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Marine Host-Microbiome Interactions: Challenges and Opportunities

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Talk by Jonathan Eisen at Scripps Institute for Oceanography., November 22, 2019

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Marine Host-Microbiome Interactions: Challenges and Opportunities

  1. 1. Marine Host-Microbiome Interactions: Challenges and Opportunities November 22, 2019 Talk at SIO Jonathan A. Eisen University of California, Davis @phylogenomics
  2. 2. Eisen Lab •Mutation •Duplication •Deletion •Rearrangement •Recombination Intrinsic Novelty Origin Evolvability: variation in these processes w/in & between taxa
 Phylogenomics: integrating genomics & evolution, helps interpret / predict evolvability
  3. 3. •Mutation •Duplication •Deletion •Rearrangement •Recombination Intrinsic •Recombination •Gene transfer •Symbiosis •Symbioses •CommunitiesExtrinsic Novelty Origin Evolvability & Phylogenomics of Extrinsic Novelties Eisen Lab
  4. 4. Eisen Lab “Topics” Phylogenomic Methods & Tools Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  5. 5. Eisen Lab • Rules
  6. 6. Eisen Lab “Topics” Phylogenomic Methods & Tools Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  7. 7. Automated Accurate Genome Tree Lang JM, Darling AE, Eisen JA (2013) Phylogeny of Bacterial and Archaeal Genomes Using Conserved Genes: Supertrees and Supermatrices. PLoS ONE 8(4): e62510. doi:10.1371/journal.pone.0062510 Jenna Lang
  8. 8. PhyEco Markers Phylogenetic group Genome Number Gene Number Maker Candidates Archaea 62 145415 106 Actinobacteria 63 267783 136 Alphaproteobacteria 94 347287 121 Betaproteobacteria 56 266362 311 Gammaproteobacteria 126 483632 118 Deltaproteobacteria 25 102115 206 Epislonproteobacteria 18 33416 455 Bacteriodes 25 71531 286 Chlamydae 13 13823 560 Chloroflexi 10 33577 323 Cyanobacteria 36 124080 590 Firmicutes 106 312309 87 Spirochaetes 18 38832 176 Thermi 5 14160 974 Thermotogae 9 17037 684 Wu D, Jospin G, Eisen JA (2013) Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. PLoS ONE 8(10): e77033. doi:10.1371/journal.pone.0077033
  9. 9. Eisen Lab “Topics” Phylogenomic Methods & Tools Microbial Phylogenomics & Evolvability Phylogenomic Resources & Reference Data Communication & Participation In Microbiology & Science Model Systems
  10. 10. 2002-2007: TIGR Tree of Life Project Figure from Barton, Eisen et al. “Evolution”, CSHL Press based on Baldauf et al Tree Naomi Ward Karen Nelson
  11. 11. 2007-2014: GEBA Figure from Barton, Eisen et al. “Evolution”, CSHL Press based on Baldauf et al Tree
  12. 12. Synapomorphies Exist Wu et al.. 2009. Nature 462: 1056-1060.
  13. 13. Missing Microbes?
  14. 14. GEBA Cyanobacteria Shih et al. 2013. PNAS 10.1073/pnas.1217107110 0.3 B1 B2 C1 Paulinella Glaucophyte Green Red Chromalveolates C2 C3 A E F G B3 D A B Fig. 2. Implications on plastid evolution. (A) Maxi- mum-likelihood phylogenetic tree of plastids and cya- nobacteria, grouped by subclades (Fig. 1). The red dot Cheryl Kerfeld
  15. 15. Haloarchaeal GEBA-like Lynch et al. (2012) PLoS ONE 7(7): e41389. doi:10.1371/journal.pone.0041389 Erin Lynch
  16. 16. The Dark Matter of Biology From Wu et al. 2009 Nature 462, 1056-1060
