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UC Davis EVE161 Lecture 17 by @phylogenomics

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UC Davis EVE161 Lecture 17 by @phylogenomics

  1. 1. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Lecture 17: EVE 161:
 Microbial Phylogenomics ! Lecture #17: Era IV: Shotgun Metagenomics ! UC Davis, Winter 2014 Instructor: Jonathan Eisen !1
  2. 2. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Where we are going and where we have been !Previous lecture: ! 16: Era IV: Metagenomic Diversity ! Current Lecture: ! 17: Era IV: Metagenomic Functions ! Next Lecture: ! 18: Metagenomic Case Study and Discussion of Student Presentations !2
  3. 3. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 • Problem Set 4 will be to select a paper by the end of Thursday • And provide a 3-4 sentence description for why it is relevant for the class • Be prepared to present Tuesday next week
  4. 4. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Era IV: Genomes in the environment Era IV: Metagenomic Functions
  5. 5. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 T. T. Paull, M. Gellert, Mol. Cell 1, 969 (1998). .-H. Lee et al., J. Biol. Chem. 278, 45171 (2003). T. T. Paull, M. Gellert, Genes Dev. 13, 1276 (1999). G. Moncalian et al., J. Mol. Biol. 335, 937 (2004). R. Shroff et al., Curr. Biol. 14, 1703 (2004). M. Kastan and R. Abraham for expression constructs; D. Ramsden, M. Gellert, and M. O’Dea for Rag1/Rag2 protein; S. Stevens for technical advice; members of the Paull lab for their help; and R. Rothstein for a helpful word. This work was supported by NIH (grant 6 December 2004; accepted 24 February 2005 Published online 24 March 2005; 10.1126/science.1108297 Include this information when citing this paper. Comparative Metagenomics of Microbial Communities Susannah Green Tringe,1,2 * Christian von Mering,3 * Arthur Kobayashi,1 Asaf A. Salamov,1 Kevin Chen,4 Hwai W. Chang,5 Mircea Podar,5 Jay M. Short,5 Eric J. Mathur,5 John C. Detter,1 Peer Bork,3 Philip Hugenholtz,1 Edward M. Rubin1,2 . The species complexity of microbial communities and challenges in culturing representative isolates make it difficult to obtain assembled genomes. Here we characterize and compare the metabolic capabilities of terrestrial and marine microbial communities using largely unassembled sequence data obtained by shotgun sequencing DNA isolated from the various environ- ments. Quantitative gene content analysis reveals habitat-specific finger- prints that reflect known characteristics of the sampled environments. The identification of environment-specific genes through a gene-centric compar- ative analysis presents new opportunities for interpreting and diagnosing environments. pite their ubiquity, relatively little is known ut the majority of environmental micro- anisms, largely because of their resistance to ure under standard laboratory conditions. A ety of environmental sequencing projects eted at 16S ribosomal RNA (rRNA) (1, 2) offered a glimpse into the phylogenetic ersity of uncultured organisms. The direct uencing of environmental samples has provided further valuable insight into the life- styles and metabolic capabilities of uncultured organisms occupying various environmental niches. The latter efforts include the sequenc- ing of individual large-insert bacterial artifi- cial chromosome (BAC) clones as well as small-insert libraries made directly from envi- ronmental DNA (3–7). The application of high-throughput shotgun sequencing environ- mental samples has recently provided global views of those communities not obtainable from 16S rRNA or BAC clone–sequencing surveys (6, 7). The sequence data have also posed challenges to genome assembly, which suggests that complex communities will demand enormous sequencing expend- iture for the assembly of even the most predominant members (7). A practical question emerging from envi- ronmental sequencing projects is the extent to which the data are interpretable in the absence of significant individual genome assemblies. Most microbial communities are extremely complex and thus not amenable to genome assembly (8). This obstacle may in part be offset by the high gene density of prokaryotes EÈ1 open reading frame per 1000 base pairs (bp)^ and currently attainable read lengths (700 to 750 bp), which result in most individual sequences containing a significant portion of at Fig. 2. Identification of 1 Department of Energy (DOE) Joint Genome Insti- tute, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA. 2 Lawrence Berkeley National Laboratory, Genomics Division, Berkeley, CA 94720, USA. 3 Euro- pean Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. 4 University of Cali- fornia, Berkeley, Department of Electrical Engineering and Computer Science, Berkeley, CA 94720, USA. 5 Diversa Corporation, 4955 Directors Place, San Diego, CA 92121, USA. *S.G.T. and C.v.M. contributed equally to this work. .To whom correspondence should be addressed. E-mail: emrubin@lbl.gov ity. Rarefaction curves lone sequences for soil s. (Inset) Rarefaction lones. The three whale asin bone; 2, Santa Cruz d 3, Antarctic bone. orthologous groups with greater sequencing depth. The number of orthologous groups ob- served at least once is shown as a function of the raw sequence gen- erated. Numbers in parentheses indicate lower limits of the total number of groups in the sample. 22 APRIL 2005 VOL 308 SCIENCE www.sciencemag.org 32. Materials and methods are available as supporting material on Science Online. 33. J. R. Abo-Shaeer, C. Raman, W. Ketterle, Phys. Rev. Lett. 88, 070409 (2002). 34. D. Gue´ry-Odelin, Phys. Rev. A. 62, 033607 (2000). 35. Y. Kagan, E. L. Surkov, G. V. Shlyapnikov, Phys. Rev. A. 55, R18 (1997). 36. C. Menotti, P. Pedri, S. Stringari, Phys. Rev. Lett. 89, 250402 (2002). 37. G. B. Partridge, W. Li, R. I. Kamar, Y.-a. Liao, R. G. Hulet, Science, 311, 503 (2006); published online 22 December 2005 (10.1126/science.1122876). 38. T. Mizushima, K. Machida, M. Ichioka, Phys. Rev. Lett. 94, 060404 (2005). 39. P. Castorina, M. Grasso, M. Oertel, M. Urban, D. Zappala`, Phys. Rev. A. 72, 025601 (2005). 40. A. A. Abrikosov, L. P. Gorkov, I. E. Dzyaloshinski, Methods of Quantum Field Theory in Statistical Physics (Dover, New York, 1975). 41. G. Bertsch, INT Workshop on Effective Field Theory in Nuclear Physics (Seattle, WA, February 1999). 42. T. D. Cohen, Phys. Rev. Lett. 95, 120403 (2005). 43. We thank G. Campbell for critical reading of the manuscript and X.