1. An Introduction to Meta’omic Analyses Curtis Huttenhower Galeb Abu-Ali Eric Franzosa 08-11-16 Harvard T.H. Chan School of Public Health Department of Biostatistics

2. Sequencing as a tool for microbial community analysis 2 Amplicons Meta’omic Lyse cells Extract DNA (and/or RNA) PCR to amplify a single marker gene, e.g. 16S rRNA George Rice, Montana State University Classify sequence  microbe Samples Microbes Taxon counts / %s Taxon counts, Gene catalog / counts, Genomes, Pathway reconstructions, Genetic variants... Who’s there? (Taxonomic profiling) What are they doing? (Functional profiling) What does it all mean? (Statistical analysis) Who’s there? (Taxonomic profiling) What are they doing? (Functional profiling) What does it all mean? (Statistical analysis)

3. What to do with your metagenome? 3 Diagnostic or prognostic biomarker for host disease Public health tool monitoring population health and epidemiology Comprehensive snapshot of microbial ecology and evolution Reservoir of gene and protein functional information

4. A foundational metagenomic study: Global Ocean Sampling 4 2003/2004 - ongoing

5. The NIH Human Microbiome Project (HMP): A comprehensive microbial survey What is a “normal” human microbiome? 300 healthy human subjects Multiple body sites 15 male, 18 female Multiple visits Clinical metadata www.hmpdacc.org Slide by Dirk Gevers http://www.nature.com/nature/focus/humanmicrobiota/ 5,200 16S samples Spanning 300 subjects, 18 sites 700 shotgun samples Subset of 100 subjects, six sites 2,500

6. How to find biology in your meta’ome Looking for ecology? –Diversity metrics, k-mer analysis, curve fitting Looking for specific bugs? –Assembly:+novelty, -difficulty –Mapping:+speed/ease, -novelty Looking for specific processes? –Intrinsic annotation:+novelty, -difficulty –Extrinsic annotation:+sensitivity, -novelty Looking for variants? –Clustering:+specificity, -difficulty –Mapping:+sensitivity, -novelty What else? 6

7. Typical shotgun metagenome and metatranscriptome analyses 7 Samples Microbes Relative abundances Samples Genes or Pathways Relative abundances Taxonomic Profiling Assembly Functional Profiling

8. Funded by National Institutes of Health, Dept. of Health and Human Services From Bugs to Drugs in Inflammatory Bowel Disease The gut microbiota varies in IBD – Diversity is reduced, specific clades enriched/depleted, and consistent functional dysbioses are induced – Differential within CD and UC, and heterogeneous within these diseases To be actionable, requires... – New onset patients to stratify disease subtypes and response to treatment – Longitudinal data to predict onset and resolution of flares – Microbial molecular data for new potential bioactives – Host molecular data to identify targetable pathways

9. Funded by National Institutes of Health, Dept. of Health and Human Services Taxonomic and functional dysbioses in IBD http://huttenhower.sph.harvard.edu/ibd2012 Gevers CHM 2014 Xochitl Morgan Dirk Gevers

10. Funded by National Institutes of Health, Dept. of Health and Human Services The “HMP2” IBD Multi’omics Data resource http://ibdmdb.org

11. Preliminary IBD microbiome multi’omics CD - 69 Non-IBD Controls - 33 UC- 55 80 Shotgun Metagenomic Sequencing Non-targeted LC-MS Metabolomics CD– 5 UC– 2 IC - 1 HMP2 Cross-Sectional HMP2 Longitudinal Time1 Time10 … NLIBD - Cross-Sectional CD - 20 Non-IBD Controls - 22 UC- 23 157 65 Stool Taxonomic Profiling Functional Profiling Metabolomic Profiling 1. Lipids -Positive ion mode 2. HiliC - Polar metabolites in positive ion mode 3. FFA - Free fatty acid and bile aids - negative ion mode 4. CMH - Polar metabolites, negative ion mode http://huttenhower.sph.harvard.edu/metaphlan2 http://huttenhower.sph.harvard.edu/humann2 Alexandra Sirota-Madi

12. MetaPhlAn2: metagenomic taxonomic profiling 12 Gene X X is a unique marker gene for clade Y http://huttenhower.sph.harvard.edu/metaphlan2 Nicola Segata

13. Funded by National Institutes of Health, Dept. of Health and Human Services Representative Differentially Abundant Microbes and Metabolites http://huttenhower.sph.harvard.edu/maaslin

14. Funded by National Institutes of Health, Dept. of Health and Human Services Bacteria Up in IBD Co-variation between Gut Microbes and Metabolites Down in IBD Up in IBDDown in IBD FDR <0.1 Spearman of residuals after regressing disease, medication, and age

15. HUMAnN2: Organism-specific functional profiling of metagenomes and metatranscriptomes 15 http://huttenhower.sph.harvard.edu/humann2

16. Microbial Contributions to Bile Acid Dismetabolism Down in IBD Up in IBD Conjugated bile acid hydrolases produced by the intestinal microbiota Samples Log2

17. Gene-based fingerprints capture strain variation in individuals’ most abundant (stable) bugs 17

18. PanPhlAn: the approach Metagenomic sample Pan-gene family coverage Microbial pangenomes Cluster to Gene families Gene coverage Abundance-sorted pan-gene families Coverage Multi-copy genes Plateau of genes from one metagenome’s strain Absent genes http://bitbucket.org/CibioCM/panphlan Nicola Segata

19. 19 Gene-family distribution curves Base coverage E. coli gene-families Select samples with “step” distribution (colored curves) E. coli strain is present Reject non-step (gray) curves

20. PanPhlAn for “meta-epidemiology” Metagenomes from [Loman et al., 2013] http://bitbucket.org/CibioCM/panphlan

21. StrainPhlAn: metagenomic strain identification and tracking 21

22. Human Microbiome Project 2 Lita Procter Jon Braun Dermot McGovern Subra Kugathasan Ted Denson Janet Jansson Ramnik Xavier Jane Peterson Sarah Highlander Barbara Methe http://huttenhower.sph.harvard.edu Bruce Birren Chad Nusbaum Clary Clish Joe Petrosino Thad Stappenbeck Thanks! Human Microbiome Project Karen Nelson George Weinstock Owen White Alex Kostic Joseph Moon George Weingart Casey DuLong Xochitl Morgan Daniela Boernigen Emma Schwager Jim Kaminski Bahar Sayoldin Eric Franzosa Boyu Ren Tommi Vatanen Koji Yasuda Tiffany Hsu Siyuan Ma Randall Schwager Melanie Schirmer Himel Mallick Moran Yassour Alexandra Sirota-Madi Galeb Abu-Ali Ali Rahnavard Lauren McIver Ayshwarya Subramanian Nicola Segata Levi Waldron Hera Vlamakis Dirk Gevers Clary Clish Justin Scott Wendy Garrett