  17. 17. JGI Dark Matter Project environmental samples (n=9) isolation of single cells (n=9,600) whole genome amplification (n=3,300) SSU rRNA gene based identification (n=2,000) genome sequencing, assembly and QC (n=201) draft genomes (n=201) SAK HSM ETLTG HOT GOM GBS EPR TAETL T PR EBS AK E SM G TATTG OM OT seawater brackish/freshwater hydrothermal sediment bioreactor GN04 WS3 (Latescibacteria) GN01 +Gí LD1 WS1 Poribacteria BRC1 Lentisphaerae Verrucomicrobia OP3 (Omnitrophica) Chlamydiae Planctomycetes NKB19 (Hydrogenedentes) WYO Armatimonadetes WS4 Actinobacteria Gemmatimonadetes NC10 SC4 WS2 Cyanobacteria :36í2 Deltaproteobacteria EM19 (Calescamantes) 2FW6SDí )HUYLGLEDFWHULD
  18. 18. GAL35 Aquificae EM3 Thermotogae Dictyoglomi SPAM GAL15 CD12 (Aerophobetes) OP8 (Aminicenantes) AC1 SBR1093 Thermodesulfobacteria Deferribacteres Synergistetes OP9 (Atribacteria) :36í2 Caldiserica AD3 Chloroflexi Acidobacteria Elusimicrobia Nitrospirae 49S1 2B Caldithrix GOUTA4 6$5 0DULQLPLFURELD
  19. 19. Chlorobi )LUPLFXWHV Tenericutes )XVREDFWHULD Chrysiogenetes Proteobacteria )LEUREDFWHUHV TG3 Spirochaetes WWE1 (Cloacamonetes) 70 ZB3 093í 'HLQRFRFFXVí7KHUPXV OP1 (Acetothermia) Bacteriodetes TM7 GN02 (Gracilibacteria) SR1 BH1 OD1 (Parcubacteria) :6 OP11 (Microgenomates) Euryarchaeota Micrarchaea DSEG (Aenigmarchaea) Nanohaloarchaea Nanoarchaea Cren MCG Thaumarchaeota Cren C2 Aigarchaeota Cren pISA7 Cren Thermoprotei Korarchaeota pMC2A384 (Diapherotrites) BACTERIA ARCHAEA archaeal toxins (Nanoarchaea) lytic murein transglycosylase stringent response (Diapherotrites, Nanoarchaea) ppGpp limiting amino acids SpotT RelA (GTP or GDP) + PPi GTP or GDP +ATP limiting phosphate, fatty acids, carbon, iron DksA Expression of components for stress response sigma factor (Diapherotrites, Nanoarchaea) ı4 ȕ ȕ¶ ı2ı3 ı1 -35 -10 Į17' Į7' 51$ SROPHUDVH oxidoretucase + +e- donor e- acceptor H 1 Ribo ADP + 1+2 O Reduction Oxidation H 1 Ribo ADP 1+ O 2H 1$' + H 1$'++ + - HGT from Eukaryotes (Nanoarchaea) Eukaryota O +2+2 OH 1+ 2+3 O O +2+2 1+ 2+3 O tetra- peptide O +2+2 OH 1+ 2+3 O O +2+2 1+ 2+3 O tetra- peptide murein (peptido-glycan) archaeal type purine synthesis (Microgenomates) PurF PurD 3XU1 PurL/Q PurM PurK PurE 3XU PurB PurP ? Archaea adenine guanine O + 12 + 1 1+2 1 1 H H 1 1 1 H H H1 1 H PRPP )$,$5 IMP $,$5 A GUA G U G U A G U A U A U A U Growing AA chain W51$*O
  20. 20. recognizes UGA P51$ UGA recoded for Gly (Gracilibacteria) ribosome Woyke et al. Nature 2013. Tanja
 Woyke
  21. 21. Microbial Dark Matter Part 2 • Ramunas Stepanauskas • Tanja Woyke • Jonathan Eisen • Duane Moser • Tullis Onstott
  22. 22. Microbial Dark Matter Part 2 • Ramunas Stepanauskas • Tanja Woyke • Jonathan Eisen • Duane Moser • Tullis Onstott
  23. 23. MAGs
  24. 24. Eisen Lab “Topics” Phylogenomic Methods Tools Microbial Phylogenomics Evolvability Phylogenomic Resources Reference Data Communication Participation In Microbiology Science Model Systems
  25. 25. Google Trends Hits to Microbiome The Rise of the Microbiome
  26. 26. The Rise of the Microbiome 0 500 1000 1500 2000 2500 3000 3500 4000 4500 1956 1958 1961 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Pubmed Hits to Microbiome vs. Year
  27. 27. Why Now I: Appreciation of Microbial Diversity
  28. 28. Why Now II: Post Genome Blues The Microbiome Transcriptome VariomeEpigenome Overselling the Human Genome?
  29. 29. Why Now III: Technological Advances
  30. 30. Why Now III: Technological Advances
  31. 31. Why Now IV: Microbiome Functions Turnbaugh et al Nature. 2006 444(7122):1027-31.
