2014 Elena Lara de la Casa Casa la de Lara Elena

community dynamics, phage-host interactionsand genomic structure. communitydynamics, phage-host interactions and genomic structure. Elena Lara de la Casa, Enero 2014

UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA Departamento de Biología D/Dª José Manuel Vergara Martín SECRETARIO DEL DEPARTAMENTO DE BIOLOGÍA DE LA UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA,

CERTIFICA,

Que el Consejo de Doctores del Departamento en su sesión de fecha...... tomó el acuerdo de dar el consentimiento para su tramitación, a la tesis doctoral titulada “Viruses in the marine environment: community dynamics, phage-host interactions and genomic structure” presentada por el/la doctorando/a D/Dª Elena Lara de la Casa y dirigida por las Doctoras Dolors Vaqué y Silvia González Acinas.

Y para que así conste, y a efectos de lo previsto en el Artº 6 del Reglamento para la elaboración, defensa, tribunal y evaluación de tesis doctorales de la Universidad de Las Palmas de Gran Canaria, firmo la presente en Las Palmas de Gran Canaria, a...de...... de dos mil......

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Viruses in the marine environment: community dynamics, phage-­‐host interactions and genomic structure

(Los virus en los ecosistemas marinos: dinámica de la comunidad, interacciones entre fago y hospedador y estructura genómica) Elena Lara de la Casa

Tesis Doctoral presentada por Dª Elena Lara de la Casa para obtener el grado de Doctor por la Universidad de las Palmas de Gran Canaria, Departamento de BiologFa, Programa en GceanograHFa (Bienio 2008-­‐2010)

Directores: Dra. Dolors Vaqué y Dra. Silvia G. Acinas

Universidad de las Palmas de Gran Canaria Institut de Ciències del Mar (ICM-­‐CSIC)

La Doctoranda El director El Co-­‐director Elena Lara de la Casa Dolors Vaqué Silvia G. Acinas

En Barcelona, a de de 2014 5

This thesis has been funded by the Spanish Ministry of Science and Innovation (MICINN) through a PhD fellowship to Elena Lara de la Casa, under the program “Formación de Personal de virus que infectan a microorganismos marinos-MICROVIS” (Ref. CTM2007-62140/MAR, P.I.Dr Investigador (FPI)”, and was ascribed to the project: “Aislamiento, identificación y especificidad Dolors Vaqué).

Other projects that contributed partially to the completion of this thesis were: “Population Ecology of Model Marine Heterotrophic Flagellates–FLAME” (CGL2010-16304, funded by the MICINN, P.I. Dr. Ramón Massana), “Arctic tipping points-ATP” (contract #226248 in the FP7 program of the European Union, P.I.: P.F. Wassman. University of Tromso, (Norway), and P.I. from CSIC, C.M. Duarte. IMEDEA), “The Role and Mechanisms of Genomic Microbial Microdiversity: a perspective integrating genomics and ecological approaches-MICRODIVERSITY” (CGL2008-00762/BOS, funded by the MICINN, P.I. Dr. Silvia G. Acinas) and “Microbial Ocean Pangenomes: from single cell to bacterial population genomics–PANGENOMICS” (CGL2011-26848/BOS, funded by the MICINN, P.I. Dr. Silvia G. Acinas) and.

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A mis padres.

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CONTENTS

Abstract /Resumen/Resum 13

List of Publications 17

General Introduction 21

Aims and Outline 37

Chapter 1 Absence of seasonality on viral dynamics and salinity as a main driver modulating viral abundance in the NW Mediterranean Sea 43

Chapter 2 Experimental evaluation of the warming effect on viral, bacterial and protistan communities in two contrasting Arctic systems 77

Chapter 3 Pseudoalteromonas sp. 109 phages Marine phage-bacteria interactions at fine-scale within

Chapter 4 Life-style and mosaic genome structure of marine Pseudoalteromonas siphovirus B8b isolated from the Northwestern Mediterranean Sea 141

Chapter 5 Comparative genomics and biogeography of Pseudoalteromonas phages 177

Synthesis of results and general discussion 197

Resumen en español/Spanish Summary 213

References (Introduction, Discussion and Spanish summary) 255

Agradecimientos/Acknowledgments 271

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ABSTRACT There are an estimated 1030 viruses in the world oceans, the majority of which are phages of marine phages on microbial abundance, community structure, genetic exchange and global (viruses that infect bacteria). Extensive research has demonstrated the significant influence biogeochemical cycles. In this thesis, we contribute to increase the knowledge about the ecological role of viruses in marine systems, but also we aimed to provide a better understanding about the interactions between phages and their hosts and the genetic pool and biogeography of some the isolated phages genomes. Firstly, we followed the seasonal variability of viral communities in a coastal marine site (Blanes Bay Microbial Observatory, BBMO) and the environmental and biological factors that could modulate them. Our results showed that viral communities did not follow any clear seasonal patterns during the 5 years studied period and that viruses were mainly negatively correlated with salinity. Secondly, given the actual concern of the climate change effects on marine ecosystems, we evaluated experimentally how increasing temperatures would affect the microbial loop via protists respect to via viruses (“viral shunt”) in two contrasting Arctic marine systems. Lytic life strategy dominated instead the lysogenic strategy when we increased the temperature. But, overall the most important factor controlling bacterial abundance was bacterivory. These two studies provide us a general overview regarding viral dynamics at the community level but without knowing who infects whom and who is doing what. To provide inputs into these relevant issues, we used the model of Pseudoalteromonas bacterial strains and its phages. Our results suggest that interactions between phages and hosts are highly complex in terms on infectivity and rank reaching to the family boundaries. marine phages are unrepresented susceptibility at microdiversity level but Pseudoalteromonasalso reflect that phages can infect at larger taxonomic with only 4 genomes public available. Therefore, one of the isolated Pseudoalteromonas phage from BBMO was deeply studied; we investigated its biology, morphology, genomic and proteomic characteristics. Moreover, we carried out a genomic comparison of 3 Pseudoalteromonas phages isolated from the same bacterial specie to get insights into the genome structure and functional diversity. The genomic data analyzed not only contributed to a better understanding of phage- host interactions in marine systems but also demonstrated the complexity of their dynamics and biogeographic patterns.

13 RESUMEN

Se estima que hay 1030 virus en los océanos, la mayoría de los cuales son fagos (virus que infectan

en la abundancia microbiana, la estructura de la comunidad, el intercambio genético y los ciclos bacterias). Numerosas investigaciones han demostrado la gran influencia de los fagos marinos biogeoquímicos globales. En esta tesis, dividida en cinco capítulos, no solo se contribuye a aumentar el conocimiento sobre el papel ecológico de los virus en los sistemas marinos, sino que también se quiso proporcionar una mejor comprensión de las interacciones entre los fagos y

de fagos aislados. Para indigar sobre el papel ecológico de los virus en relación a su abundancia, sus hospedadores, la variedad genética de los virus y la biogeografía de algunos de los genomas en el primer capítulo estudiamos la variabilidad estacional de las comunidades virales en una zona costera y los factores ambientales y biológicos que podrían modular su dinámica. Nuestros resultados mostraron que las comunidades víricas no siguen ningún patrón estacional claro durante los 5 años del período estudiado y se detectó una correlación inversa entre la abundancia vírica y la salinidad. Dada la actual preocupación de los efectos del cambio climático en los ecosistemas marinos, en el segundo capítulo se evaluó experimentalmente cómo el aumento de las temperaturas afectarían al bucle microbiano a través de los protistas (“grazing”) respecto a la “viral shunt” (a través de los virus) en dos sistemas marinos del Ártico. El mecanismo de infección que dominó cuando aumentamos la temperatura fue la lisis en vez de la lisogenia. Sin embargo, el factor más importante que controlaba la abundancia bacteriana era la bacterivoría. En estos dos primeros capítulos el objetivo era tener una visión general con respecto a la dinámica viral a nivel de comunidad, aunque sin saber quién infecta a quién y quién está haciendo qué. Para proporcionar nuevo conocimiento sobre este campo, en el tercer capítulo se utilizó un modelo basado en cepas bacterianas de Pseudoalteromonas y fagos aislados de estas cepas. Nuestros resultados sugirieron que las interacciones entre fago y hospedador son altamente complejas en

que los fagos pueden infectar en un mayor rango taxonómico de lo descrito actualmente términos de infectividad y susceptibilidad a nivel de microdiversidad, pero también reflejaron ya que pueden llegar a infectar cepas bacterianas de diferente familia. Los fagos marinos de Pseudoalteromonas están representados únicamente con 4 genomas públicos disponibles. Así pues en el cuarto capítulo nos centramos en el estudio de uno de los fagos de Pseudoalteromonas que aislamos. Investigamos sus características biológicas, su morfología, la genómica y la proteómica. Finalmente, en el quinto capítulo, se realizó una comparación genómica de 3 fagos aislados de Pseudoalteromonas de la misma especie bacteriana para entender mejor la estructura de los genomas y la diversidad funcional. Los datos genómicos analizados no sólo han contribuido a una mejor comprensión de las interacciones entre fago y hospedador en los sistemas marinos,

14sino que también demuestran la complejidad de su dinámica y sus patrones biogeográficos. RESUM

S’estima que hi ha 1030 virus en els oceans, la majoria dels quals són fags (virus que infecten bacteris). Nombroses investigacions han demostrat l’influència significativa dels fags marins en globals. En aquesta tesi, dividida en cinc capítols, no sols contribuïm a augmentar el coneixement l’abundància microbiana, l’estructura de la comunitat, l’intercanvi genètic i els cicles biogeoquímics sobre el paper ecològic dels virus en els sistemes marins, sinó que també vam voler proporcionar una millor comprensió de les interaccions entre els fags i els seus hostes, la varietat genètica de el paper ecològic dels virus, en el primer capítol vam estudiar la variabilitat estacional de les els virus i la biogeografia d’alguns dels genomes de fags aïllats. Per dur a terme l’objectiu sobre comunitats virals en una zona costanera i els factors ambientals i biològics que podrien modular la seva dinàmica. Els nostres resultats van mostrar que les comunitats víriques no segueixen cap patró estacional clar durant els 5 anys del període estudiat i es va detectar una correlació inversa entre l’abundància vírica i la salinitat. Donada l’actual preocupació dels efectes del canvi climàtic en els ecosistemes marins, en el segon capítol es va avaluar experimentalment com l’augment de les temperatures afectarien al bucle microbià a través dels protistes (“grazing”) respecte a la “viral shunt” (a través dels virus) en dos sistemes marins de l’Àrtic. El mecanisme d’infecció obstant, el factor més important que controlava l’abundància bacteriana era la bacterivoría. que va dominar quan vam augmentar la temperatura va ser la lisi en comptes de la lisogènia. No En aquests dos primers capítols l’objectiu era tenir una visió general pel que fa a la dinàmica proporcionar nou coneixement sobre aquest camp, en el tercer capítol es va utilitzar un model viral a nivell de comunitat, encara que sense saber qui infecta qui i qui està fent què. Per basat en soques bacterianes de Pseudoalteromonas i fags aïllats d’aquestes soques. Els nostres resultats van suggerir que les interaccions entre fag i hoste són altament complexes en termes poden infectar en un major rang taxonòmic ja que poden arribar a infectar soques bacterianes d’infectivitat i susceptibilitat a nivell de microdiversitat, però també reflecteixen que els fags de diferent família. Els fags marins de Pseudoalteromonas estan representats únicament amb 4 genomes públics disponibles. Així doncs en el quart capítol ens centrem en l’estudi d’un dels fags de Pseudoalteromonas que vam aïllar. Es va investigar les seves característiques biològiques, la comparació genòmica de 3 fags aïllats de seva morfologia, la genòmica i la proteòmica.Pseudoalteromonas Finalment, en el cinquè capítol, es va realitzar una per obtenir coneixements sobre les estructures dels genomes i la diversitat funcional. Les dades de la mateixa espècie bacteriana genòmiques analitzades no només han contribuït a una millor comprensió de les interaccions entre fag i hoste en els sistemes marins, sinó que també demostren la complexitat de la seva dinàmica i els seus patrons biogeogràfics .

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LIST OF PUBLICATIONS This thesis is based on the following papers:

1. Lara, E.; Sà, E.L.; Ruiz-González, C.; Massana, R.; Gasol, J.M.; Acinas, S.G. and Vaqué, D. Absence of seasonality on viral dynamics and salinity as a main driver modulating viral abundance in the NW Mediterranean Sea. Manuscript.

2. Lara, E.; Arrieta, J.M.; Garcia-Zarandona, I.; Boras, J.A.; Duarte, C.M.; Agustí, S.; Wassmann, P.F. and Vaqué, D. 2013. Experimental evaluation of the warming effect on viral, bacterial and protistan communities in two contrasting Arctic systems. Aquatic Microbial Ecology 70:17-32.

3. Lara, E., Sà, E.L.; Salazar, G.; Santos, F.; Sánchez, P.; Antón, J.; Vaqué, D. and Acinas, S.G. Marine phage-bacteria interactions at fine-scale within Pseudoalteromonas spp. phages. Manuscript.

4. Lara, E.; Holmfeldt, K.; Solonenko, N.; Sà, E.L.; Ignacio-Espinoza, J.C.; Verberkmoes, N.C.; Vaqué, D.; Sullivan, M.B. and Acinas, S.G. Life-style and mosaic genome structure of marine Pseudoalteromonas siphovirus B8b isolated from the Northwestern Mediterranean Sea. Manuscript.

5. Lara, E.; Duhaime, M.B.; Ignacio-Espinoza, J.C.; Sà, E.L.; Vaqué, D., Sullivan, M.B. and Acinas, S.G. Comparative genomics and biogeography of Pseudoalteromonas phages. Manuscript.

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General Introduction

Introduction

A LITTLE BIT OF HISTORY

Bacterial viruses or bacteriophages (term derived from ‘bacteria’ and the Greek φαγεῖν phagein “to devour”) were first described between 1915 and 1917 by both Twort and d’Herelle (Duckworth, 1987). Right after their discovery, they were considered as a potential therapeutic tool to fight bacterial pathogens (Levin and Bull, 1996). The role of phages in the marine environment was not significantly appreciated until 1968 when, Wiebe and Liston in 1968 suggested that phages could exert an influence on bacterial populations and on biochemical from , and It was not until capabilitiesPseudomonas of microorganisms.Photobacterium At the sameCytophaga time, it was reported the first isolated. marine phage (Spencer, 1955, 1960, 1963) the late 1980s, when it was demonstrated that in many aquatic environments bacteriophages were present in very high concentrations and that they often exceed by one to two orders of magnitude the concentrations of bacterioplankton (Bergh et al., 1989; Borsheim et al., 1990; Bratbak et al., 1990). These findings encouraged the research on the ecology of marine viral communities and their impact on microbial food webs and biogeochemical cycles. WHAT IS A VIRUS?

A virus is a non-cellular genetic element that uses a living cell for its own reproduction. Prokaryotic viruses are termed bacteriophages (phages). They usually have a small size, between 30 and 60 nm, although smaller and larger phages can also be found (Weinbauer, 2004). Phages can contain double-stranded (ds) DNA, single-stranded (ss) DNA, ssRNA, or dsRNA. A protective coating called capsid that is composed of phage-encoded proteins surrounds the nucleic acid. A few types have a lipid-containing envelope or contain lipids as part of the particle wall. For many phage types, the capsid is attached to a tail structure that is also made from phage-encoded proteins (Fig. 1A). Phages are highly structural diverse and according to the symmetry of a viral belong to particle, theyCaudovirales can be divided in tailed, cubic, filamentous and pleomorphic. Most aquatic phages order consist of three main families: (i) with contractile tails, (ii) with , which are tailed Myoviridaephages and characterized by an icosahedralSiphoviridae head. The Podoviridae long non-contractile tails, and (iii) with short non-contractile tails (Fig. 1B, C and D). However, in the past year, studies have demonstrated the dominance of non-tailed viral particles (Brum et al., 2013), and the presence of other viral groups in the oceans as single-stranded viruses (ssDNA), RNA viruses or large DNA viruses (Hingamp et al., 2013; Labonté and Suttle, 2013; Steward et al., 2013).

21 Marine viruses

Figure 1 and (D) . (A) Diagram of a typical T4 bacteriophageMyoviridae (myovirus)Siphoviridae structure and thePodoviridae. three morphological families of tailed dsDNA bacteriophages: (B) , (C)

DEBATE ON PHAGES: TO BE OR NOT TO BE ALIVE?

Are viruses alive? The origin of life, published in 1929 considered that viruses were the missing link between the non-living and the first cell. However, the discovery in 1944 that DNA encodes genetic information (Avery et al., 1944) created a ”life is DNA” definition that held can be viewed as complex living entities that transform the infected cell into a novel organism (the information and evolution concepts as principal values. Forterre (2010) suggested that viruses virus) producing virions. However, other authors suggested several reasons to exclude viruses from the tree of life (Moreira and López-García, 2009). For instance, they stated that a virus can not be considered an alive organism because requires portions of another organism, and they do not have the machinery to express their own genes. They also argue that viruses do not replicate and they do not evolve since they are evolved by cells. Therefore the answer to the question if viruses are alive depends on our definition of life. VIRAL INFECTION MECHANISMS

Phages cannot survive independently because of their lack of a complete metabolic system and therefore, they depend on the host enzymatic systems for proliferation (Weinbauer, 2004). 22 In order to maximize the sustainable use of the host, phages have evolved a variety of life cycles: Introduction

lytic, lysogenic, pseudolysogenic and chronic infections. However, filamentous phages able to cause chronic infection have only been detected rarely in freshwater aquatic environments (Pina et al., 1998; Hofer and Sommaruga, 2001). Lytic infection is one of the most typical strategies for phage replication; phages relying in this mechanism are called virulent phages (Fig. 2). They can quickly complete proliferation and cause the release of large amounts of progeny in a short time. Virulent phages are abundant in the ocean (Zhang et al., 2011). For instance, lytic phages can account for the 65% of the isolated for phages to adapt to nutrient rich environments where bacterial abundance and production are phages in the Atlantic Ocean (Moebus and Nattkemper, 1981). The lytic way is a survival strategy high. They are generally considered to be r-selected which is characterized by high burst size and short generation cycle (Suttle, 2007). temperate phage) integrating its own genome into the host genome for a long time, and replicating Lysogenic infection refers to the process of a lysogen or lysogenic phage (also called along the host genome (Fig.2). The bacteria that contain the genome of a lysogenic phage (existing as a prophage) are called lysogenic bacteria. The prophage can spontaneously, or be induced by physical or chemical factors (e.g. pH, temperature, UV, nutrient conditions, etc.) to enter into a a low nutrient concentration results in a lower bacterial abundance and, therefore the infection lytic cycle. Lysogenic phages are more common in oligotrophic marine environments, because abundant in open sea than in coastal waters, as well as in depth waters than in surface (Jiang frequency is reduced (Wommack and Colwell, 2000). Accordingly, lysogenic bacteria are more and Paul, 1996, 1998a; Weinbauer and Suttle, 1999; Weinbauer et al., 2003). Although this is not always the rule as it is observed in the subtropical northeast Atlantic Ocean (Boras et al., 2010a). The pseudo-lysogeny is characterized by the independent existence of phage genome (called preprophage) in the host cytoplasm (Fig.2). The detailed mechanism of this survival strategy is still controversial, but Moebus (1996) considered that pseudo-lysogeny is a temporary immune state of host bacteria. The bacteria and phage can coexist during this stage and it has been suggested that this survival strategy can help marine phages survive in an unfavorable environmental conditions (Wommack and Colwell, 2000). K-selected phages are usually lysogenic or pseudo-lysogenic with small genome and burst sizes and they infect the most abundant, slow- growing members of the microbial community (Suttle, 2007).

23 Marine viruses

Figure 2

. Strategies of phage replication. In this figure are represented (a) lysis, (b) lysogeny and (c) pseudolysogeny.

24 Introduction

UNDERSTANDING MARINE VIRUSES IN A GLOBAL CONTEXT

1. Viruses are incredible abundant in the oceans! In the past two decades, methods for direct counts have evolved from transmission electron microscopy (TEM) to epifluorescence microscopy and flow citometry (Børsheim et al., 1990; Hara et al., 1991; Hara et al., 1996; Weinbauer and Suttle, 1997; Noble and Fuhrman, 1998; Marie et al., 1999; Brussaard, 2004), and viruses have been enumerated from thousands viruses ml of samples throughout the world’s oceans. According to these techniques, there are an average 7 -1 of 10 in the ocean’s surface (Marie et al., 1999; Wommack and Colwell, 2000). The general agreement is that most of viruses in the marine environment are tailed dsDNA (Wommack and Colwell, 2000; Weinbauer, 2004), but the recent discovery of ssDNA viruses and RNA viruses suggests that these viral groups are more prevalent than previously recognized (Lang et al., 2009; Labonté and Suttle, 2013; Steward et al., 2013). Moreover, recent studies have demonstrated the dominance of non-tailed viral particles (Brum et al., 2013) and have evidenced that even larger ssDNA phages are difficult to stain and visualize using epifluorescence microscopy (Holmfeldt et al., 2012). Therefore, methodological limitations may lead to an underestimation of viral abundance and diversity in environmental samples. 2. Spatial variability Latitudinal variations

The controlling factors of viral abundance over large spatial scale in the oceans surface concentration is limited, the are still poor understood. In polar regions, specially in the Arctic, temperature is low and nutrient host abundance and production are reduced probably due to these low temperatures; thus, viral abundance is 10 times lower than in temperate waters (Middelboe et al., 2002; Säwström et al., 2007; Boras et al., 2010b). A study carried out in surface waters along 3 transects deployed in broad regions of the central Pacific and Southern Oceans also demonstrate that viral abundance tend to be more abundant in lower latitudes than in the Antarctic region (Yang et al., 2010). The authors suggested that the high abundances of viruses in subtropical and tropical waters might be accounted for high abundances of cyanophages. And finally, temperate regions go through seasons gradually changing from a deep mixed winter water column to a viral abundance and more stratified summerSynechococcus waters. Studies performed in these regions also show linkages between host cell abundance ( that viral abundance is (Bettarel et al., 2002) or correlated with bacterial activity and Corinaldesi et al., 2003). These findings evidence distinct among different oceanographic regions. Understanding changes in physical, chemical and

25 Marine viruses biological characteristics occurring across different oceans it is needed for a better assessment of factors and processes influencing viral dynamics and distribution. Depth variations

Physico-chemical changes with depth can have a significant impact on marine viruses. Deep-sea ecosystems are dark and extreme environments that lack photosynthetic primary production, depend on prokaryotic production, including heterotrophic reactions using imported organic matter from upper layers or chemosynthetic reactions using reduced inorganic compounds such as ammonia or carbon monoxide (Dick et al., 2013). Given that hosts are at least an order of magnitude less abundant in bathypelagic waters than in surface waters (Tanaka and Rassoulzadegan, 2002), viral abundances decreases in the deeper water column (Suttle, 2005, 2007). Moreover, the decay of viruses in the deep sea is higher than can be supported by rates of viral production, which are believed to be relative low (Parada et al., 2007). Nevertheless, high abundances of viruses in the deep ocean have been reported (Parada et al., 2007; De Corte et al., 2010). The mechanism for maintaining high viral abundances in the bathypelagic is not clear but one potential viral input to the deep sea is through sinking particles where bacterial cells may attach in aggregates in high abundance (Hara et al., 1996; Parada et al., 2007). 3. Temporal variability Seasonal variation in viral abundance has been observed since the earliest reports of viruses in seawater (Bergh et al., 1989; Jiang and Paul, 1994) and it has also been noted in coastal systems where the viral abundance is higher in summer and autumn than in winter (reviewed in Wommack and Colwell, 2000), probably reflecting that viral proliferation depends on the abundance and activity of host cells. For example, recurring patterns in viral abundance and were observed in the North AtlanticProchlorococcus subtropical gyreRhodobacteraceae and showed strong correlations between viral abundance and SAR11, (Parsons et al., 2012). But because the volatile nature of virioplankton, their abundance changes are more evident in short- term temporal studies. Winget and Wommack (2009) demonstrated significant variations in viral production rates over 24-h cycles and Winter et al. (2004) determined that the frequency of infected cells was generally higher at night than during the day time and suggested that infection occurred during the night and viral lysis in the afternoon. This clear strong temporal variability in viral population is not only important because it shows that viruses are a very active component, but also as a main factor to consider for comparing viral abundance data from different systems.

26 Introduction

4. Marine viruses as efficient killers and their impact on biogeochemical cycles in the oceans 4.1 Viruses in the microbial loop

It was not until the 70’s and at the beginning of the 80’s when it was recognized the importance of marine bacteria in aquatic environments (Pomeroy, 1974; Porter and Feig, 1980; Azam et al., 1983). The discovery of the vast abundance, biomass and production of bacteria in aquatic systems have lead to a more complex understanding of the food webs in the oceans. Bacterioplankton became a central component of the aquatic food webs and it was accepted that a significant proportion of primary production is incorporated by bacterioplankton: dissolved organic matter (DOM), mainly excreted by phytoplankton, is utilized by aquatic bacterial populations and channeled through higher trophic levels back incorporated into biomass. This process is known as the “microbial loop” (Azam et al., 1983) (Fig. 3). Given that viruses cause a large proportion of bacterial and phytoplankton mortality (Wommack and Colwell, 2000), lytic infection converts cells into dissolved organic matter (DOM) that become available for other heterotrophic prokaryotes (Bratbak et al., 1990; Proctor and Fuhrman, 1990). This process is called “viral shunt” (Fuhrman, 1999; Wilhelm and Suttle, 1999) (Fig. 3). Through consumption of the produced lysate, bacterial productivity and respiration is stimulated (Middelboe et al., 1996; Noble and Fuhrman, 1999; Middelboe and Lyck, 2002). Besides, nutrient enrichment from lysed phytoplankton may also stimulate bacterial productivity (Gobler et al., 1997; Bratbak et al., 1998). Indeed, the release of this organic nutrients back to the water column from lysates, make that viruses would have an important role in biogeochemical cycles in the oceans, favoring recycling production (Fuhrman 1999). Thus, lysis of prokaryotes cells could liberate some restrictive nutrients, as Fe which it is needed to support primary production in the euphotic layer (Poorvin et al., 2004). In addition, viruses could contribute to mitigate climate change enhancing dimethyl sulphide propionate (DMSP) release from viral lysis of phytoplankton (Malin et al., 1998) and converted to dimethyl sulphyde (DMS) through bacterial activity, which is associated with the cloud condensation nuclei increasing sunlight reflection. 4.2 Viral lysis vs Grazing

Bacterial cells are not only removed by viral lysis, but also by bacterivory, mostly by protists (pico/nanoflagellates and ciliates). Grazing is considered to be less specific than viral lysis, although can be selective depending on criteria as bacterial cell size or motility (Hahn and Höfle, 1999). On the other hand, bacteriophages are reported as host specific and viral infection as density-dependent, therefore is phylogenetically selective (Thingstad and Lignell, 1997).

27 Marine viruses

Figure 3

. Representation of the flow of carbon in marine food web, including the role of viruses in “shunting” dissolved organic matter away from the higher trophic levels.

Heterotrophic pico/nanoflagellate (HF) are responsible for 5 to 250% of daily bacterial mortality (Miki and Jacquet, 2008), while the viral lysis is responsible often of 20 to 40% (Suttle, 2007), and can reach up to 100% (Danovaro et al. 2011, and references therein). Bacterivory not only act as agents that transfer organic matter assimilated by bacteria to higher trophic level, but also a negative impact on the transfer of bacterial production to higher levels, but also it has been it recycles organic and inorganic matter (Miki and Jacquet, 2008). The viral shunt, not only have suggested that reduce the efficiency of the biological pump since the release of dissolved organic matter in surface waters could influence the vertical flux and the sinking rates (Suttle, 2007). Few studies have contemplated the interactions between viral lysis and grazing. It has been suggested that grazing improves the activity of bacterial cells by reducing the competition for resources and improving growth conditions. Given that, phages infect preferentially more active

28 Introduction

and productive hosts (Lenski, 1988). Thus, they would benefit from bacterial mortality due to protists. Weinbauer et al. (2007) carried out an experiment in a mesotrophic reservoir and they found that in fact, the presence of grazers stimulates viral abundance and production. However, in some studies the presence of grazers reduce the viral activity (Maranger et al., 2002). Finally, recent research suggest that cells undergoing viral infection could be preferentially grazed (Evans and Wilson, 2008) or that grazing is susceptible to phage-resistant mutant in the bacterial cell surface (Zwirglmaier et al., 2009). 5. Marine viruses in the framework of the climate change

Sea-surface warming, sea-ice melting, changes in circulation and mixing regimes, and ocean acidification induced by the present climate changes are modifying marine ecosystem structure and function and have the potential to alter the cycling carbon and nutrients in the oceans. The most immediate and direct effect of climate change will be the increment in the surface seawater temperature (Sarmento et al., 2011), and this will probably be more dramatic in polar regions where there is a very narrow range of temperatures in free ice waters. Warming is particularly intense in the Arctic, where temperatures increase at rates of 0.4°C per decade (ACIA, 2004). Moreover, this rise is expected to accelerate even more, up to 9°C, over the 21st century in experimental studies on microbial communities have showed that heterotrophic production (IPCC, 2007). Viral communities are highly linked with their hosts; the increase of temperature increased with temperature (Apple et al., 2006) that it could result in higher virus production rates (Danovaro et al., 2011). On the other hand, the effects of temperature on viral life strategy are still unclear. Temperature could be a stress factor that triggered the transition from lysogenic to lytic cycle, but several studies reported different results (Weinbauer and Suttle, 1996, 1999; Wilson et al., 2001; Weinbauer et al., 2003; Williamson and Paul, 2006). Also, viruses and their host can effects from climate change could have much more important effects on global biogeochemical respond quickly to environmental changes in an evolutionary context, but the potential cascade cycles context. UNDERSTANDING MARINE VIRUSES AT SMALL SCALE

1. Viral influences on bacterial communities: Kill the winner hypothesis (Ktw)

Since the discovery that there are millions of microbes in every milliliter of seawater (Azam et al., 1983) and that marine viruses are extremely abundant (Bergh et al., 1989), the vast majority of the viruses are believed to be phages (virus that infect bacteria) because bacteria

29 Marine viruses are the most common prey. Trough phage infection, horizontal gene transfer, and inducement of bacterial phage-resistant mutations, marine virioplankton play important roles in regulating microbial population sizes, community composition and diversity. certain groups of bacteria, creating new niches or stimulating the growth of different microbial Phages are generally specific to certain hosts, their specific infection will cause the death of populations. At the same time, the process of phage adsorption is considered random and the high abundances will be more vulnerable to phage infection because of their high contact rates frequency of infection is correlated with host density. Thus, dominant bacterial groups with with phages. The “kill the winner” hypothesis (Ktw) is based on these premises (Thingstad and Lignell, 1997; Thingstad, 2000). The Ktw hypothesis suggests cycles where the abundances of a specific bacterial host and its corresponding phage oscillate in a classical predator-prey dynamic (Fig. 4). Thus, an increase in host abundance is followed by an increase in its phage, resulting in a decrease of the host and therefore in a decrease of the phages. However, the applicability of Ktw to natural communities is not clear because, for instance, Bouvier and del Giorgio (2007) demonstrated that many rare hosts became dominant when viral pressure was reduced.

Figure 4

. Representation of the “killing the winner” hyphotesis. The figure shows viral-host oscillations maintaining bacterial and viral abundance.

30 Introduction

2. The relevance of phages in the Horizontal Gene transfer (HGT) events

A substantial part of bacterial DNA is not transferred by vertical transfer, but is acquired horizontally by transformation, conjugation or transduction. For instance, a recent study with the analyses of 70 marine bacterial genomes revealed that up to 12% of the bacterial genome In the process of viral propagation, viruses transfer genes from one bacterium to another could be horizontally transferred associated to genomic islands (Fernández-Gómez et al., 2012). bacterium. If a virus infecting a new host contains genetic material from the previous host rather than its own DNA, the extra genetic information may be transmitted to the new host, resulting in transduction (Fig. 5). It has been demonstrated that transduction it is a significant process in the marine environment (Jiang and Paul, 1998b; Miller, 2001), and it could play an important role in the genetic diversity of marine microbial populations. Additionally, the transduction has the potential to maintain an enriched gene pool on which different evolutionary processes can act. Moreover, cyanophages are known to carry and transfer a variety of genes (Paul, 2008). For instance, photosynthetic genes are commonly carried in phage genomes (Mann et al., 2005) which code for functional properties that could be beneficial for the host and acting as genetic reservoirs that maintain diversity and contribute to lateral gene transfer between hosts (Sullivan et al., 2005; Sullivan et al., 2006). Viral lysis also produce dissolved DNA, which is then available for transformation (Jiang and Paul, 1995). And, finally, gene transfer agents (GTAs), have recently gained recognition (Lang and Beatty, 2007; Stanton, 2007; McDaniel et al., 2010; Lang et al., 2012). GTAs particles are smaller than a phage and they also contain a smaller amount of DNA. GTAs have a high frequency within of transducing and they can be more efficient than generalized transducingAlphaproteobacteria phages because which it is a taxa theyRhodobacterales only carry host DNA. First, GTA descriptions were found in quite abundant in the ocean, and therefore may have significant consequences for marine microbial ecology (McDaniel et al., 2010). Later, GTAs have been found in a wide variety of prokaryotic taxa (Lang et al., 2012) and they are capable of high-frequency horizontal gene transfer (Lang and Beatty, 2007; McDaniel et al., 2010). 3. Phage-Host interactions: analyses of host range

Host-range analyses is a helpful approach to get insights into phage-host interactions under a controlled scenario (Moebus, 1992b; Suttle and Chan, 1993). Basic information about a general pattern of infection by phages on hosts could improve predictions of microbial population, dynamics, ecosystem function and microbial community assembly (Thingstad, 2000). However, the results on host range analysis could depend on the methods used for phage isolation or the

31 Marine viruses

Figure 5

. Representation of transduction mechanism by which any bacterial gene may be transferred to another bacterium via a bacteriophage.

receptor specificity and the bacterial acquisition of resistance (Jensen et al., 1998; Chibani- Chennoufi et al., 2004). Moreover, the vast majority of bacteria cannot be cultured and therefore the true host range for most marine phages remain uncharacterized. It is generally accepted that phages are highly host-specific, as shown in cultured marine phages that not infect even closely related strains (Rohwer et al., 2000). But other studies showed that marine phages could have broad host ranges (Wichels et al., 1998; Wichels et al., 2002; Comeau et al., 2006; Holmfeldt et al., 2007), capable to infect across genera boundaries (Sullivan et al., 2003). It has been suggested that morphology of tailed phages could provide clues about host range (Suttle, 2005). For example, Wichels et al. (1998) found that myoviruses have a broad host range than podoviruses which are generally narrowest and siphoviruses being intermediate. Hence, it seems that host phages do not infect all bacteria, we have little understanding of the extent of the precise host range depends on the phage type and isolation host and although it is well known that individual range for any given phage. Network-based approaches have recently been proposed to help unify the quantitative analysis of the cross-infection of multiple phages with multiple bacteria (Flores et al., 2011). These authors suggested four key types of patterns: random, one-to-one, nested and modular (Fig. 6). Random patterns refer to phage-host interactions statically indistinguishable and one- to-one to an infection network with elevated specialization, such that each phage can only infect

32 Introduction

one host or closely related hosts. However, the two most frequently examined patterns in the phages that evolve to broader host ranges and bacteria evolve to increase the number of phages study of these authors were nestedness and modularity. Nestedness is the results pattern of to which they are resistant. On the other hand, modularity contains interactions that tend to occur among distinct groups of phages and hosts. These authors analyzed 38 studies of phage- host infection networks at narrow taxonomic scale and they found that the interactions patterns were mostly nested, which implies a hierarchy structure in which the most specialist phages infect those host with higher susceptibility of infection (the most generalist hosts) matching with the idea of gene for gene (GFG) co-evolutionary model (Flores et al., 2011). However, a bacteria and phages interactions are taking into account at larger geographical/or taxonomic recent study from the same authors showed that the interactions patterns were modular when scales (Flores et al., 2013). These results indicate that more host-range analyses should be done including a wide range of phylogenetic bacterial taxa. Additionally, the complexity of host-phage relationship are often overlooked by studies based on conservative gene markers (such as 16S rRNA) since phylogenetically identical bacteria can show differences in the phage-susceptibility patterns (Holmfeldt et al., 2007) and therefore other gene markers or genomic profiles should be included in host range analyses.

Figure 6

. Schematic networks representation of infection between phages and bacteria.

33 Marine viruses 4. Phage-host evolution and mechanisms of resistance

Phages and hosts are involved in continuous cycles of co-evolution, in which phage- insensitive hosts help to preserve bacterial lineages, while non-resistant host cells could result in new bacterial strains. There are many mechanisms by which hosts can become resistant to phage infection: prevention of phage adsorption, destruction of phage DNA or loss of both phage and host through abortion of phage infection (Labrie et al., 2010). Adsorption of phages to host receptors is the initial step of infection, phages must recognize a to avoid the phage adsorption are divided in three categories: blocking phage receptors, the particular cell component which are high diverse in host membranes and walls. The mechanisms production of extracellular matrix and the production of competitive inhibitors. A recently mechanism of phage DNA destruction has been described. Clustered regularly interspaced short palindromic repeats (CRISPR) has been identified and it provides acquired immunity against viruses and plasmids. CRISPR loci typically consist of several noncontiguous direct repeats separated by stretches of variable sequences called spacers and are often adjacent to Cas genes (CRISPR-associated). Cas genes encode a large and heterogeneous family of proteins that carry functional domains typical of nucleases, helicases, polymerases, and polynucleotide- binding proteins. CRISPR, in combination with Cas proteins, forms the CRISPR/Cas systems (Horvath and Barrangou, 2010). CRISPR systems have been identified in approximately 40% and 90% of Bacteria and Archaea genomes respectively (Mojica et al., 2000; Grissa et al., 2007) and are usually laterally transferred and found overrepresented in genomic islands (Ho Sui et al., 2009). It has been demonstrated that this system is an adaptive microbial mechanism of resistance to phage infection (Barrangou et al., 2007; Deveau et al., 2010). This mechanism is very specific, thus it may dominate in environments with high host density and low diversity. However, the oceans present the opposite scenario, and the importance of the CRISPR/Cas system is still unknown (Breitbart, 2012). However, data from GOS (Global Ocean Sampling) expedition demonstrated that almost 200 reliable CRISPR cassettes (Sorokin et al., 2010) and CRISPR/Cas system has been detected in some genomes of cultured marine bacteria (Thomas et al., 2008; Fernández-Gómez et al., 2012). On the other hand, phages evolved to avoid these bacterial mechanisms of resistance. Therefore, there is a constant-diversity dynamic model, in which the diversity of prokaryotic population is maintained by phage predation, because the best-adapted microorganisms are selected (Rodriguez-Valera et al., 2009).

34 Introduction

Figure 7

. Overview of bacteriophage sequences versus the total viral genomesGammaproteobacteria available in NCBI and the also represented the taxonomic distribution of all the taxonomic distribution of all sequenced phages versusGammaproteobacteria all sequenced phages. It is phages sequenced.

5. Genomic diversity of marine phages

Recognized the viral numeric importance, scientists have been characterizing them and trying to determine the extent of marine viral diversity. Nevertheless, diversity has been hard to measure because viruses do not have a universally conserved gene. Further, it has been estimated that >99% of all environmental bacteria are difficult to culture using standard techniques (Staley and Konopka, 1985) and therefore there is a paucity of marine phage hosts. Moreover, not all phages produce identifiable plaques on bacterial lawns (Seguritan et al., 2003; Breitbart, 2012). To circumvent these limitations, the diversity of viral communities have been analyzed by culture independent approaches: (i) Pulse-Field Gel Electrophoresis (PFGE), a method to allow discriminate viruses according their genome size (Steward et al., 2000; Steward, 2001), (ii) by Randomly Amplified Polymorphic DNA (RAPD-PCR) to get a general fingerprint of the whole viral community (Comeau et al., 2006; Winget and Wommack, 2008) and (iii) by metagenome sequencing of whole viral communities (metavirome) (Breitbart et al., 2002; Rohwer, 2003; Angly et al., 2006; Rodriguez-Brito et al., 2010). Through viral metagenomes it has been shown that viruses are exceptionally diverse and represent the largest reservoir of genetic diversity in the ocean (Pedulla et al., 2003; Rohwer, 2003; Angly et al., 2006). These culture- independent metagenomic methods are powerful, but they are also severely database limited due to the lack of sequenced viral genomes. Thus, isolate-based genome analyses are essential to better map viral sequence and to understand viral-host interactions in nature. In fact, most sequenced marine phage genomes belong to cyanophages (Paul and Sullivan, 2005), and although recently it has been described phages infecting several marine bacteria (Holmfeldt et al., 2013;

35 Marine viruses

is one of the most abundant bacterial class in the ocean ranged between KangGammaproteobacteria et al., 2013; Zhao et al., 2013), we are biased against other relevant marine taxa (Fig. 7). that infect the genera 5-28% in the MediterraneanGammaproteobacteria Sea based on CARD-FISHPseudoalteromonas counts (Ruiz-González et al., 2012). Phages have been previously studied in the marine environment and it has been reported their ecologicalPseudoalteromonas importance (Moebus, 1992a; Wichels et al., 1998; Wichels et al., 2002; Thomas et al., 2008). sp. strains marine may representPseudoalteromonas significant players on the bacterial production budget. But, to date, only four phages have been isolated and sequenced (Fig. 7). In summary, a better knowledge of the ecological and evolutionary roles of marine viruses and a more accurate interpretation of the rapidly increasing metavirome sequences would require the isolation and genomic analysis of individual viruses from a diverse phylogenetic range of prokaryotic taxa.

36 Introduction

AIMS AND OUTLINE

This thesis aimed to contribute to the understanding on the ecology, biology and genomics of marine viruses. Viruses are the most abundant biological entities in aquatic ecosystems, they constitute the greatest genetic diversity in the ocean and they are important agents of mortality playing a main role in marine biogeochemical cycles. Given their importance in the global oceanographic processes, here we aimed to provide data on viral dynamics in a coastal marine site in Blanes Bay Microbial Observatory (BBMO) and investigated the effects of the climate change on viral abundance and processes in the Arctic Ocean. Moreover, we also encouraged to unravel the complex relationships between viruses and their hosts. We studied the interactions of a phage-host system under controlled conditions based on the host-range analyses of an array genomes belonging to two main viral families and their biogeographical patterns in environmental of isolated phages from BBMO. Finally, we analyzed the genomic features of different phages viruses metagenomes. Based on the current knowledge of the different issues explained in the introduction, the specific goals and hypothesis of this thesis are described bellow. OBJECTIVE 1. Explore the dynamics and processes of marine phages in marine ecosystems

1.1. Absence of seasonality on viral dynamics and salinity as a main driver modulating viral abundance in the NW Mediterranean Sea

We investigated the temporal variation of viral abundance and genomic profiling during a period of 5 years (from 2008 to 2012). In order to examine which are the mechanisms driving the abundance and genomic patterns of marine viral communities in the BBMO (Blanes Bay Microbial biological parameters ( Observatory), we comparedChapter the 1observed patterns with in situ changes in physico-chemical and ). We expected to find: (i) the viral abundance highly correlated with bacterial abundance or production, or cyanobacteria driving the viral dynamics and (ii) a seasonal pattern in viral communities since BBMO is a marine site with marked seasonality patterns. 1.2 Experimental evaluation of the warming effect on viral, bacterial and protistan communities. in two contrasting Arctic systems

We were especially interested in the effects of climate change on microbial communities and its connection with viral abundance and production and the strategies of viral infection.

37 Marine viruses communities responded to various increasing temperatures, based on the predicted warming In this context, we experimentally tested how autotrophic and heterotrophic Arctic microbial microorganisms, we investigated changes in, bacterial and viral abundance and production of the sea surface temperature in the Arctic Ocean. In particular, within the heterotrophic

(lysogeny vs. lysis), and bacterial losses due to bacterivory and viral lysis. Finally, weChapter also identified 2 which temperature triggered a significant shift for each of the studied variables ( ). The hypotheses here were: (i) that viral abundance followed the same trend that bacterial abundance; would be predominant at lower temperatures and (iii) bacterial losses due to viruses and protists (ii) the lytic viral production would dominate at high temperatures and that the lysogenic strategy would increase with the temperature. OBJECTIVE 2. To gain insight into marine phage-host interactions at fine scale

The subject of the objective was to increase the number of isolated marine phages to better understand the role of the phages-host interactions in natural communities since the actual number of studies in this field is low and our knowledge is poorly understood. 2.1. Marine phage-bacteria interactions at fine-scale within Pseudoalteromonas sp. phages

To improve the existing conceptual models on the role of phagesPseudoalteromonas in bacterial diversity and population dynamics, we analyzedPseudoalteromonas a phage-host system based on spp. Several phages from BBMO infecting bacterial strains were isolated. We determined the morphology of the 18 isolated phages and the host range patternsChapter at fine 3 scale resolution by analyzing the whole genome profiles from both host and phages ( ). OBJECTIVE 3. To improve the knowledge on ecological and genomic features of marine phages genomes

Current public dataset on viral genomes are biased towards specific phage genomes restricted to few phylogenetic taxa (mostly cyanophages). Indeed, extra phage genomes are necessary to understand the ecology of viruses not only of the abundant phages but also of those representing the phage rare biosphere, and at the same time, they are crucial to get ecological meaningfulPseudoalteromonas insights of the increasing metagenomic viral dataset. For that reason, we sequenced 3 phages genomes belonging to two main family phages that represent the first ones retrieved from the Mediterranan Sea.

38 Introduction

3.1. Life-style and mosaic genome structure of marine Pseudoalteromonas siphovirus B8b isolated from the Northwestern Mediterranean Sea Pseudoalteromonas phages in relationship

We deeply characterized and analyzed one of the with its host. We performed a detailed analyses on the biological,Chapter ecological,4 phylogenetic, proteomic and genomic features of the siphovirus B8b phage ( ). 3.2. Comparative genomics and biogeography of Pseudoalteromonas phages Pseudoalteromonas phages and we studied the ecological impact and abundance of these phages using the viral Finally, we carried out a whole genome comparison of 3 sequenced metagenomes (metaviromes) available in the public databases (Chapter 5

).

39

Absence of seasonality on viral dynamics and salinity as a main driver modulating viral abundance in the NW Mediterranean Sea

Elena Lara, Elisabet Laia Sà, Clara Ruiz-González, Ramon Massana, Josep M. Gasol, Silvia G. Acinas and Dolors Vaqué

1Chapter

Viral dynamics and composition in NW Mediterranean Sea Chapter 1

ABSTRACT

It is well known the impact of viruses on marine biogeochemical cycles and microbial ecological processes. Despite their importance in the marine microbial food webs, there is a lack of studies describing the seasonality of viral abundance and diversity in marine environments. We determined changes in abundances and composition of the viral communities in a coastal

Mediterranean site (BBMO, Blanes Bay Microbial Observatory) during a five-year period (2008- ancillary variables in order to uncover main factors underlying the observed variability. Viral 2012). The observed patterns were compared with supporting physical, chemical, and biological abundance and composition did not show any obvious seasonal pattern during the studied period salinity and light penetration and the multivariate analysis also showed a negative relationship and they were not synchronized either. Total viral abundances were negatively correlated with between viruses and Prochlorococcus

abundance. Therefore, freshwater inputs and cyanobacterial communities seem to influence the abundance of the viral community at the BBMO. On the other hand, viral and total bacterial abundances were not correlated. To explore whether specific bacterioplankton groups could explain viral abundance, we determined their abundance by CARD-FISH (fluorescent in situ hybridization) for the first 2.5 years of the study (January 2008 ratio with SAR11 were found. Moreover, the – April 2010). During this 2 years a negativeRhodobacterales correlation among viruses with salinity, and VBR group also seems to co-variate with pointed out to a highly heterogenetic system with many more factors involved in regulating the viral abundance. The absence of seasonality in the viral abundance and community assemblages viral dynamics in BBMO.

43 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

INTRODUCTION

Marine viruses contribute to a broad range of processes in aquatic microbial communities.

They are not only highly abundant, but also act as agents of mortality (Wommack and Colwell, 2000) influencing microbial abundance, community structure and dynamics (Weinbauer, 2004), playing an important role in marine carbon cycling (Fuhrman, 1999) and being agents of horizontal gene transfer (Lindell et al., 2004). Hence, viruses are active members of the microbial community, often showing a significant spatial and seasonal variability (Wommack and Colwell, 2000). Systematic studies of the spatial distribution of viruses in the ocean have revealed that viral abundances are high in the euphotic zone and decrease with depth (Boehme et al., 1993; Guixa- Boixereu et al., 1999). Further, surface ocean abundances of viruses are usually greater in coastal Also, seasonal variations in abundance were observed already in the earliest reports of viruses in waters and decrease towards the open ocean (Cochlan et al., 1993; Culley and Welschmeyer, 2002). systems where the viral abundance was found to be higher in summer and autumn than in winter seawater (Bergh et al., 1989; Jiang and Paul, 1994) and it has also been noted in different coastal

(Wommack and Colwell, 2000). These findings suggest that viral proliferation depended on the were observed in the North Atlantic subtropical gyre, where strong correlations with SAR11, abundance and activity of the host cells. For example, recurring patterns in viral abundance Prochlorococcus and Rhodobacteraceae

abundances were identified (Parsons et al., 2012). However, there is a lack of studies following viral abundance patterns in a coastal Microbial Observatory (BBMO) is an oligotrophic coastal site in the NW Mediterranean Sea environment, where the conditions are more unstable than in the open ocean. The Blanes Bay with relatively low nutrient concentrations and plankton biomass (Duarte et al., 1999; Pinhassi has been demonstrated the seasonal changes of several microbial groups, probably due to the et al., 2006; Alonso-Sáez et al., 2008). This site has been investigated for several years and it marked seasonality of water temperature and solar radiation, typical of temperate zones. Since viral production and abundance in marine environments are determined by the productivity and density of host populations, especially the bacterioplankton (Wommack and Colwell, 2000), we composition of the bacterioplankton showed gradual changes throughout a year (Schauer et al. hypothesized that viral abundance in BBMO would follow a seasonal pattern. In fact, the taxonomic Bacteroidetes

2003). As well archaea, the aerobic anoxygenic phototrophs (AAPs) and the phylum also exhibited a marked seasonality in this coastal marine site (Galand et al., 2010; Díez-Vives et Moreover, we aimed to determine if the viral community composition in this marine al., 2013; Ferrera et al., 2013). 44 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

site also follow a seasonal pattern. It has been demonstrated the use of randomly amplified polymorphic DNA PCR (RAPD-PCR) to asses the viral genetic diversity (Winget and Wommack, 2008). The RAPD-PCR technique has been used to strain typing closed related viruses (Comeau et al., 2006), to study the viral communities (Weinbauer et al., 2009), the virus-host interactions (Williamson et al., 2008), and to determine compositional changes in viriobenthos assemblages over time (Helton and Wommack, 2009). This approach, requires no prior information on the DNA, a significant advantage considering the largely unknown nature of most viral genes. Therefore, allowing the comparison of samples varying over time. the RAPD-PCR fingerprinting provides a representation of the viral communities composition In addition, there is increasing evidence that physical and chemical variations (water temperature and salinity gradients) could also play an important role in viral abundance and distribution (Weinbauer et al., 1993; Jiang and Paul, 1994; Parsons et al., 2012). Therefore, other biological, physical and biogeochemical parameters is essential to understand the ecological describing the temporal variability of virioplankton abundance and composition in the context of dynamics of viruses in marine systems, still poorly understood.

The aim of this study was to assess the mechanisms driving the temporal variation of viral communities in a coastal marine site. For that purpose, we followed in monthly basis the changes in viral abundance and composition in the BBMO during a five-year period (2008-2012). parameters in order to uncover the potential factors modulating such variability. The observed patterns were compared with in situ changes of physico-chemical and biological

MATERIAL AND METHODS

Location and sampling

Water samples (0.5 m) were obtained once a month from a shallow (< 20 m depth) coastal station (the Blanes Bay Microbial Observatory (BBMO), NW Mediterranean, 41°40’N, 2°48’E) from January 2008 to December 2012. Samples were previously filtered through a 200 µm mesh net, and transported to the laboratory under dark conditions (within 1.5 h) in 10 l polyethylene carboys. Water temperature and salinity were measured with a CTD (conductivity, temperature, BBMO sampling station (Malgrat de Mar, Catalan Meteorological Service, SCM). depth). Rainfall data was obtained from a meteorological station located 5 km southwest of the

Chlorophyll a and nutrient concentrations Chlorophyll a (Chl a

) concentration was determined by filtering 250-500 ml of seawater on GF/F filters (Whatmann), extracting the pigment in acetone (90%, v/v) in the dark at 4°C for

45 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

24 h, and measuring fluorescence with a Turner Designs fluorometer. Inorganic phosphorous and nitrate were analyzed using standard methods (Grasshoff et al., 1983). Abundances of microorganisms

Viral and bacterial abundances were determined by flow cytometry. For viral abundance 2 ml subsamples were fixed with glutaraldehyde (0.5% final concentration), refrigerated, quick frozen in liquid nitrogen and stored at -80°C (Marie et al., 1999). Counts were made using FACSCalibur flow cytometer (Becton and Dickinson) with a blue laser emitting at 488 nm. The samples were stained with SYBRGreen I (Molecular Probes, Invitrogen), and run at a medium flow . speed (Brussaard, 2004). The samples were diluted such that the event rate was between 100 −1 collected for analysis using CellQuest software (Becton Dickinson). and 800 viruses s The data obtained for green fluorescence (FL1) and side scatter (SSC) were Four subgroups of viruses were medium, high and very high viruses subgroups. Aliquots for bacterial abundance were preserved distinguished on the basis of the fluorescence intensity. These subgroups were denoted as low, with 1% paraformaldehyde and 0.05% glutaraldehyde (final concentration), refrigerated for 10 min, and frozen immediately in liquid nitrogen, and stored at -80°C until quantification of cells and ) were enumerated and stained with SYBRGreen I with a FACSCaliburSynechoccocus flow cytometerProchlorococcus (Becton Dickinson) (Gasol and Del Giorgio, 2000). Phototrophic bacteria ( distinguished in the flow cytometer by their different sizes and pigment properties in unstained samples (Marie et al. 1999). Additionally, 50 ml were fixed with glutaraldehyde (1% final concentration) for pico/nanoflagellate (≤ 2-20 µm) counts. Subsamples of 20 ml were filtered through 0.6 µm black polycarbonate filters, and stained with DAPI (4,6-diamidino-2-phenylindole) -1 (Porter and Feig, 1980) to a final concentration of 5 µg ml (Sieracki et al., 1985). The abundances of these microorganisms were determined by epifluorescence microscopy (Olympus BX40-102/E, at 1000X). Between 50-300 heterotrophic or phototrophic pico/nanoflagellates were counted per filter from 3 to 4 transects of 5 to 10 mm each. Pico and nanoflagellates showing red-orange fluorescence and/or plastidic structures in blue light (B2 filter) were considered phototrophic forms (PF), while colorless flagellates showing yellow fluorescence were counted as heterotrophic pico/nanoflagellates (HF). With this method we could not distinguish mixotrophic flagellates. Bacterial production

3

Bacterial production (BP) was estimated using the H-leucine incorporation method 3 (Kirchman et al., 1985). Briefly, four 1.2 ml aliquots and two trichloroacetic acid (TCA)-killed controls (5% final concentration) of each sample were incubated with 40 nM H-leucine for 1-2 h in the dark and at in situ temperature. Incubations were stopped by adding cold TCA (5% final 46 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 concentration) and the samples were frozen until processing as described by (Smith and Azam,

factor (which is the annual average at the 1992). Radioactivity was counted on a Beckton-Dickinson LS6000 scintillation counter and -1 transformed to production with a 1.5 kgC mol leucine site (Alonso-Sáez et al., 2008; Alonso-Sáez et al., 2010). Fluorescence in situ hybridization

m polycarbonate From January 2008 to April 2010, 30 ml samples were fixedm overnight with paraformaldehyde (1% final concentration) at 4°C in the dark and filtered on 0.2 filters (GTTP, Millipore). Sections of the filters were then hybridized following the CARD-FISH protocol (Pernthaler et al., 2002). Several horseradish peroxidase (HRP) probes were used in most order Gammaproteobacteria to characterize the composition of in situ bacterial communities: Gam42a that targets (ManzRoseobacter et al., 1992), SAR11-441R for the SAR11 cluster (Morris et belonging to al., 2002), Ros537Bacteroidetes targeting the clade (Eilers et al., 2000) and CF319a for clades (Manz et al., 1996). The Eub antisense probe Non338 (Wallner et al., (1 g ml 1993)m was used as a negative control. Counterstaining of CARD-FISH filters was done with DAPI -1 ) and a minimum of 10 fields (500-800 DAPI-stained cells) were manually counted with an Olympus BX61 epifluorescence microscope. Viral community fingerprinting by RAPD-PCR.

