN previamente secuenciados (denominados en inglés contigs), ba- sándose en la similitud que comparten estos segmentos de ADN en sus extremos. La unión de los contigs da como resultado una hebra de ADN continua denominada scaffold (en inglés, su tra- ducción más literal sería andamio). En esta metodología es co- mún la utilización de fósmidos o vectores de alta capacidad, con lo que consecuentemente se limita el número de contigs y por lo tanto se facilita el ensamblaje. Sin embargo, el genoma de los virus se caracteriza por ser pequeño: es común hallar trabajos donde el tamaño de los contigs es de muy poca longitud (20-50 pb) (Angly et al., 2006; Boujelben et al., 2012), por lo que la estra- tegia de ensamblaje suele ser diferente de lo habitual. Para poder realizar este proceso de ensamblaje existen varias herramientas informáticas que han sido utilizadas en análisis metavirómicos, por ejemplo Phrap (www.phrap.org), Arachne (Willner et al., 2009; Wommack et al., 2009), Celera Assembler (Williamson et al., 2008, 2012), PGA (http://www.paracel.com/) y CAP3 (Roux et al., 2012). Una vez que se tienen las secuencias ensambladas, éstas son incorporadas a las bases de datos para posteriormente ser com- paradas.Para un adecuado ensamblaje de secuencias, es necesa- rio discriminarlas en varios grupos: fagos, otros virus, elementos móviles, elementos repetidos, material genético bacteriano, de arqueas y de eucariotas. En este sentido, los elementos trans- ponibles, secuencias de inserción, retrotransposones y plásmi- dos son agrupados primeramente como elementos móviles. En otro gran grupo se conjuntan las secuencias correspondientes a bacterias, arqueas y eucariotas. Para cada grupo de virus filo- genéticamente cercano debe hacerse un análisis bioinformático, comparando las secuencias query o problema con las bases de datos y generándose subgrupos relacionados (para posterior- mente conformar un agrupamiento que englobe a toda la diver- sidad de la muestra). Un ejemplo establecido para bacteriófagos, que se ha seguido en varios estudios, es el basado en el método de Rohwer y Edwards (2002). Este método opera de la siguientemanera: se realiza un apareamiento del metaviroma en BLAST. De entre todas las secuencias, aquellas que tienen un e-value menor de 0.001 se consideran “especies” o estirpes relaciona- das o cercanas a lo depositado en esa base de datos. Aquellas secuencias con menor índice de similitud no son utilizadas. Las secuencias seleccionadas son mapeadas en busca de marcado- res, utilizando un método de parsimonia (utilizada para detectar el número de cambios en la secuencia debidos a mutaciones) y una función de cálculo de probabilidad. Posteriormente se utiliza un software con el objetivo de identificar los diferentes contigs, por ejemplo Sequencher 4.0 (Gene Codes, Ann Arbor, MI, USA). En estos paquetes informáticos se debe predeterminar el grado de solapamiento de las diferentes lecturas (reads), por lo gene- ral se establece una longitud de secuencia solapada que permi- ta diferenciar fagos filogenéticamente muy cercanos, como por ejemplo el φT7 y φT3 en Escherichia coli (Migula) Castellani & Chalmers (Rohwer & Edwards 2002). Este tipo de análisis puede hacerse con otros paquetes informáticos para determinar qué tan parecida es la estructura de la población que se genera con am- bos modelos. El proceso general de tratamiento de los datos se muestra en la figura 3. Este tipo de metodología ha sido seguida en varios estudios de metavirómica (Rodriguez-Brito et al., 2010). En otros estudios, particularmente para la clasificación y análi- sis de la diversidad de fagos cercanos a los T4, se ha utilizado la proteína gp23, utilizando cebadores específicos para amplificar elgen codificante a partir de las muestras ambientales. Los clones resultantes se organizan en un dendrograma (López-Bueno et al., 2009). De forma general, algunas de las principales plataformas utilizada para este tipo de análisis son la Virus Database del IC- TV (http://www.ncbi.nlm.nih.gov/ICTVdb/), utilizando para ello tBLASTx. Otra de las bases de datos comúnmente utilizadas para fagos es la que se alberga en la página http://www-rohan.sdsu. edu/~brodrigu/PPT/.Finalmente, uno de los procesos que se realiza con el análi- sis de las comparaciones es la generación de árboles filogenéti- cos. Para ello se puede utilizar la herramienta PHY-FI (Fredslund, 2006).Predicción de genes y anotación. La predicción de genes (gene prediction o gene calling) es el procedimiento para la identifica- ción de proteínas y secuencias de ARN codificadas en el ADN de interés. Este procedimiento es particularmente difícil en los aná- lisis de metavirómica, debido a que los virus usualmente no com- parten la mayoría de sus genes con otras especies y esto dificulta mucho el establecimiento de relaciones filogenéticas basadas en similitud de secuencias. Este proceso puede realizarse con se- cuencias ensambladas o bien con secuencias no ensambladas del metagenoma. En cualquiera de los dos casos, existen dos metodologías generales que han sido utilizadas en análisis me- tavirómicos. 