  32. 32. Why Now V: Importance of Other Microbiomes
  33. 33. Microbiomania vs. Germophobia Germophobia Microbiomania
  34. 34. Microbiomania vs. Germophobia Germophobia Microbiomania All Microbes Are Bad Use Antimicrobials in Everything Avoid all Microbes All Microbes Are Good Use Probiotics in Everything Embraces all Microbes Lick Subway Poles Fecal Transplants Will Save World Avoid Animals Too Swab Stories
  35. 35. Solution 1: Complain
  36. 36. http://microBE.net http://gut-check.net Solution 2: Education Outreach
  37. 37. Kitty Microbiome Georgia Barguil Jack Gilbert Project MERCCURI Phone and Shoes tinyurl/kittybiome Holly Ganz David Coil Solution 3: Citizen Science
  38. 38. Eisen Lab “Topics” Phylogenomic Methods Tools Microbial Phylogenomics Evolvability Phylogenomic Resources Reference Data Communication Participation In Microbiology Science Model Systems
  39. 39. Phylogenomic Methods Tools Microbial Phylogenomics Evolvability Phylogenomic Resources Reference Data Communication Participation In Microbiology Science HMS HMS Triangle
  40. 40. Host Microbe Stress (HMS) Triangle Host Microbe Stress
  41. 41. Host Microbiome Stress Host Microbe Stress (HMS) Triangle
  42. 42. Symbiosis Under Stress When organisms are placed under selective pressure or stress where novelty would be beneficial, can we predict which pathway they will use? What leads to interactions / symbioses being a potential solution? Can we manipulate interactions and/or force new ones upon systems?
  43. 43. HMS Type 1: Nutrient Acquisition Host Microbiome Nutrients
  44. 44. HMS Type 1: Nutrient Acquisition Glassy Winged Sharpshooter Gut Endosymbionts Trying to Live on Xylem Fluid Nancy Moran Dongying Wu
  45. 45. HMS Type 1: Nutrient Acquisition Oloton Corn Mucilage Microbiome Low N
  46. 46. HMS Type 2: Pathogens Host Microbiome Pathogen
  47. 47. HMS Type 2: Pathogens Ducks Gut Microbiome Flu Walter Boyce Holly Ganz Sarah Hird Ladan Daroud Alana Firl
  48. 48. HMS Type 2: Pathogens Koala Gut Microbiome Chlamydia Antibiotics Katherine Dahlhausen
  49. 49. Frogs Skin Microbiome Chytrid Sonia Ghose Marina De Leon HMS Type 2: Pathogens
  50. 50. Host Microbiome Changing Environment and/or Human Impacts HMS Type 3: Environmental Change
  51. 51. Oct. 2010 Jim Doyle: Aquatic Monocots
  52. 52. Oct. 2010 Jim Doyle: Aquatic Monocots
  53. 53. Oct. 2010 Jim Doyle: Aquatic Monocots
  54. 54. Seagrass w/in Monocots Dicots Monocots Alismatales
  55. 55. Seagrasses w/in Alismatales Tree inferred by Jenna Lang based from rbcL sequences using RaxML
  56. 56. Seagrasses Polyphyletic Tree inferred by Jenna Lang based from rbcL sequences using RaxML
  57. 57. Seagrasses: 3 Invasions of Marine Tree inferred by Jenna Lang based from rbcL sequences using RaxML
  58. 58. Seagrass Diversity Image from Reynolds PL. Seagrass and Seagrass Beds http://ocean.si.edu/seagrass-and-seagrass-beds
  59. 59. Seagrasses: Significant Convergence Tree inferred by Jenna Lang based from rbcL sequences using RaxML
  60. 60. Seagrass Microbiomes? • Many reasons for interest • Convergence of microbiomes? • Comparison to other monocots • Adaptations to salt / marine environment • But … • No experience in our mega-group working with seagrass … • Little literature on seagrass microbiomes • So? ….
  61. 61. Jay Stachowicz - Seagrass EcoEvo • Stachowicz lab
  62. 62. Jay Stachowicz - Seagrass Guru • Stachowicz lab Image from Reynolds PL. Seagrass and Seagrass Beds http://ocean.si.edu/seagrass-and-seagrass-beds • Seagrass Importance • Ecosystem Structure • Living Habitat • Foundation of Food Webs
  63. 63. Jay Stachowicz - Seagrass Guru • Stachowicz lab Image from Reynolds PL. Seagrass and Seagrass Beds http://ocean.si.edu/seagrass-and-seagrass-beds • Seagrass Importance • Ecosystem Structure • Living Habitat • Foundation of Food Webs
  64. 64. Slide from Jay Stachowicz Z. marina is abundant throughout northern hemisphere
  65. 65. Eelgrass Ecologically Important Slide from Jay Stachowicz
  66. 66. Seagrass Microbiome ● Aim 1: How have the microbial communities associated with seagrasses co- evolved with their hosts and what roles in the past and currently do microbes play in adaptations of plants to fresh and marine water life? ● Aim 2: What drives the community assembly of the seagrass microbiome, and specifically within the Zostera marina model system? ● Aim 3: What role does the microbial community play in the functional ecology of the Zostera marina (with a specific focus on sulfur and nitrogen metabolism and primary production)? Jenna LangJessica GreenJay StachowiczJonathan Eisen
  67. 67. Seagrass Microbiome Returning to The Sea HMS Type 3: Environmental Change
  68. 68. Seagrass Microbiome ● Aim 1: How have the microbial communities associated with seagrasses co- evolved with their hosts and what roles in the past and currently do microbes play in adaptations of plants to fresh and marine water life? ● Aim 2: What drives the community assembly of the seagrass microbiome, and specifically within the Zostera marina model system? ● Aim 3: What role does the microbial community play in the functional ecology of the Zostera marina (with a specific focus on sulfur and nitrogen metabolism and primary production)? Jenna LangJessica GreenJay StachowiczJonathan Eisen
  69. 69. Intraplant Microbiome Biogeography Hannah Holland-Moritz Ruth Lee Jenna Lang rRNA gene PCR, sequencing, informatics Laura Vann
  70. 70. Shannon Diversity By Location
  71. 71. Rhizome Roots vs. Shoot Roots vs. Leaf Variation in microbial community composition in Z. marina. PCoA plot of weighted Unifrac distances between samples. Communities cluster by tissue type (PERMANOVA, p 0.001). Within root samples, rhizome roots differ from shoot roots (PERMANOVA, p 0.001).