Four litres of SW after subsequent 0.8 and a 0.22 μm pre-filtration was used for viral genetic profiling. Those samples were obtained every month during the 5 years of study and viruses were concentrated by tangential flow filtration (30 KDa VIVAFLOW) until a final volume of 20 ml of viral concentrate. Final concentration to approximately 400 µl was achieved by centrifugation through 30 kDa Amicon centrifugal filter units (Millipore). These concentrates were afterwards stored in agar plugs to prevent changes in viral community composition. Equal amounts of phage concentrate and melted 1.6% low‐melting‐point agarose (Pronadisa) were mixed, transferred to plugs molds and let solidify at room temperature for a few minutes and then 15 minutes at 4°C. The plugs were treated with DNase to get rid of any free DNA remaining in the viral concentrates. Thereafter, one of the agarose plugs for sample was molten containing the template DNA and it was used to test for 16S rRNA prokaryotic gene amplification. Some plugs were 16S gene rRNA positive and, therefore they were thereafter discarded for RAPD-PCR analysis of viral community. Then, the 25 samples used were from September 2010 and December 2010, the whole 2011 (except for September) and the 2012 year except for May. Once we were sure that we had removed proteinase K) to break any possible bacterial contamination, we extracted the viral DNA by incubating overnight at 50°C -1 in ESP buffer (0.5M EDTA, pH 9.0, 1% N‐laurylsarcosine, and 1 mg ml 47 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

down the viral capsids, and then the plugs containing the viral DNA were stored in ESP buffer at 4°C. Plugs of viral concentrates previously molten and tested for contamination were used as templates to RAPD-PCR. The decamer primer OPA-13 (5’- CAG CAC CCA C - 3’) was used acting as both, forward and reverse primer. PCR conditions were as follows: 1 cycle of 10 min at 94°C, 30 cycles of 3 min at 35°C, 1 min at 72°C and 30 s at 94°C, 1 cycle of 3 min at 35°C and 1 cycle of 10 min of extension at 72°C. RAPD-PCR products were separated by gel electrophoresis on 1% agarose gels in 0.5% TAE run at 90V for 2 h and visualized by SYBR SAFE (10,000X, Invitrogen). Statistical analysis

Shapiro–Wilk’s W-test for normality of data was applied prior to analysis and data was logarithmic transformed if necessary. ANOVA Test was used to analyze statistically significant (p<0.05) differences for comparisons among seasonal averages. Correlations between variables were assessed using the Pearson’s correlation coefficient and a principal component analysis (PCA) was performed to evaluate covariation and multivariate patterns. These statistical analyses were performed using the JMP software (SAS Institute). Similarity of banding patterns was assessed by a group-averaged cluster analysis based on a Bray-Curtis matrix. The SIMPROF permutation procedure was used to test the significance of the clusters (p<0.05). The software tool PRIMER6 (Plymouth Routines in Multivariate Ecological Research) was used to calculate these parameters (Clarke and Warwick, 2001). RESULTS

Community analysis by PCA

To understand the main environmental and biological variables modulating the dynamics of virioplankton dynamics, we performed a principal component analysis (Fig. 1). In the PCA total variance and it was mainly contributed positively by nutrient concentration, Chl analysis during 5 years (Fig. 1A), the first principal component accounted for 28.94%a of the and PF abundance. (phototrophic flagellates) and negativelySynechococcus by bacterial abundance and production, temperature, HF

(heterotrophic flagellates) and The second componentProchlorococcus accounted for 17.16% and was mainly contributed negatively by salinity, light penetration and and positively by viruses, VBR (virus-bacterium ratio) and rainfall (Fig. 1A). Given the lack of co-variation between total virioplankton and total bacterioplankton, specific bacterioplankton lineages were determined for the first 2.5 years of the study (January 2008 – April 2010). Therefore, it was carried out a second PCA analysis using the 2.5 years that

48 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

Figure 1

. Principal component analyses (PCA) of (A) the 5 years studied without CARD-FISHa data, and (B) the first 2.5 years with the CARD-FISH data. T (temperature); Chla (Chlorophyll concentration); BP (bacterial production); HF (heterotrophic flagellate abundance); PF (phototrophic flagellate ( abundance);Synechococcus Bact (bacterial abundance);Prochlorococcus Viruses (viral abundance); Rhodobacterales VBR (virus-bacterium ratio); Syn ( abundance). Gammaproteobacteria abundance); Prochl ( abundance); Ros ( Bacteroidetes abundance); Gam abundance); SAR11 (SAR11 clade abundance); CF ( Gammaproteobacteria, Bacteroidetes (or and included Cytophaga-Flavobacteria, specific quantification of bacterialRhodobacterales taxa (SAR11, CF) by CARD-FISH data (Fig. 1B). The first ordination axis accounted for 30.51% of the variance, while the second axis accounted for , as well as bacterial abundance and production, 18.04%. The first axis was mainlyRhodobacterales related to the temperature and it was included all the bacterial Chl concentration, inorganic nutrients and protist. On the other hand, viruses, salinity, lineagesa except for the group

49 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

Prochlorococcus and the group Rhodobacterales

, light penetration (secchi) and rainfall defined the second axis (Fig. 1B). Relationships between viruses and environmental and biological variables Correlations viruses and salinity, light penetration, rainfall and As a result of PCA analysis during the 5 years studied,Prochlorococcus we detected co-variations between correlation analysis among these variables showed that, in fact, viral abundance and the VBR abundance (Fig. 1A). The and rainfall was inversely correlated with salinity. On the other hand, abundance ratio were significantly negatively correlated with salinity and light penetrationProchlorococcus (secchi) (Table 1) was positively correlated with salinity (r = 0.30; p<0.02). Viruses and specific groups of bacterioplankton dynamics, probes for , SAR11, and were To provide insightsGammaproteobacteria on bacterial community thatRhodobacterales may potentially affectBacteroidetes viral communities selected. Abundances of the bacterial groups and their dynamics with viruses are represented

and in Fig. 1.SM. Results of hybridization with the probe Non338 (negativeGammaproteobacteria control) never exceeded 0.1%Bacteroidetes of DAPI counts, and were not subtracted of CARD-FISH counts. showed a strong seasonality with two marked peaks in spring and late autumn and decreasing represented a 12.5% and 18.5% respectively of the total DAPI counts and they their numbers in winter and summer (Fig. 1A.SM, 1B.SM). The SAR11 bacteria did not show any temporal variation during the studied period although peaks were detected in spring-summer group was less important within the (Fig. 1C.SM), they dominated the bacterial Rhodobacteralescommunity ranging from 20% to 60% of total DAPI counts (average of 35% of cell counts). The bacterial community (average of 5% of cell counts) with peaks in May 2008 and in April and May 2009 (Fig. 1D.SM). According with PCA results, correlations between viral abundance or VBR ratio and variables that co-varied were analyzed. The correlations were also determined with all the bacterial groups analyzed by CARD-FISH (Table 1). The SAR11 group showed a high correlation correlation was found between the sp. and abundances (r= coefficient with VBR and salinity wasProchlorococcus correlated with viruses (r=-0.48,Rhodobacterales p=<0.02) and significant

-0.40; p<0.05) (Table 1).

50 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

Table 1

. Significant correlation coefficients among variables. Variables significantlySynechococcus correlated are labeled inProchlorococcus black. Virus (viral abundance); VBRRhodobacterales (virus-bacterium ratio); Syn ( Gammaproteobacteria abundance); abundance). Prochl ( abundance); Ros ( Bacteroidetes abundance); Gam ( abundance);2008-2012 SAR11 (SAR11 (no CARD-FISH clade abundance); data) CF ( 2008-April 2010 (with CARD-FISH data) Variables n r Variables n r

Virus - Salinity 53 -0.53 <0.01P Virus - Salinity 24 -0.48 <0.02P Virus - Secchi 54 -0.30 <0.02 Prochl - Ros 26 -0.40 <0.05 VBR-Salinity 50 -0.30 <0.05 VBR - SAR11 24 -0.55 <0.01 Salinity-Rainfall 56 -0.31 <0.05

Salinity- Secchi 67 0.40 <0.01 Virus - Ros 23 0.32 0.15 Salinity-Prochl 64 0.30 <0.02 Virus - Gam 24 0.17 0.42 Virus - CF 23 -0.06 0.78 Prochl-Virus 49 -0.11 0.45 Virus - SAR11 24 -0.25 0.24 Prochl - VBR 48 -0.05 0.70 Virus - Syn 2417 -0.25 0.24 Prochl - Secchi 60 -0.02 0.85 Virus - Prochl -0.45 0.07 VBR-Secchi 49 -0.25 0.07 Virus - Secchi 2217 -0.17 0.45 Rainfall - Virus 52 0.24 0.08 Virus - Rainfall 0.32 0.21 Rainfall - VBR 48 0.10 0.49 VBR - Ros 23 0.23 0.28 Rainfall - Prochl 52 0.10 0.47 VBR - Gam 24 0.09 0.65 Rainfall - Secchi 55 -0.26 0.05 VBR - CF 23 -0.19 0.38 VBR - Syn 2417 -0.17 0.41 VBR - Proch -0.42 0.09 VBR - Salinity 24 -0.35 0.09 VBR - Secchi 2217 -0.17 0.46 VBR - Rainfall 34 0.46 0.06 Salinity - Ros 0.10 0.55 Salinity - Prochl 2631 0.25 0.21 Salinity - Secchi 0.03 0.86 Salinity - Rainfall 2431 0.04 0.83 Secchi - Ros -0.16 0.40 Secchi - Prochl 23 0.07 0.74 Secchi - Rainfall 21 -0.40 0.07 Rainfall - Ros 24 0.21 0.33 Rainfall - Prochl 16 -0.13 0.62 51 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

Viral abundance and composition dynamics during the studied period In surface waters of the Blanes Bay Microbial Observatory (BBMO), total viral abundance did not show any obvious seasonal patterns during the studied period (analysis of variance Similar yearly viral abundance values and VBR ratio were found in

(ANOVA), p > 0.05), (Fig. 2A). the first four years (2008-2011), but viral abundance decreased by an order of magnitude during 2012 (Table 2, Fig. 2D.SM). Identical pattern was observed for the VBR ratio, which presented similar values the first four years, and in 2012 significantly decreased (Table 2). Viral abundance 7 cells ml 7 cells ml ranged about an order of magnitude, and the minimum value was detected in July 2012 (0.73 x -1 -1 10 ) and the maximum in August 2011 (6.12 x 10 ) (Table 2). The distinguished subgroups of viruses belonging to low, medium and high subgroups also decreased in 2012 but the very high viruses subgroup showed a significant increased during this year (Table 2). The genomic banding patterns from the viral communities collected in Blanes Bay were compared by cluster analysis, which did not show any seasonal clustering of RAPD-PCR fingerprints (Fig. 3). Band richness varied between 5 (September 2010) and 12 (October 2012) distinct bands (Fig. 3.SM). In autumn 2010 we observed the highest numbers of bands and during the whole 2011 the lowest band richness, while in 2012 increasing RAPD band richness was detected (Fig. 3.SM). However, samples from spring and autumn seasons were clustered together, as well as samples from winter and summer periods (Fig. 3). The maximum similarity among sample banding patterns was 83% between January 2012 and February 2012. The average similarity across all banding patterns was 36%, indicating that most of the samples shared more than a half of the bands but none of the samples displayed identical RAPD pattern. Also, viral assemblages within each year were more similar than between years (Fig. 3).

52 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

A

B

C

D

Day of the year

Figure 2. Abundances of (A) viral, (B) bacterial, (C) Synechococcus and (D) Prochlorococcus throughout the seasonal study in the BBMO.

53 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 2012 0.08 (0.03-0.22) 0.88 (0.01-3.52) 2.13 (0.35-7.11) 7.84 (4.37-13.2) 0.50 (0.16-1.21) 16.12 (8.0-20.0) 1.78 (0.73-3.18) 7.07 (0.96-20.9) 2.53 (0.13-13.56) 31.93 (1.5-127.1) 26.15 (6.65-47.18) 38.14 (37.91-38.28) 17.21 (12.24-25.35) Average (min, max) Average 2011 0.80 (0.2-2.88) 76.34 (0-275.3) 0.10 (0.07-0.18) 0.78 (0.42-1.54) 1.45 (0.43-3.11) 8.71 (3.15-13.3) 14.04 (8.5-19.0) 2.18 (0.29-6.21) 4.71 (3.49-6.12) 6.75 (1.17-16.7) 17.93 (12.25-23.0) 37.82 (37.21-38.09) Average (min, max) Average 59.13 (32.66-144.68) 2010 0.14 (0.08-0.18) 1.06 (0.08-4.25) 2.10 (0.39-5.73) 7.74 (4.12-10.8) 0.74 (0.24-1.95) 12.29 (5.0-22.0) 0.81 (0.04-4.34) 4.62 (2.89-5.72) 3.89 (0.29-16.6) 72.38 (7.6-141.1) 37.8 (37.49-38.17) 17.05 (11.94-24.43) Average (min, max) Average 65.14 (31.74-105.87) 2009 7.12 (0-34.5) 0.46 (0.01-1.5) 39.32 (3.2-82.1) 0.07 (0.03-0.12) 2.21 (0.38-5.49) 8.58 (6.77-11.4) 0.55 (0.12-1.16) 14.78 (8.0-20.0) 1.09 (0.27-2.02) 3.08 (1.79-4.57) 38.05 (37.6-38.18) 16.24 (12.16-21.1) 38.50 (25.79-66.07) Average (min, max) Average 2008 8.12 (0-39.4) 14.6 (8.0-24.0) 2.05 (0.07-5.0) 4.0 (1.41-5.57) 0.05 (0.02-0.11) 1.11 (0.38-2.66) 2.24 (0.55-6.27) 7.54 (4.12-11.8) 0.49 ( 0.18-1.91) 17.80 (12.9-24.3) 61.43 (10.1-104.6) 38.04 (37.11-38.34) 57.20 (29.16-80.03) Average (min, max) Average ) -1 ) -1 ) -1 ) -1 cells ml d 3 ) -1 cells ml -1 4 ) -1 ab. (10 ab. (10 Variable cells ml 5 viruses ml 7 concentration (µg l a concentration . Average, minimum and maximum values of the physicochemical and biological parameters during the 5 years studied. during the 5 years and biological parameters of the physicochemical minimum and maximum values . Average, concentration (μM) concentration concentration (μM) concentration -3 3- 4 Salinity (psu) Secchi (m) PO NO (mm) Rainfall Chlorophyll spp. Synechococcus spp. Prochlorococcus l (µg C production Bacterial ab. (10 Bacteria ab. (10 Viruses VBR Temperature (ºC) Temperature Table 2 Table

54 Viral dynamics and composition in NW Mediterranean Sea Chapter 1 A

B

Figure 3. Multidimensional scaling (MDS) analysis of the (A) monthly samples of viral community composition as revealed by RAPD-PCR (Randomly Amplified Polymorphic DNA-PCR)., and (B) dendogram samples. of the RAPD-PCR banding pattern constructed from the absence/presence matrix grouping the Blanes Bay

55 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

Dynamics of environmental and microbial variables driving viral communities with salinity, light penetration, rainfall, cyanobacteria and some of the bacterial groups studied, Given that PCA and correlation analysis showed that viral abundance was highly correlated their dynamics was also analyzed during the 5 years studied. Salinity did not show any seasonal pattern during these 5 years but it was detected two decreasing points in late spring and in winter (Fig. 4B). In contrast, rainfall data showed a recurrent pattern, increasing during the winter months (Fig. 4C). During the 5 years studied,

A

B

C

Day of the year

Figure 4. Values of (A) temperature, (B), salinity, and (C) rainfall, throughout the seasonal study in the BBMO.

56 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

(analysis of surface water temperature in Blanes Bay displayed significant seasonal variability , NO ) Chl a variance (ANOVA), p < 0.05), (Fig. 4A) with a minimum value of approximately 12ºC4 in3 winter -3 - and a maximum of 24ºC in summer. Inorganic nutrient concentrations and (PO a concentration were characterized by low values (Table 2, Fig.2B.SM and 2C.SM). However, Chl concentration showed large peaks during the spring seasons (Fig. 2C.SM). seasonal pattern In the case of the microbial variables, bacterial abundance did not follow a significant (ANOVA, p > 0.05) and did not differ significantly among the five years of study (Table 2). However, bacterial abundance showed peaks in spring and autumn (Fig. 2B). Average bacterial production was higher during 2012, probably due to a peak detected in September which and presented was the highest value observed during the 5 studiedSynecochococcus years. Two peaksProchlorococcus of production were also a seasonal pattern ( detected in autumn 2010 and 2011 (Fig.Synecochococcus 2E.SM). ANOVA, p Prochlorococcus < 0.05). showed maximum values in spring and average values of and abundances were observed during the in autumn (Fig. 2B,Synechococcus 2C) while Prochlorochococcus had their maxima in autumn (Fig. 2C, 2D). Similar Prochlorococcus cells slightly decreased 4 cells 5 years of study, exceptSynecochococcus in 2010 when the number of ml 4 cells ml (Table 2). The lowest abundance was reached in May 2012 (0.35 x 10 -1 -1 3 cells ml Prochlorococcus, Fig. 2C.SM) and the highest in April 2012 (7.11 x 10 , Fig. 2C.SM). The minimum -1 3 cells value was detected in April 2008 (0.25 x 10 , Fig. 2C.SM) yet in some ml samples they were not detected. Their maximum was found in September 2008 (39.4 x 10 -1 , Fig. 2C.SM). Inter-annual values showed that viral abundance, VBR ratio and rainfall values decreased (Fig. 5C, 5D and 5E) during 2012 butProchlorococcus salinity and light penetration (secchi disc) values increased (Fig. 5A, 5B, and Table 2). Moreover, abundance decreased during 2010 and 2011 while the maximum average values of viral abundance where detected in these two years (Table 2).

57 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

Figure 5

. Inter-annual changes over five years of (A) salinity, (B) light penetration, (C) rainfall, (D) viral abundance, and (E) VBR in the BBMO.

58 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

DISCUSSION

Viruses and environmental variables Of all the environmental variables analyzed (temperature, salinity, light penetration, inorganic nutrients and Chl a

concentration), viruses were significant negatively correlated with salinity. This correlation was particularly detected during the 2012 year, when the viral abundance significantly decreased coinciding with a period of high salinity and low rain (Fig. 5A, 5C, 5D). Previous studies also reported negative correlations between viral abundance and salinity (Paul freshwater input transported viruses or that the input of nutrient from freshwater stimulated the et al., 1993; Weinbauer et al., 1993; Jiang and Paul, 1994). These authors hypothesized that entire microbial community and, consequently, viral production. As Blanes Bay is an oligotrophic coastal ecosystem that sporadically receives nutrients and terrestrial carbon inputs during stormy periods (Guadayol et al., 2009), the significant negative correlation between salinity and VBR detected would confirm this plausible explanation. Moreover, the negative correlation between more important role in viral dynamics than considered until now, yet the reasons underlying this viruses and salinity previously detected and confirmed in this study, suggests that salinity plays a observation remain not fully understood. linked with the negative correlation between viruses and salinity. Less salinity implies more Viruses were also negatively correlated with light penetration (Table 1), which is also freshwater inputs and more turbidity and therefore, less light penetration. The surface microbial communities, including viruses, are exposed to high ultraviolet (UV) radiation levels potentially causing DNA damage (Heldal and Bratbak, 1991; Suttle and Chen, 1992; Noble and Fuhrman, 1997; Weinbauer et al., 1997). Although viruses exhibit efficient photoreactivation (Kaiser and Herndl, 1997), UV radiation may affect viral dynamics and abundance in the water column

(Murray and Jackson, 1992). a concentration stand out as potentially the most important environmental variables drivers However, based on previous studies, temperature, nutrients and chlorophyll of viral abundance and activity. Temperature could direct affect the physical structure of the viral particles, triggering particle decay or decreasing the infectivity (Rowe et al., 2012). With nutrient availability, temperature could also indirectly influence viral production by affecting the host’s growth and physiology. Previous studies demonstrated that sea surface temperature was significantly correlated with viral abundance (Suttle and Chan, 1993; Jiang and Paul, 1994; Rowe et al., 2008; Rowe et al., 2012) and it has been hypothesized that increasing temperatures may have an influence on marine viruses and their activity (Wilson et al., 2001). Nevertheless, this was

59 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

concentration could represent a predictive variable for bacterial not confirmed in the present studya because no correlation between temperature and viruses was and viral abundance trend and a useful link between trophic conditions and viral abundance. In observed (Table 1). Chlorophyll fact, the abundance of viruses is greater in productive and nutrient-rich environments (Wommack and Colwell, 2000; Suttle, 2005); therefore, trophic state could be a driving force controlling viral abundance are highly correlated with Chl distribution. Studies examining large data setsa have revealed that viral abundance and bacterial concentration (Wommack and Colwell, 2000).a. fact is probably due to the low range variation of each variable along the whole studied period, However, our results showed no relationship between viral abundance or VBR with the Chl This as was previously observed by other authors in the same study area of the NW Mediterranean

concentration and viral abundance in Sea (Boras et al., 2009) and in the Atlantic (Parsons et al.,a 2012). Nevertheless, Siokou-Frangou et al. (2010) found a significant correlation between Chl where the Chl the open Mediterraneana Sea as well as Rowe et al. (2008) in the Sargasso Sean and North Atlantic concentration varied from 0 to 1.5, similar values found in our study. Links between viruses and microbial abundance and activity Viral production and abundance in marine environments are determined by the productivity and density of host populations, especially the bacterioplankton (Wommack and

Colwell, 2000). Consistent with this notion, several studies have found that viral abundance is strongly positively correlated with bacterial abundance in some oceanic regions (Jiang and abundance and production did not coincide temporally and did not show any correlation with Paul, 1994; Steward et al., 1996; Winter et al., 2009). However, during our study, total bacterial total viral abundance. The lack of correlation between these two parameters was observed before in previous studies (Boras et al., 2009; Parsons et al., 2012) although it is not surprising given with host density and the high diversity of the potential bacterioplankton hosts. the known specificity of phage-host interactions, wherein the frequency of infection is correlated Viral infection rates are dependent on contact rates with susceptible hosts (Murray and

Jackson, 1992), so it is more likely that viral abundances would be correlated with specific groups dynamics of some of the dominant bacterial groups in the area ( , SAR11, of bacteria that were producing most of the viruses at the time ofGammaproteobacteria sampling. For this reason the Roseobacter and Bacteroidetes)

were analyzed in detail during the first 2.5 years. We only found a significant negative correlation between the VBR and the SAR11 group (Table 1), and neither of the other groups appeared to correlate withRhodobacterales viral abundances. However, a PCA analysis showed a co-variation between virioplankton and abundances (Figs.1B, 1D.SM). Both

60 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

SAR11 and Rhodobacterales belong to the class Alphaproteobacteria, yet they are known to exhibit distinct seasonal dynamics and specific activities in Blanes Bay (Alonso-Sáez and Gasol, 2007; Ruiz-González et al., 2012). The SAR11 clade is the dominant group within bacterial during summer ( 7 assemblages in the study area, often showing increasing values in spring and maximal abundances Alonso-Sáez et al., 200 , Ruiz-González et al. 2012). The summer dominance of these bacteria might be related with their capacity to grow in nutrient-limited waters, since Instead, the group, much less abundant, was found to increase together with phosphorus limitationRhodobacterales is known to be maximal during the summer period (Pinhassi et al., 2006). Chl a phytoplankton blooms ( peaks. This group is favored by nutrient-rich conditions, and it is often associated with found in the González et al., 2000). The negative correlationRhodobacterales observed between SAR11 and viral abundance and the apparent co-variation of viruses and PCA analysis, most especially during peaks in the abundance of this bacterial group (Fig. 1D.SM), showed higher percentages due to their ability to grow under low nutrient conditions, they might suggest a “kill the winner” scenario (Thingstad and Lignell, 1997). In summer, when SAR11 could be more susceptible to viral infection than less abundant populations. In contrast, when the Rhodobacterales group became more active, they might have been subjected to more intense viral lysis. Recently, Parsons et al. (2012) also showed that virioplankton was also related to the between total virioplankton abundance and the abundance of SAR11 and . dynamics of specific bacterioplankton lineages. Their data also revealed a Rhodobacteraceae strong correlation Overall, our results suggested that the dynamics of viruses in Blanes Bay might be the result of changes in viral communities composition and that all the members of bacterioplankton are not subject to the same level of pressure from viral infection.

Cyanobacteria

Interestingly, We examinedProchlorococcus whether cyanobacteria could be a good indicator for viral dynamics. In the surface coastal waters of NW Mediterranean, is more abundant than was significantly positiveSynechococcus correlated with salinity (Table 2). Prochlorococcus rates of growth of alongSynechococcus the whole year (Schauer et al., 2003), as in our study (Fig. 2C.SM). High in Blanes Bay occurs at lower temperatures Prochlorococcus were found during summer periods (Agawin et al., 1998), while the maximum abundance of of these two groups. has also been reported in other studies to be positively (Schauer et al., 2003).Prochlorococcus But it seems that not only temperature is critical for the distribution situations of high salinity than correlated with salinity (Calvo-DíazSynechococcus et al., 2004) and demonstrated to be more competitive in (Jiao et al., 2005; Pan et al., 2005; Pan et al., 2007).

61 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

Prochlorococcus population, but also the

Therefore, salinity could be regulating not only the viral abundance, as we discussed above (see viruses and environmental variables). Freshwater inputs and related factors could explain the variations of these two parameters. However, other of the populations and we detected it trough their salinity tolerance. Moreover, plausibleProchlorococcus explanation is that a large fraction of the viral abundance is explained by the variability Prochlorococcus and viral abundance

Parsons et al. (2012) showed strong correlations between Series Study). in a decade of virioplankton abundance measurements at the BATS (Bermuda Atlantic Time-

Seasonal patterns of viral abundance and composition in Blanes Bay

The viral abundance observed in this study falls within the range of previously published values for the Western Mediterranean Sea (Guixa-Boixereu et al., 1999; Boras et al., 2009; Siokou- evolution characteristic of temperate latitudes. Several authors have been demonstrated the Frangou et al., 2010). In the BBMO, the annual cycle of surface temperature followed the seasonal seasonal changes of some bacterial groups in this marine site (Schauer et al., 2003; Galand et al., recurring annual patterns coinciding with their hosts. But, viral abundance did not show a clear 2010; Ferrera et al., 2013), therefore, we expected that virioplankton abundance would show seasonality during the 5 years studied. RAPD-PCR banding patterns could be useful to compare temporal or spatial changes among viral communities (Winget and Wommack, 2008). As with viral abundance, genomic viral relationship between viral abundance and viral composition. Interestingly, despite that the viral pattern composition in Blanes Bay did not follow any seasonality (Fig. 3). Moreover, there was not abundance was lower in 2012 compared with other years, the bands viral profiling was greater among the samples was detected and the lack of recurrent patterns in our samples possibly is due (Fig. 3.SM). Overall, the cluster analysis revealed a high variability. No identical banding pattern variability in short periods of time as viruses are a dynamic component of marine environments, to the high diversity of viral communities in this marine site. However, we probably missed the with a turnover time of 2-4 days (Suttle and Chen, 1992; Suttle, 1994). Moreover, Winter and detected substantial temporal dynamics in the viral communities in the bathypelagic zone of the Weinbauer (2010) used RAPD-PCR banding patterns during a monthly sampling and they in Chesepeake Bay but they did not detect changes in a short temporal scale but samples from the Northwestern Mediterranean Sea. Winget and Wommack (2008) also found temporal variability same station with a difference of 6 month generated different banding patterns.

62 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

ACKNOWLEDGMENTS

We are greatful to V. Balagué, C. Cardelús, I. Forn and all those students and postdocs involved with by the Spanish projects MICROVIS ( the determination of the basic microbial parameters at the BBMO. This work has been supported CTM2007-62140/MAR), STORM (CTM2009-09352/MAR), PANGENOMICS (CGL2011-26848/BOS ) and FLAME (CGL2010-16304). This work has been also funded by the Generalitat de Catalunya project GTR (Estructura i funció de Xarxes Trófiques Microbianes Marines (2009SGR/1177)). Financial support was provided by a Ph.D. fellowship from the Spanish government to E. Lara.

63 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

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Helton, R.R., and Wommack, K.E. (2009) Seasonal Dynamics and Metagenomic Characterization of Estuarine Viriobenthos75 Assemblages by Randomly Amplified Polymorphic DNA PCR. Appl Environ Microbiol : 2259-2265. Jiang, S.C., and Paul, J.H. (1994) Seasonal and diel abundance of viruses104 and occurence of lysogeny/ bacteriocinogeny in the marine environment. Mar Ecol Prog Ser : 163-172. Jiao, N.Z., Yang, Y.H., Hong, N., Ma, Y., Harada, S., Koshikawa, H., and Watanabe, M. (2005) Dynamics of25 autotrophic picoplankton and heterotrophic bacteria in the East China Sea. Cont Shelf Res : 1265-1279. Kaiser, E., and Herndl, G.J. (1997) Rapid recovery of marine bacterioplankton63 activity after inhibition by UV radiation in coastal waters. Appl Environ Microbiol : 4026-4031. Kirchman, D., Knees, E., and Hodson, R. (1985) Leucine incorporation and its potential as a measure49 of protein-synthesis by bacteria in natural aquatic systems. Appl Environ Microbiol : 599-607. : Lindell, D., Sullivan, M.B., Johnson, Z.I., Tolonen,Prochlorococcus A.C., Rohwer, F., and Chisholm, S.W. (2004)101 Transfer of photosynthesis genes to and from viruses. Proc Nat Acad Sci 11013-11018. : problems and Manz, W., Amann, R., Ludwig, W., Wagner, M., and Schleifer,Proteobacteria K.H. (1992) Phylogenetic solutions. Syst Appl Microbiol oligodeoxynucleotide probes 15 for the major subclasses of : 593-600. Manz, W., Amann, R., Ludwig, W., Vancanneyt, M., and Schleifer, K.H. (1996) Application of a suite cytophaga-flavobacter-bacteroidesof 16S rRNA-specific oligonucleotide probes designed to investigate bacteria of the142 phylum in the natural environment. Microbiology-Uk : 1097- 1106. Marie, D., Brussaard, C.P.D., Thyrhaug, R., Bratbak, G., and Vaulot, D. (1999) Enumeration65 of marine viruses in culture and natural samples by flow cytometry. Appl Environ Microbiol : 45-52. : Morris, R.M., Rappé, M.S., Connon, S.A., Vergin, K.L., Siebold, W.A., Carlson, C.A., and Giovannoni,420 S.J. (2002) SAR11 clade dominates ocean surface bacterioplankton communities. Nature 806-810. Murray, A.G., and Jackson, G.A. (1992) Viral dynamics-a model of the effects of size, shape, motion and89 abundance of siblge-celled planktonic organisms and other particles. Mar Ecol Prog Ser : 103-116. Microbiol Noble, R.T., and63 Fuhrman, J.A. (1997) Virus decay and its causes in coastal waters. Appl Environ : 77-83.

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: Pan, L.A., Zhang, J., and Zhang, L.H. (2007) Picophytoplankton, nanophytoplankton, heterotrohpic29 bacteria and viruses in the Changjiang Estuary and adjacent coastal waters. J Plankton Res 187-197. Pan, L.A., Zhang, L.H., Zhang, J., Gasol, J.M., and Chao, M. (2005) On-board flow cytometric observation of picoplankton community52 structure in the East China Sea during the fall of different years. FEMS Microbiol Ecol : 243-253. recurring seasonal patterns of virioplankton dynamics in the northwestern Sargasso Sea. Parsons, R.J., Breitbart, M., Lomas, M.W., and Carlson, C.A. (2012) Ocean time-series reveals 6

ISME J : 273-284. Paul, J.H., Rose, J.B., Jiang, S.C., Kellogg, C.A., and Dickson, L. (1993) Distribution59 of viral abundance in the Reef Environmentof Key Largo, Florida. Appl Environ Microbiol : 718-724. Pernthaler, A., Pernthaler, J., and Amann, R. (2002) Fluorescence in situ hybridization and catalyzed68 reporter deposition for the identification of marine bacteria. Appl Environ Microbiol : 3094-3101. Pinhassi, J., Gómez-Consarnau, L., Alonso-Sáez, L., Sala, M.M., Vidal, M., Pedrós-Alió, C., and Gasol, J.M. (2006) Seasonal changes in bacterioplankton nutrient limitation and their effects44 on bacterial community composition in the NW Mediterranean Sea. Aquat Microb Ecol : 241- 252. Limnol Oceanogr Porter, K.G., and Feig,25 Y.S. (1980) The use of DAPI for identifying and counting aquatic microflora. : 943-948. Rowe, J.M., DeBruyn, J.M., Poorvin, L., LeCleir, G.R., Johnson, Z.I., Zinser, E.R., and Wilhelm, S.W. (2012) Viral and bacterial abundance and production79 in the Western Pacific Ocean and the relation to other oceanic realms. FEMS Microbiol Ecol : 359-370. : Rowe, J.M., Saxton, M.A., Cottrell, M.T., DeBruyn, J.M., Berg, G.M., Kirchman, D.L. et al. (2008)52 Constraints on viral production in the Sargasso Sea and North Atlantic. Aquat Microb Ecol 233-244. in light modulation of bacterial heterotrophic activity in surface northwestern Mediterranean Ruiz-González, C., Galí, M., Lefort, T., Cardelús, C., Simó, R., and Gasol, J.M. (2012) Annual variability waters. Limnol Oceanogr 57

: 1376-1388. Schauer, M., Balagué, V., Pedrós-Alió, C., and Massana, R. (2003) Seasonal changes in the taxonomic31 174. composition of bacterioplankton in a coastal oligotrophic system. Aquat Microb Ecol : 163-

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Sieracki, M.E., Johnson, P.W., and Sieburth, J.M. (1985) Detection, enumeration, and sizing of planktonic49 bacteria by image-analyzed epifluorescence microscopy. Appl Environ Microbiol : 799-810. Siokou-Frangou, I., Christaki, U., Mazzocchi, M.G., Montresor, M., d’Alcala, M.R., Vaqué,7 D., and Zingone, A. (2010) Plankton in the open Mediterranean Sea: a review. Biogeosciences : 1543- 1586. Smith, D.C., and Azam, F. (1992) A simple, economical method for measuring6 bacterial protein synthesis rates in seawater using 3H-leucine. Mar Microb Food Webs : 107-114. Steward, G.F., Smith, D.C., and Azam, F. (1996) Abundance131 and production of bacteria and viruses in the Bering and Chukchi Seas. Mar Ecol Prog Ser : 287-300. Suttle, C.A. (1994)28 The significance of viruses to mortality in aquatic microbial communities. Microb Ecol : 237-243. 437 Suttle, C.A. (2005) Viruses in the sea. Nature : 356-361. Suttle, C.A., and Chen, F. (1992)58 Mechanisms and rates of decay of marine viruses in seawater. Appl Environ Microbiol : 3721-3729. Suttle,synechococcus C.A., and Chan, A.M. (1993) Marine cyanophages infecting oceanic and coastal strains of 92 -abundance, morphology, cross-infectivity and growth-characteristics. Mar Ecol Prog Ser : 99-109. Thingstad, T.F., and Lignell, R. (1997) Theoretical models for the 13control of bacterial growth rate, abundance, diversity and carbon demand. Aquat Microb Ecol : 19-27. Wallner, G., Amann, R., and Beisker, W. (1993) Optimizing fluorescent in situ hybridization microorganisms. Cytometry with ribosomal-RNA-targeted14 oligonucleotide probes for flow cytometric identification of : 136-143. 28 Weinbauer, M.G. (2004) Ecology of prokaryotic viruses. FEMS Microbiol Rev : 127-181. Weinbauer, M.G., Fuks, D., and Peduzzi, P. (1993) Distribution of viruses and dissolved59 DNA along a coastal trophic gradient in the Northern Adriatic sea. Appl Environ Microbiol : 4074-4082. Weinbauer, M.G., Wilhelm, S.W., Suttle, C.A., and Garza, D.R. (1997) Photoreactivation compensates Microbiol for UV damage63 and restores infectivity to natural marine virus communities. Appl Environ : 2200-2205. Weinbauer, M.G., Arrieta, J.M., Griebler, C., and Herndl, G.J. (2009) Enhanced viral production and Ocean. Limnol Oceanogr infection of bacterioplankton54 during an iron-induced phytoplankton bloom in the Southern : 774-784.

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Williamson, S.J., Cary, S.C., Williamson, K.E., Helton, R.R., Bench, S.R., Winget, D., and Wommack, K.E. (2008) Lysogenic2 virus-host interactions predominate at deep-sea diffuse-flow hydrothermal vents. ISME J : 1112-1121. Wilson, W.H., Francis, I., Ryan, K., and Davy, S.K.25 (2001) Temperature induction of viruses in symbiotic dinoflagellates. Aquat Microb Ecol : 99-102. Winget, D.M., and Wommack, K.E. (2008) Randomly amplified polymorphic74 DNA PCR as a tool for assessment of marine viral richness. Appl Environ Microbiol : 2612-2618. Winter, C., and Weinbauer, M.G. (2010) Randomly Amplified Polymorphic DNA Reveals Tight Links between Viruses and Microbes76 in the Bathypelagic Zone of the Northwestern Mediterranean Sea. Appl Environ Microbiol : 6724-6732. : Winter, C., Kerros, M.E., and Weinbauer, M.G. (2009) Seasonal changes of bacterial and archaeal54 communities in the dark ocean: Evidence from the Mediterranean Sea. Limnol Oceanogr 160-170. Mol Biol Rev Wommack, K.E., 64and Colwell, R.R. (2000) Virioplankton: Viruses in aquatic ecosystems. Microbiol : 69-114.

69 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

SUPPLEMENTAL MATERIAL

A B

C D

Figure 1.SM ( and ) and viral abundance, (B) . Seasonal dynamics Bacteroidetesof viruses and Cytophagathe bacterial groupsFlavobacteria analyzed by CARD-FISH during the and viral abundance, (C) SAR11 clade and viral abundance and (D) and firstGammaproteobacteria 2.5 years of the study: (A) Roseobacter viral abundance in the BBMO.

70 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

A

B

C

71 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

D

E

Figure 2.SM. (C) Chlorophyll concentration, and abundance, (D) bacterial and viral Temporala dynamicsSynechococcus of (A) temperatureProchlorococcus and salinity, (B) inorganic nutrient concentration, abundance, and (E) bacterial production, over the studied period (2008-2012) in the BBMO.

72 Viral dynamics and composition in NW Mediterranean Sea Chapter 1

Figure 3.SM.

RAPD-PCR banding pattern obtained from the monthly samples in BBMO from September 2010 to December 2012. Lane 1, September 2010; Lane 2, October 2010; Lane 3, December 2010; Lane 4, January 2011; Lane 5, February 2011; Lane 6, March 2011; Lane 7 April 2011; Lane 8, May 2011; Lane 9, June 2011; Lane 10, July 2011; Lane 11, August 2011; Lane 12 October 2011; Lane 13, November 2011; Lane 14, December 2011; Lane 15, January 2012; Lane 16, February 2012; Lane 17, March 2012; Lane 18, April 2012; Lane 19, June 2012; Lane 20, July 2012; Lane 21, August 2012; Lane 22, September 2012; Lane 23 October 2012; Lane 24, November 2012 and Lane 25, December 2012. Ladder: EasyLadder I (2000bp - 1000bp - 500bp - 250bp - 100bp, Bioline).

73

Experimental evaluation of the warming e ect on viral, bacterial and protistan communities in two contrasting Arctic systems

Elena Lara, Jesús M. Arrieta, Iñigo García-Zarandona, Julia A. Boras, Carlos M. Duarte, Susana Agustí, Paul F. Wassmann and Dolors Vaqué

2Chapter

Warming effect on Arctic microbial communities Chapter 2

ABSTRACT

The effect of Arctic warming, which is 3 times faster than the global average, on microbial communities was evaluated experimentally to determine how increasing temperatures affect bacterial and viral abundance and production, protist community composition, and bacterial loss rates (bacterivory and lysis) in 2 contrasting Arctic marine systems. In July 2009, we collected samples from open Arctic waters in the Barents Sea and Atlantic-influenced waters in Isfjorden, temperatures, ranging from 1.0 to 10.0°C. In the open Arctic microbial community, collected at Svalbard Islands (Fjord waters). The samples were used in 2 microcosm experiments at 7 <1.0°C, bacterial and viral abundances, bacterial production and grazing rates due to protists increased significantly above 5.5°C, and remained at high values at even higher experimental as well as some ciliates, also increased with warming. In contrast, the biomass of phototrophs temperatures. The abundance of protists, such as some heterotrophic pico/nanoflagellates, and the microbial community showed smaller variations than the Arctic community. These results decreased above 5.5°C. The water temperature in Fjord waters was 6.2°C at the time of sampling, indicate that increases in temperature stimulate heterotrophic microbial biomass and activity compared to that of phototrophs, which has important implications for carbon and nutrient cycling in the system. In addition, open Arctic communities were more vulnerable to warming than those already adapted to the warmer Fjord waters influenced by Atlantic seawater.

77 Warming effect on Arctic microbial communities Chapter 2

INTRODUCTION

The Earth’s climate is changing and global temperatures are rising at unprecedented rates (IPCC 2007). Warming is particularly intense in the Arctic Ocean, where temperatures are increasing at rates of 0.4°C per decade (ACIA 2004). Moreover, this rise is expected to continue to accelerate, resulting in a 9°C increase over the 21st century (IPCC 2007). The consequences of these increasing temperatures are already visible in the Arctic; for example, the loss of ice cover is now affecting the habitat of large mammals, birds and humans (Smetacek & Nicol biogeochemistry (Chen et al. 2003, Wassmann et al. 2011) and functioning of microbial food 2005, Wassmann et al. 2011), and extensive sea ice melting has led to large changes in the webs in Arctic waters (Boras et al. 2010). The microbial loop is fundamental to the functioning of Arctic marine ecosystems role as recyclers of the available nutrients (Thingstad & Martinussen 1991, Seuthe et al. 2011). (Nielsen & Hansen 1995, Iversen & Seuthe 2011) in which the bacterioplankton play a pivotal However, viral lysis of bacterioplankton interrupts this cycle and converts the particulate organic matter back into dissolved organic matter, which then becomes available again to other bacteria (Fuhrman 1999). Temperature is a potentially limiting factor that affects biogeochemical processes (Nedwell 1999): microbial growth, respiratory rates and organic carbon assimilation are all affected by changes in water temperature (Holding et al. 2013). Hence, it is extremely important to understand the effect of warming on microbial communities, given the central role they play in the ocean carbon cycle. Although Li & Dickie (1987) found that photosynthetic activity was much greater than heterotrophic activity in a study of seasonal variations, recent studies show that warming stimulates the respiration of plankton communities faster than

& Duarte 2012). Moreover, an increase in temperature favors smaller organisms in aquatic their photosynthetic rates (Harris et al. 2006, López-Urrutia et al. 2006, Regaudie-De-Gioux systems for a given level of resources (Daufresne et al. 2009), leading to a higher proportion of picophytoplankton among the autotrophs (Morán et al. 2010) and increasing heterotrophic closely linked with bacterial abundance (BA) and activity, so any change in the bacterial metabolic bacterial activity (Iriberri et al. 1985, White et al. 1991). Protist grazing and viral activity are state, abundance or distribution would affect these processes. It has been well established that bacterial losses due to protist grazing increase with temperature. Thus, Vaqué et al. (2009) found an increase in bacterial production (BP) and ingestion rates after a certain experimental temperature was reached at different Antarctic sites, and Boras et al. (2010) showed that protist grazing dominates in surface waters of the Arctic Ocean that receive ice-melt waters.

78 Warming effect on Arctic microbial communities Chapter 2

cells they infect. If prokaryotic growth rates increase with temperature, the length of the lytic Increases in temperature are also likely to influence the interactions between viruses and the cycle decreases and the burst size (BS) increases, thus increasing viral production (VP) (Proctor et al. 1993, Danovaro et al. 2011). The viral life strategy in the oceans (lytic and lysogeny) also depends largely on the physiological state of the host and the physico-chemical conditions of the strategy are currently not well understood, and the molecular mechanisms that govern whether environment (Miller 2001). The environmental factors that influence the adoption of a lysogenic aspects could help to clarify the effects of changing environmental conditions induced by climate a phage enters a lysogenic or lytic cycle are still unclear (Long et al. 2008). Understanding these change. Due to the prevailing conditions of limited nutrients, periods of low host abundance and production and low temperatures, lysogeny could be expected to be a common phenomenon in viral life strategy are still unclear. the Arctic Ocean (Angly et al. 2006). Nevertheless, the potential effects of temperature on the In the present study, we experimentally tested how autotrophic and heterotrophic Arctic microbial communities responded to various temperatures, based on the predicted warming of the sea surface temperature in the Arctic Ocean. In particular, we investigated changes in community composition, bacterial and viral production, and losses due to bacterivory and viral phytoplankton biomass, microbial abundances, flagellate community size structure, ciliate each of the studied variables. For this purpose, we used temperature-controlled microcosms lysis. We also identified temperatures where further warming triggered a significant shift for with seawater collected from 2 contrasting Arctic ecosystems: one located in open Arctic waters and one in Fjord waters. MATERIAL AND METHODS

Study area and sampling Two consecutive 10-d microcosm experiments were carried out in summer 2009 at the

UNIS (University of Svalbard) facilities in Longyearbean (Svalbard Islands, Norway). Seawater for the microcosm experiments was collected at 2 different Arctic locations. The first sampling point was located in the Barents Sea, southeast of Svalbard, Norway (76° 28’ 65’’ N, 28° 00’ 62’’E) (Fig. 1). Water was sampled on 28 June 2009 at 26 m depth (surface waters) on board the RV ‘Jan Mayen’ using a CTD rosette sampler. Arctic water (840 l ,T = −1.19°C, salinity 33.92) was for at least 48 h and thoroughly rinsed with seawater from the sampling site. Seawater containers collected and distributed in 14 polypropylene containers (60 l) previously treated with HCl 0.1N 79 Warming effect on Arctic microbial communities Chapter 2

(for logistical reasons) until the start of the experiment. The second sampling point was located were stored in the dark at 0°C for 48 h in a controlled temperature room on board and at UNIS at Isfjorden, the second largest fjord in Svalbard Island, Norway (78° 20’ 00’’ N, 15° 00’ 00’’E) (T boat) from 2 m depth using a peristaltic pump. The collected water was distributed and stored = 6.2°C, salinity 32.73). On 8 July 2009, 840 l of fjord water was sampled (from on board a rubber in the experimental carboys, as described for the first sampling point. The water samples were arrival. transported to the UNIS facilities, and the experimental treatment started immediately upon

Figure 1

. Sampling locations of the 2 microcosm experiments. Ex1: first sampling point located in the Barents Sea (open Arctic waters). Ex2: second sampling point located at Isfjorden (Svalbard Islands). Experimental set-up

Seawater samples from different carboys (60 l) were mixed together in larger containers cleaned polycarbonate carboys (microcosms, 20 l). Duplicate carboys for each experimental (280 l) filtered through a 150 μm mesh net to remove larger grazers and transferred to 14 acid- temperature were submersed in 7 tanks (280 l) connected to a temperature control unit

(PolyScience 9600 series) with an impelling and expelling pump. Seven experimental Temperature data loggers submerged in each tank were used to monitor the resulting water temperatures, ranging from 1.0 to 10.0°C increasing in 1.5°C steps, were tested (Fig. 2A). temperature. The experimental set-up was completed with 2 fluorescent light tubes per tank to provide the appropriate light. The light emitted from fluorescent lamps was 90 μmol photons m−280 s−1 (measured using a LI-1000 Li-Cor radiation sensor). This irradiance was selected so Warming effect on Arctic microbial communities Chapter 2 as to reproduce a light environment similar to where the plankton communities were collected, based on measurements from earlier cruises in the same season. For the open Arctic community samples, the temperature was increased gradually over the first 3 d of the experiment from 1.0°C to each final treatment temperature (Fig. 2A). For the Fjord community, the temperature was set immediately to each final treatment temperature because the temperature of the original the treatment at 7.0°C had to be discarded after a malfunction in the cooling system that caused water was close to the middle of the experimental temperature range (Fig. 2B). Unfortunately, a sustained temperature increase to well above the experimental temperature range during the first 3 d of the experiment.

Figure 2. Schematic representation of 2 experimental microcosms used to study the effects of Arctic warming on microbial communities. Duplicate carboys were incubated at 7 experimental temperatures, ranging from 1.0° to 10.0°C increasing in 1.5°C steps. (A) Arctic microcosms: the temperature was increased gradually over the first 3 d of the experiment from 1.0°C to each final treatment temperature. (B) Fjord microcosms: the temperature was immediately set to each final treatment temperature.

81 Warming effect on Arctic microbial communities Chapter 2

Chlorophyll a concentration

Daily subsamples (50 ml) from each carboy were filtered through Whatmann GF/F glass-fiber filters. After filtration the pigment was extracted in 90% acetone for 24 h and kept refrigerated in the dark. Filters were analyzed according to the fluorometric method of Parsons et al. (1984) and fluorescence was measured spectrophotometrically. Microbial abundances Samples for viral abundance (VA) and bacterial abundance (BA) were collected daily from

2 d for each experimental temperature over the entire experimental period. Subsamples (2 ml) each microcosm, while pico/ nanoflagellate and ciliate abundances were determined once every for VA were fixed with glutaraldehyde (0.5% final concentration), refrigerated, quick frozen in liquid nitrogen and stored at −80°C, as described in Marie et al. (1999). Counts were made using a FACS Calibur flow cytometer (Becton and Dickinson) with a blue laser emitting at 488 nm. events s Samples were stained with SYBR Green I and run at an optimal event rate (between 100 and 800 −1 (Brussaard 2004). ) (Marie et al. 1999), which in our cytometer corresponded to the medium flow speed

Samples (50 ml) were fixed with glutaraldehyde (1% final concentration) for bacteria and pico/nanoflagellate (≤2 to 20 μm) counts. Subsamples of 10 ml for bacteria and 20 ml for pico/nanoflagellate abundances were filtered through 0.2 and 0.6 μm black polycarbonate filters respectively, and stained with DAPI (4,6-diamidino- 2-phenylindole) (Porter & Feig 1980) to a −1 final concentration of 5 μg ml (Sieracki et al. 1985). The abundances of these microorganisms were determined by epifluorescence microscopy (Olympus BX40-102/E, at 1000 X). Between 200 and 300 bacteria were counted per sample and at least 50 to 300 heterotrophic or phototrophic pico/nanoflagellates were counted per filter from 3 to 4 transects of 5 to 10 mm each. They were grouped into 3 size classes: ≤2 μm, 2−5 μm, >5 μm. Pico and nanoflagellates showing red-orange fluorescence and/or plastidic structures in blue light (B2 filter) were considered phototrophic pico/nanoflagellates (PF), while colorless flagellates showing yellow fluorescence were counted as heterotrophic pico/ nanoflagellates (HF). With this method, we could not distinguish mixotrophic sp. were flagellates. Gyrodinium The abundances of ciliates and the phagotrophic dinoflagellate obtained using the Utermöhl method. 125 ml of sample was fixed with acidic lugol (2% final sp. were counted in an inverted concentration). Aliquots of the fixed samples (50 to Gyrodinium100 ml) were settled for 24 to 48 h before sp. were enumeration. Both the ciliates and the dinoflagellate Gyrodinium microscope82 (Zeiss AXIO - VERT35, at 400 X). Up to 200 ciliates and 100 Warming effect on Arctic microbial communities Chapter 2

and were grouped into the subclasses Oligotrichia: oligotrichs ( sp., sp. counted per sample. Ciliates were identified to genus level when possibleHalteria (LynnStrombidium & Small 2000), and Laboea sp.); Choreotrichia: naked choreo – trichs (Strobilidium sp.) and loricate choreo trichs (tintinnids); Haptoria: haptorids (Myrionecta sp. and Askenasia sp.); Scuticociliatida (Scuticociliates); and Hypotrichia (Euplotes sp.).