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Esquema general de tratamiento informático de los datos de un metaviroma.en identificar similitud con secuencias depositadas en bases de datos (mediante una comparación BLAST, por ejemplo) (Breitbart et al., 2002; Williamson et al., 2008; Cantalupo et al., 2011). La otra alternativa consiste en utilizar, al inicio de la predicción de genes, relaciones intrínsecas entre las características del ADN a discri- minar (diferenciando, por ejemplo, entre regiones codificantes y no codificantes), de manera que se puedan identificar genes sin necesidad de identificar similitud con las secuencias depositadas en las bases de datos. Hay estudios de metavirómica que utili- zan con esta finalidad plataformas informáticas como Metagene (http://metagene.cb.k.u-tokyo.ac.jp/metagene/download_mga. html) (Noguchi et al., 2008; Parsley et al., 2010; Tanenbaum et al., 2010; Lorenzi et al., 2011).Análisis de datos. Uno de los principales problemas que se pre- sentan en el análisis de los datos en estudios de metagenómica viral es cómo detectar la presencia de secuencias virales nove- dosas que sean muy divergentes respecto de aquellas deposita- das en bases de datos, y en las que por tanto no es posible en- contrar similitudes con algoritmos de búsqueda del tipo tBLASTx (Delwart, 2007). Es particularmente importante destacar, a este respecto, que entre el 5 y el 30% de las secuencias de nucleótidos o aminoácidos obtenidas en análisis de muestras ambientales no muestran similitud significativa con las secuencias contenidas en Genbank (Delwart, 2007). Por esa razón, se han desarrollado varios métodos para poder establecer las similitudes. En virus de ARN de cadena sencilla de polaridad positiva, por ejemplo, se ha descubierto una serie de dominios proteicos implicados en la función catalítica de las enzimas (conocido en inglés como core protein functions), mediante el análisis de las cuales ha sido po- sible relacionar secuencias víricas distantes pero con funciones análogas (Koonin & Dolja, 1993). Otro ejemplo de este tipo son algunas estructuras proteicas que forman parte de las cápsidas virales y pertenecen a una familia de helicasas (las denominadas en inglés jelly-roll capsid protein). Estas proteínas pueden ser encontradas en partículas virales grandes y pequeñas, tanto de ADN como de ARN (Koonin et al., 2006). Adicionalmente, el uso de matrices de sustitución es otra posible aplicación para esta- blecer relaciones entre proteínas con muy bajo nivel de similitud (Dimmic et al., 2002; Duhaime et al., 2011). Y, finalmente, el aná- lisis de estructura de proteínas in silico es otra posibilidad que aún puede ser mejorada y permitiría la detección de virus con alto grado de divergencia a través del análisis de secuencias que codifiquen estructuras conservadas en las proteínas (Delwart, 2007).Clasificación. En un estudio metagenómico se analizan secuen- cias aisladas, contigs y genes. La asociación entre estos datos y los organismos procedentes de los ambientes de donde deri- van estas muestras es siempre un objetivo deseable para la in- terpretación de la dinámica de un ecosistema en particular. Este proceso de asociación entre los datos de las secuencias y las especies se denomina clasificación (en inglés, binning). De for-ma recurrente, algunos de los genes que han sido utilizados para realizar procedimientos de clasificación se basan en secuencias de marcadores filogenéticos. De forma similar a lo anteriormente mencionado, las comparaciones entre secuencias, junto con he- rramientas para su visualización como BLAST y MEGAN (Huson et al., 2007), pueden también ser usadas para realizar clasifica- ción de grandes fragmentos de ADN, de forma que se permita generar grupos filogenéticos.Otra estrategia diferente está basada en la dinámica de pro- cesos celulares, como la utilización de codones preferenciales, sistemas de modificación-restricción o mecanismos de reparación del ADN, los cuales causan determinados patrones en la secuen- cia que pueden ser identificados como