  72. 72. Zostera Experimental Network (ZEN) • 40 Sites in 24 countries • Eelgrass genetic composition • Eelgrass above and below ground biomass • Associated epifauna and infauna Original experimental sites Zostera marina Emmett Duffy Pamela Reynolds Kevin Hovel Jay Stachowicz http://zenscience.org
  73. 73. Seagrass Microbiome ZEN Kit Jenna Lang $25 custom filters 3D-printed stand Russell Neches
  74. 74. ZEN Microbiome Sampling Emmett Duffy Pamela Reynolds Kevin Hovel Jay Stachowicz http://zenscience.org • Sent kits • Asked to sample leaves, roots, sediment and water
  75. 75. Taxonomic Composition
  76. 76. Global Structure of Eelgrass Microbiome Results PcoA Environmental Similarity • Leaf, roots and sediment different • Leaves resemble water • Leaves more similar to local water Fahimipour AK, Kardish MR, Lang JM, Green JL, Eisen JA, Stachowicz JJ. 2017. Global-scale structure of the eelgrass microbiome. Appl Environ Microbiol 83:e03391-16. https:// doi.org/10.1128/AEM.03391-16. Jenna Lang Ashkaan Fahimipour Melissa Kardish
  77. 77. Don’t Forget the Fungi Ettinger CL, Eisen JA. Characterization of the mycobiome of the seagrass, Zostera marina, reveals putative associations with marine chytrids. Frontiers in Microbiology 10: 2476. doi: 10.3389/fmicb. 2019.02476. Cassie Ettinger
  78. 78. Approach Slide by C. Ettinger
  79. 79. Approach Slide by C. Ettinger
  80. 80. Variation Slide by C. Ettinger
  81. 81. Lots of Unclassified Sequences Slide by C. Ettinger
  82. 82. But Only From A Few ASVs Slide by C. EttingerSlide by C. Ettinger
  83. 83. Classifying by Getting Extra Sequence Slide by C. Ettinger
  84. 84. SV8 = Chytrid Slide by C. Ettinger
  85. 85. Seagrass Microbiome ● Aim 1: How have the microbial communities associated with seagrasses co- evolved with their hosts and what roles in the past and currently do microbes play in adaptations of plants to fresh and marine water life? ● Aim 2: What drives the community assembly of the seagrass microbiome, and specifically within the Zostera marina model system? ● Aim 3: What role does the microbial community play in the functional ecology of the Zostera marina (with a specific focus on sulfur and nitrogen metabolism and primary production)? Jenna LangJessica GreenJay StachowiczJonathan Eisen
  86. 86. Predicted Sulfur Metabolism Enriched on Roots Results Fahimipour AK, Kardish MR, Lang JM, Green JL, Eisen JA, Stachowicz JJ. 2017. Global-scale structure of the eelgrass microbiome. Appl Environ Microbiol 83:e03391-16. https://doi.org/10.1128/ AEM.03391-16.