Bacterial production Bacterial production (BP) was measured by incorporation of radioactive 3H-leucine taken at time zero from in situ and every day from each microcosm and were dispensed into 4 following Kirchman et al. (1985) and modified by Smith et al. (1992). Aliquots of 1.5 ml were 3H-leucine was added vials (2 ml) plus 2 TCA-killed control vials. Next, 48 μl of a 1 μM solution of to the vials to obtain a final concentration of 40 nM. Incubations were run for 2 to 3 h in the same thermostatic chambers as the experimental microcosms, and stopped with TCA (50% final concentration). Tubes were then centrifuged for 10 min at 12 000 g. Pellets were rinsed with 1.5 cocktail was added. The vials were counted in a Beckman scintillation counter. For each time ml of 5% TCA, stirred and centrifuged again. Supernatant was removed and 0.5 ml of scintillation d of 3H-leucine incorporation, applying a conversion factor −1 −1 (Kirchman 1992). point, BP was expressed in μmol C l −1 of 1.5 kg C mol Leu Viral production and bacterial losses Samples for determining the viral production (VP) and bacterial mortality due to protists (PMM) and viruses (VMM) in the Arctic community were taken three times (at time zero -1ºC-, in the middle and on the eighth day of the experiment) for all experimental temperatures. For the Fjord community, samples were taken twice: at the time zero (5.5 ºC) and on the eighth day for four experimental temperatures (1.0ºC, 5.5ºC, 8.5ºC and 10.0ºC). So that there would be experimental duplicate were pooled together. enough water volume to measure the viral production and viral lysis, 0.5 l subsamples from each bacteria (FLB) disappearance method (Sherr et al. 1987, Vázquez-Domínguez et al. 1999). For Bacterial mortality due to protists was evaluated following the fluorescent-labeled each measurement of the grazing rates, duplicated 1.5 l sterile bottles were filled with 0.5 l aliquots of seawater from each experimental microcosm, and a third bottle was filled with 0.5 l of natural bacterial concentration. The FLB were prepared with a culture of grazer-free water as a control. Each duplicate and control was inoculatedBrevundimonas with FLB at 20% diminuta of the (http://cect.org/index2.html) as described in Vazquez-Dominguez et al. (1999). Bottles were

83 Warming effect on Arctic microbial communities Chapter 2 incubated in the tanks at the same experimental temperature as the corresponding microcosms at the initial time of the grazing assay. For assessing the bacterial and FLB abundances, samples and in the dark for 48 h. Samples for evaluating the pico/nanoflagellate abundances were taken were taken at the beginning and at the end of the grazing assay. Abundances of bacteria, FLB and pico/nanoflagellate were assessed by epifluorescence microscopy as explained above. Natural bacteria were identified by their blue fluorescence when excited with UV radiation, while FLB showed no decrease in FLB at the end of the incubation time. were identified by their yellow-green fluorescence when excited with blue light. Control bottles The grazing rates of bacteria were obtained according to the equations of Salat & Marrasé as follows: (1994), based on the specific grazing rate (g) and the specific net growth rate (a), and calculated

g = -(1/t) ln (FLBt/FLB0),

a = (1/t) ln (BA/BA0),

where t is the incubation time, FLBt 0 is the abundance of FLB at the initial time, and BA and BA are bacterial abundances at the end and beginning of t is the abundance0 of FLB at the final time, FLB the incubation time respectively. The net bacterial production (BPN, cells ml-1 d-1) in the incubation bottles was obtained with the equation:

at-1 BPN = BA0 x (e ).

-1 d-1) was calculated as:

Then, the grazing rate (G, cells ml G = (g/a) x BPN. -1) was calculated as the percentage of the bacterial standing stock (BSS): Finally, protist-mediated mortality of bacteria (PMM, % d

PMMBSS 0,

= (G x 100)/BA We used the virus-reduction approach to determine the viral production and bacterial losses due to phages (Wilhelm et al. 2002). Briefly, 1 liter of seawater from each experimental microcosm was pre-filtered through a 0.8 µm pore sized cellulose filter (Whatman) and then concentrated liter of seawater using a cartridge by a spiral-wound cartridge (0.22 µm pore size, VIVAFlow200) to obtain 50 ml of bacterial concentrate. Virus-free water was collected by filtering 0.5 with84 a 30 kDa molecular mass cutoff (VIVAFlow200). A mixture of virus-free water (150 ml) Warming effect on Arctic microbial communities Chapter 2

and bacterial concentrate (50 ml) was prepared and distributed into four sterile 50 ml Falcon ml-1 plastic tubes. Two of the tubes were kept as controls, and mitomycin C (Sigma) was added (1 µg tubes were incubated in the tanks at the same temperature as the microcosms and in the dark final concentration) to the other two tubes as the inducing agent of the lytic cycle. All Falcon during 12h. Samples for viral and bacterial abundances were collected at time zero and every 4 h of the incubation, fixed with glutaraldehyde (0.5 % final concentration) and stored as described cytometry. The number of viruses released by bacterial cells (burst size, BS) was estimated from above. Virus and bacterial numbers from the viral production incubations were counted by flow increase in viral abundance during short time intervals (4 h) in viral production incubations was viral production measurements, as in Middelboe & Lyck (2002) and Wells & Deming (2006). The divided by the decrease in bacterial abundance in the same time period. We assumed that the bacterial production and viral decay in this time interval were negligible. We estimated burst sizes from 11 to 82 viruses per bacterium. Viral-mediated mortality (VMM) was determined as previously described in Weinbauer tubes represents lytic viral production (VP ), and the difference between the viral increase in et al. (2002) and Winter et al. (2004). Briefly,L an increase in viral abundance in the control falcon the mitomycin C treatments and VPL gives the lysogenic production (VP ). Because part of the

LG bacteria is lost during the bacteria concentration process, the VPL and VP were multiplied by the bacterial correction factor to compare the VP values from different incubations.LG This factor was calculated by dividing the in situ bacterial concentrations by the T0 bacterial abundances in the

-1 d-1) by dividing the lytic viral production (VP ) by VP measurements (Winget et al. 2005) and in our case ranged between 0.75 and 2.15. We Lthen calculated the rate of lysed cells (RLC, cells ml percentage of the bacterial standing stock (VMM ): the burst size (BS), as described in Guixa-BoixereuBSS (1997). RLC was used to calculate VMM as a -1 VMMBSS 0 ),

= (RLCGR X 100)/BA , (% d where BA0 is the initial bacterial abundance in the viral production incubation tube. Assuming that the percentage of BSS losses due to viruses is the same in the falcon tubes and grazing bottles, we

-1 -1 used VMMBSS , cells ml d ): = (VMM x BA )/100, GR to calculate the BSSrate of lysed bacteria in the grazing bottles (RLC RLCGR GR where BA is the bacterial abundance in the grazing bottles at time 0.

GR

85 Warming effect on Arctic microbial communities Chapter 2

Statistical analysis The Shapiro-Wilk W-test was used to check the normal distribution of the data, and data were logarithmically transformed prior to analyses if necessary. 1-way ANOVA was used to studied. This means that the comparisons of all data (for each variable) before and after the shift detect a significant shift between 2 consecutive increasing temperatures for each of the variables

V4.0 and JMP programs. For each experimental temperature, we calculated the average of each were statistically significant. These statistical analyses were performed using the Kaleidagraph variable ±SE for the whole experimental period in the Arctic and Fjord microcosms. RESULTS

Physical and biological variables in Arctic and Fjord waters The 2 environments showed clear differences at the time of sampling (Table 1). The

sp.) abundances, as well as water temperature was lower in the open Arctic waters (−1.2°C)Myrionecta than in the Fjord waters (6.2°C). chlorophyll (chl ) concentrations were higher in the open sea Arctic community. In contrast, Pigmented microorganisma a (flagellates and ciliates, e.g. most heterotrophic variables, such as abundances of bacteria, viruses and Gyrodinium sp., as well abundances were similar in the 2 environments (Table 1). In both systems phototrophic pico/ as BP and VP and bacterial losses, were higher in the Fjord waters, while phagotrophic ciliate Micromonas sp. and nanoflagellatesPhaeocystis (PF) were dominated by sp. (PF ≤2 μm) and Strobilidiumfree-living forms of sp. (PF 2−5 μm) (Table 1), while phagotrophic ciliates, such as the experiments carried out with the open Arctic waters will be called Arctic microcosms and tintinnids, were the most abundant groups in the Arctic and Fjord waters respectively. Henceforth, those with water from Isfjorden will be called Fjord microcosms. The microbial communities in the open Arctic waters will be called the Arctic community, and those found in the Isfjorden waters will be called the Fjord community. Changes in biological variables during the experiments

Chlorophyll a concentration and phototrophic pico/nanoflagellate abundance The minimum and maximum values of the chl a microcosms over the entire experiment are shown in Table 2. The average chl concentrations concentration for the aArctic and Fjord at each temperature for the entire experimental period are shown in Fig. 3A,B. In the Arctic community, the chl a

concentration decreased by 50% between 5.5 and 7°C (Fig. 3A, Table 3).

86 Warming effect on Arctic microbial communities Chapter 2

Table 1 a (chlorophyll a concentration); PF

. In situ values for both Arctic and Fjord waters. Chl Micromonas (phototrophicPhaeocystis pico/nanoflagellate abundance) and the two main identified genera ( sp. ≤ 2 (lytic viral production); VP (lysogenic viral µm and sp. 2-5 µm); BA (bacterial abundance); LVA (viral abundance); HF (heterotrophic pico/ nanoflagellate abundance); BP (bacterial production); VP LG production); PMM (protist-mediated mortality as a % of the bacteria standing stock, BSS); VMM (virus- mediated mortality as a % of BSS).

VARIABLES ARCTIC FJORD

Temperature a (ºC) -­‐1.2 6.2 -­‐1 MicromonasChl (µg 3 l ) -­‐1 0.6 0.1 PhaeocysitisPF (10 cells ml ) 3 -­‐1 2.1±0.2 0.8±0.3 sp. (10 3 cells ml -­‐1) 0.4±0.09 0.7±0.2 sp. (10 cells ml ) 1.4±0.2 0.02±0.0 5 -­‐1 BA (105 cells ml -­‐1) 3.8±0.6 8.0±0.6 VA (10 virus ml ) 5.4±0.0 8.4±1.5 3 -­‐1 HF (10 cells 3 ml ) -­‐1 3.0±0.3 0.4±0.03 HF ≤ 2 µm (10 3 cells ml -­‐1) 1.0±0.1 0.02±0.00 HF 2 -­‐5 µm (10 3 cells -­‐1 ml ) 1.2±0.2 0.4±0.05 HF Gyrodinium > 5 µm (10 cells ml ) 0.7±0.0 0.03±0.02 3 -­‐1 Myrionecta sp. (10 cells 3 l ) -­‐1 0.2±0.0 2.7±0.5 Phagotrophic ciliate 3 (10 -­‐1cells l ) 1.4±0.3 1.3±0.0 sp. (10 cells l ) 3.1±0.9 0.1±0.0 -­‐2 -­‐1 -­‐1 BP (10 µmol C l d ) 3.9±0.0 36.4±1.8 5 -­‐1 -­‐1 L VP (10 viruses 5 ml -­‐1 d ) -­‐1 1.7±1.4 2.3±0.2 VPLG (10 viruses ml d ) Negligible 3.9±0.0 VMM (%BSS) 10.1±6.8 90.6±11.6 PMM (%BSS) 15.2±4.7 33.0±24.6

1 recorded between the chl concentration at lower and higher temperatures (Fig. 3B, Table 3). There was a slight decreasea in the Fjord microcosms, but no significant differences were In both experimental microcosms, Micromonas total PF abundance followed by Phaeocystis sp. (PF ≤2 μm) was the main contributor to the sp. (PF 2− 5 μm). In both systems, there was a very in Table 2. Average values of sp. in the Arctic microcosms were almost constant low abundance of PF >5 μm. MinimumMicromonas and maximum values during the experiments are shown

at increasing temperatures up to 5.5°C, at which they reached a peak then decreased again at 87 Warming effect on Arctic microbial communities Chapter 2

Micromonas sp. values remained high higher temperatures (Fig. 3C). In the Fjord microcosms, between 2.5°C and 8°C. We did not detect important changesPhaeocystis in PF abundances (Fig. 3D). In the sp. (Fig. 3C,D). 2 microcosm experiments, freeliving PF (2−5 μm), suchMicromomas as sp., and PF >5 μm showed lower abundances (Fig. 3C,D) than PF ≤2 μm, such as

ARCTIC FJORD

1.0 2.0 A B

0.8 ) ) -1

-1 1.5 g l µ g g l µ g 0.6

1.0 0.4 Chlorophyll a ( a Chlorophyll Chlorophyll a ( a Chlorophyll 0.5 0.2

0 0 0 2 4 6 8 10 12 0 2 4 6 8 10 12

Temperature ºC Temperature ºC

Figure 3. Average values (±SE) over the experimental period for each temperature treatment in Arctic and a (PF) of different size classes. The arrow in (A) indicates the temperature at which a shift in chl abundance Fjord microcosms of (A,B) chl concentration and (C,D) abundance of phototrophic pico/nanoflagellatesa occurred in Arctic samples.

88 Warming effect on Arctic microbial communities Chapter 2

Table 2. Minimum and maximum values for each variable in the two microcosm experiments. Chl a (chlorophyll a

concentration); PF (phototrophic pico/nanoflagellate abundance); BA (bacterial abundance);L (lytic viral production); VP VA (viral abundance); HF (heterotrophic pico/nanoflagellate abundance); BP (bacterial production); VP LG (lysogenic viral production); PMM (protist-mediated mortality as a % of the bacterial standing stock, BSS); VMM (virus-mediated mortality of bacteria as a % of BSS).

ARCTIC FJORD

VARIABLES

a Min Tª (ºC) Day Max Tª (ºC) Day Min Tª (ºC) Day Max Tª (ºC) Day -­‐1 0.1 10.0 10 1.7 2.5 6 0.2 10.0 1 3.0 5.5 7 Chl (µg l ) -­‐1 4 PF (cells ml 10 ) 0.06 2.5 4 2.6 5.5 9 0.05 2.5 0 13.1 2.5 7 -­‐1 5 1.3 2.5 4 9.9 5.5 7 7.4 5.5 0 20.4 8.5 3 BA (cells ml -­‐1 10 5) VA (virus ml 10 ) 1.3 2.5 8 13.8 2.5 8 3.6 10.0 9 17.6 4.0 9 Gyrodinium -­‐1 3 0.5 7.0 5 4.6 5.5 3 0.2 1.0 8 3.3 4.0 5 HF (cells ml 10 ) -­‐1 3 0.02 7.0 8 0.7 10.0 2 0.2 8.5 3 3.2 5.5 0 sp. (cells -­‐1 l 103 ) Total ciliates (cells l 10 ) 0.04 7.0 7 12.0 10.0 2 0.04 2.5 7 1.5 5.5 0 -­‐1 -­‐1 0.03 7.0 3 1.3 7.0 9 0.06 10.0 9 0.7 5.5 8 BP (µmol 5 C l d ) -­‐1 -­‐1 L Negligible 5.5/7.0 8 9.0 4.0 8 0.6 5.5 8 1.9 10.0 8 VP (10 viruses 5 ml -­‐1 d ) -­‐1 LG Negligible 1.0/2.5/4.0/5.5/7.0 0/4/8 3.7 5.5 8 Negligible 1.0/8.5 8 3.9 5.5 0 VP (10 viruses -­‐1 ml d ) Negligible 5.5/7 8 17.1 7.0 4 0.8 1.0 8 96.6 5.5 0 VMM (%BSS -­‐1 d ) PMM (%BSS d ) 4.1 4.0 4 35.4 8.5 8 3.3 1.0 8 33.0 5.5 0

1 Heterotrophic microbial communities The minimum and maximum values of microbial (bacteria, viruses and protists)

shown in Table 2. The average BA (bacterial abundance) in the Arctic microcosms increased abundances when the Arctic and Fjord microcosms were exposed to different temperatures are

significantly by around two-fold at temperatures between 4.0 and 5.5°C (Fig. 4A). In the Fjord microcosms the increase in abundances was smaller than for the Arctic microcosms (ca. 1.5 times) and occurred between 2.5 and 4.0°C (Fig. 4B). For both systems, the variations in abundances observed before and after a certain temperature were statistically significant (Table 3). The mean VA (viral abundance) for the Arctic community increased be tween 4.0 and 5.5°C (Table 3, Fig. 3C) and followed a similar trend to that of BA (Fig. 4A,C). In the Fjord community the average VA dropped significantly between 5.5 and 8.5°C (Table 3, Fig. 4D). temperature gradients for both microcosm experiments did not show clear patterns (Fig. 4E,F). Changes in the average HF (heterotrophic pico/nanoflagellates) abundances along the However, when the different HF size classes in the Arctic microcosms were considered it was

found that HF ≤2 μm significantly decreased between 4.0 and 5.5°C, while HF >5 μm increased microcosms (Fig. 4F, Table 3). significantly between 7.0 and 8.5°C (Fig. 4E, Table 3). This was not observed in the Fjord 89 Warming effect on Arctic microbial communities Chapter 2

Gyrodinium sp. did not show clear responses to in creasing temperature in either system. Thus, in the Arctic microcosms, we detected that the pigmented Ciliates and the dinoflagellate Myrionecta sp. was the most abundant ciliate and had a tendency to decrease as the temperature

ciliates sp., for the so-called ‘other ciliates’ (comprising sp., increasedStrobilidium (Fig. 4G), while we did not observe changes with temperatureStrombidium for the phagotrophicEuplotes sp., Laboea sp., tintinnids, scuticociliates, Askenasia sp. and Tontonia sp., and tintinnids Gyrodinium Myrionecta sp.) or for the dinoflagellate sp. showed lower and higher average values, respectively, than sp. (Table Gyrodinium3). In the Fjord microcosms, we found that and the dinoflagellate sp. was not always present, and when averaging the abundance in the Arctic microcosmsStrobilidium (Fig. 4G,H), and they did not show any response to warming (Table 3). However, in the Fjord for each temperature together with the other identified ciliates we observed that they decreased significantly at the highest temperatures (Fig. 4H, Table 3). Table 3 (chlorophyll concentration); . Temperature at which a significant shift was detected according to aan ANOVA, wherea N is the sample size; F is the F-test of the variance, and P is the level of significance. Chl (lytic viral production); PF (phototrophic pico/nanoflagellate abundance); BA (bacterial abundance); VAL (viral abundance); HF VP (heterotrophic pico/nanoflagellate abundance); BP (bacterial production); VP LG (lysogenic viral production); PMM (protist-mediated mortality as a % of the bacterial standing stock, BSS); VMM (virus-mediated mortality of bacteriaARCTIC as a % of BSS). FJORD

ANOVA ANOVA VARIABLES

a N F P Tª (ºC) N F P Tª (ºC)

Chl 169 10.3 0.001 5.5 -­‐ 7.0 ns -­‐ PF ns -­‐ ns -­‐

BA 112 22.0 <0.0001 4.0 -­‐ 5.5 109 4711.0 <0.0001 2.5 -­‐ 4.0 VA 127 5.8 0.01 4.0 -­‐ 5.5 95 7.2 0.009 5.5 -­‐ 8.5

HF≤ 2 µm 43 7.6 0.01 4.0 -­‐ 5.5 ns -­‐ HF=2-­‐5 µm ns -­‐ ns -­‐ HF≥5 µm 54 5.6 0.02 7.0 -­‐ 8.5 ns -­‐ Total ciliates ns Other ciliates ns -­‐ 52 22.1 0.001 5.5 -­‐ 8.5

BP 71 13.8 0.0004 4.0 -­‐ 5.5 62 3.8 0.05 4.0 -­‐ 5.5 VPL ns -­‐ ns -­‐ VPLG ns -­‐ ns -­‐ PMMBSS 41 4.7 0.03 5.5 -­‐ 7.0 ns -­‐ VMMBSS ns -­‐ ns -­‐ 1 90 Warming effect on Arctic microbial communities Chapter 2

ARCTIC FJORD

0.8 1.8 A B ) ) -1 -1 0.7 1.6 cells ml cells ml 6 6 0.6 6

1.4 0.5 Bacteria (10 Bacteria (10 1.2 0.4

0.3 1.0 0 2 4 6 8 10 12 0 2 4 6 8 10 12

D 1.4 ) -1

1.2 viruses ml 6 1.0 Viruses (10 0.8

0.6 0 2 4 6 8 10 12

HF ≤ 2 µm E HF = 2-5 µm ) HF > 5 µm -1 1.2 cells ml 3 0.8

0.4 HF abundance (10

0 Ciliate and 0 2 4 6 8 10 12

5.0 1.5 3.0 1.0 G Myrionecta sp ) H Myrionecta sp ) -1

-1 Strobilidium sp Tintinnids Gyrodinium Ciliate abundance (10  other ciliate 2.5  other ciliate 4.0 � � 0.8 Gyrodinium sp (cells l Gyrodinium sp cells l 3 3 1.0 2.0 3.0 sp. abundance (10 0.6 1.5

2.0 0.4 3 cells l sp. abundance (10

sp. abundance (10 0.5 1.0 -1 )

1.0 3 0.2 (cells l 0.5 Myrionecta Gyrodinium

0 -1 0

0 ) 0 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Temperature ºC Temperature ºC

Figure 4 temperature treatment of the abundances of (A,B) bacteria, (C,D) viruses, (E,F) heterotrophic pico/ . Average values (±SE) over the experimental period in Arctic and Fjord microcosms for each nanoflagellates (HF) and (G,H) ciliates. Arrows indicate the temperatures at which shifts in abundances occurred: in (E) the black arrow marks shifts in abundances of HF ≤2 μm and the outlined arrow those of HF >5 μm; in (H) the black arrow marks the shifts in ‘other ciliates’. 91 Warming effect on Arctic microbial communities Chapter 2

Bacterial and viral production

d (Table 2). The average BP in the 2 types of microcosms followed the same trend as BP (bacterial production) in the Arctic and Fjord microcosms varied between 0.03 and 1.3 −1 −1 μmol C l 3). VP for bacterialL abundance, showing a significant increase between 4.0 and 5.0°C (Fig. 5A,B, Table viruses ml d at 4.0°C, both on Day 8 (Table 2). VP (lysogenic viral (viral lytic production) for the Arctic microcosms ranged be tween not detectable (at 5.5 5 −1 −1 production) ranged between not detectable (at several temperatures and on severalLG days) and and 7.0°C) and 9.0 X 10

viruses ml d L was higher than VP 5 −1 −1 andLG 3.7 X 10 , recorded at 5.5°C on Day 8 (Table 2). Average VP L 5 at all temperatures, except at 5.5°C, when VPLG was 1.5 times higher than VP (1.23 X 10

Figure 5. Average values (±SE) over the experimental period of (A,B) bacterial production (BP) for each lysogenic viral production (VP) for each temperature treatment in (C) Arctic microcosms (measured at temperature treatment in Arctic and Fjord microcosms, and average of duplicate subsamples of lytic and at which the shifts in BP occurred. Days 0, 4 and 8), and (D) Fjord microcosms (measured at Days 0 and 8). Arrows indicate the temperature 92 Warming effect on Arctic microbial communities Chapter 2

4 virus ml d −1 −1 virus ml d 1.91 X 10 respectively,L Fig. 5C), and at 7.0°C, when they were the same (Fig. 5C). 5 −1 −1 10 viruses ml d In the Fjord microcosms, VP showed minimum (0.6 X 10 ) and maximum (1.9 X 5 −1 −1 virus ml d ) was recorded ) values on Day 8 at 5.5 and 10.0°C, respectively (Table 2). Lysogeny was not 5 −1 −1 detectable at 1.0 or 8.5°C on Day 8, and the highest value (3.9 X 10 at the beginning of the experiment at 5.5°C (Table 2, Fig. 5D). Bacterial mortality

In both experiments, the losses in BSS (bacterial standing stock) due to protists (PMMBSS) were higher than those due to viruses (VMMBSS) in most temperature treatments. In the Arctic microcosms, grazing mostly increased with warming and sampling time above 5.5°C (Fig. values recorded on 6A,E), showing a significant shift between 5.5 and 7.0°C, except at 10.0°C BSSon Day 8 when values ), but they significantly decreased (Table 3, Fig. 6E). In the Fjord microcosms, PMM −1 in the Arctic microcosms reached its Day 8 increased progressively with in creasing temperaturesBSS (from 3.3 to 27.0% d were always lower than the initial value (Fig. 6B,F). VMM −1 values for each experimental temperature maximum value (17.1% d ) on Day 4 at 7.0°C, and wasBSS not detectable at 5.5 and 7.0°C on Day 8 of the experiment (Table 2, Fig. 6C). The average VMM maximum value for VMM ), whereas on Day 8, bacterial decreased from 1.0 to 5.5°CBSS and increased from 7.0 to 10.0°C. In the Fjord microcosms, the −1 was recorded at time zero (96.6% d viruses were compared in the 2 systems, bacterivory was generally higher than the mortality lysis increased progressively with the temperature (Fig. 6D). When losses due to protists and caused by viruses (Fig. 6E,F), except in the Fjord microcosms at the initial time, when bacterial losses due to viruses were higher than bacterivory (Fig. 6B,D).

DISCUSSION

Evaluation of the experimental design used The natural microbial community was incubated in large microcosms at 7 experimental abundances as well as growth and mortality rates. Although microcosm experiments could temperatures, ranging from 1.0 to 10.0°C increasing in 1.5°C steps, to study the variations in introduce some bias into the development of microbial communities in comparison to natural communities, due to the manipulation of samples and because the reaction of the microorganisms approach for determining community composition and environmental change rates (Pradeep to batch wise incubation occurs in a confined environment, these experimental tools are a useful 93 Warming effect on Arctic microbial communities Chapter 2

ARCTIC FJORD

50 100 T=0 A T=0 B T=4 T=8

) T=8 40 ) 80 -1 -1

30 60

20 40 PMM (%BSS day PMM (%BSS day

10 20

0 0 1.0 2.5 4.0 5.5 7.0 8.5 10.0 1.0 5.5 8.5 10.0

25 100 10 T=0 C T=0 D T=4 T=8 T=8 20 80 8 ) ) -1 -1

15 60 6 T=8 T=0 10 40 4 VMM (%BSS day (%BSS VMM VMM (%BSS day (%BSS VMM 5 20 2

0 0 0 1.0 2.5 4.0 5.5 7.0 8.5 10.0 1.0 5.5 8.5 10.0

40 40 PMM(BSS) E VMM (BSS) ) -1 30 30 VMM (%BSS day

20 20 PMM (%BSS day -1 ) 10 10

0 0 0 2 4 6 8 10 12

Temperature ( ºC)! Temperature ( ºC)!

Figure 6. Average of duplicates of virus-mediated mortality in bacteria as a percentage of the bacterial standing stock (BSS) for each temperature treatment: (A) protist-mediated mortality (PMMBSS) in Arctic microcosms at Days 0, 4 and 8; (B) PMMBSS mortality (VMM ) in Arctic microcosms; (D) VMM BSS in FjordBSS microcosms at Days 0 and 8; (C) virus-mediatedBSS and VMM BSS in Fjord microcosms. Average values (±SE) of PMM temperatures at which shifts in bacterial mortality occurred in (E) Arctic and (F) Fjord microcosms for each temperature treatment. Arrows indicate the

94 Warming effect on Arctic microbial communities Chapter 2

the variations in activity and biomass in each microcosm can be compared with each other. In Ram & Sime-Ngando 2008). Moreover, this effect applies to all experimental microcosms. Hence, the present study, experimental temperatures were realistic considering the predicted climate change scenarios for the Arctic Ocean over the 21st Century (ACIA 2004). According to the surface satellite temperature (SST), the yearly variations in the nomad2.ncep.noaa.gov/ncep_data/). In the open Arctic location the temperature varied between temperature at the experimental sampling points were within the range of 6.0 to 7.0°C (http:// from 1.0 to 7.0°C. Therefore, the temperature ranges used in our incubations encompassed the –1.0 and ~6.0°C, while in the Atlantic influenced water (the Fjord system) the temperature ranged yearly temperature variation range plus a 4.0°C increase. However, the increase in the in situ , while in our experiments the warming −1 ). Thus, in nature the adaptation and replacement of microbial temperature over the Arctic summer is about 0.5°C wk −1 communities would take place over a longer time scale, which could not be reproduced in our rates were much higher (≥1.5°C d experimental setup. Although this is a problem that we cannot easily solve, it is important to keep in mind that the experimental approach used does not try to mimic nature, but it does allow us to visualize and understand how warming may affect the Arctic microbial food webs and viral shunt increase or decrease in a given component of the microbial community begins. These shifts mechanisms, and to detect at which temperature there is a significant shift, i.e. when a significant should not necessarily be considered irreversible (Duarte et al. 2012) given the seasonal range of temperatures in these Arctic waters (−1.7 to 7.0°C). For instance, when the Fjord microcosms were lower bacterial abundances and activities with a similar pattern to that of the Arctic microcosms. subjected to lower temperatures than the in situ temperature (6.0°C), we observed a return to In short, the study examines what might happen in an extreme situation, when perturbations such as warming could differentially affect the distinct components of the microbial food webs.

Characteristics of the in situ Arctic and Fjord waters The chl a concentrations and abundances of bacteria, viruses, phototrophic and comparable to values reported in other Arctic studies (Sherr et al. 1997, Middelboe et al. 2002, heterotrophic flagellates and ciliates in the Arctic and Fjord communities examined were

2012, Lovejoy et al. 2007, Säwström et al. 2007b, Boras et al. 2010). BP (bacterial production) was was higher. The range of the BP corresponds to the values obtained for the summer period in areas significantly lower in open Arctic waters than in Fjord waters (Table 1), where the temperature close to the Svalbard Islands (Boras et al. 2010) and in Franklin Bay (Canadian Arctic, Garneau

95 Warming effect on Arctic microbial communities Chapter 2

et al. 2008). VPL waters, while VP (viral lytic production) was higher in the Fjord waters than in the open Arctic higher than VP LG L (lysogenic viral production) was only found in the Fjord system, where it was De Corte et al. 2011). Finally, protist grazing in the Arctic community was higher than bacterial (Table 1). This has not been observed in previous studies (Säwström et al. 2007b, the impact of viruses on bacterial communities was higher than that of bacterivores, as shown by mortality due to viruses, as shown in Boras et al. (2010), unlike in the Fjord community, where an important role in controlling bacterial communities. Wells & Deming (2006). These results suggest that both types of bacterial ‘predators’ could play

Changes in microbial biomasses and activities during the experiments

Phytoplankton and bacterioplankton The effects of warming on phytoplankton biomass (chl a concentration) as well as on the pigmented ciliate Myrionecta sp. decreased with increasing temperature in all the Arctic a concentration agree with Müren microcosms (Figs. 3A,B & 4G,H). The observed trends of chl higher temperatures and reached a minimum at 10.0°C. In the same experimental microcosms as et al. (2005), who found the highest phytoplankton biomass at 5.0°C, which then decreased with ours, A. Coello-Camba (un publ.) reported that the phytoplankton community composition varied from a high abundance of large cells, such as diatoms and dinoflagellates, at low temperatures to lowMicromonas abundances at higher temperatures. In addition, our findings of high abundances of PF ≤2 μm ( sp.) at 5.0°C in the Arctic, and at 2.5, 5.0 and 7.0°C in the Fjord microcosm are in agreement with observations made by Lovejoy et al. (2007) in the Arctic Ocean, where there are In the present study, BA (bacterial abundance) and BP (bacterial production) increased blooms of this picoflagellate during spring and summer. when chl a concentration decreased, probably due to the increase in organic matter from excretion or cell lysis of primary producers that might stimulate bacterial growth, as proposed by Hoppe et al. (2008). Moreover, empirical relationships between temperature and bacterial growth in natural bacterioplankton assemblages have shown that temperature is closely related to BP (White et al. 1991, Wiebe et al. 1992, Shiah & Ducklow 1994). In particular, at low temperatures small increases cause significant changes in prokaryotic growth rates (Morán et al. 2006, Kirchman et al. 2009). Indeed, these observations are in accordance with our results, This could because psychrophilic bacteria, which are able to grow at lower temperatures (0, which show that BA and BP significantly increase with a few degrees of temperature (Table 3).

15.0 and 20.0°C, minimum, optimal and maximum temperatures, respectively), are replaced

96 Warming effect on Arctic microbial communities Chapter 2

seawater might result in a change in the bacteria community and this could be the reason why we by psychrotolerant bacteria, which grow better at higher temperatures (Morita 1975). Warmer with Krause et al. (1993), who observed shifts from psychrophilic to psychrotolerant bacterial found an increase in BA and BP above 5.5°C (Figs. 4A,B & 5A,B). These results are in agreement communities during the replacement of summer water in the Weddell Sea, as well as a decrease in nutrients and the chl a concentration.

Viral lytic and lysogenic production Bacterial and viral abundances in aquatic systems are usually positively correlated, which indicates that they are closely linked, and presumably the environmental parameters that influence bacterial assemblages could also affect the viral community (Wommack & the Arctic microcosms, VA (viral abundance) followed BA (bacterial abundance), while in the Colwell 2000, Weinbauer 2004, Pradeep Ram & Sime-Ngando 2010, Danovaro et al. 2011). In different causes, such as adsorption into host walls or into particles, as well as ingestion by pico/ Fjord microcosms VA decreased above the temperature of 5.5°C. This decay could be due to

Also, it would also have to take into account lysogeny. Lysogenic bacteria have prophages nanoflagellates (Weinbauer 2004). (phage nucleic acid) incorporated into their genomes. When the lysogenic host is stressed (e.g. by environmental shifts) the prophage is induced and the lytic cycle activated, producing new viral infective particles. High lysogeny values were found in Antarctic lakes during winter (Lisle

& Priscu 2004) and there are a variety of reports for the Arctic that found significant lysogeny 2007b). Furthermore, at different polar sites, lysogeny showed seasonal variations, with high during summer (Boras et al. 2010), but in other cases it was not detected at all (Säwström et al.

(viral lytic production) and high VP (lysogenic viral production) at rates in winter and springL (Laybourn-Parry et al. 2007, Säwström et al. 2007a). We therefore the low experimental temperatures when bacterial abundanceLG was low (Fig. 4A,B). However, expected to find low VP our results showed the opposite trend in both systems (Fig. 5C,D). In the Arctic microcosms, community occurred, VP when BA increased withL temperature (around 5.5°C) and presumably a shift in the bacterial was also important at low experimental temperatures (1.0°C),LG while VP L decreased significantly, showing similar values as VP (Fig. 5C). In the Fjord microcosms, VP LG constituted 65% of the total viral production at the in situ temperature (~6.0°C). A plausible explanation is that between 5.5 and 7.0°C, viruses were ‘comfortably installed’ inside the active conditions acted as an environmental stress factor, and the lysogenic cycle reverted to the lytic hosts as prophages, but when the temperature increased (to 8.5 and 10.0°C), the new warming 97 Warming effect on Arctic microbial communities Chapter 2 cycle. Moreover, the stimulation of bacterial growth at higher temperatures could be related to an

2010). In summary, it seems that at higher temperatures than ~7.0°C the lysogenic cycle reverts increase in the nutrient concentrations, and therefore in VP (Pradeep Ram & Sime-Ngando 2008, to a lytic cycle.

Bacterial losses

In the Arctic microcosms, protistan grazing decreased progressively up to 5.5°C (Fig. 6E) but between 5.0 and 7.0°C there was a shift and the bacterial grazing rates increased, corresponding to high values of BA, BP and lysogeny (Figs. 4A & 5A,C). Fluctuations in bacterivory with temperature were not reflected in changes in the total HF abundances. Nevertheless, we observed differences in the dynamics of the different HF size classes (Fig. 4E). Above 5.5°C, HF ≤2 μm decreased, and in bacterivory as the temperature increased; however, like in the Arctic microcosms, this did not around 7.0°C, HF >5 μm increased (Fig. 4E). In the Fjord microcosms, there was a gradual increase correspond to an increase in the total HF at different temperatures. Although HF are considered to be the main bacterivore microorganisms (Sherr & Sherr 2002), they also ingest prey larger than bacteria to maintain their biomass and growth (Vaqué et al. 2008), and thus trophic cascades on other small prey such as sp., which were very abundant in our experiments as in could occur (Vaqué et al. 2004).Micromonas For instance, HF >5 μm could feed on bacteria, on HF ≤5 μm, and

(i.e. naturalGyrodinium Arctic waters (Lovejoy et al. 2007). In addition, phagotrophic ciliates and large flagellates and shaping the community (size and composition). In polar systems, bacterivory appears to be sp.) could prey on pico/nanoflagellates (HF, PF), controlling their abundances an important factor in controlling the bacterial abundance during most of the year (Anderson & increase with temperature in the Antarctic (Vaqué et al. 2009) and in cold waters (Newfoundland, Rivkin 2001, Boras et al. 2010). Furthermore, several authors have also found that grazing rates Choi & Peters 1992). Their results are in agreement with our bacterivory responses to warming in Arctic and Fjord waters. However, in Arctic waters, we found that the effect of temperature on viral lysis was not large enough for it to surpass bacterivory, while in the Fjord microcosms at the in situ temperature; viral-induced mortality was significantly higher than mortality due to We think that it is necessary to carry out more re - search focused on different sources protists (Fig. 6B,D). of bacterial mortality, particularly due to viruses, in different polar areas, at different seasons, as in situ as well as microcosm warming experiments. The results could be used to test and understand the function of viruses in the microbial shunt in these cold marine systems in order to generate predictive models of the effects of future global warming. Indeed, viruses could be

98 Warming effect on Arctic microbial communities Chapter 2

supply dissolved nutrients in the euphotic zone, contributing to the recycled primary production a key biotic component influencing the feedback of climate change in the oceans be cause they and/or to the increase in CO2 due to the respiration of hetero trophic microbes (Danovaro et al. 2011).

CONCLUSIONS The results of this experimental study show that heterotrophic and phototrophic microbial communities responded differentially to warming conditions in 2 contrasting Arctic systems.

After a gradual increase in temperature we observed a significant increase in the activities and warmer conditions, bacteria were mainly channeled to higher trophic levels via HF, while viral biomasses of heterotrophic microorganisms, and a decrease in biomasses of phytoplankton. Under lysis contributed to increasing the pool of dissolved organic matter in the water column. All the initial microbial communities and lower and higher temperatures, respectively. In conclusion, observed changes were larger in the open Arctic waters than in the Fjord waters, with different warming triggers shifts that would favor heterotrophic communities, which could have a large impact on carbon and nutrient cycling and carbon storage in the Arctic Ocean.

ACKNOWLEDGMENTS

This study was funded by the Project Arctic Tipping Points (ATP, contract #226248) in the FP7 program of the European Union. E.L. was supported by a grant from the Spanish Ministry of Science and Innovation. We thank R. Gutiérrez and R. Martinez for sampling assistance, the crew of the RV ‘Jan Mayen’ for helping with sampling, E. Halvorsen and M. Daase for logistic support, and The University Centre in Svalbard, UNIS, for hospitality.

99 Warming effect on Arctic microbial communities Chapter 2

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105

Marine Phage-Bacteria interactions at ne-scale within Pseudoalteromonas spp. phages

Elena Lara, Elisabet Laia Sà, Guillem Salazar, Fernando Santos, Josefa Antón, Dolors Vaqué and Silvia G. Acinas

3Chapter

Pseudoalteromonas phage-host interactions Chapter 3

ABSTRACT

It is well known that interactions between marine phages and its hosts are more complex than previously noticed and studies on marine phages implying different approaches to deeply morphologically and by RAPD-PCR the genomic patterns of 18 phages that infected seven highly characterized the phages and their hosts are still very limited. In this context, we first characterized similar strains of Pseudoalteromonas spp. (> 99% identity in their 16S rRNA gene), increasing our knowledge of the high diversity within Pseudoalteromonas sp. phages. Secondly, host range analysis was done with an array of 89 distinct hosts at increasing phylogenetic signal, which let same bacterial strains but also at higher taxonomic ranks. Bacterial hosts were also characterized us to address the extent of phage host interactions at fine -scale at microdiversity level within the those markers and cross-infection analysis. The observed variations in the susceptibility among by 16S rRNA sequencing and RAPD-PCR genomic profiles, revealing inconsistence between bacterial strains and the probability of infection were better correlated by the bacterial genome and host system are highly complex in terms on infectivity and susceptibility at microdiversity profiles provided by RAPD-PCR than for 16S rRNA gene. Moreover, interactions between phage level but also reflect that phages can infect cross over genera boundaries. Modeling of the phage- hosts interactions patterns revealed a nested structure confirming previous findings where co-evolutionary processes that lead to specialization. specialist phages infect those hosts with higher susceptibility of infection fitting with the idea of

109 Pseudoalteromonas phage-host interactions Chapter 3

INTRODUCTION food webs (Bergh et al., 1989; Fuhrman, 1999; Wommack and Colwell, 2000). The majority of Marine viruses are highly abundant and ecologically important players in marine microbial the viruses in the oceans are believed to be phages (viruses that infect bacteria) and accounts for 107 per milliliter of seawater, one order of magnitude higher than their host (Paul et al., 2002). By killing bacteria, phages has a direct impact on global biogeochemical cycles (Wommack and Colwell, 2000; Weinbauer, 2004) through cell lysis transforming nutrients from particulate to dissolved organic matter that can be consumed by other prokaryotes (Proctor and Fuhrman, 1990). The effect of this viral activity is to increase community respiration and decrease the viral lysis can open new ecological niches or stimulate growth of certain members of the microbial efficiency of carbon transfer to higher trophic levels (Fuhrman, 1999; Weinbauer, 2004). Also, community (Middelboe and Lyck, 2002). considered one of the most important components of microbial evolution via horizontal gene Marine phages also influence bacterial abundance, composition, diversity and are transfer (Jiang and Paul, 1998). Actually, virus-mediated horizontal gene transfer, has had arrays of ecological relevant genes have been uncovered in marine phages genomes, such as more effect on microbial populations than was previously considered (Miller, 2001). Wide photosynthetic genes (Lindell et al., 2004; Sullivan et al. 2005, Sullivan et al. 2006), genes related to phosphate metabolims (Kathuria and Martiny, 2011; Monier et. al, 2011) or electron transport host acting as genetic reservoirs maintaining microbial diversity and probably extending their proteins (Alperovitch et al., 2010). Viral encoding functional host genes could be beneficial to the

2009.) ecological niches and to the phage increasing their fitness and host-range (Rohwer and Thurber, Host-range analyses is still a helpful approach to get insights into phage-host interactions

1993). Basic information about a general pattern of infection by phages on hosts could improve under a controlled scenario using isolated phages and bacteria (Moebus, 1992; Suttle and Chan, predictions of microbial population, dynamics and ecosystem function (Thingstad, 2000). However, the results on host range analysis could depend on the methods used for phage isolation or the receptor specificity and the bacterial acquisition of resistance (Chibani-Chennoufi et al., since continued rounds of phage infection on the same host may result in the selection of phage 2004). Host specificity could be an artifact produced by methods used for bacteriophages isolation infection in addition to the overall impact on the phylogenetic composition at the community level lysates with a limited host range (Jensen et al., 1998; Wichels et al., 2002). The specificity of phage

(Bouvier110 & del Giorgio 2007), also influence the strain composition at population level. Phage Pseudoalteromonas phage-host interactions Chapter 3

activity produces a short-term impact on the strain-specific diversity and continuous succession important parameter to determine as well as the diversity among bacterial strains populations. in bacterial assemblages (Middelboe et al. 2009). Thus, the diversity in phage susceptibility is an Unfortunately, the complexity of phage-host relationships are often overlooked by studies based either on conservative gene markers (such as 16S rRNA) or single gene locus such the ITS and it is known that phylogenetic identical bacteria can show differences in the phage-susceptibility patterns (Holmfeldt et al., 2007). Host range analysis of phage isolates against a panel of bacterial isolates also allowed us to understand which are the patterns behind viral infection and to extent the precise host range for any given phage. Network-based approaches have recently been proposed to help unify the quantitative analysis of the cross-infection of multiple phages with multiple bacteria (Flores et al., 2011). A recent study exploring universal patterns of phage-host cross-infection assays found a clear dominance of nested patterns, which implies a hierarchy structure in which the most specialist phages infect those host with higher susceptibility of infection matching with the idea of gene for gene (GFG) co-evolutionary model (Flores et al., 2011). The same authors provided distinct groups of phages and hosts. Such modularity structure is expected when cross-infection new findings on the possibility of modularity structure in virus-bacteria interactions among analyses are tested at larger geographical scale or at increasing phylogenetic signal between host and phage (Flores et al., 2013). Thus, given the lack of studies of phage-bacteria interactions at deeper resolution, we have isolated 18 marine phages co-infecting different strains of Pseudoalteromonas spp. In addition to morphological characterization of the phages and host range analyses at increasing phylogenetic scale, we have characterized phages and their hosts at higher resolution using genomic pattern profiles (RAPDs) to better understand (i) the infection ability of specific viruses distant phylogenetic hosts and (ii) the structure of the phages-bacteria infection network. Finally, lineages with specific bacterial genotypes within the same and closely related strains but also at we discussed our findings into an evolutionary context of population dynamics.

111 Pseudoalteromonas phage-host interactions Chapter 3

MATERIAL AND METHODS

Isolation of bacteria and phages Eighty nine bacterial strains were used in this study. Fifty two belonged to Pseudoalteromonas genera, which were used to phage isolation. All the bacterial strains were isolated from Blanes

2001 and 2009. The strains were selected as clear colony in Zobell agar plates (1.0 g yeast extract, Bay Microbial Observatory (BBMO), a surface coastal site in the NW Mediterranean Sea, in winter colonies was re-isolated three times to ensure the purity of the bacterial isolates. 5 g peptone, 15 g agar, 250 ml MQ water and 750 ml ultra‐filtrated seawater). Each one of the

PseudoalteromonasPhages were obtained from seven of these 52 bacterial strains belonging to genera. Phages were isolated from the same marine site (BBMO) in winter in 2009. Four liters of surface seawater was collected, after a 0.22 μm pre-filtration, phages 20 ml of viral concentrate. Phages were isolated using liquid enrichment cultures and plaque were concentrated by tangential flow filtration (30KDa VIVAFLOW) until a final volume of assays (Sambrook, 1989). In enrichments assays, concentrated viral communities were added to different Pseudoalteromonas spp. exponentially growing in a liquid culture in a 1:3 ratio. After

24h of incubation at dark, the mixture was then centrifuged (5,000 g, 10 min) and the supernatant spp. used was filtered through a 0.22 μm filter to remove any remaining Pseudoalteromonas bacterial cell. Positive phage as hosts. Dilution series from possible positive phage-containing samples were combined with enrichments were confirmed by plaque assay with the different 8 cells and plating using the agar overlay technique

400 μl of liquid bacterial culture with ca. 10 of single clonal phage population; plaques with different morphology were selected from every by adding 3.5 ml of molten soft agar (0.5% agar in Zobell; 50°C). For isolation and purification host and purified for 3 rounds of infection and lysis. High titer phages stocks were prepared by the plate-lysate method (Sambrook, 1989). Briefly, purified phages were diluted serially to obtain x 7H the appropriate dilution that would provide confluent lysis4 of the2 host in a soft-agar overlay plate. Thereafter, 5 ml of MSM (450 mM NaCl, 50 mM MgSO 0, 50 mM Tris base, pH 8) was added to a fully lysed host lawn. The plates were incubated in a shaker for 40 min. The MSM was transferred to a sterile tube and centrifuged at 5,000 g for 10 min. Finally, the lysate supernatants were 0.22 μm filtered and stored at 4ºC in the dark. Sequencing of the 16S rRNA gene of the bacterial strains used as hosts in the host-range analyses. First, completed 16S rRNA gene was sequenced for the 7 Pseudoalteromonas spp. strains

112 Pseudoalteromonas phage-host interactions Chapter 3 used to phage isolation; while only partial 16S rRNA gene was sequenced to the bacterial strains used as hosts at host-range assays. Bacterial DNA was extracted using heat shock; a single bacterial colony was resuspended in 100 μl of miliQ water and warm up to 95ºC during 10 min. The primers for bacterial 16S rRNA gene amplification were 27f (AGAGTTTGATCCTGGCTCAG) and 1492r (GGTTACCTTGTTACGACTT). The thermal program consisted of 1 cycle of 5min at 94ºC, 30 cycles of 1 min at 94ºC, 1min at 55ºC and 2 min at 72ºC, and 1 cycle of 10 min of extension at

72ºC. Completed 16S rRNA genes were sequenced bidirectionally using primers 27f, 358f and 1492r, and 358f-907rM for partial 16S rRNA. Sequences were aligned using the program ClustalW (default parameters) (Larkin et al., 2007). Maximum likelihood trees were built using the Tamura Nei model (Tamura and Nei, 1993) with boostrap analysis (500 replicates) using MEGA version 5.1 (Tamura et al., 2011). search on the NCBI Genbank database for the most closely related organism. Accession numbers All the sequences were submitted to a standard nucleotide-nucleotide BLAST (blastn) of those 16S rRNA sequences are XXX to YYYY.

Viral DNA purification and extraction Culture of the different bacterial strains was combined with phages in 20 plaque assays giving confluent lysis. Once plaques appeared, phages were eluted with MSM buffer. The phage Sullivan et al., 2010 lysate was 0.22 μm filtered. Viral DNA was obtained using a Lambda Wizard DNA kit (Promega Corp. Madison, WI) directly on phage lysates (Henn et al., 2010; ). NaCl 3M was added to 50 ml of phage lysate. The mixture was incubated 1 h at 4°C in the dark followed by centrifugation at 5,000 g, 15 min. The pellet was discarded and polyethylene glycol (PEG 8000 10%) was added to the supernatant. After an incubation of 1 h at 4°C in the dark, it was centrifuged (5,000 g, 15 min). The supernatant was discarded and the pellet was resuspended with phage and mixed gently by inverting the tube. The mixture was loaded onto a mini-column (Promega, buffer, MSM. One ml of Purification Resin (Promega, product A7181 Madison WI) was then added through with the syringe plunger. The column was then washed with 2 ml 80% isopropanol, product A7211 Madison WI) through a 5 ml syringe attached to the column, pushing the mixture

(10,000 g, 2 min, room temperature) to remove any remaining liquid. Phage DNA was then eluted the syringe removed and the minicolumn placed into a 1.5 ml Eppendorf tube and centrifuged ml Eppendorf tube and immediately centrifuging (10,000g, 30 sec, room temperature) to recover from the column by adding 100 ml TE buffer heated to 80°C, then placing the column into a 1.5 the DNA. Phage DNA was stored at -20°C for long-term storage. 113 Pseudoalteromonas phage-host interactions Chapter 3

Viral morphologic characterization by Transmission Electron Microscopy (TEM)

(Borsheim et al., 1990; Weinbauer et al., 2002). Five microliters of the viral stock was spotted for High-titer phage stocks (lysates) from the selected phages were prepared for TEM 1 min onto fresh glow-discharged Formvard-coated carbon grids. Adsorbed phages in the grid were negatively stained by adding 5 drops of uranyl acetate solution (2%, final conc) for 10 s each in a Jeol 1010 (Jeol, Japan) transmission electron microscope operating at 80 kv equipped with a time. Excess stain was drawn off with filter paper and the grid air-dried. The grids were observed

CCD camera camera SIS Megaview III and AnalySIS software. Host-range assays infectivity test was done with 89 bacterial strains used as hosts. Plaque assays were performed To determine phage host range and bacterial susceptibility to specific phages, a cross using strains of Pseudoalteromonas spp.; Alteromonas, spp.; Marinobacterium, spp. Vibrio spp. and Bacteroidetes, Rhodobacterales and Sphingomonadales clades. Two different 10-fold viral dilutions from phages stocks were used in order to distinguish between a clear lysis caused by plaque formation and inhibition of the bacterial lawn. After incubation overnight in the dark,

(Clarke and Gorley, 2001). The binary matrix was transformed into a similarity matrix using plaque formation was evaluated. A cluster analysis was carried out using PRIMER6 software Bray-Curtis measure. Dendrograms were generated using the group average method after the

SIMPROF test was performed to evaluate the significance of the clusters (p < 0.05). Genomic profiles of isolated viruses and hosts by Randomly Amplified Polymorphic DNA (RAPD) polymerase chain reaction (PCR) In order to distinguish among the isolated viruses and bacterial host at higher resolution and to infer the genetic relatedness, we used the genomic profile approach of RAPD-PCR technique (Winget and Wommack, 2008). First, viral DNA from isolated phages previously purified and extracted was used. The decamer primer CRA-23 (5’ -GCG ATC CCC A- 3’) was used acting as both, forward and reverse primer. PCR conditions were as follows: 1 cycle of 10 min at 94°C, 30 cycles of 3 min at 35°C, 1min at 72°C and 30 s at 94°C, 1 cycle of 3 min at 35°C and 1 cycle of 10 min of extension at 72°C. RAPD-PCR products were separated by gel electrophoresis on 1% agarose gel All isolates of spp. used as hosts were also analyzed by RAPD-PCR. For in 0.5% TAE run at 90VPseudoalteromonas for 2h and visualized by SYBR SAFE (10.000X, Invitrogen). bacterial DNA, the primer OPC-11 (5’ –AAAGCTGCGG- 3’) was used acting as both, forward and reverse114 primer for RAPD-PCR. PCR conditions were as follows: 1 cycle of 2 min at 94°C, 2 cycles of Pseudoalteromonas phage-host interactions Chapter 3

30 s at 94°C, 30 s at 36°C and 2 min at 72°C, 30 cycles of 20 s at 94°C, 15 s at 36°C, 15 s at 45°C and 1.5 min at 72°C and 1 cycle of 10 min of extension at 72°C. RAPD-PCR products were separated SAFE (10,000X, Invitrogen). by gel electrophoresis on 1% agarose gel in 0.5% TAE run at 90V for 2h and visualized by SYBR For both, viruses and bacterial similarity of resulting genomic banding patterns was assessed by a group-averaged cluster analysis based on a Bray-Curtis dissimilarity matrix. The SIMPROF permutation procedure was used to test the significance of the clusters (p < 0.05). The software these parameters (Clarke & Warwick, 2001). tool PRIMER6 (Plymouth Routines in Multivariate Ecological Research) was used to calculate Networks statistics Host-phage interactions were represented as a bipartite network between all tested phages and all Pseudoalteromonas and Alteromonas bacterial strains. The degree of modularity (whether interactions tend to occur among distinct groups of phages and hosts) and nestedness (whether is there a hierarchy of susceptibility to infection, and a measure of to what extent phage host ranges are subsets one of another) of the network were calculated using the MATLAB package BiMat (Flores et al., 2013). To estimate modularity, the Adaptive BRIM (Bipartite Recursively the Nestedness Temperature Calculator algorithm (NTC; (Atmar and Patterson, 1993)) as Induced Modules) algorithm (Barber, 2007) was used, while nestedness was calculated with matrices with the same number of interactions randomly positioned (Bernoulli matrix). implemented in BiMat. Both metrics were statistically tested against a null model of 10000

RESULTS

Bacterial and phage isolation Pseudoalteromonas

A total of 52 spp. strains isolated from Blanes Bay Microbial Observatory station (BBMO), a surface coastal site in the NW Mediterranean Sea, during winter different spp strains that were obtained from the same marine site during of 2001 andPseudoalteromonas 2009 were used as .potential phage host (Table 1.SM). Phages were isolated from winter of 2009. Before proceeding with phage enrichment cultures, simple direct plating of the same marine water sample was performed via plaque assay but phages infecting Pseudoalteromonas phages were obtained using liquid enrichment before perform the plaque assays. Nineteen of the spp. bacterial isolates were not sufficiently abundant to isolate them without enrichment. Thus, Pseudoalteromonas bacterial strains tested were positive in plaque formation; a set of 7 of the

52

115

Pseudoalteromonas phage-host interactions Chapter 3

1415.7

1565.9

1616.0

514.6

689.8

1866.3

2241.8

1265.5

2367.0 539.6

1340.6

1641.0

1140.4

1891.3 1891.3

1340.6

1540.9

DB44

DB25

1816.3 DB30 DB28

MED292 DB29

MED275 2091.6 DB41

* MED517 DB42 MED169 98

DB43 MED111 82 DB46

MED113 DB48 MED271 99 DB7

MED306 DB55

MED290 1012.5

DB49 1054.3

1472.6 MED107 100 DB71

DB23

DB10 DB15

DB88 DB16

DB14 DB17

DB77 DB18 ZOCONH21

DB58 DB92

0.1 DB59 ALMIC1A

DB62 DB32

DB65 DB72 DB67

DB8 2309.0 1765.3

1723.5 DB21 1221.6 DB9 845.2

DB22 DB24

ZOCONB9 DB26

ALSKE5D DB1

ZOCONB10 DB3

DB50 ZOCONA8

DB56 DB84

DB53 DB93

DB54 DB94

DB12 ZOCONA5

DB89 DB79 ZOCONA7

2183.5

1388.9

1138.9

1514.4 1305.3 343.4 1221.6

Figure 1. 16S rRNA phylogeny of the Pseudoalteromonas and Alteromonas strains used as hosts in this study. In green are labeled the Pseudoalteromonas strains and in purple the Alteromonas strains. Some of the bacterial strains have inserted the banding pattern gel image obtained by RAPD-PCR to compare the profile between strains with nearly identical 16S rRNA gene.

