Ecology of an uncultured heterotrophic flagellate lineage: MAST-4

Ecology of an uncultured heterotrophic flagellate lineage: MAST-4 (Ecología de un linaje de flagelados heterotróficos no cultivados, el MAST-4) Raquel Rodríguez Martínez Tesis Doctoral presentada por Dª Raquel Rodríguez Martínez para obtener el grado de Doctor por la Universidad de las Palmas de Gran Canaria, Departamento de Biología, Programa en Oceanografía (Bienio 2006-2008) Director: Ramon Massana i Molera Universidad de Las Palmas de Gran Canaria Institut de Ciències del Mar (ICM-CSIC) En Barcelona a, de de La Doctoranda El Director Raquel Rodríguez Martínez Ramon Massana i Molera Anexo I D/Dª...............................................................SECRETARIO/A DEL DEPARTAMENTO DE....................................................................... 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 “Ecology of an uncultured heterotrophic flagellate lineage: MAST-4” presentada por la doctoranda Dª Raquel Rodríguez Martínez y dirigida por el Doctor Ramon Massana i Molera. Y para que así conste, y a efectos de lo previsto en el Artº 73.2 del Reglamento de Estudios de Doctorado de esta Universidad, firmo la presente en Las Palmas de Gran Canaria, a.......de...............................de dos mil............... A mi familia, a mi tía María Jesús, a mi sobrino Víctor y a todos los que me habéis acompañado y apoyado en este recorrido. “Our days are precious but we gladly see them going If in their place we find a thing more precious growing: A rare, exotic plant, our gardener’s heart delighting; A child whom we are teaching, a booklet we are writing.” Hermann Hesse, Das Glasperlenspiel Contents  Glossary          10 Acronyms          11 Summary/Resumen/Resum       12 GeneralIntroduction        17 AimsandOutlineoftheThesis      35 Chapter1 Distribution of the uncultured protistMAST4 in the Indian Ocean, Drake Passage and Mediterranean Sea assessedbyrealtimequantitativePCR  43   Chapter2 Grazing rates and functional diversity of uncultured heterotrophicflagellates 57 7 Chapter3 Functional responses of three heterotrophic flagellatestaxainmixednaturalassemblages 69 Chapter4 Lowevolutionarydiversificationinawidespreadand abundantunculturedprotist(MAST4) 85 Chapter5 Biogeographyoftheunculturedmarinepicoeukaryote MAST4:temperaturedrivendistributionpatterns 101 SynthesisofResultsandGeneralDiscussion   121 Conclusions          135 Spanishsummary(Resumendelatesis)    139 Generalreferences        191 Acknowledgements        201  10 Glossary 18S rDNA Gene coding the RNA component of the small ribosomal subunit, and widely used to identify and classify eukaryotic microorganisms. Automated Ribosomal Interspacer Analysis (ARISA) Fingerprinting technique that describes the diversity of an assemblage based on the ITS size variation of its members. Compensatory base changes (CBC) Mutations that occur in both nucleotides of a paired structural position of the ribosomal RNA while retaining the paired nucleotide bond. Clone library Heterogeneous collection of cloned sequences (often 18S rDNA) derived from a complex assemblage of organisms. Environmental molecular surveys Retrieval of genetic signatures from a complex microbial assemblage for diversity studies. Epifluorescence microscopy Technique that allows observation by fluorescence of very small stained or autofluorescent cells retained on a filter. Flagellates Single-celled protists, phototrophic or heterotrophic, with one or more whip-like organelles, called flagella, often used for propulsion or to create feeding currents. Fluorescent In Situ Hybridization (FISH) Microscopic method for detection of microbial cells by labeling them with a fluorescent probe that specifically labels ribosomes. Functional response The predation rate of a consumer as a function of food density. Internal Transcribed Spacer (ITS) Non-coding regions separating the individual genes of the ribosomal DNA operon, with a higher degree of variation than the genic regions. Marine alveolates (MALV) Clades without cultured representatives that belong to the eukaryote supergroup alveolates, detected in molecular surveys of marine picoplankton. Marine stramenopiles (MAST) Clades without cultured representatives that belong to the eukaryote supergroup stramenopiles, detected in molecular surveys of marine picoplankton. Numerical response The growth rate of a consumer as a function of food density. Phylogenetic clade (or lineage) Set of related sequences that originate from a single common ancestor. Picoeukaryotes Phototrophic and heterotrophic protists smaller than 3 µm. Population All the organisms that both belong to the same group or species and live in the same geographical area. Protists General term for eukaryotes not belonging to plants, animals, fungi, or macroalgae, generally single-celled organisms of sizes from 1 µm to more than 100 µm. GeneralIntroduction General Introduction 19 Eukaryotic tree of life Eukaryotes are only one of the three domains of life, along with Bacteria and Archaea. One of the reasons of our large curiosity in them is that they include the organisms we can see. Our understanding of eukaryote biology, ecology and evolution is dominated by the study of land plants, animals and fungi. However, these are only three isolated fragments of the full diversity of existing eukaryotes. The majority of eukaryotes, in terms of main lineages, number of different taxa and also total numbers of cells, consist of predominantly unicellular lineages. A surprising number of these lineages are poorly characterized. Nonetheless, knowledge of the morphological, functional and ecological diversity of microbial eukaryotes is fundamental to our understanding of eukaryote biology and the underlying forces that shaped it (Baldauf 2008). During the second half of the 20th century, molecular developments provided a systematic way to relate all living organisms through DNA sequence comparisons, initially using the smallsubunit ribosomal RNA gene: 16S rDNA in prokaryotes and 18S rDNA in eukaryotes (Woese 1987). Using the sequences from this gene, the eukaryotic phylogenetic tree of life appeared divided into only a few supergroups (Adl et al 2005, Baldauf 2003). Including several revisions and updates (Baldauf 2008), virtually all eukaryotes can now be assigned to one of the supergroups, which form a crown radiation without clear ranking among them and with an uncertain root (figure I.1). Few morphological or ultrastructural characters connect the diverse lineages within each supergroup, but the phylogenetic signatures are robust (Jürgens and Massana 2008). Although the configuration of supergroups varies, the general consensus includes (1) Unikonts, (2) Archaeplastida, (3) Rhizaria + Alveolates + Stramenopiles (RAS), (4) Excavates and (5) Cryptophytes, Centrohelids, Telonemids plus Haptophytes (CCTH). Unikonts include all eukaryotes thought to be primitively uniflagellate, that is, Opisthokonts (including animals, fungi and some protists such as choanoflagellates) and Amoebozoa (Cavalier-Smith 2002). Archaeoplastida is the group in which eukaryotic photosynthesis first arose and includes green algae and land plants (Adl et al 2005, Archibald and Keeling 2005). The RAS group was recently proposed (Burki et al 2007, Hackett et al 2007) to unite three very heterogeneous supergroups, the rhizaria (cercozoans, radiolaria and foraminifera), the alveolates (dinoflagellates, ciliates and apicomplexa) and the stramenopiles (see below). The Excavates are formed by two distinct groups, the mitochondriate excavates that include Euglenids, Heterolobosea and core Jakobids, and the amitochondriate excavates (i. e. Diplomonads and Parabasalids), a collection of highly derived taxa with simplified internal cell structure and lacking aerobic mitochondria. CCTH is a newly described supergroup (Burki et al 2009) proposed to relate several important but difficult to locate phylogenetic lineages, such as haptophytes, cryptophytes and telonemids (Shalchian-Tabrizi et al 2006). In addition, CCTH also include the katablepharids (known to be relatives of Cryptophytes) (Okamoto and Inouye 2005) and perhaps the picobiliphytes, a novel phytoplanktonic class initially unrelated to any supergroup (Not et al 2007). A consequence of the General Introduction 20 molecular framework is that many incertae sedis protists (Patterson and Zöffel 1991) are finding their phylogenetic position in the eukaryotic tree. In addition, sequences of the 18S rRNA gene of cultured representatives are crucial in placing protists within this phylogenetic context (CavalierSmith and Chao 2003, Scheckenbach et al 2005). MALV-V MALV-I MALV-III MALV-II MAST-4 MAST-7/8 alveolates stramenopiles archaeplastida Charaphyta Chlorophyta Land plans prasinophytes Rhodophyta Glaucophyta Apusozoa Polycystinea Toxopodida Acantharea Gromia foraminifera euglyphid amoebas cercomonads Chlorarachniophyta Phaeodaria Desmothoracida rhizaria ciliates apicomplexa Actinophryidae dinoflagellates bicosoecids MAST-3 oomycetes diatoms Chrysophytes xantophytes Pelagophyceae Dictyochophytes MAST-1 labyrinthulids opalinids telonemids cryptophytes Picobiliphytes centrohelids haptophytes core jakobids acrasid amoebas heterolobosea euglenids trypanosomes leishmania diplomonads retortamonads trichomonads parabasalids oxymonads Malawimonas excavates CCTH opisthokonts amoebozoa acanthamoebas tubulinid amoebas flabellinid amoebas Dictyostelia Myxogastria Protostelia archaemoebae Thecamoeba Mayorella mesomycetozoa choanoflagellates animals microsporidia fungi nucleariids Figure I.1. Eukaryotic tree of life. A consensus phylogeny of the major eukaryotic groups based on published molecular phylogenetic and ultrastructural data (adapted from (Baldauf 2003)). Dotted lines indicate positions of major lineages known primarily from culture-independent molecular surveys. MALV (marine alveolates), MAST (marine stramenopiles) and CCTH (Cryptophytes, Centrohelids, Telonemids plus Haptophytes). A body plan very common in the eukaryotic tree of life is that of unicellular colorless microorganisms with one or a few flagella. This type of organization, generally referred as protozoan (or heterotrophic) flagellates, can be seen in 27 of the 60 protists lineages among eukaryotes (Patterson and Larsen 1991). Thus, the flagellates are a grade of organization and not a consistent monophyletic assemblage. They are those organisms that spend most of their existence moving or feeding with a small number of flagella. Their size range between 1-2 µm up to 20 µm. Flagella arose early in eukaryote evolution, and we are not able to identify any groups of protists that are primitively without flagella. It is supposed that the last eukaryotic common ancestor was also a kind of flagellate originated by a symbiogenetic fusion between eubacteria and archaebacteria (Margulis et al 2006). And obviously, this primitive eukaryote was colorless and heterotrophic. General Introduction 21 Microbial molecular surveys raise the eukaryotic diversity The use of molecular biological approaches on microbial ecology, developed during the turn of the 20th century, has transformed the field of protistan diversity. In general, the identity of most small fragile protist was very difficult to assess by direct inspection of natural samples. So, a classical way of identification is to obtain these organisms in culture for a proper classification. In the case of autotrophic protists, the cultured strains are more or less representatives of the natural communities. This is probably because it is easier to simulate the natural conditions in a culturing bottle, since these cells require mostly inorganic nutrients and light. Nevertheless, it is likely that cultures do not cover the full in situ diversity of autotrophic protists (Vaulot et al 2008). In the case of heterotrophic bacterivorous protists, the long list of formally described species (Lee and Patterson 1998) derives mainly from cultures or enrichments started by adding a substrate for bacterial growth that, in turn, are the food for the protists. Cell cultures yield fundamental ecophysiological information but while it is obvious that these easily enriched strains live in the sea, it is doubtful that they are dominant members of natural assemblages. A now classic study demonstrated that the bacterivorous protists dominating several enrichments were rare in the original samples (Lim et al 1999). More recent studies have confirmed these results and have provided the mechanistic explanation of this culture bias in heterotrophic flagellates (HF) (del Campo 2011) Environmental (culture-independent) studies of sequencing the 18S rRNA genes have resulted in an increased appreciation of the diversity of protists in nature. Thus, molecular surveys have revealed numerous sequences of unknown protists, indicating unanticipated levels of protistan diversity in many environments and retrieving very few sequences related to cultured protists (Amaral-Zettler et al 2009, Brown et al 2009, Countway et al 2007, Díez et al 2001, Head et al 1998, Lim 1996, López-García et al 2001, Moon-van der Staay et al 2001, Richards et al 2005, Stoeck et al 2006, Vigil et al 2009). In marine systems, these surveys have revealed a large number of uncultured lineages, such as the marine alveolates (MALV) and the marine stramenopiles (MAST) (Massana et al 2004a) that appear in virtually all studies. Still, most of this diversity remains poorly known. It is thus clear that culture isolations and molecular surveys are providing different views on the species composition of marine protists in general and of HF in particular. While often these environmental studies were ecologically-driven in order to identify the dominant members of natural assemblages, it is obvious that they have also provided fundamental new insights into eukaryotic phylogeny and new branches in the tree of life are exclusively composed by these so-far uncultured lineages. General Introduction 22 Stramenopiles, an important supergroup in marine systems The supergroup Stramenopiles (Adl et al 2005) are formed by many heterogenous lineages, some of them of crucial importance in marine systems. One of the few characteristics shared by most stramenopile motile cells is the presence of a flagellum with two opposite rows of mastigonemes, tripartite hairs (“stramenopiles”), which reverse the flow around the flagellum so that the cell is dragged forward rather than pushed along. Most also possess a second, shorter smooth flagellum (hence the alternative name “heterokont”). This extraordinarily diverse group includes numerous lineages of single-celled heterotrophs (bicosoecids) and phototrophs (diatoms), slime nets (labyrinthulids), plasmodial parasites (oomycetes), and large to giant multicellular algae (phaeophytes). There are at least five known lineages of non-photosynthetic stramenopiles (figure I.2) (Baldauf 2008). Oomycetes (water molds and downy mildews) were previously classified as fungi and include numerous extremely destructive plant parasites such as Phytophthora infestans, the cause of potato blight, and Plasmopara viticola, the cause of grapevine downy mildew. The bicosoecids are small heterotrophic biflagellates, such as the well-known Cafeteria (Fenchel 1988). The Blastocystis spp. are commensals in the guts of animals (Stechmann et al 2008) and some species, like Blastocystis hominis, can infect humans. Labyrinthulids (slime nets) form filamentous “railway-like” networks patrolled by amoeboid-like cells. They were placed with the Thraustochytrids (Cavalier-Smith et al 1994), which also have the tendency to form cell aggregates. The fine taxonomy of both groups requires 18S ribosomal gene comparisons (Honda et al 1999). Photosynthetic stramenopiles (figure I.3) are formed by at least eleven distinct lineages, including some of the most important and abundant algae (Baldauf 2008). Diatoms have intricately patterned bipartite silica tests that fit together like lidded boxes. They are ubiquitous and often abundant in marine and freshwaters, with ~11,000 described and possibly as much as 107 nondescribed species (Fehling et al 2007). Chrysophytes (golden algae) are generally free-swimming and unicellular, but there are also filamentous and colonial forms. Pigmented chrysophytes contain chlorophyll as well as a carotenoid called fucoxanthin that gives them a yellow-brown color. They were considered to be mostly freshwater, but recent studies suggest they could be rather abundant in the marine plankton (Fuller et al 2006, Lepère et al 2009, Shi et al 2011). Phaeophytes (brown algae) are particularly widespread in temperate intertidal and subtidal zones. They have true parenchyma and build “forests” in near-shore waters, as the giant sea kelp forests, supporting complex ecosystems including fish and marine mammals. Xanthophytes (yellow-green algae) are the dominant producers in some salt marshes and also form multicellular organisms. The remaining groups are formed by very small algae, such as Dictyochophytes, Eustigmatophytes, General Introduction 23 Figure I.2. Pictures of examples of heterotrophic stramenopiles. a) Developayella elegans; b) the bicosoecid Cafeteria roenbergensis; c) Blastocystis hominis; Labyrinthulids: d) Aplanochytrium, e) Thraustochytrium and f) Labyrinthula terrestris; Oomycetes: g) Pasmopara viticola, h) Phytophthora infestans, i) Saprolegnia. The bottom pictures are the infested hosts by the upper oomycetes, j) forming the mildew of the grapevines, k) potatoes and l) trouts. Pictures courtesy of WJ. Lee, D. Patterson, L.A. Zettler, V. Edgcomb, C. Leander, D. Porter, J. Harper, S. Lew and E. Haugen. Phaeothamniophyta, Pelagophytes and Pinguiophytes (Vaulot et al 2008). The Pelagophyceae is a recently described class (Andersen et al 1993), previously classified within the Chrysophyceae, that can be important in the oceanic picoplankton. General Introduction 24 Figure I.3. Pictures of examples of photosintetic stramenopiles. a) The xantophyte Botrydium; Phaeophytes: b) Padina, c) Colpomenia, d) Pelagophycus porra, e) Fucus vesiculosus and f) Macrocystis integrifolia; Diatoms: g) Stephanodiscus, h) Coscinodiscus, i) Cymbella tumida and j) Phaeodactylum tricornutum; k) the eustigmatophyte strain 29.96; Phaeothamniophytes: l) Stichogloea doederleinii and m) Phaeothamnion confervicola; Chrysophytes: n) Chrysocapsa epiphytica, o) Spumella sp. and p) Synura and q) the pinguiophyta Pinguiococcus pyrenoidosus. Pictures courtesy of I. Inouye, R. Tan, E. Bierman, D. Mann, A. de Martino, C. Bowler, J.C. Baley, Y. Tsukii, D. Patterson, B. Andersen and U.S. Geological Survey. General Introduction 25 MAST, uncultured marine stramenopile lineages Marine stramenopiles (MAST) were first detected as 18S rDNA sequences retrieved from the marine environment and without a clear phylogenetic placement. They form more than 10 clades at the basal part of the stramenopiles (Massana et al 2004b), where all protists are heterotrophic, including free-living phagotrophic flagellates (bicosoecids), parasites (blastocystis), or osmotrophs (oomycetes and labyrinthulids) (figure I.4). MAST are widely recurrent in molecular surveys, occurring in the five world oceans, and most sequences affiliate with a few clades (MAST-1, MAST-3, MAST-4 and MAST-7). The heterotrophic nature of MAST, first suspected by their phylogenetic placement, was confirmed by FISH (Fluorescent In Situ Hybridization) for clade-1, clade-2 and clade-4 (Massana et al 2006b), and clade-6 (Piwosz and Pernthaler 2010). MAST cells from these Figure I.4. Phylogenetic position of marine stramenopiles (MAST) within the stramenopile supergroup. Tree with complete 18S rDNA sequences [modified from (Massana et al 2004b)] showing the positions of MAST lineages (red boxes) among cultured phototrophic (green box) and heterotrophic (grey boxes) groups. 0.05 NS4 OLI11066 UEPACCp4 UEPAC05Cp2 IND33.72 IND31.95 IND60.39/41 IND31.55/61 IND58.11 IND58.12 IND31.115 IND60.13 IND60.8 ME1.19 ME1.29 BL000921.16/22 ME1.20 BL000921.40 BL001221.8 ME1.30 BL000921.36 BL000921.9 NA11.4 ENI40076.00355 HE000803.3 ENI42482.00013 ENI42482.00265 ENI47296.00046/00055 RA010613.114 RA001219.34 RA010412.23 RA000412.146 RA000412.67 RA010412.25 HE001005.47 ENI42482.00027/00097 ENI42482.00212 ENI42482.00284 Figure I.5. Phylogenetic tree with partial 18S rDNA sequences of MAST-4. Each color identifies sequences from a different region (Atlantic: red; Pacific: green; Indian: grey; Mediterranean: yellow). The black vertical line shows the coverage of the FISH probe NS4. The scale bar indicates 0.05 substitutions per position. Figure taken from (Massana et al 2006b). General Introduction 26 ab 3 2 1 3 2 1 3 2 1 5 4 3 2 1 7 6 8 3 2 1 4 0 1-10 11-100 >100 MAST-4 cells ml -1 Figure I.6. Global distribution and abundance of MAST-4 cells in the world oceans. Stars indicate sites where 18S rDNA clone libraries have been constructed, black if the library contains MAST sequences and white if it does not. Dots indicate sites where FISH counts have been performed, in different color depending on the recorded cell abundance. Figure taken from (Massana et al 2006b). Figure I.7. Epifluorescence micrographs of MAST-4 cells. (a) DAPI-stained cells and the corresponding microscopic field (b) showing that two of the four eukaryotes were MAST-4 cells after FISH (compare a and b). The scale bar is 10 µm. The nuclear region, brightest by DAPI staining, is dimmer by FISH fluorescence, consistent with the cytoplasmic localization of ribosomes. The inset in panel b shows a MAST-4 cell (magnified 3 times) with one ingested FLB. Taken from (Massana et al 2002). groups are small protists (2-8 µm in size), able to grow in the dark and to ingest bacteria. In addition, they are quite abundant in the marine plankton and account for a significant fraction of HF globally (up to 35%). One group in particular, MAST-4, is found in all samples (except the polar ones) (figure I.5 and I.6). It is a very small protist (2-3 µm in size), so it qualifies as picoeukaryote. Its abundance averages 130 cells ml-1 and accounts for 9% of heterotrophic protists in a wide range of marine systems (Massana et al 2006b). This group shows a consistent size in all samples analyzed for a wide range of temperatures (from 5 to 28ºC), not following the “temperature size rule” of decreasing body size with increasing temperature (Atkinson et al 2003). They were determined as HF because of their fast growth in the dark, the absence of chloroplast and the observation of food vacuoles containing bacteria (Massana et al 2006a) (figure I.7). Moreover, a single flagellum was observed. Due to their widespread distribution and global abundance, it is likely that MAST-4 cells contribute substantially to marine food webs in vast areas of the oceans. Concerted efforts without success until now are being made to obtain a representative in culture. It is remarkable that still-uncultured groups can be dominant in the oceans, highlighting the ecological relevance of the novel diversity detected by the molecular approach. General Introduction 33 Unamended seawater incubations The advantages of having pure cultures are that morphological and functional parameters can be estimated, such as ultrastructure by electron microscopy, functional and numerical responses, food size spectra, growth efficiency, temperature optima, and survival responses. In the absence of pure cultures for many heterotrophic flagellates, a compromise to obtain some of these parameters is the use of unamended seawater incubations (Massana et al 2006a), known to promote the growth of uncultured HF in mixed assemblages. The setup includes a prefiltration through a 3 µm filter to remove larger predators, and a dark incubation that prevents the growth of phototrophs. During the incubation, bacteria initially increase a few times (figure I.12) followed by increasing numbers of HF a few days later coincident with the decrease in bacterial numbers. Photosynthetic flagellates and Synechococcus decrease continuously in numbers during the incubations, owing to dark conditions and perhaps to predation by heterotrophic flagellates. The protistan assemblage change from one dominated by phototrophic cells to one dominated by heterotrophic cells. In most samples, this simple setup results in the growth of several MAST groups (Massana et al 2006a). The growth of these abundant, uncultured HF was probably because in the unamended incubations bacteria are kept at realistic abundances and sizes (del Campo 2011). The increase of flagellates was moderate (10-100 fold), sufficient to measure growth rates of uncultured groups and to provide excellent material for activity measurements. Although short-lived, these events provide interesting phenotypic and functional information on uncultured protists. Thus, unamended seawater incubations can select for HF abundant in situ but not yet isolated in pure culture. This simple approach was used in several chapters of this thesis. Bacteria (105 cells ml-1) 0 2 4 6 8 10 12 02468 Bacteria Time (days) Flagellates (cells ml-1) 0 2000 4000 6000 HF PF MASTs (cells ml-1) 0 200 400 600 02468 MAST-1B MAST-1A MAST-1C MAST-2 MAST-4 Time (days) Figure I.12. Dynamics of microbial components in an unamended seawater incubation performed in the Norwegian Sea (from (Massana et al 2006a). Upper panel: Cell abundance estimated by DAPI counts of bacteria (black), heterotrophic (purple) and phototrophic flagellates (green). Lower panel: Cell abundance estimated by FISH of five MAST groups. General Introduction 34 Single amplified genomes (SAGs) Recently, there have been exciting advances in single-cell analyses. The sorting capacities of modern flow cytometers, combined with the use of lysotraker, a green fluorescing probe that stains food vacuoles (Rose et al 2004), are opening new avenues in microbial ecology. Single microbial cells can then be used as inoculums to start pure cultures, or as template for whole genomic amplification prior to genome sequencing (Yoon et al 2011). This approach has been recently applied to sort HF from marine assemblages (Heywood et al 2011, Rose et al 2004). Assessing the diversity of heterotrophic protists based on single cell sorting, whole genome amplification and rDNA sequencing is better than that given by community surveys, since the number of rDNA copies per cell is not a problem anymore. Moreover, SAGs are the only way to access to genomic information of uncultured cells, and might enable to analyze ecological interactions (grazing, symbiosis) between protists and prokaryotes (Martínez-García et al 2012). Preparing and analyzing SAGs from MAST-4 seems to be a promising approach to complement the study presented in this thesis. AimsandOutlineofthe Thesis Aims and outline of the thesis 37 The general goal of the thesis was to study the ecology of a relevant uncultured heterotrophic flagellate taxon, the MAST-4 lineage. This protist is an important and widespread picoeukaryote in marine systems, and represents a measurable fraction of the heterotrophic flagellate assemblage. Moreover, it has the advantage to be readily enriched in unamended incubations and easily detected with molecular tools. Heterotrophic flagellates (HF) are routinely quantified by epifluorescence microscopy after DAPI staining (Porter and Feig 1980), but this reveals few morphological features, so they remain generally unidentified. With the appearance of molecular surveys, oligonucleotide probes against several MAST lineages have been designed (Massana et al 2002, Massana et al 2006b) and used by FISH to identify them. Further studies of their distribution and abundance revealed that they were globally distributed, and a single group, MAST-4, contributed to ~9% of HF in surface marine systems (Massana et al 2006b). So far, MAST-4 has been quantified by FISH, which is very reliable but time-consuming to process the large number of samples generated during oceanographic cruises. The first aim of this thesis (chapter 1) was to develop a fast and sensitive technique to assess the abundance and distribution of the uncultured heterotrophic flagellate MAST-4 based on the realtime quantitative polymerase chain reaction (Q-PCR) detection of its 18S rRNA genes. Bacterial grazing is of fundamental importance in aquatic ecosystems and is carried out mostly by small flagellated protists up to 5 µm in diameter (Sherr and Sherr 2002). It controls bacterial abundances in a wide range of ecosystem conditions, channels organic carbon to higher trophic levels, and releases inorganic nutrients that often are controlling primary production (Jürgens and Massana 2008, Pernthaler 2005). The second aim of the thesis was to study the grazing rates and prey preferences (chapter 2) and the functional responses (chapter 3) of uncultured HF living in natural assemblages, including the MAST-4. This part is based on the estimation of the feeding activity of specific grazers detected by FISH