  87. 87. Edge Effects: Does in Matter Where Plants Are? Ettinger CL, Voerman SE, Lang JM, Stachowicz JJ, Eisen JA. (2017) Microbial communities in sediment from Zostera marina patches, but not the Z. marina leaf or root microbiomes, vary in relation to distance from patch edge. PeerJ 5:e3246 https://doi.org/10.7717/ peerj.3246 Jenna Lang Cassie Ettinger Sofie Voerman
  88. 88. Edge Effect in Sediment Not Plant Microbiomes • Plant parts (root, leaf) and near-by sediment different from each other. • Edge effects not seen for plant microbiomes • Edge effect seen for sediment
  89. 89. Seagrass Ammonification Seagrass Root Microbiome Ammon- ification Jay Stachowicz Susan Williams Cassie Ettinger Jessica Abbott
  90. 90. Succession During Ammonification Ettinger CL, Williams SL, Abbott JM, Stachowicz JJ, Eisen JA. (2017) Microbiome succession during ammonification in eelgrass bed sediments. PeerJ 5:e3674 https://doi.org/10.7717/peerj.3674 Susan Williams Cassie Ettinger Jessica Abbott Changes appear driven by sulfur cycling w/ decreases in sulfur reducers (Desulfobacterales) and corresponding increases in sulfide oxidizers (Alteromonadales and Thiotrichales).
  91. 91. Seagrass Temperature Seagrass Root Microbiome Temperature Jay Stachowicz Alana Firl Laura Reynolds Jessica Abbott Susan Williams Katie DuBois
  92. 92. David Coil Jeanine Olsen Laura Vann Yves van De Peer Guillaume Jospin Melissa Kardish Alana Firl Laura Reynolds Jessica Abbott Susan Williams Katie DuBois Cassie Ettinger Sofie Voerman Ashkaan Fahimipour Russell Neches James Doyle Jenna LangJessica GreenJay Stachowicz Hannah Holland-Moritz Ruth Lee Pamela Reynolds • Karley Lujuan • Marcus Cohen • Katie Somers • Taylor Tucker • Hoon San Ong • Neil Brambhatt • Hena Hundal • Daniel Oberbauer • Briana Pompa-Hogan • Alex Alexiev • Ruth Lee
  93. 93. Key Lesson Seagrass Microbiome Studies Way More Difficult in Many Ways than Those of “Model” Terrestrial Organisms
  94. 94. Seagrass Microbiome Phylogenomic Methods Tools Microbial Phylogenomics Evolvability Phylogenomic Resources Reference Data Communication Participation In Microbiology Science Model Systems
  95. 95. Zostera marina as model HMS System • What makes a model system for host- microbiome studies? • Which are / are not available for ZM?
  96. 96. Drosophila microbiome 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 Jenna Lang Angus Chandler
  97. 97. Model Systems - Rice Edwards et al. 2015. Structure, variation, and assembly of the root-associated microbiomes of rice. PNAS 9 Supplementary Figures31 32 Fig. S1 Map depicting soil collection locations for greenhouse experiment.33 10 234 Fig. S2. Sampling and collection of the rhizocompartments. Roots are collected from rice235 plants and soil is shaken off the roots to leave ~1mm of soil around the roots. The ~1 mm of soil236 three separate rhizocompartments: the rhizosphere, rhizoplane, and endosphere (Fig. 1A). Because the root microbiome has been shown to correlate with the developmental stage of the plant (10), the root-associated microbial communities were sampled at 42 d (6 wk), when rice plants from all genotypes were well-established in the soil but still in their vegetative phase of growth. For our study, the rhizosphere compartment was com- w i t i ( t s z i m a r t t ( t m P h t P p ( i M P a t o s q a n v v p t p s G Fig. 1. Root-associated microbial communities are separable by rhizo- compartment and soil type. (A) A representation of a rice root cross-section depicting the locations of the microbial communities sampled. (B) Within- sample diversity (α-diversity) measurements between rhizospheric compart- ments indicate a decreasing gradient in microbial diversity from the rhizo- sphere to the endosphere independent of soil type. Estimated species richness was calculated as eShannon_entropy . The horizontal bars within boxes represent median. The tops and bottoms of boxes represent 75th and 25th quartiles, respectively. The upper and lower whiskers extend 1.5× the interquartile range from the upper edge and lower edge of the box, re- spectively. All outliers are plotted as individual points. (C) PCoA using the WUF metric indicates that the largest separation between microbial com- munities is spatial proximity to the root (PCo 1) and the second largest source of variation is soil type (PCo 2). (D) Histograms of phyla abundances in each compartment and soil. B, bulk soil; E, endosphere; P, rhizoplane; S, rhizosphere; Sac, Sacramento. 