The Sorcerer II Global Ocean Sampling Expedition: Metagenomic Characterization of Viruses within Aquatic Microbial Samples Shannon J. Williamson, Douglas B. Rusch, Shibu Yooseph, Aaron L. Halpern, Karla B. Heidelberg, John I. Glass, Cynthia Andrews-Pfannkoch, Douglas Fadrosh, Christopher S. Miller, Granger Sutton, Marvin Frazier, J. Craig Venter

Why so hard for Classical Marine Biologists and Microbiologists? Enormous # and diversity of microorganisms, Difficult to culture and study in the lab, etc. Venter’s answers, Whole Genome Shotgun Sequencing, computationally derived metabolisms…

Discovery nth 1,500 liters of water x 10 9 new bp of non-redundant sequence, >1,800 new “species”, ~148 new bacterial phylotypes, 1.2 x 10 6 new protein sequences, –~70,000 novel (no match in the database), etc. "We chose the Sargasso seas because it was supposed to be a marine " says Venter wryly. "The assumption was low diversity there because of the desert, extremely low nutrients.” - Venter (Bio-IT World, 4/16/04)

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Points to Ponder What about Unitigs, and the assembly of an environmental sample? All data went to Genbank. J. Craig Venter

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The Sorcerer II Global Ocean Sampling Expedition: Metagenomic Characterization of Viruses within Aquatic Microbial Samples Shannon J. Williamson, Douglas B. Rusch, Shibu Yooseph, Aaron L. Halpern, Karla B. Heidelberg, John I. Glass, Cynthia Andrews-Pfannkoch, Douglas Fadrosh, Christopher S. Miller, Granger Sutton, Marvin Frazier, J. Craig Venter

Detection of Viruses in the Ocean Problems Large viruses (0.1 µm–0.22 µm) get caught in the filters because of their size and geometric shape, Small free living phages flow through the filter, When filtrating large volumes, biomass accumulates on the filter and viruses get caught, Most viruses found within the aquatic microbial communities studies seemed to be in the lytic infection cycle.

Methods Cruise the world Collect L of seawater from each of 37 different stations Record pH, salinity, temperature, etc. of water First:

Methods Pass water through 2.0, 0.8, 0.1 µm filters, Store at -20°C until shipment from next port.

Sequencing Preparation Extract DNA Nebulize DNA –Average of kb fragments Gel electrophoresis extraction –purify and determine lengths Subclone into E. coli Colonies selected for inserts Shotgun sequence inserts

End sequence each insert –Average of 822 bp sequenced per end Sequencing

Same procedure as in humans, Drosophila, dogs, etc. Metagenomic Assembly Unitigs using 98% or 94% homology for overlap Scaffolding Consensus sequence

New uses for shotgun sequencing and assembly; Multiple organisms at once, Likely novel organisms. Metagenomic Assembly Problems? Mate-pair data relied on more heavily, since overlap coverage is low or unknown, Need verification of assembly somehow?

Created multiple distinct assemblies – 98% and 94% homology unitigs –non-preassembled end-pairs at various stringencies for multiple sequence alignments Multiple assemblies allowed cross-referencing, quality assurance. Metagenomic Assembly

Taxonomic Assignment Protein-ORF based strategy 5.6 million sequences from GOS All ORFs in same sequence scaffold compared to NCBI protein database using BLAST Votes tallied from each ORF into pools for scaffold Archea, Bacteria, Eukaryota, Viral 5.0 million sequence assigned using this method

Quantitative PCR Quantifying genes in environmental samples; from station to station? versus one another?

G enes clustered and compared to NCBI –Sequence alignments, not just domains Phylogeny trees generated –Multiple sequence alignments CLUSTALW –Used only long, fairly homologous samples PHYLIP used to build trees –Based on difference matrix Clustering and Phylogeny

Phylogenetic Analyses Figure 2. Phylogenetic trees of all GOS and publicly available psbA(A) and psbD(B) sequences. BS indicates bootstrap values. GOS and public viral sequences are colored aqua and pink respectively. GOS and public prokaryotic sequences are navy blue and lime green respectively. doi: /journal.pone g002

Figure 3. Phylogenetic trees of all GOS and publicly available pstS(A) and talC(B) sequences. BS indicates bootstrap values. GOS and public viral sequences are colored aqua and pink respectively. GOS and public prokaryotic sequences are navy blue and lime green respectively. GOS eukaryotic sequences are colored yellow. doi: /journal.pone g003

Identification of Viral Sequences Data from microbial fraction of water samples was examined Looked for viral sequences by comparison to the NCBI non-redundant protein database 154,662 viral peptide sequences were identified Approximately 3% of predicted proteins were identified as viral sequences Number of viral sequences thought to be largely underestimated

Classification through Protein Clustering Of 154,662 viral peptide sequences, 117,123 or 76% fell within 380 protein clusters containing at least 20 proteins Remaining sequences fell within clusters containing less than 20 proteins Average cluster size contained 258 peptide sequences

All viral gene families were positively correlated with water temperature Some viral gene families were correlated with salinity, water depth, and calculated trophic status indices Different environmental pressures may influence acquisition of these genes by viruses Table S7 shows the correlations between viral gene families and environmental parameters

Neighbor Functional Linkage Analysis Used to verify that they were on viral instead of pro-viral regions of bacterial genomes Proportion of viral same-scaffold ORFs range from 32% to 92% for the metabolic gene families studied Occurrence of viral neighbors on same scaffolds as host- derived viral genes supports hypothesis that sources of the sequences are viruses rather than bacterial

Viruses with Metabolic Genes Through lateral gene transfer, metabolic genes can be acquired from the host Acquisition, retention, and expression of metabolic genes may increase fitness Key metabolic processes and pathways running during infection allows maximum replication Previous studies on host-derived metabolic viral genes has been on the photosynthesis genes psbA and psbD of a cyanophage Previous studies did not focus on abundance or distribution of these genes in the oceans

Host-Derived Metabolic Gene Families In aquatic viral communities sampled, host-derived genes were found widely distributed in significant proportions Quantitative PCR of the these genes confirmed high abundance Not known if these genes were expressed at the time of sampling Unlikely to see these genes in high abundance if they: –Were not expressed –Did not have a fitness advantage

“Suggests that viruses may play a more substantial role in environmentally relevant metabolic processes than previously recognized such as the conversion of light to energy, photoadaptation, phosphate acquisition, and carbon metabolism”

Discussion Most studies have focused on the filtered viral fraction of the data This is the first study to focus on the viral components in the microbial fraction of the data Strong evidence for abundance and distribution of environmentally important host-derived viral gene families Distribution patterns of host-derived viral families over environmental gradients Evidence of interactions between bacteriophage and host organisms

Potential Evolutionary Viral-Host Relationships The study of the cyanophage found that the host- derived genes undergo higher mutation rates than their cyanobacterial nucleotide counterpart After phage acquisition, the genes could diversify Mutated viral genes could form gene reservoirs for the host Through horizontal gene transfer, viruses could promote diversity and distribution

Prochlorococcus – P-SSM4-like Phage Prochlorococcus is one of the most widespread picophytoplankton in the ocean P-SSM4-like phage may influence the abundance, diversity, and distribution of Prochlorococcus Statistically significant relationship between the Prochlorococcus and the P-SSM4-like phage

Metagenomic Viral-Microbial Interactions This study of viral-microbial association between communities was coincidental Horizontal transfer of metabolic genes More studies necessary on the viral-microbial diversity and genetic complement –Community relationships –Evolutionary relationships

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