116 Pseudoalteromonas phage-host interactions Chapter 3

sample different plaques morphologies were detected in the positives plates for a single bacterial positive plates was randomly selected for purification and characterization. For the same water

7 spp. strains. host,Pseudoalteromonas they were purified by three rounds of infection and a total of 25 phages were isolated from

Sequencing of the 16S rRNA gene and inferring genomic patterns of bacterial hosts Pseudoalteromonas

A total of 89Alteromonas bacterial strains were used in tis study; 52 belonged to the infection of our isolated phages. The 16S rRNA gene was sequenced completely for the 7 genera and 15 to (Table 1.SM), which represented the two genera with positive Pseudoalteromonas spp. strains used to phage isolation; while only partial 16S rRNA gene was

1). The complet sequencing of the 16S rRNA gene showed that six of the seven hosts used to sequenced of the rest of bacterial strains used as hosts for the host-range test (Table 1.SM, Fig. isolate the phages belonged to the same Pseudoalteromonas sp. type strain (Pseudoalteromonas sp. RHS-str.402), while the other isolation host strain was Pseudoalteromonas sp. QC44. The Pseudoalteromonas strains showed a 99.9% identity in their 16S rRNA gene, while the Alteromonas strains showed an 80% identity (Fig. 1). Due to the high sequence similarities of 16S rRNA genes between host strains, the genomic profile patterns was also determined using random amplification of polymorphic DNA (RAPD-PCR). This assay yielded reproducible fingerprints from the whole genome that of the spp. genomic banding patterns indicated that all the clusters were no were convertedPseudoalteromonas to a similarity dendrogram (Fig. 1A.SM and Fig. 1B.SM). Overall, the analyses significant at the 95% level when analyzed using the SIMPROF test in PRIMER 6 (data not shown) strains that according the 16S rRNA gene were identical (Fig. 1). For instance, although discrete clusters could be identified. In fact, we were able to detectPseudoalteromonas differences among strains DB56 and ZOCONA5 were grouped in the same cluster according to the 16S rRNA gene observed for,the strains DB41 and DB42 (Fig. 1). Interestingly, the only tree while displayedAlteromonas different banding patterns by RAPD-PCR (Fig. 1). Similarly findingAlteromonas were also strain that all isolated phages infected (MED111) was clustered in the same group than the strains MED169, MED517, MED275 and MED292 according to 16S rRNA gene although the RAPD-PCR DB23 and DB32 bacterial strains respectively. These phages showed similar behavior in their showed a different genomic pattern (Fig. 1). Finally, phages B8b and C5a were isolated from host range patterns, but the bacterial strains presented also different RAPD-PCR patterns despite being very close according to 16S rRNA gene (Fig. 1).

117 Pseudoalteromonas phage-host interactions Chapter 3

Pseudoalteromonas spp. phages characterization by genomic profiling and morphology

PCR analysis (Fig. 2) to distinguish among the closely related phages and infer their clustering The genomic profiling patterns of the 25 isolated phages were also examined by RAPD-

genetic information. association based on their genomic profiling comparison. This a RAPD-PCR priori approach allows getting a genomic fingerprint of each virus and does not require (Winget and Wommack, 2008) but only the primer CRA-23 yielded enough number of bands to Five decamer primers were tested on viral DNA (OPA-6, OPA-9, OPA-13, CRA-22 and CRA-23)

Figure 2. Dendogram showing the cluster analysis of the genomic RAPD-PCR band patterns obtained from the isolated Pseudoalteromonas phages. For each isolated phage is indicated the isolation host strain, viral family according to the TEM morphology, number of infections obtained in the host range analysis permutation. and the head and tail dimensions. Clusters labeled in red were not significant according the SIMPROF 118 Pseudoalteromonas phage-host interactions Chapter 3

A B C D

E F G H

I J K

L M N O

P Q R

Figure 3. Transmission electron micrographs showing negatively stained the 18 Pseudoalteromonas phages isolated. A: phage G3/2, B: phage 306a, C: phage B8b, D: phage G3/4, E: phage 306b, F: phage

271/3, G: phage 271/4, H: phage 271/2, I: phage H1/4, J: phage 306c, K: phage G3/1, L: phage H1/1, M: phage G3/5, N: phage H1/3, O: phage 271/1, P: phage G3/3, Q: phage C5a and R: phage H3/3.

119 Pseudoalteromonas phage-host interactions Chapter 3 discriminate with the maxima accuracy the differences between the isolated phages. The RAPD-

phages grouped all PCR assay yielded reproducible fingerprints that were Pseudoalteromonas converted to a clustering dendrogram isolated phages into 7 clusters (Fig. 2). Although we could not distinguish separation according (Fig. 2). In general, the genetic profile analyses of the to morphology because almost all the phages belonged to Myoviridae family, it was interesting to find the siphovirus (B8b) grouped in the same cluster than myoviruses (see cluster 1, Fig. 2). to (see clusters 6 and 7; Fig. 2). Some of the phages displayed identical patterns of PhageMyoviridae C5a and phage H3/3 were clustered in independent single groups despite belonging both genomic profiles (phage C5a and C5b, phage H1/2, H1/3, G3/2, G3/6 and H3/1, phage B8a and genome structure phages, we chose only one representative phages of such groups that shared a B8b), and although we are aware that identical RAPD profile are not indicative that identical 100% of identity for further morphological characterization. Hence, the rest of the characterization was done for 18 phages (B8b, C5a, 306a, 306b, 306c, H1/1, H1/3, H1/4, G3/1, G3/2, G3/3, G3/4, The phenotypic morphology and characterization of the 18 bacteriophages was examined G3/5, H3/3, 271/1, 271/2, 271/3 and 271/4). Caudovirales (Van Regenmortel, 2000). Seventeen of the 18 bacteriophages belonged to the family , by transmission electron microscopy, TEM (Fig. 3). All of them belonged to the order Myoviridae having icosahedral heads and long and contractile tails. The head diameters ranged from 58 to 98 belonged to the family (phage B8b), which contains phages that have icosahedral x 109 nm and the tail lengthsSiphoviridae from 106 to 132 nm (Fig. 3). Only one of the isolated bacteriophages Podoviridae. All the 17 myoviruses were isolated from the while the only siphovirus heads and long flexible tails. There werePseudoaltermonas no phages belonging sp. RHS-str.402 to the family (B8b) was isolated from the Pseudoalteromonas sp. QC44

strain (Table 1.SM). Host range analyses from obtained Pseudoalteromonas spp. phages

spp , 3 , spp., 8 PseudoalteromonasIn order to examine theAlteromonas host range of. theMarinobacterium isolated phages, infectivityVibrio was tested on 52 , 3 and 3 Bacteroidetes Rhodobacteralesspp., 15 Sphingomonadales spp. strains, 5 (Table 1.SM). All the tested bacterial observed on spp., and strains were Vibrioisolated fromBacteroidetes the same coastalAlphaproteobacterium site (BBMO) than the phages. No infections were spp strains, phages showed a large variability in infectivity (Fig. 4). Phage B8b, Pseudoalteromonas . strains (Table 1.SM). Within respectively spp. strains besides they were isolated from different bacterial the only siphovirusPseudoalteromonas isolated, and C5a showed a similar narrow host range, infecting only 3 and 4 strains. The remaining 16 of the isolated phages showed a broad cross-infectivity pattern (Fig.

120 Pseudoalteromonas phage-host interactions Chapter 3

M292 M292 82 M275 M275 M517 M517 Alteromonas strains 99 M169 M169 M111 M111 M113 M113 M107 M107 M290 M290 M271 M271 Pseudoalteromonas strains M306 M306 DB46 DB46 DB43 DB43 DB42 DB42 DB41 DB41 DB29 DB29 Alteromonas strains 98 DB28 DB28 DB25 DB25 DB44 DB44 DB30 DB30 DB48 DB48 DB14 DB14 DB88 DB88 DB10 DB10 100 DB23 DB23 DB71 DB71 DB49 DB49 DB55 DB55 DB7 DB7 DB77 DB77 ALMIC1A ALMIC1A DB92 DB92 ZOCONH12 ZOCONH12 DB94 DB94 DB93 DB93 DB84 DB84 ZOCONA8 ZOCONA8 DB3 DB3 DB1 DB1 DB26 DB26 DB24 DB24 DB9 DB9 DB8 DB8 Pseudoalteromonas strains DB72 DB72 DB32 DB32 ZOCONA5 ZOCONA5 DB89 DB89 ZOCONA7 ZOCONA7 DB79 DB79 DB12 DB12 DB54 DB54 DB53 DB53 DB56 DB56 DB50 DB50 ZOCONB10 ZOCONB10 ALSKE5D ALSKE5D ZOCONB9 ZOCONB9 DB22 DB22 DB21 DB21 DB67 DB67 DB65 DB65 DB62 DB62 DB59 DB59 DB58 DB58 DB18 DB18 DB17 DB17 infection DB15 DB15 DB16 DB16 no infection 0.1 B8b C5a H1/4 H1/3 H1/1 H3/3 G3/2 G3/5 G3/1 G3/3 G3/4 306c 306a 306b 271/2 271/4 271/3 271/1 Isolated from: DB23DB32 MED271DB84 MED271 MED306 DB84 DB89 DB77

Pseudoalteromonas sp. QC44 Pseudoalteromonas sp. RHS-str.402

Figure 4. hosts are organized according to the 16S rRNA gene phylogeny and at the top phages are distributed Heatmap displaying the infection patterns of the isolated phages. In the left of the figure, bacterial infections while the gray rectangles represented no infection. according to the cluster obtained by the RAPD-PCR banding profiles. Black rectangles indicated positive

Pseudoalteromonas spp. tested and phages 271/3, 271/1 and 271/4, 21 (Fig.4). All the broadest host ranges belonged to 4). For example, phage 271/2 was able to infect 22 of the 52 Myoviridae Alteromonas family. Phage C5a was the only myovirus that presented a narrow host range. Out of 8 all the phages were able to infect it (Fig. 4). Some of the isolated phages showed an identical spp. strains tested, only one, M111, was susceptible to infection and the most surprising was that host-range pattern, it is the case for the phage G3/3 and G3/4; phage H1/3 and phage H1/1;

phage 306a and phage 306b and finally phage 271/4 and 271/3 (Fig. 4). All of them belonged121 to Pseudoalteromonas phage-host interactions Chapter 3

Myoviridae family and they displayed different RAPD-PCR patterns, and therefore they were not considered identical phages.

Pseudoalteromonas spp. phages host-range versus genomic patterns Based on the matrix of the host ranges (lysis/no lysis) and on the banding patterns obtained from the RAPD-PCR analysis (absence/presence of bands) both dendrograms were in 7 clusters while the host range tree divided the phages in 4 groups. Both dendrograms showed generated and compared (Fig. 5). The phages genomic fingerprints separated the isolated phages

306b and 306c because they were the only ones that were grouped together in both dendrograms significantly different clusters, except for phages 271/3, 271/4, 271/1, 271/2 and phage 306a, Pseudoalteromonas phages grouped by host range were not genetically similar.

(Fig. 5). Thus, by host-range because presented a very similar capacity of infectivity; however their genomic Phage C5a and B8b belonged to different morphology family but they were clustered together profiling is very different. Hence, comparison between host-range analyses and phage genomic range exhibited distinct genomic pattern. profiles were not coherent for all cases reflecting that many phages that shared identical host

Probability of infection To investigate further the correlation between the infection dynamics and the genetic distant of the bacterial strains, the probability of infection was measured for each isolated phage based on the phylogenetic resolution provided by the 16S rRNA gene or by the RAPD-PCR expected, the probability of infection decreased when the genetic distance increased, however, genomic fingerprinting build on the whole bacterial genome (Fig. 6). For both markers and as considerable differences between 16S rRNA gene and RAPD-based methods were detected. of infection and the genetic distance based on 16S rRNA gene (p value ranged from 0.087 and Some of the isolated phages showed a significantly negative correlation between the probability accordance with the RAPD-PCR patterns. The p values ranged from 2 x 10-4 0.985) but all the phages showed lower p values when the capacity of infection was measured in also showed that the RAPD distance contribute to explain an average of 28% of the probability of to 0.04. Our results infection on the bacterial strains, while the 16S rRNA gene only explained an average of 1% (Fig. 6).

122 Pseudoalteromonas phage-host interactions Chapter 3

Genomic phage profile clusters Host range profile clusters

1.0 1.0

Figure 5. Dendograms comparing the RAPD-PCR analysis (absence/presence of bands) and host range results assays (lysis/no lysis). Phages colored in dark and medium grey represented phages grouped light gray represented phages grouped in the same cluster in both dendograms (phages 271/1, 271/2, differently in both dendograms (phages B8b and C5a; Phages G3/2, G3/3 and H1/3). Phages colored in 271/3 and 271/4).

Host–Phage Infection Statistics We investigated at which type of model corresponded the Pseudoalteromonas host-phage both and strains, with the analysis unable to identify groups of interactionsPseudoalteromonas obtained. We foundAlteromonas a not significant modularity (0.099; values between 0 and 1) for phages and bacteria with no cross-infection between them (Fig. 7A). Instead, sorting the matrix

(values between 0 and 1) while 100% of the random matrices scored below it. According to these to achieve the maximum degree of nestedness (Fig. 7B), this produced a significant value of 0.863 results, the interactions between Pseudoalteromonas and Alteromonas strains and the studied phages followed a nested pattern, where host ranges were subsets one of another.

123 Pseudoalteromonas phage-host interactions Chapter 3 G3/1 306b 271/4 Genetic distance Genetic distance Genetic distance

0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5

. . . . . 1.0 0.8 0.6 0.4 0.2 0.0 . . . . . 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0

Probability of infection of Probability Probability of infection of Probability infection of Probability H1/3 306a 271/2 Genetic distance Genetic distance Genetic distance

0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5

. . . . . 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0

Probability of infection of Probability infection of Probability infection of Probability B8b H1/4 G3.2 Genetic distance Genetic distance Genetic distance

0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5

. . . . . 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0

Probability of infection of Probability infection of Probability infection of Probability H1/1 G3/3 306c Genetic distance Genetic distance Genetic distance

0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5

. . . . . 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 . . . . . 1.0 0.8 0.6 0.4 0.2 0.0

Probability of infection of Probability infection of Probability Probability of infection of Probability C5a G3/5 271/3 Genetic distance Genetic distance Genetic distance

0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5

. . . . . 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0

Probability of infection of Probability infection of Probability infection of Probability H3/3 G3/4 271/1 Graphic representation showing the probability of infection measured according to 16S rRNA gene and to the RAPD-PCR banding pattern pattern banding RAPD-PCR the to and gene rRNA 16S to according measured infection of probability the showing representation Graphic Genetic distance Genetic distance Genetic distance

0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5

. . . . . 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8 0.6 0.4 0.2 0.0 . . . . . 1.0 0.8 0.6 0.4 0.2 0.0

Probability of infection of Probability infection of Probability Probability of infection of Probability Figure 6. Figure from the hosts. from 124 Pseudoalteromonas phage-host interactions Chapter 3

A B

DB21 306a Pseudoalteromonas strain DB94 DB79 Alteromonas strain MED111 DB53 DB32 DB84 DB21 DB77 306b DB79 DB93 DB53 DB89 DB84 DB8 DB77 DB24 H3/3 DB93 Pseudoalteromonas DB92 DB8 M306 DB24 strains M290 DB92 DB72 G3/3 MED306 M271 MED290 DB9 DB9 DB1 DB89 M107 G3/2 MED271 DB54 DB15 DB15 DB72 DB3 DB3 G3/1 DB48 DB1 DB14 MED107 DB23 DB54 DB94 G3/4 DB7 DB7 DB23 Alteromonas strain M111 DB48 DB32 Pseudoalteromonas strains DB14 DB18 G3/5 DB18 DB12 DB12 DB58 DB58 DB50 Myoviridae family DB50 DB62 H1/3 DB62 DB59 DB59 DB56 DB56 DB16 DB16 DB71 H1/1 DB71 Pseudoalteromonas DB88 DB88 strains DB55 DB55 DB49 DB49 DB26 306c DB26 DB65 DB65 DB10 DB10 DB22 DB22 DB67 H1/3 DB67 DB17 DB17 ZOCONA8 ZOCONA8 ZOCONA7 ZOCONA7 ALSKE5D 271/4 ALSKE5D ZOCONB10 ZOCONB10 ZOCONB9 ZOCONB9 ZOCONA5 ZOCONA5 271/1 ZOCONH12 ZOCONH12 ALMIC1A ALMIC1A DB25 DB25 DB28 DB28 271/3 DB29 DB29 DB30 DB30 DB41 DB41 DB42 DB42 271/2 Alteromonas strains DB43 Alteromonas strains DB43 DB44 DB44 DB46 DB46 M113 B8b Siphoviridae family MED113 M169 MED169 M275 MED275 M292 MED292 M517 C5a Myoviridae family MED517 B8b C5a H1/4 H1/3 H1/1 H3/3 G3/1 G3/5 G3/3 G3/4 G3/2 306c 306a 306b 271/3 271/4 271/1 271/4 Myoviridae family Siphoviridae family Figure 7. isolated phages and and Modeling ofPseudoalteromonas the host-range matrix showingAlteromonas (A) insignificant modularity interactions between obtained from the same host range results. Red line represents the isocline. hosts and (B) significant nestedness matrix

DISCUSSION

Genomic characterization of bacterial strains used in this study Pseudoalteromonas spp. strains are members of the Gammaproteobacteria class. This group

area have demonstrated that this group shows high percentages of active cells taking up different seems to be important in terms of carbon processing in Blanes Bay (BBMO). Several works in this

sometimes, low abundances (Alonso-Sáez and Gasol, 2007; Vila-Costa et al., 2007). Therefore, low molecular weight compounds (such as glucose, amino acid, ATP and DMSP) in spite of their, the ecology of the Pseudoalteromonas-phage system could be an important part of the carbon cycle in this coastal site. The 89 bacterial strains used in this study were analyzed by 16S rRNA sequences; the mean identity between the Pseudoalteromonas spp. strains was 99.9% nucleotide identity. According with these results, these bacterial strains were highly similar in terms of the 16S rRNA gene. Hence, the bacterial genomic pattern was determined by RAPD-PCR technique,

which, allows for whole genome comparison (strain-typing) of bacterial strains (Martinkearley

125 Pseudoalteromonas phage-host interactions Chapter 3

et al., 1994; Perumal et al., 2009). Although the RAPD-PCR were no significant at the 95% level when analyzed using the SIMPROF test in PRIMER 6 (data not shown), a higher divergence can be observed through their genomic profiles wherein contrast genomic patterns were obtained for bacterial diversity host. those strains with identical 16S rRNA genes (Fig. 1) reflecting a better resolution to distinguish

High Pseudoalteromonas phage diversity and discrepancy between phage morphology, genomic patterns and host range First at all, we try to isolate the phages with a simple direct plating of the seawater from

BBMO via plaque assay on the bacterial isolates. But, phages were not enough abundant to an oligotrophic coastal marine environment, characterized by low nutrient concentration and isolate them without enrichment, possible due to Blanes Bay Microbial Observatory (BBMO) is plankton biomass (Duarte et al., 1999; Pinhassi et al., 2006; Alonso-Sáez et al., 2008). Pseudoalteromonas spp.strains. We considered 18 genetically different according the RAPD-PCR technique and although we Twenty-five phages were isolated from 7 different cannot discard that identical RAPD genomic patterns represent actually different phages, determined their host range. The high phage and host diversity within the we morphologically characterized these phages with distinct genomic profilesPseudoalteromonas and we also spp. group support recent studies of Flavobacterium psychrophilum (Stenholm et al., 2008), Cellulophaga baltica (Holmfeldt et al., 2007) and Vibrio parahaemolyticus (Comeau et al., 2006; Comeau and Suttle, 2007) and indicates that interactions between phages and their hosts are characterized by a high degree of complexity at strain level. The phages investigated in this study belonged to two families in the Caudovirales (Myoviridae and Siphoviridae), although a previous published study also focused on isolated Pseudoalteromonas phages found the 3 bacteriophages families, the Podovirae family was underrepresented (Wichels et al., 2002). In our case, we did not found podoviruses; these results may indicate that Pseudoalteromonas phages are basically represented by myoviruses and siphoviruses. Phage B8b was the only one of the isolates bacteriophages belonged to Siphoviridae unpublished data), a narrow host range and was isolated from a different bacterial strain than family, which had a genome size of 46 kb (Lara et. al, Myoviridae phages presented a broad host sp. the rest of the phages (Table 1.SM and Fig. 4). All Pseudoalteromonas range except for phage C5a and all of them were isolated from the same pattern, bacteriophages were split into 7 groups (Fig. 2). It was not detected a tendency to group strain in relation with the 16S rRNA gene (Table 1.SM). According with the genomic banding the phages by the bacterial strain from which they were isolated or morphology. To determine

126 Pseudoalteromonas phage-host interactions Chapter 3

whether genotypic patterns reflected the host range, an analysis of specificity of phage infection patterns was carried out (Fig. 5). The major determinant of host range viral profiling was the same cluster despite of belonged to different family but both were the only ones that presented a bacterial strain with which they were originally isolated. Phage B8b and C5a were grouped in the narrow host range. Interestingly, phages genetically grouped, mismatched in host range patterns two groups of phages were clustered together (phages 306a, 306b and 306c and 271/1, 271/2, (Fig. 5) like phages H1/3, G3/2 and G3/3. Despite the differences between both dendrograms, not correlated. 271/3 and 271/4) although the general pattern was that host range and genetic profiling were

High heterogeneity in the host range patterns within Pseudoalteromonas phages The broad host ranges of phages belonging to the Myoviridae compared to the host range of the phage belonging to the Siphoviridae phages appeared supportsPseudoalteromonas the findings of the Wichels et al. (1998) or Suttle and Chan (1993). Our findings show that range within spp. bacterial strains and second (ii) all of them were able to be not speciesPseudoalteromonas specific, first because (i) phages showed a wide array of intraspecific host to infect an Alteromonas sp. bacterial strain. The fact that all the isolated phages could infect the same Alteromonas harbor any mechanisms (such CRISPR host immune) to avoid phage infection and therefore sp. strain M111 and not others could suggest this strain may not the susceptibility of the host to be infected for different phages may differ significantly at bacterial strains from other genera besides the bacterial specie from which they were originally intraspecific level (microdiversity). Previously, it has been reported phages that can infect isolated like cyanophages infecting Prochlorococcus myoviridae

(Sullivan et al., 2003), LG1 ( phages crossing over the coliphage), the T4 coliphage AR1, and the T4-like Pseudoalteromonasvibriophage KVP40 (Matsuzaki et al., 1992), barrier of genera since and belong in fact to a different families but our results showedPseudoalteromonas for it for the first time Alteromonason (Pseudoalteromonadaceae and Alteromonadaceae).

However, the mechanisms involved in these phage’s abilities to infect different hosts have in the host, or phage resistant system (Breitbart, 2012). It has been reported that vibriophage not been elucidated, may be due to variation of receptor molecules, restriction modification system KVP40 infect Vibrio and Photobacterium species and it has been implicated that Vibrio species

Wu et al. (2007) found a lytic have a membrane protein as theKlebsiella phage receptor pneumoniae for the phage in common (Miller et al., 2003). also a broad host range. This last study proposed that the conditions that may cause a phage bacteriophage, Kpp95, which presented 127 Pseudoalteromonas phage-host interactions Chapter 3

phages, which act as receptors on the surface of the bacteria. Besides the biological mechanisms to alter or extend the host range is due to the mechanisms involving the tail fiber genes from that involve the phage-host interaction, it is generally accepted that the isolation initially might select for all bacteriophages capable of interaction with a host and the use of cocktails containing several hosts should be necessary to reduce the probability of resistance development (Jensen et al., 1998). The continued rounds of phage growth on the same host result in the unintentional bacteriophage enrichment is acting against phages with a broad host range and establishing an selection of phages lysates that become more specific for the host. And if this is true, the erroneous view that bacteriophages are restricted in their range and therefore in their impact on bacterial communities. The broader host range reported here could also indicate the phage potential for mediating transduction, which it is helpful to explain the microdiversity among the bacterial strains and thus may contribute to gene exchange between diverse host species (Jiang and Paul, 1998). Therefore, the impact of phage infection and lysis on a bacterial population is dependent not only on whether the phage can infect the host and on how susceptible the host is to infection.

Bacterial genomic patterns as a better marker to discriminate the probability of phage infection The probability of infection measured for each isolated phage and based on the phylogenetic resolution provided by the 16S rRNA gene or by the RAPD-PCR showed that phage and bacteria interactions when closely related host are analyzed cannot be resolved on conserved genes such genetic distance performs better to explain the probability of phage infection on the bacterial as the 16S rRNA gene and therefore other alternatives are desirable. Our findings indicate RAPD strains (Fig. 6). As previously knowledge the 16S rRNA is too conserve gene to distinguish between bacterial strains in host range studies and correlations between the phage susceptibility genes as alternative marker with provide a better resolution at microdiversity level (Sullivan et and host phylogeny cannot be detected using 16S rRNA. Other studies has been used the ITS al., 2003; Holmfeldt et al., 2007) but this could be a problem when multiple rrn operon exist in the bacterial host of interest, an this was the case for our Pseudoalteromonas spp. that displayed at least two or three different ITS (data not shown) making comparisons between strains more complicated. Thus, the probability of infection is better correlated by the whole bacterial genome patterns provided by RAPD-PCR than 16S rRNA gene or ITS gene.

128 Pseudoalteromonas phage-host interactions Chapter 3

Evolutionary context of the phage-host interactions patterns The phage-host infection network taking into account the Pseudoalteromonas and Alteromonas

strains fits with previous models on nested pattern to explain the structure of such Flores et al. (2011). These authors analyzed 38 studies of phage-host infection networks at narrow phage-host interactions (Fig. 7). These results are in agreement with the previous findings of the majority of the studies analyzed until now were with closely related bacterial strains. In taxonomic scale and they found that the majority of the studies were nested matrices. Moreover, this study, we determine the infection network also with all the bacterial strains tested in the could increase the compartmentalization and therefore modularity would be the expected host range analysis, therefore at larger phylogenetic scale. More complex patterns of infections pattern in these conditions. In the same work, Flores and co-authors (2011) already suggested that modularity should be expected in studies at larger biogeography or phylogenetic scales. In fact, they recently published the analysis of a data set with largest phage bacteria infection

However, we found that the infection network was also nested between and network (215 phages with 286 hosts) and they found a modularity patternPseudoalteromonas (Flores et al., 2013). Alteromonas In an evolutionary context, nestedness can be explained as the result of co-evolutionary host with no significance for modularity (Fig. 7 and Fig. 3.SM). processes that lead to specialization (Flores et al., 2011). The most specialist phages infect those hosts that are more susceptible to infection rather than infecting those hosts that are more resistant to infection. This model results would translate into bacteria evolving to increase phage resistance and phages evolving to broader host ranges. Understanding how such co-evolutionary processes uncovered by the phage-interaction network would have an effect on environmental bacterial dynamics is crucial. However, these both models are idealizations and in natural environments might be intermediate mechanisms. For instance, it is possible that phages evolve the ability to infect new hosts and partially lose natural systems is that phage-host networks do not have a perfectly nested or modular structure the ability to infect existing hosts (Agrawal and Lively, 2003). Thus, the most probable in marine (Forde, 2008).

129 Pseudoalteromonas phage-host interactions Chapter 3

ACKNOWLEDGMENTS

We thank the members of the department of transmission electron microscopy in the scientific Karin Holmfeldt for their advices in this work. This work has been supported by the Spanish and technological center at the University of Barcelona. We also are grateful to Matt Sullivan and projects MICROVIS (CTM2007-62140/MAR), PANGENOMICS (CGL2011-26848/BOS) and FLAME (CGL2010-16304). Financial support was provided by a Ph.D. fellowship from the Spanish government to E. Lara.

130 Pseudoalteromonas phage-host interactions Chapter 3

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NW Mediterranean. Environ Microbiol 9: 2451-2463. Weinbauer, M.G. (2004) Ecology of prokaryotic viruses. FEMS Microbiol Rev 28: 127-181. microscopy based estimates of viral infection of bacterio-plankton using conversion factors Weinbauer, M.G., Winter, C., and Höfle, M.G. (2002) Reconsidering transmission electron 133 Pseudoalteromonas phage-host interactions Chapter 3

Wichels, A., Gerdts, G., and Schutt, C. (2002) derived from natural communities. Aquat PseudoalteromonasMicrob Ecol 27: 103-110. spp. phages, a significant group of marine bacteriophages in the North Sea. Aquat Microb Ecol 27: 233-239. Wichels, A., Biel, S.S., Gelderblom, H.R., Brinkhoff, T., Muyzer, G., and Schutt, C. (1998) Bacteriophage diversity in the North Sea. Appl Environ Microbiol 64: 4128-4133. Winget, D.M., and Wommack, K.E. (2008) Randomly amplified polymorphic DNA PCR as a tool for assessment of marine viral richness. Appl Environ Microbiol 74: 2612-2618. Wommack, K.E., and Colwell, R.R. (2000) Virioplankton: Viruses in aquatic ecosystems. Microbiol Mol Biol Rev 64: 69-114. . Appl Wu, L.T., Chang, S.Y., Yen, M.R., Yang, T.C., and Tseng, Y.H. (2007) CharacterizationKlebsiella pneumoniae of extended- host-range pseudo-T-even bacteriophage Kpp95 isolated on Environ Microbiol 73: 2532-2540.

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SUPPLEMENTAL MATERIAL

A

B

Figure 1.SM. RAPD-PCR banding pattern obtained from (A) the Pseudoalteromonas hosts and (B) Alteromonas

hosts. In figure A: Line 1, ZOCONB10; Line 2, MED290; Line 3, DB3; Line 4, DB17; Line 5, DB65; Line 6, DB67; Line 7, DB26; Line 8, DB92; Line 9, ZOCONA5; Line 10, ZOCONB9; Line 11, DB93; Line 12, DB94; Line 13, MED107; Line 14, MED271; Line 15, DB22; Line 16, DB48; Line 17, ZOCONA8; Line 18, ZOCONA7; Line 19, ALMIC1A; Line 20, ALSKE5D; Line 21, DB15; Line 22, DB16; Line 23, DB1; Line 24, DB21; Line 25, DB84; Line 26, DB77; Line 27, DB72; Line 28, DB58; Line 29, DB59; Line 30, DB54; Line 31, DB50; Line 32, DB62; Line 33, DB23; Line 34, DB12; Line 35, DB18; Line 36, DB7; Line 37, DB14; Line 38, DB79; Line 39, DB55; Line 40, DB49; Line 41, DB8; Line 42, DB24; Line 43, DB89; Line 44, DB88; Line 45, DB53; Line 46, DB56; Line 47, DB71; Line 48, DB10; Line 49, DB9; Line 50, MED306; Line 51 DB32 and Line 52, ZOCONH12. In figure B: Line 1, DB25; Line 2, MED292; Line 3, MED517; Line 4, MED169; Line 5, MED275; Line 6, MED111; Line 7; MED113; Line 8, MED517; Line 9, DB29; Line 10, DB30; Line 11, DB41; Line 12, DB42; Line 13, DB43; Line 14, DB44 and Line 15, DB46. Ladder: EasyLadder I (2000bp – 1000 bp – 500 bp – 250bp – 100bp, Bioline).

135 Pseudoalteromonas phage-host interactions Chapter 3

N= 0.886

ZN= 14.13

Figure 2. SM. analysis. Red line represents the isocline. Significant nested matrix between isolated phages and all the hosts used in the host range

136 Pseudoalteromonas phage-host interactions Chapter 3

Table 1.SM. Bacterial strains used to isolate phages and to carry out the host range analysis. Bacterial strains used to isolate phages are labeled in black. % Similaruty 16s rRNA Isolation Isolation Taxon Strain gene (GenBAnk year location accession no.) Pseudoalteromonas sp. RHS-str.402 MED306 2001 BBMO 100.0 ( HE586873) Pseudoalteromonas sp. RHS-str.402 DB89 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB32 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB77 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 MED271 2001 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB84 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 M107 2001 BBMO 100.0 (FR821205) Pseudoalteromonas sp. RHS-str.402 DB21 2009 BBMO 100.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB16 2009 BBMO 99.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB53 2009 BBMO 100.0 (HE586873) Pseudoalteromonas sp. RHS-str.402 DB79 2009 BBMO 100.0 (HE586873) Pseudoalteromonas sp. QC44 DB23 2009 BBMO 99.0 (JN384138) Pseudoalteromonas sp. QC44 ZOCONA7 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB62 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 ZOCONB9 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 ZOCONA8 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB50 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB54 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB56 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB67 2009 BBMO 100.0 (JN384138) Pseudoalteromonas sp. QC44 DB59 2009 BBMO 99.0 (JN384138) Pseudoalteromonas sp. QC44 DB58 2009 BBMO 99.0 (JN384138) Pseudoalteromonas sp. QC44 DB65 2009 BBMO 99.0 (JN384138) Pseudoalteromonas sp. HK-3 DB1 2009 BBMO 100.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB14 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB18 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB22 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB24 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB72 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB8 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB9 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB93 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. HK-3 DB94 2009 BBMO 99.0 ( FJ477041) Pseudoalteromonas sp. SXBYC5n DB55 2009 BBMO 100.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB49 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB10 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB7 2009 BBMO 99.0 (EU343664)

Gammaproteobacteria/Alteromonadales/Pseudoalteromonadaceae/Pseudoalteromonas Pseudoalteromonas sp. SXBYC5n DB71 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. SXBYC5n DB88 2009 BBMO 99.0 (EU343664) Pseudoalteromonas sp. AB333f DB15 2009 BBMO 99.0 (FR821205) Pseudoalteromonas sp. AB333f MED290 2001 BBMO 99.0 (FR821205) Pseudoalteromonas sp. DIT 46 ALSKE5D 2009 BBMO 100.0 (HQ199603) Pseudoalteromonas sp. DIT 46 ZOCONA5 2009 BBMO 100.0 (HQ199603) Pseudoalteromonas atlantica DB92 2009 BBMO 89.0 (AJ874344) Pseudoalteromonas sp. 114Z-7 DB17 2009 BBMO 99.0 (JX310123) Pseudoalteromonas sp. 19(2006) DB48 2009 BBMO 99.0 (DQ642825) Pseudoalteromonas sp. AB474f ALMIC1A 2009 BBMO 99.0 (FR821209) Pseudoalteromonas sp. BSi20316 DB26 2009 BBMO 99.0 (DQ492738) Pseudoalteromonas sp. CI4 DB3 2009 BBMO 99.0 (EU935585) Pseudoalteromonas sp. D32 ZOCONB10 2009 BBMO 100.0 (AY576005) Pseudoalteromonas sp. MB103 ZOCONH12 2009 BBMO 100.0 (AB519012) Pseudoalteromonas sp. NBRC 102015 DB12 2009 BBMO 99.0 (AB681662)

137 Pseudoalteromonas phage-host interactions Chapter 3

Alteromonas genovensis strain: LMG DB25 2009 BBMO 99.0 (NR042667) 24078 Alteromonas genovensis strain: LMG DB29 2009 BBMO 99.0 (NR042667) 24078 Alteromonas genovensis strain: LMG DB41 2009 BBMO 99.0 (NR042667) 24078 Alteromonas genovensis strain: LMG DB42 2009 BBMO 99.0 (NR042667) 24078 Alteromonas genovensis strain: LMG DB46 2009 BBMO 99.0 (NR042667) 24078 Alteromonas sp. M E D 1 1 1 2001 BBMO 100.0 (DQ681132) Alteromonas sp. MED113 2001 BBMO 100.0 (DQ681133)

Alteromonas Alteromonas sp. MED169 2001 BBMO 100.0 (DQ681147) Alteromonas sp. MED275 2001 BBMO 100.0 (DQ681166) Alteromonas sp. MED517 2001 BBMO 100.0 (DQ681179) Alteromonas genoviensis strain I96 DB28 2009 BBMO 99.0 (FJ040187) Alteromonas genoviensis strain I96 DB43 2009 BBMO 99.0 (FJ040187) Alteromonas sp. BCw006 DB30 2009 BBMO 100.0 (FJ889589) Alteromonas sp. BCw006 DB44 2009 BBMO 99.0 (FJ889589) Gammaproteobacteria/Alteromonadales/Alteromonadaceae/ Alteromonas macleodii M292 2001 BBMO 99.0 (CP003917)

Marinobacterium georgiense CECT7200 2006 Augusta (Georgia) AB681881

Marinobacterium jannaschii CECT7201 2006 United States AB680864 Marinobacterium Alteromonadales/ Marinobacterium stanieri CECT7202 2006 Oahu (Hawaii) AB021367 Alteromonadaceae/ Gammaproteobacteria/ Vibrio gigantis MED227 2001 BBMO 97.8 (AJ582807) Vibrio gigantis MED241 2001 BBMO 100.0 (AJ582807) Vibrio pectenicida MED535 2001 BBMO 99.7 (Y13830) Vibrio sp. MED222 2001 BBMO 100.0 (AF242274)

Vibrio Vibrio sp. MED126 2001 BBMO 99.8 (AJ316207) Vibrio sp. MED140 2001 BBMO 99.6 (DQ219366) Vibrio splendidus MED511 2001 BBMO 100.0 (AJ874364) Gammaproteobacteria/ Vibrionales/Vibrionaceae/ Vibrionales/Vibrionaceae/ Vibrio tasmaniensis MED181 2001 BBMO 99.1 (AJ514912)

Dokdonia donghaensis MED134 2001 BBMO 99.4 (DQ003277) Polaribacter dokdonensis MED152 2001 BBMO 99.5 (DQ004686) Leeuwenhoekiella accommodimaris MED217 2001 BBMO 97.0 (AJ780980) Salegentibacter mishustinae MED220 2001 BBMO 94.1 (AY576653) Bacteroidetes/ Bacteroidales/ Bacteroidaceae Salegentibacter slinus MED532 2001 BBMO 99.0 (EF486353)

2009 BBMO Nereida sp. ZOCOND2 99.8 (AY612764)

2009 BBMO

Nereida Nereida sp. ZOCONH4 99.8 (AY612764) Rhodobacterales/

Rhodobacteraceae/ 2001 BBMO Alphaproteobacteria/ Nereida sp. MED365 100.0 (DQ681170)

2001 BBMO Erythrobacter litoralis MED155 99.0 (AF465836)

2001 BBMO Erythrobacter citreus MED456 100.0 (AF118020) Erythrobacter 2001 BBMO Sphingomonadales/ Erythrobacteraceae/ Alphaproteobacteria/ Erythrobacter citreus M539 100.0 (AF118020)

BBMO: Blanes Bay Microbial Observatory. NW Mediterranean Sea.

138 Life-style and mosaic genome structure of marine Pseudoalteromonas siphovirus B8b isolated from the Northwestern Mediterranean Sea

Elena Lara, Karin Holmfeldt, Natalie Solonenko, J. Cesar Ignacio-Espinoza, Nathan C. Verberkmoes, Dolors Vaqué, Matthew B. Sullivan and Silvia G. Acinas

4Chapter

Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

ABSTRACT

Marine viruses play a fundamental role regarding bacterial diversity, evolution and biogeochemical cycles. Here we present biological, proteomic, genomic and phylogenetic analyses to characterize the phage B8b isolated from the Mediterranean Sea using Pseudoalteromonas sp. QC-44 (Gammaproteobacteria) as a host. By morphology and genome, phage B8b belonged to the Siphoviridae family; with its 46kb genome most closely related to a siphovirus prophage of Marinobacterium stanieri (Gammaproteobacteria). The phage B8b genome was typically siphovirus in that, it was modular and contained only a single gene (GroES) that was novel among siphoviruses. Virion structural proteomics detected 13 structural genes, only 3 of which were different burst size and latent period from other known identifiable by sequence similarity to known structural proteins.Siphoviridae One-step growth analyses showed host growth rates. Host range analysis revealed that phage B8b infected 3 of 52 probablyPseudoalteromonas reflecting differing strains (99.9% identity in their 16S rRNA gene), as well as 1 Alteromonas strain from a different bacterial family (only 80% 16S rRNA identity) among 37 non-Pseudoalteromonas strains from 6 genera test. This extensive host range in terms of phylogenetic distance helps bound a single evolutionary step in phage-mediated horizontal gene transfer. Moreover, this phage was not marine environments. abundant in the POV metagenome but it can be considered a rare and ubiquitous phage in coastal

141 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

INTRODUCTION

Marine viruses are the most abundant biological entities on the planet and play critical roles for bacterial biology, diversity, and evolution (Fuhrman, 1999; Wommack and Colwell, 2000; Weinbauer, 2004; Suttle, 2005, 2007; Brussaard et al., 2008; Rohwer et al., 2009; Breitbart, 2012). In marine environments, phages lyse bacterial cells and thereby have a global impact on marine biogeochemical cycles and microbial ecological processes (Fuhrman, 1999; Suttle, 2007; Brussaard et al., 2008; Danovaro et al., 2011). They also represent an important force in microbial evolution as one of the major vehicles for gene transfer in the ocean and mediate the diversity is hard to measure because (i) viruses lack a universally conserved gene marker (e.g., acquisition of novel genetic information (Jiang and Paul, 1998). Despite their relevance, viral ribosomal RNA genes in cellular organisms), and (ii) most (>99%) bacteria in nature are resistant to cultivation using standard techniques (Rappé and Giovannoni, 2003) which limits the hosts bacterial lawns (Seguritan et al., 2003; Breitbart, 2012). To circumvent these limitations, viral available for virus isolation efforts. Moreover, not all phages produce identifiable plaques on community diversity has been analyzed by culture independent approaches including (i) Pulse- Field Gel Electrophoresis (PFGE) which discriminates viruses by genome size (Steward et al.,

2000; Steward, 2001), (ii) Randomly Amplified Polymorphic DNA PCR (RAPD) which provides a genetic fingerprint for the whole viral community (Comeau et al., 2006; Winget and Wommack, whole viral community (Breitbart et al., 2002; Rohwer, 2003; Angly et al., 2006; Rodriguez-Brito 2008) and (iii) viral metagenomics (viromics) which provides fragmented sequence data for the et al., 2010). Through comparative genomics of limited numbers of phage isolates, it has been suggested that viruses are exceptionally diverse and represent the largest reservoir of genetic diversity in the ocean with early metagenomic analyses suggesting the same (Pedulla et al., 2003;

Rohwer, 2003; Angly et al., 2006). However, recent calculations leveraging the large-scale Pacific orders of magnitude smaller at only a few million total proteins (Ignacio-Espinoza et al., 2013). Ocean Virome (POV) dataset (Hurwitz and Sullivan, 2013) suggest the global virome may be three These culture-independent metagenomic methods are powerful, but they are also severely database limited due to the lack of sequenced viral genomes. For example, the majority (>70%) of the predicted viral open reading frames (ORFs) in metagenomes have no similarity to Williamson et al., 2008; Hurwitz and Sullivan, 2013). Moreover, dominant marine viruses are previously described sequences (Breitbart et al., 2002; Angly et al., 2006; Dinsdale et al., 2008;

Suttle, 2013; Steward et al., 2013). Thus, isolate-based genome analyses are essential to better not well represented in culture collections (Brum et al., 2013; Hingamp et al., 2013; Labonté and 142 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

map viral sequence space and viral-host interactions in nature. with recently addition of phages infecting abundant marine heterotrophic bacteria proving Most sequenced marine phage genomes belong to cyanophages (Paul and Sullivan, 2005), incredibly valuable (Holmfeldt et al., 2013; Kang et al., 2013; Zhao et al., 2013). However, we remain relatively or completely ignorant of viruses for other relevant marine taxa. Pseudoalteromonas sp. strains are members of Gammaproteobacteria, and this class of Proteobacteria may contribute up to 30% of total marine bacterioplankton with a 20 to 80% of them taking up 3H‐leucine (Ruiz- Pseudoalteromonas sp. are in many cases associated to particle attached bacterial assemblages fraction wherein it has González et al., 2012) reflecting active members of the microbial loop. Also been shown that Gammaproteobacteria abundance reached maximum peaks ranged 24-60% in that fraction (Crespo et al., 2013). Several studies have reported the ecological and evolutionary importance of the Pseudoalteromonas phages (Moebus, 1992; Wichels et al., 1998; Wichels

showed integrated prophages (Thomas et al., 2008; Duhaime et al., 2011; etPseudoalteromonas al., 2002; Thomas et al., 2008; Duhaime et al., 2011) and some of the sequenced bacterial Xie et al., 2012). However, still there are only four marine Pseudoalteromonas phage genomes Pseudoaltermonas phage PM2, Pseudoalteromonas phage H105/1, phage pYD6-A (Männistö et al., sequencesPseudoalteromonas available in public datasets: Pseudoalteromonas 1999; Duhaime et al., 2011; Hardies et al., 2013). Despite infecting bacteria of the same genus, phage RIO-1 and Corticoviridae (PM2; (Murphy et al., 1995), (H105/1; Duhaime et al., 2011), and these phages do not share anySiphoviridae genes and even belong to different phage families:Podoviridae (RIO-1 functional organization similar to and pYD6-A) (Hardies et al., 2013).l While PM2 is the only sequenced corticovirus, H105/1 have are distantly related to T7-like viruses and N4-like viruses respectively (Hardies et al., 2013). -like siphoviruses (Duhaime et al., 2011) RIO-1 and pYD6-A To better expand our understanding of Pseudoalteromonas phage diversity, phage-host interactions in the marine environment and the genomic features of marine phages, we present the biological characterization, the proteomic, and genome structure of the siphovirus B8b.

MATERIAL AND METHODS

Phage isolation Pseudoalteromonas o), a surface coastal site in the NW phage B8b was obtained from Blanes Bay Microbial Observatory Mediterranean Sea, in winter 2009. Four liters of surface seawater was collected and after a 0.22 (BBMO, http://www.icm.csic.es/bio/projects/icmicrobis/bbm

μm pre-filtration (Millipore, Whatmann), phages were concentrated by tangential flow filtration143 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

(30KDa VIVAFLOW cartridge, Sartorius) to a final volume of 20 ml. Phages were isolated using of viral concentrate was added to 3 ml sp. QC-44, exponentially growing in liquid enrichment cultures and plaque assaysPseudoalteromonas (Sambrook, 1989). In the enrichments assay, 1 ml liquid Zobell medium (1.0 g yeast extract, 5 g peptone, 15 g agar, 250 ml MQ water and 750 ml ultra‐filtrated seawater). After 24h of incubation in the dark, the mixture was centrifuged (5,000 g, 10 min) and the supernatant was filtered through a 0.22 μm filter to remove any remaining 8 cells) and bacterial cell. Phage enrichment was confirmed by plaque assay, in which 100 μl phage sample from 10x dilution series was combined with 400 μl of liquid bacterial culture (~10 plated using the agar overlay technique by adding 3.5 ml of molten soft agar (0.5% agar in Zobell; x 7H 0, 50 mM Tris base, pH 8). For isolation and 50°C). After plating, a well-resolved plaque was4 picked2 from the lawn of host cells and eluted with MSM buffer (450 mM NaCl, 50 mM MgSO stocks were prepared by adding 5 ml of MSM to fully lysed plates. The plates were incubated on purification of single clonal phage population, plaques were purified 3 times. High titer phages a shaker (110 RPM) for 40 min and the phage-MSM solution was transferred to a sterile tube and at 4°C in the dark. centrifuged at 5,000 g for 10 min where after the supernatants were 0.22 μm filtered and stored

CsCl purification Phages for transmission electron microscopy and virion structural proteome analysis were lysed plates were concentrated using polyethylene glycol (PEG). Here, 3.25 g NaCl was added to purified by CsCl centrifugation (Sambrook and Russel, 2001). Briefly, phage lysate from ~20 fully 50 ml of phage lysate. The mixture was incubated 1 h at 4°C in the dark followed by centrifugation at 5,000 g, 10 min. The pellet was discarded and PEG 8000 (10%) was added to the supernatant. After an incubation of 1 h at 4°C in the dark, it was centrifuged (5,000 g, 10 min). The supernatant was discarded and the pellet was resuspended with MSM buffer. The centrifuge tube (Ultra-Clear, Beckman, Fullerton, CA, USA) was layered with 1.125 ml each of (1) 1.7 g CsCl ml-1, (2) 1.5g CsCl ml-1, (3) 1.45 CsCl ml-1 and (4) 1.2 g CsCl ml-1 centrifuged (87,000 g, 4 h). A turbid white line containing the phages was removed with a syringe , and finally topped with the viral concentrate and

USA) three times in 1 l buffer during at least 1 hour, (1 M Tris-HCl pH 8, 10 mM MgCl ) containing (2 ml total volume) and dialyzed (Slide-A-Lyzer Dyalisis Cassete G2 10 K MWCO, Rockford,2 IL, three different NaCl concentrations (3 M NaCl; 1.8 M NaCl; 0.6 M NaCl).

144 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

Electron microscopy of Pseudoalteromonas phage B8b lysate (see above) onto 200 mesh formvar-coated copper grids (Ted Pella) for 5 min. The solution Transmission electron microscopy grids were prepared by placing 10 µl of CsCl-purified acetate solution by rinsing the grids with 2 drops of the solution and staining for 45 s with a third was subsequently removed with filter paper and grids were negatively stained with 2% uranyl drop. The grids were examined using a Philips CM12 microscope with an accelerating voltage of 80 kV.

Pulse-field gel electrophoresis

Phage genome size was determined by pulse field gel electrophoresis (PFGE) (Steward, 2001). Phage lysate was concentrated by Amicon Ultra-15 centrifugal filter units (Millipore) low‐melting‐point agarose (Pronadisa), transferred to plugs molds, left to solidify at room from 5 ml to a final volume of 400 µl. Of this, equal amounts were mixed with melted 1.6% temperature for a few minutes and then kept at 15 minutes at 4°C. Plugs were incubated overnight at 50°C in ESP (0,5 M EDTA, pH 9, 0.1% N‐laurylsarcosine and 1 mg ml-1 proteinase K) and stored at 4°C until further analysis. PFGE was performed on a CHEF‐DR III system (Bio‐Rad) using 1% buffer (1X TBE is 89 M Tris, 2 mM EDTA, and 89 mM boric acid, pH 8.3) at a 5.0‐15.0 seconds agarose gel (LE agarose SeaKem n.50005 BERLABO S.A.). The gel was run for 22h in 0.5X TBE switch time, 6V cm-1 and an included angle of 120°. After electrophoresis, the gel was stained with SYBR Gold (Molecular probes, 10,000X) diluted to 10-4 in 150 ml of TBE for 15 min and washed with MQ water for 15 min. Lambda Low Range (New England Biolabs) was used as molecular size marker.

