Nonhumans: Don't Neglect Their Microbiomes

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Talk by Jonathan Eisen for meeting on "All creatures great and small" at UC Davis.

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  • You covered a lot in this talk. I wish I'd seen you present it. :)
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Nonhumans: Don't Neglect Their Microbiomes

  1. 1. Slides by Jonathan Eisen for BIS2C at UC Davis Spring 2014 1 Don’t Neglect Their Microbiomes Jonathan A. Eisen @phylogenomics November 17, 2014 Talk for Nonhumans Meeting
  2. 2. Obsessions … !2
  3. 3. Obsessions … !2
  4. 4. Obsessions … !2
  5. 5. Obsessions … !2
  6. 6. Obsessions … !2
  7. 7. Obsessions … !2
  8. 8. Obsessions … !2
  9. 9. Obsessions … !2
  10. 10. Me and My Girl Annapurna !3
  11. 11. The Story of a Bird !4
  12. 12. 5
  13. 13. 9
  14. 14. 10
  15. 15. 11
  16. 16. Robin in London Examples
  17. 17. MICROBES
  18. 18. Microbes vs Nonhumans !14
  19. 19. Nonhumans Word Cloud 1 !15
  20. 20. Nonhumans Word Cloud 2 !16
  21. 21. Microbes Better? !17
  22. 22. Microbes Better? !17
  23. 23. Nonhumans Better? !18
  24. 24. Microbes AND Nonhumans !19
  25. 25. Microbes AND Nonhumans !19
  26. 26. Microbes and Non Humans 1: Bad Germs !20 • Animals get and transmit many pathogens • But … can lead to excess germophobia
  27. 27. Microbes and Nonhumans 2: Mutualisms !21 Sharpshooter: Cuerna sayi bacteriomes Sharpshooters harbor two obligate symbionts in their bacteriomes D Takiya Copyright © National Academy of Sciences. All rights reserved. al Biology of Microbial Communities: Workshop Summary WORKSHOP OVERVIEW 9 et al., 2012). This simple model of persistent colonization of animal epithelia by Gram-negative bacteria provides a “valuable complement to studies of both beneficial and pathogenic consortial interactions, such as in the mammalian in- testine, and chronic disease that involve persistent colonization by Gram-negative bacteria, such as cystic fibrosis” (Nyholm and McFall-Ngai, 2004). Plant roots and their partners Plants establish associations with several micro- organisms in a relationship somewhat analogous to that of mammals with their gastrointestinal microbiota. The roots of most higher plant species form mycor- rhizae, an association with specific fungal species that significantly improves the plant’s ability to acquire phosphorous, nitrogen, and water from the soil.12 A few plant families, including legumes, associate with nitrogen-fixing bacteria. They colonize the plant’s roots and form specialized nodules, where the bacteria 12 See http://agronomy.wisc.edu/symbiosis. DC Figure WO-3 A B FIGURE WO-3 The bacterium and the squid. A persistent, symbiotic association be- tween the squid Euprymna scolopes (A) and its luminous bacterial symbiont Vibrio fischeri (B) forms within the squid’s light organ (C and D). After colonization of the host’s light organ tissue, V. fischeri induces a series of irreversible developmental changes that trans- form these tissues into a mature, functional light organ (Nyholm and McFall-Ngai, 2004). SOURCE: (A) Images taken by C. Frazee, provided by M. McFall-Ngai and E. G. Ruby; (B) Image provided courtesy of Marianne Engel; (C and D). Reprinted by permission from Macmillan Publishers Ltd: Nature, Dusheck (2002), copyright 2002. The Social Biology of Microbial Communities: Workshop Summary 148 THE SOCIAL BIOLOGY OF MICROBIAL COMMUNITIES Figure A5-3.eps bitmap FIGURE A4-3 Cooperation and conflict within the fungus-growing ant microbe symbio- sis. A) Fungus-growing ants forage for substrate to nourish their cultivated fungus, which they also groom to help remove garden parasites. B) In return, the fungus serves as the primary food source for the ants; with some species producing nutrient-rich hyphal swell-
  28. 28. Microbes and Nonhumans 3: The Microbiome !22
  29. 29. The Rise of the Microbiome
  30. 30. 0 1000 2000 3000 4000 00 01 02 03 04 05 06 07 08 09 10 11 12 13 Pubmed “Microbiome” Hits The Rise of the Microbiome
  31. 31. 0 1000 2000 3000 4000 00 01 02 03 04 05 06 07 08 09 10 11 12 13 Pubmed “Microbiome” Hits The Rise of the Microbiome
  32. 32. 0 1000 2000 3000 4000 00 01 02 03 04 05 06 07 08 09 10 11 12 13 Pubmed “Microbiome” Hits The Rise of the Microbiome
  33. 33. 0 1000 2000 3000 4000 00 01 02 03 04 05 06 07 08 09 10 11 12 13 Pubmed “Microbiome” Hits The Rise of the Microbiome
  34. 34. Not Just About Humans !25
  35. 35. • Animals are covered in a cloud of microbes !26 The Rise of the Microbiome
  36. 36. • Animals are covered in a cloud of microbes • This “microbiome” likely is involved in many important animal phenotypes !27 The Rise of the Microbiome
  37. 37. • Animals are covered in a cloud of microbes • This “microbiome” LIKELY is involved in many important animal phenotypes !28 The Rise of the Microbiome
  38. 38. • Animals are covered in a cloud of microbes • This “microbiome” LIKELY is INVOLVED in many important animal phenotypes !29 The Rise of the Microbiome
  39. 39. Why Now?
  40. 40. Why Now I: Growing Appreciation of Microbial Diversity !31
  41. 41. Why Now I: Growing Appreciation of Microbial Diversity !31
  42. 42. Why Now I: Growing Appreciation of Microbial Diversity !31 Diversity of Form
  43. 43. Why Now I: Growing Appreciation of Microbial Diversity !31 Diversity of Form Phylogenetic Diversity
  44. 44. Why Now I: Growing Appreciation of Microbial Diversity !31 Functional Diversity Diversity of Form Phylogenetic Diversity
  45. 45. Why Now I: Growing Appreciation of Microbial Diversity !31 Functional Diversity Diversity of Form Phylogenetic Diversity MICROBES RUN THE PLANET
  46. 46. Why Now II: Post Genome Blues !32
  47. 47. Why Now II: Post Genome Blues !32 Overselling the Human Genome?
  48. 48. Why Now II: Post Genome Blues !32 Transcriptome Overselling the Human Genome?
  49. 49. Why Now II: Post Genome Blues !32 Transcriptome Epigenome Overselling the Human Genome?
  50. 50. Why Now II: Post Genome Blues !32 Transcriptome VariomeEpigenome Overselling the Human Genome?
  51. 51. Why Now II: Post Genome Blues !32 The Microbiome Transcriptome VariomeEpigenome Overselling the Human Genome?