-G. Wen, E. Demler, and S. Sachdev for stimulating discussions. This wo Naval Research, Arm Supporting Online M www.sciencemag.org/cgi/ Materials and Methods Figs. S1 and S2 References and Notes 7 November 2005; acce Published online 22 Dec 10.1126/science.112231 Include this information Community Genomics Among Stratified Microbial Assemblages in the Ocean’s Interior Edward F. DeLong,1 * Christina M. Preston,2 Tracy Mincer,1 Virginia Rich,1 Steven J. Hallam,1 Niels-Ulrik Frigaard,1 Asuncion Martinez,1 Matthew B. Sullivan,1 Robert Edwards,3 Beltran Rodriguez Brito,3 Sallie W. Chisholm,1 David M. Karl4 Microbial life predominates in the ocean, yet little is known about its genomic variability, especially along the depth continuum. We report here genomic analyses of planktonic microbial communities in the North Pacific Subtropical Gyre, from the ocean’s surface to near–sea floor depths. Sequence variation in microbial community genes reflected vertical zonation of taxonomic groups, functional gene repertoires, and metabolic potential. The distributional patterns of microbial genes suggested depth-variable community trends in carbon and energy metabolism, attachment and motility, gene mobility, and host-viral interactions. Comparative genomic analyses of stratified microbial communities have the potential to provide significant insight into higher-order community organization and dynamics. (NPSG) at the op ALOHA (22). The bial genes from th depths was determi fosmid clone termin cloning, and sequen (ranging from 10 large-insert genomi stratified microbial c sequencing of fosm per depth) and co were used to identi pathways that cha microbial assemblag Study Site and Sa Our sampling site (HOT) station AL represents one of characterized sites been a focal point f RESEARCH ARTICLES
  6. 6. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 trient sources (plant material for soil and lipid- rich bone for deep-sea whale fall samples). We first analyzed the microbial diversity in these samples through polymerase chain re- action (PCR)–amplified small rRNA libraries generated for each sample by using primers specific for Bacteria, Archaea, and Eukaryota. In the soil sample, a wide diversity of bacteria, few archaeal species, and some fungi and unicellular eukaryotes were found (fig. S2). We sequenced a total of 1700 clones from two independent libraries of PCR-amplified bacte- rial 16S rRNA sequences prepared from the deep W com inser light soil s of se each pred samp large 150, exhi ribotype accounts for 112 (6.6%) of the clones (fig. S2A) when a 97% identity cutoff is used and 81 (4.8%) when 98% identity is required. The whale fall samples are both less diverse and less evenly distributed than the soil cohort and are estimated to contain between 25 and 150 distinct ribotypes of which the most abundant accounts for 15 to 25% of the library (Fig. 1; fig. S4). The reduced species and phyla diversity of the whale fall microbial commu- nities as compared with soil is consistent with the extreme and specialized nature of this deep-ocean ecological niche. We explored the genomic diversity of the Mb ed i mos for of t ican com inve ing gen gen una (13
  7. 7. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure 1 sequencing of environmental samples has high-throughput shotgun se mental samples has recent Fig. 1. Species complexity. Rarefaction curves of bacterial 16S rRNA clone sequences for soil and whale fall samples. (Inset) Rarefaction curve for all 1700 soil clones. The three whale falls are: 1, Santa Cruz Basin bone; 2, Santa Cruz Basin microbial mat; and 3, Antarctic bone. 22 APRIL 2005 VOL 308 SCIEN554
  8. 8. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure S1 Science Supporting Online Material Tringe et al., p. 11 Figures and legends Fig. S1. Rarefaction curves for 16S phylotypes observed (blue triangles), Chao1 total richness estimator (blue line), and ACE total richness estimator (dotted blue line) for soil. 0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 16S clones sequenced Phylotypes
  9. 9. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure S2 Science Supporting Online Material Tringe et al., p. 12 Fig. S2. rRNA analysis of soil. (A) Rank-abundance curve for bacterial 16S rRNA sequences. (B) Phylogenetic distribution of soil 16S rRNA sequences from PCR clone library (solid) and genomic library (hatched). (C and D) Allocation of (C) archaeal 16S and 9D) eukaryotic 18S rRNA sequences into phyla. A) Soil bacterial 16S sequences 0 20 40 60 80 100 120 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 Rank Abundance
  10. 10. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure S2 Science Supporting Online Material Tringe et al., p. 13 B) Science Supporting Online Material Tringe et al., p. 12 Fig. S2. rRNA analysis of soil. (A) Rank-abundance curve for bacterial 16S rRNA sequences. (B) Phylogenetic distribution of soil 16S rRNA sequences from PCR clone library (solid) and genomic library (hatched). (C and D) Allocation of (C) archaeal 16S and 9D) eukaryotic 18S rRNA sequences into phyla. A) Soil bacterial 16S sequences 0 20 40 60 80 100 120 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 Rank Abundance
  11. 11. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure S2 Science Supporting Online Material Tringe et al., p. 12 Fig. S2. rRNA analysis of soil. (A) Rank-abundance curve for bacterial 16S rRNA sequences. (B) Phylogenetic distribution of soil 16S rRNA sequences from PCR clone library (solid) and genomic library (hatched). (C and D) Allocation of (C) archaeal 16S and 9D) eukaryotic 18S rRNA sequences into phyla. A) Soil bacterial 16S sequences 0 20 40 60 80 100 120 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 Rank Abundance C) Crenarchaeota Euryarchaeota: Methanomicrobia Unknown
  12. 12. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure S2 Science Supporting Online Material Tringe et al., p. 12 Fig. S2. rRNA analysis of soil. (A) Rank-abundance curve for bacterial 16S rRNA sequences. (B) Phylogenetic distribution of soil 16S rRNA sequences from PCR clone library (solid) and genomic library (hatched). (C and D) Allocation of (C) archaeal 16S and 9D) eukaryotic 18S rRNA sequences into phyla. A) Soil bacterial 16S sequences 0 20 40 60 80 100 120 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 Rank Abundance Science Supporting Online Material Tringe et al., p. 14 D) Fungi Alveolata Cercozoa stramenopiles Viridiplantae Lobosea Pelobiontida Metazoa Plasmodiophorida Unclassified
  13. 13. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Science Supporting Online Material Tringe et al., p. 15 Fig. S3. Rarefaction curves for 16S phylotypes observed (triangles), Chao1 total richness estimator (lines), and ACE total richness estimator (dotted lines) for 3 whale falls. Whale fall 1, dark green; whale fall 2, bright green, whale fall 3, light green. 0 20 40 60 80 100 120 140 160 0 20 40 60 80 16S clones sequenced Phylotypes
  14. 14. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Science Supporting Online Material Tringe et al., p. 16 Fig. S4. Rank-abundance curves for whale fall bacterial 16S sequences. (A) Assignment of 16S rRNA sequences to bacterial phyla for both PCR clone libraries (solid bars) and genomic libraries (hatched bars). (B) Whale fall 1, Santa Cruz bone; (C) Whale fall 2, Santa Cruz microbial mat; (D) Whale fall 3, Antarctic bone.