One-step growth experiments The burst sizes and one-step growth curves were determined as described by (Weiss et al., Pseudoalteromonas sp. QC44 overnight culture was transferred to 10 ml of fresh 20% Zobell media and incubated with shaking (120 RPM) for 1994), with minor modifications. One milliliter of 8 about 20 min, until the A600 cells ml-1. The concentration of bacterial cells at A was ~ 0.02, which was600 equivalent to a viable cell count of around 10 ~ 0.02 was verified by flow cytometry (Gasol tube and mixed with phage at a multiplicity of infection of 0.1. The mixture was incubated at and Del Giorgio, 2000). One milliliter of the bacterial culture was then transferred to an eppendorf room temperature for 15 min to allow phage adsorption. After this adsorption, the mixture was diluted to 10-2 in 20 ml of 20% Zobell media. Samples were removed to enumerate total and free

phage concentration. In order to detect the free phages, samples were 0.22 µm filtrated before145 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 plating. The number of phages in both cases was determined, in duplicate, using the double-agar- particles to the initial count of infected bacterial cells during the latent period. layer method. Finally, burst size was calculated as the ratio of the final count of liberated phage

Phage specificity To determine phage host range and bacterial susceptibility, a cross infectivity test was Pseudoalteromonas sp. as well as 37 strains of spp., spp., spp., , done where plaque assaysAlteromonas were performed Marinobacterwith phage B8b on Vibrio 52 strains Bacteroidetesof Nereida spp., and Erytrobacter (10-5 and 10-8 spp. (see Table 1.SM) using 100 μl of two different phage stock dilutions ), in order to distinguish between a clear lysis caused by plaque formation or inhibition of the bacterial lawn. Lysis was evaluated after overnight incubation in the dark. One the bacterial strains that showed phage susceptibility in the first test, a more thorough analysis was performed to determine the efficiency of infection on each strain. Here, plaque assays were days incubation. performed with a range of 10X diluted phage stock and plaques were enumerated after 1 and 2

Viral DNA purification and genome sequencing Viral DNA was obtained using the Lambda Wizard DNA kit (Promega Corp. Madison,

WI) (Henn et al., 2010; Sullivan et al., 2010). Phage lysate from ~15 fully lysed plates were concentrated using polyethylene glycol as described earlier (CsCl purification section). One ml (the PEG pellet resuspended with MSM) and mixed gently by inverting the tube. The mixture of Purification Resin (Promega, product A7181 Madison WI) was added to 1.5 ml of phages was loaded onto a mini-column (Promega, product A7211 Madison WI) through a 5 ml syringe attached to the column, pushing the mixture through with the syringe plunger. The column was then washed with 2 ml 80% isopropanol, the syringe was removed and the mini-column placed into a 1.5 ml eppendorf tube and centrifuged (10,000 g, 2 min, room temperature) to remove any the DNA was recovered in a 1.5 ml eppendorf tube through centrifugation (10,000 g, 30 s, room remaining liquid. Phage DNA was eluted from the column by adding 100 ml TE buffer (80°C), and temperature). Phage DNA was stored at -20°C. The genome was sequenced by the Lifesequencing company (Valencia, Spain) using the standard shotgun sequencing reagents and a 454 GS FLX Titanium Sequencing System (Roche), according to the manufacturer’s instructions. Genome assembly and annotation

146 B8b phage genome sequences were assembled into 4 contigs using Newbler (Roche). In Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 the absence of complete genome coverage, attempts were made to close the gaps using PCR and by direct Sanger sequencing. Forward and reverse primers were designed for every contig using PRIMER3 VERSION 0.4.0 (Clarke, 1993), producing a 300-400 bp overlap among the different (Promega), 0.5 µl (10 µM) of each primer pair and 1 µl of phage stock adjusting the volume with contigs. The PCR was carried out in a 25 µl volume containing 12.5 µl of GoTaq Green Master Mix sterile water. PCR profiles consisted of an initial denaturation step of 5 min at 94°C, followed by 30 cycles at 94°C 30 s, 55°C 30 s, and 72°C 2.5 min with a final extension at 72°C for 5 min. TAE run at 90V for 30 min and visualized by SYBR SAFE (10,000X, Invitrogen). Unfortunately, The amplification products were separated by gel electrophoresis on 1% agarose gel in 0.5% we failed to close the genome since we could not get readable sequences from PCR amplicons derived from any contig combination and we did not obtain any good enough sequence from direct sequencing. ORFs were predicted using a pseudo-automated pipeline where ORFs first were assigned by GeneMark Heuristic (Besemer and Borodovsky, 1999) followed by refinement through synteny annotation was done using the BLASTP program against the NCBI non-redundant (nr) database and maximizing ORF size where alternative start sites were present. Gene identification and (e-value cut off <0.001, August 2013).

Proteome analysis

Phages were harvested with MSM from fully lysed plates and CsCl purified as described optimization of the FASP kit (Protein Discovery, Knoxville, TN) (Wisniewski et al., 2009). All above. The purified phage particles were prepared prior to 2d-LC-MS/MS analyses using an reagents were provided for in the kit. Briefly purified phage were re-suspended in 8M Urea/10mM iodoacetamide (IAA) to label cysteine residues. IAA was washed away with 8M Urea and then DTT, denatured and passed over the 30kDa filter, then washed with 8M Urea and treated with

50mM Ammonium Biocarbonate. Sequencing grade trypsin was then added and digestion processed overnight. The next day peptides were eluted from the 30kDa filter via Ammonium sample and frozen at -80°C until 2d-LC-MS/MS analyses. The FASP prepared peptides (>500 ng) Biocarbonate buffer, NaCl buffer and water/0.1% Formic acid. Three aliquots were prepared per

(all packing materials purchased from Phenomenex, Torrance, CA). The column was loaded to were loaded onto the back column of a split phase 2D column (~3-5cmSCX and 3-5cm C-18) the HPLC and washed with 100% aqueous solution for 5 min, followed by a ramp from 100% (RP C-18, 15cm) with a nanospray source on LTQVelos and run for 5 – 12 h two dimensional aqueous to 100% organic solution for 10 min. The column was connected to a front column 147 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 separation of increasing salt pulses (ammonium acetate) followed by water to organic gradients (see (Verberkmoes et al., 2009). All instrument were run in a data-dependent manner as previous described (Erickson et al.; Verberkmoes et al., 2009). To recruit peptides to the phage genomes, the resultant MS/MS spectra were searched against a database consisting of annotated phage

bacteria ( TAC125, proteins, all phage ORFs >Pseudoalteromonas 30 aa (to identify ORFs possiblyPseudoalteromonas missed through haloplanktisthe annotation), and sp. TW-7, T6c, Pseudoalteromonasproteins from sequenced Pseudoalteromonas atlantica Pseudoalteromonas tunicata D2) and eukaryotic organisms (human and mouse) to use as indicator for false positives. Data

(Verberkmoes et al., 2009). For proteomics, databases, peptide and protein results, MS/MS spectra analyses were performed using SEQUEST and filtered with DTA Select with conservative filters and supplementary tables are archived and available at e https://compbio.ornl.gov/Cellulophaga_ phages_proteom , while MS .raw files or other extracted formats are available upon request. Phylogenetic analysis

DNA polymerase, phage portal protein, and phage large terminase amino acid sequences alignment has been automatically performed using the program ClustalW (default parameters) of known bacteriophages were used to investigate the phage B8b phylogeny. Multiple sequence with bootstrap analysis (1000 replicates) using MEGA version 5.1 (Tamura et al., 2011). (Larkin, 2007). Maximum likelihood trees were built using the JTT model (Jones et al., 1992)

Fragment recruitment analysis of B8b phage on POV (Pacific Ocean Viral metagenomics). Phage B8b genome fragment recruitment analyses (FRA) were performed to get a sense of the relative abundance of this phage in 32 marine viral metagenomes from the Pacific Ocean Virome1 (Hurwitz and Sullivan, 2013) (available at CAMERA (http://camera.calit2.net) Reciprocal Best Blast approach (RBB) (Raes et al., 2007) applying the same rationale to that under the following project accessions: CAM_P_0000914 and CAM_P_0000915). We used the employed elsewhere (Zhao et al., 2013). Briefly, individual metagenomics samples are made into a BLAST database, and then the predicted ORFS of B8b are searched against it using TBLASTn. BLAST search (BLASTx) against a internal protein genome reference database with a total size of After this initial blast, hits to the POV database are extracted and become the query for a second

8.512.217 ORFs that included: (i) protein viral genomes (Refseq Release 60; 4.958 genomes and and (iii) the 163.830 ORFs),Pseudoalteromonas (ii) bacterial genomes (RefSeq Release 60; 197.527 contigs and 8.348.231 ORFs) phage B8b phage B8b (4 Contigs, 55 ORFs).Pseudoalteromonas Only metagenomic sequences that148 returned as a best hit a sequence from the genome of the Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

relative abundance of B8b phage and two other phages used as reference genomes (the abundant are extracted from the database and count as hits for subsequent step. Finally, to calculate the Pelagiphage HTV0C10P (KC465898) and the non marine Enterobacteria

phage T4 (NC_000866) was in the POV dataset, we normalized the number of hits to: 1) protein length, 2) sequencing Hdepth and 2) to the amino acid length of the hit protein . and 3) mean abundances across the 32 POVN metagenomes. This is: the number of hits L divided to the total number of sequences abundances across all samples. Finally, to avoid larger number of significant figures, the abundances were rescaled to the mean

!! !! 𝐻𝐻 ∗ 𝑁𝑁 ∗ 𝐿𝐿 𝐴𝐴!"! = !! !! Phage genome accession numbers 𝐻𝐻 ∗ 𝑁𝑁 ∗ 𝐿𝐿 Accession number of the B8b phage genes was deposited into NCBI under the following : accession number XXXX.

RESULTS AND DISCUSSION

Morphology and genome structure of the siphovirus B8 genome

Phage B8b was isolated from Blanes Bay Microbial Observatory (BBMO), an oligotrophic grown on its host of isolation, sp. QC-44. Morphological examination showed surface coastal site in the NWPseudoalteromonas Mediterranean Sea, and it formed clear, round plaques when that phage B8b belonged to the Siphoviridae family based on ICTV rules of nomenclature (Van Regenmortel et al., 2000) and had a isometric capsid of 46 nm in diameter connected to a long

While the PFGE analyses predicted that phage B8b had a genome size and flexible tail of 235 nm in length (Fig. 1). Pseudoalteromonas represented two major contigs (20.209 and 19.353 bp) and two short contigs (2.155 and 1.012 of 46 kb (Fig. 1.SM), the combined length of the 4 sequenced contigs were only 42.700 bp. These bp) and as these contigs could not be closed, the complete genome is likely larger although we concatemeric genome, which are produced by rolling circle replication and/or recombination sequenced about 90% of the phage. Moreover, the PFGE results showed that our phage had a and is a common phenomenon for virus genomes (Rao and Feiss, 2008). The obtained banding pattern in the PFGE gel suggested a multiple copies of the original DNA linked in a continuous series of different sizes (Fig 1.SM). The genome had a GC content of 50% and 58 ORFs were in GenBank, but only 12 could be annotated to a function (Table 1), which is similar to other predicted proteins (Table 1). Thirty of these ORFs had significant sequence similarity to proteins 149 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

Figure 1. Transmission electron micrograph showing negatively stained Pseudoalteromonas phage B8b.

Pseudoalteromonas phages (Männistö et al., 1999; Duhaime et al., 2011; Hardies et al., 2013). Among the genes with detected similarity, 40% were most similar previously sequenced marine to viruses, 26.66% to prophages and 33.33% showed similarity to genes detected in bacterial genomes (Table 1). Pseudoalteromonas contig 1 had several genes involved in DNA replication and nucleotide metabolism such as DNA phage B8b displayed two distinctive supermodules: primase (Contig1_ORF10), helicase (Contig1_ORF21) and DNA polymerase (Contig1_ORF23). contig 1 and 7 of these were most similar to siphoviruses. A packaging/structural module was Furthermore, the majority of ORFs with highest similar to phages (9 of 12) were detected in observed in contig 2 and contained proteins including phage terminases (Contig2_ORF2 and ORF4), phage portal protein (Contig2_ORF6), prohead peptidase (Contig2_ORF14), and tail tape repressor and antirepressor) or encoding for transcription regulatory functions were detected. measure protein (Contig2_ORF22). No genes involved in lysogenic function (integrase, excisionase,

Distinctive genes in Pseudoalteromonas phage B8b homologous recombination of importance to a variety of cellular processes, including the The B8b genome encoded a RecT protein (Contig1_ORF15), which is involved in maintenance of genomic integrity (Kogoma, 1996). It provide means for repair of DNA double- stranded breaks, which can arise during DNA replication as well as after damage by external factors such as irradiation (Haber, 1999) and as a ssDNA-binding protein, RecT promotes ssDNA annealing, strand transfer, and strand invasion in vitro (Hall and Kolodner, 1994). In Escherichia coli DNA engineering in various hosts (Zhang et al., 1998). Thus, RecT might facilitate integration , homologous recombinationE. coli is mediated by bacteriophage RecT protein that permits efficient of the phage B8b genome into the bacterial hosts genome, opening up for the potential of phage B8b to act as a prophage. 150 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

The presence of chaperone GroES (also called chaperonin 10; Contig2_ORF19) in B8b is in myoviruses and podoviruses (Holmfeldt et al., 2013). Chaperonins are known to promote unique as it is the first time GroES been reported in a siphovirus, while it previously been detected the correct folding of newly synthesized polypeptides and to prevent aggregation of proteins denatured under stress (Kurochkina et al., 2012). In Escherichia coli, the genes that encode for

GroES/GroEL chaperonin system were first identified as host factors required for bacteriophage Keppel et morphogenesis and subsequent work established that the GroES and GroEL proteins were al., 2002). The presence of this gene in phage B8b might point out that possibly could have a more essential for the correct assembly of λ proheads and T5 tails (Georgopoulos et al., 1973; own chaperonin. complex viral capsid or tail structure than other siphoviruses, which requires that it provide its

Proteomic analysis virion structural proteomic analyses to experimentally determine the remaining structural Given that only 5 structural proteins were identified by sequence similarity, we performed unknown function in contig 2 were detected as part of the phage particle (Table 1). Further, the proteins. The portal protein, prohead peptidase, tail tape measure protein as well as 8 ORFs of proteins of unknown function in contig 1. Three spectra also matched against the DNA polymerase 2 ORFs of unknown function in contig 3 are part of the phage structural particle, as well as two gene, however, they were considered false positives as the total peptides detected covered <4% of the gene.

Phylogenetic relationships In order to get insights of the phylogenetic relatedness of phage B8b compared to other terminase, and phage portal protein (Figs. 2A.SM, 2B.SM and 2C.SM). phages, three relevant key genes were investigated: the B8b DNA polymerase, the phage large DNA polymerase genes are crucial in genomic replication and mutagenic repair and it has been used in new isolated phages to know the phylogenetic relationships (Chen and Suttle, 1996; Angly et al., 2009; Baudoux et al., 2012). Surprisingly, the B8b DNA polymerase clustered together with several myoviruses (Fig. 2A.SM; Table 1A.SM). Two of them were isolated from marine bacteria (Edwardsiella phage MSW-3 and Klebsiella and most of them were lytic phages, except for phage CP-T1 that is known to be capable phageVibrio JDOO1) (Cui et al., 2012; Yasuike et al., 2013) of temperate behavior (Comeau et al., 2012). Although DNA polymerases have been suggested to be good phylogenetic marker for investigating viral phylogeny, since they offer the greatest

151 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 3.9 7.8 10.9 42.20 76.40 8 3 4 46 10 Proteomic data 3 2 4 5 18 Sequence count Spectral count Sequence coverage (%) Taxonomy (Prophage) (Prophage) (Prophage) Myoviridae, Myoviridae, Myoviridae, Siphoviridae, Siphoviridae, Siphoviridae, Siphoviridae, Siphoviridae, Siphoviridae, Siphoviridae, Caudovirales Caudovirales Caudovirales Caudovirales Caudovirales Caudovirales Caudovirales Caudovirales Caudovirales Caudovirales Chromatiales Alteromonadales Alteromonadales Alteromonadales Alteromonadales Alteromonadales Alteromonadales Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, YP_750332 AFV51054.1 AGF89287.1 AGF89282.1 AGF89284.1 AGF89344.1 ADE87936.1 YP_006634.1 YP_007348969 YP_004009376 WP_005220619 WP_008951684 WP_010325175 WP_008172253 WP_010322164 WP_010322159 984 S30 S30 S30 NCIMB KCTC 22429 sp. BSi20652 phage phage Ac42 phage phage Q33 phage MSW-3 phage MSW-3 phage phiKO2 (7.0E-5) (1.0E-4) phage FSL SP-062 phage FSL SP-062 phage FSL SP-062 phage FSL SP-062 phage FSL phage E1 (4.0E-45) WP_003849806.1 (7.0E-11) (5.0E-28) (2.0E-20) (1.0E-72) (2.0E-33) (3.0E-92) (7.12E-7) (8.0E-13) (2.0E-17) (6.0E-101) (5.0E-126) (9.0E-145) (3.00E-125) 400 (8.32E-4) Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Escherichia vB_EcoM_ECO1230-10 Lactococcus Klebsiella Acinetobacter Edwardsiella Marinobacterium stanieri Marinobacterium stanieri Marinobacterium stanieri Salmonella Salmonella Salmonella Salmonella Salmonella Shewanella frigidimarina Strain with closest hit (Evalue) Accesion number Marichromatium purpuratum Pseudoalteromonas Alishewanella jeotgali Genomic data subunit Helicase dUTPase RecT protein RecT DNA primase DNA phage protein phage protein phage protein phage protein phage protein phage protein phage protein phage protein DNA polymerase DNA DNA binding protein DNA Phage portal protein function of product Predicted identity or Phage large terminase Conserved hyphotetical Conserved hyphotetical Conserved hyphotetical Conserved hyphotetical Conserved hyphotetical Conserved Hypothetical Conserved Hypothetical Conserved Hypothetical Small terminase subunit Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein 33.0 % aa ID 72 85 76 71 1 1 1 906 42.0 225 51.0 624 546 42.0 198170 25.0 28.0 282 33.0 429 47.0 264 198 171 237 603 234 471228 40.0 41.0 165 135 168 762201 37.0 663 59.0 499142 40.0 236 1167 1125 2211 39.0 1671 35.0 2355 35.0 2190 30.0 Product lenght (aa) ------+ + + + + + + + + + + + + + + + + + + + Strand 630 11281 10813 - 13628 + Nucleotide end position start 1 1 1 1 7 11037 112861154611709 11549 11958 11743 11891 position Nucleotide Genomic annotation and structural proteomics results proteomics Genomic annotation and structural Contig_ORF Contig1_ORF1Contig1_ORF2Contig1_ORF3 7 Contig1_ORF4 676Contig1_ORF5 1218Contig1_ORF6 1622 2121Contig1_ORF7 1221 Contig1_ORF8 2536 1625 Contig1_ORF9 2128 3282 2456 3503 3753 3297 3500 3760 4376 Contig2_ORF1Contig2_ORF2 256Contig2_ORF3 410 486 Contig2_ORF4 990 1006 Contig2_ORF5 1519Contig2_ORF6 1502 3514Contig2_ORF7 3510 3719 3729 5232 5218 5660 Contig1_ORF11 7155 6919 Contig1_ORF15 10783 9878 Contig1_ORF29 20170 19874 - Contig1_ORF10 6914Contig1_ORF12Contig1_ORF13 7580 4560 Contig1_ORF14 8746 9873 7152 Contig1_ORF16 7580 Contig1_ORF17 8749 Contig1_ORF18 Contig1_ORF19 Contig1_ORF20 Contig1_ORF21 Contig1_ORF22Contig1_ORF23 13621Contig1_ORF24 13847Contig1_ORF25 16103 13857Contig1_ORF26 16705 16057Contig1_ORF27 17471 16705 + 16938 17695Contig1_ORF28 + 17001 19877 + 17468 + - 17688 - -

152 1. Table Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4 43.50 36.19 27.63 42.70 24.40 37.30 43.00 54.90 57.90 30.10 33.60 3 3 3 74 21 38 10 67 22 937 290 6 7 3 9 6 3 3 28 46 12 12 Vibrionales (Prophage) (Prophage) (Prophage) (Prophage) (Prophage) Podoviridae, Podoviridae, Caudovirales Caudovirales Alteromonadales Alteromonadales Alteromonadales Alteromonadales Alteromonadales Alteromonadales Alteromonadales Enterobacteriales Pseudomonadales Pseudomonadales Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, Gammaproteobacteria, WP_010322158 WP_010322157 WP_017059000 WP_010322154 WP_009840504 WP_010322152 WP_010322151 WP_010490777 WP_004131755 YP_008051111.1 YP_008051111.1 WP_019396974.1 WP_019396974.1

S30 S30 S30 S30 S30 sp. S9 phage RIO-1 phage RIO-1 (4.0E-4) (2.0E-6) (2.0E-6) (9.0E-5) (1.0E-4) (8.0E-17) (1.0E-17) (3.0E-44) (5.0 E-82) (7.0 E-73) (8.01E-39) (3.0E-168) (1.29E-161) Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Non-significant Klebsiella oxytoca Vibrio crassostreae Vibrio Pseudoaltermonas Pseudomonas aeruginosa Pseudomonas aeruginosa Pseudoalteromonas tunicata Marinobacterium stanieri Marinobacterium stanieri Marinobacterium stanieri Marinobacterium stanieri Marinobacterium stanieri Pseudoalteromonas Pseudoalteromonas phage protein phage protein phage protein phage protein phage protein phage protein phage protein phage protein phage protein phage protein prohead HK97 Chaperone GroES Peptidase U35 phage Conserved hyphotetical Conserved hyphotetical Conserved hyphotetical Conserved hyphotetical Conserved hyphotetical Conserved hyphotetical Conserved hyphotetical Conserved Hypothetical Conserved Hypothetical Conserved Hypothetical Phage tail tape measure protein TP901, core region protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein Hypothetical phage protein 48.0 86 56 76 98 31.0 1 1 2 1 1 3 124 35.0 136 39.0 339 29.0 206125 40.0 684 44.0 207158 41.0 30.0 253 40.0 523 35.0 148 154 267 126 1644 38.0 1402 32.0 - - - - + + + + + + + + + + 350 1694 11298 12148 + 11774 11295 11771 Contig3_ORF1 9 Contig2_ORF8Contig2_ORF9 5662 6053 6108 6283 Contig3_ORF2Contig4_ORF1 1694Contig4_ORF2Contig4_ORF3 12 343 2154 824 609 699 Contig2_ORF11 6821 6525 Contig2_ORF23 17359 17769 + Contig2_ORF15 10022 10360 + Contig2_ORF19 Contig2_ORF22 13148 17356 + Contig2_ORF10 6264Contig2_ORF12Contig2_ORF13 7423 6524 Contig2_ORF14 7800 7912 6803 7423 Contig2_ORF16 9966 Contig2_ORF17 10341 10675 Contig2_ORF18 10682Contig2_ORF20 + Contig2_ORF21 12148 12318 12318 13079 + Contig2_ORF24 + 17769 19343 + 153 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

not compatible with relationships based on phage morphology and that morphological features number of viral homologs (Fileé et al., 2002), our results showed that genomic sequences were possibly be a very large group (Lawrence et al., 2002). The situation may be complicated further available would be not sufficient to reveal the relationships among the members of what may if one considers the potential opportunity for dynamic genetic exchange in marine environments. Notably, while such rampant mosacism may be the case in siphoviruses, other groups (e.g., T4- like myoviruses) appear to have clear signals of vertical descent, particularly in their core gene sets (Ignacio-Espinoza and Sullivan, 2012; Ignacio-Espinoza et al., 2013). The phage large terminase and phage portal protein are commonly highly conserved phylogeny has been investigated using these genes in several other studies (Serwer et al., 2004; among phage genes, possibly due to their specific enzymatic functions (Casjens, 2005) and phage Sullivan et al., 2009; Comeau et al., 2012; Huang et al., 2012). The phage terminases are DNA packaging enzymes that contain the ATPase activity that powers DNA translocation and most terminases also contain an endonuclease that during DNA packaging cuts concatemeric DNA into genome lengths. Terminases must also recognize viral DNA in a pool that may also include host DNA (Catalano et al., 1995; Rao and Feiss, 2008). Phage portal proteins one the other hand, are structurally associated with the phage capsid and facilitate DNA packaging during head assembly (Rao and Feiss, 2008). Phylogenetically, both B8b terminase and portal protein were closest related to Stenotrophomonas phage S1 (Figs. 2B and 2C.SM; Table 1B and 1C.SM), a temperate siphovirus isolated from sewage (Garcia et al., 2008). They also clustered together with the putative temperate siphoviruses Synechococcus S-CBS1 (terminase and portal) and S-CBS3 (terminase) (Huang et al., 2012) as well as several temperate myoviruses, like Acidithiobacillus phage AcaML (terminase) (Tapia et al., 2012), Halomonas phage phiHAP-1 (portal) (Mobberley et al., 2008), and Vibrio phage VP882 (Lan et al., 2009).

Biology characterization of Pseudoalteromonas phage B8b The one-step growth curve of phage B8b showed a latent period of 70 min and approximately 172 new viral particles were released from each infected Pseudoalteromonas sp. QC-44 cell (Fig. 2). These values differed from the marine Pseudoalteromonas phage PM2, which produced 300 viral particles per infected cell about 70-90 min after infection (Kivelä et al., 1999), as well as other marine siphoviruses, e.g. Vibrio min and an average burst size of 60 (Baudoux et al., 2012) or the cyanosiphovirus S-BBS1, which phage SIO-2, which had a latent period of 45-60 had a 540 min (9 h) of latent period and approximately 250 progeny viruses were produced per infected host cell (Suttle and Chan, 1993). However, this is not surprising as burst size and latent

154 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

Figure 2 Pseudoalteromonas phage B8b on Pseudoalteromonas sp. QC-44 strain n total PFU; ¢ free PFU). ( , . One-step growth curve of period is known to vary between phages, but also depend on which host they infect (Holmfeldt et

1997). Additionally, marine bacteria thrive under lower nutrient concentrations than provided in al., submitted), nutrient availability, specific growth rate of the host, and temperature (Hadas et al., in situ burst size is likely smaller than the values we measured (Hadas et al., 1997). the lab and, consequently, To examine the host range of the isolated phage, infectivity was tested on 52 Pseudoalteromonas sp., 15 Alteromonas sp., 8 Vibrio sp. strains, 3 Marinobacterium, sp., all those belonging to Gammaproteobacteria class plus 5 Bacteroidetes (Flavobacteria) and 6 Alphaproteobacteria (Rhodobacterales and Sphingomonadales). All tested bacterial strains were isolatedPseudoalteromonas from the same BBMO marine station as the phage. Phage B8b only infected 3 of 52 100% on the 3 different spp.Pseudoalteromonas strains (Fig. 3) and the phage’s efficiency of infection range between 67- with previous phage host ranges – PM2 infected 2 of 13 Pseudoalteromonas strains (Fig. 3). These narrow host rangePseudoalteomonas findings agree strains (Kivelä et al., 1999), H105/5 infected 3 of 59 Pseudoalteromonas strains (Wichels et al., Pseudoalteromonas strains (Hardies et al., 2013). While the use of a single-host enrichment method in this study might bias the results towards a narrow host 1998), and RIO-1 infected 4 of 11 range phage (Jensen et al., 1998), this is unlikely the cause here as 18 co-isolated myoviruses155 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

58.3%

i s

aniensis

Vibrio splendidus splendidus Vibrio

Vibrio sp. sp. Vibrio Alteromonas sp. Alteromonas

Alteromonas sp. Marinobacterium georgiense Marinobacterium Alteromonas sp.

Alteromonas sp.

Erythrobacter citreu Erythrobacter Alteromonas macleodi

Nereida sp. Nereida Alteromonas sp.

Erythrobacter citreus Erythrobacter Vibrio tasm

Erythrobacter litoralis Erythrobacter Vibrio sp.

Salegentibacter slinus slinus Salegentibacter Vibrio gigantis

Polaribacter dokdonensis Polaribacter Vibrio pectenicida

Dokdonia donghaensis Dokdonia Marinobacterium jannaschii Nereida sp. Nereida Marinobacterium stanieri onas sp. AB333f

Nereida sp. Nereida 100 Vibrio sp.

Vibrio gigantis gigantis Vibrio Pseudoalteromonas sp. RHS−str.402

Leeuwenhoekiella accommodimaris Leeuwenhoekiella Pseudoalterom Salegentibacter mishustinae Salegentibacter Pseudoalteromonas sp. RHS−str.402

Pseudoalteromonas sp. RHS−str.402

Pseudoalteromonas sp. MB103

Pseudoalteromonas sp. RHS−str.402

Alteromonas genovensis LMG 24078 LMG genovensis Alteromonas Pseudoalteromonas sp. AB474f Alteromonas genovensis LMG 24078 LMG genovensis Alteromonas 99 Pseudoalteromonas sp. 114Z−7

Alteromonas genovensis LMG 24078 LMG genovensis Alteromonas Pseudoalteromonas sp. D32

Alteromonas genovensis LMG 24078 LMG genovensis Alteromonas Pseudoalteromonas sp. CI4

Alteromonas genovensis LMG 24078 LMG genovensis Alteromonas Pseudoalteromonas sp. QC44

Alteromonas genovensis LMG 24078 LMG genovensis Alteromonas Pseudoalteromonas sp. QC44

Alteromonas genovensis LMG 24078 LMG genovensis Alteromonas Pseudoalteromonas sp. QC44 Alteromonas sp. BCw006 sp. Alteromonas 88 0.1

Pseudoalteromonas sp. DIT 4 Alteromonas sp. BCw006 sp. Alteromonas Pseudoalteromonas sp. DIT 46 6

Pseudoalteromonas sp. 19(2006) sp. Pseudoalteromonas Pseudoalteromonas sp. AB333f

Pseudoalteromonas sp. SXBYC5n sp. Pseudoalteromonas Pseudoalteromonas sp. HK−3

Pseudoalteromonas sp. SXBYC5n sp. Pseudoalteromonas Pseudoalteromonas sp. HK−3

Pseudoalteromonas sp. SXBYC5n sp. Pseudoalteromonas Pseudoalteromonas sp. RHS−str.40

Pseudoalterom Pseudoalteromonas sp. RHS−str.402 100%

Pseudoalteromonas sp. QC44 sp. Pseudoalteromonas Pseudoalteromonas sp. HK−3

Pseudoalteromonas sp. SXBYC5n sp. Pseudoalteromonas

onas sp. SXBYC5n sp. onas Pseudoalteromonas sp. RHS−str.402 Pseudoalteromonas sp. SXBYC5 sp. Pseudoalteromonas Pseudoalteromonas sp. HK−3

Pseudoalteromonas sp. HK−3 sp. Pseudoalteromonas Pseudoalteromonas sp. HK−3 2 Pseudoalteromonas sp. QC44 sp. Pseudoalteromonas Pseudoalterom

Pseudoalteromonas sp. QC44 sp. Pseudoalteromonas Pseudoalteromonas sp. HK−3 Pseudoalteromonas sp. QC44 sp. Pseudoalteromonas Pseudoalteromonas sp. HK−3

Pseudoalteromonas sp. HK−3 sp. Pseudoalteromonas Pseudoalteromonas sp. QC44

Pseudoalteromonas sp. NBRC sp. Pseudoalteromonas Pseudoalteromonas sp. QC44

Pseudoalteromonas sp. HK−3 sp. Pseudoalteromonas

Pseudoalteromonas sp. RHS−str.402 sp. Pseudoalteromonas 83.3% Pseudoalteromonas sp. QC44

Pseudoalteromonas sp. RHS−str.402 sp. Pseudoalteromonas Pseudoalteromonas sp. BSi20316

Pseudoalteromonas sp. RHS−str.402 sp. Pseudoalteromonas Pseudoalteromonas sp. QC44 onas sp. RHS−str.402 Pseudoalteromonas atlantica

Pseudoalteromonas sp. QC44 n

68.8%

Figure 3. Bacterial hosts used to test the Pseudoalteromonas phage B8b phage host range. Bacterial strains infected by siphovirus B8b are labeled in black and the efficiency of phage B8b in hosts infected is titer dilution was used for all the bacterial strains (106). indicated. Efficiency is expressed in relative PFU, where the highest was set to 100% and the same phage

156 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

Pseudoalteromonas spp. each (Lara et. al, in preparation). This extended myovirus host range and narrow siphovirus host range is consistent with previous presented broad host ranges of ~20 Pseudoalteromonas phages and cyanophages (Wichels et al., 1998; Sullivan et al., 2003), but contrasts observations in the phages (Holmfeldt et al., 2013). findings on Cellulophaga belonging to , which even belong to a different family ( ) than the Of theAlteromonas 37 bacterial strains of other genera, phage B8b couldAlteromonadaceae infect a bacterial strain phage B8b original host (Pseudoalteromonadaceae) and has a phylogenetic similarity of 80% nucleotide identity with respect to the Pseudoalteromonas spp. infected (Fig. 3). Also, a lower

(Fig. 3). Previously, phages infecting across genera boundaries have been reported, but this is efficiency of infection (58%) was observed compared to the infection on the host of isolation commonly among large myoviruses, like cyanophages infecting Prochlorococcus (Sullivan et al., 2003), enterophage LG1 and AR1 (Goodridge et al., 2003), and vibriophage KVP40 (Matsuzaki et al., 1992), and the two genera do not represent different families of host microbes. Among siphoviruses, one isolate has been reported to infect two bacterial strains of different genera in wastewater (Chang and Kim, 2011; Kim and Ryu, 2011), but no such cross-genera infections have been reported for siphoviruses from the marine environment. Mechanistically, while not known, broad host range phages must rely on consistency between hosts of multiple layers of the host (Breitbart, 2012). The fact that this phage may infect over genus boundaries highlights phage-host ‘interaction space’ including receptor molecules or restriction modification system in the potential of this phage for mediating transduction, and thereby increasing microdiversity, not

Paul, 1998). only among closely related bacterial strains but also across larger taxonomic space (Jiang and

Possibility of lysogenic replication strategy step growth curve. However, from our phylogenetic analyses and the presence of the RecT Phage B8b was isolated as a lytic phage and its lytic nature was confirmed by the one- protein, phage B8b was closely related to several phages that are known to be able to perform the microbial genome, which begged its comparison (Fig.4). lysogeny.Marinobacterium Also, a large number stanieri of S30 phage B8b’s structural proteins were similar to proteins from This relative synteny suggests a prophage in this microbial genome and represents the closest available relative to phage B8b. This highlights the possibility that B8b could be a temperate phage, despite the lack of genes involved in lysogenic function (integrase, excisionase, repressor and antirepressor) or encoding for transcription regulatory functions, which could possibly occur

in the part of the genome still not sequenced. Also, it is possible that lysogenic replication157 of Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

Contig 1; 20,209 bp Helicase polymerase DNA dUTPase DNA binding DNA protein RecT B8b primase DNA

Contig 4; 1,012 bp B8b

Contig 2; Contig 3; 19,353 bp 2,155 bp terminase, small subunit terminase, large subunit portal protein prohead peptidase U35 Chaperone GroES tape Tail measure protein B8b

M. stanieri protein prohead Tail tape Tail measure terminase, large subunit portal protein

peptidase U35 %-id (aa) DNA packaging DNA 60 Chaperone GroES

Host interaction 50 Protein folding 40 Structural proteins Unknown proteins 30 DNA packaging 20 Figure 4. Genome structure of Pseudoalteromonas phage B8b represented by 4 contigs and genome comparison with the putative prophage of Marinobacterium stanieri. Lines drawn between the genomes represent shared sequence similarity, which is given next to each line as percentage amino acid identity. phage B8b might be detected if different host strains are infected as observed in the Bacteriodetes podophages (Holmfeldt et al, submitted), under conditions which might promote lysogeny such as altered host cell size (St-Pierre and Endy, 2008), or phage-host density ratios used in the oligotrophic marine environment, such as the NW Mediterranean Sea source waters, as lysogeny infection assay (Kourilsk, 1973; Joh and Weitz, 2011). Lysogeny would be an attractive lifestyle in is a survival strategy during conditions of low host cell encounter rates (Stewart and Levin, 1984; Weinbauer et al., 2003; Boras et al., 2009).

Marine Gammaproteobacteria host genes Given that cyanophage genomes commonly contain “host genes” (e.g., (Mann et al., 2003;

Sullivan158 et al., 2006; Sullivan et al., 2010) and recent metagenomic findings that such viral-encoded Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

Coastal Intermeadiate Open Ocean Photic Aphotic Photic Aphotic Photic Aphotic

100

90

80

70

60 % aa identity 50

40

30

20

A 29 97 35 24 63 27 29 86 27 15 70 25 22 93 29 27 80 30 B 0.5 0.8 - - - - T4 T4 T4 T4 T4 T4 B8b B8b B8b B8b B8b B8b HTVC010P HTVC010P HTVC010P HTVC010P HTVC010P HTVC010P

Figure 5. recruiting to predicted genes from phage B8b as well as the abundant Pelagiphage Box plots show the percentPseudoalteromonas amino acid identity for metagenomics reads (32 metagenomes, POV) HTV0C10P (GenBank No/ KC465898) and the non-marine Enterobacteriaphage T4 (GenBank No. site location as appear in Hurwitz and Sullivan (2013). A) Percentage of the genome that is being covered NC_000866). Metagenomes were grouped in 6 categories product of the combination of photic zone and

(as per bitscore comparison) than the rest of NR. In box plots the box are marking the upper and lower by the metagenomics reads. B) Percentage of reads that better align to ORFs in the indicated genome quartile, the median is shown in red, whiskers are marking 1.5 times the interquartile range. host genes cover broad metabolic categories (Sharon et al., 2011) including nearly all of central carbon metabolism (Hurwitz et al., 2013a), we wondered whether such AMGs (Auxiliar Metabolic prophage related, most of them related to (Gammaproteobacteria), suggesting Genes) existed in this new phage B8b genome.Alteromonadales Of phage B8b’s 58 ORFs, 10 were bacterial and 8 that the genetic exchange might occur between Pseudoalteromonas and distant bacterial strains and their phages. If phages acted as important vectors to genetically transfer DNA across bacterial transfer (LGT) (Canchaya et al., 2003). This trend has been previously observed in cyanophages taxa one would expect to find host genes within phages that infect similar hosts through lateral gen (Sullivan et al., 2005) and in Pseudoalteromonas phages (Duhaime et al., 2011). Genetic interaction

159 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

models (Desiere et al., 2001) although it is well know that phages can infect hosts from different of phage and bacterial genomes has been predicted to be highly specific such in co-evolutionary species and even genera (Sullivan et al., 2003) emerging phage-bacteria interactions are now being viewed as networks rather than coupled simplistic interactions (Weitz et al., 2013). The genus-crossing host range detected in phage B8b and most of the bacterial host genes found in our B8 genomes were related to other host or prophage, Marinobacter within stressing that possibility of genetic exchange between different spp. Gammaproteobacteria specifically to the genus host genera.

Relative abundance of phage B8b in Pacific Ocean Viral metagenomes

Viromes, (Hurwitz and Sullivan, 2013) that was consistently prepared using extensively well- Given the recent availability of a large-scale viral metagenomic dataset (32 Pacific Ocean

2012; Hurwitz et al., 2013b; Solonenko et al., 2013; Solonenko and Sullivan, 2013), we wondered documented quantitative methods (John et al., 2011; Duhaime et al., 2012; Duhaime and Sullivan, whether this relatively novel phage B8b genome was observed in nature and if so how abundant it was. The normalized relative abundance showed that phage B8b was mainly present in the surface of the coastal environment with 1.15% assigned reads to phage B8b (data not shown) although we also found it in aphotic samples with a 0.46% of assigned reads. These numbers are low compared with the obtained for the highly abundant pelagiphages (58.7%) or for phages infecting Synechococcus and Prochlorococcus that reached a 21.6% and 12.4% respectively in the same database (Zhao et al., 2013). However, the relative abundance of the Pseudoalteromonas B8b phage is similar to Cellulophaga phages detected in Holmfeldt et al. (2013) . The percentage of the genome covered by the metagenomics reads in POV database was on average 24.2%, although conserved in a broad array of phages (Fig. 5). In fact, we observed that the AA percentage identity only 0.2% was exclusive to phage B8b meaning that many of the ORFs from phage B8b are proteins of the reads of phage B8b genes (24.2%) were similar to that observed for T-4 like phages (29%) (Fig. 5). However, pelagiphages showed higher average percent identity with 81.4% (Fig. 5 and Zhao et al. (2013) probably due to Pseudoalteromonas class is not as abundant as SAR11, which is considered the most abundant in seawater (Morris et al., 2002; Rappé et al., 2002). In summary, our findings support that idea that this phage is not abundant but it can be considered a rare and ubiquitous phage in coastal marine environments.

160 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

CONCLUSIONS The Pseudoalteromonas phage B8b genome adds a new genome for marine Gammaproteobacteria similar morphology and has the potential for lysogeny as other siphoviruses available. However, phages. Our results point out that phage B8b is a siphovirus that shares chaperone GroES and despite the narrow within species host-range, the phage B8b has the the phage B8b also exhibited unique genomic features for siphovirus such as the presence of capacity to infect a host strain belonging to a different family than the original phage B8b host. Components for horizontal gene transfer from host or phage genomes are present in the B8b phage genome stressing the extent of genetic exchange between phage-host genomes and providing interesting insights into the mosaic nature of the siphoviruses genomes.

ACKNOWLEDGMENTS We thank Bonnie Poulos, Cristina Howard and the Tucson Marine Phage Lab for support during a recipient of the FPI-predoc fellowship from the Spanish Ministry of Science and Innovation a stay in Tucson, Arizona. We also thank Daniel J. Nasko for his help in genome assembly. EL was (MICINN). SGA was supported by a Ramon y Cajal contract from MICINN and by MicroB3 (FP7-

OCEAN-2011). KH was supported by a postdoctoral fellowship from the Swedish Research Council. This work was supported by grants MICROVIS (CTM2007-62140) and FLAME (CGL2010-16304) SGA from the MICINN, and a Gordon and Betty Moore Foundation (grant #2631) to MBS. to DV, MICRODIVERSITY (CGL2008-00762/BOS) and PANGENOMICS (CGL2011-26848/BOS) to

161 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

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SUPPLEMENTAL MATERIAL

194.0 Kb

145.5 Kb

97.0 Kb

48.5 Kb

Figure 1.SM. Genome size of phage B8b analyzed by Pulse Field Gel Electrophoresis (PFGE).

170 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

A B

C

Figure 2.SM. Phylogenetic relationships of the (A) DNA polymerase, (B) phage terminase and (C) phage portal protein across diverse bacteriophages. In green are represented the Myoviridae phages, in black the Siphoviridae and in blue the Podoviridae. Phage B8b is represented in red.

171 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

Table 1A.SM.

PhagePhage nameDNA polymerase gene Familysequences used for phylogeneticHost analysis. Accession Myoviridae Vibrio cholerae Vibrio phageCP-T1 Myoviridae Vibrio cholerae ElTor Vibrio phage vB_VchM-138 YP_007006391.1 Myoviridae Edwardsiella tarda YP_007003043 Myoviridae Klebsiella pneumoniae Edwardsiella_phage_MSW-3 YP_007348961 Agrobacterium phage 7-7-1 Myoviridae Agrobacterium sp. H13-3 Klebsiella phage JD001 JDM777 YP_007392876 Siphoviridae Pseudomonas aeruginosa YP_007006473 Siphoviridae Pseudomonas aeruginosa Pseudomonas phage vB_Pae-Kakheti25 YP_006299891.1 Salmonella phage PhiSH19 Myoviridae Salmonella Typhimurium Pseudomonas_phage_73 YP_001293433.1 Myoviridae Vibrio parahaemolyticus YP_007008133.1 Enterobacteria phage ime09 Myoviridae Escherichia coli Vibrio_phage_KVP40 NP_899330.1 Myoviridae Yersinia enterocolitica YP_007004431.1 Vibrio phage VP5 Podoviridae Vibrio cholerae Yersinia_phage_phiR1-RT YP_007235888.1 Podoviridae Vibrio parahaemolyticus YP_024986.1 Podoviridae Pseudomonas fluorescens SBW25 Vibrio_phage_VpV262 NP_640280.1 Klebsiella phage KP34 Podoviridae Klebsiella pneumoniae Pseudomonas_phage_phi-2 YP_003345482.1 Siphoviridae Vibrio sp. SWAT-3 YP_003347671.1 Siphoviridae Vibrio parahaemolyticus AFB83871.1 Vibrio phage SIO-2 YP_004957553 Siphoviridae Salmonella enterica Vibrio_phage_pVp-1 Enterobacteria_phage_SPC35 YP_004306595.1 Table 1B.SM.

Phage Phage name large terminase geneFamily sequences used for phylogeneticHost analysis. Accession Synechococcus phage S-CBS3 Siphoviridae Synechococcus sp. CB0202 Synechococcus phage S-CBS1 Siphoviridae Synechococcus sp. CB0201 YP_004421723.1 Acidithiobacillus phage AcaML1 Myoviridae Acidithiobacillus caldus ATCC 51756 AFU62879.1 YP_004934601.1 Stenotrophomonas S1 Siphoviridae Stenotrophomonas maltophilia Synechococcus phage S-CAM8 Myoviridae Synechococcus sp. WH7803 YP_002321454.1 Myoviridae Vibrio parahaemolyticus YP_008125637.1 Vibrio phage VHML Myoviridae Vibrio harveyi Vibrio phage vB_VpaM_MAR YP_007112478.1 Vibrio phage VP58.5 Myoviridae Vibrio parahaemolyticus CAX64983.1 NP_758915.1 Siphoviridae Lactobacillus casei Escherichia phage HK75 Siphoviridae Escherichia coli Lactobacillus phage_A2 NP_680484.1 Burkholderia phage KS9 Siphoviridae Burkholderia cepacia YP_004934109.1 Pseudoalteromonas phage H105/1 Siphoviridae Pseudoalteromonas sp. H105 YP_003090178.1 Vibrio phage pVp-1 Siphoviridae Vibrio parahaemolyticus AFB83996.1 YP_004327112.1 Enterobacteria phage SPC35 Siphoviridae Salmonella enterica Enterobacteria phage Min27 Podoviridae Escherichia coli YP_004306624.1 Aeromonas phage 65 Myoviridae Aeromonas salmonicida O157:H7 str. Min27 YP_001648943.1 Acinetobacter phage 133 Myoviridae Acinetobacter johnsonii YP_004300928.1 Myoviridae Escherichia coli YP_004300751.1 Klebsiella phage KP15 Myoviridae Klebsiella pneumoniae Enterobacteria phage JS10 YP_002922510.1 YP_003580049.1

172 Life-style and genome structure of Pseudoalteromonas phage B8b Chapter 4

Table 1C.SM.

Phage Portal name protein gene sequencesFamily used for phylogeneticHost analysis. Accession Synechococcus phage S-CAM8 Myoviridae Synechococcus sp. WH7803 Synechococcus phage syn9 Myoviridae Synechococcus sp. WH 8012 YP_008125640.1 Cyanophage S-SSM2 Myoviridae Synechococcus sp. WH8102 AGH57437.1 YP_717798.1 Aeromonas phage 25 Myoviridae Aeromonas salmonicida Vibriophage phi-pp2 Myoviridae Vibrio parahaemolyticus AFN37590.1 YP_656382.1 Enterobacteria phage T5 Siphoviridae Escherichia coli Vibrio phage pVp-1 Siphoviridae Vibrio parahaemolyticus ATCC 33844 AFB83998.1 YP_006980.1 Flavobacterium phage 11b Siphoviridae Flavobacterium sp. Marinomonas phage P12026 Siphoviridae Marinomonas sp. IMCC12026 YP_112492.1 Enterobacteria phage HK97 Siphoviridae Escherichia coli YP_006560242.1 Pseudomonas phage D3 Siphoviridae Pseudomonas aeruginosa NP_037699.1 Podoviridae Pseudoalteromonas marina NP_061500.1 Burkholderia phage AH2 Siphoviridae Burkholderia cenocepacia C6433 AEY69575.1 Pseudoalteromonas phage RIO-1 YP_008051121.1 Acidithiobacillus phage AcaML1 Myoviridae Acidithiobacillus caldus ATCC 51756 AFU62881.1 Myoviridae Vibrio parahaemolyticus Vibrio phage VHML Myoviridae Vibrio harveyi Vibrio phage vB_VpaM_MAR YP_007112480.1 Stenotrophomonas S1 Siphoviridae Stenotrophomonas maltophilia NP_758916.1 Halomonas phage phiHAP-1 Myoviridae Halomonas aquamarina ABY90372.1 YP_002321454.1 Vibrio phage VP882 Myoviridae Vibrio parahaemolyticus

YP_001039817.1

173

Comparative genomics and biogeography of Pseudoalteromonas phages

Elena Lara, Melissa B. Duhaime, J. Cesar Ignacio-Espinoza, Elisabet Laia Sà, Dolors Vaqué, Matthew B. Sullivan and Silvia G. Acinas

5ChapterChapter

Comparative genomics of Pseudoalteromonas phages Chapter 5

ABSTRACT

Marine viruses genomes are not well represented but spread into few phylogenetic host taxa. Isolate-based genome analyses are essential to better understand the huge viral diversity and used them to map environmental viral metagenomes. Here, we report the genomic sequences and the predicted proteins of 3 Pseudoalteromonas phages: the siphovirus B8b and the myoviruses 306b and C5a. Comparative nucleotide and protein analysis revealed that myovirus 306b was most similar to siphovirus B8b than the other myovirus C5a. Phage C5a was very conserved (93% of known ORFs in its genome), which is highly uncommon for environmental phages where majority of the ORFs does not match any ORFs in public databases. Moreover, several proteins involved in lysogenic function were detected in this phage (integrase, transcriptional regulator) indicating the ability of this myovirus to be temperate. The metagenomic recruitment in the POV database revealed that these phages are rare but they were detected not only in coastal photic samples, but also in aphotic samples of the intermediate and open ocean suggesting that these phage genera are spread distributed and they could represent a fraction of the rare biosphere of unknown marine viral diversity.

177 Comparative genomics of Pseudoalteromonas phages Chapter 5

INTRODUCTION

The concept of the microbial loop established by Azam et al. (1983) led to a recognition of the important role of bacteria in aquatic ecosystems. Later the discovery of high concentrations of viruses in the sea Bergh et al. (1989), suggested that the vast majority of the viruses are believed to be phages (virus that infect bacteria) because bacteria are the most common prey. Phages play important roles on bacterial communities affecting their diversity, evolution and dynamics (Wommack and Colwell, 2000; Weinbauer, 2004; Suttle, 2005; Suttle, 2007; Breitbart, 2012). However, our knowledge of phage ecology and evolution is biased towards few sequenced marine phage genomes that belongs to a few hosts. The genus Pseudoalteromonas is a marine group of bacteria belonging to the class Gammaproteobacteria. This genus has been widely found in marine habitats from sea ice (Bian et al., 2012), coastal environments (BBMO, (Alonso-Sáez et al., 2007; Ruiz-González et al., 2012), to deep-sea sediments (Qin et al., 2011). Also, Pseudoalteromonas antimicrobial and algicidal substances (Bowman, 2007). Phages that infect has been shown to influence biofilm formation Pseudoalteromonas and production of strains have been previously studied in the marine environment and it has been reported their ecological and evolutionary importance (Moebus, 1992; Wichels et al., 1998; Wichels et al., 2002; Thomas et al., 2008). However, to date, only four marine Pseudoalteromonas phages have been isolated and sequenced: Pseudoaltermonas phage PM2 (Cortoviridae), Pseudoalteromonas phage H105/1 (Siphoviridae), Pseudoalteromonas phage RIO-1 (Podoviridae) and Pseudoalteromonas phage and pYD6-A (Podoviridae) (Männistö et al., 1999; Hardies et al., 2003; Duhaime et al., 2011). In the current study, we address the absence of myoviruses infecting Pseudoalteromonas by the isolation and genomic characterization of two myoviruses (306b and C5a) from Pseudoalteromonas sp. RHS-str.402. In addition, we compared these myoviruses with the siphovirus B8b isolated from Pseudoalteromonas sp. QC-44, all of them isolated from the same coastal marine site (BBMO). The comparative analysis of multiple phages retrieved from a single bacterial species offers an opportunity to evaluate the heterogeneity of their genomic structure and functional diversity. Moreover, metagenomic comparisons were carried out to elucidate their distribution in natural aquatic systems and explore putative biogeographic patterns.

178 Comparative genomics of Pseudoalteromonas phages Chapter 5

MATERIAL AND METHODS

Phage isolation Pseudoalteromonas phage 306b and C5a were obtained from Blanes Bay Microbial Observatory (BBMO, http://www.icm.csic.es/bio/projects/icmicrobis/bbmo), a surface coastal site in the NW Mediterranean Sea, in winter 2009. Four liters of surface seawater was collected and after a 0.22 μm prefiltration (Millipore, Whatmann), phages were concentrated Phages were isolated using liquid enrichment cultures and plaque assays (Sambrook, 1989). In by tangential flow filtration (30KDa VIVAFLOW cartridge, Sartorius) to a final volume of 20 ml. the enrichments assay, 1 ml of viral concentrate was added to 3 ml of Pseudoalteromonas sp. RHS-str.402 for phages 306b and C5a, exponentially growing in liquid Zobell medium (1.0 g yeast of incubation in the dark, the mixture was centrifuged (5,000 g, 10 min) and the supernatant was extract, 5 g peptone, 15 g agar, 250 ml MQ water and 750 ml ultra‐filtrated seawater). After 24h filtered through a 0.22 μm filter to remove any remaining bacterial cell. Phage enrichment was 8 cells) and plated using the agar overlay technique confirmed by plaque assay, in which 100 μl phage sample from 10X dilution series was combined by adding 3.5 ml of molten soft agar (0.5% agar in Zobell; 50°C). After plating, a well-resolved with 400 μl of liquid bacterial culture (~10 plaque was picked from the lawn of host cells and eluted with MSM buffer (450 mM NaCl, 50 mM MgSO4 x 7H2

0, 50 mM Tris base, pH 8). For isolation and purification of single clonal phage ml of MSM fully lysed plates. The plates were incubated in a shaker (110 RPM) for 40 min and the population, plaques were purified 3 times. High titer phages stocks were prepared by adding 5 phage-MSM solution was transferred to a sterile tube and centrifuged at 5,000 g for 10 min where after the supernatants were 0.22 μm filtered and stored at 4°C in the dark. Electron microscopy High-titer phage stocks (lysates) from the selected phages were prepared for TEM (transmission electron microscopy) (Børsheim et al., 1990; Weinbauer et al., 2002). Five microlitres of the viral stock was spotted for 1 min onto fresh glow-discharged Formvard-coated carbon grids. Adsorbed phages were negatively stained by adding 5 drops of 2% uranyl acetate

The grids were observed in a Jeol 1010 (Jeol, Japan) transmission electron microscope operating solution for 10 s each time. Excess stain was drawn off with filter paper and the grid air-dried. at 80 kV equipped with a CCD camera camera SIS Megaview III and AnalySIS software.

179 Comparative genomics of Pseudoalteromonas phages Chapter 5

Viral DNA purification and genome sequencing Viral DNA was obtained using the Lambda Wizard DNA kit (Promega Corp. Madison, concentrated using polyethylene glycol. Here, 3.25 g NaCl was added to 50 ml of phage lysate. WI) (Henn et al., 2010; Sullivan et al., 2010). Phage lysate from ~15 fully lysed plates were

The mixture was incubated 1 h at 4°C in the dark followed by centrifugation at 5,000 g, 10 min. The pellet was discarded and polyethylene glycol (PEG 8000 10%) was added to the supernatant. After an incubation of 1 h at 4°C in the dark, it was centrifuged (5,000 g, 10 min). The supernatant

(Promega, product A7181 Madison WI) was added to 1.5 ml of phages (the PEG pellet resuspended was discarded and the pellet was resuspended with MSM buffer. One ml of Purification Resin with MSM buffer) and mixed gently by inverting the tube. The mixture was loaded onto a mini- column (Promega, product A7211 Madison WI) through a 5 ml syringe attached to the column, pushing the mixture through with the syringe plunger. The column was then washed with 2 ml 80% isopropanol, the syringe was removed and the mini-column placed into a 1.5 ml eppendorf tube and centrifuged (10,000 g, 2 min, room temperature) to remove any remaining liquid. Phage DNA was eluted from the column by adding 100 ml TE buffer (80°C), and the DNA was recovered in a 1.5 ml eppendorf tube through centrifugation (10,000 g, 30 s, room temperature). Phage DNA was stored at -20°C. The genome was sequenced by the Lifesequencing company (Valencia,

System (Roche), according to the manufacturer’s instructions. Spain) using the standard shotgun sequencing reagents and a 454 GS FLX Titanium Sequencing

Genome assembly and annotation Quality control was done to improve assembly entailed, dereplicating reads (default parameters, cdhit-454, v4.6.1) (Huang et al., 2010) and removing all reads with ambiguous bases (Ns) using prinseq lite (v 0.2.3) (Schmieder and Edwards, 2011). This reduced the original read number for Phage C5a (44.244) and Phage 306a (19.283) to 28.374 and 12.253 after dereplication and 22.915 and 9.437 after removing reads with Ns. Reads were then maximally assembled using Newbler v2.5.3 with minimum overlap length of 80 bp, resulting in dominant contigs 35.389 bp (Phage C5a) and 43.789 (Phage 306a) long. Coding sequences on the dominant contigs were predicted using prodigal v1.2.0 (default parameters) (Hyatt et al., 2010). Predicted proteins were compared to NCBI nr RefSeq protein database (downloaded June 21, 2013) and InterProScan (accessed September 6, 2013) for functional annotation.