  52. 52. !33 Why Now III: Advances in Culture-Independent Work
  53. 53. !33 Why Now III: Advances in Culture-Independent Work
  54. 54. !33 Observation Why Now III: Advances in Culture-Independent Work
  55. 55. !33 Culturing Observation Why Now III: Advances in Culture-Independent Work
  56. 56. !33 Culturing Observation CountCount Why Now III: Advances in Culture-Independent Work
  57. 57. !33 <<<< Culturing Observation CountCount Why Now III: Advances in Culture-Independent Work
  58. 58. !33 <<<< Culturing Observation CountCount http://www.google.com/url? sa=i&rct=j&q=&esrc=s&source=images& cd=&docid=rLu5sL207WlE1M&tbnid=CR LQYP7d9d_TcM:&ved=0CAUQjRw&url=h ttp%3A%2F%2Fwww.biol.unt.edu %2F~jajohnson %2FDNA_sequencing_process&ei=hFu7 U_TyCtOqsQSu9YGwBg&psig=AFQjCN G-8EBdEljE7- yHFG2KPuBZt8kIPw&ust=140487395121 1424 Why Now III: Advances in Culture-Independent Work
  59. 59. !33 <<<< Culturing Observation CountCount http://www.google.com/url? sa=i&rct=j&q=&esrc=s&source=images& cd=&docid=rLu5sL207WlE1M&tbnid=CR LQYP7d9d_TcM:&ved=0CAUQjRw&url=h ttp%3A%2F%2Fwww.biol.unt.edu %2F~jajohnson %2FDNA_sequencing_process&ei=hFu7 U_TyCtOqsQSu9YGwBg&psig=AFQjCN G-8EBdEljE7- yHFG2KPuBZt8kIPw&ust=140487395121 1424 DNA Why Now III: Advances in Culture-Independent Work
  60. 60. !34 Why Now IV: Sequencing Has Gone Crazy !34
  61. 61. !3535 Approaching to NGS Discovery of DNA structure (Cold Spring Harb. Symp. Quant. Biol. 1953;18:123-31) 1953 Sanger sequencing method by F. Sanger (PNAS ,1977, 74: 560-564) 1977 PCR by K. Mullis (Cold Spring Harb Symp Quant Biol. 1986;51 Pt 1:263-73) 1983 Development of pyrosequencing (Anal. Biochem., 1993, 208: 171-175; Science ,1998, 281: 363-365) 1993 1980 1990 2000 2010 Single molecule emulsion PCR 1998 Human Genome Project (Nature , 2001, 409: 860–92; Science, 2001, 291: 1304–1351) Founded 454 Life Science 2000 454 GS20 sequencer (First NGS sequencer) 2005 Founded Solexa 1998 Solexa Genome Analyzer (First short-read NGS sequencer) 2006 GS FLX sequencer (NGS with 400-500 bp read lenght) 2008 Hi-Seq2000 (200Gbp per Flow Cell) 2010 Illumina acquires Solexa (Illumina enters the NGS business) 2006 ABI SOLiD (Short-read sequencer based upon ligation) 2007 Roche acquires 454 Life Sciences (Roche enters the NGS business) 2007 NGS Human Genome sequencing (First Human Genome sequencing based upon NGS technology) 2008 From Slideshare presentation of Cosentino Cristian http://www.slideshare.net/cosentia/high-throughput-equencing Miseq Roche Jr Ion Torrent PacBio Oxford Sequencing Has Gone Crazy
  62. 62. Sequencing Revolution !36 •More genes and genomes •Deeper sequencing • The rare biosphere • Relative abundance estimates •More samples (with barcoding) • Times series • Spatially diverse sampling • Fine scale sampling
  63. 63. !37 Turnbaugh et al Nature. 2006 444(7122):1027-31. Why Now V: Microbiome Functions
  64. 64. IBD vs. normal • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Healthy Crohn’s disease Ulcerative colitis P value: 0.031 PC2 PC1 Figure 4 | Bacterial species abundance differentiates IBD patients and healthy individuals. Principal component analysis with health status as ARTICLES !38
  65. 65. Microbiome Forensics !39
  66. 66. Microbiomes and Plant Health !40
  67. 67. Model Animal Microbiomes !4141 Both natural surveys and laboratory experiments indicate that host diet plays a major role in shaping the Drosophila bacterial microbiome. Laboratory strains provide only a limited model of natural host– microbe interactions
  68. 68. Asthma, Dust, Dogs and Microbiomes !42
  69. 69. Nice Counter to Germophobia but … !43
  70. 70. Public Service Reminder Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation Correlation ≠ causation !44
  71. 71. Microbiome 101 !45
  72. 72. Methods !46
  73. 73. Woese: Classification of Cultured Taxa by rRNA !47
  74. 74. Woese: Classification of Cultured Taxa by rRNA !47
  75. 75. Woese: Classification of Cultured Taxa by rRNA !47
  76. 76. Woese: Classification of Cultured Taxa by rRNA !47
  77. 77. Woese: Classification of Cultured Taxa by rRNA !47
  78. 78. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA
  79. 79. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA
  80. 80. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG
  81. 81. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG
  82. 82. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG
  83. 83. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG
  84. 84. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG
  85. 85. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG
  86. 86. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG
  87. 87. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG
  88. 88. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG Eukaryotes
  89. 89. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG EukaryotesBacteria
  90. 90. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG EukaryotesBacteria ?????
  91. 91. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG EukaryotesBacteria ?????Archaebacteria
  92. 92. Woese: Classification of Cultured Taxa by rRNA !47 rRNA rRNArRNA ACUGC ACCUAU CGUUCG ACUCC AGCUAU CGAUCG ACCCC AGCUCU CGCUCG Taxa Characters S ACUGCACCUAUCGUUCG R ACUCCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG F ACUCCAGGUAUCGAUCG C ACCCCAGCUCUCGCUCG W ACCCCAGCUCUGGCUCG Taxa Characters S ACUGCACCUAUCGUUCG E ACUCCAGCUAUCGAUCG C ACCCCAGCUCUCGCUCG EukaryotesBacteria ?????ArchaebacteriaArchaea
  93. 93. Culture Independent rRNA PCR: One Taxon • v DNA ACTGC ACCTAT CGTTCG ACTGC ACCTAT CGTTCG ACTGC ACCTAT CGTTCG Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACTGCACCTATCGTTCG EukaryotesBacteria Archaea !48 Many sequences from one sample all point to the same branch on the tree
  94. 94. DNA ACTGC ACCTAT CGTTCG ACTGC ACCTAT CGTTCG ACCCC AGCTCT CGCTCG Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 ACTGCACCTATCGTTCG EukaryotesBacteria Archaea !49 One can estimate cell counts from the number of times each sequence is seen. Culture Independent rRNA PCR: Two Taxa
  95. 95. DNA ACTGC ACCTAT CGTTCG ACTGC ACCTAT CGTTCG ACCCC AGCTCT CGCTCG Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 ACTGCACCTATCGTTCG EukaryotesBacteria Archaea !49 One can estimate cell counts from the number of times each sequence is seen. Culture Independent rRNA PCR: Two Taxa
  96. 96. DNA ACTGC ACCTAT CGTTCG ACTGC ACCTAT CGTTCG ACCCC AGCTCT CGCTCG Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 ACTGCACCTATCGTTCG EukaryotesBacteria Archaea !49 One can estimate cell counts from the number of times each sequence is seen. Culture Independent rRNA PCR: Two Taxa
  97. 97. DNA Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 AGGGGAGCTCTGCCTCG New3 ACTCCAGCTATCGATCG New4 ACTGCACCTATCGTTCG EukaryotesBacteria Archaea !50 ACTGC ACCTAT CGTTCG ACTCC AGCTAT CGATCG ACCCC AGCTCT CGCTCG AGGGG AGCTCT CGCTCG AGGGG AGCTCT CGCTCG ACTGC ACCTAT CGTTCG Even with more taxa it still works Culture Independent rRNA PCR: Four Taxa
  98. 98. Culture Independent rRNA PCR: Communities DNA DNADNA ACTGC ACCTAT CGTTCG ACTCC AGCTAT CGATCG ACCCC AGCTCT CGCTCG Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 ACGGCAGCTCTGCCTCG EukaryotesBacteria Archaea !51
  99. 99. Culture Independent rRNA PCR: Communities DNA DNADNA ACTGC ACCTAT CGTTCG ACTCC AGCTAT CGATCG ACCCC AGCTCT CGCTCG Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 ACGGCAGCTCTGCCTCG !52
  100. 100. Culture Independent “Metagenomics” DNA DNADNA !53 Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 AGGGGAGCTCTGCCTCG New3 ACTCCAGCTATCGATCG New4 ACTGCACCTATCGTTCG RecA RecARecA http://genomebiology.com/2008/9/10/R151 Genome Biology 2008, Volume 9, Issue 10, Article R151 Wu and Eisen R151.7 Genome Biology 2008, 9:R151 sequences are not conserved at the nucleotide level [29]. As a result, the nr database does not actually contain many more protein marker sequences that can be used as references than those available from complete genome sequences. Comparison of phylogeny-based and similarity-based phylotyping Although our phylogeny-based phylotyping is fully auto- mated, it still requires many more steps than, and is slower than, similarity based phylotyping methods such as a MEGAN [30]. Is it worth the trouble? Similarity based phylo- typing works by searching a query sequence against a refer- ence database such as NCBI nr and deriving taxonomic information from the best matches or 'hits'. When species that are closely related to the query sequence exist in the ref- erence database, similarity-based phylotyping can work well. However, if the reference database is a biased sample or if it contains no closely related species to the query, then the top hits returned could be misleading [31]. Furthermore, similar- ity-based methods require an arbitrary similarity cut-off value to define the top hits. Because individual bacterial genomes and proteins can evolve at very different rates, a uni- versal cut-off that works under all conditions does not exist. As a result, the final results can be very subjective. In contrast, our tree-based bracketing algorithm places the query sequence within the context of a phylogenetic tree and only assigns it to a taxonomic level if that level has adequate sampling (see Materials and methods [below] for details of the algorithm). With the well sampled species Prochlorococ- cus marinus, for example, our method can distinguish closely related organisms and make taxonomic identifications at the species level. Our reanalysis of the Sargasso Sea data placed 672 sequences (3.6% of the total) within a P. marinus clade. On the other hand, for sparsely sampled clades such as Aquifex, assignments will be made only at the phylum level. Thus, our phylogeny-based analysis is less susceptible to data sampling bias than a similarity based approach, and it makes Major phylotypes identified in Sargasso Sea metagenomic dataFigure 3 Major phylotypes identified in Sargasso Sea metagenomic data. The metagenomic data previously obtained from the Sargasso Sea was reanalyzed using AMPHORA and the 31 protein phylogenetic markers. The microbial diversity profiles obtained from individual markers are remarkably consistent. The breakdown of the phylotyping assignments by markers and major taxonomic groups is listed in Additional data file 5. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Alphaproteobacteria Betaproteobacteria G am m aproteobacteria D eltaproteobacteria Epsilonproteobacteria U nclassified proteobacteria Bacteroidetes C hlam ydiae C yanobacteria Acidobacteria Therm otogae Fusobacteria ActinobacteriaAquificae Planctom ycetes Spirochaetes Firm icutes C hloroflexiC hlorobi U nclassified bacteria dnaG frr infC nusA pgk pyrG rplA rplB rplC rplD rplE rplF rplK rplL rplM rplN rplP rplS rplT rpmA rpoB rpsB rpsC rpsE rpsI rpsJ rpsK rpsM rpsS smpB tsf Relativeabundance RpoB RpoBRpoB Rpl4 Rpl4Rpl4 rRNA rRNArRNA Hsp70 Hsp70Hsp70 EFTu EFTuEFTu Many other genes better than rRNA