2 of 10 | www.pnas.org/cgi/doi/10.1073/pnas.1414592112 igate the relationship between rice ge- icrobiome, domesticated rice varieties rated growing regions were tested. Six spanning two species within the Oryza 2 d in the greenhouse before sampling. a) cultivars M104, Nipponbare (both ties), IR50, and 93-11 (both indica va- gside two cultivars of African cultivated g7102 (Glab B) and TOg7267 (Glab E). ed that rice genotype accounted for ariation between microbial communities % of the variance, P 0.001; Dataset f the variance, P 0.066; Dataset S5H); ntations for clustering patterns of the nt on the first two axes of unconstrained ppendix, Fig. S10). We then used CAP effect of rice genotype on the microbial ng on rice cultivar and controlling for and technical factors, we found that ge- ice have a significant effect on root- mmunities (5.1%, P = 0.005, WUF, Fig. , UUF, SI Appendix, Fig. S11A). Ordi- AP analysis revealed clustering patterns only partially consistent with genetic UF and UUF metrics. The two japonica her and the two O. glaberrima cultivars ver, the indica cultivars were split, with O. glaberrima cultivars and IR50 clus- cultivars. enotypic effect manifests in individual eparated the whole dataset to focus on vidually and conducted CAP analysis and technical factors. The rhizosphere eight sites were operated under two cultivation practices: organic cultivation and a more conventional cultivation practice termed “ecofarming” (see below). Because genotype explained the least variance in the greenhouse data, we limited the analysis to one cultivar, S102, a California temperate japonica variety that is widely cultivated by commercial growers and is closely related to M104 (26). Field samples were collected from vegetatively growing rice plants in flooded fields and the previously defined rhizocompartments were analyzed as before. Unfortunately, collection of bulk soil controls for the field experiment was not Fig. 3. Host plant genotype significantly affects microbial communities in the rhizospheric compartments. (A) Ordination of CAP analysis using the WUF metric constrained to rice genotype. (B) Within-sample diversity measurements of rhizosphere samples of each cultivar grown in each soil. Estimated species richness was calculated as eShannon_entropy . The horizontal bars within boxes represent median. The tops and bottoms of boxes repre- sent 75th and 25th quartiles, respectively. The upper and lower whiskers extend 1.5× the interquartile range from the upper edge and lower edge of the box, respectively. All outliers are plotted as individual points. oi/10.1073/pnas.1414592112 Edwards et al. fields are too high to find representative soil that is unlikely to be affected by nearby plants. Amplification and sequencing of the field microbiome samples yielded 13,349,538 high-quality sequences (median: 54,069 reads per sample; range: 12,535– 148,233 reads per sample; Dataset S13). The sequences were clustered into OTUs using the same criteria as the greenhouse experiment, yielding 222,691 microbial OTUs and 47,983 OTUs with counts 5 across the field dataset. We found that the microbial diversity of field rice plants is significantly influenced by the field site. α-Diversity measure- ments of the field rhizospheres indicated that the cultivation site significantly impacts microbial diversity (SI Appendix, Fig. S14A, P = 2.00E-16, ANOVA and Dataset S14). Unconstrained PCoA using both the WUF and UUF metrics showed that microbial communities separated by field site across the first axis (Fig. 4B, WUF and SI Appendix, Fig. S14B, UUF). PERMANOVA agreed with the unconstrained PCoA in that field site explained the largest proportion of variance between the microbial communi- ties for field plants (30.4% of variance, P 0.001, WUF, Dataset S5O and 26.6% of variance, P 0.001, UUF, Dataset S5P). CAP analysis constrained to field site and controlled for rhizocom- partment, cultivation practice, and technical factors (sequencing batch and biological replicate) agreed with the PERMANOVA results in that the field site explains the largest proportion of variance between the root-associated microbial communities in field plants (27.3%, P = 0.005, WUF, SI Appendix, Fig. S15A and 28.9%, P = 0.005, UUF, SI Appendix, Fig. S15E), sug- gesting that geographical factors may shape root-associated microbial communities. Rhizospheric Compartmentalization Is Retained in Field Plants. Sim- ilar to the greenhouse plants, the rhizospheric microbiomes of field plants are distinguishable by compartment. α-Diversity of the field plants again showed that the rhizosphere had the highest microbial diversity, whereas the endosphere had the least S15). PCoA the WUF a compartmen Appendix, F separation i ond largest (20.76%, P UUF, Data biomes cons trolled for f agreed with variance bet compartmen and 10.9%, Taxonomi overall sim Chloroflexi, microbiota. endosphere Proteobacter and Plancto distribution trend from t Appendix, F We again OTUs in the S16). We fo endosphere c representing Fig. S17). Th the genus A and Alphap terestingly, found to b greenhouse OTUs were sisted of tax and Myxoco bidopsis roo Cultivation Pr The rice fiel practices, org tion called farming in th are all perm harvest fumi itself does si partments ov a significant the rhizocom indicating th affected diffe the rhizosph practice, with zospheres th Dataset S14) crobial comm tests; Datase practices are the WUF m S14D). PERFig. 