Fragment Recruitment Analysis Genomes from phages 306b and C5a (myovirus) and the phage B8b (siphovirus) isolated

180 Comparative genomics of Pseudoalteromonas phages Chapter 5 from Pseudoalteromonas sp. RHS-str. 402 strain from the same site were used for genome fragment recruitment analyses (FRA) to analyze the distribution and relative abundance of

Sullivan, 2013) (available at CAMERA (http://camera.calit2.net) under the following project these phages in 32 marine viral metagenomes from the Pacific Ocean Virome1 (Hurwitz and accessions: CAM_P_0000914 and CAM_P_0000915). We used the Reciprocal Best Blast approach (RBB) (Raes et al., 2007) applying the same rationale to that employed elsewhere (Zhao et al., predicted ORFS of B8b, 306b and C5a are searched against it using TBLASTn. After this initial 2013). Briefly, individual metagenomics samples are made into a BLAST database, and then the blast, hits to the POV database are extracted and become the query for a second BLAST search (BLASTx) against a internal protein genome reference database with a total size of 8.512.217 ORFs that included: (i) protein viral genomes (Refseq Release 60; 4.958 genomes and 163.830 ORFs), (ii) bacterial genomes (RefSeq Release 60; 197.527 contigs and 8.348.231 ORFs) and (iii) the Pseudoalteromonas phage B8b (4 Contigs, 55 ORFs), 306b (54 ORFs) and C5a (47 ORFs), total size of the database 8.512.217 ORFs. Only metagenomic sequences that returned as a best hit a sequence from the genome of the Pseudoalteromonas phage B8b, 306b and C5a are extracted from the database and count as hits for subsequent step. Finally, to calculate the relative abundance of each Pseudoalteromonas phage and two other phages used as reference genomes (the abundant Enterobacteria phage T4 (NC_000866) in the POV dataset, we normalized the number of hits to: 1) protein length, 2) sequencing depth Pelagiphage HTV0C10P (KC465898) and the non marine and 3) mean abundances across the 32 POV metagenomes. This is: the number of hits H was divided to the total number of sequences N and 2) to the amino acid length of the hit protein L. abundances across all samples. Finally, to avoid larger number of significant figures, the abundances were rescaled to the mean

!! !! 𝐻𝐻 ∗ 𝑁𝑁 ∗ 𝐿𝐿 𝐴𝐴!"! = !! !! 𝐻𝐻 ∗ 𝑁𝑁 ∗ 𝐿𝐿 RESULTS AND DISCUSSION

Morphology and general genomic features of 306b and C5a phages Phages 306b and C5a were isolated from Blanes Bay Microbial Observatory using an enrichment method. Phage 306b formed clear, small and round plaques while C5a plaques were larger, turbid and with irregular edges. These two phages are morphologically similar to each other according TEM observation, with isometric head, contractile tail, baseplate, and Myoviridae family according to current tail fiber structures (Fig. 1B, 1C). Thus they belonged to 181 Comparative genomics of Pseudoalteromonas phages Chapter 5

A B C

Figure 1. Transmission electron micrograph showing negatively stained Pseudoalteromonas phages (A) B8b, (B) 306b and (C) C5a.

Both 306b and C5a phages are double-stranded DNA viruses and sequencing data showed International Committee on Taxonomy of Viruses taxonomy (King AMQ et al., 2012). a length of 43.4 kb for 306b phage and 35 kb for phage C5a. Out of the 54 open reading frames of these encoded protein had hits with known function. In the case of phage C5a, 47 ORFs were (ORFs) identified in 306b, 29 (53.7%) had recognizable homologues in GenBank, and only 13 no identity to proteins in GenBank database (Table 1C). The conservation of the phage C5a was identified and 44 had recognizable homologues in GenBank. Thus, only 3 (6.4%) ORFs showed highly surprising since it is uncommon for environmental phages where usually most of their ORFs have no matches with anything in databases (Duhaime et al., 2011; Baudoux et al., 2012; Holmfeldt et al., 2013).

Genomic and gene comparison among Pseudoalteromonas phages Phages 306b and C5a were compared with the siphovirus B8b (Fig. 1A), isolated from the same marine site but from different host (Lara et al., in prep). Despite myoviruses 306b and organization (Fig. 2). Interestingly, phage 306b is most similar to phage C5a were isolated from the same bacterialPseudoalteromonas host, these phages differ significantly overall genome B8b, the last one belong to a different viral family (phage 306b belongs to Myoviridae family and phage B8b to Siphoviridae). These phages are similar with respect to genome length and predicted proteins (Fig. 2). Both displayed two distinctive supermodules: genes involved in DNA replication and nucleotide metabolism were located in the left arm of both genomes and a packaging/structural module in the right arm. Moreover, no genes involved in lysogenic function

182 Comparative genomics of Pseudoalteromonas phages Chapter 5

(integrase, excisionase, repressor and antirepressor) or encoding for transcription regulatory functions were detected. In contrast, phage C5a had smaller genome but with higher number of predicted proteins (Fig. 2, Table 1C). This phage presented a structural module with a well-conserved gene order:

detected in this phage (integrase, transcriptional regulator) located, almost al of them, in the left portal – terminase – head – tail – tail fibers. Several proteins involved in lysogenic function were

it supports the view that phages are mobile genomic extensions of the hosts they infect (Sullivan arm. Thus, the presence of these proteins confirms the ability of this myovirus to be temperate and et al., 2005; Siefert, 2009) and that genetic exchange occurred between Pseudoalteromonas host and its phage.

5 kb 10 kb 15 kb 5 kb 10 kb 15 kb 5 kb

dUTPase Helicase Small terminase subunit DNA binding protein DNA polymerase Large Portalterminase protein subunit Phage proheadChaperone U35 GroESPhage tail tape mesure protein

Phage B8b Primase proteinRecT (Siphovirus)

Phage 306b (Myovirus)

dUTPase Helicase Primase

DNA polymerase Chaperone ParB-like nuclease Portal protein Phage tail tape GroES DNA binding Phage prohead Large terminase protein mesure protein

Adhesin regulatory domain

Phage C5a SH3 (Myovirus)

il spike

il protein il

il protein il il protein il

Ta

Ta Ta Integrase Ta

Peptidase Repressor DNAEndonuclease methylase Head protein Head

DNA methylase protein tube il Ta

Capsid protein

Portal protein Assembly protein TranscirptionalTranscirptional regulator activator

Large terminase terminase Small

Transcirptional regulator morphogenesis Virion

Major capsid protein capsid Major

Transcirptional regulator Transcirptional Phage tail tape mesure mesure tape tail Phage 5 kb 10 kb 15 kb 20 kb 25 kb 30 kb 35 kb 40 kb

Figure 2. Representation and comparison of the genome organization for the 3 isolated Pseudoalteromonas phages.

183 Comparative genomics of Pseudoalteromonas phages Chapter 5

Table 1A. Genomic annotation of phage B8b

Genomic data

Nucleotide Nucleotide Product Predicted identity or Strain with closest hit Contig_ORF start Strand % aa ID Accesion number Taxonomy end position lenght (aa) function of product (Evalue) position Hypothetical phage Contig1_ORF1 7 630 + 624 Non-significant protein Lactococcus phage Q33 Contig1_ORF2 676 1221 + 546 42.0 dUTPase AFV51054.1 Siphoviridae, Caudovirales (8.0E-13) Hypothetical phage Contig1_ORF3 1218 1625 + 135 Non-significant protein Hypothetical phage Contig1_ORF4 1622 2128 + 168 Non-significant protein Hypothetical phage Contig1_ORF5 2121 2456 + 1 1 1 Non-significant protein Salmonella phage E1 Contig1_ORF6 2536 3297 + 762 37.0 DNA binding protein WP_003849806.1 Siphoviridae, Caudovirales (4.0E-45) Hypothetical phage Contig1_ORF7 3282 3500 + 72 Non-significant protein Hypothetical phage Contig1_ORF8 3503 3760 + 85 Non-significant protein Hypothetical phage Contig1_ORF9 3753 4376 + 201 Non-significant protein Salmonella phage FSL SP- Contig1_ORF10 6914 4560 - 2355 35.0 DNA primase AGF89287.1 Siphoviridae, Caudovirales 062 (1.0E-72) Hypothetical phage Contig1_ORF11 7155 6919 - 236 Non-significant protein Conserved hyphotetical Edwardsiella phage MSW- Contig1_ORF12 7580 7152 - 429 47.0 YP_007348969 Myoviridae, Caudovirales phage protein 3 (6.0E-101) Hypothetical phage Contig1_ORF13 8746 7580 - 1167 Non-significant protein Conserved hyphotetical Salmonella phage FSL SP- Contig1_ORF14 9873 8749 - 1125 33.0 AGF89282.1 Siphoviridae, Caudovirales phage protein 062 (2.0E-33) Marichromatium Contig1_ORF15 10783 9878 - 906 42.0 RecT protein WP_005220619 Gammaproteobacteria, Chromatiales purpuratum 984 (1.0E-4) Conserved hyphotetical Acinetobacter phage Ac42 Contig1_ORF16 11037 10813 - 225 51.0 YP_004009376 Myoviridae, Caudovirales phage protein (2.0E-20) Hypothetical phage Contig1_ORF17 1 1 1 1 7 11281 + 165 Non-significant protein Hypothetical phage Contig1_ORF18 11286 11549 + 264 Non-significant protein Hypothetical phage Contig1_ORF19 11546 11743 + 198 Non-significant protein Hypothetical phage Contig1_ORF20 11709 11891 + 171 Non-significant protein Salmonella phage FSL SP- Contig1_ORF21 11958 13628 + 1671 35.0 Helicase AGF89284.1 Siphoviridae, Caudovirales 062 (3.0E-92) Hypothetical phage Contig1_ORF22 13621 13857 + 237 Non-significant protein Salmonella phage FSL SP- Contig1_ORF23 13847 16057 + 2211 39.0 DNA polymerase AGF89344.1 Siphoviridae, Caudovirales 062 (9.0E-145) Hypothetical phage Contig1_ORF24 16103 16705 + 603 Non-significant protein Hypothetical phage Contig1_ORF25 16705 16938 + 234 Non-significant protein Conserved hyphotetical Klebsiella phage phiKO2 Contig1_ORF26 17471 17001 - 471 40.0 YP_006634.1 Siphoviridae, Caudovirales phage protein (2.0E-17) Conserved Hypothetical Alishewanella jeotgali Gammaproteobacteria, Contig1_ORF27 17695 17468 - 228 41.0 WP_008951684 phage protein KCTC 22429 (7.0E-11) Alteromonadales Conserved Hypothetical Marinobacterium stanieri Gammaproteobacteria, Contig1_ORF28 19877 17688 - 2190 30.0 WP_010325175 phage protein S30 (5.0E-28) Alteromonadales (Prophage) Conserved Hypothetical Pseudoalteromonas sp. Gammaproteobacteria, Contig1_ORF29 20170 19874 - 282 33.0 WP_008172253 phage protein BSi20652 (7.0E-5) Alteromonadales Hypothetical phage Contig2_ORF1 256 486 + 76 Non-significant protein Escherichia phage Contig2_ORF2 410 1006 + 198 25.0 Small terminase subunit vB_EcoM_ECO1230-10 ADE87936.1 Myoviridae, Caudovirales (7.12E-7) Conserved hyphotetical Shewanella frigidimarina Gammaproteobacteria, Contig2_ORF3 990 1502 + 170 28.0 YP_750332 phage protein NCIMB 400 (8.32E-4) Alteromonadales Phage large terminase Marinobacterium stanieri Gammaproteobacteria, Contig2_ORF4 1519 3510 + 663 59.0 WP_010322164 subunit S30 (3.00E-125) Alteromonadales (Prophage) Hypothetical phage Contig2_ORF5 3514 3729 + 71 Non-significant protein Marinobacterium stanieri Gammaproteobacteria, Contig2_ORF6 3719 5218 + 499 40.0 Phage portal protein WP_010322159 S30 (5.0E-126) Alteromonadales (Prophage) Hypothetical phage Contig2_ORF7 5232 5660 + 142 Non-significant protein Hypothetical phage Contig2_ORF8 5662 6108 + 148 Non-significant protein Hypothetical phage Contig2_ORF9 6053 6283 + 76 Non-significant protein Hypothetical phage Contig2_ORF10 6264 6524 + 86 Non-significant protein Conserved hyphotetical Pseudoalteromonas phage Contig2_ORF11 6821 6525 - 98 31.0 YP_008051111.1 Podoviridae, Caudovirales phage protein RIO-1 (2.0E-6) Conserved Hypothetical Pseudoalteromonas phage Contig2_ORF12 7423 6803 - 206 40.0 YP_008051111.1 Podoviridae, Caudovirales phage protein RIO-1 (2.0E-6) Hypothetical phage Contig2_ORF13 7800 7423 - 125 Non-significant protein Peptidase U35 phage Marinobacterium stanieri Gammaproteobacteria, Contig2_ORF14 7912 9966 + 684 44.0 WP_010322158 prohead HK97 S30 (3.0E-168) Alteromonadales (Prophage) Conserved hyphotetical Marinobacterium stanieri Gammaproteobacteria, Contig2_ORF15 10022 10360 + 1 1 2 48.0 WP_010322157 phage protein S30 (1.0E-17) Alteromonadales (Prophage) Hypothetical phage Contig2_ORF16 10341 10682 + 1 1 3 Non-significant protein Conserved hyphotetical Vibrio crassostreae Contig2_ORF17 10675 11298 + 207 41.0 WP_017059000 Gammaproteobacteria, Vibrionales phage protein (3.0E-44) Conserved Hypothetical Marinobacterium stanieri Gammaproteobacteria, Contig2_ORF18 11295 11771 + 158 30.0 WP_010322154 phage protein S30 (4.0E-4) Alteromonadales (Prophage) Pseudoalteromonas Gammaproteobacteria, Contig2_ORF19 11774 12148 + 124 35.0 Chaperone GroES WP_009840504 tunicata (1.0E-4) Alteromonadales Hypothetical phage Contig2_ORF20 12148 12318 + 56 Non-significant protein Conserved hyphotetical Marinobacterium stanieri Gammaproteobacteria, Contig2_ORF21 12318 13079 + 253 40.0 WP_010322152 phage protein S30 (8.01E-39) Alteromonadales (Prophage) Phage tail tape measure Marinobacterium stanieri Gammaproteobacteria, Contig2_ORF22 13148 17356 + 1402 32.0 protein TP901, core WP_010322151 S30 (1.29E-161) Alteromonadales (Prophage) region Conserved hyphotetical Pseudoaltermonas sp. S9 Gammaproteobacteria, Contig2_ORF23 17359 17769 + 136 39.0 WP_010490777 phage protein (8.0E-17) Alteromonadales Conserved Hypothetical Pseudomonas aeruginosa Gammaproteobacteria, Contig2_ORF24 17769 19343 + 523 35.0 WP_019396974.1 phage protein (7.0 E-73) Pseudomonadales Conserved hyphotetical Pseudomonas aeruginosa Gammaproteobacteria, Contig3_ORF1 9 1694 + 1644 38.0 WP_019396974.1 phage protein (5.0 E-82) Pseudomonadales Hypothetical phage Contig3_ORF2 1694 2154 + 154 Non-significant protein Conserved hyphotetical Klebsiella oxytoca Gammaproteobacteria, Contig4_ORF1 12 350 + 339 29.0 WP_004131755 phage protein (9.0E-5) Enterobacteriales Hypothetical phage Contig4_ORF2 343 609 + 267 Non-significant protein Hypothetical phage Contig4_ORF3 824 699 - 126 Non-significant 184 protein Comparative genomics of Pseudoalteromonas phages Chapter 5

Table 1B. Genomic annotation of 306b phage.

Genomic data

Nucleotide Nucleotide Product % aa Predicted identity or function of Strain with closest hit ORF start Strand Accesion number Taxonomy end position lenght (aa) ID product (Evalue) position ORF_1 288 9 1 1 + 623 Hyphotetical phage protein Non-significant Megamonas funiformis (3.0E- ORF_2 957 1502 + 545 39.39 dUTPase WP_008537393.1 Negativicutes, Selenomonadales 14) ORF_3 1499 1906 + 407 Hyphotetical phage protein Non-significant ORF_4 1903 2409 + 506 Hyphotetical phage protein Non-significant ORF_5 2402 2737 + 335 Hyphotetical phage protein Non-significant ParB-like nuclease domain Erwinia tasmaniensis Et1/99 Gammaproteobacteria, ORF_6 2817 3578 + 761 46.64 YP_001911111.1 (ParBc) (4.0E-54) Enterobacteriales ORF_7 3581 3781 + 200 Hyphotetical phage protein Non-significant ORF_8 3784 4041 + 257 Hyphotetical phage protein Non-significant ORF_9 4034 4657 + 623 Hyphotetical phage protein Non-significant ORF_10 4669 4830 - 161 Hyphotetical phage protein Non-significant Segniliparus rotundus DSM ORF_11 4841 7195 - 2354 31.75 Primase YP_003658116.1 Actinobacteria, Actinomycetales 44985 (9.0E-56) ORF_12 7200 7436 - 236 Hyphotetical phage protein Non-significant Conserved hyphotetical phage Edwardsiella phage MSW-3 ORF_13 7433 7861 - 428 47.33 YP_007348969.1 Myoviridae, Caudovirales protein (7.0E-26) ORF_14 7861 9003 - 1142 Hyphotetical phage protein Non-significant Conserved hyphotetical phage Paenibacillus sp. JC66 ORF_15 9006 10130 - 1124 29.64 WP_010271397.1 Bacilli, Bacillales protein (1.0E-33) Conserved hyphotetical phage Marichromatium purpuratum ORF_16 10135 11043 - 908 41.94 WP_005220619.1 Gammaproteobacteria, Chromatiales protein (2.0E-04) Conserved hyphotetical phage ORF_17 11073 11297 - 224 73.33 Vibrio cholerae (2.0E-33) WP_000431250.1 Gammaproteobacteria, Vibrionales protein ORF_18 11437 11553 + 1 1 6 Hyphotetical phage protein Non-significant ORF_19 11544 11807 + 263 Hyphotetical phage protein Non-significant ORF_20 11804 12001 + 197 Hyphotetical phage protein Non-significant ORF_21 11979 12149 + 170 Hyphotetical phage protein Non-significant Edwardsiella phage MSW-3 ORF_22 12216 13886 + 1670 36.38 Helicase YP_007348968.1 Myoviridae, Caudovirales (4.0E-101) ORF_23 13879 14115 + 236 Hyphotetical phage protein Non-significant Vibrio phage vB_VchM-138 ORF_24 14105 16315 + 2210 39.06 DNA polymerase YP_007006391.1 Myoviridae, Caudovirales (2.0E-153) ORF_25 16361 16963 + 602 Hyphotetical phage protein Non-significant ORF_26 16963 17196 + 233 Hyphotetical phage protein Non-significant Conserved hyphotetical phage Alteromonas macleodii str. Gammaproteobacteria, ORF_27 17259 17729 - 470 38.22 YP_006798858.1 protein 'English Channel 673' (4.0E-28) Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_28 17726 17953 - 227 41.33 Alishewanella jeotgali (4.0E-11) WP_008951684.1 protein Alteromonadales Conserved hyphotetical phage Pseudomonas fulva 12-X Gammaproteobacteria, ORF_29 17946 20135 - 2189 29.51 YP_004473534.1 protein (1.0E-42) Alteromonadales Conserved hyphotetical phage Pseudoalteromonas sp. Gammaproteobacteria, ORF_30 20132 20413 - 281 33.33 WP_008172253.1 protein BSi20652 (4.0E-05) Alteromonadales ORF_31 20410 21357 - 947 Hyphotetical phage protein Non-significant Conserved hyphotetical phage Gammaproteobacteria, ORF_32 21370 22563 - 1193 27.18 Aeromonas veronii (9.0E-05) WP_005346858.1 protein Aeromonadales Conserved hyphotetical phage Pseudomonas fulva 12-X Gammaproteobacteria, ORF_33 22563 26015 - 3452 32.69 YP_004473531.1 protein (1.0E-160) Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_34 26015 26425 - 410 38.71 Pseudomonas sp. S9 (5.0E-17) WP_010490777.1 protein Alteromonadales Shewanella baltica OS155 Gammaproteobacteria, ORF_35 26428 30636 - 4208 33.36 Phage tail tape measure protein YP_001051936.1 (8.0E-177) Alteromonadales Conserved hyphotetical phage Marinobacterium stanieri Gammaproteobacteria, ORF_36 30705 31466 - 761 40 WP_010322152.1 protein (2.0E-46) Alteromonadales ORF_37 31466 31636 - 170 Hyphotetical phage protein Non-significant Lactobacillus kefiranofaciens ORF_38 31636 32010 - 374 37.5 Chaperone GroES YP_004561897.1 Bacilli, Lactobacillales ZW3 (9.0E-05) Conserved hyphotetical phage Marinobacterium stanieri (4.0E- Gammaproteobacteria, ORF_39 32013 32489 - 476 30.56 WP_010322154.1 protein 04) Alteromonadales Conserved hyphotetical phage Hahella chejuensis KCTC 2396 Gammaproteobacteria, ORF_40 32486 32974 - 488 28.3 YP_434100.1 protein (1.0E-05) Oceanospirales ORF_41 33102 33398 - 296 Hyphotetical phage protein Non-significant Conserved hyphotetical phage Marinobacterium stanieri Gammaproteobacteria, ORF_42 33424 33762 - 338 47.75 WP_010322157.1 protein (7.0E-18) Alteromonadales Conserved hyphotetical phage Marinobacterium stanieri (3.0E- Gammaproteobacteria, ORF_43 33818 35845 - 2027 43.81 WP_010322158.1 protein 168) Alteromonadales ORF_44 35984 36361 + 377 Hyphotetical phage protein Non-significant Conserved hyphotetical phage Pseudoalteromonas sp. Gammaproteobacteria, ORF_45 36361 36981 + 620 40.21 WP_008172280.1 protein BSi20652 (3.0E-25) Alteromonadales Conserved hyphotetical phage Pseudoalteromonas sp. Gammaproteobacteria, ORF_46 36978 37259 + 281 32.18 WP_008172278.1 protein BSi20652 (1.0E-08) Alteromonadales ORF_47 37260 37496 - 236 Hyphotetical phage protein Non-significant ORF_48 37676 38122 - 446 Hyphotetical phage protein Non-significant ORF_49 38124 38522 - 398 Hyphotetical phage protein Non-significant Marinobacterium stanieri Gammaproteobacteria, ORF_50 38566 40065 - 1499 40.36 Phage portal protein WP_010322159.1 (3.0E-126) Alteromonadales ORF_51 40055 40270 - 215 Hyphotetical phage protein Non-significant Gammaproteobacteria, ORF_52 40274 42265 - 1991 58.82 Phage large terminase subunit Marinobacterium stanieri (0) WP_010322164.1 Alteromonadales Shewanella frigidimarina Gammaproteobacteria, ORF_53 42282 42794 - 512 27.72 Adhesin regulatory domain YP_750332.1 NCIMB 400 (7.0E-05) Alteromonadales Bradyrhizobium oligotrophicum ORF_54 42778 43374 - 596 26.92 DNA-binding domain YP_007511257.1 Alphaproteobacteria, Rhizobiales S58 (2.0E-06)

185 Comparative genomics of Pseudoalteromonas phages Chapter 5

Table 1C. Genomic annotation of C5a phage

Genomic data

Nucleotide Nucleotide Product % aa Predicted identity or function of ORF start Strand Strain with closest hit (Evalue) Accesion number Taxonomy end position lenght (aa) ID product position Conserved hyphotetical phage Escherichia coli APEC O78 Gammaproteobacteria, ORF_1 377 784 + 407 64.29 YP_007382111 protein (1.0E-46) Enterobacteriales Gammaproteobacteria, ORF_2 791 1648 - 857 85.16 Phage integrase Pseudoalteromonas tunicata (0) WP_009838283 Alteromonadales ORF_3 2409 2840 + 431 Hyphotetical phage protein Non-significant Pseudoalteromonas sp. BSi20311 Gammaproteobacteria, ORF_4 2897 3265 - 368 69.42 Peptidase S24 WP_008111719 (3.0E-54) Alteromonadales ORF_5 3580 3699 + 1 1 9 Hyphotetical phage protein Non-significant Conserved hyphotetical phage Gammaproteobacteria, ORF_6 3759 4310 + 551 39.08 Pseudoalteromonas rubra (2.0E-37) WP_010385115 protein Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_7 4311 4655 + 344 27.19 Pseudoalteromonas rubra (8.0E-04) WP_010385114 protein Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_8 4660 5253 - 593 42.62 Pseudoalteromonas rubra (7.0E-40) WP_010379924 protein Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_9 5509 5694 - 185 47.17 Pseudoalteromonas rubra (6.0E-11) WP_010379925 protein Alteromonadales Conserved hyphotetical phage ORF_10 6006 6620 + 614 35.82 Vibrio cholerae (2.0E-14) WP_000438276 Gammaproteobacteria, Vibrionales protein Gammaproteobacteria, ORF_11 6663 7127 - 464 50 Repressor Shigella sonnei 53G (7.0E-21) YP_005456223 Enterobacteriales Pseudoalteromonas agarivorans Gammaproteobacteria, ORF_12 7320 7601 + 281 38.46 Transcriptional regulator WP_004589035 (8.0E-14) Alteromonadales Transcriptional activator for Gammaproteobacteria, ORF_13 7598 7852 + 254 58.33 Shewanella sp. MR-7 (2.0E-29) YP_736760 pyocin synthesis Alteromonadales Conserved hyphotetical phage Pseudoalteromonas agarivorans Gammaproteobacteria, ORF_14 7864 8139 + 275 97.65 WP_004589037 protein (2.0E-54) Alteromonadales Conserved hyphotetical phage Pseudoalteromonas agarivorans Gammaproteobacteria, ORF_15 8142 8546 + 404 59.13 WP_004587555 protein (2.0E-39) Alteromonadales ORF_16 8556 8804 + 248 Hyphotetical phage protein Non-significant Pseudoalteromonas agarivorans Gammaproteobacteria, ORF_17 8801 9397 + 596 60.78 DNA methylase WP_004589039 (7.0E-76) Alteromonadales Conserved hyphotetical phage Pseudoalteromonas agarivorans Gammaproteobacteria, ORF_18 9416 9697 + 281 95.7 WP_004587553 protein (3.0E-58) Alteromonadales Gammaproteobacteria, ORF_19 9698 12838 + 3140 99.24 Replication gene A endonuclease Pseudoalteromonas agarivorans (0) WP_004589041 Alteromonadales Conserved hyphotetical phage Pseudoalteromonas agarivorans Gammaproteobacteria, ORF_20 12847 13122 + 275 93.33 WP_004589043 protein (4.0E-56) Alteromonadales ORF_21 13112 14536 + 1424 64.23 DNA methylase Vibrio cyclitrophicus (0) WP_010436982 Gammaproteobacteria, Vibrionales Pseudoalteromonas tunicata (5.0E- Gammaproteobacteria, ORF_22 14705 14950 - 245 98.77 Transcriptional regulator WP_009838248 52) Alteromonadales Gammaproteobacteria, ORF_23 15113 15538 + 425 38.06 SH3 type 3 domain Shewanella sp. MR-7 (2.0E-20) YP_739977 Alteromonadales Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_24 15603 16631 - 1028 84.59 Portal protein YP_001673546 (0) Alteromonadales Gammaproteobacteria, ORF_25 16613 18397 - 1784 81.19 Phage large terminase subunit Shewanella sp. MR-7 (0) YP_736777 Alteromonadales Gammaproteobacteria, ORF_26 18677 19507 + 830 58.63 Capsid scaffolding protein Shewanella sp. MR-7 (0) YP_736778 Alteromonadales Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_27 19562 20569 + 1007 71.09 Major capsid protein YP_001673549 (2.0E-179) Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_28 20635 21162 + 527 45.23 Shewanella sp. W3-18-1 (2.0E-28) YP_964268 protein Alteromonadales Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_29 21291 21821 + 530 65.05 Phage small terminase subunit YP_001673551 (3.0E-84) Alteromonadales Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_30 21935 22384 + 449 73.15 Head completion protein YP_001673552 (9.0E-71) Alteromonadales Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_31 22381 22863 + 482 72.44 Tail completion protein YP_001673553 (3.0E-77) Alteromonadales Shewanella baltica OS155 (2.0E- Gammaproteobacteria, ORF_32 22860 23516 + 656 65.9 Virion morphogenesis protein YP_001049676 103) Alteromonadales Gammaproteobacteria, ORF_33 23521 24627 + 1106 80.65 Tail sheath protein Shewanella sp. MR-7 (0) YP_736785 Alteromonadales Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_34 24639 25088 + 449 90.6 Tail tube protein YP_001673556 (9.0E-93) Alteromonadales Gammaproteobacteria, ORF_35 25101 25301 + 200 46.97 Transcriptional regulator Shewanella baltica OS155 (8.0E-10) YP_001049679 Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_36 25306 25857 + 551 58.29 Shewanella sp. MR-7 (9.0E-65) YP_736789 protein Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_37 25860 26414 + 554 65.32 Shewanella sp. MR-7 (4.0E-77) YP_736790 protein Alteromonadales Conserved hyphotetical phage Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_38 26411 26656 + 245 50.62 YP_964258 protein (0) Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_39 26660 26926 + 266 65.91 Shewanella sp. MR-7 (4.0E-36) YP_736792 protein Alteromonadales Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_40 27136 28914 + 1778 71.45 Phage tail tape measure protein YP_001673562 (0) Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_41 28911 29246 + 335 74.07 Shewanella sp. W3-18-1 (2.0E-49) YP_964255 protein Alteromonadales Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_42 29247 30437 + 1190 73.05 Baseplate assembly protein YP_001673564 (0) Alteromonadales Conserved hyphotetical phage Gammaproteobacteria, ORF_43 30443 31000 + 557 63.19 Shewanella sp. W3-18-1 (3.0E-81) YP_964253 protein Alteromonadales Gammaproteobacteria, ORF_44 31004 33088 + 2084 62.8 Tail fibre protein Shewanella sp. W3-18-1 (6.0E-116) YP_964252 Alteromonadales Conserved hyphotetical phage Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_45 33095 33586 + 491 70.3 YP_001673566 protein (2.0E-76) Alteromonadales Conserved hyphotetical phage Shewanella halifaxensis HAW-EB4 Gammaproteobacteria, ORF_46 33586 34275 + 689 70.31 YP_001673567 protein (3.0E-124) Alteromonadales Gammaproteobacteria, ORF_47 34278 35039 + 761 75.89 Tail spike Shewanella sp. W3-18-1 (1.0E-136) YP_964249 Alteromonadales

186 Comparative genomics of Pseudoalteromonas phages Chapter 5

lysis function were detected in any of these 3 genomes. Finally, although all these 3 phages were isolated as lytic phages, no identifiable genes involved in The comparison among the annotated genes among the 3 phages was represented in a Venn diagram (Fig. 3) and it showed that the myovirus 306b and the siphovirus B8b shared more genes than both isolated myoviruses (306b and C5a). 306b and C5a phages only share 3 among portal protein (Fig. 3, Tables 1B, 1C). While, B8b and 306b shared 6 genes: the primase, the all identified ORFs: the phage tail tape measure protein, the terminase large subunit and the DNA-binding protein, the chaperone GroEs, the dUTPase, the helicase and the DNA polymerase. Moreover, the phage C5a had 20 unique proteins not detected in the other 2 phages (Fig. 3). Therefore, the functional genomic analysis revealed the high similarity between phages belonging to different family and at the same time, the differences between phages isolated from the same host and with identical morphology. Moreover, the functional analysis allowed describing the Myoviridae Pseudoalteromonas phage able to integrate in the host genome. first

B8b 306b

4 6 4

3 1 0

C5a

Figure 3. Venn diagram showing the comparison among the annotated genes among the 3 Pseudoalteromonas phages.

187 Comparative genomics of Pseudoalteromonas phages Chapter 5

10

8

6

4

Normalized Relative Abundance Relative Normalized 2 C5a C5a C5a B8b B8b B8b C5a B8b C5a C5a B8b B8b 306b 306b 306b 306b Photic 306b Aphotic 306b Photic Aphotic Photic Aphotic Coastal Intermeadiate Open Ocean Figure 4. Normalized relative abundances of the Pseudoalteromonas phages. Metagenomes were grouped in 6 categories product of the combination of photic zone and site location as appear in (Hurwitz and Sullivan, 2013a).

Ecological distribution of Pseudoalteromonas phages We investigated the relative abundance of the Pseudoalteromonas phages 306b, C5a and

B8b in a recently published collection of quantitative marine viral metagenomes from the Pacific (Fig. 4) since these phages were originally isolated from a surface Mediterranean Sea sample. Ocean (Hurwitz and Sullivan, 2013). It was interesting to find related proteins in aphotic samples Normalized relative abundance also showed that phages C5a and 306b were not only detected in aphotic samples, but also they were more abundant in dark waters than in photic samples in the intermediate and open ocean environments (Fig. 4). Thus, these Pseudoalteromonas phages showed to be spread distributed and not restricted to photic waters and therefore some of the host of this phages should be highly abundant in dark waters. The percentatge of the number of genes that were covered by the metagenomic reads of POV database was on average 24.2, 19.5 and 24.4 for phage B8b, C5a and 306b respectively. However, of these recruited reads only 0.2, 7.8 and 1.3 for phage B8b, C5a and 306b were, on average, exclusive to Pseudoalteromonas phages (Fig. 5). Thus, phage B8b presented a more restrictive distribution compared with phages 306b and C5a that were ubiquitous in POV database. These results contrast with the obtained for Zhao et al. (2013) who found that a 58.7% of the reads were assigned to pelagiphages (phages that infect SAR11 bacterial isolates), with 38.8% assigned to HTVC010P in the POV database.

188 Comparative genomics of Pseudoalteromonas phages Chapter 5

Coastal Intermeadiate Open Ocean Photic Aphotic Photic Aphotic Photic Aphotic 100

90

80

70

60

50 % aa identity 40

30

20 A 29 43 28 97 35 24 15 26 63 27 29 11 28 86 27 15 11 15 70 25 22 15 22 93 29 27 23 28 80 30 B 0.5 6 0.7 0.8 11 - - 5 0.4 - 21 0.8 - - 0.8 - 4 5 T4 T4 T4 T4 T4 T4 B8b C5a B8b C5a C5a C5a B8b B8b B8b C5a C5a B8b 306b 306b 306b 306b 306b 306b HTVC010P HTVC010P HTVC010P HTVC010P HTVC010P HTVC010P

Figure 5. Box plots show the percent amino acid identity for metagenomics reads (32 metagenomes, POV) recruiting to predicted genes from Pseudoalteromonas phage B8b, 306b and C5a, as well as the abundant Enterobacteria phage T4 (GenBank No. NC_000866). Metagenomes were grouped in 6 categories product of the combination of photic zone Pelagiphage HTV0C10P (GenBank No/ KC465898) and the non-marine and site location as appear in Hurwitz and Sullivan (2013b). A) Percentage of the genome that is being covered by the metagenomics reads. B) Percentage of reads that better align to ORFs in the indicated genome (as per bitscore comparison) than the rest of NR. In box plots the box are marking the upper and lower quartile, the median is shown in red, whiskers are marking 1.5 times the interquartile range.

Moreover, Fig. 6 also showed that recruited reads of Pseudoalteromonas phages genes were similar to that observed for T-4 like phages. Similar results were found for Cellulophaga phages which presented a 15% of recruited reads exclusive to these phages in POV database and they also presented similar % aa identity to T4-like phages (Holmfeldt et al., 2013). These results suggested that although Pseudoalteromonas phages are not abundant in these metagenomes, it seems that they rare phages that are ubiquitious and spread distributed and some of them such the C5a phage with better representation in dark oceans.

189 Comparative genomics of Pseudoalteromonas phages Chapter 5

ACKNOWLEDGMENTS

This work has been supported by the Spanish projects MICROVIS (CTM2007-62140/MAR), FLAME (CGL2010-16304) and PANGENOMICS (CGL2011-26848/BOS). Financial support was provided by a Ph.D. fellowship from the Spanish government to E. Lara.

190 Comparative genomics of Pseudoalteromonas phages Chapter 5

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193

Synthesis of Results and General discussion

Synthesis and Discussion

SYNTHESIS OF RESULTS AND GENERAL DISCUSSION

1. Marine viruses from an ecological to a genomic perspective In the past years, marine viruses have changed our view of marine planktonic food webs. It is now realized that phages play an important role in marine ecology (e.g., phage impact on the cycling of organic matter in the biosphere at a global scale) (Fuhrman, 1999; Wilhelm and Suttle, communities (Fuhrman and Schwalbach, 2003; Breitbart, 2012; Weitz and Wilhelm, 2012). 1999; Weinbauer, 2004) and also influence the evolution, diversity and dynamics of bacterial However, there is a lack of data describing viral abundance and diversity over time in a marine system, except for one example (Parsons et al, 2012). In fact, most studies are limited to a small number of samples and they do not represent studies on viral community structure and diversity

) in order to follow and at larger scales in time. In this scenario, viral abundance dynamicsChapter 1 and community profiling understand the viral seasonal changes with respect to environmental and microbial variables. were determined monthly during five years in the BBMO ( Furthermore, given the actual concern of the climate change effects on marine ecosystems, and the poor knowledge of the role of viruses in warming scenarios, we evaluated experimentally how increasing temperatures would affect the microbial loop (via protists) respect to the microbial shunt (via viruses) in 2 contrasting Arctic marine systems (Chapter 2). These two studies provide us a general overview regarding interactions among microorganisms (including viruses), and between them and the environment but without knowing who infects whom and who is doing what. Therefore, studies relaying on particular interactions of a bacterium with its phages as models, would improve our understanding about the ecological roles of marine viruses. Little is known on the general mechanism of infection and resistance between most phages and bacteria and fundamental questions still remain unanswered. interactions characterized by complex patterns or are they randomly distributed? What is the For instance: which phages infect which hosts in natural communities? Or, are bacteria-phage extent of such patterns at increasing phylogenetic signal? To provide input in these relevant questions, we used the model of Pseudoalteromonas bacterial strains and its phages isolated Chapter 3). In addition, to increase our knowledge on phage biology, genomics and evolution, 3 of 18 isolated phages were completed sequenced. Currently, from BBMO ( Pseudoalteromonas Pseudoalteromonas marine phages are unrepresented with only 4 phages genomes available in its biology, morphology, genomic and proteomic characteristics ( ). Finally, we carried GenBank. Therefore, one of the isolated phage from BBMO was deeplyChapter studied; 4 we investigated out a genomic comparison of 3 Pseudoalteromonas phages isolated from a single bacterial species

197 Synthesis and Discussion to get insights into the genome structure, functional diversity and biogeographical patterns and distribution of such phages in marine viruses metagenomes available in public datasets (Chapter 5).

2. Do natural viral communities follow seasonal patterns? Although seasonal variability in viral abundance has been shown at several marine sites (Bergh et al., 1989; Jiang and Paul, 1994; Wommack and Colwell, 2000), few studies have analyzed the seasonal changes more than two years to extract any recurrent pattern that followed seasonality. The only multiyear time series study examining viral abundance was carried out in the open ocean in the Sargasso Sea (Parsons et al., 2012). This study demonstrated seasonally the distribution of SAR11, and abundances. However, there is a recurring patterns in viralProchlorococcus abundance related toRhodobacteraceae physical changes in water-column stability and lack of studies following viral abundance patterns in a coastal environment, where the conditions are more unstable than in the open ocean. For that reason, and given the existence of continuous the seasonal variability of viral communities in this ecosystem and the factors that could modulate records of microbial and physico-chemical parameters from BBMO since 1992, we aimed to follow them. The results reported in Chapter 1 indicated that both viral abundance and composition with environmental and biological variables showed that viruses were correlated negatively did not follow any clear seasonal patterns during the five years analyzed. Furthermore, analysis with salinity and light penetration and positively with rainfall and Prochlorococcus abundance (Table 1, Chapter 1). Several studies have found that viral abundance is strongly positively correlated with bacterial abundance in some oceanic regions (Jiang and Paul, 1994; Steward et in marine environments are determined by the productivity and density of host populations, al., 1996; Winter et al., 2009; Siokou-Frangou et al., 2010) since viral production and abundance especially the bacterioplankton (Wommack and Colwell, 2000). But in our study, we were not able to detect any correlation between viruses and bacteria abundances. For that reason, we During this time, negative correlation among viruses with salinity and abundance were analysed the bacterial communities lineages during the first 2.5Prochlorococcus years by CARD-FISH. found and Roseobacter abundances were positively correlated to viruses. Therefore, it seems that Prochlorococcus and salinity are the main variables driving the viral abundance in this marine site. Here, we hypothesized that changes in salinity in marine environments could be producing a synergistic effect between viruses and Prochlorococcus. Changes in viral abundance could be explained by the variability of the Prochlorococcus population and we detected it trough the Procholorococcus salinity,198 that it has been demonstrated to influence populations (Calvo-Díaz et Synthesis and Discussion

January February March Jan+Feb+Mar

April May June Apr+May+Jun

July August September Jul+Aug+Sep Seasonal anomaly Monthly anomalies

October November December Oct+Nov+Dec

2000 2010 2000 2010 2000 2010 2000 2010

Annual

Annual anomalies 2000 2010

Figure 1

. Monthly, seasonal and annual anomalies patterns of viral abundance during a period of 8 years (2005-2012) in Blanes Bay.

In order to demonstrate this hypothesis and corroborate that viral abundance does not al., 2004; Jiao et al., 2005; Pan et al., 2005; Pan et al., 2007). variables that play an important role in viral abundance during our study ( follow seasonal patterns in BBMO even when larger temporal scale is studied, weProchlorococcus analyzed the abundance, temperature, Chlorophyll a, secchi depth, salinity, bacterial production and abundance) during a period of 8 years (2005-2012) in the same marine site. The results showed that effectively, viral abundance does not follow a seasonal pattern in BBMO. Neither monthly, seems that viral abundance tended to increase from August to December with years but in 2012 seasonal or annual anomalies patterns were found during these 8 years (Fig. 1). Moreover, it decreased every month (Fig. 1). The tendency with years from the other variables it was also analyzed (Fig. 2). Viral, bacterial and Prochlorococcus abundance, as well as salinity increased with time suggesting that these 4 variables could be related as we showed in Chapter 1. Instead, temperature, light penetration and bacterial production tended to decrease and Chl a showed similar values during the 8 years studied (Fig. 2). Finally, we carried out a correlation analysis Chapter 1 that suggested that viral abundance is correlated negatively with salinity and light penetration and positively with rainfall among these variables to confirm our previous results in and Prochlorococcus

. However, when we added 3 years in our time-series we found that this 199 Synthesis and Discussion

¡ l 0=iru~,ml') .~ U~ "" "'" =, ,=, ,=, "=""=""=,,=,,=" ,=, ,,=,,= ,,,=""= ,"=""= '''=,, ";,,,, 2 3 4 5 6 7 8 9 10 1112 1950 1960 1970 1980 1990 2(nl 2OJO

Prochlorococcus abundance (ce1l5 ml-' ) J~ ¡ ;: i " " iI¡IIIIIIi ii ii ii ii ii ii ""'" 11111111" ii ii i , 9 10 1112 ,,., '''' '''"' Temperature (OC) "" '"Y ¡:~ ~ l ~i i i i i i i j i i " " iI¡IIIIIIi ii ii ii ii ii ii ""'" i ii i ii i ii i i i i i i -2_00 , 9 10 1112 , ,,., '''' .. '''"'

¡~ l ~(~ m') -.-,OnnlD'I....",,-' U ~ "" "'" =, ="=" = '''=,,,= '''=, ,=, ,=,,=, ,=, ,=, ,=, , ='''="=,,='''='''=,,=" =" , 9101112 , ,,., '''' .. '''"' Secchi deplh (m) ;~ '.00 -LOO ~ ¡ -2.00 ~ l v/vi i i i i i i j i i " " iI¡IIIIIIi ii ii ii ii ii ii ""'" 11111111" ii ii i 123 4 56789101112 , ,,., "50 .. '''"' ~E 1Sali"ily (p~) ~;~ j~ ¡t.so :: 37.40 i i i i i i i j i i =,,=' ''=' ''=' ''=' ''=' ''=,,=,,=,,=,,=,,=,,=,,='" ='''='''='''='''=,,=,,=''' 1 3 ~ S6789101112 , ,,., 2010 '''' "'" ,,'" ...

Bacterial produclion ( ( ~gC 1_1 ¡t')

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: l ~i i i i i i i i i i " " iI¡IIIIIIi ii ii ii ii ii ii ""'" 11111111" ii ii i 9101112 , ,,., "50 .. '''"'

Bacterial abundance (oolls ml-')

=, T,n, T,n, ' ~~ l ~123 4 567891011121950 1960T, nliT , nilT, nilT, 1970n'T, n'T,n'T,n ' T,n' T,n' T,n' ,,.,Tiln' Tiln ' T,n' Tiln2

Figure 2. Tendency with years of viral abundance, Prochlorococcus abundance, temperature, Chloropyll a concentration, light penetration, salinity, bacterial production and bacterial abundance in Blanes Bay from

2005200 to 2012. Synthesis and Discussion

<, ••• t R'_ 0.03111 " R'- Q.20n •• \! " •• •• • ! •• .. •• •• •• i -" ••• -" I ~ •• •• •• •• .~ i .~ • ! R'- O.0461 , .' R"_ O.6962 • , .m i • 1 í -, • i .~ ~ .~ •• .~ ••• •• ••• •• ;; R'- O.051 • R'- O.2IM1 .~ • ~ ! •• • • •• .. • . . j •• • •• I •• • .~ •• ••• J •• •• ..

¡ •• R'_ 0.3701 • ! ••• ,1• j •• •• •• .~

Figure 3. Correlations among viral abundance and Prochlorococcus abundance, temperature, Chlorophyll a 2012 in Blanes Bay. concentration, light penetration, salinity, bacterial production and bacterial abundance from 2005 to

201 Synthesis and Discussion scenario changed. Viral abundance was highly correlated with light penetration (R2= 0.698) and no correlation among salinity and Prochlorococcus was detected (Fig. 3). These results suggested that the exposure to light and ultraviolet radiation (UV) potentially causing DNA damage to viruses (Heldal and Bratbak, 1991; Suttle and Chen, 1992; Noble and Fuhrman, 1997; Weinbauer et al., 1997) and determining the viral population abundance. BBMO is a temperate region with periods of mixed waters in winter and stratification in summer. Moreover, it is highly influenced marine site that it is translated to an irregular pattern in the viral communities and in several by freshwater inputs and periods of strong storms. All these factors made of BBMO a very unstable factors influencing their dynamics at different timing scales. 3. How marine viruses would be affected by climate change? energy from solar radiation and exchanging heat with the atmosphere. They also modulate the Oceans play an important role in regulating climate by storing, distributing and dissipating

2. The effects emissions have direct and indirect consequences in the oceans but evaporation and precipitation 2and they are able to absorb large quantities of the gas CO of climate change linked to CO is likely amplified in the Arctic by several processes including ice and snow melting, atmospheric temperature in the Arctic is increasing at a rate of two to three times that of the global average stability and cloud dynamics that magnify any changes (Overpeck et al., 1997). Consequently, the temperature estimated to be 0.4°C over the past 150 years (IPCC, 2007). In the Arctic Ocean are located key elements of Earth system (Lenton et al., 2008) that includes the freshwater outflow from the Arctic, potentially affecting deep-water formation in the North Atlantic and the global this region. Given that the majority of biomass in oceans consists of microorganisms (Whitman thermohaline circulation (Notz, 2009). Therefore, research efforts are particularly important in et al., 1998), it is expected that microbial communities and viruses play important roles as agents and recipients of global climate change. In this scenario, in Chapter 2 we investigated how autotrophic and heterotrophic Arctic microbial communities responded to various temperatures, based on the predicted warming of the sea surface temperature in the Arctic Ocean. In particular, size structure, ciliate community composition, and especially whether virus (through lysis or we investigated changes in phytoplankton biomass, microbial abundances, flagellate community lysogenic infection) and protists (as bacterivores) had a differential impact on bacterial biomass and production. Previous studies found that warming stimulates the respiration of plankton communities faster than it stimulates photosynthetic rates (Harris et al., 2006; López-Urrutia et al., 2006; Regaudie-De-Gioux202 and Duarte, 2012) and favors heterotrophic bacterial activity (Iriberri Synthesis and Discussion

temperature in our experiments stimulate heterotrophic bacterial biomass and activity as well et al., 1985; White et al., 1991). Our results agreed with these findings since increases in as viral abundance (Fig. 4, Chapter 2). The life strategy of viruses submitted to increasing temperatures was also reported in this study. Temperature could be a stress factor that triggered the transition from lysogenic to lytic cycle, but lytic life strategy was dominant in almost all experimental temperatures (Fig. 5, Chapter 2). Finally, the most important factor controlling bacterial abundance was bacterivory instead of viral lysis (Fig. 6, Chapter 2). These results were in agreement with other studies realized in Arctic regions, influenced by Atlantic waters (Boras et al., 2010). However, an ongoing Pan-Arctic revision, showed that in Arctic areas such as in Chukchi and Beaufort Sea, influenced by the Pacific Ocean virus lyses have a higher impact on bacteria than bacterivory (Maranger & Vaqué et al. submitted). Furthermore, several authors have also found that grazing rates increase with temperature in the Antarctic (Vaqué et al., 2009) that increases in temperature stimulate heterotrophic microbial biomass and activity compared and in cold waters (Newfoundland) (Choi and Peters, 1992). Therefore, these results indicate to that on phototrophs, which has important implications for carbon and nutrient cycling in the system.

4. Marine phage-host interactions 4.1. Host range analyses using the marine Pseudoalteromonas bacterial strain as model

Despite the increasing recognition that phages play a significant role in shaping microbial remain unclear. It has been proposed a mechanism for controlling the biodiversity of the bacterial communities, the ecological interactions and evolutionary processes between phage-bacteria population called “Kill the Winner” hypothesis (KtW). This hypothesis relaying on that viruses control the most abundant or fastest growing host populations thereby enables less competitive the high diversity in the environment (Thingstad and Lignell, 1997; Thingstad, 2000). However, or slower growing populations to co-exist with the dominant, fast growing hosts thus maintaining based on experimental approaches, other authors have proposed that the most dominant host in the marine environment are the least susceptible to viral lysis and the rare marine bacterial groups are the most susceptible (Bouvier and del Giorgio, 2007). Moreover, the KtW assumes that virus-host relationship is extremely specific as shown in some cultured marine phages that not infect even closely related strains (Moebus and Nattkemper, 1981; Moebus, 1992; Kellogg of evidence to suggest that phages commonly infect multiple distinct bacterial strains in natural et al., 1995; Wichels et al., 1998; Rohwer et al., 2000). However, nowadays there is a long record environments (Wichels et al., 1998; Wichels et al., 2002; Comeau et al., 2006; Chiura et al., 2009),

203 Synthesis and Discussion including phages that can infect hosts from different genera (Sullivan et al., 2003). The results on host range analysis could depend on the methods used for bacteriophage isolation or the receptor specificity and the bacterial acquisition of resistance (Chibani-Chennoufi et al., 2004). bacteriophages isolation. Continued rounds of phage infection on the same host may also result Thus, host specificity could be at some extent not realistic depending on the methods used for in the selection of phage lysates with more limited host range (Jensen et al., 1998; Wichels et al., 2002). Chapter 1, samples for viral isolation were taken in autumn 2009. Eighteen phages infecting 7 highly similar strains of During the sampling for viral seasonality in BBMO in Pseudoalteromonas

spp. were isolated. They were characterized morphologically by TEM; genotypic fingerprinting using the RAPD-PCR technique and the host range test was determinedChapter ). Seventeen of 18 isolated phages belonged to family and only one belonged to for3 every isolated phage to know both, their genomic profileMyoviridae and specificity of infection ( Siphoviridae family (phage B8b). According with the genotypic analysis, isolated phages were separated into 7 groups and the single siphovirus isolated was grouped together within other myoviruses in one of the clusters. Thus, these results showed that there was no relationship between these morphologic and genotypic features. Also, it has been suggested that morphological family have broader host ranges than , which have broader host ranges taxonomyMyoviridae of phages could be used as a proxy to inferSiphoviridae the host range specificity. Phages of the than Podoviridae this established pattern. Within the (Wichels et al., 1998;Pseudoalteromonas Suttle, 2005). However, our isolated phages did not follow family. group, phage C5a (myovirus)Myoviridae showed the same narrow host range than siphovirus B8b despite that the first belongs to from a different bacterial family in the genus. These results break two established Moreover, all the isolated phages, includingAlteromonas the siphovirus were able to infect a bacterial strain paradigms: (i) that taxonomy could predict the host range of marine phages and (ii) the specificity host interactions at larger phylogenetic distance crossing over the barrier of phages infecting of phage-host interactions are more complex and poorly understood, in which we found phage- distinct family bacteria (Chapter 3). The discrepancy among the different host ranges reported in different studies may also due to the level of resolution on which bacterial and phages strains were measured. Host diversity et al., 2007). Holmfeldt et al. (2007) showed that phylogenetically identical bacteria based on is often based on single marker genes such as 16S rRNA or ITS (Sullivan et al., 2003; Holmfeldt

16SChapter rRNA 3 sequencing can show differences in the phage-susceptibility patterns. Our results in 204 corroborate these findings; we analyzed the host diversity not only by the 16S rRNA Synthesis and Discussion

gene, but also doing RAPD-PCR that allowed whole genome profiling comparison (strain-typing) of bacterial strains (Martinkearley et al., 1994; Perumal et al., 2009). We found that the genetic distance (for each bacterial strain) detected by RAPD-PCR contribute to explain an average explained an average of 1% ( of 28% of the probability of Fig.infection 6, Chapter on the 3bacterial strains, while the 16S rRNA gene only ). Hence, our results pointed out that 16S rRNA sequencing on host bacteria from closely related strains is not efficient to detect the observed better resolution. variations in infection susceptibility among bacterial strains while RAPD profiles provides a

4. 2. Phage-host networks and their evolutionary processes Host range analysis of phage isolates against a panel of bacterial isolates also allowed us to understand which are the patterns behind viral infection. Network-based approaches have phages with multiple bacteria (Flores et al., 2011). The obtained matrix from our host range test recently been proposed to help unify the quantitative analysis of the cross-infection of multiple using the Pseudoalteromonas by these authors ( ) but further modeling test is needed to accurately understand Fig. 7, Chapter spp. 3 phage-host system followed the nestedness patterns proposed the infection patterns of complex phage-host system. In evolutionary terms, nestedness, as well as (Lenski, 1988). Under this model, bacteria evolve ever increasing resistance to more and more modularity, results from a sequence of adaptations that are arbitrated by gene-for-gene processes phage genotypes, and phages evolve broader host range. An alternative model of gene-for-gene is However, these both models are idealizations and in natural environments might be intermediate that phages must have alleles that facilitate infection against specific bacterial defensive alleles. mechanisms. For instance, it is possible that phages evolve the ability to infect new hosts and partially lose the ability to infect existing hosts (Agrawal and Lively, 2003). Thus, the most or modular structure (Forde, 2008). probable in marine natural systems is that phage-host networks do not have a perfectly nested

5. Going one step further: phage genomics Three of the 18 isolated phages described in Chapter 3 were further sequenced to better Pseudoalteromonas group in marine systems (Bowman, 2007) and the important role of phages in microbial understand their biology and ecology. Knowing the ecological significance of communities (Weinbauer, 2004; Weinbauer and Rassoulzadegan, 2004), only 4 Pseudoalteromonas phages are available in GenBank database: Pseudoaltermonas Cortoviridae), ), ) Pseudoalteromonas Siphoviridae Pseudoalteromonasphage PM2 ( Podoviridae phage H105/1 ( phage RIO-1 (

205 Synthesis and Discussion and Pseudoalteromonas (Podoviridae Hardies et al., 2013). phage pYD6-A ) (Männistö et al., 1999; Duhaime et al., 2011; Therefore, to increase the knowledge of the Pseudoalteromonas host interactions and the genomic features of marine phages, we investigated the biological phage diversity, phage- characterization, the proteomic, and genome structure of the siphovirus B8b at great detail (Chapter 4 infect a bacterial strain from different family than the host ( ). Previously, phages ). One of the relevant biological outcomes about thisChapter siphovirus 3 and was4 that it was able to infecting across genera boundaries have been reported, but these are commonly large myoviruses, like cyanophages infecting Prochlorococcus and Synechococcus (Sullivan et al., 2003), enterophage siphoviruses, one isolate has been reported to infect two bacterial strains of different genera in LG1 and AR1 (Goodridge et al., 2003), and vibriophage KVP40 (Matsuzaki et al., 1992). Among been reported for siphoviruses from the marine environment. wastewater (Chang and Kim, 2011; Kim and Ryu, 2011), but no such cross-genera infections have analysis was the similarity of the siphovirus B8b with other temperate phages ( ). The Other interesting feature of this phage revealed by phylogenetic, proteomicChapter and genomic 4 presence of the RecT protein, that it is related with the capacity to perform lysogeny in several phages and the large number of phage B8b’s structural proteins similar to proteins from a putative prophage in Marinobacterium stanieri S30 bacterial genome that belong to a different genera within Gammaproteobacteria, suggests the ability of B8b phage to integrate into its host genome.