after short-term ingestion experiments with tracer preys, which are then counted inside the protist food vacuoles. Microbes have vital roles for the functioning of the biosphere (Falkowski et al 2008), but currently we are far from having acceptable estimates of their diversity. Furthermore, it is unclear how microbial diversity is distributed in space and time, and how diversity ranks are translated into ecologically meaningful interactions or processes. The marine protists of very small size, the picoeukaryotes, are among the underexplored microbes with large ecological importance (Massana 2011). The third aim of this thesis (Chapter 4) was to understand the genetic structure and evolutionary patterns of the MAST-4 picoeukaryote. It was based on sequencing a large fragment of the rDNA operon and investigating ITS (Internal Transcribed Spacer) secondary structures to explore possible sexual boundaries among related types. Aims and outline of the thesis 38 Biogeography is the study of the distribution of biodiversity over space and time. The current evidence confirms that environmental selection is fundamental for the spatial variation in microbial diversity (Martiny et al 2006). The next frontier is to figure out whether these patterns are also influenced by geographical barriers that facilitate evolution and diversification. Contradictory results have been obtained in the last years, with no universal picture emerging, partly because the answer may depend on the particular situation analyzed. The last aim of this thesis (chapter 5) was to study marine protist biogeography using the MAST-4 as a model. Its community structure and distribution was assessed by combining automated ribosomal intergenic spacer analysis (ARISA) (Fisher and Triplett 1999) and 18S-ITS1 gene libraries. The outline of the different topics studied is: • Objective 1: Abundance and distribution Chapter 1 “Distribution of the uncultured protist MAST-4 in the Indian Ocean, Drake Passage and Mediterranean Sea assessed by real-time quantitative PCR” We developed a Q-PCR protocol to determine rapidly the abundance of this group using environmental DNA. We designed a primer set targeting the 18S rRNA genes of MAST-4 and optimized and calibrated the Q-PCR protocol using a plasmid with the target sequence as insert. The Q-PCR was applied to quantify MAST-4 along three transects, longitudinal in the Indian Ocean, latitudinal in the Drake Passage and coastal–offshore in the Mediterranean Sea, and to a temporal study in a Mediterranean Sea coastal station. • Objective 2: Trophic role Chapter 2 “Grazing rates and functional diversity of uncultured heterotrophic flagellates” Here we measured grazing rates of uncultured protists in natural assemblages (detected by FISH), and investigated their prey preference over several bacterial preys in short-term ingestion experiments. These included fluorescently labeled bacteria (FLB) and two strains of the Rhodobacteraceae and Flavobacteriaceae families, of various cell sizes, which were offered alive and detected by catalyzed reporter deposition-FISH after the ingestion. We obtained grazing rates of MAST-4 and MAST-1C flagellates. Chapter 3 “Functional responses of three heterotrophic flagellates taxa in mixed natural assemblages” Here we determined the functional response (maximum ingestion rate and half-saturation constant) of three heterotrophic flagellates taxa (MAST-4, Minorisa minuta candidatus and Paraphysomonas sp.) and of the total community from a mixed natural assemblage. We used fluorescence labeled bacteria added at different final abundance (from 105 to 107 cells ml-1) and counted them inside the Aims and outline of the thesis 39 protist food vacuoles. • Objective 3: Genetic structure and evolutionary patterns Chapter 4 “Low evolutionary diversification in a widespread and abundant uncultured protist (MAST-4)” In this study, we investigated the diversity of MAST-4, aiming to assess its limits and structure. We used rDNA sequences obtained here (both pyrosequencing reads and clones with large rDNA operon coverage), complemented with GenBank sequences. Conserved regions of the ITS1 and ITS2 secondary structures were evaluated for delineating different biological species. • Objective 4: Biogeography Chapter 5 “Biogeography of the uncultured marine picoeukaryote MAST-4: temperature driven distribution patterns” We studied the biogeography of MAST-4 by combining ARISA fingerprints and gene libraries of the ITS1 region. This study addresses this question by examining both spatial and temporal trends in MAST-4 assemblages and associated environmental factors. “And so it was indeed: she was now only ten inches high, and her face brightened up at the thought that she was now the right size for going through the little door into that lovely garden.” Lewis Carroll (1865) Chapter 1 49 MAST-4 rDNA molecules ml-1 30 20 1632 1128 2426 1 Depth (m) 1 2 3 5 7 9 11 13 14 16 18 20 23 24 26 MAST-4 rDNA molecules ml-1 Depth (m) MAST-4 rDNA molecules ml-1 A B C Depth (m) 32 SUB-ANTARCTIC FRONT POLAR FRONT 11 1 WEDDELL-SCOTIA CONFLUENCE ANTARCTIC PENINSULA TIERRA DE FUEGO DRAKE PASSAGE 30 28 26 24 20 16 FALKLAND IS. D 70ºW 65ºW 60ºW 55ºW 50ºW 65ºS 60ºS 55ºS 50ºS 12 3 57911 1314 16 18 20 23 24 26 B PORT HEDLAND CAPE TOWN MADAGASCAR A B C SPAIN MOROCCO 5ºW5.5ºW 4.5ºW 4ºW6ºW 35ºN 35.5ºN 37ºN ALGERIA Fig. 4 36.5ºN 36ºN C A B CD Fig. 3. Overview of the marine systems investigated (A) and abundance of MAST-4 (18S rDNA molecules ml-1) at several depths in three of them: (B) Indian Ocean transect. The green line marks the DCM and stations analysed by FISH are encircled. (C) Alboran Sea transect. (D) Drake Passage transect. Samples with a cross in (B), (C) and (D) indicate negative amplification. Fast quantification of the uncultured MAST-4 401 © 2008 The Authors Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology,11, 397–408 Fast quantification of the uncultured MAST-4 50 lagic region generally gives no or very low amplification. Finally, MAST-4 was absent from the coldAntarctic waters below 5°C and present at low abundances (150 molecules ml-1) north of the South Atlantic Front. Comparison of Q-PCR and FISH quantifications The FISH counts were done in a subset of samples from Blanes Bay (monthly during years 2001 and 2003 and some more during 2005, Fig. 4) and from the Indian Ocean (vertical profiles at stations 1, 9 and 23, Fig. 3). Except the deepest Indian samples investigated (200 m), all samples yielded a significant MAST-4 count by FISH, with concentrations ranging from 18 to 244 cells ml-1 (Table 1). Correlating Q-PCR and FISH estimates for these two data sets was moderate, with a R2of 0.47 for Blanes samples (Fig. 5A) and of 0.65 for Indian samples (Fig. 5B). The slope of these correlations, representing the rDNAcopy number per MAST-4 cell, was estimated to be 11 and 6 respectively. In the unamended seawater incubations, samples to be processed by Q-PCR were collected to maximize DNA recovery, in contrast with environmental samples that were generally collected to optimize DNA quality (see Discussion). In the Indian Ocean incubations, MAST-4 peaked after 2 days (2 ¥104molecules ml-1), whereas in the Blanes Bay incubations the peak occurred the fourth day (3 ¥104molecules ml-1) (data not shown). As expected in these incubations, the abundance of MAST-4 molecules decreased after the peak to very low levels in both cases. The comparison of Q-PCR signal and FISH counts was much better with these samples, with a R2of 0.95 for Blanes samples (Fig. 5C) and of 0.99 for Indian samples (Fig. 5D). The number of the rDNA copies per cell was estimated to be 29 in the Blanes and 37 in the Indian incubation data sets. Interestingly, in an additional incubation performed in Blanes Bay on July 2005, the comparison of Q-PCR and FISH data yielded an rDNA copy number of 22 (R2=0.99; n=4) (data not shown). 0 1000 2000 3000 4000 5000 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2001 2002 2003 2004 2005 2006 Years MAST-4 rDNA molecules ml-1 Julian days Fig. 4. Abundance (mean and SE; n=4) of MAST-4 molecules in Blanes Bay during a 6-year study sampled monthly. Black lines at the top of the figure mark the periods where FISH data were also obtained. Fig. 5. Relationship between the MAST-4 signal estimated by Q-PCR (rDNA molecules ml-1) and by FISH (cells ml-1) in environmental samples from the Mediterranean Sea (A) and the Indian Ocean (B), and samples from unamended seawater incubations from the Mediterranean Sea (C) and the Indian Ocean (D). MAST-4 cells ml-1 R2= 0.47 slope = 11 R2= 0.65 slope = 6 R2= 0.95 slope = 29 R2= 0.99 slope = 37 A B C D MAST-4 rDNA molecules ml-1 0 500 1000 1500 2000 2500 3000 0 50 100 150 200 250 0 50 100 150 200 250 0 1 104 2 104 3 104 0 500 1000 0 500 1000 402 R. Rodríguez-Martínez et al. © 2008 The Authors Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology,11, 397–408 Chapter 1 51 Discussion Quantification of an uncultured protist taxa by Q-PCR Here we have optimized, calibrated and validated a Q-PCR protocol to assess the abundance in marine waters of the uncultured protist MAST-4. Whereas some previous studies have applied Q-PCR for uncultured marine prokaryotes, such as Sulphurimonas denitrificanslike (Labrenz et al., 2004), our study represents the first application of Q-PCR to uncultured protists, whose presence has been only inferred by molecular tools (environmental sequencing and FISH probing). The main difference when dealing with uncultured organisms is that the standard to calibrate the Q-PCR signal cannot be cultured cells. Here we have used a plasmid carrying the target sequence and, therefore, target molecules are quantified instead of target cells. To convert the abundance of rDNA molecules to cells, the rDNA operon copy number in the genome must be known, and this can be derived by comparing the Q-PCR and the FISH signals from the same samples (see later). The reliability of the Q-PCR data depends on several critical aspects, such as the quality of the DNAextract, the specificity of the amplified PCR product and obtaining optimal amplification efficiencies (Cankar et al., 2006). In our study, it has been essential to further purify the DNA extracts from environmental samples, which did not amplify initially, by a DNA precipitation step with ethanol. The plasmid DNA samples, extracted with a different system, did not need the precipitation step to amplify properly. This confirms that different extraction methods can influence the purity of the DNA and have a great impact on the results obtained by Q-PCR (Peano et al., 2004). After this cleaning step, the regular Q-PCR checks gave very satisfactory results. Thus, the primer set used was highly specific for MAST-4, the efficiencies of the standard and the environmental samples were similar and close to 100%, and the melting curve analysis indicate that a single PCR product (Tm =84°C) was always generated in the samples with positive signal. Moreover, once the Q-PCR protocol was stabilized, we performed additional tests to assess its applicability on environmental samples. First, we studied the influence of freeze/thaw cycles on the percentage of recovered target molecules, as it is known that this process can compromise the integrity of DNA (Bellete et al., 2003). Here we show that up to seven cycles do not affect the MAST-4 quantified in environmental samples. This result gives confidence that the numbers obtained are realistic despite years of storage of the DNA extracts, including several thawing events. This might be due to the use of a very short amplicon (only 188 bp), which would still be amplified properly even if some DNA breakage occurs. Other studies have shown the stability of DNA extracts during long-term storage (Jerome et al., 2002). Second, with the spiking experiments we demonstrated the absence of PCR inhibitors in the negative samples (so they really lacked target molecules) and that the same number of target molecules amplify roughly equally even when diluted with a lot of non-target DNA (Bellete et al., 2003). Thus, the Q-PCR protocol presented here appears robust and adequate to quantify the rDNA molecules of MAST-4 in marine samples. Q-PCR and FISH comparison and estimated rDNA copy number There is a very good correlation between Q-PCR and FISH signals in the incubation samples, particularly robust in the Blanes incubation (Fig. 5C). With these results we can estimate that MAST-4 cells have around 30 copies of the rDNA operon. This value is fundamental to interpret the Q-PCR results targeting the rDNA genes, specially in the light that the rDNA copy number can vary orders of magnitude in protists, from 1–4 (some green algae) to more than 10 000 (some dinoflagellates). Comparing different eukaryotic species, a strong correlation has been found between the rDNA copy number and genome size (Prokopowich et al., 2003) and cell length (Zhu et al., 2005). Having around 30 copies of the rDNA operon (a comparatively low number) in MAST-4 cells is consistent with its small size, 2–3 mm in diameter (Massana et al., 2006a), and fits well within the relationship described for 18 phytoplankton strains (Zhu et al., 2005). The correlation between Q-PCR and FISH signals in environmental samples is considerably less robust (Fig. 5Aand B) and the copy number is lower (around 10). A possible explanation for this noisier signal is that the distinct MAST-4 lineages in the environmental samples (the probes target a phylogenetic group with up to 3% divergence in the complete 18S rDNA) also vary in their rDNA copy number, whereas the incubations are likely selecting a single genotype. Nevertheless, we consider that the main cause of the larger variability and the lower rDNAcopy number in the environmental samples was that they were not collected to be quantitatively processed (as, for instance, detailed in Boström et al., 2004). First, in some samples the volume of seawater filtered and the volume of DNA extract obtained were only approximate. Second, samples were collected on encapsulated Sterivex filters that are known to be less efficient in DNA recovery. We have seen that Sterivex units might recover only half of the DNA quantity as compared with regular filters (R. Massana, unpubl. data). Third, during the DNA extraction, emphasis was done on the quality of DNA and this surely caused significant DNA losses, particularly during the phenolization step. These factors could cause a less efficient and inexact DNA recovery, therefore Fast quantification of the uncultured MAST-4 403 © 2008 The Authors Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology,11, 397–408 Fast quantification of the uncultured MAST-4 52 yielding a lower and noisier Q-PCR signal. In fact, when these issues were properly addressed in the incubation samples, both techniques correlated very well and the rDNA copy number was higher. So, the Q-PCR is very suitable to quantify MAST-4 as long as it is combined with a careful sample collection and DNA extraction. Taking into account the constrains of applying Q-PCR with our environmental samples, it is clear that the data obtained provides interesting insights into the distribution and abundance of MAST-4 (Fig. 3). Samples were processed similarly, so they can be compared to provide useful global views of MAST-4 distributional patterns (presence and predominance). In addition, the data generated can be regarded as minimal estimates of the abundance of MAST-4 molecules. Distribution of MAST-4 in the oceans In a previous study, 24 surface samples from different world oceans were processed by FISH to estimate the abundance of MAST-4 cells (Massana et al., 2006a). This protist appeared in all samples investigated, except the polar ones, with averaged abundances of 131 cells ml-1. The Q-PCR protocol allows a faster sample processing, so we have increased the number of samples analysed by one order of magnitude (214 samples), addressing new aspects such as the depth distribution or the interannual variation. Our data confirm and expand the FISH data indicating that MAST-4 is a widespread protist and, interestingly, also finds some environmental constrains that limit this broad distribution. The vertical profiles in the Alboran Sea and the Indian Ocean show that MAST-4 is found in the upper ocean, including the photic zone and the upper aphotic zone. MAST-4 seems to be more abundant at subsurface (near the DCM) than at surface (5 m). Perhaps there is a negative effect of UV light at surface (Moran and Zepp, 2000) or simply the DCM is a more active and convenient habitat for MAST-4. Conversely, MAST-4 was hardly found in the deeper aphotic region, which is in accordance to the fact that heterotrophic flagellates and their bacterial food are becoming more scarce in mesopelagic waters (Tanaka and Rassoulzadegan, 2002; Fukuda et al., 2007). So, if present, MAST-4 cells were likely below the detection limit of Q-PCR. In fact, the three samples quantified by FISH at 200 m in the Indian Ocean did not reveal any target cell (abundance <2 cells ml-1). Moreover, cloning and sequencing of mesopelagic and bathypelagic waters has never retrieved a MAST-4 sequence (López-García et al., 2001; Countway et al., 2007; Not et al., 2007). Seawater temperature seems to be a second constrain in the distribution of MAST-4. In a previous study, this protist could not be detected by FISH in polar samples (Massana et al., 2006a) and here we had a unique opportunity to identify its real boundary by analysing a transect in the Drake Passage from South Atlantic to Antarctic waters. Indeed, the coldest samples from this transect, with temperatures up to 4°C, gave negative signal for MAST-4. Only the northernmost stations of the transect, with warmer temperatures (5–6°C), show MAST-4 signal, although rather low as compared with that from the other systems. Thus, MAST-4 seems to be excluded in polar waters below 5°C, an intriguing feature shared by other microorganisms such as marine picocyanobacteria (Partensky et al., 1999). So, combining the FISH data with the Q-PCR data it appears that MAST-4 is a structural component of protist assemblages in marine temperate photic waters. Virtually all samples from epipelagic waters (surface to 120 m) and with temperatures above 5°C have MAST-4 molecules. This broad and systematic presence is shared with several marine bacteria like SAR11 (Morris et al., 2002), and Roseobacter (Selje et al., 2004) and could be a common trait of smallest marine protists, as clone libraries of picoeukaryotes retrieve similar groups in distant oceans (Epstein and López-García, 2008). There are few reports on the abundance and distribution of picoeukaryotes at large oceanographic scales. Recently, small prasinophytes have been studied (Not et al., 2005; Not et al., 2008), and they change orders of magnitude from the coast, where they are more abundant, to the open sea. For larger protists, those that can be identified by microscopy, it is well known that they are not always present in marine samples, and temporality seems to be extremely important. Nevertheless, we have to keep in mind the significant phylogenetic diversity of MAST-4. So, although they look the same by FISH (Massana et al., 2006a), they can include distinct lineages with different and complementary ecological adaptations that might explain this broad distribution, as has been proposed for other picoeukaryotes (Rodríguez et al., 2005). Sampling a coastal station allows a detailed temporal assessment of microbial dynamics. This cannot be easily done in the open sea, where each cruise represents a singletemporalsnapshot.Wecouldnotidentifyaseasonal patternfor the abundance of MAST-4 inthe Mediterranean coastal station. There was a large variation (up to 3000 molecules ml-1) between consecutive dates, and averaging different periods did not yield systematic trends. Probably MAST-4 varies on a shorter time scale and sampling only once a month does not properly describe its temporal variation. Data from the incubation experiments indicate that MAST-4 might be a typical r-strategist that responds with high growth rates to increase in prey and declines rapidlywhenpreydiminishes.Thiscouldexplaintheirregular peaks during the season. Surprisingly, we detected important interannual variations, and we could not explain these by the environmental parameters currently taken, 404 R. Rodríguez-Martínez et al. © 2008 The Authors Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology,11, 397–408 Chapter 1 53 such as temperature, salinity, inorganic nutrients, chlorophyll or microbial counts. In summary, we present a very robust Q-PCR protocol for a fast quantification of rDNA molecules of the uncultured protist MAST-4. The extent that this protocol gives absolute abundance on environmental samples depends on the care with which sample collection and DNAextraction were done. The application of this protocol to a large sample collection from different oceanographic cruises, including some where only DNA extracts were available, yields a global vision of the distribution of this taxon. MAST-4 appears as a constitutive member (always found) of most marine systems and also identifies some habitats where it is excluded, such as mesopelagic and polar waters. Experimental procedures Environmental DNA from marine assemblages of small protists Samples from three oceanographic cruises were taken at different depths with Niskin bottles attached to a CTD rosette. Alboran Sea samples were collected on 2–4 May 1998 during cruise MTP-II-MATER/HESP/04-98 on board the Spanish RV Hespérides. Drake Passage samples were collected on 6–14 December 1998 during cruise DHARMA on board RV Hespérides. Indian Ocean samples were collected on 16 May to 11 June 2003 on board RV Melville (Scripps Institution of Oceanography, US). Some physico-chemical (temperature, salinity, inorganic nutrients, chlorophyll) and biological data have already been published for the Alboran Sea (Arin et al., 2002), Drake Passage (Díez et al., 2004) and Indian Ocean (Not et al., 2008) cruises. We also collected surface samples from the Blanes Bay Microbial Observatory in the Mediterranean Sea (41°40′N, 2°48′E), monthly from March 2001 to June 2006. Samples (5–20 l) were first prefiltrated through a 200 mm nylon mesh and then collected in Sterivex filter units of 0.2 mm pore size (Durapore; Millipore) after being prefiltrated through 3 mm (Indian Ocean and Blanes Bay) or 5 mm (Alboran Sea and Drake Passage). Sterivex units were filled up with lysis buffer (40 mM EDTA, 50 mM Tris-HCl and 0.75 M sucrose) and kept frozen (-20°C during the cruises and -80°C afterwards) until DNA extraction. Unamended incubations were prepared by filtering surface seawater by gravity first through a nylon mesh of 200 mm and second through 3-mm-pore-size polycarbonate filters (Massana et al., 2006b). Subsequently, the filtered seawater was dispensed into Nalgene polycarbonate bottles and incubated at near in situ temperature. Two bottles were prepared in the Indian Ocean (this is the Coastal incubation in Massana et al., 2006b), one incubated at ambient light and the other in the dark. Another two bottles were prepared in the Blanes Bay (sampled on 7 March 2006) and incubated in the dark. Mean temperatures for the Indian Ocean and Blanes Bay incubations were 21°C and 13°C respectively. Bottles were sampled every 1–2 days: 100– 200 ml of seawater was filtered onto 25 mm Durapore filters of 0.2 mm pore size, submerged in lysis buffer and kept as before. DNA extraction was done as described before (Massana et al., 2000). Cell lysis was performed by digestion with lysozyme followed by proteinase K and SDS treatments. DNA was purified twice with phenol : chloroform : isoamyl alcohol (25:24:1, pH 8) and once with chloroform : isoamyl alcohol (24:1), desalted and concentrated with a Centricon100 (Millipore). Special effort was done with the samples from the unamended incubations to maximize the quantity of DNA recovered during the phenolization step. The integrity of the DNA was checked by agarose gel electrophoresis. Nucleic acid extracts were stored at -80°C until they were analysed. To obtain a positive amplification, all the environmental samples needed a DNA precipitation step. The DNA extract was mixed with 2.8 vols of precipitation mix (absolute ethanol, 2 M NaAc and 1 M MgCl2) and kept more than half an hour at -80°C. Samples were then centrifuged at 14.000 r.p.m. for 15 min, washed twice with ethanol 70%, and re-suspended in milliQ water. Development and optimization of the Q-PCR protocol Design of specific primers for 18S rDNA of MAST-4. The forward primer M41f (5′-GTC TGC ACT GGA GTC GG-3′) was designed in base of all MAST-4 sequences available so far (34 clones from nine different marine sites). It matches perfectly all these clones, except two from the Indian Ocean (IND 31.115 and IND 58.11) that have one mismatch. It has more than five mismatches to all non-target sequences in GenBank. The reverse primer has the sequence of the probe NS4 designed for FISH (5′-TAC TTC GGT CTG CAA ACC3′), which matches all target sequences and has at least two to three internal mismatches with all non-target sequences in GenBank (Massana et al., 2002). Primers were optimized using the PerlPrimer software (Marshall, 2004) in order to check the no formation of primer-dimers, the GC content and the theoretical melting temperature. The amplicon was 188 bp of length. The specificity of the primer set was checked by standard PCR. It gave negative amplification for 15 non-target clones and positive amplification for six target clones (including the two clones with one mismatch). Preparation of the standard plasmid. 