  101. 101. Culture Independent “Metagenomics” DNA DNADNA !54 Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 AGGGGAGCTCTGCCTCG New3 ACTCCAGCTATCGATCG New4 ACTGCACCTATCGTTCG inputs of fixed carbon or nitrogen from external sources. As with Leptospirillum group I, both Leptospirillum group II and III have the genes needed to fix carbon by means of the Calvin–Benson– Bassham cycle (using type II ribulose 1,5-bisphosphate carboxy- lase–oxygenase). All genomes recovered from the AMD system contain formate hydrogenlyase complexes. These, in combination with carbon monoxide dehydrogenase, may be used for carbon fixation via the reductive acetyl coenzyme A (acetyl-CoA) pathway by some, or all, organisms. Given the large number of ABC-type sugar and amino acid transporters encoded in the Ferroplasma type Figure 4 Cell metabolic cartoons constructed from the annotation of 2,180 ORFs identified in the Leptospirillum group II genome (63% with putative assigned function) and 1,931 ORFs in the Ferroplasma type II genome (58% with assigned function). The cell cartoons are shown within a biofilm that is attached to the surface of an acid mine drainage stream (viewed in cross-section). Tight coupling between ferrous iron oxidation, pyrite dissolution and acid generation is indicated. Rubisco, ribulose 1,5-bisphosphate carboxylase–oxygenase. THF, tetrahydrofolate. articles NATURE | doi:10.1038/nature02340 | www.nature.com/nature 5©2004 NaturePublishing Group
  102. 102. Culture Independent “Metagenomics” DNA DNADNA !55 Taxa Characters B1 ACTGCACCTATCGTTCG B2 ACTCCACCTATCGTTCG E1 ACTCCAGCTATCGATCG E2 ACTCCAGGTATCGATCG A1 ACCCCAGCTCTCGCTCG A2 ACCCCAGCTCTGGCTCG New1 ACCCCAGCTCTGCCTCG New2 AGGGGAGCTCTGCCTCG New3 ACTCCAGCTATCGATCG New4 ACTGCACCTATCGTTCG
  103. 103. Animal Microbiomes as Ecosystems !56
  104. 104. Biogeography !57
  105. 105. Biogeography !57 a broader range of Proteobacteria, but yielded similar results (Fig. S1 and Tables S2 and S3). Across all samples, we identified 4,931 quality Nitrosomadales sequences, which grouped into 176 OTUs (operational taxo- nomic units) using an arbitrary 99% sequence similarity cutoff. This cutoff retained a high amount of sequence diversity, but minimized the chance of including diversity because of se- quencing or PCR errors. Most (95%) of the sequences appear closely related either to the marine Nitrosospira-like clade, known to be abundant in estuarine sediments (e.g., ref. 19) or to marine bacterium C-17, classified as Nitrosomonas (20) (Fig. S2). Pairwise community similarity between the samples was calcu- lated based on the presence or absence of each OTU using a rarefied Sørensen’s index (4). Community similarity using this incidence index was highly correlated with the abundance-based Sørensen index (Mantel test: ρ = 0.9239; P = 0.0001) (21). A plot of community similarity versus geographic distance for each pairwise set of samples revealed that the Nitrosomonadales display a significant, negative distance-decay curve (slope = −0.08, P < 0.0001) (Fig. 2). Furthermore, the slope of this curve varied significantly among the three spatial scales. The distance-decay slope within marshes was significantly shallower than the overall slope (slope = −0.04; P < 0.0334) and steeper across marshes within a region than the overall slope (slope = −0.27, P < 0.0007) (Fig. 2). In contrast, at the continental scale, the distance-decay curve did not differ from zero (P = 0.0953). Thus, there is no evidence that somonadales community similarity. Geographic distance con- tributed the largest partial regression coefficient (b = 0.40, P < 0.0001), with sediment moisture, nitrate concentration, plant cover, salinity, and air and water temperature contributing to smaller, but significant, partial regression coefficients (b = 0.09– 0.17, P < 0.05) (Table 1). Because salt marsh bacteria may be dispersing through ocean currents, we also used a global ocean circulation model (23), as applied previously (24), to estimate relative dispersal times of hypothetical microbial cells between each sampling location. Dispersal times between sampling points did not explain more variability in bacterial community similarity (ln dispersal time: b = 0.06, P = −0.0799; with dispersal R2 = 0.47 vs. without 0.46). Therefore, in the remaining analyses we use geographic distance rather than dispersal time. As hypothesized, the relative importance of environmental factors versus geographic distance to Nitrosomadales community similarity differed across the three spatial scales. Contrary to our expectations, however, geographic distance had a strong effect Fig. 1. The 13 marshes sampled (see Table S1 for details). Marshes com- pared with one another within regions are circled. (Inset) The arrangement of sampling points within marshes. Six points were sampled along a 100-m transect, and a seventh point was sampled ∼1 km away. Two marshes in the Northeast United States (outlined stars) were sampled more intensively, along four 100-m transects in a grid pattern. Fig. 2. Distance-decay curves for the Nitrosomadales communities. The dashed, blue line denotes the least-squares linear regression across all spatial scales. The solid lines denote separate regressions within each of the three spatial scales: within marshes, regional (across marshes within regions circled in Fig. 1), and continental (across regions). The slopes of all lines (except the solid light blue line) are significantly less than zero. The slopes of the solid red lines are significantly different from the slope of the all scale (blue dashed) line. ECOLOGY ults ales xo- off. but se- om- ent 0-m the ely, Fig. 2. Distance-decay curves for the Nitrosomadales communities. The dashed, blue line denotes the least-squares linear regression across all spatial scales. The solid lines denote separate regressions within each of the three spatial scales: within marshes, regional (across marshes within regions circled in Fig. 1), and continental (across regions). The slopes of all lines (except the solid light blue line) are significantly less than zero. The slopes of the solid red lines are significantly different from the slope of the all scale (blue dashed) line.
  106. 106. Biogeography !57 a broader range of Proteobacteria, but yielded similar results (Fig. S1 and Tables S2 and S3). Across all samples, we identified 4,931 quality Nitrosomadales sequences, which grouped into 176 OTUs (operational taxo- nomic units) using an arbitrary 99% sequence similarity cutoff. This cutoff retained a high amount of sequence diversity, but minimized the chance of including diversity because of se- quencing or PCR errors. Most (95%) of the sequences appear closely related either to the marine Nitrosospira-like clade, known to be abundant in estuarine sediments (e.g., ref. 19) or to marine bacterium C-17, classified as Nitrosomonas (20) (Fig. S2). Pairwise community similarity between the samples was calcu- lated based on the presence or absence of each OTU using a rarefied Sørensen’s index (4). Community similarity using this incidence index was highly correlated with the abundance-based Sørensen index (Mantel test: ρ = 0.9239; P = 0.0001) (21). A plot of community similarity versus geographic distance for each pairwise set of samples revealed that the Nitrosomonadales display a significant, negative distance-decay curve (slope = −0.08, P < 0.0001) (Fig. 2). Furthermore, the slope of this curve varied significantly among the three spatial scales. The distance-decay slope within marshes was significantly shallower than the overall slope (slope = −0.04; P < 0.0334) and steeper across marshes within a region than the overall slope (slope = −0.27, P < 0.0007) (Fig. 2). In contrast, at the continental scale, the distance-decay curve did not differ from zero (P = 0.0953). Thus, there is no evidence that somonadales community similarity. Geographic distance con- tributed the largest partial regression coefficient (b = 0.40, P < 0.0001), with sediment moisture, nitrate concentration, plant cover, salinity, and air and water temperature contributing to smaller, but significant, partial regression coefficients (b = 0.09– 0.17, P < 0.05) (Table 1). Because salt marsh bacteria may be dispersing through ocean currents, we also used a global ocean circulation model (23), as applied previously (24), to estimate relative dispersal times of hypothetical microbial cells between each sampling location. Dispersal times between sampling points did not explain more variability in bacterial community similarity (ln dispersal time: b = 0.06, P = −0.0799; with dispersal R2 = 0.47 vs. without 0.46). Therefore, in the remaining analyses we use geographic distance rather than dispersal time. As hypothesized, the relative importance of environmental factors versus geographic distance to Nitrosomadales community similarity differed across the three spatial scales. Contrary to our expectations, however, geographic distance had a strong effect Fig. 1. The 13 marshes sampled (see Table S1 for details). Marshes com- pared with one another within regions are circled. (Inset) The arrangement of sampling points within marshes. Six points were sampled along a 100-m transect, and a seventh point was sampled ∼1 km away. Two marshes in the Northeast United States (outlined stars) were sampled more intensively, along four 100-m transects in a grid pattern. Fig. 2. Distance-decay curves for the Nitrosomadales communities. The dashed, blue line denotes the least-squares linear regression across all spatial scales. The solid lines denote separate regressions within each of the three spatial scales: within marshes, regional (across marshes within regions circled in Fig. 1), and continental (across regions). The slopes of all lines (except the solid light blue line) are significantly less than zero. The slopes of the solid red lines are significantly different from the slope of the all scale (blue dashed) line. ECOLOGY ults ales xo- off. but se- om- ent 0-m the ely, Fig. 2. Distance-decay curves for the Nitrosomadales communities. The dashed, blue line denotes the least-squares linear regression across all spatial scales. The solid lines denote separate regressions within each of the three spatial scales: within marshes, regional (across marshes within regions circled in Fig. 1), and continental (across regions). The slopes of all lines (except the solid light blue line) are significantly less than zero. The slopes of the solid red lines are significantly different from the slope of the all scale (blue dashed) line.