4. Root-associated microbiomes from field-grown plants are separable by cultivation site, rhizospheric compartment, and cultivation practice. (A) Variation w/in Plant Cultivation Site Effects Rice Genotype Effects and mitochondrial) reads to analyze microbial abundance in the endosphere over time (Fig. 6A). Using this technique, we confirmed the sterility of seedling roots before transplantation. We found that microbial penetrance into the endosphere oc- curred at or before 24 h after transplantation and that the pro- portion of microbial reads to organellar reads increased over the first 2 wk after transplantation (Fig. 6A). To further support the evidence for microbiome acquisition within the first 24 h, we sampled root endospheric microbiomes from sterilely germi- nated seedlings before transplanting into Davis field soil as well as immediately after transplantation and 24 h after transplan- tation (SI Appendix, Fig. S24). The root endospheres of sterilely germinated seedlings, as well as seedlings transplanted into Davis field soil for 1 min, both had a very low percentage of microbial reads compared with organellar reads (0.22% and 0.71%), with the differences not statistically significant (P = 0.1, Wilcoxon test). As before, endospheric microbial abundance increased significantly, by 10-fold after 24 h in field soil (3.95%, P = 0.05, Wilcoxon test). We conclude that brief soil contact does not strongly increase the proportion of microbial reads, and therefore the increase in microbial reads at 24 h is indicative of endophyte acquisition within 1 d after transplantation. α-Diversity significantly varied by rhizocompartment (P 2E- 16; Dataset S23) and there was a significant interaction between rhizocompartment and collection time (P = 0.042; Dataset S23); however, when each rhizocompartment was analyzed individ- (13 d) approach the endosphere and rhizoplane microbiome compositions for plants that have been grown in the green- house for 42 d. There are slight shifts in the distribution of phyla over time; however, there are significant distinctions between the com- partments starting as early as 24 h after transplantation into soil (Fig. 6D, SI Appendix, Figs. S24B and S26, and Dataset S24). Because each phylum consists of diverse OTUs that could ex- hibit very different behaviors during acquisition, we next ex- amined the dynamics and colonization patterns of specific OTUs within the time-course experiment. The core set of 92 endosphere-enriched OTUs obtained from the previous green- house experiment (SI Appendix, Fig. S9C) was analyzed for relative abundances at different time points (Fig. 6E). Of the 92 core endosphere-enriched microbes present in the greenhouse experiment, 53 OTUs were detectable in the endosphere in the time-course experiment. The average abundance profile over time revealed a colonization pattern for the core endospheric microbiome. Relative abundance of the core endosphere- enriched microbiome peaks early (3 d) in the rhizosphere and then decreases back to a steady, low level for the remainder of the time points. Similarly, the rhizoplane profile shows an in- crease after 3 d with a peak at 8 d with a decline at 13 d. The endosphere generally follows the rhizoplane profile, except that relative abundance is still increasing at 13 d. These results sug- gest that the core endospheric microbes are first attracted to the Fig. 5. OTU coabundance network reveals modules of OTUs associated with methane cycling. (A) Subset of the entire network corresponding to 11 modules with methane cycling potential. Each node represents one OTU and an edge is drawn between OTUs if they share a Pearson correlation of greater than or equal to 0.6. (B) Depiction of module 119 showing the relationship between methanogens, syntrophs, methanotrophs, and other methane cycling taxonomies. Each node represents one OTU and is labeled by the presumed function of that OTU’s taxonomy in methane cycling. An edge is drawn between two OTUs if they have a Pearson correlation of greater than or equal to 0.6. (C) Mean abundance profile for OTUs in module 119 across all rhizocompartments and field sites. The position along the x axis corresponds to a different field site. Error bars represent SE. The x and y axes represent no particular scale. PLANTBIOLOGYPNASPLUS Function x Genotype of magnitude greater than in any single plant species to date. Under controlled greenhouse conditions, the rhizocompartments described the largest source of variation in the microbial com- munities sampled (Dataset S5A). The pattern of separation be- tween the microbial communities in each compartment is consistent with a spatial gradient from the bulk soil across the rhizosphere and rhizoplane into the endosphere (Fig. 1C). Similarly, microbial