18S rRNA genes from an Indian Ocean sample (Not et al., 2008) were amplified with theuniversaleukaryoticprimersEukAandEukB(Medlinet al., 1988) and cloned with the TOPO TAcloning kit (Invitrogen).A clone (IND58.12) having a MAST-4 insert was used as standard for the Q-PCR. Its plasmid was extracted with the Plasmid DNA Purification kit (QIAGEN) and linearized by digesting the supercoiled plasmid with the restriction endonuclease NotI (Sigma). Similar to other studies (Suzuki et al., 2000), we have seen that the Q-PCR signal of the linearized plasmid is 10 times higher than the supercoiled plasmid (data not shown). The plasmid extract was purified with the precipitation step (described before) to process it equally to the environmental samples. The DNAconcentration and purity of theplasmidextractwereassessedwithaNanoDrop(ND-1000 Spectrophotometer). The number of rDNA molecules in the plasmid extract was calculated using the following formula: molecules ml-1=[a/(5736 ¥660)] ¥6.022 ¥1023 where ais the plasmid DNAconcentration (g ml-1), 5736 is the plasmid length (3931 bp of the vector plus 1805 bp of the 18S Fast quantification of the uncultured MAST-4 405 © 2008 The Authors Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology,11, 397–408 Fast quantification of the uncultured MAST-4 54 rDNA insert), 660 is the average molecular weight of one base pair and 6.022 ¥1023 is the molar constant (Avogadro constant). Optimization of the Q-PCR conditions. The Q-PCR reactions (final volume of 15 ml) were done with 0.45 ml of forward and reverse primers (both 10 mM), 7.5 ml of iQ SYBR Green Supermix (Bio-Rad), 5.6 ml of sterile milliQ water and 1 ml of DNA template. The reaction mixtures were prepared in thinwall tubes and cap strips (Bio-Rad) and filter SafesealTips (Biozym), DNase, RNase, Pyrogen-free, inside a UV-sterilized chamber. Reactions were performed in a iCycler iQ Multi-Color Detection System (Bio-Rad) programmed with an enzyme activation step (95°C, 3 min) and 40 cycles of 10 s of denaturation at 94°C, 30 s of annealing extension at 59°C and 30 s of data collection at 72°C. Data were analysed using the Multicolor Real-Time PCR Detection System v 3.1 software (Bio-Rad). These conditions were decided after different tests done with the positive plasmid. The optimal primer concentration (0.3 mM each) was the one that gave the earliest target amplification and the lowest amount of primer-dimer (nine combinations tested). The optimal annealing-extension temperature (58.9°C) was found by testing a gradient from 55°C to 65°C. For each Q-PCR run (96 tubes) we prepared three replicates of seven serial dilutions (from 108to 102rDNA molecules) from the standard (IND58.12 plasmid), three replicates of four serial dilutions (10-1-10-5ml of DNA extract) from the environmental sample used as relative standard and PCR efficiency control, three negative samples with milliQ water and two replicates of two dilutions (10-1and 10-2ml of DNA extract; undiluted samples generally did not amplify) from each environmental sample to assay (15 in total). After the Q-PCR run, the number of rDNA molecules in the tubes with environmental samples was obtained. These values were converted to true concentration (molecules ml-1of seawater) in three steps: (i) considering the dilution factor to find out the number of molecules ml-1of DNA extract, (ii) multiplying the later number by the volume of DNA extract (100– 300 ml) to obtain the total number of molecules in the extract and (iii) dividing the later number by the volume of seawater collected (5–20 l). Melting curve analysis. The SYBR Green I binds all double-stranded DNA, including specific and unspecific PCR products and primer-dimers. These can be distinguished by their different melting temperatures, which depend on their base composition and length. In the melting curve analysis the temperature is raised by a fraction of a degree and the change in fluorescence is measured. At Tm, the two DNA strands separate and the fluorescence rapidly decreases. The software plots the rate of change of the relative fluorescence units (RFU) with temperature (T) (-d(RFU)/dT) on the y-axis versus the temperature on the x-axis, and this will peak at the Tm. A dissociation curve from 55°C to 94°C was measured after the last Q-PCR cycle in all samples. Q-PCR efficiency with plasmid and environmental samples. The Q-PCR efficiency was assessed by comparing the number of molecules estimated in the dilutions from the same sample. With an optimal efficiency of 100%, a 1–10 dilution should yield 10% of the molecules (or a Ct difference between dilutions of 3.3). The efficiency of the standard IND58.12 plasmid (three replicates in seven serial dilutions) was compared each time with the efficiency of one environmental sample used as relative standard (three replicates in four serial dilutions). Samples with different PCR efficiencies (or too far from 100%) were excluded from further analyses. FISH Samples for FISH were collected during the Indian Ocean cruise and the seasonal sampling in Blanes Bay. Seawater (100–200 ml) was fixed with filtered formaldehyde (3.7% final concentration) and filtered through 0.6 mm pore diameter polycarbonate filters.The FISH samples (80–150 ml) during the unamended seawater incubations were similarly collected. Filters were kept at -80°C until processed. For FISH we used the probe NS4, specific for MAST-4 (Massana et al., 2002), supplied with a CY3 fluorophore at the 5′end. Thin pieces of filters were hybridized with the CY3-NS4 probe following the protocol described before (Pernthaler et al., 2001; Massana et al., 2002) and counter-stained with DAPI. Positive cells were then observed by epifluorescence with green light excitation (CY3-specific signal) and checked with UV radiation (DAPI staining). Acknowledgements We thank the captains and crews of Research Vessels Hespérides and Melville and the chief scientists (T. Calafat, C. Pedrós-Alió and D. Blackman) for providing an optimal environment for sampling. This study was supported by projects TRANSINDICO (REN2000-1471-CO2-01/MAR), ESTRAMAR (CTM2004-12631/MAR, MEC) and PROTAL (HA2004-088, MCyT) to R.M. and by an F.P.I. fellowship from the Spanish Ministry of Education and Science to R.R.M. We thank R. Martínez-Gutiérrez, F. Not, H Brockmöller and I. Ontoria for laboratory assistance, V. Balagué and C. Cardelús for maintaining the Blanes Bay sampling station, M. Pastor for ODV advice, M. Ribes for statistic help and F. Unrein for useful comments. References Ahlgren, N.A., Rocap, G., and Chisholm, S.W. (2006) Measurement of Prochlorococcus ecotypes using real-time polymerase chain reaction reveals different abundances of genotypes with similar light physiologies. Environ Microbiol 8: 441–454. Arin, L., Morán, X.A.G., and Estrada, M. (2002) Phytoplankton size distribution and growth rates in the Alboran Sea (SW Mediterranean): short term variability related to mesoscale hydrodynamics. J Plankton Res 24: 1019– 1033. Audemard, C., Reece, K.S., and Burreson, E.M. (2004) Realtime PCR for detection and quantification of the protistan parasite Perkinsus marinus in environmental waters. Appl Environ Microb 70: 6611–6618. 406 R. Rodríguez-Martínez et al. © 2008 The Authors Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology,11, 397–408 Chapter 1 55 Becker, S., Fahrbach, M., Böger, P., and Ernst, A. (2002) Quantitative tracing, by Taq nuclease assays, of a Synechococcus ecotype in a highly diversified natural population. Appl Environ Microb 68: 4486–4494. Bellete, B., Flori, P., Hafid, J., Raberin, H., and Sung, R.T.M. (2003) Influence of the quantity of nonspecific DNA and repeated freezing and thawing of samples on the quantification of DNA by the Light Cycler. J Microbiol Methods 55: 213–219. Boström, K.H., Simu, K., Hagstrom, A., and Riemann, L. (2004) Optimization of DNA extraction for quantitative marine bacterioplankton community analysis. Limnol Oceanogr Meth 2: 365–373. Bowers, H.A., Tengs, T., Glasgow, H.B., Burkholder, J.M., Rublee, P.A., and Oldach, D.W. (2000) Development of real-time PCR assays for rapid detection of Pfiesteria piscicida and related dinoflagellates. Appl Environ Microb 66: 4641–4648. Cankar, K., Štebih, D., Dreo, T., Žel, J., and Gruden, K. (2006) Critical points of DNA quantification by real-time PCR – effects of DNAextraction method and sample matrix on quantification of genetically modified organisms. BMC Biotechnol 6: 37. Countway, P.D., and Caron, D.A. (2006) Abundance and distribution of Ostreococcus sp. in the San Pedro Channel, California, as revealed by quantitative PCR. Appl Environ Microb 72: 2496–2506. Countway, P.D., Gast, R.J., Dennett, M.R., Savai, P., Rose, J.M., and Caron, D.A. (2007) Distinct protistan assemblages characterize the euphotic zone and deep sea (2500 m) of the western North Atlantic (Sargasso Sea and Gulf Stream). Environ Microbiol 9: 1219–1232. Díez, B., Pedrós-Alió, C., and Massana, R. (2001) Study of genetic diversity of eukaryotic picoplankton in different oceanic regions by small-subunit rRNA gene cloning and sequencing. Appl Environ Microb 67: 2932–2941. Díez, B., Massana, R., Estrada, M., and Pedrós-Alió, C. (2004) Distribution of eukaryotic picoplankton assemblages across hydrographic fronts in the Southern Ocean, studied by denaturing gradient gel electrophoresis. Limnol Oceanogr 49: 1022–1034. Epstein, S., and López-García, P. (2008) ‘Missing’ protists: a molecular prospective. Biodivers Conserv 17: 261–276. Fenchel, T. (1986) The ecology of heterotrophic microflagellates. Adv Microb Ecol 9: 57–97. Fukuda, H., Sohrin, R., Nagata, T., and Koike, I. (2007) Size distribution and biomass of nanoflagellates in mesoand bathypelagic layers of the subarctic Pacific. Aquat Microb Ecol 46: 203–207. Galluzzi, L., Penna, A., Bertozzini, E., Vila, M., Garcés, E., and Magnani, M. (2004) Development of a real-time PCR assay for rapid detection and quantification of Alexandrium minutum (a dinoflagellate). Appl Environ Microb 70: 1199– 1206. Heid, C.A., Stevens, J., Livak, K.J., and Williams, P.M. (1996) Real time quantitative PCR. Genome Res 6: 986– 994. Jerome, K.R., Huang, M.L., Wald, A., Selke, S., and Corey, L. (2002) Quantitative stability of DNA after extended storage of clinical specimens as determined by real-time PCR. J Clin Microbiol 40: 2609–2611. Johnson, Z.I., Zinser, E.R., Coe, A., McNulty, N.P., Woodward, E.M.S., and Chisholm, S.W. (2006) Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311: 1737–1740. Labrenz, M., Brettar, I., Christen, R., Flavier, S., Bötel, J., and Höfle, M.G. (2004) Development and application of a realtime PCR approach for quantification of uncultured bacteria in the central Baltic Sea. Appl Environ Microb 70: 4971– 4979. López-García, P., Rodríguez-Valera, F., Pedrós-Alió, C., and Moreira, D. (2001) Unexpected diversity of small eukaryotes in deep-sea Antarctic plankton. Nature 409: 603–607. Marie, D., Zhu, F., Balague, V., Ras, J., and Vaulot, D. (2006) Eukaryotic picoplankton communities of the Mediterranean Sea in summer assessed by molecular approaches (DGGE, TTGE, QPCR). FEMS Microbiol Ecol 55: 403– 415. Marshall, O.J. (2004) PerlPrimer: cross-platform, graphical primer design for standard, bisulphite and real-time PCR. Bioinformatics 20: 2471–2472. Massana, R., DeLong, E.F., and Pedrós-Alió, C. (2000) Afew cosmopolitan phylotypes dominate planktonic archaeal assemblages in widely different oceanic provinces. Appl Environ Microb 66: 1777–1787. Massana, R., Guillou, L., Díez, B., and Pedrós-Alió, C. (2002) Unveiling the organisms behind novel eukaryotic ribosomal DNA sequences from the ocean. Appl Environ Microb 68: 4554–4558. Massana, R., Castresana, J., Balagué, V., Guillou, L., Romari, K., Groisillier, A., et al. (2004) Phylogenetic and ecological analysis of novel marine stramenopiles. Appl Environ Microb 70: 3528–3534. Massana, R., Terrado, R., Forn, I., Lovejoy, C., and PedrósAlió, C. (2006a) Distribution and abundance of uncultured heterotrophic flagellates in the world oceans. Environ Microbiol 8: 1515–1522. Massana, R., Guillou, L., Terrado, R., Forn, I., and PedrósAlió, C. (2006b) Growth of uncultured heterotrophic flagellates in unamended seawater incubations. Aquat Microb Ecol 45: 171–180. Medlin, L., Elwood, H.J., Stickel, S., and Sogin, M.L. (1988) The characterization of enzymatically amplified eukaryotic 16s-like rRNA-coding regions. Gene 71: 491–499. Moon-van der Staay, S.Y., De Wachter, R., and Vaulot, D. (2001) Oceanic 18S rDNA sequences from picoplankton reveal unsuspected eukaryotic diversity. Nature 409: 607– 610. Moran, M.A., and Zepp, R.G. (2000) UV radiation effects on microbes and microbial processes. In Microbial Ecology of the Oceans. Kirchman, D.L. (ed.) New York, NY, USA: Wiley-Liss, pp. 201–228. Morris, R.M., Rappé, M.S., Connon, S.A., Vergin, K.L., Siebold, W.A., Carlson, C.A., and Giovannoni, S.J. (2002) SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420: 806–810. Not, F., Massana, R., Latasa, M., Marie, D., Colson, C., Eikrem, W., et al. (2005) Late summer community composition and abundance of photosynthetic picoeukaryotes in Norwegian and Barents Seas. Limnol Oceanogr 50: 1677– 1686. Fast quantification of the uncultured MAST-4 407 © 2008 The Authors Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology,11, 397–408 Fast quantification of the uncultured MAST-4 56 Not, F., Gausling, R., Azam, F., Heidelberg, J.F., and Worden, A.Z. (2007) Vertical distribution of picoeukaryotic diversity in the Sargasso Sea. Environ Microbiol 9: 1233– 1252. Not, F., Latasa, M., Scharek, M., Viprey, P., Karleskind, V., Balagué, V., et al. (2008) Protistan assemblages across the Indian Ocean, with a specific emphasis on the picoeukaryotes. Deep-Sea Res I (in press): 10.1016/ j.dsr.2008.1006.1007. Partensky, F., Hess, W.R., and Vaulot, D. (1999) Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiol Mol Biol Rev 63: 106– 127. Peano, C., Samson, M.C., Palmieri, L., Gulli, M., and Marmiroli, N. (2004) Qualitative and quantitative evaluation of the genomic DNA extracted from GMO and Non-GMO foodstuffs with four different extraction methods. J Agric Food Chem 52: 6962–6968. Pernthaler, J. (2005) Predation on prokaryotes in the water column and its ecological implications. Nat Rev Microbiol 3: 537–546. Pernthaler, J., Glöckner, F.O., Schönhuber, W., and Amann, R. (2001) Fluorescence in situ hybridization (FISH) with rRNA-targeted oligonucleotide probes. Methods Microbiol 30: 207–226. Porter, K.G., and Feig, Y.S. (1980) The use of DAPI for identifying and counting aquatic microflora. Limnol Oceanogr 25: 943–948. Prokopowich, C.D., Gregory, T.R., and Crease, T.J. (2003) The correlation between rDNA copy number and genome size in eukaryotes. Genome 46: 48–50. Rodríguez, F., Derelle, E., Guillou, L., Le Gall, F., Vaulot, D., and Moreau, H. (2005) Ecotype diversity in the marine picoeukaryote Ostreococcus (Chlorophyta, Prasinophyceae). Environ Microbiol 7: 853–859. Schneeberger, C., Speiser, P., Kury, F., and Zeillinger, R. (1995) Quantitative detection of reverse transcriptase-PCR products by means of a novel and sensitive DNA stain. PCR Methods Appl 4: 234–238. Selje, N., Simon, M., and Brinkhoff, T. (2004) A newly discovered Roseobacter cluster in temperate and polar oceans. Nature 427: 445–448. Suttle, C.A. (2005) Viruses in the sea. Nature 437: 356–361. Suzuki, M.T., Taylor, L.T., and DeLong, E.F. (2000) Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5’-nuclease assays. Appl Environ Microb 66: 4605–4614. Tanaka, T., and Rassoulzadegan, F. (2002) Full-depth profile (0–2000 m) of bacteria, heterotrophic nanoflagellates and ciliates in the NW Mediterranean Sea: vertical partitioning of microbial trophic structures. Deep-Sea Res II 49: 2093– 2107. Walker, N.J. (2002) A technique whose time has come. Science 296: 557–559. Zhu, F., Massana, R., Not, F., Marie, D., and Vaulot, D. (2005) Mapping of picoeukaryotes in marine ecosystems with quantitative PCR of the 18S rRNAgene. FEMS Microbiol Ecol 52: 79–92. 408 R. Rodríguez-Martínez et al. © 2008 The Authors Journal compilation © 2008 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology,11, 397–408 Grazingratesandfunctionaldiversityof unculturedheterotrophicGlagellates Chapter2 Massana R, Unrein F, Rodríguez-Martínez R, Forn I, Lefort T, Pinhassi J et al (2009). Grazing rates and functional diversity of uncultured heterotrophic flagellates. The ISME Journal 3: 588-596. Chapter 2 65 and Fukami, 2004; Shannon et al., 2007). Prey preference has been recently studied using FISH against specific bacterial prey within food vacuoles (Jezbera et al., 2005), but this approach could not provide concurrently grazing rates nor specific activity for particular grazers. Grazing rates of uncultured flagellates We obtained in situ grazing rates of the uncultured HF taxa MAST-4 and MAST-1C. These have been detected only in molecular surveys (18S rDNA sequences and FISH-targeted cells) and are important members of marine assemblages, accounting for 9.2% and 2.7% of HF cells globally (Massana et al., 2006a). Using the classical FLB procedure, clearance and ingestion rates for MAST-4 were 0.7 nl per predator per h and 1.0–1.5 bacteria per predator per h, and rates for MAST-1C were 1.6 nl per predator per h and 3.6 bacteria per predator per h. As their functional responses are still unknown, these values likely underestimate maximal CRs (if half-saturation constant (km) is low relative to actual bacterial abundances), or maximal ingestion rates (if km is high). Comparing these rates with those from the whole HF assemblage, it appears that MAST-4 is less active and MAST-1C more active than the average HF cell. This is consistent with a larger cell size of MAST-1C than MAST-4 (5.6 and 3.3 mm diameter on average in these samples). The specific grazing rates of these MAST taxa are comparable to in situ rates measured in Blanes Bay (Unrein et al., 2007) and worldwide (Vaque ´et al., 1994), but much lower than most estimates derived from cultured strains. Indeed, maximal CRs from cultured HF strains range from 1 to 58 nl per predator per h, and maximal ingestion rates range from 5 to 259 bacteria per predator per h (Eccleston-Parry and Leadbeater, 1994). So, these cultured HF could be poor models of natural and dominant HF taxa. Comparing FLB and live bacteria in ingestion experiments with MAST-4 Several studies have analyzed the effect of using dead bacteria as tracers in ingestion experiments. These generally show that using live bacteria result in grazing rates significantly higher than FLB (Landry et al., 1991; Boenigk et al., 2001). Differences can also be seen when comparing growing versus starving bacteria, the first being preferentially consumed (Gonza ´lez et al., 1993). Our results fit well with this general picture, and higher grazing rates of MAST-4 were obtained when using live bacteria over FLB (2–3 times higher). Moreover, MAST-4 grazing rates could be plotted to respect cell viability of the tracer suspensions, with maximal values being reached from ca 20% of live cells. An extreme case of negative selection is shown by the experiments using heat-killed MED134 cells. Those were not ingested at all, and the underlying mechanism for this prey avoidance is unknown. Finally, besides differences related to cell viability, no other differences in measured grazing rates were seen when using the two bacterial strains, even though they belong to distant phylogenetic groups with different life strategies. Members of the Roseobacter lineage tend to be free-living bacteria and typical of somewhat rich conditions (Buchan et al., 2005), whereas flavobacteria tend to be particle-associated bacteria with high exoenzymatic activity (Kirchman, 2002). In our study, Roseobacter and flavobacteria cells were ingested equally by MAST-4, so these important differences in phylogeny and life strategy did not determine prey preference. Functional differences between MAST-4 and MAST-1C There were clear functional differences between these two taxa. MAST-4 cells preferred live prey and somehow represented the average in situ HF. MAST1C cells, on the other hand, behaved very differently and the dead FLB yielded the highest rates. Most strikingly, the flavobacteria MED134 were almost not ingested by MAST-1C, which was unexpected because these bacterial cells showed the best physiological state and, in the same bottle (experiment 8), gave highest rates for MAST-4 and eukaryotes. A possible explanation would be that MAST-1C does not like this particular strain as food, opening an interesting and complex scenario of specific trophic interactions for some flagellates (but not for others). However, a more plausible explanation would be that the boundary of optimal prey size for MAST-1C falls within the size range of the tested bacteria. MED134, being the smallest of the bacteria tested, could be outside the size range of edible bacteria and escape predation. This could also explain the moderate rates measured with MED479 and the highest rates with the largest FLB. The fact that MAST-1C could be adapted to feed on larger bacteria than MAST-4 is consistent with its larger size, following the established relationship between predator and prey size (Fenchel, 1987). Our results clearly show functional differences between MAST4 and MAST-1C, but the underlying mechanisms for such differences remain to be elucidated. The functional diversity we observed gives an ecological meaning to the high phylogenetic diversity of marine heterotrophic protists (Vaulot et al., 2002). Our study opens up the black box of bacterivory in marine ecosystems by showing different specific activity and prey preferences in distinct uncultured taxa. If our interpretation is correct, cell size was the main factor in prey vulnerability, as commonly accepted (Gonza ´lez et al., 1990), and there were sharp size boundaries outside which preys could not be ingested (Fenchel, 1987; Ju ¨rgens and Matz, 2002). Secondarily, when all preys fell within the edible size range then other factors interplayed with a less dramatic impact. For instance, MAST-4 Grazing rates of uncultured flagellates R Massana et al 594 The ISME Journal Grazing rates of uncultured flagellates 66 preferred live bacteria in good physiological state, but still fed on dead FLB at one third of the maximal rate. Our data did not reveal differential feeding behavior related to the phylogenetic affiliation of the tested bacteria. This study assigned differential functional roles to distinct uncultured HF taxa, effectively linking phylogenetic and functional diversity within a natural assemblage. Our combination of FISH for specific predators with the use of live bacteria as prey surrogates allows addressing the huge complexity of microbial food webs. Acknowledgements This study was supported by the project ESTRAMAR (CTM2004-12631/MAR, MEC) to RM. FN was supported by the Marie-Curie fellowship ESUMAST (MEIF-CT-2005025000) and TL by the project METAOCEANS (MEST-CT2005-019678). We thank Josep M Gasol for help in flow cytometry, Matthias Engel for microscopic counts and Marta Ribes for statistical advice. References Alonso-Sa ´ez L, Balague ´V, Sa `EL, Sa ´nchez O, Gonza ´lez JM, Pinhassi J et al. (2007). Seasonality in bacterial diversity in north-west Mediterranean coastal waters: assessment through clone libraries, fingerprinting and FISH. FEMS Microbiol Ecol 60: 98–112. Boenigk J, Arndt H, Cleven EJ. (2001). The problematic nature of fluorescently labeled bacteria (FLB) in Spumella feeding experiments—an explanation by using video microscopy. Arch Hydrobiol 152: 329–338. Boenigk J, Jost S, Stoeck T, Garstecki T. (2007). Differential thermal adaptation of clonal strains of a protist morphospecies originating from different climatic zones. Environ Microbiol 9: 593–602. Buchan A, Gonza ´lez JM, Moran MA. (2005). Overview of the marine Roseobacter lineage. Appl Environ Microbiol 71: 5665–5677. Eccleston-Parry JD, Leadbeater BSC. (1994). A comparison of the growth kinetics of six marine heterotrophic nanoflagellates fed with one bacterial species. Mar Ecol Prog Ser 105: 167–177. Epstein S, Lo ´pez-Garcı ´a P. (2008). ‘Missing’ protists: a molecular prospective. Biodivers Conserv 17: 261–276. Falcioni T, Papa S, Gasol JM. (2008). Evaluating the flowcytometric nucleic acid double-staining protocol in realistic situations of planktonic bacterial death. Appl Environ Microbiol 74: 1767–1779. Fenchel T. (1982). Ecology of heterotrophic microflagellates. II. Bioenergetics and growth. Mar Ecol Prog Ser 8: 225–231. Fenchel T. (1987). Ecology of Protozoa: The Biology of Free-Living Phagotrophic Protists. Brock/Springer Series in Contemporary Bioscience, Springer: Berlin. Gonza ´lez JM, Sherr EB, Sherr BF. (1990). Size-selective grazing on bacteria by natural assemblages of estuarine flagellates and ciliates. Appl Environ Microbiol 56: 583–589. Gonza ´lez JM, Sherr EB, Sherr BF. (1993). Differential feeding by marine flagellates on growing versus starving, and on motile versus nonmotile, bacterial prey. Mar Ecol Prog Ser 102: 257–267. Gre ´gori G, Citterio S, Ghiani A, Labra M, Sgorbati S, Brown S et al. (2001). Resolution of viable and membrane-compromised bacteria in freshwater and marine waters based on analytical flow cytometry and nucleic acid double staining. Appl Environ Microbiol 67: 4662–4670. Jezbera J, Hornak K, Simek K. (2005). Food selection by bacterivorous protists: insight from the analysis of the food vacuole content by means of fluorescence in situ hybridization. FEMS Microbiol Ecol 52: 351–363. Ju ¨rgens K, Matz C. (2002). Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. Antonie van Leeuwenhoek 81: 413–434. Ju ¨rgens K, Massana R. (2008). Protistan grazing on marine bacterioplankton. In: Kirchman DL (ed). Microbial Ecology of the Oceans, 2nd edn. Wiley: New York, pp 383–441. Kirchman DL. (2002). The ecology of Cytophaga-Flavobacteria in aquatic environments. FEMS Microbiol Ecol 39: 91–100. Landry MR, Lehner-Fournier JM, Sundstrom JA, Fagerness VL, Selph KE. (1991). Discrimination between living and heat-killed prey by a marine zooflagellate Paraphysomonas vestita (Stokes). J Exp Mar Biol Ecol 146: 139–152. Lim EL, Dennet MR, Caron DA. (1999). The ecology of Paraphysomonas imperforata based on studies employing oligonucleotide probe identification in coastal water samples and enrichment cultures. Limnol Oceanogr 44: 37–51. Massana R, Gasol JM, Bjørnsen PK, Blackburn N, Hagstro ¨m A ˚, Hietanen S et al. (1997). Measurement of bacterial size via image analysis of epifluorescence preparations: description of an inexpensive system and solutions to some of the most common problems. Sci Mar 61: 397–407. Massana R, Guillou L, Dı ´ez B, Pedro ´s-Alio ´C. (2002). Unveiling the organisms behind novel eukaryotic ribosomal DNA sequences from the ocean. Appl Environ Microbiol 68: 4554–4558. Massana R, Terrado R, Forn I, Lovejoy C, Pedro ´s-Alio ´C. (2006a). Distribution and abundance of uncultured heterotrophic flagellates in the world oceans. Environ Microbiol 8: 1515–1522. Massana R, Guillou L, Terrado R, Forn I, Pedro ´s-Alio ´C. (2006b). Growth of uncultured heterotrophic flagellates in unamended seawater incubations. Aquat Microb Ecol 45: 171–180. Matz C, Ju ¨rgens K. (2001). Effects of hydrophobic and electrostatic cell surface properties of bacteria on feeding rates of heterotrophic nanoflagellates. Appl Environ Microbiol 67: 814–820. Matz C, Ju ¨rgens K. (2005). High motility reduces grazing mortality of planktonic bacteria. Appl Environ Microbiol 71: 921–929. Mohapatra BR, Fukami K. (2004). Comparison of the numerical grazing response of two marine heterotrophic nanoflagellates fed with different bacteria. J Sea Res 52: 99–107. Moon-van der Staay SY, De Wachter R, Vaulot D. (2001). Oceanic 18S rDNA sequences from picoplankton reveal unsuspected eukaryotic diversity. Nature 409: 607–610. Not F, Latasa M, Marie D, Cariou T, Vaulot D, Simon N. (2004). A single species, Micromonas pusilla (Prasinophyceae), dominates the eukaryotic picoplankton in Grazing rates of uncultured flagellates R Massana et al 595 The ISME Journal Chapter 2 67 the Western English Channel. Appl Environ Microbiol 70: 4064–4072. Pernthaler A, Pernthaler J, Amann R. (2002). Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Env Microbiol 68: 3094–3101. Pernthaler J, Glo ¨ckner FO, Scho ¨nhuber W, Amann R. (2001). Fluorescence in situ hybridization (FISH) with rRNA-targeted oligonucleotide probes. In: Paul JH (ed). Marine Microbiology. Academic Press: London, pp 207–226. Pernthaler J. (2005). Predation on prokaryotes in the water column and its ecological implications. Nat Rev Microbiol 3: 537–546. Porter KG, Feig YS. (1980). The use of DAPI for identifying and counting aquatic microflora. Limnol Oceanogr 25: 943–948. Rodrı ´guez F, Derelle E, Guillou L, Le Gall F, Vaulot D, Moreau H. (2005). Ecotype diversity in the marine picoeukaryote Ostreococcus (Chlorophyta, Prasinophyceae). Environ Microbiol 7: 853–859. Sanders RW, Caron DA, Berninger UG. (1992). Relationships between bacteria and heterotrophic nanoplankton in marine and fresh waters: an inter-ecosystem comparison. Mar Ecol Prog Ser 86: 1–14. Shannon SP, Chrzanowski TH, Grover JP. (2007). Prey food quality affects flagellate ingestion rates. Microb Ecol 53: 66–73. Sherr BF, Sherr EB, Fallon RD. (1987). Use of monodispersed, fluorescently labeled bacteria to estimate in situ protozoan bacterivory. Appl Environ Microbiol 53: 958–965. Sherr EB, Sherr BF. (2002). Significance of predation by protists in aquatic microbial food webs. Antonie van Leeuwenhoek 81: 293–308. Sieracki ME, Haas LW, Caron DA, Lessard EJ. (1987). Effect of fixation on particle retention by microflagellates: underestimation of grazing rates. Mar Ecol Prog Ser 38: 251–258. Simek K, Jezbera J, Horna ´k K, Vrba J, Sed0a J. (2004). Role of diatom-attached choanoflagellate of the genus Salpingoeca as pelagic bacterivores. Aquat Microb Ecol 36: 257–269. Strom SL. (2000). Bacterivory: interactions between bacteria and their grazers. In: Kirchman DL (ed). Microbial Ecology of the Oceans. Wiley-Liss Inc: New York, pp 351–386. Unrein F, Massana R, Alonso-Sa ´ez L, Gasol JM. (2007). Significant year-round effect of small mixotrophic flagellates on bacterioplankton in an oligotrophic coastal system. Limnol Oceanogr 52: 456–469. Urdea MS, Warner BD, Running JA, Stempien M, Clyne J, Horn T. (1988). A comparison of non-radioisotopic hybridization assay methods using fluorescent, chemiluminescent and enzyme labeled synthetic oligodeoxyribonucleotide probes. Nucleic Acids Res 16: 4937– 4956. Vaque ´D, Gasol JM, Marrase ´C. (1994). Grazing rates on bacteria: the significance of methodology and ecological factors. Mar Ecol Prog Ser 109: 263–274. Vaulot D, Romari K, Not F. (2002). Are autotrophs less diverse than heterotrophs in marine picoplankton? Trends Microbiol 10: 266–267. Grazing rates of uncultured flagellates R Massana et al 596 The ISME Journal Functionalresponsesofthree heterotrophicGlagellatestaxainmixed naturalassemblages Chapter3 Rodríguez-Martínez R, Vaqué D, Forn I and Massana R. Functional responses of three heterotrophic flagellates taxa in mixed natural assemblages. In preparation to Protist Chapter 3 71 Functional responses of three heterotrophic flagellates taxa in mixed natural assemblages Raquel Rodríguez-Martínez*, Dolors Vaqué, Irene Forn and Ramon Massana* Institut de Ciències del Mar (CSIC). Passeig Marítim de la Barceloneta, 37-49, 08003 Barcelona, Spain Summary Grazing controls bacterial abundances and composition in a wide range of ecosystems and in aquatic systems, heterotrophic flagellates seem to be the main bacterial predators. Natural assemblages of marine heterotrophic flagellates are primarily formed by taxa that remain uncultured or that are cultured mimicking oligotrophic conditions. Thus, many aspects of their trophic behavior, including their functional response, are poorly known. Here we assessed by the first time the functional response (maximum ingestion rates and half-saturation constant) of three heterotrophic flagellates (MAST-4, Minorisa minuta candidatus and Paraphysomonas sp.) and of the total natural heterotrophic flagellates assemblage. We used fluorescently labeled bacteria as tracers and counted them inside protist food vacuoles. Natural heterotrophic flagellates had a Ks of 6.7 105 prey ml-1 lower than that of the traditional cultured flagellates, thus being well adapted to typical bacterial abundances of marine planktonic environments. Moreover, inside the heterotrophic flagellate assemblage, there were different taxa adapted to different ecological niches. M. minuta candidatus, recently cultured by mimicking natural conditions, was well adapted to low prey abundances being very efficient in ingesting FLBs. Uncultured MAST-4 cells were also well adapted to the typical abundances of marine bacteria but were less voracious. In contrast, Paraphysomonas sp., a typical cultured flagellate, did not achieve saturation of the ingestion rate even at the highest prey concentrations assayed (near 107 prey ml-1). Our study sets the basis for the fundamental differences between cultured and uncultured bacterial grazers. Keywords: Functional response, grazing, heterotrophic flagellates, MAST-4, Minorisa minuta candidatus, Paraphysomonas imperforata Introduction Heterotrophic flagellates (HF) are small predators considered to be the main consumers of aquatic bacteria (Sherr and Sherr 2002). Grazing by small flagellates controls bacterial abundance and composition in a wide range of ecosystem conditions, channels organic carbon to higher trophic levels, and releases inorganic nutrients that often are controlling primary production (Jürgens and Massana 2008, Pernthaler 2005). In fact, HF are central in the microbial loop concept (Azam et al 1983). Grazing rates of natural HF assemblages are estimated using tracer techniques that follow the fate of an added bacterial surrogate, or by manipulation techniques that uncouple predators and preys (Strom 2000, Vaqué et al 1994). Grazing rates may be then used to calculate community growth rates (Fenchel 1987). These rates average the activities of all populations in the community, each one perhaps having different rates and preferences. Indeed, recent molecular surveys have unveiled a large diversity of protists assemblages in the marine environment (Díez et al 2001, López-García et al 2001, Moon-van der Staay et al 2001), including HF (Jürgens and Massana 2008, Massana et al 2006b, Vaulot et al 2002). So, for a better understanding Functional responses of heterotrophic flagellates 72 of bacterial grazing in the sea, it would help to investigate particular physiological parameters of the building HF populations. Prey abundance is one of the most important factors influencing grazing rates (Holling 1959). Its effect on grazing rates, named functional response, has been estimated in a wide variety of marine predators, including copepods (Henriksen et al 2007, Isari and Saiz 2011), dinoflagellates (Jeong et al 2005, Kim and Jeong 2004) or ciliates (Jonsson 1986, Jürgens and Simek 2000, Massana et al 1994). Functional responses can be adjusted to different mathematical models (Holling 1959), being the most popular among ecologists the equivalent to the enzyme kinetic model developed in 1913 by Leonor Michaelis and Maude Menten. This model is described by two parameters, the maximum ingestion rate (IRmax), determined by the mechanistic capacity of the predator to capture, handle and digest the prey, and the half-saturation constant (Ks: prey concentration that allows half the maximal rate), which is a proxy of the food concentrations at which the predator is adapted to live (Fenchel 1980). Only a few studies have measured directly the functional response of small flagellates (Jürgens and Matz 2002), since it is much easier to measure the numerical response, the relationship of growth rates with prey abundance. For these small predators, the growth efficiency is considered constant regardless prey abundance, so growth rates are directly proportional to ingestion rates and the numerical and functional responses have the same form (Fenchel 1987). Numerical responses have been studied on model flagellates grown in cultures (Anderson et al 2011, Eccleston-Parry and Leadbeater 1994, Fenchel 1982, Mohapatra and Fukami 2004), but it has been suggested that these do not represent the dominant grazers in the sea (Massana et al 2006b), which in many cases remain uncultured. A few trophic experiments (i.e. grazing rates and prey preferences) have been conducted with some of these uncultured flagellates (Massana et al 2009, Piwosz and Pernthaler 2010), but it is still unknown if they have a fundamentally different functional response than model cultured flagellates. The aim of this study was to determine the functional response of uncultured flagellates living in mixed natural assemblages. We combined short-term ingestion experiments based on the use of fluorescently labeled bacteria (FLB) counted inside the protist food vacuoles (Sherr et al 1987), with specific FISH counts of HF within mixed assemblages. This time-consuming approach is so far the only way to provide specific ingestion rates for taxonomic classes of flagellates. Our study could have been done directly in natural assemblages, but low in situ abundances would have complicated the measurements. Instead, we used unamended seawater incubations, which are known to promote the growth of natural assemblages of uncultured HF (Massana et al 2006a). We obtained the functional responses of the total natural community and of three taxa of heterotrophic flagellates (MAST-4, Minorisa minuta candidatus and Paraphysomonas sp.). This is the first report of the functional response of specific uncultured flagellates, and highlights fundamental differences that might explain why they are not cultured by classical approaches. Methods Enrichment of uncultured heterotrophic flagellates by an unamended incubation Surface seawater from the Blanes Bay Microbial Observatory was taken on October 16, 2007 and carried to the laboratory in less than 2 h. Six liters of seawater were filtered by gravity through a 200 µm nylon mesh and then through 3 µm pore size polycarbonate filters to reduce the presence of large predators in the sample. This water was incubated into Nalgene polycarbonate bottles at in situ temperature (19ºC) in the dark, to prevent the growth of phototrophic cells (Massana et al 2006a), and sampled daily during 5 days. Glutaraldehydefixed aliquots (2% final concentration) were stained with 4,6-diamidino-2-phenylindole (DAPI; 5 mg ml-1) and filtered on 0.2 (for bacteria) or 0.6 µm (for flagellates) pore size black polycarbonate filters. Counts of bacteria (heterotrophic bacteria plus archaea) and heterotrophic flagellates (HF) were carried out by epifluorescence microscopy Chapter 3 73 (Porter and Feig 1980) with UV irradiance and blue light in an Olympus BX61 microscope at 1000X magnification. The grazing experiment reported here was done with the sample after three days of unamended incubation. Detection of possible predators by clone library and FISH Before starting the grazing experiment, 100 ml of seawater were collected onto a 0.2 µm pore size Durapore filter and DNA extraction was done as described before (Massana et al 2000). Cell lysis was performed by digestion with lysozyme followed by proteinase K and SDS treatments. DNA was purified with phenol:chloroform:isoamyl alcohol and concentrated with a Centricon-100 (Millipore). The PCR mixture (50 µl) contained 2 ng of template DNA, 0.5 µM of each primer, 200 µM of each dNTP, 1.5 mM MgCl2, 1.25 units of a Taq DNA polymerase (ProOmega), and the enzyme buffer. We used the eukaryotic 18S rDNA primers 528F (Elwood et al 1985) and EUKR (Medlin et al 1988). PCR cycling, carried out in a BioRad thermocycler, was: initial denaturation at 94ºC for 3 min; 30 cycles with denaturation at 94ºC for 45 sec, annealing at 55ºC for 1 min and extension at 72ºC for 3 min; and a final extension at 72ºC for 10 min. PCR products were purified with the QIAquick PCR Purification kit (QIAGEN) and cloned using the TOPO-TA cloning kit (Invitrogen) with the vector pCR2.1. Putative positive bacterial colonies were picked to a new LB (Luria-Bertani) plate and finally into LB-glycerol solution for -80°C stocks. Presence of correct insert was checked by PCR reamplification with the same primers using a small aliquot of bacterial culture as template. Amplicons with the right insert size were sequenced at the Macrogen sequencing service (Korea). The phylogenetic affiliation of clones and the detection of putative chimeras were obtained by a basic local alignment search tool (BLAST). Aliquots for FISH targeting small protists were fixed with formaldehyde (3.7% final concentration), filtered on 0.8 or 1 µm pore size polycarbonate filters and kept at -80ºC until processed. Oligonucleotide probes (Table 1) labeled with the fluorescent dye CY3 at the 5’ end were supplied by Thermo Electron Corporation (Waltham, MA, USA). For FISH we followed the protocol and conditions detailed before (Massana et al 2006b, Pernthaler et al 2001). Briefly, filter portions with protist cells were hybridized for 3 h at 46ºC in the appropriate buffer (with 30% formamide), washed at 48ºC in a second buffer, counter-stained with DAPI and mounted in a slide. Cells were then observed by epifluorescence microscopy at 1000X under green light excitation. Fluorescence labeled bacteria used as prey Brevundimonas diminuta (syn. Pseudomonas diminuta; Caulobacteraceae, alfa-Proteobacteria) was obtained from the Colección Española de Cultivos Tipo (Valencia, Spain), grown in LB agar plates and used to prepare FLB (Sherr et al 1987). B. diminuta has already been used to prepare FLB (Vazquez-Dominguez et al 1999) because of their small size comparable to that of natural marine bacteria. Two-week-old colonies were scraped, diluted in carbonate–bicarbonate buffer (CO3Na2HCO3Na pH 9.5), and stained with 100 pg mL-1 of 5-(4,6-dichlorotriazinyl)-aminofluorescein (DTAF) for 2 h in a water bath at 60ºC. Stained cells were rinsed with 0.2 µm-filtered carbonate-bicarbonate buffer, resuspended, and centrifuged 5 times (10 min, 10,000 rpm) to prevent the transfer of leftover dye to experimental samples. Cell suspensions (average cell biovolume 0.073 µm3) were kept frozen at -20ºC. Before using in the grazing experiments, the FLB solution was thawed and gently sonicated for three 10-s rounds with the microtip at 35% of power output (Dynatech sonic dismembrator, Model 300) to prevent cell clustering as explained before (Unrein et al 2007). Grazing experiments Seawater with the natural assemblages of bacteria and protists from the unamended incubation was divided in two sets (Fig. 1). One set was diluted (1 to 5.5) in order to decrease the initial bacterial concentration, whereas the other set remained undiluted. Five hundred ml bottles were filled-up with seawater from both sets (400 and 350 ml, respectively) and acclimated in a large container for Functional responses of heterotrophic flagellates 74 2-4 h at in situ temperature (19ºC). Then, increasing amounts of FLBs were added to the bottles, at tracer concentrations (~15% of total bacteria) in the first bottle, and becoming the main bacterial prey in the other bottles (~600% in the last bottle). Instead replicating the same prey concentration, we decided to obtain more points along the prey gradient, a recommended strategy in a regression analysis (Montagnes and Berges 2004). Aliquots for DAPI-stained microbial counts (bacteria and small protists) and for FISH analyses (only small protists) were taken immediately after the addition of FLB and after 40 min of incubation. Fixation was carried out with an equal volume of diluted fixative to reduce cell egestion (Sieracki et al 1987), reaching the same final concentrations detailed above. The incubation time (40 min) was chosen based on a previous time series that showed a plateau in the number of ingested bacteria at 45 min (Unrein et al 2007). Grazing of the natural assemblage of heterotrophic flagellates was estimated by counting FLBs inside colorless flagellates in the DAPIstained samples. Grazing of specific predators was assessed counting FLBs inside FISH positive cells, by combining green light excitation (FISH signal) and blue light excitation (FLB detection). The mean number of cells counted was 325, 300, 100 and 50 for community HF, M. minuta, MAST-4 and Praphysomonas sp., respectively. The number of FLBs per predator was multiplied times the ratio of total prey (native bacteria plus FLBs, obtained by separate DAPI counts) to FLB, to obtain the preys ingested at time 0 (I0) and at 40 min (I40). Ingestion rates (IRs: preys ingested per predator per h) were then calculated according to: IR = (I40 – I0) x (60/40) Clearance rates (CR: nl filtered per predator per h) were calculated by dividing the ingestion rate by the prey concentration (in nl). Data for IR and prey abundance were fitted by iteration to the hyperbolic Michaelis-Menten equation: IR = x IRmax/(Ks + x) where IRmax is the maximum ingestion rate, x is prey concentration (prey per milliliter) and Ks is the half-saturation constant (prey per milliliter). The maximum clearance rate (CRmax) was calculated by IRmax/ Ks (Fenchel 1986) Results The grazing experiment was done with a natural sample incubated for 3 days, during which in situ bacterial abundance (6.5 105 cells ml-1) increased to 1.1 106 cells ml-1 and in situ HF abundance (840 cells ml-1) increased to 6200 cells ml-1, being in exponential growth at the moment of the experiment (data not shown). For some experimental bottles, this HF-enriched sample was diluted, and shortterm ingestion experiments were prepared along a gradient of prey (native bacteria plus FLBs) covering almost two orders of magnitude (105 to 107 preys ml-1) in 13 bottles (Fig. 1). This gradient covers the natural marine bacterial concentration, typically around 106 cells ml-1. This experimental 654321 7 8 9 10 11 12 13 0.24 0.35 0.49 0.57 0.68 0.76 1.3 2.2 3.3 4.6 5.7 6.6 7.8 Diluted seawater: Bac 2.1x105, HF 1100 Undiluted seawater: Bac 1.1x106, HF 6200 Total prey Figure 1. Scheme of the experimental design for assessing the functional response of natural HF. Several bottles with increasing amounts of prey (native bacteria plus FLBs added, in 106 cell ml-1) were prepared. Bacterial and HF concentration (cells ml-1) are indicated for the diluted (left) and undiluted (right) sets. A short-term ingestion experiment was performed in each experimental bottle. Chapter 3 81 Fenchel T (1982). Ecology of heterotrophic microflagellates. II. Bioenergetics and growth. Mar Ecol Prog Ser 8: 225-231. Fenchel T (1986). The Ecology of Heterotrophic Microflagellates. Adv Microb Ecol 9: 57-97. Fenchel T (1987). Ecology of Protozoa. The Biology of Free-living Phagotrophic Protists. Brock/Springer Series in Contemporary Bioscience 47 figs.: 197pp. Fu Y, O’Kelly C, Sieracki M, Distel DL (2003). Protistan grazing analysis by flow cytometry using prey labeled by in vivo expression of fluorescent proteins. Appl Environ Microbiol 69: 6848-6855. Fuhrman JA, Hagström A (2008). Bacteria and archaeal community structure and its patterns. In Kirchman DL (ed) Microbial Ecology of the Oceans Wiley Series: 45-80. Geider RJ, Leadbeater BSC (1988). Kinetics and energetics of growth of the marine choanoflagellate Stephanoeca diplocostata. . Mar Ecol Prog Ser 47: 169-177. Hammond SJ (1991). Comparative growth characteristics of heterotrophic flagellates. M Sc thesis, Bristol University: 26-37. Henriksen CI, Saiz E, Calbet A, Hansen BW (2007). Feeding activity and swimming patterns of Acartia grani and Oithona davisae nauplii in the presence of motile and non-motile prey. Mar Ecol Prog Ser 331: 119-129. Holling CS (1959). The Components of Predation as Revealed by a Study of Small-Mammal Predation of the European Pine Sawfly. Can Entomol 91: 293-320. Isari S, Saiz E (2011). Feeding performance of the copepod Clausocalanus lividus (Frost and Fleminger, 1968). J Plankton Res 33: 715-728. Ishigaki T, Sleigh M (2001). Grazing characteristics and growth efficiencies at two different temperatures for three nanoflagellates fed with Vibrio bacteria at three different concentrations. Microb Ecol 41: 264271. Jeong HJ, Yoo YD, Park JY, Song JY, Kim ST, Lee SH et al (2005). Feeding by phototrophic red-tide dinoflagellates: five species newly revealed and six species previously known to be misotrophic. Aquat Microb Ecol 40: 133-150. Jezbera J, Hornák K, Simek K (2005). Food selection by bacterivorous protists: insight from the analysis of the food vacuole content by means of fluorescence in situ hybridization. FEMS Microbiol Ecol 52: 351363. Jonsson R (1986). Particle size selection, feeding rates and growth dynamics of marine planktonic oligotrichous ciliates (Ciliophora: Oligotrichina). Mar Ecol Prog Ser 33: 265-277. Jürgens K (1995). Die Bedeutung heterotropher Nanoflagellaten als Bakterienkonsumenten sowie deren Regulation durch Prädation und Ressourcen. PhD thesis: 1-1. Jürgens K, Simek K (2000). Functional response and particle size selection of Halteria cf. grandinella, a common freshwater oligotrichous ciliate. Aquat Microb Ecol 22: 57-58. Jürgens K, Matz C (2002). Predation as a shaping force for the phenotypic and genotypic composition of planktonic bacteria. A Van Leeuw 81: 413-434. Jürgens K, Massana R (2008). Protist grazing on marine bacterioplankton. Microbial ecology of the oceans: 383-441. Kim JS, Jeong HJ (2004). Feeding by the heterotrophic dinoflagellates Gyrodinium dominans and G. spirale on the red-tide dinoflagellate Prorocentrum minimum. Mar Ecol Prog Ser 280: 85-94. Landry M, Lehner-Fournier J, Sundstrom J, Fagerness V, Selph K (1991). Discrimination between living and heat-killed prey by a marine zooflagellate, Paraphysomonas vestita (Stokes). J Exp Mar Biol Ecol 146: 139-151. Lim E, Dennett M, Caron D (1999). The ecology of Paraphysomonas imperforata based on studies employing oligonucleotide probe identification in coastal water samples and enrichment cultures. Limnol Oceanogr 44: 37-51. López-García P, Rodríguez-Valera F, Pedrós-Alió C, Moreira D (2001). Unexpected diversity of small eukaryotes in deep-sea Antarctic plankton. Nature 409: 603-607. Martínez-García M, Brazel D, Poulton… N (2011). Unveiling in situ interactions between marine protists and bacteria through single cell sequencing. ISME J 6: 703-707 Massana R, Stumm CK, Pedrós-Alió C (1994). Effects of temperature, sulfide, and food abundance on growth and feeding of anaerobic ciliates. Appl Environ Microbiol 60: 1317-1324. Massana R, DeLong EF, Pedrós-Alió C (2000). A few cosmopolitan phylotypes dominateplanktonic archaeal assemblages in widely different oceanic provinces. Appl Environ Microbiol 66: 1777-1787. Functional responses of heterotrophic flagellates 82 Massana R, Guillou L, Díez B, Pedrós-Alió C (2002). Unveiling the organisms behind novel eukaryotic ribosomal DNA sequences from the ocean. Appl Environ Microbiol 68: 4554-4558. Massana R, Guillou L, Terrado R, Forn I, PedrósAlió C (2006a). Growth of uncultured heterotrophic flagellates in unamended seawater incubations. Aquat Microb Ecol 45: 171-180. Massana R, Terrado R, Forn I, Lovejoy C, PedrósAlió C (2006b). Distribution and abundance of uncultured heterotrophic flagellates in the world oceans. Environ Microbiol 8: 1515-1522. Massana R, Del Campo J, Dinter C, Sommaruga R (2007). Crash of a population of the marine heterotrophic flagellate Cafeteria roenbergensis by viral infection. Environ Microbiol 9: 2660-2669. Massana R, Unrein F, Rodríguez-Martínez R, Forn I, Lefort T, Pinhassi J et al (2009). Grazing rates and functional diversity of uncultured heterotrophic flagellates. ISME J 3: 588-596. McManus GB, Okubo A (1991). On the use of surrogate food particles to measure protistan ingestion. Limnol Oceanogr 36: 613-617. Medlin L, Elwood HJ, Stickel S, Sogin ML (1988). The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71: 491-499. Mohapatra B, Fukami K (2004). Comparison of the numerical grazing response of two marine heterotrophic nanoflagellates fed with different bacteria. J Sea Res 52: 99-107. Montagnes DJS, Berges JA (2004). Determining Parameters of the Numerical Response. Microb Ecol 48: 139-144. Moon-van der Staay SY, De Wachter R, Vaulot D (2001). Oceanic 18S rDNA sequences from picoplankton reveal unsuspected eukaryotic diversity. Nature 409: 607-610. Pernthaler J, Glöckner F, Schönhuber W, Amann R (2001). Fluorescence in situ hybridization (FISH) with rRNA-targeted oligonucleotide probes. Meth Microb 30: 207-226. Pernthaler J (2005). Predation on prokaryotes in the water column and its ecological implications. Nat Rev Microbiol 3: 537-546. Pfandl K, Boenigk J (2006). Stuck in the mud: suspended sediments as a key issue for survival of chrysomonad flagellates. Aquat Microb Ecol 45: 89. Piwosz K, Pernthaler J (2010). Seasonal population dynamics and trophic role of planktonic nanoflagellates in coastal surface waters of the Southern Baltic Sea. Environ Microbiol 12: 364-377. Porter K, Feig Y (1980). The use of DAPI for identifying and counting aquatic microflora. Limnol Oceanogr 25: 943-948. Rodríguez-Martínez R, Rocap G, Logares R, Romac S, Massana R (2012). Low evolutionary diversification in a widespread and abundant uncultured protist (MAST-4). Mol Biol Evol: e-pub ahead of print 13 December 2011, doi: 2010.1093/molbev/msr2303. Sanders RW, Porter KG, Bennett SJ, DeBiase AE (1989). Seasonal patterns of bacterivory by flagellates, ciliates, rotifers, and cladocerans in a freshwater planktonic community. Limnol Oceanogr 34: 673-687. Sherr BF, Sherr EB, Fallon RD (1987). Use of monodispersed, fluorescently labeledbacteria to estimate in situ protozoan bacterivory. Appl Environ Microbiol 53: 958-965. Sherr EB, Sherr BF (2002). Significance of predation by protists in aquatic microbial food webs. A Van Leeuw 81: 293-308. Sieracki ME, Haas LW, Caron DA, Lessard EJ (1987). Effect of fixation on particle retention by microflagellates: underestimation of grazing rates. Mar Ecol Prog Ser 38: 251-258. Simek K, Chrzanowski TH (1992). Direct and Indirect Evidence of Size-Selective Grazing on Pelagic Bacteria by Freshwater Nanoflagellates. Appl Environ Microbiol 58: 3715-3720. Strom S (2000). Bacterivory: Interactions between bacteria and their grazers. In: Kirchmand DL (ed) Microbial ecology of the oceans Wiley-Liss Inc: New York: 351-386. Unrein F, Massana R, Alonso-Sáez L, Gasol J (2007). Significant year-round effect of small mixotrophic flagellates on bacterioplankton in an oligotrophic coastal system. Limnol Oceanogr 52: 456-469. Vaqué D, Pace ML, Findlay S, Lints D (1992). Fate of bacterial production in a heterotrophic ecosystem: grazing by protists and metazoans in the Hudson Estuary. Mar Ecol Prog Ser 89: 155-163. Vaqué D, Gasol JM, Marrasé C (1994). Grazing rates on bacteria: the significance of methodology and ecological factors. Mar Ecol Prog Ser 109: 263-274. Vaulot D, Romari K, Not F (2002). Are autotrophs less diverse than heterotrophs in marine picoplankton? Trends Microbiol 10: 266-267. Chapter 3 83 Vázquez-Domínguez E, Peters F, Gasol JM, Vaqué D (1999). Measuring the grazing losses of picoplankton: methodological improvements in the use of fluorescently labeled tracers combined with flow cytometry. Aquat Microb Ecol 20: 119-128. LowevolutionarydiversiGicationina widespreadandabundantuncultured protist(MAST4) Chapter4 Rodríguez-Martínez R, Rocap G, Logares R, Romac S, Massana R (2012). Low evolutionary diversification in a widespread and abundant uncultured protist (MAST-4). Molecular Biology and Evolution: e-pub ahead of print 13 December 2011, doi: 2010.1093/molbev/msr2303. Chapter 4 87 Low Evolutionary Diversification in a Widespread and Abundant Uncultured Protist (MAST-4) Raquel Rodrı ´guez-Martı ´nez,* ,1 Gabrielle Rocap, 2 Ramiro Logares, 1 Sarah Romac, 3 and Ramon Massana* ,1 1 Institut de Cie `ncies del Mar, Consejo Superior de Investigaciones Cientı ´ficas (CSIC), Barcelona, Spain 2 School of Oceanography, University of Washington 3 Station Biologique de Roscoff, Centre National de la Recherche Scientifique (CNRS) et Universite Pierre et Marie Curie (UPMC), Roscoff, France *Corresponding author: E-mail: [email protected]; [email protected]. Associate editor: Douglas Crawford Abstract Recent culture-independent studies of marine planktonic protists have unveiled a large diversity at all phylogenetic scales and the existence of novel groups. MAST-4 represents one of these novel uncultured lineages, and it is composed of small (;2lm) bacterivorous eukaryotes that are widely distributed in marine systems. MAST-4 accounts for a significant fraction of the marine heterotrophic flagellates at the global level, playing key roles in the marine ecological network. In this study, we investigated the diversity of MAST-4, aiming to assess its limits and structure. Using ribosomal DNA (rDNA) sequences obtained in this study (both pyrosequencing reads and clones with large rDNA operon coverage), complemented with GenBank sequences, we show that MAST-4 is composed of only five main clades, which are well supported by small subunit and large subunit phylogenies. The differences in the conserved regions of the internal transcribed spacers 1 and 2 (ITS1 and ITS2) secondary structures strongly suggest that these five clades are different biological species. Based on intraclade divergence, ITS secondary structures and comparisons of ITS1 and ITS2 trees, we did not find evidence of more than one species within clade A, whereas as many as three species might be present within other clades. Overall, the genetic divergence of MAST-4 was surprisingly low for an organism with a global population size estimated to be around 10 24 , indicating a very low evolutionary diversification within the group. Key words: MAST-4, low evolutionary diversification, uncultured protist, pyrosequencing, ITS secondary structure. Introduction Microbes have vital roles for the functioning of the biosphere (Falkowski et al. 2008), but currently, we are far from having acceptable estimates of their diversity. Furthermore, it is unclear how microbial diversity is distributed in space and time, and how diversity ranks are translated into ecologically meaningful interactions or processes. The marine protists of very small size, the picoeukaryotes, are among the underexplored microbes with large ecological importance (Massana 2011). Picoeukaryotes have key ecological roles in the oceans as primary producers, bacterial grazers, or parasites. They are found in all planktonic marine samples at concentrations ranging between 10 3 and 10 4 cells ml 1 . During the last 10 years, molecular tools based on sequencing environmental 18S ribosomal DNA (rDNA) genes have revealed a wide diversity of microeukaryote assemblages as well as the existence of novel and uncultured lineages (Dı ´ez et al. 2001; Lo ´pez-Garcı ´a et al. 2001;Moon-van der Staay et al. 2001). Still, most of this diversity remains poorly known. The assignation of this novel and uncultured diversity to taxonomic groups is a challenging task. An approach to address this issue is to explore the correspondence between genetic divergence and species limits using cultured strains and then use that data as a proxy to investigate species limits in uncultured strains. Studies combining molecular and morphological data have been done within different taxonomic groups, such as prasinophytes (Slapeta et al. 2006), prymnesiophytes (Lange et al. 2002), diatoms (Amato et al. 2007; Evans et al. 2007;Casteleyn et al. 2008;Rynearson et al. 2009;Sorhannus et al. 2010), and dinoflagellates (Montresor et al. 2003;Litaker et al. 2007;Lowe et al. 2010). Gene markers used in the mentioned studies generally involve the 18S rDNA and other more variable genes (such as rbcL or cox1) since the former may be too conserved to differentiate among related but different species (Edvardsen et al. 2000; Logares et al. 2007). For uncultured protists detected in 18S rDNA surveys, the obvious loci for increasing phylogenetic resolution are the contiguous internal transcribed spacer (ITS) regions (ITS1 and ITS2). The above-mentioned functional genes, proposed as more robust phylogenetic markers (A ´lvarez and Wendel 2003), are currently inaccessible for uncultured microorganisms. ITS regions are noncoding loci that display high sequence variability but also key functionally constrained positions since transcripts need to fold into a secondary structure to permit their own splicing and the correct processing of the rDNA genes (Schlo¨tterer et al. 1994;Co ˆte ´ et al. 2002). They have been proposed as the best tool for barcoding in diatoms (Moniz and Kaczmarska 2010) and are useful for species and genus phylogenetic inferences (Coleman 2003). ©The Author 2012. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: [email protected] Mol. Biol. Evol. doi:10.1093/molbev/msr303 Advance Access publication December 13, 2011 1 Research article MBE Advance Access published February 7, 2012 at CSIC on February 8, 2012http://mbe.oxfordjournals.org/Downloaded from Limited diversity of a widespread picoeukaryote 88 The secondary structure of the ITS2 region has been used for delimiting biological species. Compensatory base changes (CBCs) in particular regions of the secondary structure have been associated with sexual incompatibility (Coleman 2007,2009). Taxa exhibiting at least one CBC in these conserved regions most likely belong to different biological species (Amato et al. 2007). Significant progress has been made in identifying such relevant positions in Volvocaceae, Haliotis, and Fagales (Coleman 2000,2003; Coleman and Vacquier 2002;Mu ¨ller et al. 2007). In addition, this hypothesis has been subjected to a large-scale testing using the ITS2 database containing 100,000 secondary structures (Schultz et al. 2006;Selig et al. 2007) and has been supported in 93% of the cases (Mu ¨ller et al. 2007). However, this is a one-way diagnostic; a lack of CBCs does not mean that organisms are members of the same species. In this study, we investigate an important and poorly known uncultured picoeukaryote group, the MAST-4 (Massana et al. 2004). This protist group is widespread in surface marine waters (except polar systems), where it represents approximately 9% of heterotrophic flagellates (Massana, Terrado, et al. 2006;Rodrı ´guez-Martı ´nez et al. 2009). Although MAST-4 remains uncultured, it is easily detected in environmental samples using molecular tools. So far, only the 18S rDNA of MAST-4 has been sequenced. To understand the genetic structure and evolutionary patterns of this uncultured model picoeukaryote, we sequenced a large fragment of the rDNA operon, including the ITS region and the beginning of the 28S (using Sanger sequencing) as well as the V4 region of the 18S (454 pyrosequencing). We have also compiled and analyzed all publicly available MAST-4 18S rDNA sequences. The emerging scenario is that despite being hugely abundant and widely distributed, this lineage has experienced a limited evolutionary diversification. A more detailed study of the biogeography of the group will appear elsewhere (Rodrı ´guez-Martı ´nez R, unpublished data). Materials and Methods Compilation of Published MAST-4 Sequences BLAST (basic local alignment search tool) searches against NCBI-nr were done using as seeds different regions (1–500, 501–1000, and 1001–1700) of the published MAST-4 18S rDNA sequences ME1.19, ME1.20 (Dı ´ez et al. 2001), UEPACCp4, UEPAC05Cp2 (Worden 2006), and SSRPD78 (Not et al. 2007). Best hits (sorted in decreasing order by identity) were selected until a sequence classified to another group appeared. The retrieved 134 sequences were screened to remove chimeras, identical sequences from the same study, and sequences that did not cover the V4–V5 regions, leaving 72 sequences that aligned at least ;550 bp. Seven partial GenBank sequences, of clones from our own libraries, were completely sequenced. This resulted in 17 complete MAST-4 sequences (‘‘small subunit [SSU]–complete’’ data set). The remaining 55 partial sequences (between 461 and 1,266 bp) formed the ‘‘SSU-partial’’ data set. Retrieval of MAST-4 Using 454 Pyrosequencing Seawater samples were collected through the BioMarKs consortium (http://www.biomarks.org/) in several European coastal stations (offshore Oslo, Naples, Blanes, Roscoff, Gijon,andVarna)withNiskinbottles attachedtoaconductivity, temperature, and depth rosette at surface and deep chlorophyll maximum depths. Water samples were prefiltered through 20 lm. Afterward, they were sequentially filteredthrough3and0.8lm142mmpolycarbonatefilters. Filters were flash frozen and stored at 80 C. Total DNA and RNA were extracted simultaneously from the same filter using the NucleoSpin RNA L kit (Macherey-Nagel) and quantified using a Nanodrop ND-1000 Spectrophotometer. Extract quality was checked on a 1.5% agarose gel. To remove contaminating DNA from RNA, we used the TurboDNA kit (Ambion).ExtractedRNAwasimmediatelyreversetranscribed to DNA using the RT Superscript III_random primers kit (Invitrogen).TheuniversalprimersTAReuk454FWD1andTAReukREV3 were used to amplify the V4 region (;380 bp) of eukaryotic 18S rDNA (Stoeck et al. 2010). The primers were adapted for 454 using the manufacturers specificationsand had the configuration A adapter Tag (8 bp)–forward primer and B adapter–reverse primer. Polymerase chain reactions (PCRs) were performed in 25 ll and consisted 1MasterMix Phusion High-Fidelity DNA Polymerase (Finnzymes), 0.35 lM ofeachprimer,and 3%dimethylsulfoxide. Weaddeda totalof 5 ng of template DNA/cDNA to each PCR reaction. PCR reactions consisted of an initial denaturation step at 98 C during 30s, followedby 10 cyclesof10sat98C, 30 s at53C,and30s at72C,andafterwardby15cyclesof10sat98C,30sat48C, and30sat72C.Ampliconswerecheckedina1.5%agarosegel for successful amplification. Triplicate amplicons were pooled andpurifiedusingtheNucleoSpinExtractII(Macherey-Nagel). Purified amplicons were eluted in 30 ll of elution buffer and quantified again using a Nanodrop ND-1000 Spectrophotometer. The total final amount of pooled amplicons for 454 tag sequencing was approximately of 5 lg. Amplicon sequencing was carried out on a 454 GS FLX Titanium system (454 Life Sciences, USA) installed at Genoscope (http://www.genoscope.cns.fr/spip/, France). Only reads having exact forward and reverse primers and an estimated error of 0.1% were kept (682,390 reads) and were annotated using a custom made and curated 18S rDNA database (Guillou L, unpublished data). Sequences withtheMAST-4astheclosestgroup(similarity.90%)were extracted (2,808 reads). Identical reads were removed with Mothur (Schloss et al. 2009) and then clustered at 0.0049 distance, resulting in 169 unique sequences. Subsequently, 81 chimeras were removed with the Chimera Slayer algorithm (Haas et al. 2011) as implemented in Mothur, using a custom-made protist database as a template. Sixteen remaining chimeras were removed manually, after partial sequence BLASTs against NCBI-nr. The final 72 sequences of ;380 bp formed the ‘‘SSU-pyrosequencing’’ data set. Clone Libraries Covering the 18S to 28S rDNA Regions Offshore surface samples were selected from separate oceanographic cruises in the Indian Ocean (IND70), Rodrı ´guez-Martı ´nez et al. ·doi:10.1093/molbev/msr303 MBE 2 at CSIC on February 8, 2012http://mbe.oxfordjournals.org/Downloaded from Chapter 4 89 Sargasso Sea (BE3), North Pacific (WE7), and Mediterranean Sea (BL43, taken on August 2004). These sites correspond to stations INO3, ATL7, PAC1, and MED as shown in Massana, Terrado, et al. (2006). The 0.2–3 lm microbial fraction of surface seawater was collected by peristaltic filtration. A fifth sample was selected (OA4), derived from the peak of heterotrophic flagellates in an unamended incubation from the MED station (March 2006) processed as in Massana, Guillou, et al. (2006). DNA extraction was done using enzymatic and sodium dodecyl sulfate digestion plus phenol purification (Massana et al. 2000). PCR amplification was done with the MAST-4-specific primer M42F (5#-GGTTTGCAGACCGAAGTA-3#) located in the 18S rDNA (after the V4 region) and the universal eukaryotic primer LSUR (5#-TTGGTCCGTGTTTCAAGACG-3#) located in the 28S rDNA (in the middle of the D2 region) (Jerome and Lynn 1996). Primer M42F is the reverse sequence of the FISH probe NS4 (Massana et al. 2002) and has a good specificity for MAST-4 (Massana, Terrado, et al. 2006). Primer LSUR matches 183 of 187 stramenopile large subunit (LSU) sequences extracted from SILVA database (Pruesse et al. 2007). Primers were checked for formation of primer dimers, GC content, and theoretical melting temperature in the website www.operon.com, using the Oligo Analysis & Plotting Tool. This primer set gave an amplicon size of ;2,300 bp covering the end of the SSU gene, the whole ITS region (ITS1-5.8S-ITS2), and the beginning of the LSU gene (fig. 1). The PCR mixture (25 ll) contained 1 ll of DNA template, 0.5 lM of each primer, 200 lM of each dNTP, 3 mM MgCl 2 , 1.25 units of a proofreading Taq polymerase (ACCUZYME), and the enzyme buffer. PCR cycling, carried out in an MJ Research thermal cycler, was initial denaturation at 94 C for 5 min, 30 cycles with denaturation at 94 C for 1 min, annealing at 60 C for 1 min, and extension at 72 C for 3 min and a final extension at 72 C for 10 min. We added a reconditioning PCR step to eliminate heteroduplexes from mixed-template PCR products (Thompson et al. 2002). The PCR reaction was diluted 5-fold into fresh reaction mixture and cycled three times as above. We tested the MgCl 2 concentration (from 1.5 to 3 mM) and the annealing temperature (from 57 to 64 C) and chose the more stringent conditions giving the expected band. The PCR product from four parallel reactions per sample was pooled and reduced to 25 ll by ethanol precipitation or vacuum concentration and run in a 1% agarose gel electrophoresis. Bands of 2,000–3,000 bp were cut and purified with the QIAquick Gel Extraction kit (QIAGEN). We added 3#A-overhangs to the final PCR product and cloned it using the TOPO-TA cloning kit (Invitrogen) with the vector (pCR4) following manufacturer’s recommendations. Putative positive colonies were picked and transferred to a new Luria–Bertani (LB) plate and finally into LB-glycerol solution for frozen stocks (80 C). Presence of correct insert was checked by PCR reamplification with vector primers M13F and M13R using a small aliquot of culture as template. Amplicons with the right insert size were sequenced in both directions at the Macrogen sequencing service (Korea) with eight primers (fig. 1). After inspecting the first sequences, we modified primers EUKR, ITS2, and ITS4 for a perfect match with MAST-4 sequences (fig. 1). Chromatograms were examined with 4Peaks (A. Griekspoor and T. Groothuis, http://www. mekentosj.com), and sequences for each clone were assembled with Geneious (Drummond et al. 2010), which also allows careful inspection of chromatograms and sequence editing. A total of 22 sequences from clone libraries were FIG. 1. Map of the rDNA operon showing the covered region of each sequence data set (A) and a detailed diagram of the ;2,300 bp MAST-4 rDNA amplicons (B). For this last data set, the positions and sequences of primers used are presented. Limited Diversity of a Widespread Picoeukaryote ·doi:10.1093/molbev/msr303 MBE 3 at CSIC on February 8, 2012http://mbe.oxfordjournals.org/Downloaded from Limited diversity of a widespread picoeukaryote 90 used for subsequent analyses (‘‘SSU-LSU’’ data set). These sequences were aligned with MAFFT v6.853 (Katoh and Toh 2008) with the E-INS-I algorithm, and the alignment was inspected visually. Boundaries of rDNA genes were determined by comparison with published reference sequences belonging to closely related organisms, resulting in five separate DNA regions: 3#end of the SSU gene (946 bp), ITS1 (184–256 bp), 5.8S gene (162 bp), ITS2 (252–317 bp), and 5#beginning of the LSU gene (706 bp). Sequences have been deposited in GenBank under accession numbers JN836289–JN836310. Sequence Analyses Sequences from the SSU-complete data set were aligned together with a MAST-7 outgroup using MAFFT as specified above. This alignment (1688 positions) was used as a skeleton, and shorter sequences from GenBank (SSU-partial data set) or from pyrosequencing (SSUpyrosequencing data set) were incorporated into it using the ‘‘-add’’ option of MAFFT. Alignments with 5.8S and 28S regions were done using Phytophthora infestans as outgroup (GenBank accession numbers HQ191489 and EU079637, respectively). Due to the large sequence variability in ITS regions, ITS1 and ITS2 alignments were done separately for each SSU-defined clade, using secondary structure models for alignment improvement (Rocap et al. 2002;Wang et al. 2007;Tippery and Les 2008). All these alignments were used to calculate sequence divergences (uncorrected pairwise distances) using Mothur (Schloss et al. 2009). Maximum likelihood (ML) phylogenetic trees were reconstructed using RAxML v7.0.4 MPI version (Stamatakis 2006), using the General Time Reversible model of nucleotide substitution and a Gamma distributed rate of variation across sites (GTRþG). As suggested in RaxML, we did not estimate the proportion of invariable sites, and missing data were not considered (i.e., treated as missing data). The shape parameter (a) of the gamma distribution was estimated from the data set using default options. Phylogenies were reconstructed at both the University of Oslo Bioportal (www.bioportal.uio.no) and the Instituto Astrofı ´sico de Canarias (IAC) computer cluster. One thousand alternative ML trees were run, and the tree with the best likelihood was selected and visualized in FigTree v1.3.1 (Rambaut 2009) or iTOL (Letunic and Bork 2007). Bootstrap analyses were run with 1,000 pseudoreplicates, and a consensus tree was constructed with MrBayes (Huelsenbeck and Ronquist 2001). ITS1 and ITS2 Secondary Structures ITS1 and ITS2 sequences extracted from the SSU-LSU data set were folded in mFOLD (Zuker 2003), which generates multiple possible secondary structures. We used default settings for a linear molecule with a folding temperature fixed at 37 C and 1 M NaCl with no divalent ions for ionic conditions. The best conformation for each sequence was the one that possessed the previously defined ITS hallmarks and was also similar between related clones. This generally coincided with the minimum free energy configuration. For ITS2 models, we searched for the familiar four-helix domain seen in eukaryotic taxa, such as green algae and flowering plants (Mai and Coleman 1997), dinoflagellates (Gottschling 2004), and metazoans (Joseph et al. 1999; Coleman and Vacquier 2002;Mu ¨ller et al. 2007;Wiemers et al. 2009). The core structure and hallmarks for the ITS1 secondary structure are less clear (see Discussion). Exported secondary structures in Vienna format (http:// www.tbi.univie.ac.at/~ivo/RNA/) were aligned and visualized as a consensus of each clade with 4SALE version 1.5 (Seibel et al. 2006). Structural models were further analyzed for the presence of CBCs (e.g., a change of paired G-C into paired A-U) in conserved regions (Gutell et al. 1994;Coleman et al. 1998). We used the models proposed by Coleman to identify the ITS2 conserved regions having a biological meaning (Coleman 2003,2007,2009). Results Low Diversity within the MAST-4 18S rDNA Gene The phylogenetic tree with the distinct MAST-4 18S rDNA sequences retrieved from our thorough GenBank search (SSU-complete plus SSU-partial data sets) displayed the complete MAST-4 variability published so far. MAST-4 diversity was limited to only five clades (A–E), each one containing at least two complete sequences and being well supported (except clade B) by bootstrap values above 80% (fig. 2). Only clone IND31.115 (Massana, Terrado, et al. 2006) did not belong to a given clade. The intraclade sequence divergence (calculated in the SSU-complete data set) was typically below 0.010, whereas among clades, the average divergence was 0.030 (table 1), with a maximum of 0.044. In addition, a BLAST search of MAST-4 sequences against NCBI-nr displayed a maximum of 91% similarity to the closest outgroup sequence, which belonged to MAST-7 or MAST-8. Sequences with intermediate similarity (i.e., between 91% and 96%) were chimeras. High-throughput sequencing approaches allow deeper sampling of environmental diversity than traditional cloning and Sanger sequencing. In order to determine whether additional MAST-4 clades were present in the marine plankton, we analyzed 454 reads (;380 bp) obtained from several coastal locations around Europe using eukaryotic universal primers. After processing an initial set of 2,808 MAST-4 sequences, the 72 distinct sequences of the SSU-pyrosequencing data set were used for phylogenetic reconstructions together with a subset of the GenBank sequences (31 remaining sequences after clustering the SSUcomplete and SSU-partial data sets at a 0.0049 distance) (fig. 3). Pyrosequences distributed among the five clades reported before, and, most interestingly, no additional clades appeared. The sequence IND31.115 still remained alone. Analysis of Other rDNA Regions Support Five Main Clades We obtained good quality sequences of ;2,300 bp (SSULSU data set) for 22 clones derived from four Rodrı ´guez-Martı ´nez et al. ·doi:10.1093/molbev/msr303 MBE 4 at CSIC on February 8, 2012http://mbe.oxfordjournals.org/Downloaded from Chapter 4 97 Table 2. Different Examples of ITS Sequence Divergences (uncorrected p-distance; shown as minimum–maximum) among Related Strains or Species of Cultured Eukaryotes. Species Intraclonal Intraspecies Interspecies ITS ITS1 ITS2 ITS ITS1 ITS2 ITS ITS1 ITS2 Ref. a Diatoms Eunotia bilunaris 0.000–0.052 0.000–0.123 1 E. bilunaris 0.000–0.043 0.000–0.044 2 Pseudo-nitzschia multistriata 0.006 0.010 0.006 3 P. pungens 0.000–0.070 0.000–0.044 0.000–0.050 0.000–0.064 4 P. seriata and P. australis 0.036 0.027 5 P. decipiens and P. dolorosa 0.000–0.005 0.105–0.108 6 P. delicatissima and P. decipiens 0.000–0.049 0.075–0.090 P. dolorosa and P. delicatissima 0.000–0.002 0.129–0.151 Several species (5.8S1ITS2) 0.000–0.070 0.110–0.260 7 Dinoflagellates Symbiodinium 0.006–0.061 0.009–0.043 0.010–0.124 8 Several species 0.000–0.017 0.000–0.034 0.000–0.026 0.000–0.021 0.000–0.040 0.000–0.021 0.042–0.577 0.038–0.734 0.020–0.732 9 Several species 0.000–0.014 10 Peridinium limbatum and P. willei 0.000–0.099 0.000–0.111 0.551–0.566 0.432–0.463 11 Scrippsiella trochoidea 0.002–0.015 12 Ciliates Halteria grandinella 0.001–0.082 13 Mollusca Haliotis 0.000–0.049 0.000–0.044 0.380–0.590 0.380–0.480 14 Copepod Several species 0.000–0.008 0.002–0.034 15 Magnoliophyta Several species 0.000–0.480 0.000–0.440 16 Averages 0.001–0.049 0.005–0.039 0.005–0.075 0.000–0.042 0.002–0.050 0.001–0.042 0.077–0.200 0.201–0.481 0.144–0.363 a References: 1) Vanormelingen et al. (2008), 2) Vanormelingen et al. (2007), 3) D’Alelio et al. (2008), 4) Casteleyn et al. (2008), 5) Fehling et al. (2004), 6) Lundholm et al. (2006), 7) Moniz and Kaczmarska (2009), 8) Thornhill et al. (2007), 9) Litaker et al. (2007), 10) Logares et al. (2008), 11) Kim et al. (2004), 12) Montresor et al. (2003), 13) Katz et al. (2005), 14) Coleman and Vacquier (2002), 15) Goetze (2003), and 16) Goertzen (2003). Limited Diversity of a Widespread Picoeukaryote ·doi:10.1093/molbev/msr303 MBE 11 at CSIC on February 8, 2012http://mbe.oxfordjournals.org/Downloaded from Limited diversity of a widespread picoeukaryote 98 that this variability is due to experimental artifacts, as we used very stringent sequencing and analysis methods, but it could be caused by intragenomic polymorphisms (Prokopowich et al. 2003). It is generally assumed that these polymorphisms are rapidly eliminated through a series of homogenizing mechanisms referred to as concerted evolution (Elder and Turner 1995;Ganley and Kobayashi 2007), but it is also known that some species have a fraction of the rDNA units that have escaped the process of concerted evolution (Keller et al. 2006;Simon and Weiss 2008). Intragenomic variation in species where this occurs is very low (lower than interspecific variation, Litaker et al. 2007) and typically only in extremely variable positions that are never paired in secondary structure (Behnke et al. 2004;Orsini et al. 2004;Casteleyn et al. 2008). Thus, the ITS can be treated as single copy region (Coleman 2003). Finally, we observed that MAST-4 cells have a relatively low rDNA copy number (around 30, Rodrı ´guez-Martı ´nez et al. 2009), which reduces the possibility of mutations. In summary, despite the presence of a huge number of MAST-4 cells in the oceans, its diversity is structured into just five main clades, each representing at least one biological species. Clade A is particularly interesting because it seems to be composed of only one species, appearing in distant oceanic areas. Specifically, clade A showed no polymorphisms in the critical regions of the ITS2 and ITS1 secondary structures (figs. 5 and 6), the topologies of their ITS1 and ITS2 trees were incongruent (fig. 4), and the clade presented a very low sequence divergence (table 1). On the other hand, using the same three criteria, clade B would include at least two species, clade C three species, and clade E two species (clade D is still undersampled with only one ITS sequence). Altogether there is currently evidence of a maximum of only ten separate species within MAST-4. Within each of the clades, diversification appears to be very low, as indicated by the 18S and ITS rDNA markers. This low evolutionary diversification points to either a very recent evolutionary divergence and worldwide dispersal or to a very strong environmental filtering that penalizes any deviation from an optimal cell design. Acknowledgments Funding has been provided by FLAME (CGL2010-16304, MICINN, Spain) and BioMarKs (2008-6530, ERA-net Biodiversa, EU) projects to R.M., by an FPI fellowship from the Spanish Ministry of Education and Science to R.R.M., by a Marie Curie Intra-European Fellowship grant PIEFGA-2009-235365 to R.L., and by NSF ATOL 0629521 to G.R. We thank M. Pernice for Mothur help, V. Balague ´ and C. Williams for molecular help, R.E. Collins for useful advice, and all people involved in the BioMarKs consortium, in particular, the coordinator Colomban de Vargas. References A ´lvarez I, Wendel J. 2003. Ribosomal ITS sequences and plant phylogenetic inference. Mol Phylogenet Evol. 29:417–434. Amato A, Kooistra W, Levialdi Ghiron J, Mann D, Pro¨schold T, Montresor M. 2007. Reproductive isolation among sympatric cryptic species in marine diatoms. Protist 158:193–207. Behnke A, Friedl T, Chepurnov V, Mann D. 2004. Reproductive compatibility and rDNA sequence analyses in the Sellaphora pupula species complex (Bacillariophyta). J Phycol. 40:193–208. Bridge PD, Schlitt T, Cannon PF, Buddie AG, Baker M, Borman AM. 2008. Domain II hairpin structure in ITS1 sequences as an aid in differentiating recently evolved animal and plant pathogenic fungi. Mycopathologia 166:1–16. Casteleyn G, Chepurnov V, Leliaert F, Mann D, Bates S, Lundholm N, Rhodes L, Sabbe K, Vyverman W. 2008. Pseudo-nitzschia pungens (Bacillariophyceae): a cosmopolitan diatom species? Harmful Algae 7:241–257. Coleman A. 2000. The significance of a coincidence between evolutionary landmarks found in mating affinity and a DNA sequence. Protist 151:1. Coleman A. 2003. ITS2 is a double-edged tool for eukaryote evolutionary comparisons. Trends Genet. 19:370–375. Coleman A. 2007. Pan-eukaryote ITS2 homologies revealed by RNA secondary structure. Nucleic Acids Res. 35:3322–3329. Coleman AW. 2009. Is there a molecular key to the level of ‘‘biological species’’ in eukaryotes? A DNA guide. Mol Phylogenet Evol. 50:197–203. Coleman AW, Maria Preparata R, Mehrotra B, Mai JC. 1998. Derivation of the secondary structure of the ITS-1 transcript in Volvocales and its taxonomic correlations. Protist 149:135–146. Coleman AW, Vacquier VD. 2002. Exploring the phylogenetic utility of ITS sequences for animals: a test case for abalone (Haliotis). J Mol Evol. 54:246–257. Co ˆte ´CA, Greer CL, Peculis BA. 2002. Dynamic conformational model for the role of ITS2 in pre-rRNA processing in yeast. RNA 8:786–797. D’Alelio D, Amato A, Kooistra W, Procaccini G, Casotti R, Montresor M. 2008. Internal transcribed spacer polymorphism in Pseudo-nitzschia multistriata (Bacillariophyceae) in the Gulf of Naples: recent divergence or intraspecific hybridization? Protist 160:9–20. Dı ´ez B, Pedro ´s-Alio ´C, Massana R. 2001. Study of genetic diversity of eukaryotic picoplankton in different oceanic regions by smallsubunit rRNA gene cloning and sequencing. Appl Environ Microbiol. 67:2932–2941. Drummond AJ, Ashton B, Buxton S, et al. (12 co-authors). 2010. Geneious v5.1 [Internet]. Available from: http://www.geneious.com. Edvardsen B, Eikrem W, Green J, Andersen RA, Moon-van der Staay SY, Medlin LK. 2000. Phylogenetic reconstructions of the Haptophyta inferred from 18S ribosomal DNA sequences and available morphological data. Phycologia 39:19–35. Elder JF, Turner BJ. 1995. Concerted evolution of repetitive DNA sequences in eukaryotes. Q Rev Biol. 70:297–320. Evans KM, Wortley AH, Mann DG. 2007. An assessment of potential diatom ‘‘barcode’’ genes (cox1, rbcL, 18S and ITS rDNA) and their effectiveness in determining relationships in Sellaphora (Bacillariophyta). Protist 158:349–364. Falkowski PG, Fenchel T, Delong EF. 2008. The microbial engines that drive earth’s biogeochemical cycles. Science 320:1034–1039. Fehling J, Green DH, Davidson K, Bolch CJ, Bates SS. 2004. Domoic acid production by Pseudo-nitzschia seriata (Bacillariophyceae) in Scottish waters. J Phycol. 40:622–630. Ganley ARD, Kobayashi T. 2007. Highly efficient concerted evolution in the ribosomal DNA repeats: total rDNA repeat variation revealed by whole-genome shotgun sequence data. Genome Res. 17:184–191. Gaston KJ, Blackburn TM. 1997. How many birds are there? Biodivers Conserv. 6:615–625. Goertzen L. 2003. ITS secondary structure derived from comparative analysis: implications for sequence alignment and phylogeny of the Asteraceae. Mol Phylogenet Evol. 29:216–234. Rodrı ´guez-Martı ´nez et al. ·doi:10.1093/molbev/msr303 MBE 12 at CSIC on February 8, 2012http://mbe.oxfordjournals.org/Downloaded from Chapter 4 99 Goetze E. 2003. Cryptic speciation on the high seas; global phylogenetics of the copepod family Eucalanidae. Proc Biol Sci. 270:2321–2331. Gottschling M. 2004. Secondary structure models of the nuclear internal transcribed spacer regions and 5.8S rRNA in Calciodinelloideae (Peridiniaceae) and other dinoflagellates. Nucleic Acids Res. 32:307–315. Gottschling M, Hilger H, Wolf M, Diane N. 2001. Secondary structure of the ITS1 transcript and its application in a reconstruction of the phylogeny of Boraginales. Plant Biol. 3: 629–636. Gutell R, Larsen N, Woese C. 1994. Lessons from an evolving rRNA: 16S and 23S rRNA structures from a comparative perspective. Microbiol Mol Biol Rev. 58:10. Haas BJ, Gevers D, Earl AM, et al. (15 co-authors). 2011. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21:494–504. Hoshina R. 2010. Secondary structural analyses of ITS1 in Paramecium. Microbes Environ. 25:313–316. Huelsenbeck JP, Ronquist F. 2001. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17:754–755. Itskovich V, Gontcharov A, Masuda Y, Nohno T, Belikov S, Efremova S, Meixner M, Janussen D. 2008. Ribosomal ITS sequences allow resolution of freshwater sponge phylogeny with alignments guided by secondary structure prediction. J Mol Evol. 67:608–620. Jerome C, Lynn D. 1996. Identifying and distinguishing sibling species in the Tetrahymena pyriformis complex (Ciliophora, Oligohymenophorea) using PCR/RFLP analysis of nuclear ribosomal DNA. J Eukaryot Microbiol. 43:492–497. Joseph N, Krauskopf E, Vera M, Michot B. 1999. Ribosomal internal transcribed spacer 2 (ITS2) exhibits a common core of secondary structure in vertebrates and yeast. Nucleic Acids Res. 27:4533. Katoh K, Toh H. 2008. Recent developments in the MAFFT multiple sequence alignment program. Brief Bioinform. 9:286–298. Katz LA, McManus GB, Snoeyenbos-West OLO, Griffin A, Pirog K, Costas B, Foissner W. 2005. Reframing the ‘Everything is everywhere’ debate: evidence for high gene flow and diversity in ciliate morphospecies. Aquat Microb Ecol. 41:55–65. Keller I, Chintauan-Marquier IC, Veltsos P, Nichols RA. 2006. Ribosomal DNA in the grasshopper Podisma pedestris: escape from concerted evolution. Genetics 174:863–874. Kim E, Wilcox L, Graham L, Graham J. 2004. Genetically distinct populations of the dinoflagellate Peridinium limbatum in neighboring Northern Wisconsin lakes. Microb Ecol. 48:521–527. Lange M, Chen YQ, Medlin LK. 2002. Molecular genetic delineation of Phaeocystis species (Prymnesiophyceae) using coding and non-coding regions of nuclear and plastid genomes. Eur J Phycol. 37:77–92. Letunic I, Bork P. 2007. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23:127–128. Litaker RW, Vandersea MW, Kibler SR, Reece KS, Stokes NA, Lutzoni FM, Yonish BA, West MA, Black MND, Tester PA. 2007. Recognizing dinoflagellate species using ITS rDNA sequences. J Phycol. 43:344–355. Logares R, Daugbjerg N, Boltovskoy A, Kremp A, Laybourn-Parry J, Rengefors K. 2008. Recent evolutionary diversification of a protist lineage. Environ Microbiol. 10:1231–1243. Logares R, Rengefors K, Kremp A, Shalchian-Tabrizi K, Boltovskoy A, Tengs T, Shurtleff A, Klaveness D. 2007. Phenotypically different microalgal morphospecies with identical ribosomal DNA: a case of rapid adaptive evolution? Microb Ecol. 53:549–561. Lo ´pez-Garcı ´a P, Rodrı ´guez-Valera F, Pedro ´s-Alio ´C, Moreira D. 2001. Unexpected diversity of small eukaryotes in deep-sea Antarctic plankton. Nature 409:603–607. Lowe CD, Montagnes DJS, Martin LE, Watts PC. 2010. Patterns of genetic diversity in the marine heterotrophic flagellate Oxyrrhis marina (Alveolata: Dinophyceae). Protist 161:212–221. Lundholm N, Moestrup Ø, Kotaki Y, Hoef-Emden K, Scholin C, Miller P. 2006. Inter-and intraspecific variation of the Pseudonitzschia delicatissima complex (Bacillariophyceae) illustrated by rRNA probes, morphological data and phylogenetic analyses. J Phycol. 