  107. 107. !58Huttenhower et al. 2012. Population Variability !58Morgan et al. Genome Biology 2012, 13:R79 MJ Blaser et al. ISMEJ 2012 US Amerindian Actinobacteria (Propionibacteria) Firmicutes (Staphylococcus) Relativeabundance Actinobacteria dominates in the US Boulder NY Platanillal A Platanillal B Proteobacteria Between Countries Age Vaginal Microbiome Corn at Different Locations Individuals
  108. 108. Community Assembly
  109. 109. Community Assembly From Mom
  110. 110. Community Assembly From Mom Other People
  111. 111. Community Assembly From Mom From Pets Other People
  112. 112. Community Assembly From Mom From Food From Pets Other People
  113. 113. Community Assembly From Mom From Food From Pets From Built Environment Other People
  114. 114. Disturbance !60
  115. 115. Disturbance !60
  116. 116. Disturbance !60
  117. 117. Disturbance !60 Switch to solid foods
  118. 118. Disturbance !60 Switch to solid foods
  119. 119. Disturbance !60 Switch to solid foods
  120. 120. Disturbance !60 Switch to solid foods
  121. 121. Captivity and Conservation !61 Research article Captivity results in disparate loss of gut microbial diversity in closely related hosts Kevin D. Kohl1*, Michele M. Skopec2 and M. Denise Dearing1 2 *Corresponding author: + The gastrointestinal tracts of animals contain diverse communities of microbes that provide a number of services to their hosts. There is recent concern that these communities may be lost as animals enter captive breeding programmes, due to changes in diet and/or exposure to environmental sources. However, empirical evidence documenting the effects of captivity and captive birth on gut communities is lacking. We conducted three studies to advance our knowledge in this area. First, we compared changes in microbial diversity of the gut communities of two species of woodrats (Neotoma albigula, a dietary gen- eralist, and Neotoma stephensi, which specializes on juniper) before and after 6–9 months in captivity. Second, we investi- gated whether reintroduction of the natural diet of N. stephensi could restore microbial diversity. Third, we compared the microbial communities between offspring born in captivity and their mothers. We found that the dietary specialist, N. ste- phensi, lost a greater proportion of its native gut microbiota and overall diversity in response to captivity compared with N. albigula. Addition of the natural diet increased the proportion of the original microbiota but did not restore overall diversity in N. stephensi. Offspring of N. albigula more closely resembled their mothers compared with offspring–mother pairs of N. stephensi.Thisresearchsuggeststhatthemicrobiotaofdietaryspecialistsmaybemoresusceptibletocaptivity.Furthermore, this work highlights the need for further studies investigating the mechanisms underlying how loss of microbial diversity may vary between hosts and what an acceptable level of diversity loss may be to a host.This knowledge will aid conservation biolo- gists in designing captive breeding programmes effective at maintaining microbial diversity. Sequence Accession Numbers: NCBI’s Sequence Read Archive (SRA) – SRP033616 Key words: Neotoma Editor: Cite as: Conserv Physiol Introduction The gut microbial communities of animals are hyperdiverse and influence many aspects of their physiology, such as nutri- tion, immune development and even behaviour (Amato, 2013). The preservation of the microbial diversity present in resulting in microbial communities that are more susceptible to invasion or by altering host immune function (Blaser and Falkow, 2009). Additionally, gut microbes serve as sources of novel gene products, such as enzymes for biomass degradation (Hess et al., 2011) or bioremediation (Verma et al., 2006). byguestonNovember16,2014http://conphys.oxfordjournals.org/Downloadedfrom Zoos and Shelters
  122. 122. !62 Antimicrobials are in Everything
  123. 123. Restoration !63
  124. 124. Restoration !63 Probiotics
  125. 125. Restoration !63 Animal TransfaunationProbiotics
  126. 126. Restoration !63 Animal Transfaunation Ileal Transplant Probiotics
  127. 127. Restoration !63 Fecal Transplants Animal Transfaunation Ileal Transplant Probiotics
  128. 128. History Important Too
  129. 129. History Important Too Genera that cross the divide. Another way to visualize the vertebrate gut–environment dichotomy is by using a network diagram that displays, in addition to the clus- tering of hosts with similar microbiotas, the bacterial genera they share. In this representation of the data, the vertebrate gut samples are more connected to one another than to the environmental samples (FIG. 4a,b). As in the UniFrac-based analysis, the non-gut human samples also occupy an intermediate position between the free-living andthegutcommunities. FIGURE 5 showsthephylogenetic classification of operational taxonomic units (OTUs) that are shared between samples: among humans, an over- whelming number of these are from the Firmicutes, with a smaller number from the Bacteroidetes. By contrast, the free-living communities share OTUs from a wider range of phyla. Samples from the guts of obese humans cluster away from the samples of healthy subjects, and most of theirsharedOTUsarefoundintheFirmicutes.Thisobser- vation is consistent with the finding that samples from obese individuals have a higher number of OTUs family of the gammaproteobacteria class. This fam- ily contained OTUs from both the vertebrate gut and free-living communities in saline and non-saline habitats. Members of the Enterobacteriales order (also from the gammaproteobacteria) were detected in the vertebrate gut, termite gut and other invertebrates, as well as in a surface soil sample and anoxic saline water. Staphylococcaceae family members (from the phylum Firmicutes and class Bacilli) were common in the ver- tebrate gut samples, but were also detected in soil and cultures derived from freshwater and saline habitats. Finally, members of the Fusobacterium genus were detected in salt-water sediments, in addition to the vertebrate gut. The cosmopolitan distribution of these organisms might have made them particularly impor- tant for introducing novel functions during evolution of the gut microbiota, as they could bring new useful genes from the global microbiome into the gut microbiome through horizontal gene transfer. However, it should be noted that some OTUs that are common in humans Nature Reviews | Microbiology 16SribosomalRNAsequences(%) 0 20 40 60 80 100 Bacteroidetes (red) Firmicutes (blue) Vertebrate gut Termite gut Salt-water surface Salt water Subsurface, anoxic or sediment Other human Non-saline cultured Insects or earthworms Soils or freshwater sediments Mixed water Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown. ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)). SS Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.
  130. 130. History Important Too Genera that cross the divide. Another way to visualize the vertebrate gut–environment dichotomy is by using a network diagram that displays, in addition to the clus- tering of hosts with similar microbiotas, the bacterial genera they share. In this representation of the data, the vertebrate gut samples are more connected to one another than to the environmental samples (FIG. 4a,b). As in the UniFrac-based analysis, the non-gut human samples also occupy an intermediate position between the free-living andthegutcommunities. FIGURE 5 showsthephylogenetic classification of operational taxonomic units (OTUs) that are shared between samples: among humans, an over- whelming number of these are from the Firmicutes, with a smaller number from the Bacteroidetes. By contrast, the free-living communities share OTUs from a wider range of phyla. Samples from the guts of obese humans cluster away from the samples of healthy subjects, and most of theirsharedOTUsarefoundintheFirmicutes.Thisobser- vation is consistent with the finding that samples from obese individuals have a higher number of OTUs family of the gammaproteobacteria class. This fam- ily contained OTUs from both the vertebrate gut and free-living communities in saline and non-saline habitats. Members of the Enterobacteriales order (also from the gammaproteobacteria) were detected in the vertebrate gut, termite gut and other invertebrates, as well as in a surface soil sample and anoxic saline water. Staphylococcaceae family members (from the phylum Firmicutes and class Bacilli) were common in the ver- tebrate gut samples, but were also detected in soil and cultures derived from freshwater and saline habitats. Finally, members of the Fusobacterium genus were detected in salt-water sediments, in addition to the vertebrate gut. The cosmopolitan distribution of these organisms might have made them particularly impor- tant for introducing novel functions during evolution of the gut microbiota, as they could bring new useful genes from the global microbiome into the gut microbiome through horizontal gene transfer. However, it should be noted that some OTUs that are common in humans Nature Reviews | Microbiology 16SribosomalRNAsequences(%) 0 20 40 60 80 100 Bacteroidetes (red) Firmicutes (blue) Vertebrate gut Termite gut Salt-water surface Salt water Subsurface, anoxic or sediment Other human Non-saline cultured Insects or earthworms Soils or freshwater sediments Mixed water Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown. ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)). SS Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.