  15. 15. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Science Supporting Online Material Tringe et al., p. 17 B) Whale fall 3051 0 2 4 6 8 10 12 14 16 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 Rank Abundance
  16. 16. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Rank C) Whale fall 3052 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 17 19 21 Rank Abundance
  17. 17. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Science Supporting Online Material Tringe et al., p. 18 D) Whale fall 3053 0 2 4 6 8 10 12 14 16 18 20 1 3 5 7 9 11 13 15 17 Rank Abundance
  18. 18. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 nts u- d- s). in e- es rs ta. a, nd 2). wo e- he nities as compared with soil is consistent with the extreme and specialized nature of this deep-ocean ecological niche. We explored the genomic diversity of the communities by sequencing genomic small- insert libraries made from all four samples. In light of the organismal complexity seen in the soil sample, we generated 100 million bp (Mbp) of sequence from this sample and 25 Mbp for each whale fall library. Consistent with the predicted high species diversity in the soil sample, attempts at sequence assembly were largely unsuccessful. Less than 1% of the nearly 150,000 reads generated from the soil library exhibited overlap with reads from independent genome. In preliminary studies, w gene predictions from assembled se unassembled, using available metag (13). With our analysis supporting of gene predictions on unassembl applied an automated annotation p sequence data from several differ mental samples. As our analysis r ily on the predicted genes on fragments, the majority of which w ual sequence reads, we termed e mental sequence an environmen (EGT), to distinguish EGTs from ing reads primarily used for the genomes. The gene contents of ary hyla mu- with this the all- . In the bp) for the soil were arly ary dent complete genomes from the samples, we investigated the genes present without attempt- ing to place them in the context of an individual genome. In preliminary studies, we compared gene predictions from assembled sequence with unassembled, using available metagenomic data (13). With our analysis supporting the validity of gene predictions on unassembled reads, we applied an automated annotation process to the sequence data from several different environ- mental samples. As our analysis relied primar- ily on the predicted genes on small DNA fragments, the majority of which were individ- ual sequence reads, we termed each environ- mental sequence an environmental gene tag (EGT), to distinguish EGTs from the sequenc- ing reads primarily used for the assembly of genomes. The gene contents of the partially
  19. 19. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 ronmental DNA (3–7). The application of high-throughput shotgun sequencing environ- mental samples has recently provided global Fig. 2. Identification of orthologous groups with greater sequencing depth. The number of orthologous groups ob- served at least once is shown as a function of the raw sequence gen- erated. Numbers in parentheses indicate lower limits of the total number of groups in the sample. *S.G.T. and C.v.M. contributed equally to this work. .To whom correspondence should be addressed. E-mail: emrubin@lbl.gov RIL 2005 VOL 308 SCIENCE www.sciencemag.org
  20. 20. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 unicellular eukaryotes were found (fig. S2). We sequenced a total of 1700 clones from two independent libraries of PCR-amplified bacte- rial 16S rRNA sequences prepared from the sample, attempts at sequence assembly were largely unsuccessful. Less than 1% of the nearly 150,000 reads generated from the soil library exhibited overlap with reads from independent mental sequence an environmental gene tag (EGT), to distinguish EGTs from the sequenc- ing reads primarily used for the assembly of genomes. The gene contents of the partially Fig. 3. Functional profiling of microbial communities. Two-way clustering of samples and encoded functions based on relative enrichment of KEGG functional processes. The 15 most discriminating processes are high- lighted. Asterisks indicate that environmental genes mapping to these processes probably have a broader range of substrates than the KEGG process title indicates. www.sciencemag.org SCIENCE VOL 308 22 APRIL 2005 555
  21. 21. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 number of orthologous groups detected at increasing levels of sequencing depth. For all samples, saturation for frequently occurring domain-oriented Pfam database (17) (fig. S5), which suggests that this phenomenon is not an artifact of comparison to a particular database. fourth, broad functional categories from COG database (13,15). Assembled contigs w weighted to account for the number of in pendent clones contributing to them. A two-way clustering of samples a KEGG maps, in which over- and underr resented categories are indicated by red a blue blocks, respectively, is displayed in Fig (fig. S6 shows similar figures based on CO and operons). Regardless of the functional b ning employed, the independent Sargasso samples clustered together, as did the wh fall samples. These profiles clearly suggest t the predicted protein complement of a co munity is similar to that of other communi whose environments of origin pose sim metabolic demands. Our results further supp the hypothesis that the Bfunctional[ profile o community is influenced by its environm and that EGT data can be used to deve fingerprints for particular environments. To assess the significance of these similari and differences and to identify functions importance for communities existing in spec environments, we systematically examined differences in gene content between samples (F 4). For this analysis, the three whale fall samp were pooled together, as were the three oc samples. At each level, significant differen among the respective