diversity patterns within samples hold the same pattern where there is a gradient in α-diversity from the rhizosphere to the endosphere (Fig. 1B). Enrichment and de- pletion of certain microbes across the rhizocompartments indi- cates that microbial colonization of rice roots is not a passive process and that plants have the ability to select for certain mi- crobial consortia or that some microbes are better at filling the root colonizing niche. Similar to studies in Arabidopsis, we found that the relative abundance of Proteobacteria is increased in the endosphere compared with soil, and that the relative abundances of Acidobacteria and Gemmatimonadetes decrease from the soil to the endosphere (9–11), suggesting that the distribution of different bacterial phyla inside the roots might be similar for all land plants (Fig. 1D and Dataset S6). Under controlled green- house conditions, soil type described the second largest source of variation within the microbial communities of each sample. However, the soil source did not affect the pattern of separation between the rhizospheric compartments, suggesting that the rhizocompartments exert a recruitment effect on microbial con- sortia independent of the microbiome source. By using differential OTU abundance analysis in the com- partments, we observed that the rhizosphere serves an enrich- ment role for a subset of microbial OTUs relative to bulk soil (Fig. 2). Further, the majority of the OTUs enriched in the rhizosphere are simultaneously enriched in the rhizoplane and/or endosphere of rice roots (Fig. 2B and SI Appendix, Fig. S16B), consistent with a recruitment model in which factors produced by the root attract taxa that can colonize the endosphere. We found that the rhizoplane, although enriched for OTUs that are also Time Series
  98. 98. Z. marina as a model system Jay Stachowicz Maggie Sogin
  99. 99. JGI Seagrass Pop Geno/Microbiomics 216 Zostera marina Thalassia testudinum Cymodocea nodosa Posidonia oceanica Potamogeton crispus Spirodela Jeanine Olsen Jay Stachowicz Slide by Laura Vann from Tree from Les et al., Syst. Bot. 1997 Yves van De Peer Laura Vann
  100. 100. http://zenscience.org • Sent kits • Sampled microbiomes of leaves, roots, sediment • Sampled leaves for genomes Jeanine Olsen Laura Vann Jay Stachowicz JGI Seagrass Population Sampling
  101. 101. Microbial Manipulation of Seagrass? Raquel PeixotoMelissa KardishJay Stachowicz
  102. 102. Probiotic consortium from Pocillopora damicornis BMC screening 7 strains Microbial Manipulation of Coral
  103. 103. Massively Parallel Undergraduates Pic of Karley Lujuan David Coil • Karley Lujuan • Marcus Cohen • Katie Somers • Taylor Tucker • Hoon San Ong • Neil Brambhatt • Hena Hundal • Daniel Oberbauer • Briana Pompa-Hogan • Alex Alexiev • Ruth Lee
  104. 104. Jolie LoBrutto Jolie LoBrutto Cassie Ettinger Lena Capece Massively Parallel Undergraduates
  105. 105. Reference Genomes
  106. 106. Culturing Hit List
  107. 107. Last Lessons • 1. What Goes Around Comes Around • 2. Seagrass Is Part of a Larger System
  108. 108. Metagenomic Sequencing Laura Vann
  109. 109. Chemosymbionts
  110. 110. Eisen et al. 1992 Eisen et al. 1992. J. Bact.174: 3416 Colleen Cavanaugh Chemosynthetic Symbioses
  111. 111. Genomics of Chemosymbionts
  112. 112. Clams in Seagrass Beds
  113. 113. HMS: Istmobiome 1000s of Species Microbiome Sand
  114. 114. HMS: Istmobiome 1000s of Species Microbiome Sand
  115. 115. Istmobiome Project ~ 3 million years ago… Formation of the Panama Isthmus split the Atlantic and Pacific Oceans This geographic barrier facilitated the speciation of macro- and micro-organisms “Divergence of Marine Symbiosis After the Rise of the Isthmus of Panama” Collaboration Between STRI and UC Davis See http://istmobiome.net Bill Wcislo
  116. 116. Lucinid Clams Laetitia Wilkins
  117. 117. Diana Chin, Ph.D. candidate Stony Brook Ipek Yasmin Meric, UC Davis undergraduate reasearcher Gustav Paulay, Florida Museum Jay Osvatic Ph.D. candidate Uni Vienna Benedict Yuen, Postdoc Uni Vienna Jillian Petersen, Professor Uni Vienna Lucinid collaborators
  118. 118. A.1 A.2 A.3 A.4 A.1-A.4; Wilkins 2019, Mol Ecol B Codakia sister pair Ctena sister pair Bacterial symbiont genomes Sister species locations Other lucinids sampled (Clathrolucina spp.) Other lucinids sampled (Ctena spp.) Yellow: Caribbean Blue: Pacific
  119. 119. 4 Phacoides Atlantic Promiscuous Istmobiome Atlantic clade 4 Lucinoma Dall clade Atlantic Istmobiome Atlantic 5 Chiquita clade (Pacific) Istmobiome Pacific Ctena Hawai’i Clathrolucina clad Galapagos Codakia 233 high quality bins clustered roughly into 8 clades ! 80% completion, 4% contamination GTDBTk Phylogeny Istmobiome means Ctena and Codakia hosts Symbionts: Other lucinids sampled (Ctena spp.) Yellow: Caribbean Blue: Pacific
  120. 120. Eisen Lab • Rules

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