  131. 131. History Important Too Genera that cross the divide. Another way to visualize the vertebrate gut–environment dichotomy is by using a network diagram that displays, in addition to the clus- tering of hosts with similar microbiotas, the bacterial genera they share. In this representation of the data, the vertebrate gut samples are more connected to one another than to the environmental samples (FIG. 4a,b). As in the UniFrac-based analysis, the non-gut human samples also occupy an intermediate position between the free-living andthegutcommunities. FIGURE 5 showsthephylogenetic classification of operational taxonomic units (OTUs) that are shared between samples: among humans, an over- whelming number of these are from the Firmicutes, with a smaller number from the Bacteroidetes. By contrast, the free-living communities share OTUs from a wider range of phyla. Samples from the guts of obese humans cluster away from the samples of healthy subjects, and most of theirsharedOTUsarefoundintheFirmicutes.Thisobser- vation is consistent with the finding that samples from obese individuals have a higher number of OTUs family of the gammaproteobacteria class. This fam- ily contained OTUs from both the vertebrate gut and free-living communities in saline and non-saline habitats. Members of the Enterobacteriales order (also from the gammaproteobacteria) were detected in the vertebrate gut, termite gut and other invertebrates, as well as in a surface soil sample and anoxic saline water. Staphylococcaceae family members (from the phylum Firmicutes and class Bacilli) were common in the ver- tebrate gut samples, but were also detected in soil and cultures derived from freshwater and saline habitats. Finally, members of the Fusobacterium genus were detected in salt-water sediments, in addition to the vertebrate gut. The cosmopolitan distribution of these organisms might have made them particularly impor- tant for introducing novel functions during evolution of the gut microbiota, as they could bring new useful genes from the global microbiome into the gut microbiome through horizontal gene transfer. However, it should be noted that some OTUs that are common in humans Nature Reviews | Microbiology 16SribosomalRNAsequences(%) 0 20 40 60 80 100 Bacteroidetes (red) Firmicutes (blue) Vertebrate gut Termite gut Salt-water surface Salt water Subsurface, anoxic or sediment Other human Non-saline cultured Insects or earthworms Soils or freshwater sediments Mixed water Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown. ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)). SS Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.
  132. 132. History Important Too Genera that cross the divide. Another way to visualize the vertebrate gut–environment dichotomy is by using a network diagram that displays, in addition to the clus- tering of hosts with similar microbiotas, the bacterial genera they share. In this representation of the data, the vertebrate gut samples are more connected to one another than to the environmental samples (FIG. 4a,b). As in the UniFrac-based analysis, the non-gut human samples also occupy an intermediate position between the free-living andthegutcommunities. FIGURE 5 showsthephylogenetic classification of operational taxonomic units (OTUs) that are shared between samples: among humans, an over- whelming number of these are from the Firmicutes, with a smaller number from the Bacteroidetes. By contrast, the free-living communities share OTUs from a wider range of phyla. Samples from the guts of obese humans cluster away from the samples of healthy subjects, and most of theirsharedOTUsarefoundintheFirmicutes.Thisobser- vation is consistent with the finding that samples from obese individuals have a higher number of OTUs family of the gammaproteobacteria class. This fam- ily contained OTUs from both the vertebrate gut and free-living communities in saline and non-saline habitats. Members of the Enterobacteriales order (also from the gammaproteobacteria) were detected in the vertebrate gut, termite gut and other invertebrates, as well as in a surface soil sample and anoxic saline water. Staphylococcaceae family members (from the phylum Firmicutes and class Bacilli) were common in the ver- tebrate gut samples, but were also detected in soil and cultures derived from freshwater and saline habitats. Finally, members of the Fusobacterium genus were detected in salt-water sediments, in addition to the vertebrate gut. The cosmopolitan distribution of these organisms might have made them particularly impor- tant for introducing novel functions during evolution of the gut microbiota, as they could bring new useful genes from the global microbiome into the gut microbiome through horizontal gene transfer. However, it should be noted that some OTUs that are common in humans Nature Reviews | Microbiology 16SribosomalRNAsequences(%) 0 20 40 60 80 100 Bacteroidetes (red) Firmicutes (blue) Vertebrate gut Termite gut Salt-water surface Salt water Subsurface, anoxic or sediment Other human Non-saline cultured Insects or earthworms Soils or freshwater sediments Mixed water Figure 3 | Relative abundance of phyla in samples. Bargraphshowingtheproportionofsequencesfromeachsample thatcouldbeclassifiedatthephylumlevel.ThecolourcodesforthedominantFirmicutesandBacteroidetesphylaareshown. ForacompletedescriptionofthecolourcodesseeSupplementary information S2(figure).‘Otherhumans’referstobody habitatsotherthanthegut;forexample,themouth,ear,skin,vaginaandvulva(seeSupplementary information S1(table)). SS Nat Rev Microbiol. 2008 October ; 6(10): 776–788. doi:10.1038/nrmicro1978.
  133. 133. Example: Behavior !65 PERSPECTIVES H uman bodies house trillions of sym- biotic microorganisms. The genes in this human microbiome outnum- ber human genes by 100 to 1, and their study is providing profound insights into human health. But humans are not the only ani- mals with microbiomes, and microbiomes do not just impact health. Recent research is revealing surprising roles for microbiomes in shaping behaviors across many animal taxa—shedding light on how behaviors from diet to social interactions affect the compo- sition of host-associated microbial commu- nities (1, 2), and how microbes in turn influ- ence host behavior in dramatic ways (2–6). Our understanding of interactions between host behavior and microbes stems largely from studies of pathogens. Animal social and mating activities have profound effects on pathogen transmission, and many animals use behavioral strategies to avoid or remove pathogens (7). Pathogens can also manipulate host behavior in overt or covert ways. However, given the diversity of microbes in nature, it is important to expand the view of behavior-microbe interactions to include nonpathogens. For diverse animals, including iguanas, squids, and many insects, behavior plays a central role in the establishment and regula- tion of microbial associations (see the first figure). For example, the Kudzu bug (Mega- copta cribraria), an agricultural pest, is born without any symbionts. After birth it acquires a specific symbiont from bacterial capsules left by its mother. If these capsules fit of social living in many species may be the transmission of beneficial microbes (9). Koch and Schmid-Hempel have shown that in the case of bumble bees (Bombus terres- tris), either direct contact with nest mates or feeding on feces of nest mates was neces- sary for establishing the normal gut micro- biota. Bees never exposed to feces had an altered gut microbiota and were more sus- ceptible to the parasite Crithidia bombi (1). Once host-microbe associations are established, microbes can influence host behavior in ways that have far-reaching implications for host ecology and evolution (see the second figure). Sharon et al. recently found that fruit flies (Drosophila melano- gaster) strongly prefer to mate with individ- uals reared on the same diet on which they were reared. Antibiotic treatment abolished the mating preference, and inoculation of Animal Behavior and the Microbiome MICROBIOLOGY Vanessa O. Ezenwa1 , Nicole M. Gerardo2 , David W. Inouye3,4 , Mónica Medina5 , Joao B. Xavier6 Feedbacks between microbiomes and their hosts affect a range of animal behaviors. Gut microbiota Behaviorsimpactmicrobiomes Juvenile iguanas eat soil or feces to tailor the microbiota to their current diet Animals may adjust the microbiota at different life-history stages Ishikawaella capsulata When born, bugs feed on capsules of symbionts; if no capsules are present, nymphs wander in search of microbes Behaviors shape symbiont acquisition Vibrio fischeri Squids eject bioluminescent bacteria daily Suggests animals can actively control their symbiont populations Green iguana (Iguana iguana) Bobtail squid (Euprymna scolopes) Kudzu bug (Megacopta cribraria) Animal Implication Microbial species or consortium Interaction with behavior Behaviors alter microbiomes. In Kudzu bugs (8), green iguanas (15), and bobtail squid (16), host behaviors alter microbial acquisition and maintenance. EUNIVERSITY,NORTHRIDGE;(SQUID)W.ORMEROD,COURTESYOFM.MCFALL-NGAI/UNIVERSITYOFWISCONSIN;(BUG)N.GERARDO/EMORYUNIVERSITY onNovember21,2012www.sciencemag.orgDownloadedfrom S m- es m- dy an ni- es is es mal om o- u- u- ). ns ms mal nd ny id an or of nd to as, s a la- rst a- is it fit of social living in many species may be the transmission of beneficial microbes (9). Koch and Schmid-Hempel have shown that in the case of bumble bees (Bombus terres- tris), either direct contact with nest mates or feeding on feces of nest mates was neces- sary for establishing the normal gut micro- biota. Bees never exposed to feces had an Once