42:464–481. Mai JC, Coleman AW. 1997. The internal transcribed spacer 2 exhibits a common secondary structure in green algae and flowering plants. J Mol Evol. 44:258–271. Massana R. 2011. Eukaryotic picoplankton in surface oceans. Annu Rev Microbiol. 65:1–47. Massana R, Castresana J, Balague ´V, Guillou L, Romari K, Groisillier A, Valentin K, Pedro ´s-Alio ´C. 2004. Phylogenetic and ecological analysis of novel marine stramenopiles. Appl Environ Microbiol. 70:3528–3534. Massana R, DeLong EF, Pedro ´s-Alio ´C. 2000. A few cosmopolitan phylotypes dominate planktonic archaeal assemblages in widely different oceanic provinces. Appl Environ Microbiol. 66:1777–1787. Massana R, Guillou L, Dı ´ez B, Pedro ´s-Alio ´C. 2002. Unveiling the organisms behind novel eukaryotic ribosomal DNA sequences from the ocean. Appl Environ Microbiol. 68:4554–4558. Massana R, Guillou L, Terrado R, Forn I, Pedros-Alio C. 2006. Growth of uncultured heterotrophic flagellates in unamended seawater incubations. Aquat Microb Ecol. 45:171–180. Massana R, Terrado R, Forn I, Lovejoy C, Pedro ´s-Alio ´C. 2006. Distribution and abundance of uncultured heterotrophic flagellates in the world oceans. Environ Microbiol. 8:1515–1522. May RM, Beverton RJH. 1990. How many species? Philos Trans R Soc Lond B Biol Sci. 330:293. Moniz MBJ, Kaczmarska I. 2009. Barcoding diatoms: is there a good marker? Mol Ecol Resour. 9:65–74. Moniz MBJ, Kaczmarska I. 2010. Barcoding of diatoms: nuclear encoded ITS revisited. Protist 161:7–34. Montresor M, Sgrosso S, Procaccini G, WHCF Kooistra. 2003. Intraspecific diversity in Scrippsiella trochoidea (Dinophyceae): evidence for cryptic species. Phycologia 42:56–70. Moon-van der Staay SY, De Wachter R, Vaulot D. 2001. Oceanic 18S rDNA sequences from picoplankton reveal unsuspected eukaryotic diversity. Nature 409:607–610. Mu ¨ller T, Philippi N, Dandekar T, Schultz J, Wolf M. 2007. Distinguishing species. RNA 13:1469. Mullineux T, Hausner G. 2009. Evolution of rDNA ITS1 and ITS2 sequences and RNA secondary structures within members of the fungal genera Grosmannia and Leptographium. Fungal Genet Biol. 46:855–867. Not F, Gausling R, Azam F, Heidelberg JF, Worden AZ. 2007. Vertical distribution of picoeukaryotic diversity in the Sargasso Sea. Environ Microbiol. 9:1233–1252. Orsini L, Procaccini G, Sarno D, Montresor M. 2004. Multiple rDNA ITS-types within the diatom Pseudo-nitzschia delicatissima (Bacillariophyceae) and their relative abundances across a spring bloom in the Gulf of Naples. Mar Ecol Prog Ser. 271:87–98. Prokopowich CD, Gregory TR, Crease TJ. 2003. The correlation between rDNA copy number and genome size in eukaryotes. Genome 46:48–50. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, Glockner FO. 2007. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35:7188–7196. Rambaut A. 2009. FigTree. Edinburgh (United Kingdom): Institute of Evolutionary Biology, University of Edinburgh. Available from: http://tree.bio.ed.ac.uk/software/figtree. Limited Diversity of a Widespread Picoeukaryote ·doi:10.1093/molbev/msr303 MBE 13 at CSIC on February 8, 2012http://mbe.oxfordjournals.org/Downloaded from Limited diversity of a widespread picoeukaryote 100 Rocap G, Distel D, Waterbury J, Chisholm S. 2002. Resolution of Prochlorococcus and Synechococcus ecotypes by using 16S-23S ribosomal DNA internal transcribed spacer sequences. Appl Environ Microbiol. 68:1180. Rodrı ´guez-Martı ´nez R, Labrenz M, Del Campo J, Forn I, Ju ¨rgens K, Massana R. 2009. Distribution of the uncultured protist MAST-4 in the Indian Ocean, Drake Passage and Mediterranean Sea assessed by real-time quantitative PCR. Environ Microbiol. 11: 397–408. Rynearson TA, Lin EO, Armbrust EV. 2009. Metapopulation structure in the planktonic diatom Ditylum brightwellii (Bacillariophyceae). Protist 160:111–121. Schloss PD, Westcott SL, Ryabin T, et al. (15 co-authors). 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 75:7537–7541. Schlo¨tterer C, Hauser MT, von Haeseler A, Tautz D. 1994. Comparative evolutionary analysis of rDNA ITS regions in Drosophila. Mol Biol Evol. 11:513. Schultz J, Muller T, Achtziger M, Seibel P, Dandekar T, Wolf M. 2006. The internal transcribed spacer 2 database—a web server for (not only) low level phylogenetic analyses. Nucleic Acids Res. 34:W704. Seibel P, Mu ¨ller T, Dandekar T, Schultz J, Wolf M. 2006. 4 SALE—a tool for synchronous RNA sequence and secondary structure alignment and editing. BMC Bioinformatics 7:498. Selig C, Wolf M, Muller T, Dandekar T, Schultz J. 2007. The ITS2 Database II: homology modelling RNA structure for molecular systematics. Nucleic Acids Res. 36:D377–D380. Simon UK, Weiss M. 2008. Intragenomic variation of fungal ribosomal genes is higher than previously thought. Mol Biol Evol. 25:2251–2254. Slapeta J, Lo ´pez-Garcı ´a P, Moreira D. 2006. Global dispersal and ancient cryptic species in the smallest marine eukaryotes. Mol Biol Evol. 23:23–29. Sorhannus U, Ortiz JD, Wolf M, Fox MG. 2010. Microevolution and speciation in Thalassiosira weissflogii (Bacillariophyta). Protist 161:237–249. Stamatakis A. 2006. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22:2688–2690. Stoeck T, Bass D, Nebel M, Christen R, Jones MDM, Breiner H-W, Richards TA. 2010. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 19:21–31. Taylor JW, Jacobson DJ, Kroken S, Kasuga T, Geiser DM, Hibbett DS, Fisher MC. 2000. Phylogenetic species recognition and species concepts in fungi. Fungal Genet Biol. 31:21–32. Thompson J, Marcelino L, Polz M. 2002. Heteroduplexes in mixedtemplate amplifications: formation, consequence and elimination by ‘reconditioning PCR’. Nucleic Acids Res. 30:2083. Thornhill DJ, Lajeunesse TC, Santos SR. 2007. Measuring rDNA diversity in eukaryotic microbial systems: how intragenomic variation, pseudogenes, and PCR artifacts confound biodiversity estimates. Mol Ecol. 16:5326–5340. Thornhill DJ, Lord JB. 2010. Secondary structure models for the internal transcribed spacer (ITS) region 1 from symbiotic dinoflagellates. Protist 161:434–451. Tippery N, Les D. 2008. Phylogenetic analysis of the internal transcribed spacer (ITS) region in Menyanthaceae using predicted secondary structure. Mol Phylogenet Evol. 49:526–537. Vanormelingen P, Chepurnov V, Mann D, Cousin S, Vyverman W. 2007. Congruence of morphological, reproductive and ITS rDNA sequence data in some Australasian Eunotia bilunaris (Bacillariophyta). Eur J Phycol. 42:61–79. Vanormelingen P, Chepurnov V, Mann D, Sabbe K, Vyverman W. 2008. Genetic divergence and reproductive barriers among morphologically heterogeneous sympatric clones of Eunotia bilunaris sensu lato (Bacillariophyta). Protist 159:73–90. Wang S, Bao Z, Li N, Zhang L, Hu J. 2007. Analysis of the secondary structure of ITS1 in Pectinidae: implications for phylogenetic reconstruction and structural evolution. Mar Biotechnol. 9: 231–242. Wiemers M, Keller A, Wolf M. 2009. ITS2 secondary structure improves phylogeny estimation in a radiation of blue butterflies of the subgenus Agrodiaetus (Lepidoptera: Lycaenidae: Polyommatus). BMC Evol Biol. 9:300. Worden A. 2006. Picoeukaryote diversity in coastal waters of the Pacific Ocean. Aquat Microb Ecol. 43:165–175. Zuker M. 2003. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31:3406. Rodrı ´guez-Martı ´nez et al. ·doi:10.1093/molbev/msr303 MBE 14 at CSIC on February 8, 2012http://mbe.oxfordjournals.org/Downloaded from Biogeographyoftheunculturedmarine picoeukaryoteMAST4:temperature drivendistributionpatterns Chapter5 Rodríguez-Martínez R, Rocap G, Salazar G, Massana R. Biogeography of the uncultured marine picoeukaryote MAST4: temperature driven distribution patterns. Submitted to The ISME Journal. Chapter 5 103 Biogeography of the uncultured marine picoeukaryote MAST-4: temperature driven distribution patterns Raquel Rodríguez-Martínez1*, Gabrielle Rocap2, Guillem Salazar1 and Ramon Massana1* 1 Institut de Ciències del Mar, CMIMA (CSIC). Passeig Marítim de la Barceloneta, 37-49, 08003 Barcelona, Spain 2 School of Oceanography, University of Washington, Box 357940, Seattle, Washington, USA Abstract The MAST-4 is a widespread uncultured picoeukaryote that accounts for an important fraction of marine heterotrophic flagellates. This group has low genetic divergence and is composed of a small number of species. We combined ARISA (automated ribosomal intergenic spacer analysis) and ITS clone libraries to study the biogeography of this marine protist, examining both spatial and temporal trends in MAST-4 assemblages and associated environmental factors. We did not see marine geographical barriers for the dispersal of the most represented MAST-4 clades, which appeared adapted to different temperature ranges. Distant samples sharing the same temperature presented very similar assemblages, especially in cold temperatures, where only one clade dominated. The most highly represented clades, A and E1, had high gene flow between very distant geographic regions and may each consist of a single species. Our results contribute to the general discussion on microbial biogeography by showing no dispersal limitation together with strong environmental selection for some picoeukaryotes in the marine environment. Keywords: Biogeography, ITS1, ARISA, temperature, gene flow, MAST-4 Introduction Biogeography is the study of the distribution of biodiversity over space and time. Most research and theories have been established through the study of plants and animals, revealing that their metapopulations are geographically restricted (MacArthur and Wilson 1967), due to the result of both deterministic (environmental) and stochastic (dispersal) processes. The debate about whether or not microorganisms follow similar patterns dates back decades ago (Baas-Becking 1934, Beijerinck 1913), and has seen a renovated interest in recent years (O’Malley 2008). A long-held concept in microbial ecology is that free-living microbes smaller than 1 mm body size (all prokaryotes and most protists) are probably sufficiently abundant to have worldwide distribution owing to their dispersal ability (Fenchel and Finlay 2004, Finlay 2002, Finlay and Fenchel 2004). Microbial cells inevitably will hitch a ride via wind, water, birds, or floating vegetation, and many species have an astonishing ability to hunker down in harsh environments until their moment arises (Whitfield 2005). Consequently, microbial organisms are believed to occur wherever the environment permits: ‘everything is everywhere, but the environment selects’ (Baas-Becking 1934, De Wit and Bouvier 2006). Implicit in this tenet is that free-living microbial taxa are not randomly distributed, but rather exhibit biogeographic patterns, and in some cases these patterns may be qualitatively similar to those observed for macroorganisms (Green et al 2004, Horner-Devine et al 2004, Horner-Devine et al 2007). However, these patterns would be the result of local environmental selection rather than dispersal limitation. Biogeography of an uncultured marine picoeukaryote 104 The current evidence confirms that environmental selection is fundamental for the spatial variation observed in microbial diversity (Martiny et al 2006). The next frontier is to figure out whether these patterns are also influenced by geographical barriers that facilitate evolution and diversification. However, because geographic distance is often correlated with specific environmental characteristics, disentangling the relative influence of these two factors on community divergence represents a major challenge in elucidating whether or not microbes are limited by dispersal. It is known that freshwater diatoms present real dispersal limitations because of desiccation intolerances (Vyverman et al 2007). Other highly specialized microorganisms, such as the hyperthermophiles, are unlikely to make a long dispersal journey, so they would be easily isolated by geographic barriers, resulting in the development of a global diversity structure (Whitaker 2003). However, more dispersal limitation is expected in terrestrial and freshwater systems than in marine systems. The oceans are an interconnected geophysical fluid that potentially allows planktonic organisms to disperse globally. A global conveyor belt is mixing oceanic waters at scales of thousand years (Broecker 1991). Thus, tectonic and water mass dispersal barriers are often weak and unable to geographically isolate pelagic planktonic populations for extended periods of time (Sexton and Norris 2008). The geographic distribution of marine planktonic diatoms does not seem to be limited by dispersal and it is environmental selection which dominates diatom community structure (Cermeño and Falkowski 2009). The general view is that of a broad dispersal of marine planktonic microbes (Cermeño et al 2010). An optimal target to further investigate biogeographical patterns of marine microbes are the picoeukaryotes. These are small protists 1-3 μm in size populating surface oceans at abundances of 102 to 104 cells ml-1, playing important ecological roles, and exhibiting a high and underexplored diversity (Massana 2011). In a previous study (Rodríguez-Martínez et al 2012), we investigated the diversity structure of an important uncultured picoeukaryote, the MAST-4 lineage (Massana et al 2004), which is widespread in surface marine waters (except polar systems) and represents approximately 9% of heterotrophic flagellates (Massana et al 2006, Rodríguez-Martínez et al 2009). Despite its huge number of cells in the oceans, MAST-4 has a very low genetic divergence and is composed of only five main clades, each representing at least one biological species. The small size (~2 µm), high abundance, worldwide distribution and low genetic diversity make MAST-4 a good model to study marine protist biogeography. In this work we determined the MAST-4 community structure and distribution by combining automated ribosomal intergenic spacer analysis (ARISA) (Fisher and Triplett 1999) and 18S-ITS1 gene libraries. MAST-4 diversity was analyzed in 40 different marine locations and we found evidence for a strong environmental selection and no-dispersal limitation for the most represented clades. Materials and Methods Study sites and sampling Environmental samples were selected from different oceanographic cruises performed at the North Atlantic Ocean (NOR, NAT, RG, BE and COC), the North Pacific Ocean (WE), the Mediterranean Sea (BL and AL) and the southern hemisphere (IND and DH) (Figure 1). Details of some of these cruises have been already published: NOR (Not et al 2005), NAT (González et al 2000), COC (Alonso-Sáez et al 2007), WE (del Giorgio et al 2011), BL (Alonso-Sáez et al 2008), AL (Arin et al 2002), IND (Not et al 2008) and DH (Díez et al 2004). Seawater at different depths was collected with Niskin bottles attached to a CTD rosette and filtered for the 0.2-3 µm microbial fraction with a peristaltic pump. DNA extraction was done using enzymatic and SDS digestion plus phenol purification (Massana et al 2000). The quality and quantity of extracted genomic DNA was determined with a NanoDrop 1000 (Thermo Fisher Scientific Inc., Wilmington, DE). Physico-chemical data (temperature, salinity) and chlorophyll concentration from the samples were compiled. Chapter 5 105 ARISA fingerprinting was done for surface samples in all the stations marked in Figure 1, for additional depths in most stations, and for a temporal survey at the Blanes Bay Microbial Observatory (BL). Surface samples from stations marked with a star were used for the clone libraries. Design of PCR primers Specific primers were designed to amplify the end of the 18S rDNA (290bp) the Internal Transcribed Spacer 1 (ITS1) and the beginning of the 5.8S (39bp). The forward primer M4.18S-F (5’-TGGGTAATCTTTGAACGTGAAT-3’), located before the V9 region of the 18S rDNA, was designed based on all MAST-4 sequences for this region available so far (52 unique clones). It matched perfectly all these clones, except two (ME1.29 and OLI11066) that had an extra nucleotide (likely a sequencing error). It had more than two mismatches (except for 1 sequence with one mismatch and 3 sequences with two mismatches) to non-target sequences from the SILVA database (Pruesse et al 2007). The reverse primer M4.58S-R (5’-GTTGCGAGAACCTAGAC-3’), located in the 5.8S rDNA, was designed to have a perfect match with the 22 MAST-4 sequences (Rodríguez-Martínez et al 2012). This primer had two or more mismatches with all stramenopile sequences extracted from GenBank, except for some Labyrinthulida sequences (3 with no mismatches and 62 with one mismatch). Primers were checked for formation of primer dimers, GC content and theoretical melting temperature in the website www.operon.com, using the Oligo Analysis & Plotting Tool. This primer set gave an amplicon size ranging from 500 to 650 bp. Construction of clone libraries The PCR mixture (30 µl) contained 15 ng of DNA template, 0.5 µM of each primer, 200 µM of each dNTP, 1 mM MgCl2, 1.5 units of a Taq DNA polymerase (Thermo Scientific ThermoPrime), and the enzyme buffer. PCR cycling, carried out in a BioRad thermocycler, was: initial denaturation at 94ºC for 5 min; 30 cycles with denaturation at 94ºC for 1 min, annealing at 60ºC for 45 sec and extension at 72ºC for 1 min; and a final extension at 72ºC for 10 min. We tested the MgCl2 concentration (from 0.5 to 3 mM) and the annealing temperature (from 55 to 66°C) and chose the most stringent conditions giving the expected band. To check the specificity of the primer set, we confirmed the negative signal with 9 non-target cultures (diatoms, haptophytes, dinoflagellates and cyanobacteria). PCR products were purified with the QIAquick PCR Purification kit (QIAGEN) and cloned using the TOPO-TA cloning kit (Invitrogen) with the vector pCR4 following manufacturer’s recommendations and a vectorinsert ratio of 1:5. Putative positive bacterial colonies IND DH COC BE AL BL RG WE NAT NOR Figure 1. Global map indicating sampling sites used for ARISA fingerprinting (dots) and sites used for clone libraries (stars). The acronym of the cruise is indicated close to the stations. Biogeography of an uncultured marine picoeukaryote 106 were picked and transferred to a new LB (LuriaBertani) plate and finally into LB-glycerol solution for frozen stocks (-80ºC). Presence of correct insert was checked by PCR reamplification with vector primers M13F and M13R using a small aliquot of bacterial culture as template. Amplicons with the right insert size (39 to 49 clones per library) were sequenced at the Macrogen sequencing service in Korea. Chromatograms were examined with 4Peaks (A. Griekspoor and T. Groothuis, mekentosj.com). Sequence analysis Complete sequences from the clone libraries together with sequences from the same region in the “SSU-LSU” dataset in (Rodríguez-Martínez et al 2012) were aligned with MAFFT v6.853 (Katoh and Toh 2008) with the EINS-I algorithm, using a MAST-7 sequence as outgroup. The alignment was inspected visually and modified using secondary structure models folded in mFOLD (Zuker 2003) as in (Rodríguez-Martínez et al 2012) A Maximum Likelihood (ML) phylogenetic tree was reconstructed using RAxML v7.0.4 MPI version (Stamatakis 2006), using the General Time Reversible model of nucleotide substitution and a Gamma distributed rate of variation across sites (GTR+G). We did not estimate the proportion of invariable sites and missing data were not considered (i.e. treated as missing data). The shape parameter (α) of the Gamma distribution was estimated from the dataset using default options. Phylogenies were done at the University of Oslo Bioportal (www.bioportal.uio. no). One thousand alternative ML trees were run, and the tree with the best likelihood was selected and visualized in FigTree v1.3.1 (Rambaut 2009). Bootstrap analyses were run with 1000 pseudoreplicates and a consensus tree was constructed with RAxML. To infer intraspecific phylogenies and visualize alternative potential evolutionary paths we constructed median-joining networks (Bandelt et al 1999) with the Network 4.6.0.0 program (Fluxus Technology). The genetic differentiation between populations was estimated with the fixation index (Fst) computed with DNAsp 5.10.1 and can range from 0 (no genetic differentiation) to a maximum of 1 (complete differentiation). Generation of ARISA profiles Environmental DNA samples were PCR-amplified in triplicate for ARISA in a MJ Research cycler. PCR conditions were the same as described before using a volume of 25 µl with 10 ng of DNA template, another enzyme (Taq DNA Polymerase “Gene Choice”) and the forward primer fluorescently labeled (5-HEX). PCR products stored in the dark at 4ºC were purified with MultiScreen ® PCRµ96 Plates and quantified using PicoGreen fluorescence (Invitrogen, Carlsbad, CA, USA) in a SpectraMax M2 microplate reader (Molecular Devices Corp., Sunnyvale, CA, USA). Ten ng DNA were ethanol precipitated from triplicates or from pooled PCR products (when the yield of the PCR was low), followed by resuspension with 0.078 µl tween, 9.67 µl water and 0.25 µl fluorescently-labeled internal size standard, CST ROX 60-1500 bp (http://www.bioventures.com/). Samples were run on a MegaBACE 1000 automated capillary sequencer (Molecular Dynamics). The electropherograms were then analyzed using DAx software (v8.0, Van Mierlo Software Consultancy). Only peaks exceeding 4 times the noise signal of the electropherogram curve were taken into account. Analysis of fingerprinting data From DAx output tables, peak heights were binned using the “fixed window” binning strategy to take into account the size calling imprecision from ARISA fingerprints (Hewson and Fuhrman 2006). In order to determine the best window size with our data, we applied the “automatic binning algorithm” (Ramette 2009) developed in a R script (The R Foundation for Statistical Computing [http://cran.r-project.org/]); we chose 2 bp. To identify the best window frame (out of the 20 possible starting with a shift value of 0.1), we used the “interactive binning algorithm” (Ramette 2009). This algorithm binned the peaks for each frame, calculated the relative fluorescence intensity of each binned peak by dividing its height by the total peak height of the sample and omitted peaks with values <0.5% (considered as background). We added an option in the script to compare frames considering only triplicate samples (instead of all samples). The frame with the best correlation among triplicates was chosen; starting Chapter 5 113 levels. A PERMANOVA test considering grouped factors showed that temperature explained 36% of the variability in community composition, whereas all factors together explained 55% (Table 1). Since temperature appeared as the most important factor, we tested if the established groups (cold, temperate and warm) were statistically different by the ANOSIM pairwise tests. Indeed, they were significantly different in all cases, with a global R of 0.44 (significance level 0.1%) and highest R-value (0.88) for the cold-warm comparison. The ANOSIM test for the other environmental factors generally gave lower R-values, and was significantly different in only a few cases. Finally, when samples within each temperature group were analyzed separately, temperature was less important than salinity and sampling depth (Table 1). We then searched for the OTUs driving the differences among cold, temperate and warm samples. The SIMPER test identified five OTUs with a dissimilarity contribution between groups higher than 5% (Table 2). OTU 589 had the largest CCA CCA (Separate temperature groups) PERMANOVA COLD TEMPER. WARM Variable R2 Sign R2 Sign R2 Sign R2 Sign Range R2 Sign Temperature (ºC) 0.51 *** 0.01 0.12 ** 0.21 *** 2_9.5, 9.5_17, 17_30 0.36 *** Z (m) 0.18 *** 0.02 0.41 *** 0.13 *** 0-25, 25-75, 75-180, 180-250 0.08 *** Salinity 0.11 *** 0.06 ** 0.10 * 0.25 *** 32, 33, 34, 35, 36, 37, 38, 39 0.07 *** Zmax (m) 0.06 *** 0.01 0.06 0.08 ** 0-200, 200-1000, 1000-6000 0.02 * Chlorophyll (µg L-1) 0.05 * 0.01 0.04 0.15 *** 0.1-0.5, 0.5-1.5, 1.5-4 0.02 * Total 0.91 0.06 0.63 0.82 0.55 Significance codes: ***:< 0.001; **0.0010.01; *0.010.05 Bold numbers represent the highest R2 of the particular analysis  Table 1. Separate statistical analysis (CCA, CCA per groups and PERMANOVA) to estimate the contribution (from R2 values) of five environmental factors in the ARISA fingerprinting variance. dissimilarity contribution in all pairwise analyses, especially when including cold samples. This was consistent with the 99% similarity contribution of OTU 589 to the cold group (Table 2) and its predominant presence in cold waters (Figure 7). OTU 527 was the most important in warm samples (22% similarity contribution) and contributed 7% to the dissimilarity between all group comparisons with warm samples. OTUs 529 and 531 appeared important in temperate and warm samples, whereas OTU 567 was characteristic for temperate samples and absent in cold samples. Using the agreement between ARISA and clone libraries peak profiles we predicted that OTU 589 corresponded to a clone of 581 bp in size, belonging to clade E, whereas OTUs 527, 529 and 531 (clone sizes between 519 and 524 bp) could belonged to either clades A or C and OTU 567 (clone size 560 bp) belonged to clade B. Therefore, clade E was the best adapted to cold water, clades A and/or C were typical of temperate and warm waters while clade B was most characteristic of temperate waters.  