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Scalable metabolic reconstruction for metagenomic data and the human microbiome

Scalable metabolic reconstruction for metagenomic data and the human microbiome. Sahar Abubucker , Nicola Segata ,

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Scalable metabolic reconstruction for metagenomic data and the human microbiome

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  1. Scalable metabolic reconstructionfor metagenomic dataand the human microbiome SaharAbubucker, Nicola Segata, Johannes Goll, Alyxandria M. Schubert, Jacques Izard,Brandi L. Cantarel, Beltran Rodriguez-Mueller, Jeremy Zucker, MathangiThiagarajan, Bernard Henrissat, Owen White,Scott T. Kelley, Barbara Methé, Patrick D. Schloss,Dirk Gevers, MakedonkaMitreva, Curtis Huttenhower 07-16-11 Harvard School of Public Health Department of Biostatistics

  2. What’s metagenomics? Total collection of microorganisms within a community Also microbial communityormicrobiota Total genomic potential of a microbial community Study of uncultured microorganisms from the environment, which can include humans or other living hosts Total biomolecular repertoire of a microbial community

  3. Valm et al, PNAS 2011

  4. What to do with your metagenome? Reservoir of gene and protein functional information Comprehensive snapshot of microbial ecology and evolution Who’s there? What are they doing? Who’s there varies: your microbiota is plastic and personalized. What they’re doing is adapting totheir environment:you, your body, and your environment. Public health tool monitoring population health and interactions Diagnostic or prognostic biomarker for host disease

  5. The Human Microbiome Project for a normal population Multifaceted analyses Multifaceted data 300 People/15(18) Body Sites • >6,000 samples • >50M 16S seqs. • 4Tbp uniquemetagenomicsequence • >1,900 referencegenomes • Full clinicalmetadata • Human population • Microbial population • Novel organisms • Biotypes • Viruses • Metabolism 2 clin. centers, 4 seq. centers, data generation,technology development, computational tools, ethics…

  6. Metabolic/Functional Reconstruction: The Goal Healthy/IBD BMI Diet Geneexpression SNPgenotypes Taxon abundances Enzyme family abundances Pathway abundances LEfSe:LDA Effect Size Metagenomic biomarker discovery Nicola Segata http://huttenhower.sph.harvard.edu/lefse

  7. HMP: Metabolic reconstruction Functional seq. KEGG + MetaCYC CAZy, TCDB,VFDB, MEROPS… 100 subjects 1-3 visits/subject ~7 body sites/visit 10-200M reads/sample 100bp reads BLAST HUMAnN:HMP UnifiedMetabolic AnalysisNetwork http://huttenhower.sph.harvard.edu/humann Smoothing Witten-Bell BLAST → Genes Genes → Pathways MinPath(Ye 2009) WGS reads Genes(KOs) Taxonomic limitation Rem. paths in taxa < ave. ? Pathways(KEGGs) Pathways/modules Xipe Distinguish zero/low(Rodriguez-Mueller in review) Gap filling c(g) = max( c(g), median )

  8. HUMAnN: Metabolic reconstruction Vaginal Oral (BM) Gut Oral (SupP) Oral (TD) Skin Nares ← Pathways→ ← Samples → Vaginal Skin Nares Oral (SupP) Oral (BM) Oral (TD) Gut ← Pathways→ ← Samples → Pathway coverage Pathway abundance

  9. HUMAnN: Validating gene and pathwayabundances on synthetic data • Validated on individual gene families, module coverage, and abundance • 4 synthetic communities: Low (20 org.) and high (100 org.) complexity Even and lognormal abundances • Best-BLAST-hit overshoots false positives, undershoot real pathways as a result • HUMAnN FNs: short genes (<100bp), taxonomically rare pathways • HUMAnN FPs: large andmulticopy (not many in bacteria) Individual gene families ρ=0.91

  10. A portrait of the healthy human microbiome:Who’s there vs. what they’re doing ←Phylotypes→ ← Relative abundance → ← Relative abundance → Oral (BM) Oral (SupP) Oral (TD) Nares Vaginal Skin Gut ← Relative abundance → ← Relative abundance → ← Pathways →