microbial communi were observed that suggested environme specific variations in both biochemistry and p logeny. The acid mine drainage was not inclu in this analysis because of its great dissimi ity from the other samples (Fig. 3; fig. S6) low species diversity, both likely reflective the very extreme nature of this environmen At the individual gene level, quite a f orthologous groups are exclusive to a particu environment (Fig. 4, upper left). For exam 73 putative orthologs of cellobiose phospho ase, involved in degradation of plant mater are found in the È100 Mbp of soil sequence, none are found in the È700 Mbp of seque examined from the Sargasso Sea. On the ot Fig. 4. Specific enrichments. Three-way comparisons of soil, whale fall, and Sargasso Sea environments in terms of COGs, operons, KEGG processes, and COG functional categories. Each dot shows the relative abundance of an item in the three environmental samples, such that proximity to a vertex is proportional to the level of enrichment in the respective sample. Color indicates statistical significance of the enrichment. Marked items discussed in main text: 1, COG5524 bacteriorhodopsin; 5, COG3459 cellobiose phosphorylase; 7, ABC-type proline/glycine betaine transport system; 10, Naþ-transporting NADH:ubiquinone reductase; 14, osmosensitive, active Kþ-transport system; 18, photosynthesis; and 19, type I polyketide biosynthesis (antibiotics). A complete listing of numbered items is available in the SOM, and an enhanced version of the figure is at (23). 22 APRIL 2005 VOL 308 SCIENCE www.sciencemag.org556
  22. 22. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  23. 23. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  24. 24. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 occurring artifact of comparison to a particular database. weighte pendent A t KEGG resented blue blo (fig. S6 and ope ning em samples fall sam the pred munity whose metabol the hypo commun and tha
  25. 25. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 the hypo commun and tha fingerpr To a and diff importan environm differenc 4). For t were po samples. among were o specific logeny. in this a
  26. 26. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Science Supporting Online Material Tringe et al., p. 19 Fig. S5. Functional accumulation curves for all samples. Number of unique hits in the (A) COG and (B) Pfam database as a function of sequence depth. The y axis maximum is set to the total number of categories in each database. A) 0 1000 2000 3000 4000 5000 0 50 100 150 200 Sequence (Mb) NumberofCOGs Soil AMD Whale fall 1 Whale fall 2 Whale fall 3 Sargasso 2 Sargasso 3 Sargasso 4
  27. 27. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 B) 0 1500 3000 4500 6000 0 50 100 150 200 Sequence (Mb) Pfamdomainsobserved Soil AMD Whale fall 1 Whale fall 2 Whale fall 3 Sargasso 2 Sargasso 3 Sargasso 4
  28. 28. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Science Supporting Online Material Tringe et al., p. 20 Fig. S6. Two-way clustering of data based on (A) COGs and (B) operons.
  29. 29. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014
  30. 30. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Science Supporting Online Material Tringe et al., p. 21 Fig. S7. Sample tree based on 10 Mb of unassembled sequence from each sample. Total hits to each of 4873 COGs were taken as components of a COG vector; Euclidean distances were calculated among the vectors to create a distance matrix. Tree was generated using Phylip (University of Washington, http://evolution.genetics.washington.edu/phylip.html) and visualized with Phylodendron (University of Indiana, http://www.es.embnet.org/Doc/phylodendron/treeprint- form.html).
  31. 31. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 32. Materials and methods are available as supporting material on Science Online. 33. J. R. Abo-Shaeer, C. Raman, W. Ketterle, Phys. Rev. Lett. 88, 070409 (2002). 34. D. Gue´ry-Odelin, Phys. Rev. A. 62, 033607 (2000). 35. Y. Kagan, E. L. Surkov, G. V. Shlyapnikov, Phys. Rev. A. 55, R18 (1997). 36. C. Menotti, P. Pedri, S. Stringari, Phys. Rev. Lett. 89, 250402 (2002). 37. G. B. Partridge, W. Li, R. I. Kamar, Y.-a. Liao, R. G. Hulet, Science, 311, 503 (2006); published online 22 December 2005 (10.1126/science.1122876). 38. T. Mizushima, K. Machida, M. Ichioka, Phys. Rev. Lett. 94, 060404 (2005). 39. P. Castorina, M. Grasso, M. Oertel, M. Urban, D. Zappala`, Phys. Rev. A. 72, 025601 (2005). 40. A. A. Abrikosov, L. P. Gorkov, I. E. Dzyaloshinski, Methods of Quantum Field Theory in Statistical Physics (Dover, New York, 1975). 41. G. Bertsch, INT Workshop on Effective Field Theory in Nuclear Physics (Seattle, WA, February 1999). 42. T. D. Cohen, Phys. Rev. Lett. 95, 120403 (2005). 43. We thank G. Campbell for critical reading of the manuscript and X.-G. Wen, E. Demler, and S. Sachdev for stimulating discussions. This work was supported by the NSF, Office of Naval Research, Army Research Office, and NASA. Supporting Online Material www.sciencemag.org/cgi/content/full/1122318/DC1 Materials and Methods Figs. S1 and S2 References and Notes 7 November 2005; accepted 14 December 2005 Published online 22 December 2005; 10.1126/science.1122318 Include this information when citing this paper. Community Genomics Among Stratified Microbial Assemblages in the Ocean’s Interior Edward F. DeLong,1 * Christina M. Preston,2 Tracy Mincer,1 Virginia Rich,1 Steven J. Hallam,1 Niels-Ulrik Frigaard,1 Asuncion Martinez,1 Matthew B. Sullivan,1 Robert Edwards,3 Beltran Rodriguez Brito,3 Sallie W. Chisholm,1 David M. Karl4 Microbial life predominates in the ocean, yet little is known about its genomic variability, especially along the depth continuum. We report here genomic analyses of planktonic microbial communities in the North Pacific Subtropical Gyre, from the ocean’s surface to near–sea floor depths. Sequence variation in microbial community genes reflected vertical zonation of