These findings stressed the idea of more broadly genetic exchange between bacterial and phages gene (GroES) that was novel among siphoviruses. at higher phylogenetic signal than we previously thought. Moreover, this phage contained a single

6. Pseudoalteromonas phages genomic comparison and their global distribution

Our knowledge of phage genomics is biased towards phages that infect only a few hosts. For , , and of the example, 85% of the 1.100 sequenced phageActinobacteria genomes in GenBankFirmicutes are isolatedProteobacteria by using bacteria class ), predominantly involved in human diseases and food processing. In from onlyGammaproteobacteria three of 45 known bacterial phyla ( contrast, with the exception of phages infecting cyanobacteria (cyanophages), phages that infect environmental microbes are largely unstudied and unknown. This lack of genomic representation metagenome (Breitbart et al., 2002; Angly et al., 2006; Dinsdale et al., 2008; Williamson et al., results in unidentified DNA sequences accounting for ∼70% of the sequences in nearly any viral 2008; Hurwitz and Sullivan, 2013). Thus, genome analyses of representative members of marine viral communities are

206 Synthesis and Discussion essential to provide a better understanding of the high genetic diversity and their interactions of novel phage taxa and signature genes. Therefore, we addressed the absence of myoviruses with their hosts. Also, completed phage genomes are required to facilitate the identification infecting Pseudoalteromonas spp. by the genomic characterization of two myoviruses (306b and Pseudoalteromonas Chapter 5). Phage genomic sequencing

C5a) isolated from sp. RHS-str.402 ( data revealed that a 46% of the open reading frames (ORFs) identified in phage 306b did not show any homology to proteins in GenBank database. Instead, the majority of ORFs detected in which usually presented a high percentage of unknown proteins in their genomes (Duhaime et C5a phage genome (41 of 44) were known. This is very uncommon in environmental phages, genomic and comparison analysis between these two phages was that despite they were isolated al., 2011; Baudoux et al., 2012; Holmfeldt et al., 2013). Other interesting feature detected in the from the same bacterial host and both are myoviruses, they differ overall genome organization (Fig. 2, Chapter 5

). Phage 306b displayed two distinctive supermodules: genes involved in DNA replication and nucleotide metabolism were located in the left arm and a packaging/structural module in the right arm. However, phage C5a presented a structural module with a well-conserved function (integrase, transcriptional regulator) located, almost all of them, in the left arm. This gene order: portal – terminase – head – tail – tail fibers and several proteins involved in lysogenic was very surprisingly, since it is considered that phages with the ability to integrate into the host

Finally, these two phages were compared with the previous studied B8b genome are the siphoviruses and myoviruses are typically lytic (Suttle, 2005).Pseudoalteromonas siphovirus (Chapter 4). Curiously, the comparison among the annotated genes among the 3 phages showed that the myovirus 306b and the siphovirus B8b shared more genes than both Fig. 2, 3, Chapter 5 analysis carried out in isolated myoviruses (306bChapter and 3 C5a) ( ). On the other hand, the host range showed also that phage C5a and B8b were more similar in terms versus the 16 bacterial strains than phage 306b was able to infect ( ). Therefore, of specificity of infection. Phage B8b and C5a infected only 4 and 5Fig. bacterial 4, Chapter strains 3 respectively these results change the paradigm about the relationships established between morphology, and biological traits of marine phages. Up to now, it was suggested that myoviruses are lytic phages with broad host range and siphovirus were mostly temperate exhibiting narrow host range. Our has genes to develop the lysogenic function and it presented a narrower host range than other inputs on host range and genomic results indicated that phage C5a despite being a myovirus Pseudoalteromonas myoviruses, so its behavior reminds a siphovirus. In addition, we also found that myovirus 306b is more similar to B8b siphovirus at genomic level even when both phages

were isolated from different hosts. Thus, the data presented contribute to a new view of phage-207 Synthesis and Discussion host interactions in marine systems and demonstrate how complex and poorly understood is their dynamics. Furthermore, it reveals the need to improve the existing conceptual models on the role of phages in natural bacterial communities. Given the novelty of these Pseudoalteromonas phages, their distribution was investigated in a recently published collection of quantitative marine viral metagenomes from the Pacific and 306b were not only detected in aphotic samples, but also they were more abundant than Ocean (Hurwitz and Sullivan, 2013). Normalized relative abundance showed that phages C5a in photic samples from the intermediate and open ocean environments (Fig. 4, Chapter 5). Thus, these Pseudoalteromonas phages showed to be spread distributed. The percentatge of the number of genes that were covered by the metagenomic and exclusive to Pseudoalteromonas Fig. 5, Chapter 5). Thus, phage corresponded to 0.2, 7.8 and 1.3 for phage B8b, C5a and 306b ( siphovirus B8b had more restrictive distribution than the other 2 myoviruses in POV database. reads were assigned to pelagiphages (phages that infect SAR11 bacterial isolates), probably due These results contrast with the obtained for Zhao et al. (2013) who found that a 58.7% of the to Pseudoalteromonas class is not as abundant as SAR11, which is considered the most abundant inCellulophaga seawater (Morris et al., 2002; Rappé et al., 2002). However, similar results were found for phages which presented a 15% of recruited reads exclusive to these phages in 2013). These results proved that phages might be wide distributed across POV database and they also presentedPseudoalteromonas similar % aa identity to T4-like phages (Holmfeldt et al., the oceans, and even thought they are not very abundant, they are ubiquitous in several marine environments.

208 Synthesis and Discussion

CONCLUSIONS Chapter 1 1. Viral abundance and composition did not follow any seasonal pattern in the investigated

coastal marine environment (BBMO) during the period of 5 years. 2.

Salinity seems to be the major factor that co-variate negatively with viral abundance in our temporal study in BBMO. 3. Despite the lack of correlation between viral and bacterial abundance, the abundance of Procholorococcus, Rhodobacterales and SAR11 could play an important role in the viral community in Blanes Bay. specific bacterioplankton lineages such as Chapter 2 4. The experiments carried out in the Arctic to determine how increasing temperatures would affect the microbial loop showed that a warming scenario stimulate heterotrophic microbial biomass and activity as well as viral abundance against phototrophic communities.

Lytic life strategy was dominant when viruses were forced to increasing temperatures but the most important factor controlling bacterial abundance was bacterivory instead of 5. viral lysis. Chapter 3 6. The interaction between Pseudoalteromonas isolated phages and Pseudoalteromonas host

infect hosts from different phylogenetic families. strains showed a high variability in specificity of infection and we found that phages can

7.

Comparison between host-range analyses and phage genomic profiles by RAPD-PCR distinct genomic pattern. were not coherent reflecting that many phages that shared identical host range exhibited

8. Variations in susceptibility among bacterial strains and the probability of infection is

better correlated by the whole bacterial genome patterns provided by RAPD-PCR than 16S rRNA gene. 9.

Phage-host interactions patterns obtained in this study followed a nested structure. This209 Synthesis and Discussion

implies that the most specialist phages infect those hosts that are more susceptible to infection rather than infecting those hosts that are more resistant to infection. Chapter 4 10. The isolated Pseudoalteromonas phage B8b belonged to siphoviridae family based on ICTV rules of nomenclature. This phage showed a latent period of 70 min and approximately 172 new viral particles were released from each bacterial cell infected. Host range Pseudoalteromonas spp. strains but it could also infect a bacterial strain belonging to , which represents a taxa of analysis revealed that phage B8b only infected 3 ofAlteromonas 52 a different family.

11. Phage B8b has a 46kb genome and it is closely related to the prophage of Marinobacterium stanieri (Gammaproteobacteria). The phage B8b genome was modular and contained a single gene (GroES) that was novel among siphoviruses. Chapter 5 12. 306b shared more genes with the siphovirus B8b than both isolated myoviruses. Two myoviruses phages (306b and C5a) were also sequenced and surprisingly the myovirus

13.

The phage C5a was very conserved (93% of known ORFs in its genome), which is highly uncommon for environmental phages where majority of the ORFs does not match any ORFs in public databases. 14. transcriptional regulator) suggesting the ability of a myovirus to be temperate. Several proteins involved in lysogenic function were detected in phage C5a (integrase and

The relative abundance of these Pseudoalteromonas phages determined in the marine viral

15. not only in coastal photic samples, but also in aphotic samples of the intermediate and metagenomes from the Pacific Ocean (POV database) revealed that they were detected open ocean.

16. Pseudoalteromonas are ubiquitious and spread distributed and they could represent a fraction of the rare phages are not abundant in the POV database but it seems that they biosphere of unknown marine viral diversity.

210 Resumen en Español (Spanish Summary)

Resumen en Español

INTRODUCCIÓN GENERAL

UN POCO DE HISTORIA Los virus que infectan a bacterias, también llamados bacteriófagos o fagos (término derivado de “bacterias” y del griego φαγεῖν (phagein) de “alimento, ingestión”) fueron descritos por primera vez entre 1915 y 1917 por Twort y d’ Herelle (Duckworth, 1987). Inmediatamente después de su descubrimiento, se consideraron como posible herramienta terapéutica para combatir bacterias patógenas (Levin & Bull, 1996). El papel de los fagos en el medio marino no fue apreciado significativamente hasta 1968 cuando, Wiebe & Liston (1968) sugirieron que los fagos podrían ejercer una influencia sobre las poblaciones bacterianas y en las capacidades marinos de , y bioquímicasPseudomonas de los microorganismos.Photobacterium Además,Cytophaga casi al mismo tiempo se aislaban los primeros fagos (Spencer, 1955, 1960, 1963). Pero no fue estaban presentes en concentraciones muy altas y que a menudo superan en una a dos órdenes hasta finales de 1980, cuando se demostró que en muchos ambientes acuáticos los bacteriófagos de magnitud a las concentraciones de bacterioplancton (Bergh et al., 1989; Borsheim et al., 1990; Bratbak et al., 1990). Estos hallazgos alentaron la investigación sobre la ecología de comunidades virales marinas y su impacto en las redes tróficas microbianas y los ciclos biogeoquímicos. ¿QUÉ ES UN VIRUS? Un virus es una entidad genética no celular que usa una célula viva para su propia reproducción. Por lo general tienen un tamaño pequeño, entre 30 y 60 nm, aunque se han Una partícula vírica completa, conocida como virión encontrado fagos más pequeños y más grandes (Weinbauer, 2004). una capa de protección proteica llamada e , consiste en un ácido nucleico rodeado por cápsid . Los virus pueden contener ADN de doble cadena (ds), ADN monocatenario (ss), dsARN o ssARN. Las cápsides están compuestas de subunidades de la membrana celular proteicas idénticas llamadas capsómeros. Algunos virus tienen un «envoltorio lipídico» derivado del hospedador. La cápside está formada por proteínas codificadas por el pertenecen a la orden de genoma vírico, y su formaCaudovirales es la base de la distinción morfológica. La mayoría de los fagos acuáticos

, que son fagos de dsADN, con cola y se caracterizanMyoviridae por tener una cabeza icosaédrica (Fig. 1A).Siphoviridae Este orden consiste en tres familias principales: (i)Podoviridae con las colas contráctiles, (ii) con largas colas no contráctiles, y (iii) con colas cortas no contráctiles (Figs. 1B, C y D). Sin embargo, estudios realizados en el último año, han demostrado el predominio de virus sin cola (Brum et al., 2013), y la presencia de otros 213 Spanish Summary A

B C D

200 nm 100 nm 100 nm E. Lara E. Lara Born, 2011 Myoviridae Siphoviridae Podoviridae

Figura 1.

Diagrama de la estructura típica de un Myoviridae bacteriófago T4Siphoviridae (myovirus) (A) yPodoviridae las tres familias morfológicas de bacteriófagos dsDNA con colas: (B) , (C) y (D) . grupos virales en los océanos, como los virus de cadena sencilla (ssADN), virus de ARN, o los mimivirus (Labonté & Suttle, 2013; Steward et al, 2013; Hingamp et al., 2013). ¿LOS VIRUS SON ORGANISMOS VIVOS? El origen de la vida, publicado en 1929, considera que los virus son el eslabón perdido entre la materia inerte y la primera célula. Sin embargo, el descubrimiento en 1944 de que el ADN codifica la información genética (Avery et al., 1944) creó una nueva ideología: “la vida es el ADN“ definición que tiene en cuenta conceptos como información genética y evolución como valores principales. Forterre (2010) sugirió que los virus pueden ser considerados como entidades vivas complejas que transforman la célula infectada en un microorganismo nuevo (el virus), el cual produce viriones. Sin embargo, otros autores sugieren varias razones para excluir los virus en el puede ser considerado como un organismo vivo porque requiere partes de otro organismo, o que árbol de la vida (Moreira & López- García, 2009). Por ejemplo, ellos defienden que un virus no así como que los virus no se multiplican y no evolucionan ya que se desarrollan a través de las los virus no contienen todos los genes necesarios para expresar su propia maquinaria genética, células. Por lo tanto la respuesta a la pregunta de si los virus están vivos depende de nuestra definición de la vida. 214 Resumen en Español

MECANISMOS DE INFECCIÓN VIRICA Los fagos no pueden sobrevivir de forma independiente debido a su falta de un sistema metabólico completo y por lo tanto, dependerán de los sistemas enzimáticos del hospedador para la proliferación (Weinbauer, 2004). Con el fin de maximizar el uso sostenible de la maquinaria enzimática, los fagos han desarrollado una variedad de ciclos de vida: el ciclo lítico, infecciones lisogénicas, la pseudo-lisogénia y las infecciones crónicas. Ahora bien, los fagos filamentosos capaces de causar infección crónica sólo se han detectado raramente en ambientes acuáticos de agua dulce (Pina et al, 1998; Hofer & Sommaruga, 2001). La infección lítica es una de las estrategias más típicas para la replicación del fago; los fagos que utilizan este mecanismo son llamados fagos virulentos (Fig. 2). En el ciclo lítico, las las partículas virales, de forma que los nuevos virus quedan libres para llevar a cabo una nueva células hospedadoras del fago son lisadas (destruidas) tras la replicación y encapsidación de infección. Los fagos virulentos son abundantes en el océano (Zhang et al., 2011). Por ejemplo, el 65 % de los fagos aislados en el Océano Atlántico corresponden a este grupo (Moebus & fagos para adaptarse a los ambientes ricos en nutrientes donde la abundancia bacteriana y la Nattkemper, 1981). Esta estrategia de proliferación vírica, es una vía de supervivencia de los producción son elevadas. Por lo general, se considera que los fagos víricos siguen la estrategia r, que se caracteriza por la liberación de un gran número de virus después de la lisis celular del hospedador y tienen un ciclo de generación corto (Suttle, 2007). cromosómico Por el contrario, en el ciclo lisogénico no se produce la lisis inmediata de la célula. El genoma o del fago puede integrase en el ADN de la bacteria hospedadora, replicándose a la vez que lo hace la bacteria o bien puede mantenerse estable en forma de plásmid , replicándose de forma independiente a la replicación bacteriana (Fig. 2). En cualquier caso, el genoma del fago se transmitirá a toda la progenie de la bacteria originalmente infectada. El fago queda en estado de latencia hasta que las condiciones del medio se vean deterioradas por una disminución de nutrientes, un aumento de agentes mutagénicos, etc. En este momento, los fagos endógenos o profagos se activan y dan lugar al ciclo lítico que termina con la lisis celular. Los fagos lisogénicos son resulta en una menor abundancia de células bacterianas y se reduce la frecuencia de infecciones más comunes en los ambientes marinos oligotróficos, ya que la baja concentración de nutrientes

(Wommack & Colwell, 2000). Por lo tanto, las bacterias lisogénicas son más abundantes en mar abierto que en aguas costeras, así como en aguas de profundidad que en la superficie (Jiang & Paul, 1996, 1998b; Weinbauer & Suttle, 1999; Weinbauer et al., 2003) aunque esta premisa no siempre se observa en las comunidades naturales como es el caso del Océano Atlántico Noreste (Boras et al., 2010a). 215 Spanish Summary

Figura 2

. Estrategias de replicación de los fagos. En esta figura están representados (a) lisis, (b) lisogenia y (c) pseudolisogenia.

216 Resumen en Español

La pseudo-lisogénia se caracteriza por la existencia del genoma del fago (llamado “preprophage”) en el citoplasma del hospedador de forma independiente. El mecanismo detallado de esta estrategia es aún polémico, aunque Moebus (1996) consideró que la pseudo-lisogénia es un estado inmune temporal de la bacteria hospedadora. Debido a que las bacterias y los fagos pueden coexistir en este estado, se ha sugerido que esta estrategia de supervivencia puede ayudar a los fagos marinos a sobrevivir en unas condiciones ambientales desfavorables (Wommack & Colwell, 2000). Los fagos que utilizan la lisogénia y la pseudo-lisogénia como mecanismo de proliferación se considera que siguen la estrategia K. Poseen un tamaño de genoma pequeño e infectan los miembros más abundantes y de crecimiento lento de las comunidades microbianas (Suttle, 2007). ENTENDER LOS VIRUS MARINOS EN UN CONTEXTO GLOBAL

1. Los virus son increíblemente abundantes en los océanos!

En las últimas dos décadas, los métodos para los recuentos directos de virus han evolucionado ø y se utiliza la microscopía electrónica de transmisión (TEM), la microscopía de epifluorescencia o la citometría de flujo (B rsheim et al., 1990; Hara et al., 1991; Hara et al., 1996; Weinbauer & Suttle, 1997; Noble & Fuhrman, 1998; Marie et al., 1999; Brussaard, 2004). Gracias a estas 7 virus ml técnicas, los virus han sido enumerados en miles de muestras de todos los océanos del mundo. -1 De acuerdo con estos resultados, hay un promedio de 10 en la superficie del océano (Marie et al., 1999; Wommack & Colwell, 2000). El consenso general es que la mayoría de los virus en el medio ambiente marino son dsADN con cola (Wommack & Colwell, 2000; Weinbauer, 2004), pero el reciente descubrimiento de los virus de cadena simple de ADN (ssADN) y los virus de ARN en el océano, sugiere que estos grupos virales pueden ser más frecuentes de lo que se ha establecido hasta ahora (Lang et al., 2009; Labonté & Suttle, 2013; Steward et al., 2013). Además, un estudio reciente ha demostrado el predominio de las partículas virales sin cola. Brum et al., (2013) y Holmfeldt et al. (2012), evidenciaron que incluso fagos de ssADN con un tanto, las limitaciones metodológicas pueden dar lugar a una subestimación de la abundancia y genoma largo son difíciles de teñir y visualizar mediante microscopía de epifluorescencia. Por lo la diversidad viral en muestras ambientales. 2. Variabilidad espacial Variaciones latitudinales

Los factores que controlan la abundancia de virus a gran escala en la superficie de217 los Spanish Summary donde la temperatura es baja y la concentración de nutrientes es limitada, la abundancia de los océanos aún no se comprenden del todo. En las regiones polares, especialmente en el Ártico, por lo que la abundancia viral puede llegar a ser 10 veces menor que en aguas templadas hospedadores y su producción se reducen debido probablemente a estas bajas temperaturas,

(Middelboe et al., 2002; Säwström et al., 2007; Boras et al., 2010b). Un estudio llevado a cabo en aguas superficiales en 3 transectos a lo largo del Pacífico central y en el Océano Austral también Los autores sugieren que la alta abundancia de virus detectada en aguas subtropicales y tropicales demuestra que la abundancia de virus tiende a ser menor en la región antártica (Yang et al., 2010). podría ser explicada por elevadas abundancia de cianofagos (virus que infectan a cianobacterias). estación, de esta forma, en invierno la columna de agua es una capa de mezcla mientras que También hay que tener en cuenta que las regiones templadas pasan por cambios graduales de muestran vínculos entre la abundancia de virus y en verano hay una mayor estratificación. EstudiosSynechococcus llevados a cabo en estas regiones también (Bettarel et al., 2002) o con la actividad bacteriana y la abundancia de células del hospedador (Corinaldesi et al., 2003). Por lo tanto, estos hallazgos evidencian que la abundancia viral es distinta entre las diferentes regiones oceanográficas. Entender mejor los cambios físicos, químicos y biológicos que se producen en los diferentes océanos permite una mejor evaluación de los factores y procesos que influyen en la dinámica viral y su distribución. Variaciones en profundidad

Los cambios físico-químicos que tienen lugar con la profundidad pueden tener un impacto significativo sobre los virus marinos. Los ecosistemas de aguas profundas son ambientes oscuros y extremos que carecen de la producción primaria fotosintética, dependen de la producción procariota, incluyendo reacciones heterotróficas que utilizan la materia orgánica procedente de las capas superiores o reacciones quimiosintéticas utilizando compuestos inorgánicos reducidos como el amoníaco o el monóxido de carbono (Dick et al., 2013). Dado que los hospedadores son al menos un orden de magnitud menos abundantes en las aguas batipelágicas que en las aguas de superficie (Tanaka & Rassoulzadegan, 2002), la abundancia de virus disminuye en las aguas más profundas (Suttle, 2005, 2007). Sin embargo, se ha observado una alta abundancia de virus en las profundidades del océano (Parada et al., 2007; De Corte et al., 2010). El mecanismo para el mantenimiento de esta alta abundancia en el batipelágico no está claro, pero una posibilidad es la entrada de virus mediante partículas que se hunden (Hara et al., 1996; Parada et al., 2007).

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3. La variabilidad temporal

La variación estacional en la abundancia de virus se ha observado desde los primeros estudios de virus en el mar (Bergh et al., 1989; Jiang & Paul, 1994) así como que en sistemas costeros la abundancia viral es mayor en verano y otoño que en invierno (Wommack & Colwell, 2000), lo que probablemente refleja que la proliferación viral depende de la abundancia y la actividad de las células hospedadoras. Por ejemplo, se observaron patrones recurrentes en la abundancia de y virusProchlorococcus en el giro subtropicalRhodobacteraceae del Atlántico Norte y mostraron una fuerte correlación con SAR11, (Parsons et al., 2012). Pero debido a la naturaleza volátil del virioplancton, sus cambios de abundancia son más evidentes en los estudios temporales a corto plazo. Por ejemplo, Winget & Wommack (2009) demostraron variaciones significativas en las tasas de producción virales en ciclos de 24 h y Winter et al. (2004) determinaron que la frecuencia de las células infectadas fue generalmente mayor por la noche que durante el día y sugirió que la temporal en la población vírica no sólo es importante porque demuestra que los virus son un infección se produjo durante la noche y la lisis viral por la tarde. Ésta clara y fuerte variabilidad componente muy activo, sino también es importante tener en cuenta esta variabilidad temporal para la comparación de datos en diferentes sistemas. 4. Los virus marinos lisan las células hospedadoras y afectan a los ciclos biogeoquímicos en los océanos 4.1 Los virus en el bucle microbiano

No fue hasta los años 70 y a principios de los años 80 cuando se reconoció la importancia de las bacterias marinas en ambientes acuáticos (Pomeroy, 1974; Porter & Feig, 1980; Azam et al., 1983). El descubrimiento de la gran abundancia, biomasa y producción de bacterias en los sistemas acuáticos ha llevado a una comprensión más compleja de las redes tróficas de los océanos. El bacterioplancton se convirtió en un componente central de las redes tróficas acuáticas y se aceptó que una proporción significativa de la producción primaria se incorpora por el bacterioplancton. La materia orgánica disuelta (DOM), principalmente excretada por el fitoplancton, es utilizada por las poblaciones de bacterias y canalizada hacia los niveles tróficos superiores. Este proceso se conoce como el “bucle microbiano” (Azam et al., 1983) (Fig. 3). En este contexto el papel de los virus es importante ya que causan una gran proporción de la mortalidad bacteriana y el fitoplancton (Wommack & Colwell, 2000). La infección lítica convierte las células en materia orgánica disuelta (DOM) que se convertirá en materia orgánica disponible para otros procariotas heterótrofos (Bratbak et al., 1990; Proctor & Fuhrman, 1990). Este proceso se denomina “viral shunt” (Fuhrman, 1999; Wilhelm & Suttle, 1999) (Fig. 3). A través del consumo219 Spanish Summary

Figura 3

. Representación de los flujos de carbono en red trófica marina, incluyendo el papel de los virus en la desviación de la materia orgánica disuelta lejos de los niveles tróficos superiores.

del lisado producido, se estimula la productividad bacteriana y la respiración (Middelboe et al., 1996; Noble & Fuhrman, 1999; Middelboe & Lyck, 2002). Además, el enriquecimiento de nutrientes debido al lisado del fitoplancton también puede estimular la productividad bacteriana (Gobler et al., 1997; Bratbak et al., 1998). De hecho, la liberación de esta materia orgánica de nuevo a la columna de agua a partir de lisados,​​ hace que los virus tengan un papel importante podría liberar algunos nutrientes restrictivos, como el Fe que se necesita para la producción en los ciclos biogeoquímicos en los océanos (Fuhrman, 1999). La lisis de las células procariotas primaria en la capa fótica (Poorvin et al., 2004). Otra consecuencia es que debido a la liberación de dimetilsulfoniopropionato (DMSP) a través de la lisis viral del fitoplancton, los virus podrían contribuir a mitigar el cambio climático (Malin et al., 1998). El DMSP se convierte en dimetilsulfuro (DMS) a través de la actividad bacteriana, que se asocia con la condensación de las nubes y el aumento de la reflexión solar.

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4.2 Lisis viral vs bacterivoría Las células bacterianas no sólo se eliminan por lisis viral, sino también por bacterivoría, en gran medida por los protistas (pico/nanoflagelados y ciliados). La bacterivoría se considera que es menos específica que la lisis viral, aunque puede ser selectiva en función de algunos criterios como el tamaño, la actividad o la motilidad celular bacteriana (Hahn & Höfle, 1999). Por otro lado, los bacteriófagos son considerados específicos de un hospedador y que la infección viral depende de la densidad del mismo, por lo tanto, es filogenéticamente selectiva (Thingstad & Lignell, 1997). Los HF (pico/nanoflagelados heterotróficos) son responsables del 5 al 250 % de la mortalidad diaria bacteriana (Miki & Jacquet, 2008), mientras que la lisis viral es responsable a menudo de un 20 al 40 % (Suttle, 2007) y puede alcanzar un 100% (Danovaro et al., 2011). La bacterivoría actúa como agente que transfiere la materia orgánica asimilada por las bacterias a un nivel trófico superior, y también recicla materia orgánica e inorgánica (Miki & Jacquet, 2008). El “viral shunt”, en cambio, tiene un impacto negativo sobre la transferencia de la producción bacteriana a niveles más altos de las cadenas tróficas, y también se ha sugerido que reduce la eficiencia de la bomba biológica porque la liberación de la materia orgánica disuelta en las aguas superficiales podría influir en el flujo vertical y las tasas de hundimiento (Suttle , 2007). sugerido que la bacterivoría mejora la actividad de las células bacterianas mediante la reducción Pocos estudios han contemplado las interacciones entre la lisis viral y la bacterivoría. Se ha de la competencia por los recursos y por lo tanto, en la mejora de las condiciones de crecimiento. Teniendo en cuenta que, los fagos infectan hospedadores preferentemente más activos y productivos (Lenski, 1988) los virus se beneficiarían de la mortalidad bacteriana debido a los protistas. Weinbauer et al. (2007) llevaron a cabo un experimento y observaron que, de hecho, la presencia de bacterívoros estimula la abundancia y la producción vírica. Pero, en algunos una investigación reciente sugiere que las células sometidas a infección viral podrían ser presas estudios la presencia de bacterívoros reduce la actividad viral (Maranger et al., 2002). Finalmente, preferentes para la bacterivoría (Evans & Wilson, 2008) o que la bacterivoría es susceptible a bacterias con factores de resistencia a fagos en la superficie de la célula (Zwirglmaier et al., 2009). 5. Virus marinos en el marco actual : el cambio climático

El calentamiento de la superficie del mar, el derretimiento del hielo, los cambios en la circulación y regímenes de mezcla, y la acidificación de los océanos provocados por el actual cambio climático están modificando la estructura y la función del ecosistema marino y tienen el potencial de alterar los ciclos del carbono y nutrientes en los océanos. El efecto más inmediato221 y Spanish Summary directo del cambio climático será el incremento en la temperatura del agua superficial (Sarmento et al., 2011), lo que probablemente será más dramático en las regiones polares, donde hay un rango ° muy estrecho de temperaturas en las aguas libres de hielo. El calentamiento es particularmente ° intenso en el Ártico, donde las temperaturas aumentan a un ritmo de 0,4 C por década (ACIA, 2004). Por otra parte, se espera que este aumento se acelere aún más, hasta 9 C, a lo largo del siglo XXI (IPCC, 2007). Estudios sobre el aumento de la temperatura en las comunidades microbianas han demostrado que la producción heterotrófica aumentó con la temperatura (Apple et al., 2006). Como las comunidades virales están muy vinculadas con sus hospedadores, esto podría dar lugar a mayores tasas de producción de virus (Danovaro et al., 2011). Por otro lado, podría ser un factor de estrés que desencadene la transición del ciclo lisogénico al ciclo lítico, los efectos de la temperatura sobre la estrategia de vida viral aún no están claros. La temperatura pero varios estudios obtuvieron resultados dispares (Weinbauer & Suttle, 1996, 1999 y Wilson et al., 2001; Weinbauer et al., 2003; Williamson & Paul, 2006). Sin embargo, los virus pueden responder rápidamente a los cambios del medio ambiente en un contexto evolutivo, pero los posibles efectos en cascada por el cambio climático podría tener efectos mucho más importantes en el contexto global de los ciclos biogeoquímicos. COMPRENDER LOS VIRUS MARINOS A PEQUEÑA ESCALA

1. Influencia de los virus en las comunidades bacterianas: La hipótesis “Kill the winner” (KtW)

Desde el descubrimiento de que hay millones de microbios en cada mililitro de agua de mar (Azam et al., 1983) y que los virus marinos son extremadamente abundantes (Bergh et al., 1989), se cree que la gran mayoría de los virus son fagos (virus que infectan bacterias) porque las bacterias son la presa más común. A través de la infección vírica, la transferencia un papel importante en la regulación del tamaño, composición y diversidad de las comunidades horizontal de genes y la inducción de mutaciones resistentes a los fagos, el virioplancton juega microbianas. Los fagos son generalmente específicos a determinados hospedadores, la infección específica causará la muerte de ciertos grupos de bacterias y dará lugar a la creación de nuevos proceso de adsorción de fagos se considera aleatorio y la frecuencia de la infección se correlaciona nichos o estimulará el crecimiento de diferentes poblaciones bacterianas. Al mismo tiempo, el con la densidad del hospedador. Así, los grupos dominantes de bacterias con abundancias elevadas serán222 más vulnerables a la infección por fagos, debido a sus altas tasas de contacto con los fagos. Resumen en Español

Figura 4.

Representación de la “Kill the winner” hipótesis. La figura muestra las oscilaciones entre virus y hospedador manteniendo constante la abundancia de virus y bacterias.

La hipótesis “Kill the winner” (KtW) se basa en estas premisas (Thingstad & Lignell, 1997; Thingstad, 2000). La hipótesis sugiere ciclos en los que la abundancia de un huésped bacteriano específico y su correspondiente fago oscilan en una dinámica depredador-presa clásica (Fig. 4). Por lo tanto, un aumento en la abundancia del hospedador es seguido por un aumento de su fago, lo que resulta en una disminución del hospedador y por lo tanto en una disminución de los fagos. De todas formas, la aplicabilidad de la hipótesis “Ktw” a las comunidades naturales no está clara ya que Bouvier y del Giorgio (2007) demostraron que muchos hospedadores poco abundantes se convirtieron en dominante cuando la presión vírica se reducía. 2. La relevancia de los fagos en la transferencia horizontal de genes (HGT)

Una parte sustancial de ADN bacteriano no se transfiere por transferencia vertical, sino que se adquiere horizontalmente por transformación, conjugación o transducción. Por ejemplo, un estudio reciente que analizó 70 genomas de bacterias marinas reveló que hasta el 12% del genoma bacteriano podría ser transferido horizontalmente a través de las islas genómicas (Fernández- Gómez et al., 2012). En el proceso de la propagación viral, los virus transfieren material genético de una bacteria a otra. Si un virus que infecta un nuevo hospedador contiene material genético del hospedador anterior en lugar de su propio ADN, esa información genética adicional puede223 Spanish Summary ser transmitida al nuevo hospedador, lo que da lugar a la transducción (Fig. 5). Se ha demostrado que la transducción es un proceso importante en el medio marino (Jiang & Paul, 1998a; Miller, microbianas marinas, así como tener el potencial para mantener una reserva de genes en los 2001), y podría desempeñar un papel importante en la diversidad genética de las poblaciones que puede actuar la evolución. Además, los cianofagos son conocidos por llevar y transferir una variedad de genes (Paul, 2008). Por ejemplo, los genes fotosintéticos se encuentran comúnmente en los genomas de fagos (Mann et al., 2005) los cuales codifican para las propiedades funcionales del hospedador y actúan como reservorios genéticos manteniendo la diversidad y contribuyendo a la transferencia lateral de genes entre los hospedadores (Sullivan et al., 2005; Sullivan et al., 2006). La lisis viral también produce ADN disuelto, que estará disponible para que tenga lugar la transformación (Jiang y Paul, 1995). Y, por último, los agentes de transferencia de genes (GTA) han ganado recientemente reconocimiento (Lang & Beatty, 2007; Stanton, 2007; McDaniel et al., 2010; Lang et al., 2012). Los GTAs son partículas más pequeñas que un fago y también contienen una cantidad más pequeña de ADN. Asimismo, tienen una alta frecuencia de transducción y ( pueden ser más eficientes que los fagos de transducción generalizada,Alphaproteobacteria ya que sóloRhodobacterales llevan ADN del hospedador. En primer lugar, los GTAs se encontraron en )

Figura 5. Representación del mecanismo de transducción por el cual cualquier gen bacteriano puede ser transferido a otra bacteria a través de un bacteriófago.

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dentro de los cuales, se hayan taxones muy abundantes en el océano, por lo que puede tener consecuencias importantes para la ecología microbiana marina (McDaniel et al., 2010). Más tarde, los GTAs se han encontrado en una amplia variedad de taxones procarióticos (Lang et al., 2012) y son capaces de altas frecuencias de transferencia horizontal de genes (Lang & Beatty, 2007; McDaniel et al., 2010). 3. Interacción entre fago y hospedador: rangos de infección

El análisis de rangos de infección es un enfoque útil para obtener conocimientos sobre las interacciones entre un fago y su hospedador en un escenario controlado (Moebus, 1992; Suttle & Chan, 1993). La información básica sobre el patrón general de infección puede mejorar las predicciones acerca de las poblaciones microbianas, la dinámica, la función y la diversidad de los ecosistemas (Thingstad, 2000). Ahora bien, los resultados de los rangos de infección podrían depender de los métodos utilizados para el aislamiento del fago o la especificidad del receptor y la adquisición de resistencia bacteriana (Jensen et al., 1998; Chibani-Chennoufi et al., 2004). Además, la gran mayoría de las bacterias marinas no pueden ser cultivadas en el laboratorio y esto dificulta el conocimiento del verdadero rango de infección para la mayoría de los fagos. En general se acepta que los fagos son depredadores específicos, como se ha demostrado en fagos marinos cultivados que no infectan cepas bacterianas incluso estrechamente relacionadas con el hospedador del que fueron aislados (Rohwer et al., 2000). De todas formas, algunos estudios mostraron que los fagos marinos podrían tener amplios rangos de infección (Wichels et al., 1998; Wichels et al., 2002; Comeau et al., 2006; Holmfeldt et al., 2007), incluso han sido descritos fagos Resultados de la literatura sugieren que la morfología de los fagos puede relacionarse capaces de infectar bacterias pertenecientes a diferente género (Sullivan et al., 2003). con los rangos de infección (Suttle, 2005). Así, Wichels y co-autores (1998) encontraron que los “myoviruses” tienen un rango de infección más amplio que los “podoviruses”, que por lo general son más específicos, y los “siphoviruses” son un intermedio entre los dos anteriores. Por lo tanto, parece que los rangos de infección dependen del tipo de fago y hospedador a partir del cual fueron aislados. A pesar de que es sabido que los fagos no infectan todas las bacterias, es necesario un conocimiento más preciso de la extensión del rango de infección para un determinado fago. Recientemente se han propuesto una serie de modelos para ayudar a unificar el análisis cuantitativo de la infección cruzada de múltiples fagos con múltiples bacterias (Flores et al., 2011). Estos autores sugieren cuatro tipos principales de patrones: al azar, uno-a-uno, “nestedness” y modulares (Fig. 6). Los patrones aleatorios se refieren a las interacciones fago- hospedador estáticamente indistinguibles y el modelo uno-a-uno hace referencia a un tipo225 de Spanish Summary infección con especialización elevada, de tal manera que cada fago sólo puede infectar a uno o a varios hospedadores muy cercanos filogenéticamente. Sin embargo, los dos patrones detectados con mayor frecuencia en el estudio de estos autores fueron “nestedness” y modularidad. “Nestedness” es el resultado de fagos que evolucionan a rangos de infección más amplios y lado, la modularidad contiene interacciones que tienden a ocurrir entre grupos distintos de fagos y bacterias que evolucionan para aumentar el número de fagos a los que son resistentes. Por otro hospedadores. Estos autores analizaron 38 estudios de infección cruzada entre fago-hospedador, y mostraron que los patrones de interacciones eran en su mayoría “nested”. Esto implica una estructura de jerarquía en la que los fagos más especialistas infectan a hospedadores con mayor susceptibilidad a ser infectados. Este patrón encaja con el modelo co-evolutivo “gene for gene” patrón de interacción era modular (un grupo de fagos puede infectar a un grupo de bacterias, pero (GFG) (Flores et al., 2011). Sin embargo, un reciente estudio de los mismos autores mostró que el no hay interacciones entre grupos) cuando una mayor diversidad de bacterias y fagos interactúan (Flores et al., 2013). Estos resultados indican que es necesario un mayor número de estudios sobre rangos de infección incluyendo grupos bacterianos de amplia diferencia filogenética. Además, la complejidad de la relación fago-hospedador a menudo se pasa por alto en estudios basados en genes conservados (por ejemplo, 16S ARNr) ya que las bacterias filogenéticamente idénticas pueden mostrar diferencias en los patrones de infección (Holmfeldt et al., 2007) y por lo tanto otros genes o perfiles genómicos deben ser incluidos en los análisis de rangos de infección. 4. Evolución entre fago y hospedador y mecanismos de resistencia

Los fagos y sus hospedadores están implicados en ciclos continuos de co-evolución, en el que los hospedadores insensibles a los fagos ayudan a preservar linajes bacterianos, mientras que las células hospedadoras no resistentes podrían dar lugar a nuevas cepas bacterianas. Hay muchos mecanismos por los que los hospedadores pueden convertirse en resistentes a la infección: la prevención de la adsorción del fago, la destrucción del ADN del fago o pérdida tanto del ADN del fago y del hospedador mediante el aborto de la infección (Labrie et al., 2010). La adsorción del fago es el paso inicial de la infección. Los fagos deben reconocer un componente celular particular en las membranas y paredes del hospedador. Los mecanismos para evitar la adsorción del fago se dividen en tres categorías: los receptores de fagos de bloqueo, la producción de la matriz extracelular y la producción de inhibidores competitivos. Recientemente, se descubrió un mecanismo para la destrucción del ADN del fago, los CRISPR (Clustered regularly interspaced short palindromic repeats). Los CRISPR son loci que generalmente constan226 de múltiples repeticiones directas cortas, separadas por tramos de secuencias variables Resumen en Español

Figura 6

. Representación esquemática de las “networks” sobre la infección entre fagos y bacterias. denominadas separadores y con frecuencia son adyacentes a genes Cas. Los genes Cas codifican a una familia grande y heterogénea de proteínas que llevan dominios funcionales típicos de nucleasas, polimerasas, helicasas, y las proteínas de unión al polinucleótido. Los CRISPR combinado con las proteínas Cas , forma los sistemas de CRISPR/Cas (Horvath & Barrangou, 2010) los cuales se han identificado en aproximadamente el 40% y el 90% de los genomas de bacterias y Archaea respectivamente (Mojica et al, 2000; Grissa et al, 2007). Por lo general son transferidos lateralmente y se encuentran muchas veces en islas genómicas (Ho Sui et al., 2009). Los sistemas CRISPR/Cas son un mecanismo adaptativo de resistencia a la infección vírica (Barrangou et al, 2007; Deveau et al, 2010), además es muy específico, por lo que puede dominar en ambientes con alta densidad de hospedadores y baja diversidad. Pero, los océanos presentan el escenario opuesto, y la importancia del sistema CRISPR/Cas aún se desconoce (Breitbart, 2012). Sin embargo, se detectaron casi 200 casetes de CRISPR fiables (Sorokin et al., 2010) en la expedición GOS (Global Ocean Sampling). Por último, el sistema de CRISPR/Cas se ha detectado en algunos genomas de bacterias marinas cultivadas (Thomas et al., 2008). Por otro lado, los fagos evolucionaron para evitar estos mecanismos bacterianos de diversidad de la población procariota se mantiene por la depredación vírica, debido a que los resistencia. Por lo tanto, existe un modelo constante de diversidad dinámico, en el que la microorganismos mejor adaptados se seleccionan (Rodríguez - Valera et al., 2009). 227 Spanish Summary

Figura 7

. Información general de las secuencias de bacteriófagos disponibles en NCBI (Octubre 2013) todos los fagos de versus los genomasGammaproteobacteria virales totales y la distribución taxonómica de todos los fagos secuenciados frente a los fagos de Gammaproteobacteria . También está representada la distribución taxonómica de todos secuenciados.

5. La diversidad genómica de los fagos marinos

Reconocida la importancia de la abundancia vírica, los científicos han estado caracterizando y tratando de determinar el alcance de la diversidad viral marina. Sin embargo, la diversidad ha sido y es difícil de medir porque los virus no tienen un gen universalmente conservado. Además, se ha estimado que >99 % de todas las bacterias ambientales son difíciles de cultivar con técnicas estándar (Staley & Konopka, 1985) y por lo tanto hay una escasez de disponibilidad de hospedadores de fagos marinos. Por otra parte, no todos los fagos producen calvas de lisis identificables en céspedes bacterianos (Seguritan et al., 2003; Breitbart, 2012). Para superar estas limitaciones, la diversidad de las comunidades virales ha sido analizada mediante técnicas independientes del cultivo: (i) Electroforesis de campo pulsado (PFGE), un método que permite discriminar los virus según el tamaño del genoma (Steward et al., 2000; Steward, 2001), (ii) mediante amplificación de ADN polimórfico al azar (RAPD-PCR) para obtener un “fingerprinting” general de toda la comunidad vírica (Comeau et al., 2006; Winget & Wommack, 2008) y (iii) por secuenciación masiva de las comunidades virales (metaviroma) (Breitbart et al., 2002; Rohwer, 2003; Angly et al., 2006; Rodríguez - Brito et al., 2010). A través de los metagenomas virales se ha demostrado que los virus son excepcionalmente diversos y representan la mayor reserva de diversidad genética en el océano (Pedulla et al., 2003; Rohwer, 2003; Angly et al., 2006). No obstante, la metagenómica está limitada debido a la falta de genomas víricos secuenciados. Así, el análisis de genomas de virus aislados es esencial para entender mejor los resultados de las técnicas de secuenciación en masa y las interacciones entre fago y hospedador. De hecho, la mayoría de los genomas secuenciados de fagos marinos son cianofagos (Paul & Sullivan, 2005),

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y aunque recientemente se han descrito fagos que infectan otras bacterias marinas (Holmfeldt es una de las clases bacterianas et al., 2013; Kang et al., 2013; Zhao et al.,Gammaproteobacterias 2013), aún hay muchos grupos bacterianos de los que no se han aislado virus (Fig. 7). Las más abundante en el mar, representan entre un 5-28 % en el Mar Mediterráneo en base a los que infectan al género ( recuentos de CARD-FISHPseudoalteromonas (Ruiz-González et al.,Gammaproteobacteria 2012). Previamente, ya han sido aislados fagos ) y ya ha sido reconocida su importancia ecológica (Moebus, 1992a; Wichels et al., 1998; Wichels et al., 2002; Thomas et al., 2008) debido a que juegan un Pseudoalteromonaspapel importante en la producción bacteriana. Sin embargo, hasta Una mejor comprensión de las funciones ecológicas de los virus marinos y una interpretación la fecha, sólo cuatro fagos de sp. han sido aislados y secuenciados (Fig. 7). más precisa de las secuencias de los metaviromas requeriría el aislamiento y el análisis genómico de virus individuales a partir de una amplia gama filogenética de cepas bacterianas.

229 Spanish Summary

OBJETIVOS Esta tesis pretende avanzar en el conocimiento de la ecología, la biología y la genómica constituyen la mayor diversidad genética en el océanos, son agentes importantes de mortalidad de los virus marinos. Los virus son las entidades biológicas más abundantes en los ecosistemas, y juegan un papel fundamental en los ciclos geoquímicos globales. Dada su importancia en los procesos oceanográficos globales, en esta tesis se obtuvieron datos sobre la dinámica de los virus en el observatorio microbiano de Blanes (BBMO) y se investigaron los efectos del cambio climático en la abundancia y dinámica vírica en el Océano Ártico. Otro de los propósitos de esta tesis fue desentrañar las complejas relaciones entre los virus y sus hospedadores mediante el secuenciaron los fagos aislados pertenecientes a dos familias virales diferentes y se estudiaron estudio de rangos de infección entre fagos aislados de BBMO y sus hospedadores. Por último, se sus patrones biogeográficos en metagenomas ambientales. En base a los conocimientos actuales explicados en la introducción, a continuación se detallan los objetivos específicos y las hipótesis de esta tesis. OBJETIVO 1. Entender la dinámica y los procesos de los fagos marinos en los ecosistemas marinos

1.1. Ausencia de estacionalidad en la dinámica viral y la salinidad como el principal factor que modula la abundancia viral en el Noroeste del Mar Mediterráneo.

Se investigó la variación temporal de la abundancia de la comunidad vírica y su perfil genómico en un periodo de 5 años (2008-2012) en BBMO. Con el fin de examinar cuáles son los mecanismos que influyen en la abundancia y la diversidad de las comunidades virales marinas en la Bahía de Blanes, los patronesCapítulo observados 1 fueron comparados con los cambios de los parámetros viral estuviera altamente correlacionada con la abundancia o producción bacteriana, o bien que físico-químicos y biológicos ( ). Las hipótesis planteadas son: (i) que la abundancia las cianobacterias marcaran la dinámica viral y (ii) que encontráramos un patrón estacional en las comunidades virales ya que la Bahía de Blanes se caracteriza por tener unos patrones estacionales marcados. 1.2 Evaluación experimental del efecto del calentamiento en las comunidades víricas, bacterianas y protistas en dos ecosistemas del Ártico.

230 Estamos especialmente interesados ​​en los efectos del cambio climático sobre las Resumen en Español

comunidades microbianas y su conexión con la abundancia, la producción y las estrategias de la infección vírica. En este contexto, se realizó una aproximación experimental en microcosmos para entender cómo las comunidades microbianas autótrofas y heterótrofas del Ártico responderían a un incremento de la temperatura del agua de mar. Las temperaturas experimentales escogidas están basadas ​​en el calentamiento previsto del agua superficial del mar en el Océano Ártico. En particular, hemos investigado los cambios en la biomasa de fitoplancton, abundancias microbianas, ciliados, la producción bacteriana y viral y las pérdidas bacterianas debidas a la lisis vírica y a la estructura de tamaños de la comunidad de flagelados, la composición de la comunidad de entre dos temperaturas para cada una de las variables estudiadas ( bacterivoría. También se identificaron las temperaturas que provocabanCapítulo un cambio 2 significativo ). Las hipótesis en este apartado fueron: (i) que las abundancias virales siguieran la misma tendencia que las abundancias y producciones bacterianas, (ii) que la lisis dominaran a altas temperaturas y en cambio, que la lisogénia predominara a temperaturas más bajas y (iii) que la mortalidad bacteriana (debida a la bacterivoría o a la lisis) aumentara con la temperatura . OBJETIVO 2. Entender las interacciones entre fago y hospedador a pequeña escala

Este objetivo se llevó a cabo con el propósito de aumentar el número de fagos marinos aislados y entender mejor el papel de las interacciones entre fagos y hospedadores en las comunidades naturales, ya que el número de estudios es bajo y es necesaria una mejor comprensión en este campo. 2.1 Interacciones entre fago y hospedador a nivel de microdiversidad dentro de fagos de Pseudoalteromonas sp.

Para mejorar los modelos conceptuales existentes sobre el papel de los fagos en la basado en diversidad Pseudoalteromonasbacteriana y la dinámica de la población, se analizó un sistema de fago-hospedador bacterianas pertenecientes al género . Se aislaronPseudoalteromonas varios fagos de la Bahía de Blanes que infectan cepas del genoma y los patrones de rangos de infección en una escala de resolución muy detallada . Determinamos la morfología, tamaño

( medianteCapítulo el3 análisis de perfiles de todo el genoma tanto del hospedador como de los fagos ). OBJETIVO 3. Aumentar el conocimiento de las características ecológicas y genómicas de los genomas de fagos marinos

Actualmente, las bases de datos públicas de genomas de virus están sesgadas hacia231 Spanish Summary genomas de fagos específicos y por lo tanto, restringidas a unos pocos taxones filogenéticos (la para comprender la ecología de los virus, y no sólo de los fagos abundantes, sino también de mayoría cianofagos). Pero los genomas de fagos de otros géneros bacterianos son necesarios los representantes de la biosfera rara, y que al mismo tiempo son cruciales para obtener mayor Pseudoalteromonas pertenecientes a 2 familias de virus diferentes y que representan los primeros aislados del mar resolución en los datos metagenómicos. Por esa razón, secuenciamos 3 fagos de

Mediterráneo. 3.1. Estilo de vida y estructura genómica del siphovirus B8b de Pseudoalteromonas aislado del Noroeste del Mar Mediterráneo. Pseudoalteromonas

Caracterizamos y analizamos exhaustivamente uno de los fagos de en relación con su hospedador. Se realizó un análisis detallado de los aspectosCapítulo biológicos 4 y ecológicos, así como de la filogenia, la proteómica y genómica de este fago ( ). 3.2. Genómica comparativa y biogeografía de los fagos de Pseudoalteromonas impacto ecológico y su distribución global utilizando metagenomas de comunidades naturales Por último, se llevó a cabo la comparación de los 3 fagos secuenciados y se estudió su Capítulo 5 virales (metaviromas) disponibles en las bases de datos públicas ( ).