host-microbe associations are established, microbes can influence host behavior in ways that have far-reaching implications for host ecology and evolution (see the second figure). Sharon et al. recently found that fruit flies (Drosophila melano- gaster) strongly prefer to mate with individ- uals reared on the same diet on which they r and avid W. Inouye3,4 , Mónica Medina5 , Joao B. Xavier6 Feedbacks between microbiomes and their hosts affect a range of animal behaviors. Gut microbiota Behaviorsimpactmicrobiomes Juvenile iguanas eat soil or feces to tailor the microbiota to their current diet Animals may adjust the microbiota at different life-history stages Ishikawaella capsulata When born, bugs feed on capsules of symbionts; if no capsules are present, nymphs wander in search of microbes Behaviors shape symbiont acquisition Vibrio fischeri Squids eject bioluminescent bacteria daily Suggests animals can actively control their symbiont populations Green iguana (Iguana iguana) Bobtail squid (Euprymna scolopes) Kudzu bug (Megacopta cribraria) Animal Implication Microbial species or consortium Interaction with behavior Behaviors alter microbiomes. In Kudzu bugs (8), green iguanas (15), and bobtail squid (16), host behaviors alter microbial acquisition and maintenance. SITY,NORTHRIDGE;(SQUID)W.ORMEROD,COURTESYOFM.MCFALL-NGAI/UNIVERSITYOFWISCONSIN;(BUG)N.GERARDO/EMORYUNIVERSITY onNovember21,2012www.sciencemag.orgDownloadedfrom Microbial effects on animal chemistry also recently have been linked to changes in predator-prey interactions (11) and feed- ing behavior (12). Females of the African malaria mosquito, Anopheles gambiae, use chemical cues released from human skin to locate hosts. By analyzing skin emana- tions from 48 subjects, Verhulst et al. (12) found that humans with higher microbial diversity on their skin were less attractive to these mosquitoes. High abundances of Pseudomonas spp. and Variovorax spp. were also associated with poor attractive- ness to A. gambiae. These bacteria may pro- duce chemicals that repel mosquitoes or mask attractive volatiles emanating from human skin. Given the importance of chem- marine tubeworm Hydroides elegans. Bac- terial biofilms play a key role in the settle- ment behavior of many marine inverte- brates, from corals to sea urchins. To study the H. elegans system, the authors used transposon mutagenesis to knock out a num- ber of genes from the bacterium Pseudoal- teromonas luteoviolacea, which is required for larval settlement. Mutagenesis of four genes related to cell adhesion and secretion generated bacterial strains that altered worm settlement behavior and metamorphosis (4). It remains to be shown whether similar bac- terial phenotypes drive this important life- history transition across metazoans. Some animal behaviors will be linked to single microbial species, but many will can (5, bac swi sho acid of t betw otic mit (5). mo bra mo beh the man stan man by s enh tion alte the olo und mu nut hid 1. 2. 3. 4. 5. 6. 7. 8. Animal Microbiomesimpactbehaviors Implication Microbial species or consortium Interaction with behavior Human skin microbiota Skin microbes of humans influence attraction to mosquitoes Differential attraction could impact disease spread Lactobacillus rhamnosus The probiotic L. rhamnosus decreases anxiety in mice Suggests bacteria can alter mood Gut microbiota Diet-specific microbiota influence mating preferences Microbes could drive speciation Mosquito (Anopheles gambiae) Mouse (Mus musculus) Fruit fly (Drosophila melanogaster) Microbiomes alter behaviors. In fruit flies (2), mosquitoes (12), and mice (5, 6), microbes alter mating, feeding, and anxiety levels. EASECONTROL;(MOUSE)G.SHUKLIN/WIKIMEDIACOMMONS;(FLIES)T.CHAPMAN/UNIVERSITYOFEASTANGLIA www.sciencemag.org SCIENCE VOL 338 12 OCTOBER 2012 allowing characterization of microbiomes beyond the few cultivable microbes (10, 13, 14). However, determining which animal behaviors influence and are influenced by microbial symbionts, and the mechanisms underlying these interactions, will require a combination of molecular and experimen- tal approaches. For example, Huang et al. have studied the settlement behavior in the two.This requires manipulative experiments and will be facilitated by studying the under- lying mechanisms by which signals are sent between hosts and microbes. Recent experiments with mice, showing that the gut microbiome can influence stress, anxiety, and depression-related behavior via effects on the host’s neuroendrocrine sys- tem, provide insight into how information Physiol. A Neuroethol. Sens. Neura 65 (1996). Acknowledgments: This perspective thanks to NSF meeting grant IOS 12294 Future of Research in Animal Behavior.” A. Laughton, B. Wehrle, C. Fontaine, and discussion and comments. PHOTOCREDITSSECONDFIGURE:(M 10.1 Published by AAAS
  134. 134. Where You Reside / Spend Time Important !66 ORIGINAL ARTICLE Architectural design influences the diversity and structure of the built environment microbiome Steven W Kembel1 , Evan Jones1 , Jeff Kline1,2 , Dale Northcutt1,2 , Jason Stenson1,2 , Ann M Womack1 , Brendan JM Bohannan1 , G Z Brown1,2 and Jessica L Green1,3 1 Biology and the Built Environment Center, Institute of Ecology and Evolution, Department of Biology, University of Oregon, Eugene, OR, USA; 2 Energy Studies in Buildings Laboratory, Department of Architecture, University of Oregon, Eugene, OR, USA and 3 Santa Fe Institute, Santa Fe, NM, USA Buildings are complex ecosystems that house trillions of microorganisms interacting with each other, with humans and with their environment. Understanding the ecological and evolutionary processes that determine the diversity and composition of the built environment microbiome—the community of microorganisms that live indoors—is important for understanding the relationship between building design, biodiversity and human health. In this study, we used high-throughput sequencing of the bacterial 16S rRNA gene to quantify relationships between building attributes and airborne bacterial communities at a health-care facility. We quantified airborne bacterial community structure and environmental conditions in patient rooms exposed to mechanical or window ventilation and in outdoor air. The phylogenetic diversity of airborne bacterial communities was lower indoors than outdoors, and mechanically ventilated rooms contained less diverse microbial communities than did window-ventilated rooms. Bacterial communities in indoor environments contained many taxa that are absent or rare outdoors, including taxa closely related to potential human pathogens. Building attributes, specifically the source of ventilation air, airflow rates, relative humidity and temperature, were correlated with the diversity and composition of indoor bacterial communities. The relative abundance of bacteria closely related to human pathogens was higher indoors than outdoors, and higher in rooms with lower airflow rates and lower relative humidity. The observed relationship between building design and airborne bacterial diversity suggests that we can manage indoor environments, altering through building design and operation the community of microbial species that potentially colonize the human microbiome during our time indoors. The ISME Journal advance online publication, 26 January 2012; doi:10.1038/ismej.2011.211 Subject Category: microbial population and community ecology Keywords: aeromicrobiology; bacteria; built environment microbiome; community ecology; dispersal; environmental filtering Introduction microbiome—includes human pathogens and com- mensals interacting with each other and with their The ISME Journal (2012), 1–11 & 2012 International Society for Microbial Ecology All rights reserved 1751-7362/12 www.nature.com/ismej Microbial Biogeography of Public Restroom Surfaces Gilberto E. Flores1 , Scott T. Bates1 , Dan Knights2 , Christian L. Lauber1 , Jesse Stombaugh3 , Rob Knight3,4 , Noah Fierer1,5 * 1 Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, Colorado, United States of America, 2 Department of Computer Science, University of Colorado, Boulder, Colorado, United States of America, 3 Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado, United States of America, 4 Howard Hughes Medical Institute, University of Colorado, Boulder, Colorado, United States of America, 5 Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, United States of America Abstract We spend the majority of our lives indoors where we are constantly exposed to bacteria residing on surfaces. However, the diversity of these surface-associated communities is largely unknown. We explored the biogeographical patterns exhibited by bacteria across ten surfaces within each of twelve public restrooms. Using high-throughput barcoded pyrosequencing of the 16 S rRNA gene, we identified 19 bacterial phyla across all surfaces. Most sequences belonged to four phyla: Actinobacteria, Bacteriodetes, Firmicutes and Proteobacteria. The communities clustered into three general categories: those found on surfaces associated with toilets, those on the restroom floor, and those found on surfaces routinely touched with hands. On toilet surfaces, gut-associated taxa were more prevalent, suggesting fecal contamination of these surfaces. Floor surfaces were the most diverse of all communities and contained several taxa commonly found in soils. Skin-associated bacteria, especially the Propionibacteriaceae, dominated surfaces routinely touched with our hands. Certain taxa were more common in female than in male restrooms as vagina-associated Lactobacillaceae were widely distributed in female restrooms, likely from urine contamination. Use of