taxonomic groups, functional gene repertoires, and metabolic potential. The distributional patterns of microbial genes suggested depth-variable community trends in carbon and energy metabolism, attachment and motility, gene mobility, and host-viral interactions. Comparative genomic analyses of stratified microbial communities have the potential to provide significant insight into higher-order community organization and dynamics. M icrobial plankton are centrally involved in fluxes of energy and matter in the sea, yet their vertical distribution and func- tional variability in the ocean_s interior is still only poorly known. In contrast, the vertical zonation of eukaryotic phytoplankton and zooplankton in the ocean_s water column has been well documented for over a century (1). In the photic zone, steep gradients of light quality and intensity, temperature, part by their obligate growth requirement for elevated hydrostatic pressures (6). In addition, recent cultivation-independent (7–15) surveys have shown vertical zonation patterns among spe- cific groups of planktonic Bacteria, Archaea, and Eukarya. Despite recent progress however, a comprehensive description of the biological properties and vertical distributions of plank- tonic microbial species is far from complete. (NPSG) at the open-ocean time-series station ALOHA (22). The vertical distribution of micro- bial genes from the ocean_s surface to abyssal depths was determined by shotgun sequencing of fosmid clone termini. Applying identical collection, cloning, and sequencing strategies at seven depths (ranging from 10 m to 4000 m), we archived large-insert genomic libraries from each depth- stratified microbial community. Bidirectional DNA sequencing of fosmid clones (È10,000 sequences per depth) and comparative sequence analyses were used to identify taxa, genes, and metabolic pathways that characterized vertically stratified microbial assemblages in the water column. Study Site and Sampling Strategy Our sampling site, Hawaii Ocean Time-series (HOT) station ALOHA (22-45’ N, 158-W), represents one of the most comprehensively characterized sites in the global ocean and has been a focal point for time series–oriented ocean- ographic studies since 1988 (22). HOT inves- tigators have produced high-quality spatial and time-series measurements of the defining physi- cal, chemical, and biological oceanographic pa- rameters from surface waters to the seafloor. These detailed spatial and temporal datasets present unique opportunities for placing microbial ge- nomic depth profiles into appropriate oceano- graphic context (22–24) and leverage these data to formulate meaningful ecological hypotheses. RESEARCH ARTICLES especially along the depth continuum. We report here genomic analyses of planktonic microbial communities in the North Pacific Subtropical Gyre, from the ocean’s surface to near–sea floor depths. Sequence variation in microbial community genes reflected vertical zonation of taxonomic groups, functional gene repertoires, and metabolic potential. The distributional patterns of microbial genes suggested depth-variable community trends in carbon and energy metabolism, attachment and motility, gene mobility, and host-viral interactions. Comparative genomic analyses of stratified microbial communities have the potential to provide significant insight into higher-order community organization and dynamics. M icrobial plankton are centrally involved in fluxes of energy and matter in the sea, yet their vertical distribution and func- tional variability in the ocean_s interior is still only poorly known. In contrast, the vertical zonation of eukaryotic phytoplankton and zooplankton in the ocean_s water column has been well documented for over a century (1). In the photic zone, steep gradients of light quality and intensity, temperature, and macronutrient and trace-metal concentrations all influence species distributions in the water column (2). At greater depths, low temperature, increasing hydrostatic pressure, the disappearance of light, and dwindling energy supplies largely determine vertical stratification of oceanic biota. For a few prokaryotic groups, vertical distrib- utions and depth-variable physiological properties are becoming known. Genotypic and phenotypic properties of stratified Prochlorococcus Becotypes[ for example, are suggestive of depth-variable adaptation to light intensity and nutrient availabil- ity (3–5). In the abyss, the vertical zonation of deep-sea piezophilic bacteria can be explained in part by their obligate growth requirement for elevated hydrostatic pressures (6). In addition, recent cultivation-independent (7–15) surveys have shown vertical zonation patterns among spe- cific groups of planktonic Bacteria, Archaea, and Eukarya. Despite recent progress however, a comprehensive description of the biological properties and vertical distributions of plank- tonic microbial species is far from complete. Cultivation-independent genomic surveys represent a potentially useful approach for char- acterizing natural microbial assemblages (16, 17). BShotgun[ sequencing and whole genome assem- bly from mixed microbial assemblages has been attempted in several environments, with varying success (18, 19). In addition, Tringe et al. (20) compared shotgun sequences of several disparate microbial assemblages to identify community- specific patterns in gene distributions. Metabolic reconstruction has also been attempted with en- vironmental genomic approaches (21). Never- theless, integrated genomic surveys of microbial communities along well-defined environmental gradients (such as the ocean_s water column) have not been reported. To provide genomic perspective on microbial biology in the ocean_s vertical dimension, we cloned large EÈ36 kilobase pairs (kbp)^ DNA fragments from microbial communities at differ- ent depths in the North Pacific Subtropical Gyre microbial assemblages in the water column. Study Site and Sampling Strategy Our