  11. Niche specialization in human microbiome function Metabolic modules in theKEGG functional catalogenriched at one or morebody habitats http://huttenhower.sph.harvard.edu/lefse Nicola Segata

  12. Proteoglycan degradationby the gut microbiota Glycosaminoglycans(Polysaccharide chains) AA core

  13. Proteoglycan degradation:From pathways to enzymes Enzyme relative abundance 10-8 10-3 • Heparan sulfate degradation missing due to the absence ofheparanase, a eukaryotic enzyme • Other pathways not bottlenecked by individual genes • HUMAnN links microbiome-wide pathway reconstructions → site-specific pathways → individual gene families

  14. Patterns of variation in human microbiome function by niche

  15. Patterns of variation in human microbiome function by niche • Three main axes of variation • Eukaryotic exterior • Low-diversity vaginal • Gut metabolism • Oral vs. tooth hard surface • Only broad patterns: every human-associated habitat is functionally distinct!

  16. How do microbes and function varywithin each body site across the population?

  17. How do body sites compare between individuals across the population?

  18. HMP: Prevalence of species (OTUs)across the population Cumulative prevalence

  19. HMP: Prevalence of pathwaysacross the population Cumulative prevalence • 16 (of 251) modules strongly “core” at 90%+ coverage in 90%+ individuals at 7 body sites • 24 modules at 33%+ coverage • 71 modules (28%) weakly “core” at 33%+ coverage in 66%+ individuals at 6+ body sites • Contrast zerophylotypes or OTUs meeting this threshold! • Only 24 modules (<10%) differentially covered by body site • Compare with 168 modules (>66%) differentially abundant by body site

  20. Linking function to community composition Plus ubiquitous pathways: transcription, translation, cell wall, portions of central carbon metabolism… ← 52 posterior fornix microbiomes → Phosphate and peptide transport Lactobacillus crispatus Lactobacillus jensenii Sugar transport Embden-Meyerhof glycolysis, phosphotransferases Lactobacillus gasseri Lactobacillus iners F-type ATPase, THF ← Taxa and correlated metabolic pathways → AA and small molecule biosynthesis Gardnerella/Atopobium Candida/Bifidobacterium Eukaryotic pathways

  21. Linking communities to host phenotype Top correlates with BMI in stool Body Mass Index Normalized relative abundance Vaginal pH (posterior fornix) Vaginal pH, community metabolism, and community composition represent a strong, direct link between phenotype and function in these data. Vaginal pH (posterior fornix)

  22. Microbial biomolecular function and metabolism in the human microbiome: the story so far? • HUMAnN • Accurate metagenomic metabolic reconstruction • Sequences → genes → pathways → phenotypes • Validated on 4x synthetic communities • Who’s there varies even in health • What they’re doing doesn’t (as much) • There are patterns in this variation • Communities in related environments adapt using related functions • Function correlates with membership and phenotype • ~1/3 to 2/3 of human metagenome characterized • Job security!

  23. Ask both what you can do for your microbiomeand what your microbiome can do for you

  24. Thanks! Human Microbiome Project George Weinstock Owen White Rob Knight MakedonkaMitreva Erica Sodergren Mihai Pop VivienBonazzi Jane Peterson Lita Proctor Johannes Goll Yuzhen Ye Beltran Rodriguez-Mueller Jeremy Zucker MathangiThiagarajan Brandi Cantarel QiandongZeng Maria Rivera Barbara Methe Bill Klimke Daniel Haft Dirk Gevers SaharAbubucker Jacques Izard Nicola Segata Levi Waldron HMP Metabolic Reconstruction Bruce BirrenRamnik Xavier Doyle Ward Eric Alm Ashlee Earl Lisa Cosimi Alyx Schubert Pat Schloss BenGanzfried FahSathira Interested? We’re recruitingstudents and postdocs! http://huttenhower.sph.harvard.edu http://huttenhower.sph.harvard.edu/humann http://huttenhower.sph.harvard.edu/lefse VagheeshNarasimhan LarisaMiropolsky

  25. HMP: Prevalence of genera (phylotypes)across the population Cumulative prevalence

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