marcadores moleculares (Karlin & Burge, 1995; Karlin et al., 1997; Deschavanne et al., 1999). En trabajos previos, Karlin et al. (1997) reportaron la presencia de dinucleótidos (repeticiones de mononucleótidos en la secuencia de ADN, por ejemplo AA, GG o CC) como marcadores moleculares o, como los denominaron, una “firma genética”. En este sentido, los genomas víricos pueden ser modificados por la acción de me- canismos de regulación de la célula a la que parasitan, algunos de los cuales ya fueron mencionados anteriormente. Sería este efecto el que podría generar de modo reiterativo secuencias que compartan los patrones de distribución de estos dinucleótidos, elementos que servirían en ese caso para el proceso de agru- pación (y servirían también como marcadores moleculares). Esta propiedad de los genomas ha sido utilizada en diversos estudios para la identificación de grupos de secuencias con cierto grado de similitud y para el establecimiento de relaciones filogenéticas (Nakashima et al., 1998; Sandberg et al., 2001; Teeling et al., 2004; Abe et al., 2005; Dalevi et al., 2006). Un estudio interesante para la clasificación en metagenómica viral ha permitido el desarrollo de herramientas informáticas como ProVIDE, con la que se ha podi- do procesar entre un 3 y un 19% de las secuencias que perma- necían sin clasificar utilizando otras aplicaciones, como MEGAN (Shankar Ghosh et al., 2011).PERSPECTIVASEn los últimos diez años se ha producido un importante esfuerzo por comprender el papel de los virus en los ecosistemas acuáti- cos. Un indicativo de esto son los numerosos trabajos desarrolla- dos cuyo objetivo específico fue comparar diferentes variables de la ecología vírica en ecosistemas acuáticos de agua dulce y salada (Clasen et al., 2008; Danovaro et al., 2008). En este sentido, una de las conclusiones que se ha podido establecer es que es necesario particularizar en las condiciones ecológicas de cada ambiente para poder definir las relaciones complejas entre di- ferentes formas de materia orgánica y los grupos de bacterias presentes. Otra cuestión que es posible extrapolar en cuanto a la dinámica del funcionamiento de las comunidades víricas es que la mayoría de las investigaciones indican una gran complejidad y variabilidad de las interacciones de estos microorganismos enlos ecosistemas (Middelboe et al., 2008). Desde este particular punto de vista, las herramientas metagenómicas son idóneas pa- ra el análisis de los virus.En sus inicios, la aplicación de la metagenómica se enfocaba al estudio de biomoléculas de interés, fundamentalmente proteí- nas en comunidades bacterianas, lo que también se ha denomi- nado metagenómica funcional (Uchiyama & Miyazaki, 2009). Sin embargo, el rápido desarrollo de nuevos métodos en el aislamien- to de ácidos nucleicos, estrategias de clonación y técnicas de rastreo han permitido la exploración y análisis de las secuencias de las comunidades microbianas, lo que a su vez ha facilitado su comparación con base en criterios taxonómicos y metabólicos (Simon & Daniel, 2011). Esto está siendo utilizado para investigar no sólo la diversidad en cuanto a especies, sino incluso la propia dinámica de los ecosistemas (Vega Thurber et al., 2008).Una de las perspectivas futuras más importantes para la metagenómica (en general, y la metavirómica en particular), se- rá el desarrollo de programas de cómputo que permitan detectar bajos niveles de similitud entre secuencias, los cuales serán de gran ayuda para el análisis de este tipo de datos y el estableci- miento de posibles relaciones entre organismos (Altschul et al., 1997; Zhang et al., 1998). Adicionalmente, el desarrollo de nuevas matrices BLOSUM (Blocks of Amino Acid Substitution Matrix) (Henikoff & Henikoff, 1992) permitiría medir con mayor precisión la distancia entre secuencias de proteínas relacionadas, lo que podría ser utilizado para la detección de virus con mayor grado de divergencia (Delwart, 2007).Es importante tener en cuenta que la enorme cantidad de datos producidos en los estudios de metagenómica viral puede trascender el propio marco disciplinario de la ecología micro- biana, lo que en algún momento podría hacer necesario plantear nuevas hipótesis que permitan la inclusión de los datos metage- nómicos en disciplinas con un marco conceptual cercano, como un mecanismo de aproximación complementario (Ward, 2006). Un ejemplo de este tipo de aproximaciones se podría dar en el ámbito sanitario, en el que muchos de los patógenos de naturaleza vírica permanecen aún sin caracterizar. 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