the SourceTracker algorithm confirmed many of our taxonomic observations as human skin was the primary source of bacteria on restroom surfaces. Overall, these results demonstrate that restroom surfaces host relatively diverse microbial communities dominated by human-associated bacteria with clear linkages between communities on or in different body sites and those communities found on restroom surfaces. More generally, this work is relevant to the public health field as we show that human-associated microbes are commonly found on restroom surfaces suggesting that bacterial pathogens could readily be transmitted between individuals by the touching of surfaces. Furthermore, we demonstrate that we can use high-throughput analyses of bacterial communities to determine sources of bacteria on indoor surfaces, an approach which could be used to track pathogen transmission and test the efficacy of hygiene practices. Citation: Flores GE, Bates ST, Knights D, Lauber CL, Stombaugh J, et al. (2011) Microbial Biogeography of Public Restroom Surfaces. PLoS ONE 6(11): e28132. doi:10.1371/journal.pone.0028132 Editor: Mark R. Liles, Auburn University, United States of America Received September 12, 2011; Accepted November 1, 2011; Published November 23, 2011 Copyright: ß 2011 Flores et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported with funding from the Alfred P. Sloan Foundation and their Indoor Environment program, and in part by the National Institutes of Health and the Howard Hughes Medical Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: noah.fierer@colorado.edu Introduction More than ever, individuals across the globe spend a large portion of their lives indoors, yet relatively little is known about the microbial diversity of indoor environments. Of the studies that have examined microorganisms associated with indoor environ- ments, most have relied upon cultivation-based techniques to detect organisms residing on a variety of household surfaces [1–5]. Not surprisingly, these studies have identified surfaces in kitchens and restrooms as being hot spots of bacterial contamination. Because several pathogenic bacteria are known to survive on surfaces for extended periods of time [6–8], these studies are of obvious importance in preventing the spread of human disease. However, it is now widely recognized that the majority of communities and revealed a greater diversity of bacteria on indoor surfaces than captured using cultivation-based techniques [10–13]. Most of the organisms identified in these studies are related to human commensals suggesting that the organisms are not actively growing on the surfaces but rather were deposited directly (i.e. touching) or indirectly (e.g. shedding of skin cells) by humans. Despite these efforts, we still have an incomplete understanding of bacterial communities associated with indoor environments because limitations of traditional 16 S rRNA gene cloning and sequencing techniques have made replicate sampling and in-depth characterizations of the communities prohibitive. With the advent of high-throughput sequencing techniques, we can now investigate indoor microbial communities at an unprecedented depth and begin to understand the relationship the stall in), they were likely dispersed manually after women used the toilet. Coupling these observations with those of the distribution of gut-associated bacteria indicate that routine use of toilets results in the dispersal of urine- and fecal-associated bacteria throughout the restroom. While these results are not unexpected, they do highlight the importance of hand-hygiene when using public restrooms since these surfaces could also be potential vehicles for the transmission of human pathogens. Unfortunately, previous studies have documented that college students (who are likely the most frequent users of the studied restrooms) are not always the most diligent of hand-washers [42,43]. Results of SourceTracker analysis support the taxonomic patterns highlighted above, indicating that human skin was the primary source of bacteria on all public restroom surfaces examined, while the human gut was an important source on or around the toilet, and urine was an important source in women’s restrooms (Figure 4, Table S4). Contrary to expectations (see above), soil was not identified by the SourceTracker algorithm as being a major source of bacteria on any of the surfaces, including floors (Figure 4). Although the floor samples contained family-level taxa that are common in soil, the SourceTracker algorithm probably underestimates the relative importance of sources, like Figure 3. Cartoon illustrations of the relative abundance of discriminating taxa on public restroom surfaces. Light blue indicates low abundance while dark blue indicates high abundance of taxa. (A) Although skin-associated taxa (Propionibacteriaceae, Corynebacteriaceae, Staphylococcaceae and Streptococcaceae) were abundant on all surfaces, they were relatively more abundant on surfaces routinely touched with hands. (B) Gut-associated taxa (Clostridiales, Clostridiales group XI, Ruminococcaceae, Lachnospiraceae, Prevotellaceae and Bacteroidaceae) were most abundant on toilet surfaces. (C) Although soil-associated taxa (Rhodobacteraceae, Rhizobiales, Microbacteriaceae and Nocardioidaceae) were in low abundance on all restroom surfaces, they were relatively more abundant on the floor of the restrooms we surveyed. Figure not drawn to scale. doi:10.1371/journal.pone.0028132.g003 Bacteria of Public Restrooms high diversity of floor communities is likely due to the frequency of contact with the bottom of shoes, which would track in a diversity of microorganisms from a variety of sources including soil, which is known to be a highly-diverse microbial habitat [27,39]. Indeed, bacteria commonly associated with soil (e.g. Rhodobacteraceae, Rhizobiales, Microbacteriaceae and Nocardioidaceae) were, on average, related differences in the relative abundances of s some surfaces (Figure 1B, Table S2). Most notably were clearly more abundant on certain surfaces restrooms than male restrooms (Figure 1B). Some family are the most common, and often most abun found in the vagina of healthy reproductive age w Figure 2. Relationship between bacterial communities associated with ten public restroom surfaces. Communities were PCoA of the unweighted UniFrac distance matrix. Each point represents a single sample. Note that the floor (triangles) and toilet (as form clusters distinct from surfaces touched with hands. doi:10.1371/journal.pone.0028132.g002 Bacteria of P time, the un to take of outside om plants ours after ere shut ortion of e human ck to pre- which 26 Janu- Journal, hanically had lower y than ones with open win- ility of fresh air translated tions of microbes associ- an body, and consequently, pathogens. Although this hat having natural airflow Green says answering that clinical data; she’s hoping they move around. But to quantify those con- tributions, Peccia’s team has had to develop new methods to collect airborne bacteria and extract their DNA, as the microbes are much less abundant in air than on surfaces. In one recent study, they used air filters to sample airborne particles and microbes in a classroom during 4 days during which pant in indoor microbial ecology research, Peccia thinks that the field has yet to gel. And the Sloan Foundation’s Olsiewski shares some of his con- cern. “Everybody’s gen- erating vast amounts of data,” she says, but looking across data sets can be difficult because groups choose dif- ferent analytical tools. With Sloan support, though, a data archive and integrated analyt- ical tools are in the works. To foster collaborations between micro- biologists, architects, and building scientists, the foundation also sponsored a symposium 100 80 60 40 20 0 Averagecontribution(%) DoorinDoorout StallinStallout Faucethandles SoapdispenserToiletseat ToiletflushhandleToiletfloorSinkfloor SOURCES Soil Water Mouth Urine Gut Skin Bathroom biogeography. By swabbing different surfaces in public restrooms, researchers determinedthatmicrobesvaryin where they come from depend- ing on the surface (chart). February9,2012
  135. 135. Example: Context !67 K.R. Amato with reduced resource availability [71]. Such a trend is likelyfrequencies of social interaction and contact are likely to have Figure 1. Basic model of factors influencing host fitness, including predicted interactions between host and gut microbiota. Relationships and factors represented by dashed lines indicate areas that are not well studied in wild animal populations. Research Article • DOI: 10.2478/micsm-2013-0002 • MICSM • 2013 • 10-29 MicrobioMe Science and Medicine Introduction As sequencing technology makes data generation faster, cheaper, and more comprehensive, studies of gut microbial communities are multiplying at an astonishing rate. As a result, our understanding of the host-gut microbe relationship is constantly improving. Studies to date have demonstrated that the gut microbiota contributes to host nutrition, health and behavioral patterns by providing energy and nutrients, improving immune function, and influencing the production of neuroactive molecules [1-12]. Changes in the composition of the gut microbial community are known to lead to changes in its function, which can alter host nutrition, health and behavior [6,13-23]. Environmental factors such as diet or social contact are largely responsible for determining the composition of the gut microbial community [24-31], but host genotype also affects the abundances of some microbial genera [28,32,33]. Because host-gut microbe relationships are influenced to some extent by host