sampling site, Hawaii Ocean Time-series (HOT) station ALOHA (22-45’ N, 158-W), represents one of the most comprehensively characterized sites in the global ocean and has been a focal point for time series–oriented ocean- ographic studies since 1988 (22). HOT inves- tigators have produced high-quality spatial and time-series measurements of the defining physi- cal, chemical, and biological oceanographic pa- rameters from surface waters to the seafloor. These detailed spatial and temporal datasets present unique opportunities for placing microbial ge- nomic depth profiles into appropriate oceano- graphic context (22–24) and leverage these data to formulate meaningful ecological hypotheses. Sample depths were selected, on the basis of well-defined physical, chemical, and biotic char- acteristics, to represent discrete zones in the water column (Tables 1 and 2, Fig. 1; figs. S1 and S2). Specifically, seawater samples from the upper euphotic zone (10 m and 70 m), the base of the chlorophyll maximum (130 m), below the base of the euphotic zone (200 m), well below the upper mesopelagic (500 m), in the core of the dissolved oxygen minimum layer (770 m), and in the deep abyss, 750 m above the seafloor (4000 m), were collected for preparing microbial community DNA libraries (Tables 1 and 2, Fig. 1; figs. S1 and S2). The depth variability of gene distributions was examined by random, bidirectional end-sequencing of È5000 fosmids from each depth, yielding È64 Mbp of DNA sequence total from the 4.5 Gbp archive (Table 1). This represents raw sequence coverage of about 5 (1.8 Mbp sized) genome equivalents per depth. Because we surveyed È180 Mbp of cloned DNA (5000 clones by 1 Massachusetts Institute of Technology, Cambridge, MA 02139, USA. 2 Monterey Bay Aquarium Research Institute, Moss Landing, CA 95064, USA. 3 San Diego State Univer- sity, San Diego, CA 92182, USA. 4 University of Hawaii Honolulu, HI 96822, USA. *To whom correspondence should be addressed. E-mail: delong@mit.edu 27 JANUARY 2006 VOL 311 SCIENCE www.sciencemag.org496
  32. 32. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 would facilitate detection of ecologically meaning- ful taxonomic, functional, and community trends. Vertical Profiles of Microbial Taxa Vertical distributions of bacterial groups were assessed by amplifying and sequencing small (SAR92, OM60, SAR86 clades); Alphaproteo- bacteria (SAR116, OM75 clades); and Delta- proteobacteria (OM27 clade) (Fig. 2). Bacterial groups from deeper waters included members of Deferribacteres; Planctomycetaceae; Acido- bacteriales; Gemmatamonadaceae; Nitrospina; fo fig ge w of la re lik hi Rh tio ge gr lik oc Pl w w ro Table 1. HOT samples and fosmid libraries. Sample site, 22-45’ N, 158-W. All seawater samples were pre-filtered through a 1.6-mm glass fiber filter, and collected on a 0.22-mm filter. See (35) for methods. Depth (m) Sample date Volume filtered (liters) Total fosmid clones Total DNA (Mbp) Archived Sequenced 10 10/7/02 40 12,288 442 7.54 70 10/7/02 40 12,672 456 11.03 130 10/6/02 40 13,536 487 6.28 200 10/6/02 40 19,008 684 7.96 500 10/6/02 80 15,264 550 8.86 770 12/21/03 240 11,520 415 11.18 4,000 12/21/03 670 41,472 1,493 11.10 Table 2. HOT sample oceanographic data. Samples described in Table 1. Oceanographic parameters were measured as specified at (49); values shown are those from the same CTD casts as the samples, where available. Values in parentheses are the mean T 1 SD of each core parameter during the period October 1988 to December 2004, with the total number of measurements dissolved inorganic carbon. column samples (assuming a and a light extinction coeffic of surface), 70 m 0 1.63 (5% 200 m 0 0.07 (0.02% of su
  33. 33. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 All archaeal SSU rRNA–containing fosmids were identified at each depth, quantified by mac- roarray hybridization, and their rRNAs sequenced 500 10/6/02 80 15,264 550 8.86 770 12/21/03 240 11,520 415 11.18 4,000 12/21/03 670 41,472 1,493 11.10 Table 2. HOT sample oceanographic data. Samples described in Table 1. Oceanographic parameters were measured as specified at (49); values shown are those from the same CTD casts as the samples, where available. Values in parentheses are the mean T 1 SD of each core parameter during the period October 1988 to December 2004, with the total number of measurements collected for each parameter shown in brackets. The parameter abbreviations are Temp., Temperature; Chl a, chlorophyll a; DOC, dissolved organic carbon; NþN, nitrate plus nitrite; DIP, dissolved inorganic phosphate; and DIC, dissolved inorganic carbon. The estimated photon fluxes for upper water column samples (assuming a surface irradiance of 32 mol quanta mj2 dj1 and a light extinction coefficient of 0.0425 mj1) were: 10 m 0 20.92 (65% of surface), 70 m 0 1.63 (5% of surface), 130 m 0 0.128 (0.4% of surface), 200 m 0 0.07 (0.02% of surface). The mean surface mixed-layer during the October 2002 sampling was 61 m. Data are available at (50). *Biomass derived from particulate adenosine triphosphate (ATP) measurements as- suming a carbon:ATP ratio of 250. ND, Not determined. Depth (m) Temp. (-C) Salinity Chl a (mg/kg) Biomass* (mg/kg) DOC (mmol/kg) N þ N (nmol/kg) DIP (nmol/kg) Oxygen (mmol/kg) DIC (mmol/kg) 10 26.40 (24.83 T 1.27) [2,104] 35.08 (35.05 T 0.21) [1,611] 0.08 (0.08 T 0.03) [320] 7.21 T 2.68 [78] 78 (90.6 T 14.3) [140] 1.0 (2.6 T 3.7) [126] 41.0 (56.0 T 33.7) [146] 204.6 (209.3 T 4.5) [348] 1,967.6 (1,972.1 T 16.4) [107] 70 24.93 (23.58 T 1.00) [1,202] 35.21 (35.17 T 0.16) [1,084] 0.18 (0.15 T 0.05) [363] 8.51 T 3.22 [86] 79 (81.4 T 11.3) [79] 1.3 (14.7 T 60.3) [78] 16.0 (43.1 T 25.1) [104] 217.4 (215.8 T 5.4) [144] 1,981.8 (1,986.9 T 15.4) [84] 130 22.19 (21.37 T 0.96) [1,139] 35.31 (35.20 T 0.10) [980] 0.10 (0.15 T 0.06) [350] 5.03 T 2.30 [90] 69 (75.2 T 9.1) [86] 284.8 (282.9 T 270.2) [78] 66.2 (106.0 T 49.7) [68] 204.9 (206.6 T 6.2) [173] 2,026.5 (2,013.4 T 13.4) [69] 200 18.53 (18.39 T 1.29) [662] 35.04 (34.96 T 0.18) [576] 0.02 (0.02 T 0.02) [97] 1.66 T 0.24 [2] 63 (64.0 T 9.8) [113] 1,161.9 T 762.5 [7] 274.2 T 109.1 [84] 198.8 (197.6 T 7.1) [190] 2,047.7 (2,042.8 T 10.5) [125] 500 7.25 (7.22 T 0.44) [1,969] 34.07 (34.06 T 0.03) [1,769] ND 0.48 T 0.23 [107] 47 (47.8 T 6.3) [112] 28,850 (28,460 T 2210) [326] 2,153 (2,051 T 175.7) [322] 118.0 (120.5 T 18.3) [505] 2197.3 (2,200.2 T 17.8) [134] 770 4.78 (4.86 T 0.21) [888] 34.32 (34.32 T 0.04) [773] ND 0.29 T 0.16 [107] 39.9 (41.5 T 4.4) [34] 41,890 (40,940 T 500) [137] 3,070 (3,000 T 47.1) [135] 32.3 (27.9 T 4.1) [275] 2323.8 (2,324.3 T 6.1) [34] 4,000 1.46 (1.46 T 0.01) [262] 34.69 (34.69 T 0.00) [245] ND ND 37.5 (42.3 T 4.9) [83] 36,560 (35,970 T 290) [108] 2,558 (2,507 T 19) [104] 147.8 (147.8 T 1.3) [210] 2325.5 (2,329.1 T 4.8) [28] www.sciencemag.org SCIENCE VOL 311 27 JANUARY 2006 497