Dissimilarity contribution % Similarity contribution % OTU Clade assignment TEMP & COLD WARM & COLD TEMP & WARM COLD TEMPERATE WARM 589 E 47.53 47.8 21.38 98.75 43.32 4.59 527 A or C 3.74 7.09 7.37 0.04 6.17 21.89 529 A or C 7.4 4.77 6.42 0.17 12.46 12.22 531 A or C 5.25 3.75 3.98 0.08 8.47 13.06 567 B 4.75 3.11 5.83 0 4.8 3.72 Contributions below 5% are represented in gray Table 2. Results of the SIMPER analysis to identify the contribution of the five most important OTUs to the dissimilarity and similarity of the groups defined by temperature. Biogeography of an uncultured marine picoeukaryote 114 0 20 40 60 80 100 0 5 10 15 20 25 30 % OTU 589 0 5 10 15 20 25 0 5 10 15 20 25 30 % OTU 531 0 10 20 30 40 50 0 5 10 15 20 25 30 % OTU 527 0 10 20 30 40 50 0 5 10 15 20 25 30 % OTU 529 Temperature ºC 0 10 20 30 40 50 0 5 10 15 20 25 30 % OTU 567 Figure 7. Relative intensity of the most important OTUs in the ARISA fingerprints in all samples displayed according to temperature. Gray circles represent samples where the OTU was not detected. Temporal changes at Blanes Bay At the Blanes Bay (BL) station in the NW Mediterranean Coast, we analyzed 23 different ARISA fingerprints covering a monthly seasonal sampling in 2003 and random dates from 2001 to 2006. Samples exhibited a large variability in the composition of MAST-4 and grouped, in an associated dendrogram (not shown), in two clusters differentiated by seawater temperature (above or below 17°C), as highlighted in the NMDS plot (Figure 8). The similarity of temperate samples was above 58% and the similarity of warm samples was above 37%. Each group contained samples from different years, highlighting the importance of temperature in defining community composition along this temporal scale. Aug01 Nov01 Jul02 Jan03 Jan03 Mar03 Mar03 Apr03 May03 Jun03 Jul03 Aug03 Sep03 Oct03 Nov03 Dec03 Apr04 Aug04 Jan05 Jun05 Jan06 Mar06 May06 2-9.4ºC 9.5-16.9ºC 17-30ºC Stress: 0.1 58% 37% Figure 8. NMDS statistical analysis of ARISA fingerprints in the temporal study at Blanes Bay. Samples are displayed with symbols for temperature as in Figure 6 and with the date of sampling (month-year). Points enclosed by dashed line cluster at 37% similarity and points enclosed by solid line cluster at 58% similarity in a separate (not shown) dendrogram. Discussion Despite the recent interest in microbial biogeography as a mechanism to better understand the ecological and evolutionary properties of microbial taxa, this field has suffered from diverse conceptual and methodological limitations that confuse the emerging conclusions (Martiny et al 2006). A first problem can be how the microbial taxa are sampled, as many studies are based in a handful of isolated strains from separate geographic sites (Ki and Han 2005, Kooistra et al 2008) In the present study, the method of sampling is not based on culturing Chapter 5 115 but on molecular markers easily amplified from environmental DNA. This has the advantage that the sampled diversity is more representative of the natural assemblage (by avoiding culturing bias), and that many individuals (sequences) can be retrieved at once. Second, it is very important to choose a good taxonomic marker, because the view that no biogeographic patterns exist in microorganisms (Finlay et al 2006) could be caused by a blurry identification of protist species (Lomolino et al 2006). Indeed many protist species appear to be widely distributed when identified via morphology, but these “morphological species” most likely include cryptic species that are not able to interbreed (Dolan 2006). Our approach, initially based on the 18S rDNA, also targeted the highly variable ITS1 region, which provides enhanced taxonomic resolution for diversity and biogeography studies (Brown and Fuhrman 2005). The ITS size variation is the basis of the fingerprinting technique ARISA, which can differentiate OTUs at high taxonomic resolution. ITS1 has been used to assess the diversity of Pseudo-nitzschia populations at interand intraspecific levels (Orsini et al 2004) and for elucidating Pseudo-nitzschia community structure using ARISA fingerprinting on environmental samples (Hubbard et al 2008). Here we amplify the ITS1 region and analyze this marker at the sequence level (in clone libraries), and at a fingerprinting level to compare assemblages. Clone libraries and ARISA profiles showed a remarkable agreement, with a shift of 5-9 bp depending of the length of the sequence, which is similar to previously reported shifts (Collins et al 2010, Hahn et al 2001). Thus, we confirm the ITS region as a good marker for studying microbial biogeography. Third, it is important to clearly determine the genetic complexity of the studied taxa. We focus here in the uncultured protist MAST-4, which is known to exhibit a limited evolutionary diversification (Rodríguez-Martínez et al 2012). By designing specific primers amplifying the ITS1 region of MAST-4, we increased the sequences within this group by ten-fold, and we still did not have evidence of more than 12 species, as judged by the base of the conserved helix III stem in the ITS1 secondary structure. This confirmed the low diversity of MAST-4, adding only two more putative species. Clade A was still a single species, and all 119 clones (with 3 exceptions) presented the same helix III motif. Subclade E1 seemed to form also a single species. Despite having two helix III motifs it presented very well mixed populations between sites and the difference between these motifs was by only one hemiCBC in the more variable fifth base pair. The structure of MAST-4 in a few genetically distinct species is similar to that found in other marine protists, such as the cosmopolitan diatom Skeletonema costatum (Kooistra et al 2008). In addition, MAST-4 has the advantage that it is a very abundant and widespread group, so ideal as a model for biogeographical studies. It has been found in a wide range of temperatures (2-30ºC) as the biliphytes lineage (Cuvelier et al 2008). Finally, a limitation of many studies is the inability to disentangle the relative contribution of contemporary environmental conditions and historical contingencies in shaping the spatial variations of microbial communities (Martiny et al 2006). Our study addresses this question by analyzing spatial changes together with the associated factors of the samples. In addition, since single sampling may not cover the complete biodiversity of a given place, we also evaluated temporal changes. In fact, it has been suggested that to obtain reliable estimates of biodiversity in a given habitat it is necessary to cover the entire season (Nolte et al 2010). The analysis of MAST-4 assemblages using ARISA fingerprints showed that temperature was the main factor influencing the distribution patterns, as has been observed in other marine microbes like Prochlorococcus (Martiny et al 2009). Distant samples sharing the same temperature could have similar MAST-4 diversity, whereas samples with different temperature were very different. This global pattern was also observable in a temporal study in the same geographic site (Blanes Bay). In this place with a thermal seasonal cycle from 12 to 24°C, samples grouped again by temperature, confirming this as an important structuring factor. Moreover, the Fst analysis revealed a very large gene flow Biogeography of an uncultured marine picoeukaryote 116 between samples geographically distant but with a similar temperature. It is worth mentioning that, in theory, a similar pattern could have been obtained due to another driving factor not measured here but strongly correlated with temperature (Martiny et al 2009). Thus, the spatial distribution of MAST-4 seemed to be mainly controlled by contemporary environmental factors with a null or low degree of provincialism. In addition, temperature had a strong positive correlation with OTU richness (r = 0.54), as has been previously reported (Fuhrman et al 2008, Pommier et al 2006). The positive effect of temperature on diversity could be due to the kinetics of biological processes, including rates of reproduction, dispersal, species interaction, adaptive evolution, and speciation (Allen 2002, Rohde 1992). Finally, other abiotic factors that are known to be important in defining microbial community composition, such as salinity (Logares et al 2009) and sampling depth (Winter et al 2008), took importance within each defined temperature group. A more detailed analysis of clade A, probably constituting a single species, revealed that it was widely distributed (it appeared in all five clone libraries) and very well mixed. The five populations examined from this clade exhibited a high gene flow, and samples from distant places with the same temperature displayed completely mixed populations in the MJ network with few estimated mutations per ribotype. A similar scenario occurred within subclade E1, probably representing a single species as well. This subclade even had a lower proportion of mutations per ribotype. On the contrary, clades B and C, probably composed by more than one species, exhibited some spatial structuring, with subclades appearing in only one library. Moreover, the mutations per ribotype increased two times in clade C or five times in clade B as compared with clade A. This intriguing feature, perhaps indicating some dispersal restriction for clades B and C, could also be due to undersampling and deserves more attention in future surveys. If confirmed by further results, the dispersal limitation of clades B and C would be comparable to that found with the cosmopolitan marine planktonic diatom Pseudo-nitzschia pungens (Casteleyn et al 2010). Our data also highlight specific lineages within MAST-4 that seem adapted to different temperature regimes. Thus, clade B, and either or possibly both clades A or C seemed to be characteristic of temperate and warm waters, whereas clade E1, represented by OTU 589, was the only one able to inhabit cold waters. The genetic structure of MAST-4 with different lineages, some ubiquitous in the oceans and with particular ecological properties, resembles that of other marine picoeukaryotes. Thus, different Ostreococcus (Rodríguez et al 2005) and Synechococcus lineages (Ahlgren and Rocap 2006) are adapted to different light levels, whereas a Micromonas pusilla clade adapted to cold temperature has also been reported (Lovejoy et al 2006). It has been proposed that this ecotypic differentiation can partly explain the success of these picoeukaryotes, allowing them to exploit the whole spectrum of habitat variability. The uncultured free-living protist MAST-4 is widely abundant and very small, and thus possesses the properties for a worldwide distribution (Finlay 2002). Moreover it lives in a marine habitat, where wind, waves and currents produce mixing events that facilitate the dispersion. However, we did not observe all MAST-4 clades at all locations but saw biogeographical patterns, stressing the importance of the end of the tenet, “but the environment selects”. It is reasonable to hypothesize that MAST-4 has a huge dispersal capacity and can arrive everywhere within a marine habitat. For instance, there is one record of a MAST-4 sequence in the Arctic Ocean (Lovejoy and Potvin 2011), showing the potential to arrive to such high latitudes probably dragged by coastal currents of Pacific water. But then, depending the environmental conditions, different organisms will settle and grow up, resulting in different community patterns. These are related to some form of ecological differentiation between related types, as described for Skeletonema species (Kooistra et al 2008). Although microorganisms could spread across all suitable habitats, local adaptations eventually reduce the gene flow and promote speciation (Medlin 2007). Chapter 5 117 As a conclusion, we did not see strong marine geographical barriers for the dispersal of MAST-4, whereas temperature was the main driver for community composition, as has been observed in marine bacterial and archaeal assemblages (Winter et al 2008). In our study the environment seemed to make a taxonomic selection, with clades with supposed physiological adaptations established in different regions. In particular, clade E1 represented by OTU 589 was the only one acclimated to cold waters. Our study also confirmed the low MAST-4 diversity previously detected. Acknowledgements Funding has been provided by FPI fellowships from the Spanish Ministry of Education and Science to RRM and GS and by projects FLAME (CGL201016304, MICINN, Spain) to RM, GEMMA (CTM200763753-C02-01/MAR, MEC) to Carlos Pedrós-Alió, and ATOL (NSF, 0629521) to GR. We thank R.E. Collins for DAx software help, E. Sañé for Primer software help, R. Logares for statistical advices and C. Williams for molecular help. References Ahlgren NA, Rocap G (2006). Culture Isolation and Culture-Independent Clone Libraries Reveal New Marine Synechococcus Ecotypes with Distinctive Light and N Physiologies. Appl Environ Microbiol 72: 7193-7204. Allen AP (2002). Global Biodiversity, Biochemical Kinetics, and the Energetic-Equivalence Rule. Science 297: 1545-1548. Alonso-Sáez L, Arístegui J, Pinhassi J, GómezConsarnau L, González JM, Vaqué D et al (2007). Bacterial assemblage structure and carbon metabolism along a productivity gradient in the NE Atlantic Ocean. Aquat Microb Ecol 46: 43. Alonso-Sáez L, Vázquez-Domínguez E, Cardelús C, Pinhassi J, Sala MM, Lekunberri I et al (2008). Factors Controlling the Year-Round Variability in Carbon Flux Through Bacteria in a Coastal Marine System. Ecosystems 11: 397-409. Arin L, Morán X, Estrada M (2002). Phytoplankton size distribution and growth rates in the Alboran Sea (SW Mediterranean): short term variability related to mesoscale hydrodynamics. J Plankton Res 24: 1019. Baas-Becking LGM (1934). Geobiologie of inleiding tot de milieukunde. The Hague, the Netherlands: WP Van Stockum & Zoon (in Dutch) 18. Bandelt H, Forster P, Röhl A (1999). Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol 16: 37-48. Beijerinck MW (1913). De infusies en de ontdekking der backteriën. Jaarboek van de Koninklijke Akademie van Wetenschappen: 1–28. Broecker WS (1991). The great ocean conveyor. Oceanography 4: 79-89. Brown M, Fuhrman J (2005). Marine bacterial microdiversity as revealed by internal transcribed spacer analysis. Aquat Microb Ecol 41: 15-23. Casteleyn G, Leliaert F, Backeljau T, Debeer A, Kotaki Y, Rhodes L et al (2010). Limits to gene flow in a cosmopolitan marine planktonic diatom. Proc Natl Acad Sci USA 107: 12952-12957. Cermeño P, Falkowski PG (2009). Controls on diatom biogeography in the ocean. Science 325: 1539-1541. Cermeño P, De Vargas C, Abrantes F, Falkowski PG (2010). Phytoplankton Biogeography and Community Stability in the Ocean. PLoS ONE 5: e10037. Collins RE, Rocap G, Deming JW (2010). Persistence of bacterial and archaeal communities in sea ice through an Arctic winter. Environ Microbiol 12: 1828-1841. Cuvelier ML, Ortiz A, Kim E, Moehlig H, Richardson DE, Heidelberg JF et al (2008). Widespread distribution of a unique marine protistan lineage. Environ Microbiol 10: 1621-1634. De Wit R, Bouvier T (2006). ‘Everything is everywhere, but, the environment selects’; what did Baas Becking and Beijerinck really say? Environ Microbiol 8: 755-758. del Giorgio PA, Condon R, Bouvier T, Longnecker K, Bouvier C, Sherr E et al (2011). Coherent patterns in bacterial growth, growth efficiency, and leucine metabolism along a northeastern Pacific inshore– offshore transect. Limnol Oceanogr 56: 1-16. Díez B, Massana R, Estrada M, Pedrós-Alió C (2004). Distribution of eukaryotic picoplankton assemblages across hydrographic fronts in the Southern Ocean, Biogeography of an uncultured marine picoeukaryote 118 studied by denaturing gradient gel electrophoresis. Limnol Oceanogr 49: 1022-1034. Dolan J (2006). Microbial biogeography? J Biogeogr 33: 199-200. Fenchel T, Finlay BJ (2004). The ubiquity of small species: patterns of local and global diversity. Bioscience 54: 777-784. Finlay B, Esteban G, Brown S, Fenchel T, Hoef-Emden K (2006). Multiple cosmopolitan ecotypes within a microbial eukaryote morphospecies. Protist 157: 377-390. Finlay BJ (2002). Global Dispersal of Free-Living Microbial Eukaryote Species. Science 296: 10611063. Finlay BJ, Fenchel T (2004). Cosmopolitan metapopulations of free-living microbial eukaryotes. Protist 155: 237-244. Fisher MM, Triplett EW (1999). Automated approach for ribosomal intergenic spacer analysis of microbial diversity and its application to freshwater bacterial communities. Appl Environ Microbiol 65: 4630-4636. Fuhrman JA, Steele JA, Hewson I, Schwalbach MS, Brown MV, Green JL et al (2008). A latitudinal diversity gradient in planktonic marine bacteria. Proc Natl Acad Sci USA 105: 7774-7778. González JM, Simó R, Massana R, Covert JS, Casamayor EO, Pedrós-Alió C et al (2000). Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom. Appl Environ Microbiol 66: 4237-4246. Green JL, Holmes AJ, Westoby M, Oliver I, Briscoe D, Dangerfield M et al (2004). Spatial scaling of microbial eukaryote diversity. Nature 432: 747-750. Hahn M, Wilhelm J, Pingoud A (2001). Influence of fluorophor dye labels on the migration behavior of polymerase chain reaction--amplified short tandem repeats during denaturing capillary electrophoresis. Electrophoresis 22: 2691-2700. Hewson I, Fuhrman J (2006). Improved strategy for comparing microbial assemblage fingerprints. Microb Ecol 51: 147-153. Horner-Devine MC, Carney KM, Bohannan BJM (2004). An ecological perspective on bacterial biodiversity. P Roy Soc Lond B Bio 271: 113-122. Horner-Devine MC, Silver JM, Leibold MA, Bohannan BJM, Colwell RK, Fuhrman JA et al (2007). A comparison of taxon co-occurrence patterns for macroand microorganisms. Ecology 88: 13451353. Hubbard K, Rocap G, Armbrust E (2008). Interand intraspecific community structure within the diatom genus Pseudo-nitzschia (Bacillariophyceae). J Phycol 44: 637-649. Katoh K, Toh H (2008). Recent developments in the MAFFT multiple sequence alignment program. Brief Bioinform 9: 286-298. Ki J, Han M (2005). Molecular analysis of complete SSU to LSU rDNA sequence in the harmful dinoflagellate Alexandrium tamarense (korean isolate, HY970328M). Ocean Sci J 40: 43-54. Kooistra WHCF, Sarno D, Balzano S, Gu H, Andersen RA, Zingone A (2008). Global Diversity and Biogeography of Skeletonema Species (Bacillariophyta). Protist 159: 177-193. Logares R, Bråte J, Bertilsson S, Clasen JL, ShalchianTabrizi K, Rengefors K (2009). Infrequent marinefreshwater transitions in the microbial world. Trends Microbiol 17: 414-422. Lomolino MV, Riddle BR, Brown JH (2006). Biogeography. Sinauer Associates, Inc, Sunderland, Massachusetts, ed 3. Lovejoy C, Massana R, Pedrós-Alió C (2006). Diversity and distribution of marine microbial eukaryotes in the Arctic Ocean and adjacent seas. Appl Environ Microbiol 72: 3085-3095. Lovejoy C, Potvin M (2011). Microbial eukaryotic distribution in a dynamic Beaufort Sea and the Arctic Ocean. J Plankton Res 3: 431-444. MacArthur RH, Wilson EO (1967). The theory of island biogeography. Martiny AC, Tai APK, Veneziano D, Primeau F, Chisholm SW (2009). Taxonomic resolution, ecotypes and the biogeography ofProchlorococcus. Environ Microbiol 11: 823-832. Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL et al (2006). Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol 4: 102-112. Massana R, DeLong EF, Pedrós-Alió C (2000). A few cosmopolitan phylotypes dominate planktonic archaeal assemblages in widely different oceanic provinces. Appl Environ Microbiol 66: 1777-1787. Massana R, Castresana J, Balagué V, Guillou L, Romari K, Groisillier A et al (2004). Phylogenetic and ecological analysis of novel marine stramenopiles. Appl Environ Microbiol 70: 3528-3534. Chapter 5 119 Massana R, Terrado R, Forn I, Lovejoy C, Pedrós-Alió C (2006). Distribution and abundance of uncultured heterotrophic flagellates in the world oceans. Environ Microbiol 8: 1515-1522. Massana R (2011). Eukaryotic picoplankton in surface oceans. Annu Rev Microbiol 65: 1-47. Medlin L (2007). If everything is everywhere, do they share a common gene pool? Gene: 1-4. Nolte V, Pandey RV, Jost S, Medinger R, Ottenwälder B, Boenigk J et al (2010). Contrasting seasonal niche separation between rare and abundant taxa conceals the extent of protist diversity. Mol Ecol 19: 2908-2915. Not F, Massana R, Latasa M, Marie D, Colson C, Eikrem W et al (2005). Late summer community composition and abundance of photosynthetic picoeukaryotes in Norwegian and Barents Seas. Limnol Oceanogr 50: 1677-1686. Not F, Latasa M, Scharek R, Viprey M, Karleskind P, Balagué V et al (2008). Protistan assemblages across the Indian Ocean, with a specific emphasis on the picoeukaryotes. Deep-Sea Res I 55: 1456-1473. O’Malley MA (2008). ‘Everything is everywhere: but the environment selects’: ubiquitous distribution and ecological determinism in microbial biogeography. Stud His Phil Biol Biomed Sci 39: 314-325. Orsini L, Procaccini G, Sarno D, Montresor M (2004). Multiple rDNA ITS-types within the diatom Pseudonitzschia delicatissima (Bacillariophyceae) and their relative abundances across a spring bloom in the Gulf of Naples. Mar Ecol Prog Ser 271: 87-98. Pommier T, Canbäck B, Riemann L, Boström KH, Simu K, Lundberg P et al (2006). Global patterns of diversity and community structure in marine bacterioplankton. Mol Ecol 16: 867-880. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J et al (2007). SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 35: 7188-7196. Rambaut A (2009). FigTree. Institute of Evolutionary Biology, University of Edinburgh, United Kingdom. http://tree.bio.ed.ac.uk/software/figtree. Ramette A (2009). Quantitative Community Fingerprinting Methods for Estimating the Abundance of Operational Taxonomic Units in Natural Microbial Communities. Appl Environ Microbiol 75: 2495-2505. Rodríguez F, Derelle E, Guillou L, Le Gall F, Vaulot D, Moreau H (2005). Ecotype diversity in the marine picoeukaryote Ostreococcus (Chlorophyta, Prasinophyceae). Environ Microbiol 7: 853-859. Rodríguez-Martínez R, Labrenz M, Del Campo J, Forn I, Jürgens K, Massana R (2009). Distribution of the uncultured protist MAST-4 in the Indian Ocean, Drake Passage and Mediterranean Sea assessed by real-time quantitative PCR. Environ Microbiol 11: 397-408. Rodríguez-Martínez R, Rocap G, Logares R, Romac S, Massana R (2012). Low evolutionary diversification in a widespread and abundant uncultured protist (MAST-4). Mol Biol Evol: e-pub ahead of print 13 December 2011, doi: 2010.1093/molbev/msr2303. Rohde K (1992). Latitudinal gradients in species diversity: the search for the primary cause. Oikos: 514-527. Sexton PF, Norris RD (2008). Dispersal and biogeography of marine plankton: Long-distance dispersal of the foraminifer Truncorotalia truncatulinoides. Geology 36: 899-902. Stamatakis A (2006). RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22: 2688-2690. ter Braak C, Smilauer P (2002). CANOCO reference manual on CanoDraw for Windows User’s guide: Software for Canonical Community Ordination (version 4.5). Ithaca, NY: Microcomputer Power. Vyverman W, Verleyen E, Sabbe K, Vanhoutte K, Sterken M, Hodgson DA et al (2007). Historical processes constrain patterns in global diatom diversity. Ecology 88: 1924-1931. Whitaker RJ (2003). Geographic Barriers Isolate Endemic Populations of Hyperthermophilic Archaea. Science 301: 976-978. Whitfield J (2005). Biogeography: Is Everything Everywhere? Science 310: 960-961. Winter C, Moeseneder MM, Herndl GJ, Weinbauer MG (2008). Relationship of Geographic Distance, Depth, Temperature, and Viruses with Prokaryotic Communities in the Eastern Tropical Atlantic Ocean. Microb Ecol 56: 383-389. Zuker M (2003). Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31: 3406. SynthesisofResults andGeneralDiscussion Synthesis of results and general discussion 129 chapter 4 was consistent with the same clades. The 28S rDNA tree displayed the same five clades as the 18S rDNA tree, although they appeared better resolved and separated with longer phylogenetic distances. Finally, the tree based on the end of the 18S rDNA and the very variable ITS1 region revealed the same five clades previously observed, sometimes organized in separate subclades (figure 2, chapter 5). So, despite the presence of a huge number of MAST-4 cells in the oceans and the analysis of hundreds to thousands of environmental sequences, its diversity is structured into only five main clades. The hypothetical number of species Different approaches have been used for the theoretical delimitation of different biological species within MAST-4 (chapters 4 and 5). First, we considered the ITS2 secondary structure (figure 5, chapter 4), since it has been shown that strains exhibiting at least one compensatory base change (CBC) in the conserved nucleotides of helices II (5 paired bases) and III (18-30 continuous positions) belonged to different biological species (Coleman 2003, Coleman 2007, Coleman 2009). The presence of a hemiCBC (one sided) could allow some weak degree of interbreeding (Coleman 2009). For instance, two Pseudo-nitzschia strains differing by three hemiCBCs produced zygotes but never gave viable offspring, thus being considered as separate species (Amato et al 2007). A second criterion was the inspection of analogous regions in the ITS1 secondary structure. Whereas the ITS2 has been widely investigated, the ITS1 still lacks of a universal core secondary structure model. For our ITS1 sequences, we established a common core secondary structure with three helices and found a region with a similar behavior than the ITS2 with respect to species delimitation. It consisted in four base pairs at the stem III that were conserved within defined clades but differed with CBCs between them. When expanded to the fifth base pair, polymorphisms appeared within some clades (figure 6, chapter 4). Third, we contrasted the tree topologies recovered by the ITS1 and ITS2 regions (figure 4B, chapter 4). We hypothesize that groups that have diversified enough to constitute different species will display congruent topologies in their ITS1 and ITS2 trees, since no recombination would exist among these markers. In contrast, groups that may still constitute one single species might display incongruent topologies due to recent recombination events. Basically, this is the concordance–discordance principle used for the recognition of phylogenetic species (Taylor et al 2000), which can be readily applied using these rapidly evolving spacers (Coleman 2007, Mullineux and Hausner 2009). Forth, we compared the intraclade divergence in the ITS region of MAST-4 clades with that observed in other species (tables 1 and 2, chapter 4). Finally, we applied tools from population genetics in the species delimitation: median-joining (MJ) networks (Bandelt et al 1999) that infer intraspecific phylogenies and visualize alternative potential evolutionary paths (figure 2, chapter 5), and the fixation index (Fst) that estimates the genetic differentiation between populations (figure 3, chapter 5). Synthesis of results and general discussion 130 We combined these five criteria to investigate the correspondence of phylogenetic clades and biological species within MAST-4. ITS1 and ITS2 secondary structures differed by CBCs among clades in the conserved regions, indicating that each clade is clearly a separate biological species. Then, we looked within each particular clade. Clade A seemed to be composed of only one species. It showed no polymorphism in the critical ITS regions, the tree topologies were incongruent and the sequence divergence was very low. Moreover, it presented a well mixed MJ network and a high gene flow between different sites. Clade B presented only one motif in the ITS conserved regions, although the ITS2 conserved fragment in the helix III was short. Sequence divergence was high, forming three phylogenetic subclades and exhibiting a strong separated structuring in the MJ network. For these reasons this clade may include two or three species. Clade C had one hemiCBC between several clones in helix II and a short conserved fragment for helix III of the ITS2 and four helix III motifs of the ITS1. The tree topology revealed three subclades and the MJ network presented a strong population structuring between sites. In addition, the intraclade divergence was similar to the average minimum divergence between species. So, we concluded that clade C might include three to four species. Clade D was undersampled, and the four available sequences already suggest two different species. Clade E presented four ITS1 motifs, two of them forming well differentiated lineages, and the other two, included in the E1 subclade, with only one hemiCBC in the more variable fifth base pair. Sequences within subclade E1 showed no polymorphisms in ITS2 conserved regions, had low sequence divergence, some incongruent ITS tree topologies, and well mixed populations between distant sites with a low Fst, so it seemed to form a single species. Overall, three species may be present in clade E. Despite the huge number of MAST-4 cells in the oceans, the current evidence after the inspection of 250 sequences derived from 5 different sites indicates a maximum of 13 separate biological species. Low evolutionary diversification MAST-4 diversity is structured into just five main clades, each representing at least one biological species with a current evidence of a maximum of 13 (chapters 4 and 5). This lineage appears as a well-supported discrete group in 18S rDNA phylogenies, and the closest relative sequences are only 91% similar. In addition, the maximal 18S rDNA sequence divergence (chapter 4) within MAST-4 is 0.044, a very low value as compared with other protist groups (Pernice M, personal communication). Overall, the genetic divergence of MAST-4 is surprisingly low for such a widespread and abundant organism, indicating a very low evolutionary diversification within the group. This low evolutionary diversification points to either a very recent evolutionary divergence and worldwide dispersal or to a very strong environmental filtering that penalizes any deviation from an optimal cell design. A similar scenario of low diversity and cosmopolitan distribution seems to exist in other picoeukaryotes, such as the prasinophyte Micromonas (Slapeta et al 2006). Synthesis of results and general discussion 131 Biogeography MAST-4 was present in virtually all samples from epipelagic waters and with temperatures above 5°C (chapter 1). However, it has been determined that the MAST-4 group includes several species with sequences retrieved coming from distant oceanic sites (chapter 4). The question of the distribution of each of these species still remains to be elucidated and defining particular specific distributions can also give some clues to ecotypic differentiation. Sampling of MAST-4 diversity was based on molecular markers easily amplified from natural samples. This approach, initially based on the 18S rDNA, also targeted the highly variable ITS1 region, which provides enhanced taxonomic resolution for diversity and biogeography studies (Brown and Fuhrman 2005). The ITS1 region has been used for studying interand intraspecific population variations (Orsini et al 2004) or to compare assemblages through ARISA (Automated Ribosomal Intergenic Spacer Analysis) fingerprinting (Hubbard et al 2008). In chapter 5, to study the biogeography of MAST-4, we combined ARISA and 18S-ITS1 clone libraries and examined both spatial and temporal community changes and associated environmental factors. We confirmed the ITS region as a good marker for studying microbial biogeography. The analysis of MAST-4 assemblages using ARISA fingerprints showed that temperature was the main factor influencing the distribution patterns (figure 6, chapter 5). This has been observed in other marine microbes like Prochlorococcus (Martiny et al 2009). Mantel tests between genetic and geographic distances gave weak correlations, indicating that spatial distance was not important in explaining sample composition. In contrast the correlation was high comparing temperature instead of geographic distances. In a second analysis, distributing the samples in a 2-D space (NMDS plot) did not reveal any trend for the different cruises (geographic location), but suggested a clear grouping according to sample