genotype, and gut microbial community composition differs according to host phylogeny [34-36], discussions of the co-evolution of host and gut microbiota are common in the current literature [7,34-37]. Some researchers argue that since microbes are found in animals as simple as Co-evolution in context: The importance of studying gut microbiomes in wild animals 1 Program in Ecology Evolution and Conservation Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 61801 2 Department of Anthropology, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 61801 Katherine R. Amato1,2 * Received 05 August 2013 Accepted 29 September 2013 Abstract Because the gut microbiota contributes to host nutrition, health and behavior, and gut microbial community composition differs according to host phylogeny, co-evolution is believed to have been an important mechanism in the formation of the host-gut microbe relationship. However, current research is not ideal for examining this theme. Most studies of the gut microbiota are performed in controlled settings, but gut microbial community composition is strongly influenced by environmental factors. To truly explore the co-evolution of host and microbe, it is necessary to have data describing host-microbe dynamics in natural environments with variation in factors such as climate, food availability, disease prevalence, and host behavior. In this review, I use current knowledge of host-gut microbe dynamics to explore the potential interactions between host and microbe in natural habitats. These interactions include the influence of host habitat on gut microbial community composition as well as the impacts of the gut microbiota on host fitness in a given habitat. Based on what we currently know, the potential connections between host habitat, the gut microbiota, and host fitness are great. Studies of wild animals will be an essential next step to test these connections and to advance our understanding of host-gut microbe co-evolution. Keywords Gut microbiota • host-microbe • co-evolution • habitat • ecology • fitness occurring for more than 800 million years [38,39]. Additionally, the increased complexity and stability of gut microbial communities in vertebrates as well as the presence of fewer physical barriers to bacteria has been used to suggest that the adaptive immune system evolved in vertebrates to recognize gut bacteria and improve host-gut microbe interactions [40]. Nevertheless, while it seems likely that co-evolution is an important mechanism for understanding host-gut microbe relationships, current research is not ideal for examining the co-evolution of host and microbe. Most studies of the gut microbiota are performed in controlled laboratory settings or are focused solely on human populations [9,16,25,41-49]. Therefore, despite an understanding that environmental factors greatly influence the host-gut microbe relationship [25,27-29,31], the effects of natural environmental variation in factors such as food availability on the host-gut microbe relationship have generally not been explored. Because the host-gut microbe mutualism evolved in a natural environment with complex patterns of climate, food availability, disease prevalence, and host behavior, a comprehensive examination of host-gut microbe dynamics must consider these factors. Specifically, we must establish the ways in which a host’s habitat influences the selective environment the host imposes upon its gut microbiota, and in turn, how the gut microbiota influences © 2013 Katherine R. Amato, licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author
  136. 136. !68 From Wu et al. 2009 Nature 462, 1056-1060 Challenge 1: Biological Dark Matter
  137. 137. Challenge 2: Function Prediction Difficult Lateral Gene Transfer Metagenomic Binning Hypothetical Proteins
  138. 138. Solution: Better Prediction Methods and Data !70 Characterizing the niche-space distributions of components Sites North American East Coast_GS005_Embayment North American East Coast_GS002_Coastal North American East Coast_GS003_Coastal North American East Coast_GS007_Coastal North American East Coast_GS004_Coastal North American East Coast_GS013_Coastal North American East Coast_GS008_Coastal North American East Coast_GS011_Estuary North American East Coast_GS009_Coastal Eastern Tropical Pacific_GS021_Coastal North American East Coast_GS006_Estuary North American East Coast_GS014_Coastal Polynesia Archipelagos_GS051_Coral Reef Atoll Galapagos Islands_GS036_Coastal Galapagos Islands_GS028_Coastal Indian Ocean_GS117a_Coastal sample Galapagos Islands_GS031_Coastal upwelling Galapagos Islands_GS029_Coastal Galapagos Islands_GS030_Warm Seep Galapagos Islands_GS035_Coastal Sargasso Sea_GS001c_Open Ocean Eastern Tropical Pacific_GS022_Open Ocean Galapagos Islands_GS027_Coastal Indian Ocean_GS149_Harbor Indian Ocean_GS123_Open Ocean Caribbean Sea_GS016_Coastal Sea Indian Ocean_GS148_Fringing Reef Indian Ocean_GS113_Open Ocean Indian Ocean_GS112a_Open Ocean Caribbean Sea_GS017_Open Ocean Indian Ocean_GS121_Open Ocean Indian Ocean_GS122a_Open Ocean Galapagos Islands_GS034_Coastal Caribbean Sea_GS018_Open Ocean Indian Ocean_GS108a_Lagoon Reef Indian Ocean_GS110a_Open Ocean Eastern Tropical Pacific_GS023_Open Ocean Indian Ocean_GS114_Open Ocean Caribbean Sea_GS019_Coastal Caribbean Sea_GS015_Coastal Indian Ocean_GS119_Open Ocean Galapagos Islands_GS026_Open Ocean Polynesia Archipelagos_GS049_Coastal Indian Ocean_GS120_Open Ocean Polynesia Archipelagos_GS048a_Coral Reef Component 1 Component 2 Component 3 Component 4 Component 5 0.1 0.2 0.3 0.4 0.5 0.6 0.2 0.4 0.6 0.8 1.0 Salinity SampleDepth Chlorophyll Temperature Insolation WaterDepth General High Medium Low NA High Medium Low NA Water depth >4000m 2000!4000m 900!2000m 100!200m 20!100m 0!20m >4000m 2000!4000m 900!2000m 100!200m 20!100m 0!20m (a) (b) (c) Figure 3: a) Niche-space distributions for our five components (HT ); b) the site- similarity matrix ( ˆHT ˆH); c) environmental variables for the sites. The matrices are aligned so that the same row corresponds to the same site in each matrix. Sites are ordered by applying spectral reordering to the similarity matrix (see Materials and Methods). Rows are aligned across the three matrices. Figure 3a shows the estimated niche-space distribution for each of the five com- ponents. Components 2 (Photosystem) and 4 (Unidentified) are broadly distributed; Components 1 (Signalling) and 5 (Unidentified) are largely restricted to a handful of sites; and component 3 shows an intermediate pattern. There is a great deal of overlap between niche-space distributions for di erent components. Figure 3b shows the pattern of filtered similarity between sites. We see clear pat- terns of grouping, that do not emerge when we calculate functional distances without filtering, or using PCA rather than NMF filtering (Figure 3 in Text S1). As with the Pfams, we see clusters roughly associated with our components, but there is more overlapping than with the Pfam clusters (Figure 2b). Figure 3c shows the distribution of environmental variables measured at each site. Inspection of Figure 3 reveals qualitative correspondence between environmental factors Better Prediction Methods More Reference Data In Situ Function A B C Representative Genomes Extract Protein Annotation All v. All BLAST Homology Clustering (MCL) SFams Align & Build HMMs HMMs Screen for Homologs New Genomes Extract Protein Annotation Figure 1
  139. 139. Challenge 3: Systems are Complex !71 • How distinguish ! Good vs. Bad ! Correlation vs. Causation • Solutions: ! More controlled ecological studies ! Better analysis tools Good? Bad?
  140. 140. How define “bad” vs. “good” ecosystems • Can (try to) define features that indicate whether an ecosystem is good or bad ! Productivity ! Diversity ! Stability ! Resilience • Key major challenge is predicting future “health” of ecosystem !72
  141. 141. How define “bad” vs. “good” ecosystems • Can (try to) define features that indicate whether a microbiome is good or bad ! Productivity ! Diversity ! Stability ! Resilience • Key major challenge is predicting future “health” of a microbiome !73
  142. 142. How define “bad” vs. “good” ecosystems • Can (try to) define features that indicate whether a microbiome is good or bad ! Health of host ! Diversity ! Stability ! Resilience • Key major challenge is predicting future “health” of a microbiome !74 How do these relate to health?
  143. 143. • Idea of a healthy community vs. a unhealthy community is very complex • The enormous variation between people and over time makes it VERY difficult and very risky to try and say what is “normal” !75
  144. 144. Challenge 4: Need More Reference Data Historical Collections Global Automated Sampling Filling in the 
 Tree of Life
  145. 145. Acknowledgements • GEBA: • $$: DOE-JGI, DSMZ • Eddy Rubin, Phil Hugenholtz, Hans-Peter Klenk, Nikos Kyrpides, Tanya Woyke, Dongying Wu, Aaron Darling, Jenna Lang • GEBA Cyanobacteria • $$: DOE-JGI • Cheryl Kerfeld, Dongying Wu, Patrick Shih • Haloarchaea • $$$ NSF • Marc Facciotti, Aaron Darling, Erin Lynch, • Phylosift • $$$ DHS • Aaron Darling, Erik Matsen, Holly Bik, Guillaume Jospin • iSEEM: • $$: GBMF • Katie Pollard, Jessica Green, Martin Wu, Steven Kembel, Tom Sharpton, Morgan Langille, Guillaume Jospin, Dongying Wu, • aTOL • $$: NSF • Naomi Ward, Jonathan Badger, Frank Robb, Martin Wu, Dongying Wu • Others (not mentioned in detail) • $$: NSF, NIH, DOE, GBMF, DARPA, Sloan • Frank Robb, Craig Venter, Doug Rusch, Shibu Yooseph, Nancy Moran, Colleen Cavanaugh, Josh Weitz • EisenLab: Srijak Bhatnagar, Russell Neches, Lizzy Wilbanks, Holly Bik

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