  34. 34. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 (figs. S5 and S6). The general patterns of archaeal distribution we observed were consistent with pre- viousfieldsurveys(15, 25, 26). Recovery of ‘‘group II’’ planktonic Euryarchaeota genomic DNA was greatest in the upper water column and declined below the photic zone. This distribution corrob- orates recent observations of ion-translocating pho- toproteins (called proteorhodopsins), now known to occur in group II Euryarchaeota inhabiting the photic zone (27). ‘‘Group III’’ Euryarchaeota DNA was recovered at all depths, but at a much lower frequency (figs. S5 and S6). A novel crenarchaeal group, closely related to a putatively thermophilic Crenarchaeota (28), was observed at the greatest depths (fig. S6). Vertically Distributed Genes and Metabolic Pathways The depths sampled were specifically chosen to capture microbial sequences at discrete biogeo- chemical zones in the water column encompassing key physicochemical features (Tables 1 and 2, Fig. 1; figs. S1 and S2). To evaluate sequences from each depth, fosmid end sequences were compared against different databases including the Kyoto Encyclopedia of Genes and Genomes (KEGG) (29), National Center for Biotechnology Information (NCBI)’s Clusters of Orthologous Groups (COG) (30), and SEED subsystems (31). After categorizing sequences from each depth in BLAST searches (32) against each database, we identified protein categories that were more or less well represented in one sample versus an- other, using cluster analysis (33, 34) and boot- strap resampling methodologies (35). Cluster analyses of predicted protein sequence representation identified specific genes and meta- bolic traits that were differentially distributed in the water column (fig. S7). In the photic zone (10, deoxyribopyrimidine photolyase, diaminopimelate decarboxylase, membrane guanosine triphospha- tase (GTPase) with the lysyl endopeptidase gene product LepA, and branched-chain amino acid– transport system components (fig. S8). In con- trast, COGs with greater representation in deep-water samples included transposases, sev- eral dehydrogenase categories, and integrases (fig. S8). Sequences more highly represented in the deep-water samples in SEED subsystem (31) that track major depth-variable environmental features. Specifically, sequence homologs found only in the photic zone unique sequences (from 10, 70, and 130 m), or deepwater unique sequences (from 500, 770, and 4000 m) were identified (Fig. 3). To categorize potential functions encoded in these photic zone unique (PZ) or deep-water unique (DW) sequence bins, each was compared with KEGG, COG, and NCBI protein databases in separate analy- 34 34.5 35 35.5 0 5 10 15 20 25 30 10 70 130 200 500 770 4000 Potentialtemperature(°C) Depth(meters) Salinity Fig. 1. Temperature versus salinity (T-S) relations for the North Pacific Subtropical Gyre at station ALOHA (22-45’N, 158-W). The blue circles indicate the positions, in T-S ‘‘hydrospace’’ of the seven water samples analyzed in this study. The data envelope shows the temperature and salinity conditions observed during the period October 1988 to December 2004 emphasizing both the temporal variability of near-surface waters and the relative constancy of deep waters. ESEARCH ARTICLES
  35. 35. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014ly flagellar motor and hook protein-encoding DW sequences were enriched in several like genes); protein folding and processing (pre- Fig. 2. Taxon distributions of top HSPs. The percent top HSPs that match the taxon categories shown at expectation values of e1 Â 10j60. Values in parentheses indicate number of genomes in each category, complete or draft, that were in the database at the time of analysis. The dots in the lower panel tabulate the SSU rRNAs detected in fosmid libraries from each taxonomic group at each depth (35) (figs. S3 and S6). RESEARCH ARTICLES
  36. 36. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 mortality number o was unex expected our colle similar a butions fr of viral libraries ing virus Viral DN zone, wit senting 1 5), and 60 Below 20 than 0.3% Most pho similarity the Podo sistent wi a widespr ocean. Analy vided furt sequence Fig. 3. Habitat-specific sequences in photic zone versusdeep-watercommuni- ties. The dendrogram shows a cluster analysis based on cumulative bitscores de- rivedfromreciprocalTBLASTX comparisons between all depths. Only the branch- ing pattern resulting from neighbor-joining analy- ses (not branch-lengths) are shown in the dendro- gram. The Venn diagrams depict the percentage of se- quences that were present only in PZ sequences (n 0 12,713) or DW sequences (n 0 14,132), as deter- mined in reciprocal BLAST searches of all sequences in each depth versus every other. The percentage out of the total PZ or DW sequence bins represented in each subset is shown. See SOM for methods (35). RESEARCH ARTICLES