temperature. Distant samples sharing the same temperature could have similar MAST-4 diversity, whereas samples with different temperature were very different. Further statistical tests confirmed that temperature was the factor explaining most of the variance (CCA and PERMANOVA) and that temperature groups were significantly different among them (ANOSIM). This global pattern was seen in a temporal study in a single geographic site (with a 12-24°C thermal cycle) (figure 8, chapter 5). Samples from different years, grouped again by temperature, confirmed the importance of this structuring factor also in a temporal scale. Moreover, the Fst analysis revealed a very large gene flow (within clades A and E1) between distant samples at similar temperatures. Other abiotic factors that are known to be important in defining microbial community composition, such as salinity (Logares et al 2009) and sampling depth (Winter et al 2008), took importance within each defined temperature group. Thus, the spatial distribution of MAST-4 seemed to be mainly controlled by contemporary environmental factors with a null or low degree of provincialism. Synthesis of results and general discussion 132 A more detailed analysis of clade A, probably constituting a single species, revealed that it was widely distributed (it appeared in all five clone libraries) and very well mixed (figure 2, chapter 5). The five populations examined from this clade exhibited a high gene flow (figure 3, chapter 5) and samples from distant places with the same temperature displayed completely mixed populations in the MJ network with few estimated mutations per ribotype. A similar scenario occurred within subclade E1, probably a single species as well. This subclade even had a lower proportion of mutations per ribotype. On the contrary, clades B and C, probably composed by more than one species, exhibited some spatial structuring, with subclades appearing in only one library and with many more mutations per ribotype among subclades. This intriguing feature, perhaps indicating some dispersal restriction for clades B and C, could also be due to undersampling and deserves more attention in future surveys. If confirmed by further results, the dispersal limitation of clades B and C would be comparable to that found with the cosmopolitan marine planktonic diatom Pseudo-nitzschia pungens (Casteleyn et al 2010). There were five OTUs (ARISA peaks) that drove the differences among cold, temperate and warm samples, highlighting specific MAST-4 lineages that seem adapted to different temperature regimes. Thus, clade B, and either or possibly both clades A or C, seemed to be characteristic of temperate and warm waters, whereas clade E1, represented by OTU 589, was the only one able to inhabit cold waters. The genetic structure of MAST-4 with different lineages, some ubiquitous in the oceans and with particular ecological properties, resembles that of other marine picoeukaryotes. Thus, different Ostreococcus (Rodríguez et al 2005) and Synechococcus lineages (Ahlgren and Rocap 2006) are adapted to different light levels, whereas a Micromonas pusilla clade adapted to cold temperature has also been reported (Lovejoy et al 2006). It has been proposed that this ecotypic differentiation can partly explain the success of these picoeukaryotes, allowing them to exploit the whole spectrum of habitat variability. The uncultured free-living protist MAST-4 is widely abundant and very small, and thus possesses the properties for a worldwide distribution (Finlay 2002). Moreover it lives in a marine habitat, where wind, waves and currents produce mixing events that facilitate the dispersion. However, we did not observe all MAST-4 clades at all locations but saw biogeographical patterns, stressing the importance of the end of the tenet, “but the environment selects”. It is reasonable to hypothesize that MAST-4 has a huge dispersal capacity and can arrive everywhere within a marine habitat. For instance, there is one record of a MAST-4 sequence in the Arctic Ocean (Lovejoy and Potvin 2011), showing the potential to arrive to such high latitudes probably dragged by coastal currents of Pacific water. But then, depending on the environmental conditions, different organisms will settle and grow up, resulting in different community patterns. These are related to some form of ecological differentiation between related types, as described for Skeletonema species (Kooistra Synthesis of results and general discussion 133 et al 2008). Although microorganisms could spread across all suitable habitats, local adaptations eventually reduce the gene flow and promote speciation (Medlin 2007). As conclusion, the widespread MAST-4 protist is an ideal model for studying microbial biogeography, displaying no dispersal limitation in marine systems together with strong environmental selection. Conclusions Conclusions 137 1) MAST-4isastructuralcomponentofprotistassemblagesinmarinetemperatephoticwaters. Itispresentinvirtuallyallsamplesfromepipelagicwaters(surfaceto120m)andwith temperaturesabove~5°C. 2) The average abundance of MAST-4 in systems with warm temperatures (16-24°C) was similar,attherangeof100to150cellsml-1.Atcoldertemperaturesitwaslessabundant. 3) Bacteriatestedinthis thesis(from0.07to0.18 µm3)haveacellsizethatiswithinthe ediblerangeforMAST-4.Ittypicallyeats1to3bacteriaperhour,whichdefinesitasnot very voracious predator. Moreover, MAST-4 appears to prefer bacteria that are in good physiologicalstate,with2-3timeshighergrazingratesoflivebacteriaversusdeadFLB. 4) Natural heterotrophic flagellates had a Ks lower than that of the traditional cultured flagellates,thusbeingwelladaptedtotypicalbacterialabundancesofmarineplanktonic environments.Specifically,MAST-4presentedaKsof8.7105bacteriaml-1. 5) ThefunctionaldiversityobservedbetweenMAST-4andotherheterotrophicflagellates(such asMAST-1C,M. minutacandidatusandParaphysomonas)givesanecologicalmeaningtothe highphylogeneticdiversityofmarineheterotrophicprotists,withdifferenttaxaadaptedto differentecologicalniches. 6) DespitethepresenceofahugenumberofMAST-4cellsintheoceansitsdiversityisstructured intojustfivemainclades,eachrepresentingatleastonebiologicalspecies.Thecurrent evidenceindicatesamaximumof13separatespecies. 7) Thegeneticdivergenceof MAST-4 was surprisingly lowforan organismsowidespread andabundant,indicatingaverylowevolutionarydiversification,pointingtoeitheravery recentevolutionarydivergenceortoaverystrongenvironmentalfilteringthatpenalizes anydeviationfromanoptimalcelldesign. 8) We did not see marine geographical barriers for the dispersal of the most represented MAST-4 clades, whereas temperature was the main factor influencing the distribution patterns. 9) Theenvironmentseemedtomakeataxonomicselection,withdifferentcladesofMAST-4 withsupposedphysiologicaladaptationsestablishedindifferentconditions.Inparticular, cladeE1,representedbyOTU589,wastheonlyoneacclimatedtocoldwaters.Thisecotypic differentiationcouldpartlyexplainthesuccessofthispicoeukaryote,allowingittoexploit thewholespectrumofhabitatvariability. Conclusions 138 Future perspectives 1) WehaveseenthroughoutthisthesistheimportanceoftheMAST-4lineage.Therefore,there isanurgentneedtohaveitinculture.Wethinkthatthisisfeasible,speciallywhentaking advantageofthenewdatagatheredaboutthisunculturedmicrobe,suchasitsfeedinghabits, environmentalconstrains,andoptimizedprobesforfastmoleculardetection. 2) CladesAandEprovideageneralpictureofbiogeographicpatterns,governedbytemperature andnotgeographicdistance.However,lessclearresultsareseenintheotherclades,probably duetoundersampling.Itwouldbeverymotivatingtocompletethisstudyaddingmanymore sequencesfromdifferentsites.ThiscouldbedonebyexploitingtheprimersandPCRsets designedherefortheuseinmassivesequencinglikepyrosequencing.Increasingthenumber ofsequenceswillindeedrefinethepopulationgeneticswithinthisprotist. 3) WehaveproposedaconservedregionintheITS1secondarystructureforthedelimitation ofbiologicalspecies,similarlytoconservedregionsattheITS2.Itwouldbeinterestingto corroboratethiswithbreedingexperiments. 4) Singleamplifiedgenomicsis a promisingapproach togetthegenomiccontentofthese unculturedmicrobesandtoidentifyspecificbiologicalinteractions. 5) ItwouldbeinterestingtointegrateMAST-4inmicrobialfoodwebs,andanswerhowmuch carbonitisprocessingand,moreintriguingly,whatareitsmortalityfactors(likelypredation andperhapsviralinfection). Resumen de la tesis 145 MALV-V MALV-I MALV-III MALV-II MAST-4 MAST-7/8 alveolates stramenopiles archaeplastida Charaphyta Chlorophyta Land plans prasinophytes Rhodophyta Glaucophyta Apusozoa Polycystinea Toxopodida Acantharea Gromia foraminifera euglyphid amoebas cercomonads Chlorarachniophyta Phaeodaria Desmothoracida rhizaria ciliates apicomplexa Actinophryidae dinoflagellates bicosoecids MAST-3 oomycetes diatoms Chrysophytes xantophytes Pelagophyceae Dictyochophytes MAST-1 labyrinthulids opalinids telonemids cryptophytes Picobiliphytes centrohelids haptophytes core jakobids acrasid amoebas heterolobosea euglenids trypanosomes leishmania diplomonads retortamonads trichomonads parabasalids oxymonads Malawimonas excavates CCTH opisthokonts amoebozoa acanthamoebas tubulinid amoebas flabellinid amoebas Dictyostelia Myxogastria Protostelia archaemoebae Thecamoeba Mayorella mesomycetozoa choanoflagellates animals microsporidia fungi nucleariids taxones altamente derivados con la estructura celular interna simplificada y carentes de mitocondria aeróbica. CCTH es un nuevo supergrupo (Burki et al 2009) propuesto para relacionar varios linajes filogenéticos importantes pero difíciles de localizar, como los Haptófitos, Criptófitos y Telonemida (Shalchian-Tabrizi et al 2006). Además el CCTH también incluye los Ketablefáridos (sabiendo que están emparentados con los Criptófitos) (Okamoto y Inouye 2005) y quizás los Picobilifitas, una nueva clase de fitoplancton que inicialmente no aparecía relacionada con ningún supergrupo (Not et al 2007). Una consecuencia del marco molecular es que muchos protistas incertae sedis (Patterson y Zöffel 1991) están encontrando su posición filogenética en el árbol eucariota. Además, las secuencias del gen 18S ARNr de los representantes cultivados son cruciales en la colocación de los protistas dentro de este contexto filogenético (Cavalier-Smith y Chao 2003, Scheckenbach et al 2005). Figura I.1. Árbol de la vida eucariota. Filogenia consenso de los principales grupos eucariotas basada en datos publicados de filogenia molecular y utraestructural. Las líneas punteadas indican las posiciones de los principales linajes, conocidos principalmente mediante técnicas moleculares independientes de cultivo. MALV (alveolados marinos), MAST (estramenópilos marinos) y CCTH (Criptófitos, Centrohelida, Telonemida más Haptófitos). Figura adaptada de (Baldauf 2003). Spanish summary 146 Un plan corporal muy común en el árbol de la vida eucariota es el de los microorganismos unicelulares no coloreados con uno o pocos flagelos. Este tipo de organización, que por lo general se refiere a los protozoos (o heterótrofos) flagelados, puede ser observado en 27 de los 60 linajes de protistas dentro de los eucariotas (Patterson y Larsen 1991). Por lo tanto, los flagelados son un grado de organización y no un conjunto monofilético consistente. Son organismos que pasan la mayor parte de su existencia moviéndose o alimentándose con un número reducido de flagelos. Su tamaño oscila entre 1-2 µm y 20 µm. El flagelo surgió temprano en la evolución de los eucariotas y no somos capaces de identificar ningún grupo de protistas que primitivamente no tengan flagelo. Se cree que el último ancestro común eucariota era también una especie de flagelado originado por una fusión simbiogenética entre eubacterias y arqueobacterias (Margulis et al 2006). Y obviamente, este eucariota primitivo era incoloro y heterótrofo. Estudios moleculares microbianos incrementan la diversidad eucariota El uso de la biología molecular en la ecología microbiana, desarrollado durante el final del siglo XX, ha transformado el campo de la diversidad de los protistas. En general, la identidad de la mayoría de los frágiles y diminutos protistas era muy difícil de evaluar mediante el examen directo de las muestras naturales. Por lo tanto, un método clásico de identificación era, y sigue siendo, obtener estos organismos en cultivo para una clasificación adecuada. En el caso de los protistas autotróficos, las cepas cultivadas son más o menos representativas de las comunidades naturales. Probablemente esto se debe a que es fácil simular las condiciones naturales para ellos en una botella de cultivo, ya que estas células requieren principalmente nutrientes inorgánicos y luz. No obstante, es probable que los cultivos no cubran la diversidad total de los protistas autotróficos in situ (Vaulot et al 2008). En el caso de los protistas heterotróficos, la larga lista de especies formalmente descritas (Lee y Patterson 1998) deriva principalmente de cultivos o enriquecimientos que parten del suministro de un sustrato para el crecimiento de las bacterias, que a su vez, son el alimento de los protistas. Las células cultivadas proporcionan información ecofisiológica fundamental, pero al mismo tiempo que es obvio que estas cepas fácilmente enriquecidas viven en el mar, es dudoso que sean los miembros dominantes de los ensamblajes naturales. Un estudio ya clásico demostró que los protistas bacterívoros dominantes en varios enriquecimientos eran raros en las muestras originales (Lim et al 1999). Estudios más recientes han confirmado estos resultados y han proporcionado la explicación mecanicista de este sesgo del cultivo de los flagelados heterotróficos (HF) (del Campo 2011). Estudios ambientales de secuenciación de los genes del ARNr 18S (independientes de cultivo) se han traducido en una mayor apreciación de la diversidad de los protistas en la naturaleza. De esta manera, los estudios moleculares han revelado numerosas secuencias de protistas desconocidos, Resumen de la tesis 147 indicando niveles no previstos de la diversidad de los protistas en muchos ambientes y recuperando muy pocas secuencias relacionadas con protistas cultivados (Amaral-Zettler et al 2009, Brown et al 2009, Countway et al 2007, Díez et al 2001, Head et al 1998, Lim 1996, López-García et al 2001, Moon-van der Staay et al 2001, Richards et al 2005, Stoeck et al 2006, Vigil et al 2009). En estudios marinos estos análisis han revelado un gran número de linajes no cultivados, como los alveolados marinos (MALV) y los estramenópilos marinos (MAST) (Massana et al 2004a), los cuales prácticamente aparecen en todos los estudios, aunque todavía la mayor parte de esta diversidad sigue siendo poco conocida. Por lo tanto, está claro que el aislamiento de cepas en cultivo y los estudios moleculares proporcionan diferentes puntos de vista en la composición de especies de protistas marinos en general y de HF en particular. Mientras que estos estudios ambientales fueron a menudo utilizados con el fin ecológico de identificar los miembros dominantes de los ensamblajes naturales, es obvio que también han aportado nuevos conocimientos fundamentales de la filogenia eucariota, así como nuevas ramas en el árbol de la vida formadas exclusivamente por estos linajes no cultivados. Los estramenópilos, un importante supergrupo en los sistemas marinos El supergrupo de los Estramenópilos (Adl et al 2005) está formado por muchos linajes heterogéneos, algunos de ellos de vital importancia en los sistemas marinos. Una de las pocas características compartidas por la mayoría de las células móviles de los estramenópilos es la presencia de un flagelo con dos filas opuestas de mastigonemas, pelos tripartitos (“estramenópilos”), los cuales invierten el flujo alrededor del flagelo de manera que la célula se arrastra hacia delante más que se impulsa. La mayoría también tienen un segundo flagelo más corto y liso (de ahí el nombre alternativo “heterokontos”). Este grupo extraordinariamente diverso incluye numerosos linajes unicelulares de heterótrofos (Bicosoécidos) y fotótrofos (Diatomeas), hongos mucilaginosos (Laberintúlidos), parásitos plasmodiales (Oomycetes) y algas multicelulares que pueden alcanzar un gran tamaño (Feófitas). Existen por lo menos cinco linajes conocidos de estramenópilos no fotosintéticos (figura I.2) (Baldauf 2008). Los Oomycetes (mohos marinos y mohos vellosos) fueron previamente clasificados como hongos e incluyen numerosos parásitos de plantas, extremadamente destructivos, como Phytophthora infestans, causante de la plaga de la patata y Plasmopara viticola, causante del moho de la vid. Los Bicosoécidos son pequeños biflagelados heterotróficos tales como la bien conocida Cafeteria (Fenchel 1988). Los Blastocystis spp. son comensales en los intestinos de animales (Stechmann et al 2008) y algunas especies, como el Blastocystis hominis, pueden infectar a humanos. Los Laberintúlidos (hongos mucilaginosos) forman redes filamentosas, en forma de raíles, creadas por células ameboides. Se colocaron junto con los Traustoquítridos (Cavalier-Smith et al 1994), Spanish summary 148 que también tienen la tendencia de formar agregados celulares. Una taxonomía más fina de ambos grupos requiere comparaciones del gen ribosomal del 18S (Honda et al 1999). Figura I.2. Fotografías de varios ejemplos de estramenópilos heterotróficos. a) Developayella elegans; b) el bicosoecido Cafeteria roenbergensis; c) Blastocystis hominis; Laberintulidos: d) Aplanochytrium, e) Thraustochytrium y f) Labyrinthula terrestris; Oomycetes: g) Pasmopara viticola, h) Phytophthora infestans, i) Saprolegnia. Las imágenes inferiores representan organismos infectados por los oomycetes superiores, j) mildiú de la vid, k) patatas y l) trucha. Las fotos son cortesía de WJ. Lee, D. Patterson, L.A. Zettler, V. Edgcomb, C. Leander, D. Porter, J. Harper, S. Lew y E. Haugen. Resumen de la tesis 149 Los estramenópilos fotosintéticos (figura I.3) están formados por al menos once linajes diferentes, donde se incluyen algunas de las algas más importantes y abundantes (Baldauf 2008). Las Diatomeas tienen generalmente dos tecas de sílice con patrones intrincados que encajan entre sí como una caja y su tapa. Son ubicuas y a menudo abundantes en aguas dulces y marinas, con ≈11.000 especies descritas y posiblemente hasta 107 especies no descritas (Fehling et al 2007). Las Crisófitas (algas doradas) son generalmente organismos unicelulares de vida libre, pero también forman colonias y filamentos. Las Crisófitas pigmentadas contienen clorofila y un carotenoide llamado fucoxantina que les confiere un color marrón amarillento. Se consideraron mayormente de agua dulce, pero estudios recientes sugieren que también podrían ser bastante abundantes en el plancton marino (Fuller et al 2006, Lepère et al 2009, Shi et al 2011). Las Feófitas (algas pardas) están particularmente extendidas en zonas templadas intermareales y submareales. Tienen verdadero parénquima y forman extensiones de aspecto boscoso en aguas cercanas a la costa, como los llamados bosques de kelp, algas marinas gigantes que contienen ecosistemas complejos incluyendo peces y mamíferos marinos. Las Xantofíceas (algas verde-amarillentas) son los principales productores en algunas marismas de agua salobre y forman también organismos multicelulares. Los grupos restantes están formados por algas muy pequeñas, como las Dicteocófitas, Eustigmatófitas, Faeotamniófitas, Pelagofíceas y Pinguiófitas (Vaulot et al 2008). Las Pelagofíceas son una clase descrita hace poco tiempo (Andersen et al 1993), clasificadas previamente dentro de las Crisofíceas, y podrían ser importantes en el picoplancton oceánico. Spanish summary 150 Figura I.3. Fotografías de varios ejemplos de estramenópilos fotosintéticos. a) El xantófito Botrydium; Feofitos: b) Padina, c) Colpomenia, d) Pelagophycus porra, e) Fucus vesiculosus y f) Macrocystis integrifolia; Diatomeas: g) Stephanodiscus, h) Coscinodiscus, i) Cymbella tumida y j) Phaeodactylum tricornutum; k) el eustigmatofito cepa 29.96; Phaeothamniophytes: l) Stichogloea doederleinii y m) Phaeothamnion confervicola; Crisofitas: n) Chrysocapsa epiphytica, o) Spumella sp. y p) Synura y q) la pinguiofita Pinguiococcus pyrenoidosus. Las fotos son cortesía de I. Inouye, R. Tan, E. Bierman, D. Mann, A. de Martino, C. Bowler, J.C. Baley, Y. Tsukii, D. Patterson, B. Andersen y U.S. Geological Survey. Resumen de la tesis 151 MAST, linajes de estramenópilos marinos no cultivados Los estramenópilos marinos (MAST) fueron detectados por primera vez a partir de secuencias del ADNr 18S obtenidas de ambientes marinos y sin una ubicación filogenética clara. Forman por lo menos 10 clados en la parte basal de los estramenópilos (Massana et al 2004b), en donde todos los protistas son heterótrofos, e incluyen flagelados fagotróficos de vida libre (bicosoécidos), parásitos (blastocistos) u osmótrofos (oomycetes y laberintúlidos) (figura I.4). Los MAST son muy recurrentes en los estudios moleculares, encontrándose en los cinco océanos mundiales, con la mayoría de las secuencias afiliadas a unos pocos clados (MAST-1, MAST-3, MAST-4 y MAST-7). La naturaleza heterotrófica de los MAST, sospechada inicialmente por su ubicación filogenética, fue confirmada posteriormente mediante FISH (hibridación fluorescente in situ) para el clado-1, clado-2 y clado-4 (Massana et al 2006b) y para el clado-6 (Piwosz y Pernthaler 2010). Las células MAST que forman estos grupos son pequeños protistas (2-8 µm de tamaño), capaces de crecer en la oscuridad y de ingerir bacterias. Además, son bastante abundantes en el plancton marino y representan una fracción significativa de los HF a nivel mundial (hasta un 35%). Figura I.4. Posición filogenética de los estramenópilos marinos (MAST) dentro del supergrupo de los estramenópilos. Árbol con secuencias completas del ADNr 18S [modificado de (Massana et al 2004b)] mostrando las posiciones del los linajes de MAST (rojo) entre los fotótrofos cultivados (verde) y los heterótrofos (gris). Spanish summary 152 0.05 NS4 OLI11066 UEPACCp4 UEPAC05Cp2 IND33.72 IND31.95 IND60.39/41 IND31.55/61 IND58.11 IND58.12 IND31.115 IND60.13 IND60.8 ME1.19 ME1.29 BL000921.16/22 ME1.20 BL000921.40 BL001221.8 ME1.30 BL000921.36 BL000921.9 NA11.4 ENI40076.00355 HE000803.3 ENI42482.00013 ENI42482.00265 ENI47296.00046/00055 RA010613.114 RA001219.34 RA010412.23 RA000412.146 RA000412.67 RA010412.25 HE001005.47 ENI42482.00027/00097 ENI42482.00212 ENI42482.00284 Figura I.5. Árbol filogenético formado por secuencias parciales del ADNr 18S del MAST-4. Cada color identifica secuencias de diferentes regiones (Atlántico: rojo; Pacífico: verde; Índico: gris; Mediterráneo: amarillo). La línea vertical negra muestra la cobertura de la sonda NS4 para FISH. La barra de escala indica 0.05 sustituciones por posición. Figura tomada de (Massana et al 2006b). Un grupo en particular, el MAST-4, se ha encontrado en todas las muestras analizadas (excepto las polares) (figura I.5 e I.6). Es un protista muy pequeño (2-3 µm de tamaño), por lo tanto clasificado como picoeucariota. Su promedio de abundancia es de 130 cél ml-1 y representa el 9% de los protistas heterotróficos en un amplio rango de sistemas marinos (Massana et al 2006b). Este grupo muestra un tamaño corporal consistente en todas las muestras analizadas para un extenso registro de temperaturas (de 5 a 28ºC), incumpliendo la regla de “temperatura-tamaño” según la cual el tamaño corporal disminuye en función del incremento de la temperatura (Atkinson et al 2003). Se definió como HF debido a su rápido crecimiento en la oscuridad, la ausencia de cloroplastos, la observación de vacuolas alimentarias que contenían bacterias y la presencia de un flagelo (Massana et al 2006a) (figura I.7). Debido a su amplia distribución y su abundancia global es probable que las células MAST-4 contribuyan sustancialmente a la cadena alimentaria marina en extensas áreas oceánicas. Hasta el momento se han realizado numerosos esfuerzos, aunque sin éxito, para obtener un representante en cultivo. Es destacable que grupos dominantes en los océanos todavía no estén cultivados y esto remarca la importancia ecológica de la nueva diversidad detectada por métodos moleculares. Resumen de la tesis 153 ab 3 2 1 3 2 1 3 2 1 5 4 3 2 1 7 6 8 3 2 1 4 0 1-10 11-100 >100 MAST-4 cells ml -1 Figura I.6. Distribución global y abundancia de las células del MAST-4 en los océanos. Las estrellas indican lugares donde se han realizado bibliotecas de clones del ADNr 18S, en negro si la biblioteca contiene secuencias del MAST y en blanco si no. Los puntos indican lugares donde se han realizado recuentos de FISH, con diferente color dependiendo de la abundancia de células encontrada. Figura tomada de (Massana et al 2006b). Figura I.7. Micrografías de epifluorescencia de las células del MAST-4. (a) células teñidas con DAPI y su correspondiente visión microscópica bajo luz ultravioleta, (b) células positivas utilizando la técnica de FISH observadas con luz azul. Al comparar a y b se muestra que dos de las cuatro células eucariotas eran MAST-4. La barra de escala es de 10 µm. Se observa que la región nuclear, la más brillante por el DAPI (a), es más tenue para la fluorescencia de FISH (b), coincidiendo con la localización de los ribosomas. El inserto en el panel b muestra una célula del MAST-4 (amplificada 3 veces) con una FLB ingerida. Fotos tomadas de (Massana et al 2002). Resumen de las características del MAST-4: • Eucariota unicelular (protista) • Estramenópilo • Picoeucariota: tamaño de la célula inferior a 3 µm • Flagelado heterotrófico marino • Depredador de bacterias • Mundialmente distribuido excepto en los sistemas polares • Abundante en los ecosistemas marinos (~10% de los flagelados heterotróficos) • Todavía no cultivados Spanish summary 154 La importancia de los picoeucariotas marinos La existencia de vida microbiana en suspensión en la columna de agua marina se conoce desde hace mucho tiempo, pero sólo en las últimas décadas ha surgido una apreciación de la importancia de su significado ecológico y biológico. La mayor parte del conocimiento de los protistas marinos se ha limitado a los grandes taxones microscópicos reconocibles, como las microalgas, los ciliados y los flagelados más grandes. Por el contrario, los picoeucariotas marinos son en gran parte indistinguibles al microscopio óptico. Forman un conjunto de pequeñas células inconspicuas, sólo un poco más grandes que las bacterias marinas (Massana 2011). Los picoeucariotas fototróficos (células pigmentadas) son importantes productores primarios que están en la base de la cadena trófica. Los picoeucariotas heterotróficos (células incoloras) son en su mayoría bacterívoros y desempeñan un papel clave en la canalización de las bacterias hacia niveles tróficos superiores, así como en el reciclaje de nutrientes. La mixotrofía y el parasitismo también son relevantes pero sus relaciones tróficas no han sido tan estudiadas. Hasta ahora, sólo unos pocos picoeucariotas han sido aislados y caracterizados, por lo que permanecen en gran parte sin describir. El picoplancton eucariota es una colección heterogénea de pequeños protistas con un diámetro comprendido entre 0.8 µm en el caso de Ostreococcus tauri, el eucariota más pequeño conocido, hasta un rango superior de 2-3 µm. En 1978 se definió un sistema para la clasificación de los organismos marinos en función de su tamaño, basado principalmente en el proceso de tamizado. De esta forma, los microorganismos fueron divididos en tres categorías: picoplancton (0.2-2 µm de diámetro celular), nanoplancton (2-20 µm) y microplancton (20-200 µm). Inicialmente el picoplancton se pensó para estar casi exclusivamente formado por procariotas y el nanoplancton en su mayoría formado por pequeños eucariotas unicelulares. Sin embargo, pronto se reconoció la existencia y abundancia de los protistas dentro del tamaño del picoplancton. Hoy en día, el término picoeucariota es a menudo utilizado menos rigurosamente para incluir a los protistas con un tamaño de hasta 3 µm (Vaulot et al 2008). Observaciones directas del conjunto de los protistas indican que el límite previamente establecido de 2 µm se sitúa frecuentemente en el medio del espectro de tamaño y que se delimita un grupo más coherente si utilizamos un límite de hasta 3 µm (Massana 2011). Muchos picoeucariotas, tanto fototróficos (como Micromonas pusilla) como heterotróficos (como el MAST-4), son células flageladas (Patterson y Larsen 1991). Por lo tanto una gran proporción de los ensamblajes, conocidos como flagelados fototróficos y heterotróficos, se clasificarían dentro de los picoeucariotas. De esta manera, los picoeucariotas son muy importantes tanto como productores primarios como depredadores de bacterias en los sistemas acuáticos. Hoy en día sabemos que los picoeucariotas son ubicuos en los ambientes marinos poblando la superficie oceánica con una abundancia de alrededor de 1000 cél ml-1. Los picoeucariotas son, sin lugar a dudas, miembros esenciales de los ecosistemas marinos en términos de abundancia celular, biomasa, actividad y