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METODOLOGÍA

Para la elaboración de esta tesis se han empleado diferentes tipos de técnicas. Por un microorganismos, de medida de procesamiento de carbono a nivel de comunidad como la lado, en el estudio de los virus en un contexto global, se han usado técnicas de recuento de producción bacteriana y de virus (Capítulos 1 y 2). Capítulo 2 también

Para la realización del se desarrolló el sistema experimental para determinar la respuesta de los microorganismos a los cambios de temperatura en el Ártico. utilizaron técnicas de cultivo, aislamiento de virus y bacterias, y diferentes métodos moleculares Por otra parte, en el estudio de la interacción entre fago y hospedador a pequeña escala, se (Capítulos 3, 4 y 5

). continuación se describen brevemente las técnicas especialmente utilizadas para trabajar con Si bien la metodología particular de cada capítulo está detallada en los mismos, a virus marinos. Abundancia de virus

La abundancia de virus se analizó por citometría de flujo (Brussaard, 2004). Se fijaron 2 ml de muestra con glutaraldehído (0,5% concentración final). Las muestras se mantuvieron en ° oscuridad después de la fijación durante 15 min, a continuación se congelaron rápidamente en nitrógeno líquido y se almacenaron a -80 C hasta su posterior análisis. Los recuentos se realizaron en un citómetro de flujo FACSCalibur (Becton-Dickinson) con un emisor de láser azul a 488 nm. Las muestras se tiñeron con SYBR Green I (Molecular Probes) y se analizaron a una velocidad de flujo medio (Marie et al., 1999). Producción de virus

El método utilizado es el descrito por Weinbauer et al. (2002), conocido como técnica de reducción-dilución. Con este método se determinó la producción vírica debida a la lisis bacteriana de células infectadas con virus líticos, así como a la lisis inducida por Mitomicina C de bacterias infectadas con virus lisogénicos. La lisis inducida en las muestras tratadas con Mitomicina C nos dio la producción total de virus (líticos más lisogénicos). A partir de aquí se pudo calcular cuál es la concentración de virus lisogénicos: producción total de virus menos producción por lisis. Se obtuvieron 200 ml de agua de mar libre de virus mediante filtración tangencial con una membrana de 30 KDa y se concentraron las bacterias de 1 l de agua de mar filtrado por 0,8μm en 100 ml mediante filtración tangencial con una membrana de 0,22 µm. A continuación,233 Spanish Summary se mezclaron los 100 ml de concentrado de bacterias con 200 ml de agua de mar libre de virus. La idea es tener la misma concentración inicial de bacterias que en la muestra natural, pero habiendo eliminado la mayor parte de los virus. La mezcla se repartió en 6 tubos de 50 ml y en 3 de ellos se -1 añadió 0,5 ml de Mitomicina C (concentración final 1 µg ml ), para obtener la producción de virus y se tomaron muestras, por duplicado, para abundancia de virus y bacterias para analizar en el total (infección lítica y lisogénica). Se incubaron los 6 tubos en oscuridad a la temperatura in situ citómetro, cada 4 horas (0, 4, 8 y 12h). Tanto las tasas de producción de virus como las estimas de tasas de lisis se calcularon según se describe en Boras et al. (2009) y Boras et al. (2010). Aislamiento de fagos

Se recogieron 4 l de agua de mar y después de una pre-filtración de 0,22 µm, los fagos Los fagos fueron aislados utilizando cultivos de enriquecimiento líquido y ensayos de placa se concentraron por filtración tangencial (Vivaflow 30kDa) hasta un volumen final de 20 ml.

(Sambrook, 1989). En los enriquecimientos, se añadió el concentrado de virus a un cultivo líquido del hospedador en estado exponencial. Después de 24 h de incubación en la oscuridad, se centrifugó la mezcla (5,000 g, 10 min) y el sobrenadante se filtró a través de un filtro de 0,22 μm para eliminar cualquier célula bacteriana restante. Los enriquecimientos se confirmaron mediante ensayo en placa con los diferentes hospedadores utilizados. Se realizaron diluciones ° de las posible muestras que contienen fagos y se combinaron con 400 μl de cultivo bacteriano líquido y 3,5 ml de agar blando fundido (0,5% de agar Zobell, 50 C). Para el aislamiento y la purificación de la población de un solo fago; calvas de diferente morfología fueron seleccionadas de cada hospedador y se purificaron en 3 rondas de infección y lisis. Los fagos purificados se diluyeron para obtener la dilución apropiada que proporcionó una lisis confluente en una placa donde el hospedador crece en césped.2 A partir de entonces, se añadió 5 ml de MSM (450 mM de NaCl, 50 mM de MgSO4 x 7H 0, 50 mM de base Tris, pH 8) al la placa con lisis confluente y se incubaron en un agitador durante 40 min. El MSM se transfirió a un tubo estéril y se centrifugó a ° 5,000 g durante 10 min. Finalmente, los sobrenadantes de lisado fueron filtrados por 0,22 μm y se almacenaron a 4 C en la oscuridad. Rangos de infección del stock de fagos en suspensión (10 y 10 Para determinar el rango de infección se mezclaron 100 μl de dos diluciones diferentes -5 -8 ) con 300 μl de cultivo bacteriano y se plaquearon. La aparición de calvas se evaluó después de la incubación de las placas durante la noche en la oscuridad.

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TEM (Microscopio Electrónico de Transmisión)

Transmisión (Bø La morfología de los virus aislados fue determinada por Microscopía Electrónica de virus fueron depositados durante 1 min sobre una rejilla con película Formvar y carbón negro rsheim et al., 1990; Weinbauer et al., 2002). Cinco microlitros de un stock de previamente cargada eléctricamente. Los fagos fueron teñidos con uranil acetato al 2% durante 10 s. Las rejillas fueron observadas en un microscopio Jeol 1010 (Jeol, Japan) operando a 80kV y las fotografías fueron tomadas con la cámara SIS Megaview III. Experimentos one-step (curvas de crecimiento de fagos)

El número de fagos producidos por célula lisada (tamaño de explosión: “burst size”) y las curvas de crecimiento de fagos se determinaron tal y como se describe en Weiss et al. (1994) con modificaciones menores. Un mililitro de un cultivo líquido del hospedador se transfirió a 10 ml de medio Zobell (1,0 g de extracto de levadura, 5 g de peptona, 15 g de agar, 250 ml de agua era ~ 0,02, que era equivalente a un recuento de MQ y 750 ml de agua de mar ultrafiltrada) al 60020% y se incubó con agitación (120 RPM) durante células viables de alrededor de 10 células ml aproximadamente 20 minutos, hasta que la A 8 -1 . Un mililitro del cultivo bacteriano se transfirió a un tubo eppendorf y se mezcló con el fago a una multiplicidad de infección de 0,1. La mezcla se de esta adsorción, la mezcla se diluyó a 10 incubó a temperatura ambiente durante 15 minutos para permitir la adsorción del fago. Después -2 en 20 ml de Zobell al 20%. Se cogieron muestras para enumerar concentración de fagos total y libres. Para detectar los fagos libres, las muestras se filtraron por 0,22 μm micras antes de plaquearlas. En ambos casos se determinó el número de fagos, por duplicado, plaqueando los virus junto su hospedador. Por último, el “burst size” se calculó como la relación entre el recuento final de las partículas de fago liberadas con el recuento inicial de células bacterianas infectadas durante el período de latencia. Electroforesis de campo pulsado (PFGE)

Para determinar el tamaño del genoma de los virus se utilizó la Electroforesis de Campo Pulsado (PFGE) (Steward, 2001). En primer lugar se concentraron los fagos a partir de una muestra natural o de un stock de un fago aislado usando Amicon Ultra de 30 KDa hasta un volumen final de 400 μl. A continuación, se mezclaron cantidades iguales de concentrado de fago y agarosa de ° bajo punto de fusión (Pronadisa) a una concentración final de 1,6%. Esta mezcla se transfirió a ° moldes para que solidificaran (llamados insertos), los cuales se incubaron a 50 C en ESP (0,5 M EDTA, pH 9,0, 1% de N-laurylsarcosine y 1 mg/ml de proteinasa K) y se almacenaron 4 C. 235 El PFGE se realizó en el sistema CHEF‐DR III (Bio‐Rad) utilizando gel de agarosa al 1%. El Spanish Summary gel corrió durante 22h en tampón TBE 0,5 X (TBE 1X es 89 M Tris, 2 mM de EDTA, y ácido bórico de 120° 89 mM, pH 8,3), aplicando cambios de campo eléctrico cada 5,0 a 15,0 s, 6V/cm y un ángulo . Después de la electroforesis, el gel se tiñó con SYBR Gold (Molecular Probes, 10,000X) durante 15 min y se lavó con agua MQ durante 15 min. Purificación de ADN vírico

El ADN vírico se obtuvo utilizando el kit Lambda Wizard DNA kit (Promega Corp. Madison, WI) directamente en lisados ​​de fagos (Henn et al., 2010; Sullivan et al., 2010). En primer lugar, se purificó el lisado de fagos con glicol de polietileno (PEG 8000 10%) a continuación se añadió 1 ml de resina de purificación (Promega, Madison WI producto A7181) y se mezcló suavemente invirtiendo el tubo. La mezcla se cargó en una mini columna (Promega, product A7211 Madison WI) a través de una jeringa de 5 ml unida a la columna. Seguidamente la columna se lavó con 2 ml ° de isopropanol al 80%. El ADN del fago fue diluido de la columna mediante la adición de 100 ml de tampón TE previamente calentado a 80°C y se almacenó a -20 C. RAPD-PCR (Random Amplified Polymorphic DNA)

Mediante la técnica de “fingerprinting” RAPD-PCR estimamos los cambios de en la composición de las comunidades de virus y tipificamos los virus aislados, con lo que conseguiremos descifrar fragmentos de secuencias víricas, gracias a una amplificación en la que se usan cebadores de secuencia corta y aleatoria que se unirán a diversos puntos del genoma vírico (Winget and Wommack, 2008). Para realizar esta técnica usamos los insertos que habíamos preparado anteriormente (ver ° apartado PFGE) o el ADN vírico previamente purificado. Se usó un encebador que actuaba como 30 ciclos de 3 min a 35° ° ° ° “forward” y “reverse”. Las condiciones de la PCR fueron las siguientes: 1 ciclo de 10 min a 94 C, ° C, 1 min a 72 C y 30 s a 94 C, 1 ciclo de 3 min a 35 C y 1 ciclo de 10 min de extensión a 72 C. Los productos del RAPD-PCR se separaron en un gel de agarosa al 1% con 0,5%TAE, 90V durante 2h y se visualizó con SYBR SAFE (10,000X, Invitrogen).

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SÍNTESIS DE RESULTADOS Y DISCUSIÓN GENERAL

1 . Los virus marinos desde un punto de vista ecológico y genómico

En los últimos años, los virus marinos han cambiado nuestra visión de las redes tróficas planctónicas marinas. Ahora se sabe que los fagos juegan un papel importante en la ecología marina (por ejemplo, el impacto de los fagos en el ciclo de la materia orgánica en la biosfera a escala mundial) (Fuhrman, 1999; Wilhelm & Suttle, 1999; Weinbauer, 2004) y también influyen en la evolución, la diversidad y la dinámica de las comunidades bacterianas (Fuhrman & que describan la abundancia y la diversidad vírica a escala temporal en un ecosistema marino, Schwalbach, 2003; Breitbart, 2012; Weitz & Wilhelm, 2012). Sin embargo, hay una falta de datos excepto para un ejemplo (Parsons et al, 2012). De hecho, la mayoría de los estudios se limitan a un pequeño número de muestras y no representan la estructura y la diversidad de la comunidad cambios estacionales virales con respecto a las variables ambientales y microbianas, se determinó vírica a escalas más grandes en el tiempo. En este contexto y con el fin de seguir y entender los mensualmente la dinámicaCapítulo de la 1 abundancia y el perfil genómico de la comunidad vírica durante cinco años en BBMO ( ). ecosistemas marinos y la falta de conocimiento del papel de los virus en el calentamiento global, Por otro lado, dada la preocupación real de los efectos del cambio climático en los teniendo en cuenta el papel de los protistas, respecto al papel de los virus en la comunidad se evaluó experimentalmente cómo el aumento de la temperatura afectaría al bucle microbiano Capítulo 2 Estos dos estudios nos proporcionan una visión general acerca de las interacciones entre bacteriana en 2 sistemas marinos del Ártico ( ). los microorganismos (incluidos los virus) y el medio ambiente, pero sin saber quién infecta a bacteria con sus fagos podría mejorar nuestra comprensión acerca de las funciones ecológicas quién y quién está haciendo qué. Por lo tanto, el estudio de las interacciones particulares de una de los virus marinos. Poco se sabe sobre el mecanismo general de infección y resistencia entre la mayoría de los fagos y bacterias y cuestiones fundamentales aún no tienen respuesta. Por ejemplo: O, las interacciones entre fagos y bacterias, ¿son al azar o se caracterizan por seguir patrones ¿cuáles son las bacterias infectadas por determinados fagos en las comunidades naturales? complejos?, ¿Cuál es el alcance de dichos patrones al disminuir la distancia filogenética entre cepas bacterianas de cepas bacterianas? NuestraPseudoalteromonas aportación para responder estas preguntas fueCapítulo utilizar 3 un modelo de y sus fagos aislados de BBMO ( ). Para aumentar nuestros conocimientos sobre la biología, la genómica y la evolución237 de Spanish Summary losPseudoalteromonas fagos, 3 de los 18 fagos aislados fueron secuenciados. Además, actualmente los fagos de están representados con sólo 4 genomas disponibles en GenBank. Por lo tanto, las características genómicas y proteómicas ( llevamos a cabo un análisis exhaustivo de unoCapítulo de los fagos; 4 se determinó su biología, morfología, genómica de los 3 fagos aislados para obtener un mejor conocimiento sobre la estructura de los ). Por último, se realizó una comparación genomas, la diversidad funcional y poder establecer patronesCapítulo5 biogeográficos de dichos fagos en las bases de datos públicas de metagenomas de virus. ( ). 2 . ¿Siguen patrones estacionales las comunidades naturales de virus?

A pesar de que la variabilidad estacional en la abundancia viral se ha mostrado en varios ecosistemas marinos (Bergh et al., 1989; Jiang & Paul, 1994; Wommack & Colwell, 2000), pocos estudios han analizado los cambios estacionales más de dos años para obtener un patrón recurrente. El único estudio realizado durante varios años y que examinó la abundancia de virus mostró patrones estacionales recurrentes de la abundancia viral relacionada con los cambios fue llevado a cabo en océano abierto en el mar de los Sargassos (Parsons et al., 2012). Este trabajo

y físicosProchlorococcus en la estabilidadRhodobacteraceae de la columna de agua, la distribución de SAR11 y la abundancia de . Sin embargo, no existe ningún trabajo del mismo tipo en un sistema costero, donde las condiciones son más inestables que en el océano abierto. Por ello, y dada la existencia del registro continuo de variables microbianas y parámetros físico-químicos en BBMO desde 1992, realizamos un seguimiento de la variabilidad estacional de las comunidades presentados en el virales en este ecosistemaCapítulo y1 los factores que pueden modular los cambios. Los resultados indican que tanto la abundancia como el perfil genómico de los virus no siguieron ningún patrón estacional claro durante los cinco años analizados. Además, negativamente con la salinidad y la penetración de la luz y positivamente con el registro de lluvia el análisis de las variables ambientales y biológicas mostraron que los virus se correlacionaron y la abundancia de Prochlorococcus (Tabla 1, Capítulo 1)

. Varios estudios han encontrado que la abundancia viral está altamente correlacionada de forma positiva con la abundancia bacteriana en algunas regiones oceánicas (Jiang & Paul, 1994; Steward et al., 1996; Winter et al., 2009; Siokou - Frangou et al., 2010), ya que la producción y la abundancia vírica en ambientes marinos viene determinada por la productividad y la densidad de las poblaciones del hospedador, especialmente por el bacterioplancton (Wommack & Colwell, 2000). Sin embargo, en nuestro estudio no detectamos ninguna correlación entre los virus y la abundancia de bacterias. Por ello, se analizó la diversidad de las comunidades bacterianas con CARD-FISH durante los primeros 2.5 años. Durante este tiempo, observamos una correlación negativa entre los virus con la salinidad y

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January February March Jan+Feb+Mar

April May June Apr+May+Jun

July August September Jul+Aug+Sep Seasonal anomaly Monthly anomalies

October November December Oct+Nov+Dec

2000 2010 2000 2010 2000 2010 2000 2010

Annual

Annual anomalies 2000 2010

Figura 1

. Anomalías mensuales, estacionales y anuales en los patrones de abundancia viral durante un unaperíodo correlación de 8 años positiva (2005-2012) con laen abundancia la Bahía de Blanes. de Prochlorococcus y Roseobacter (Tabla 1, Capítulo 1) Prochlorococcus y la salinidad son las principales variables que

. Por lo tanto, parece que los la salinidad de los ambientes marinos podrían producir un efecto sinérgico entre los virus y determinan la abundancia vírica en BBMO. En este contexto, hipotetizamos que los cambios en Prochlorococcus la población Prochlorococcus. Los cambios en la abundancia vírica podrían explicarse por la variabilidad de Procholorococcus y detectarlos a través de la salinidad, que se ha demostrado que influye en las poblaciones de (Calvo-Díaz et al., 2004; Jiao et al., 2005; Pan et al., 2005; Pan et al., 2007). Para demostrar esta hipótesis y corroborar que la abundancia vírica no sigue unos las variables que juegan un papel importante en la abundancia viral durante nuestro estudio patrones estacionales incluso cuando se estudia a una escala temporal más grande, se analizaron (abundancia de Prochlorococcus a, profundidad del

, temperatura, concentración de clorofila disco de Secchi, salinidad, producción y abundancia bacteriana) durante un período de 8 años sigue un patrón estacional y tampoco se detectaron anomalías mensuales, estacionales o anuales (2005-2012) en BBMO. Los resultados mostraron que efectivamente, la abundancia viral no se durante estos 8 años (Fig. 1). Por otra parte, parece que la abundancia de virus tiende a aumentar desde agosto a diciembre con los años, pero en 2012 disminuyó cada mes (Fig. 1). La tendencia con los años de las demás variables (Fig. 2) muestra que la abundancia de virus, bacterias239 y Spanish Summary

Figura 2 Prochlorococcus, temperatura, concentración

. Tendenciaa con los años de la abundancia viral, de de clorofila , penetración de la luz, salinidad, producción bacteriana y abundancia de bacterias en la Bahía de Blanes desde 2005 hasta 2012.

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Figura 3 Prochlorococcus, temperatura, , penetración de la luz, salinidad, producción bacteriana y abundancia . Correlaciones entrea la abundancia viral y abundancia de concentración de clorofila bacteriana desde 2005 hasta 2012 en la Bahía de Blanes.

241 Spanish Summary Prochlorococcus aumentó con el tiempo, lo que sugiere que estas 3 variables podrían estar relacionados como vimos en el Capítulo 1

. En cambio, la temperatura, la penetración de la luz y la producción bacteriana tienden a disminuir, mientras que la clorofila mostró valores similares , los cuales sugerían durante los 8 años estudiados (Fig. 2). Por último, se realizó un análisisCapítulo de correlación 1 entre estas que la abundancia vírica se correlaciona negativamente con la salinidad y la penetración de la variables para confirmar los resultados anteriores obtenidos en el luz y de forma positiva con las precipitaciones y Prochlorococcus

. En cambio, cuando añadimos se correlaciona con la penetración de la luz (R2 3 años en nuestra serie de datos, encontramos que el escenario cambia. La abundancia de virus y Prochlorococcus = 0,698) y no hay correlación entre la salinidad (Fig. 3). Estos resultados sugieren que la exposición a la radiación de la luz y la luz ultravioleta (UV) puede causar daños en el ADN de los virus (Heldal & Bratbak, 1991; Suttle & Chen, 1992; Noble & Fuhrman, 1997; Weinbauer et al., 1997) y por lo tanto determinar el tipo de población vírica. BBMO es una región templada con períodos de mezcla en invierno y estratificación en verano. Por otra parte, está muy influenciada por los aportes de agua dulce y los períodos de fuertes tormentas. Todos estos factores hacen de BBMO un punto de muestreo con características cambiantes, que se traduce en un patrón irregular en la dinámica de las comunidades víricas debido a diferentes factores ambientales. 3 . ¿Cómo se verán afectados los virus marinos por el cambio climático ? Los océanos juegan un papel importante en la regulación del clima mediante el almacenamiento, distribución y disipación de la energía de la radiación solar y el intercambio

de calor con la atmósfera. También modulan2 la evaporación y la precipitación y son capaces de absorber grandes2 cantidades de CO . Los efectos del cambio climático relacionado con las emisiones de CO tienen consecuencias directas e indirectas en los océanos, pero se amplifican en el Ártico por varios procesos, incluyendo el deshielo o la estabilidad atmosférica y la dinámica de las nubes que magnifican cualquier cambio (Overpeck et al., 1997). En consecuencia, la ° temperatura en el Ártico está aumentando a un ritmo de dos a tres veces mayor que la de la temperatura media global estimada en 0,4 C en los últimos 150 años (IPCC, 2007). En el Océano Ártico se encuentran elementos clave del sistema de la Tierra (Lenton et al. , 2008) como el flujo de agua dulce del Ártico, lo que podría afectar la formación de aguas profundas en el Atlántico Norte y la circulación termohalina global (Notz, 2009). Por lo tanto, los esfuerzos de investigación son especialmente importantes en esta región. Dado que la mayoría de la biomasa en los océanos consiste en microorganismos (Whitman et al., 1998), se espera que las comunidades microbianas y los virus juegan un papel importante como agentes del cambio climático global.

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Capítulo 2 se investigó cómo las comunidades microbianas

En este contexto, en el del Ártico autótrofas y heterótrofas respondieron a diferentes temperaturas, basadas ​​en el calentamiento previsto de la temperatura superficial del mar en el Océano Ártico. En particular, hemos estudiado los cambios en la biomasa de fitoplancton, abundancias microbianas, estructura de tamaños de la comunidad de flagelados, composición de la comunidad de ciliados, y sobre todo si los virus (a través de la lisis o la infección lisogénica ) y los protistas (como los bacterívoros) demostrado que el aumento de la temperatura estimula la respiración de las comunidades del tenían un impacto diferencial en la biomasa y producción bacteriana. Estudios previos han plancton más rápido de lo que estimula las tasas fotosintéticas (Harris et al., 2006; López- Urrutia et al., 2006; Regaudie-De-Gioux & Duarte, 2012) y favorece la actividad bacteriana heterotrófica (Iriberri et al., 1985; White et al., 1991). Nuestro experimento mostró resultados similares ya que abundancia vírica ( el aumento de la temperaturaFig. 4, Capítulo estimuló 2 la biomasa y actividad bacteriana heterótrofa, así como la ). También se estudió cuál es la estrategia de vida de los virus que desencadena la transición del ciclo lisogénico al lítico, pero en casi todas las temperaturas cuando se someten a un aumento de temperatura. La temperatura podría ser un factor de estrés Fig. 5, Capítulo 2 experimentales la lisis fue dominante ( ). Por último, encontramos que el factor viral ( más importanteFig. 6, Capítulo que controla 2 la abundancia bacteriana era la bacterivoría en lugar de la lisis ). Estos resultados están de acuerdo con otros estudios realizados en las regiones árticas, influenciadas por las aguas del Atlántico (Boras et al. , 2010). Sin embargo, una revisión (Pan-Ártica), mostró que tanto en las zonas del Ártico como en el Mar de Beaufort y Chukchi influenciadas por el Océano Pacífico, el ciclo lítico tiene un mayor impacto en las bacterias que la bacterivoría (Maranger & Vaqué et al., submitted). Además, varios autores han encontrado que las tasas de bacterivoría aumentan con la temperatura en la Antártida (Vaqué resultados indican que el aumento de la temperatura estimula la biomasa y actividad microbiana et al., 2009) y en aguas frías (Newfoundland) (Choi & Peters, 1992 ). En todo caso, nuestros heterotrófica en comparación con la fototrófica, lo que implica consecuencias importantes para el ciclo del carbono y otros nutrientes en el sistema . 4 . Interacciones entre hospedadores y fagos marinos

4.1. Análisis de rangos de infección con la cepa bacteriana Pseudoalteromonas como modelo en la conformación de las comunidades microbianas, las interacciones ecológicas y procesos A pesar del creciente reconocimiento de que los fagos desempeñan un papel importante

243 Spanish Summary evolutivos entre fago y bacterias siguen sin estar claras. Thinstad & Lignell (1997) propusieron la “Kill the Winner“ hipótesis (Ktw), la cual se basa en que los virus controlan las poblaciones de hospedadores más abundantes o que tienen un crecimiento más rápido, de ese modo se permite que poblaciones menos competitivas o con crecimiento más lento co-existan con las poblaciones dominantes, manteniendo así la alta diversidad (Thingstad & Lignell, 1997; Thingstad, 2000). Sin embargo, en base a ensayos experimentales, otros autores han propuesto que los hospedadores más dominantes en el medio marino son los menos susceptibles a la lisis viral y los grupos de bacterias marinas raras son las más susceptibles (Bouvier & del Giorgio, 2007). Por otra parte, la Ktw hipótesis asume que la relación virus-hospedador es extremadamente específica, como se muestra en algunos fagos marinos cultivados que no infectan cepas incluso estrechamente relacionadas (Moebus & Nattkemper, 1981; Moebus, 1992; Kellogg et al., 1995; Wichels et al., 1998; Rohwer et al., 2000). No obstante, hoy en día hay un largo historial de evidencia que sugiere que los fagos infectan a múltiples cepas bacterianas distintas en entornos naturales (Wichels et al., 1998; Wichels et al., 2002; Comeau et al., 2006; Chiura et al., 2009), incluyendo fagos que pueden infectar a hospedadores de diferentes géneros (Sullivan et al., 2003). Se ha demostrado que los resultados en los análisis de rangos de infección podrían depender de los métodos utilizados para el aislamiento de bacteriófagos o la especificidad del receptor y de la adquisición de resistencia ser no realista en función de los métodos utilizados para el aislamiento de bacteriófagos ya que bacteriana (Chibani - Chennoufi et al., 2004). Por lo tanto, la especificidad del hospedador podría rondas continuas de infección por fagos en el mismo hospedador también pueden resultar en la selección de fagos con rango de infección más limitado (Jensen et al., 1998; Wichels et al., 2002). Capítulo 1 Durante la toma de muestras para la estacionalidad viral en BBMO del Pseudoalteromonas, en otoño del 2009, seFig. aislaron 1.SM, dieciochoCapítulo 1 fagos que infectaban 7 cepas muy similares de spp. ( ). Se caracterizaron morfológicamente por TEM; se hizo un “fingerprinting” genotípico de los hospedadores y de losCapítulo fagos utilizando 3 la técnica de fagos aislados pertenecían a la familia y sólo uno pertenecía a la familia RAPD-PCR y se determino para cada fagoMyoviridae su rango de infección ( ). DiecisieteSiphoviridae de los 18

(fago B8b). De acuerdo con el análisis genotípico, los fagos (Fig. aislados 2 Capítulo se agruparon 3) en 7 clusters, donde el siphovirus aislado se agrupó con otros myovirus . Por lo tanto, estos resultados mostraron que no había relación entre las características morfológicas y genotípicas tienen rangos de los fagos. Además, se ha sugerido que la taxonomía morfológica de Myoviridaefagos podría ser utilizada , que a su vez tienen para inferir en la especificidad de infección. Los fagos de la Siphoviridae familia de infección más amplios que los que pertenecen a la familia

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Podoviridae rangos de infección más amplios que la familia (Wichels et al., 1998; Suttle, 2005). SinPseudoalteromona embargo, los fagos aislados no siguen este patrón establecido. Dentro del grupo de fagos de s, el fago C5a (myovirus) mostró el mismo estrechoMyoviridae rango de infección que el siphovirus B8b a pesar de que el primero pertenece a la familia . Por otra parte, todos género los fagosAlteromonas aislados, incluyendo el siphovirus fueron capaces de infectar una cepa bacteriana del , que pertence a una familia bacteriana diferente de la que fueron aislados. Con estos resultados se rompen dos paradigmas establecidos: (i) que la taxonomía podría predecir la especificidad de infección de los fagos marinos y (ii) la especificidad de las interacciones entre aislados de infectan a bacterias de distinta familia ( hospedadorPseudoalteromonas y fago son complejas y poco conocidas, y en nuestro caso, encontramosCapítulo 3 que los fagos La discrepancia entre los diferentes rangos de infección presentados en diferentes estudios ). puede también ser debido al nivel de resolución en el que se midieron las cepas bacterianas y los fagos. La diversidad bacteriana se basa a menudo en los genes marcadores individuales tales como 16S rRNA o ITS (Sullivan et al., 2003; Holmfeldt et al., 2007). Holmfeldt et al. (2007) mostraron que las bacterias filogenéticamente idénticas basadas ​​en la secuenciación del gen 16S rRNA del puedenCapítulo mostrar 3 diferencias en los patrones de susceptibilidad a los fagos. Nuestros resultados corroboran estos hallazgos ya que se analizó la diversidad de los hospedadores no sólo por el gen 16S rRNA, sino también haciendo RAPD- PCR que permite la comparación de perfiles de todo el genoma bacteriano (Martinkearley et al., 1994; Perumal et al., 2009). Nuestros resultados mostraron que la distancia genética (para cada cepa bacteriana) detectada por RAPD- PCR contribuye a explicar un promedio del 28% de la probabilidad de infecciónFig. 6, deCapítulo las cepas 3 bacterianas, mientras que el gen 16S rRNA sólo explicó un promedio del 1% ( ). Por lo tanto, nuestros resultados indicaron que la secuenciación del gen 16S rRNA de bacterias no es eficiente para detectar las variaciones observadas en los patrones de infección entre las diferentes cepas bacterianas, mientras que los perfiles de RAPD-PCR proporcionan una mejor resolución. 4.2. “Networks” entre fago y hospedador y sus procesos evolutivos

Los análisis de rangos de infección entre fagos y bacterias nos permitió comprender cuales son los patrones que sigue la infección vírica. Recientemente se han propuesto una serie de modelos para ayudar a unificar el análisis cuantitativo de la infección cruzada de múltiples fagos con múltiples bacterias (Flores et al., 2011). La matriz que se obtiene aPseudoalteromonas partir de nuestros datos de rango de infección usando el modelo de fagos y hospedadoresFig. de 7, Capítulo 3 spp. siguió los patrones de “nestedness” propuestos por estos autores ( ), pero

245 Spanish Summary es necesario realizar más ensayos de otros modelos para entender con precisión los patrones como la modularidad, es el resultado de una secuencia de adaptaciones que son arbitrarias por complejos de infección del sistema fago-hospedador. En términos evolutivos, “nestedness”, así vez a una mayor resistencia a los fagos y los fagos evolucionan para infectar a una amplia gama los procesos “gene-for-gene” (Lenski, 1988). Bajo este modelo, las bacterias evolucionan cada de hospedadores. Un modelo alternativo al ““gene-for-gene” es que los fagos deben tener alelos que facilitan la infección contra alelos defensivos específicos bacterianos. Sin embargo, ambos modelos son idealizaciones y en entornos naturales podrían ser mecanismos intermedios. Por ejemplo, es posible que los fagos evolucionen la capacidad de infectar a nuevos hospedadores y perder parcialmente la capacidad de infectar a los hospedadores existentes (Agrawal & Lively, 2003). Por lo tanto, lo más probable en los sistemas naturales marinos es que las redes entre fago y hospedadores no tengan una estructura perfectamente “nestedness” o modular (Forde, 2008). 5 . Un paso adelante: la genómica de fagos Capítulo 3 se secuenciaron para entender

Tres de los 18 fagos aislados descritos en el mejorPseudoalteromonas su biología y ecología. Sabiendo la importancia ecológica del grupo bacteriano en los sistemas marinos (Bowman, 2007) y el importante papel de los fagos en las comunidadesPseudoalteromonas microbianas (Weinbauer, 2004; Weinbauer & Rassoulzadegan, 2004), H105/1 ( sóloPseudoaltermonas 4 fagos de Cortoviridae están disponiblesPseudoalteromonas en la base de datos GenBank:Siphoviridae el fago de el fago de Pseudoalteromonas PM2 ( ), Podoviridae el fago de Pseudoalteromonas ), ( Podoviridae RIO-1 ( ) y el fago de pYD6 -A para aumentar el conocimiento de la diversidad de fagos de , así como de ) (Männistö et al., 1999; Duhaime et al., 2011; HardiesPseudoalteromonas et al., 2013). Por lo tanto, las interacciones fago-hospedador y las características genómicas, se estudió en detalleCapítulo las características4 biológicas, la proteómica y la estructura del genoma del siphovirus B8b ( ). Uno de los resultados biológicos de este siphovirus es que era capaz de infectar una cepa ( bacterianaCapítulo 3que y 4 pertenecía a una familia bacteriana diferente del hospedador de la que fue aislado ). Anteriormente, ya se habían detectado fagos que infectan a hospedadores de y diferentesProchlorococcus familias,Synechococcus pero estos eran comúnmente grandes myovirus, como cianofagos que infectan (Sullivan et al., 2003), el enterofago LG1 y AR1 (Goodridge et al., 2003) y el vibriofago KVP40 (Matsuzaki et al., 1992). Entre los siphovirus, se sabe de un aislado capaz de infectar dos cepas de bacterias de diferentes géneros en aguas residuales (Chang & Kim, 2011; Kim & Ryu, 2011), pero no hay tal información para siphovirus en el medio marino. Otra característica interesante de este fago revelada por el análisis filogenético, proteómico

246 Resumen en Español

Capítulo 4 y genómico fue la similitud del siphovirus B8b con otros fagos atemperados ( ). Por un lado, la presencia de la proteína RecT que está relacionada con la capacidad para llevar a cabo la lisogénia en varios fagos y además detectamos un gran número de proteínas estructurales de , el cual pertenece a un género diferente dentro del grupo en elMarinobacterium fago de B8b similares stanieri a S30 las proteínas de un profago putativo en el genoma bacteriano Gammaproteobacteria.

Todo esto, sugiere la capacidad del fago B8b de integrarse en el genoma del hospedador y estas características subrayan la idea de que el intercambio genético entre bacterias y fagos es mayor de los que se pensaba anteriormente. Finalmente, este fago contenía el gen “GroES”, el cual es la primera vez que se encuentra en un genoma de un siphovirus. 6 . Comparación genómica y distribución global de los fagos de Pseudoalteromonas

Nuestro conocimiento de la genómica de fagos está sesgada hacia los fagos que infectan sólo unos pocos hospedadores. Por ejemplo, el 85% de los 1.100 genomas de fagos ( , y de la clase secuenciadosActinobacteria en GenBankFirmicutes pertenecenProteobacteria únicamente a tres deGammaproteobacteria los 45 phylums bacterianos conocidos ), que participan predominantemente en enfermedades humanas y de procesamiento de alimentos. En cambio, con la excepción de fagos que infectan cianobacterias (cianofagos), los fagos que infectan a microbios ambientales son en gran medida desconocidos. Esta falta de resultados representa aproximadamente el 70% de las secuencias en casi cualquier metagenoma viral (Breitbart et al., 2002; Angly et al., 2006; Dinsdale et al., 2008; Williamson et al., 2008; Hurwitz & Sullivan, 2013). marinas son esenciales para proporcionar una mejor comprensión de la alta diversidad genética Así pues, los análisis de genomas de los miembros representantes de las comunidades virales y su interacción con sus hospedadores. Además, se requiere de genomas de fagos completados que infectan para facilitar Pseudoalteromonas la identificación de nuevos genes. Por lo tanto, a falta de genomas de myovirus bacteriana ( Pseudoalteromonas , secuenciamos dosCapítulo myovirus 5 (306b y C5a) aislados de la cepa sp . RHS- str.402 ). De los datos de la secuenciación de estos fagos obtuvimos que un 46% de los ORFs identificados en el fago 306b fago no tenían ninguna homología con proteínas en la base de datos GenBank. Por el contrario, la mayoría de los ORFs detectados en el genoma del fago C5a (41 de 44) eran conocidos. Esto es muy poco común en los fagos ambientales, ya que por lo general presentan un alto porcentaje de proteínas desconocidas en sus genomas (Duhaime et al., 2011; genómico y la comparación entre estos dos fagos fue que a pesar de que se aislaron del mismo Baudoux et al., 2012; Holmfeldt et al., 2013). Otro rasgo interesante detectado en el análisis hospedador bacteriano y ambos son myovirus, difieren en la organización global del genoma

247 Spanish Summary (Fig. 2, Capítulo 5

). El fago 306b tiene dos súper módulos distintivos: genes implicados en la replicación del ADN y el metabolismo de nucleótidos que se encuentran en el brazo izquierdo del genoma y un módulo estructural en el brazo derecho. Sin embargo, el fago C5a presentaba un módulo estructural con un orden de los genes bien conservado: portal - terminasa - cabeza - cola - fibras de la cola y varias proteínas implicadas en la función lisogénica (integrasa, regulador transcripcional) situados, casi todos ellos, en el brazo izquierdo. Esto fue muy sorprendente, ya que se considera que los fagos con la capacidad de integrarse en el genoma del hospedador son los siphovirus y los myovirus suelen ser líticos (Suttle, 2005). Capítulo 4 Por último, estos dos fagos se compararon con el estudiado siphovirus B8b ( ). Curiosamente, la comparación entre los genes anotados entre los 3 fagos mostró que el myovirusFig. también mostró 306b2, 3, Capítulo y el siphovirus 5 B8b comparten más genes que ambos myovirus Capítuloaislados (306b 3 y C5a) ( ). El análisis de rango de infección llevado a cabo en el que el fago C5a y B8b eran más similares en términos de especificidad de la infección ya que el bacterianas que el fago 306b fue capaz de infectar ( fago B8b y C5a infectaban sólo 4 y 5 cepas bacterianasFig. 4, respectivamente,Capítulo 3 frente a las 16 cepas ). Estos resultados cambian el paradigma de las relaciones que se habían establecido entre la morfología y las características biológicas de los fagos marinos. Hasta el momento, está establecido que los myovirus son fagos líticos con una amplia gama de hospedadores y que los siphovirus son generalmente lisogénicos y de ser un myovirus tiene genes para desarrollar la función lisogénica y presentó un rango de tienen un estrecho rango de infección. Pero nuestros resultados indicaron que el fago C5a a pesar Pseudoalteromonas. infección más estrecho que otros myovirus de Además, también encontramos que el myovirus 306b es más similar al siphovirus B8b a nivel genómico. Así pues, los datos que hemos presentado contribuyen a una nueva visión de las interacciones entre fago y hospedador en los sistemas marinos y demuestran lo compleja que es su dinámica. Por otro lado, se pone de manifiesto la necesidad de mejorar los modelos conceptuales existentes sobre el papel de los Dada la novedad de estos fagos de , se investigó su distribución global fagos en las comunidades bacterianas naturales.Pseudoalteromonas en una base de datos recientemente publicada de metagenomas virales marinos cuantitativos del Océano Pacífico (Hurwitz & Sullivan, 2013). Los datos de la abundancia relativa normalizada mostraron que los fagos C5a y 306b no sólo fueron detectados en muestras obtenidas de la zona afótica, sino que eran más abundantes en estas muestrasFig. 4, Capítulo que en las 5) de zonas fóticas en fagos de los ambientesPseudoalteromonas oceánicos intermedios y de mar abierto ( . Por lo tanto, estos se pueden considerar ampliamente distribuidos. El porcentaje del número de genes que fueron encontrados en los metagenomas y eran exclusivos para fagos

248 Resumen en Español de Pseudoalteromonas respectivamente ( Fig. 5,correspondió Capítulo 5) a un 0,2, 7,8 y un 1,3% para los fagos B8b, C5a y 306b . Esto indicó que el siphovirus B8b tenía una distribución más restrictiva que los otros 2 myovirus en la base de datos POV. Estos resultados contrastan con los obtenidos por Zhao et al. (2013) quienes encontraron que se le asigna un 58,7 % de los nuestros para los fagos de genes a los fagos aislados deCellulophaga SAR11. De todas formas, se encontraron resultados similares a los que presentaron un 15% de genes exclusivos de estos de podrían distribuirse ampliamente en los océanos, e incluso a pesar de que fagosPseudoalteromonas en la base de datos POV (Holmfeldt et al., 2013). Estos resultados demostraron que los fagos no son muy abundantes, se pueden considerar omnipresentes en varios ambientes marinos.

249 Spanish Summary CONCLUSIONES

Capítulo 1

1. La abundancia y la composición de la comunidad vírica no siguió ningún patrón años estudiados estacional en el Observatorio Microbiano de la Bahía de Blanes (BBMO) durante los 5

La salinidad parece ser el factor principal que covaría negativamente con la abundancia

2. vírica en nuestro estudio temporal en BBMO.

, 3. A pesar de la falta de correlación entre la abundanciaProcholorococcus vírica y laRhodobacterales bacteriana, la abundancia de grupos bacterianos específicos como y SAR11 Capítulopodría 2 desempeñar un papel importante en la comunidad vírica en BBMO.

de la temperatura al bucle microbiano mostraron un estímulo de las comunidades 4. Los experimentos llevados a cabo en el Ártico para determinar cómo afectaría un aumento

microbianas heterótrofas en comparación con las fototróficas.

importante que controla la abundancia y producción bacteriana fue la bacterivoría en 5. La infección lítica fue dominante cuando aumentamos la temperatura, pero el factor más

Capítulolugar 3 de la lisis viral. Las interacciones entre los fagos aislados de Pseudoalteromonas

6. y sus hospedadores mostraron una alta variabilidad en la especificidad de la infección y encontramos que los fagos pueden infectar a hospedadores de diferentes familias filogenéticas.

7. La comparación entre rangos de infección y el perfil genómico de los fagos obtenido por RAPD-PCR no mostró patrones similares ya que fagos con idéntico rango de infección mostraron patrones genómicos diferentes. Las variaciones observadas en la susceptibilidad entre las cepas bacterianas y la

8. probabilidad de infección está mejor correlacionada por el patrón de todo el genoma bacteriano obtenido por RAPD-PCR que por el gen 16S rRNA. 250 Resumen en Español

9. Las interacciones entre fago y hospedador de este estudio siguen una estructura “nested”. Esto implica que los fagos más especializados infectan los hospedadores que son más susceptibles a la infección en lugar de infectar a los hospedadores que son más resistentes Capítuloa la 4 infección. El fago aislado de Pseudoalteromonas Siphoviridae

10. B8b pertenecía a la familia según las reglas ICTV de nomenclatura. Este fago mostró un período de latencia de 70 min y aproximadamente 172 nuevas partículas virales fueron liberados de cada célula bacteriana cepas de infectada.Pseudoalteromonas El análisis de rango de infección reveló que el fago B8b infecta sólo 3 de 52 pertenecía a Alteromonas spp., pero también podía infectar una cepa bacteriana que , a una familia bacteriana diferente del hospedador del que fue aislado este fago.

( 11. MarinobacteriumEl fago B8b tiene unstanieri genomaGammaproteobacteria de 46kb y está estrechamente relacionado con el profago de ). El genoma del fago B8bSiphoviridae era modular y Capítulocontiene 5 el gen GroES que es la primera vez que se detecta en la familia . Dos fagos pertenecientes a la familia Myoviridae

12. (306b y C5a) también fueron secuenciados y, sorprendentemente, el myovirus 306b compartía más genes con el siphovirus B8b que con el otro myovirus aislado.

13. El fago C5a tenía un genoma muy conservado (el 93% de los ORFs de su genoma tenían homologos), lo cual es muy raro en fagos aislados en medios naturales ya que en la mayoría no se detectan ORFs con homología en la bases de datos públicas.

14. Varias proteínas involucradas en la función lisogénica fueron detectadas en el fago C5a (integrasa y regulador transcripcional), lo que sugirió la habilidad de este myovirus de ser temperado. La abundancia relativa de estos fagos de Pseudoalteromonas fue determinada en los

15. sólo se detectaron en muestras fóticas costeras, sino también en las muestras afóticas del metagenomas virales marinos del Océano Pacífico (base de datos POV) y reveló que no

océano intermedio y abierto. 251 Spanish Summary Estos fagos de Pseudoalteromonas que son omnipresentes y ampliamente distribuidos, de manera que podrían representar 16. no son abundantes en la base de datos POV pero parece

una fracción de la biosfera rara en la diversidad viral marina.

252 References

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267

AGRADECIMIENTOS/ACKNOWLEDGMENTS

Durante estos 5 años de doctorado he pensado varias veces en el momento en el que me tocaría y ahora que ha llegado no sé ni por donde empezar. Crees que cuando llegue este momento te escribir los agradecimientos de la tesis para poder plasmar todo lo que ha significado para mi, invadirá una sensación de alegría y estarás pensando… por fin! Parecía que el final no llegaba nunca! Y en parte es así, pero también va acompañada de un sentimiento de pena porque esta maravillosa etapa ha llegado a su fin. Estos años han estado cargados de experiencias, de viajes inolvidables, de buenos momentos, de mucho trabajo, pero sobretodo si este tiempo ha sido tan por si acaso GRACIAS a todos con los que he compartido este doctorado. increíble ha sido por la gente que me ha acompañado. Así que no quiero dejarme a nadie, pero

gràcies per haver cregut en mi i haver-me donat aquesta oportunitat. Gràcies també pel teu Todo esto no hubiera sido posible sin mi directora de tesis, Dolors Vaqué. Dolors, moltíssimes optimisme i recolzament en tot moment i sobretot perquè gràcies a tu he pogut viatjar a llocs que virus i bacteris o de les hores davant l’ordinador llegint i escrivint he après moltíssimes coses. mai hagués imaginat. De cada viatge, de cada experiment, de les hores al laboratori fent créixer

Així que MOLTES GRÀCIES PER TOT.

Le toca el turno a la co-dire…SILVIA!!! Que habría hecho yo sin ti! Me abriste el camino a la biología molecular y a la genómica y no solo eso, sino a nuevas oportunidades y a aprender cosas nuevas. MIL GRACIAS por haberte atrevido a meterte en este mundo de los virus marinos porque trabajar contigo ha sido un AUTÉNTICO placer. Admiro y envidio tu energía y tus infinitas ganas de seguir adelante sea como sea, venga como venga, que grande eres!! Gracias de corazón.

La siguiente, es la técnico…Eli Sà. Eli, no només ets una gran professional, sinó que ets una de les millors persones que he conegut. Amb tu he passat els millors i pitjors moments al laboratori. Amb tu he discutit cada pas que donava en aquesta tesi i sempre m’has fet costat. MOLTES GRÀCIES. Una part d’aquesta tesi és teva, perquè començar de 0 amb un tema al laboratori requereix molt esforç, probatines i preguntar-se quan les coses no funcionen…ens haurem deixat algo? Estan les plaques prou fresques? i si és que la temperatura estava molt alta? o potser molt baixa? Els posem a la foscor? I ara perquè no fan calves si ahir sortien?? ;) Per tots aquests moments i tants d’altres, gràcies, no canviïs mai!

271 Y habiendo nombrado a mis 3 pilares en esta tesis, sigo con el resto…a verrrr…que no me quiero dejar a nadie!!! cuando todo esto de los virus era una enorme nube gris... A Pepa y a Fer, y a todo su equipo de la Universidad de Alicante que me ayudaron tanto al principio

Las siguientes van a ser mis bien paríassss!!! (Clara, Pati, Arancha y Sara). Yo creo que no os voy a decir nada que ya no sepáis….que sus quiero muchísimo, que esta tesis no habría sido igual sin vosotras y que ahí quedan todos los momentazos que hemos pasado juntas. A las que ya no andáis por aquí (Clara y Arancha) ya sabéis como os echo de menos. Los paseos perrunos “relajaos” con Bruja y Lack, esas cervecitas toashuntas, los chascarrillos, las escapadas a Cadaqués o Mallorca, los fines de semana ravaleando…volveeerrrrrrr!!! Y a las demás…Pati, la alegría la huerta, gracias por tu apoyo sobretodo en esta etapa final que ya sé que en muchos momento no había dios quien me aguantara y tu siempre has estado ahí para sacarme una sonrisa. Sariqui, pero que apañá que compartido. es mi Sariqui por dió! ;) Empezamos esta aventura juntas y me alegro que además la hayamos

A toda la gente que anda o ha andado por el ICM que han hecho que venir cada día a trabajar sea un gustazo por el buen ambiente que se respira en el ICM: Clara C., Vane, Miriam, Albert R., Esther G., Montse C., Martí, Raquel, Bea, Xavi, Sarah-Jeanne, Elisa, María, Pablo, Suso, Caterina, Rodrigo, Roy, Ariadna, Jordi G., Stefano, Isabel, Paula, Julia B., Ero, Ramiro, Ivo, Thomas, Hugo S., …y a todos los demás!!! A Ana Mari, Franciiiihhhhcooo, Juancho y Guillem por su arteee, y su buen rollo. A Massimo, mi gran apoyo durante este final de tesis. Gracias por tu cariño. A Marta S., mi postdoc y Bea. favorita jejejeje, eres muy grande neni!!! A mis compañeros de despacho: Pedro, Rosana, Cris, Ana

A todos los “jefazos” que andan por el departamento y siempre están dispuestos a echarte una mano, resolverte dudas y dar su opinión: Carles, Ramón, Rafel, Cèlia, etc. En especial a Pep Gasol. Pep, moltes gràcies pels teus consells (sobretot amb l’estadística!), per les teves idees, perquè de cada reunió amb tu fas que les coses es vegin més clares quan semblava que ja no hi havia per on agafar-ho. També gràcies, perquè per mi has estat una gran motivació, ets un d’aquests científics que transmet la passió pel que fem i fas que un recuperi les ganes de seguir endavant.

A Eli A., mi compañera no solo durante esta tesis, sino durante los últimos casi 10 años…Xurri!!! Gràcies per escoltar-me sempre, per fer-me costat en els mals moments, per aguantar-me quan 272 tot això se’m feia gran i pesat. Però no només m’has fet costat a la feina sinó també a la meva vida, per tot això i per les bones estones que sempre passem juntes o con nuestros perrones, mil gràcies!!!. Espero que aquests 10 anys només siguin els primers que hem de compartir. A tod@s mis amig@s de Barcelona, Altafulla, en especial a Esther C. (Xupiiii), Esther P., Laia, Marta, Annabel y a los demás!!

A todos mis compañeros de campañas; en la del Ártico: Raquel, Iñigo, Regino, Johnna, Jordi F., Txetxu y Lolo. Y en la de Malaspina: Raquel, Víctor, Irene, Cris (aiiii Crisss, como se te echa de menos!!!), Paqui (mi Paaaaquiiii, que grandes momentos compartidos en el Hespérides), Ángel, Zuqui, Oscar, Rafel (vas fer que els participants del leg 6 fóssim l’enveja del altres per tenir-te de jefe de campanya, inoblidables les nits amb la teva guitarra repassant els moments del dia, gràcies!) y a Ricardo, por hacer de esta campaña un viaje inolvidable. las campañas no hubieran sido lo mismo sino las hubiera compartido contigo. Gracias por tu Raquel y Iñigo se merecen una mención especial…Raquelilla…gran amiga y gran compañera, apoyo, por nuestros ratos en cubierta, por los ratos de risas durante experimentos y muestreos interminables. Espero que nuestros caminos se unan en el futuro. durante estos 5 años. Contigo he pasado momentos inolvidables y ratos en los que más me he Iñiguin!! Y a ti que te voy a decir que ya no sepas…eres una de las mejores cosas que me ha pasado reído en mi vida. Te adoro!!! Pero eso ya lo sabes jodio…solo espero que sigamos en contacto como hasta ahora porque eres uno de los amigos que quiero cerca!!!

A Carlos Duarte y Paul Wassman, por darme la oportunidad de embarcarme en estas dos campañas y a toda la tripulación del Jan Mayen y del Hespérides por todos esos momentos que hacen que uno nunca quiera bajarse del barco.

I will always be thankful to Matt Sullivan and all the people at his lab (especially thanks to Cristina Howard, Natalie Solonenko, Karin Holmfeldt and Boonie Poulos). Matt, thanks a lot for your hospitality and for you support and help during and after my stay in Tucson. Half of this thesis would have not been possible without all that I have learned from you and from your people. THANKS SO MUCH.

Pero sobretodo esta tesis no hubiera sido posible sin el apoyo de mi familia, en especial de mis padres. Papa!!!! Si es que esto para ti no empezó aquí. La cosa viene de más lejos…cuando ya estaba yo empeñada en estudiar biología y me fui a Girona. Ahí empezaron los viajes con 273 multinacional para irme a hacer el doctorado en virus marinos…santa paciencia que has tenido el Nissan pariiba y pabajo…después te salí con la increíble idea de dejar un trabajo fijo en una que tener! Pero a pesar de lo descabelladas que te hayan parecido mis inquietudes SIEMPRE me has apoyado y ayudado en todo. Mama, siempre has luchado porque estudiara, para que llegara lejos…mira donde he llegado! Y no lo hubiera hecho sin vosotros! Gracias por tus infinitos ánimos y paciencia, por tu cariño y por motivarme a seguir adelante siempre con energía positiva! Nunca podré agradeceros suficiente todo lo que habéis hecho por mi. Además también he podido llegar hasta aquí gracias a todo lo que siempre me habéis inculcado y enseñado: trabajar por lo que ha sido y será imprescindible en cada nuevo paso que dé. crees y lo que te gusta y dedicarle tiempo y esfuerzo. GRACIAS POR TODO. Vuestro apoyo y cariño

A mi hermano, ejemplo de lucha constante y del que he aprendido que reírse de uno mismo y de tus miedos es el mejor remedio para seguir adelante. Pero mi familia no solo consta de 4 miembros, sino que para mi siempre ha sido de 8. A mis tios (Malina y Tiet) y mis primos (Ainhoa y David). Hemos crecido juntos, y para mi sois mis segundos padres y mis hermanos. Tengo que agradeceros vuestro apoyo y cariño. En especial a mi Malina, que siempre ha estado ahí para darme buenos consejos, para escucharme, para sacarme las fuerzas cuando ya no me quedan y porque siempre he admirado tu energía y tu fortaleza. También mención especial a mi primo David. Ya sabes cuanto te quiero, siempre alegrándome la vida! Que grande eres couso!!! A Bea, que no es solo familia sino buena amiga. Gracias a los dos por los buenos ratos en el chiringuito, los paseos perrunos playeros, las cenas juntos, las excursiones. Grandes recuerdos de Castefa!! Y a Pol!!!! El nuevo miembro de la familia…la cosa más bonita que hay en el mundo! A mi prima Ainhoa también tengo que agradecerle el diseño gráfico de esta tesis. Mil gracias Nhoica, ha quedao preciosííísssimaaaa! No me quiero olvidar del resto de la familia, tios, tias, primos, etc y en especial a mis abuelicas… estaríais tan orgullosas de vuestra nieta si estuvierais aquí! Por último...no podía faltar (sino no sería yo...) que nombre a mi compañero de 4 patas más fiel...mi acompañanante en todas mis aventuras durante los últimos 8 años...MI TITO LACK!! Ese perrón al que adoro y que forma parte de mi en todo lo que hago y allí a donde voy...así que estas líneas son para él!!

Cada uno de vosotros ha sido importante para poder realizar y finalizar esta tesis, así que… VA POR USTEDES!!!

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