  37. 37. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014Ecological Implications and Future Prospects ses about potential adaptive strategies of het- erotrophic bacteria in the photic zone that may ability’’ along environmental gradients, as evidenced by the partitioning of high- and low- Fig. 4. Cluster analyses of KEGG and COG annotated PZ and DW sequence bins versus depth. Sequence homologs unique to or shared within the photic zone (10, 70, and 130 m) and those unique to or shared in DW (500, 770, and 4000 m) were annotated against the KEGG or COG databases with TBLASTX with an expectation threshold of 1 Â 10j5. Yellow shading is proportional to the percentage of categorized sequences in each category. Cluster analyses of gene categories (left dendrograms) were performed with the Kendall’s tau nonparametric distance metric, and the Pearson correlation was used to generate the top dendrograms relating the depth series (33, 34). Dendrograms were displayed by using self-organizing mapping with the Pearson correlation metric (33, 34). Green lines in top dendrograms show PZ sequences, blue lines DW sequences. (A) KEGG category representation versus depth. KEGG categories with a standard deviation greater than 0.4 of observed values, having at least two depths R0.6% of the total KEGG-categorized genes at each depth, are shown. For display purposes, categories 98% in more than two depths are not shown. (B) COG category representation versus depth. COG categories with standard deviations greater than 0.2 of observed values, having at least two depths R0.3% of the total COG-categorized genes at each depth, are shown. RESEARCH ARTICLES
  38. 38. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Fig. 4. Cluster analyses of KEGG and COG annotate sequence bins versus depth. Sequence homologs uniqu within the photic zone (10, 70, and 130 m) and those uniq in DW (500, 770, and 4000 m) were annotated against th databases with TBLASTX with an expectation threshold Yellow shading is proportional to the percentage of catego in each category. Cluster analyses of gene categories (lef were performed with the Kendall’s tau nonparametric d and the Pearson correlation was used to generate the to relating the depth series (33, 34). Dendrograms were dis self-organizing mapping with the Pearson correlation metric lines in top dendrograms show PZ sequences, blue lines DW KEGG category representation versus depth. KEGG cat standard deviation greater than 0.4 of observed values, hav depths R0.6% of the total KEGG-categorized genes at e shown. For display purposes, categories 98% in more than not shown. (B) COG category representation versus depth. with standard deviations greater than 0.2 of observed values, having at least two depths R0.3% of the total COG-categorized genes at shown. RESEAR
  39. 39. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 RESEARCH ARTICLES
  40. 40. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Some depth-specific gene distributions we ob- served [e.g., transposases found predominantly at greater depths (Fig. 4B; fig. S8)], appear to originate from a wide variety of gene families and genomic sources. These gene distributional productivity, and lower effective population sizes of deep-sea microbial communities. In future comparative studies, similar deviations in environmental gene stoichiometries might be expected to provide even further insight enabled ecological studies matures, it should become possible to model microbial community genomic, temporal, and spatial variability with other environmental features. Significant future attention will no doubt focus on interpreting the complex interplay between genes, orga- nisms, communities and the environment, as well as the properties revealed that regulate global biogeochemical cycles. Future efforts in this area will advance our general perspective on microbial ecology and evolution and elu- cidate the biological dynamics that mediate the flux of matter and energy in the world’s oceans. References and Notes 1. E. Forbes, in Physical Atlas of Natural Phenomena, A. K. Johnston, Ed. (William Blackwood & Sons, London and Edinburgh, 1856). 2. P. W. Hochachka, G. N. Somero, Biochemical Adaptation (Princeton Univ. Press, Princeton, NJ, 1984), pp. 450–495. 3. G. Rocap et al., Nature 424, 1042 (2003). 4. Z. I. Johnson et al., manuscript submitted. 5. N. J. West et al., Microbiology 147, 1731 (2001). 6. A. A. Yayanos, Annu. Rev. Microbiol. 49, 777 (1995). 7. N. R. Pace, Science 276, 734 (1997). 8. M. S. Rappe´, S. J. Giovannoni, Annu. Rev. Microbiol. 57, 369 (2003). 9. M. T. Suzuki et al., Microb Ecol (2005). 10. O. Be´ja` et al., Environ. Microbiol. 2, 516 (2000). 11. R. M. Morris, M. S. Rappe´, E. Urbach, S. A. Connon, S. J. Giovannoni, Appl. Environ. Microbiol. 70, 2836 (2004). 12. S. Y. Moon-van der Staay et al., Nature 409, 607 (2001). 13. J. A. Fuhrman, K. McCallum, A. A. Davis, Nature 356, 148 (1992). 14. E. F. DeLong, Proc. Natl. Acad. Sci. U.S.A. 89, 5685 (1992). 15. M. B. Karner et al., Nature 409, 507 (2001). 16. J. Handelsman, Microbiol. Mol. Biol. Rev. 68, 669 (2004). 17. E. F. Delong, Nat Rev Microbiol (2005). 18. G. W. Tyson et al., Nature 428, 37 (2004). 19. J. C. Venter et al., Science 304, 66 (2004). 20. S. G. Tringe et al., Science 308, 554 (2005). 21. S. J. Hallam et al., Science 305, 1457 (2004). 22. D. M. Karl, R. Lukas, Deep-Sea Res. II 43, 129 (1996). 23. D. M. Karl et al., Deep-Sea Res. II 48, 1449 (2001). Fig. 5. Cyanophage and cyanobacteria dis- tributions in microbial community DNA. The percentage of total sequences derived from cyanophage, total cyanobacteria, total Prochlor- ococcus spp., high-light Prochlorococcus, low- light Prochlorococcus spp., or Synechococcus spp., from each depth. Taxa were tentatively assigned according to the origin of top HSPs in TBLASTX searches, followed by subsequent manual inspection and curation. 0 100 200 300 400 500 0 10 20 cyanobacteria Prochlorococcus Synechococcus HL Prochlorococcus LL Prochlorococcus cyanophages Percent total sequences per depth Depth(m) RESEARCH ARTICLES
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