Transcript 2016_Huttenhower_Stamps01

An Introduction to Meta’omic Analyses
Curtis Huttenhower
Galeb Abu-Ali
Eric Franzosa
Harvard T.H. Chan School of Public Health
Department of Biostatistics
08-11-16
Sequencing as a tool for
microbial community analysis
Lyse cells
Extract DNA (and/or RNA)
Who’s there?
(Taxonomic profiling)
Amplicons
Meta’omic
George Rice, Montana State University
PCR to amplify a single
marker gene, e.g. 16S rRNA
What are they doing?
Classify
sequence
(Functional
 microbe
profiling)
Samples
Microbes
What does it all mean?
(Statistical analysis)
Taxon
counts / %s
Taxon counts,
Gene catalog / counts,
Genomes,
Pathway reconstructions,
Genetic variants...
2
What to do with your metagenome?
Reservoir of
gene and protein
functional
information
Public health tool
monitoring
population health
and epidemiology
Comprehensive
snapshot of
microbial ecology
and evolution
Diagnostic or
prognostic
biomarker for
host disease
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A foundational metagenomic study:
Global Ocean Sampling
2003/2004 - ongoing
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Slide by Dirk Gevers
The NIH Human Microbiome Project (HMP):
A comprehensive microbial survey
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What is a “normal” human microbiome?
300 healthy human subjects
Multiple body sites
5,200 16S samples
Spanning 300 subjects, 18 sites
• 15 male, 18 female
700 shotgun samples
Subset of 100 subjects, six sites
2,500
Multiple visits
Clinical metadata
http://www.nature.com/nature/focus/humanmicrobiota/
www.hmpdacc.org
How to find biology in your meta’ome
• Looking for ecology?
– Diversity metrics, k-mer analysis, curve fitting
• Looking for specific bugs?
– Assembly:
– Mapping:
+novelty, -difficulty
+speed/ease, -novelty
• Looking for specific processes?
– Intrinsic annotation:
– Extrinsic annotation:
+novelty, -difficulty
+sensitivity, -novelty
• Looking for variants?
– Clustering:
– Mapping:
• What else?
+specificity, -difficulty
+sensitivity, -novelty
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Typical shotgun metagenome and
metatranscriptome analyses
Taxonomic
Profiling
Assembly
Functional
Profiling
Relative
abundances
Samples
Genes or
Pathways
Microbes
Samples
Relative
abundances
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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
Funded by National Institutes of Health, Dept. of Health and Human Services
Taxonomic and functional dysbioses in IBD
Dirk Gevers
Xochitl
Morgan
http://huttenhower.sph.harvard.edu/ibd2012
Gevers CHM 2014
Funded by National Institutes of Health, Dept. of Health and Human Services
The “HMP2” IBD Multi’omics Data resource
http://ibdmdb.org
Funded by National Institutes of Health, Dept. of Health and Human Services
Alexandra
Sirota-Madi
Preliminary IBD microbiome multi’omics
HMP2 Cross-Sectional
Stool
http://huttenhower.sph.harvard.edu/metaphlan2
Taxonomic
Profiling
CD - 69
157
UC- 55
http://huttenhower.sph.harvard.edu/humann2
Shotgun Metagenomic
Sequencing
Non-IBD
Controls - 33
Functional
Profiling
HMP2 Longitudinal
…
CD– 5 Time1
UC– 2
IC - 1
Time10
80
Metabolomic
Profiling
NLIBD - Cross-Sectional
Non-targeted LC-MS
Metabolomics
CD - 20
UC- 23
Non-IBD
Controls - 22
65
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
MetaPhlAn2: metagenomic
taxonomic profiling
Nicola Segata
http://huttenhower.sph.harvard.edu/metaphlan2
X is a unique marker gene for clade Y
Gene X
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Representative Differentially Abundant Microbes and Metabolites
http://huttenhower.sph.harvard.edu/maaslin
Funded by National Institutes of Health, Dept. of Health and Human Services
Co-variation between Gut Microbes and Metabolites
Up in IBD
Down in IBD
Spearman of residuals
after regressing disease,
medication, and age
Bacteria
Up in IBD
Down in IBD
Funded by National Institutes of Health, Dept. of Health and Human Services
FDR <0.1
HUMAnN2: Organism-specific functional profiling
of metagenomes and metatranscriptomes
Gene Family
UniRef50_A6L0N6
UniRef50_A6L0N6|s__Bacteroides_fragilis
UniRef50_A6L0N6|s__Bacteroides_finegoldii
UniRef50_A6L0N6|s__Bacteroides_stercoris
UniRef50_A6L0N6|unclassified
UniRef50_G9S1V7
UniRef50_G9S1V7|s__Bacteroides_vulgatus
UniRef50_G9S1V7|s__Bacteroides_thetaiotaomicron
UniRef50_G9S1V7|s__Bacteroides_stercoris
RPKs
67
8
5
4
1
60
31
22
7
Pathway
indole-3-acetate activation
indole-3-acetate activation|unclassified
indole-3-acetate activation|s__Bacteroides_ovatus
indole-3-acetate activation|s__Alistipes_putredinis
indole-3-acetate activation|s__Bacteroides_caccae
melibiose degradation
melibiose degradation|unclassified
melibiose degradation|s__Parabacteroides_merdae
melibiose degradation|s__Bacteroides_caccae
RPKs
57
32.3
4.5
3
2.25
55
17
8
6
Eric
Lauren
Franzosa McIver
http://huttenhower.sph.harvard.edu/humann2
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Microbial Contributions to Bile Acid Dismetabolism
Log2
Up in IBD
Down in IBD
Samples
Conjugated bile acid hydrolases produced by the intestinal microbiota
Gene-based fingerprints capture strain variation
in individuals’ most abundant (stable) bugs
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PanPhlAn: the approach
http://bitbucket.org/CibioCM/panphlan
Nicola
Segata
Gene coverage
Read
mapping
Metagenomic
sample
Microbial
pangenomes
Cluster to
Gene families
Coverage
Pan-gene family coverage
Multi-copy
genes
Plateau of genes from one metagenome’s strain
Absent genes
Abundance-sorted pan-gene families
Base coverage
Gene-family distribution curves
Select samples with “step” distribution
(colored curves)
E. coli strain is present
Reject non-step
(gray) curves
E. coli gene-families
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PanPhlAn for “meta-epidemiology”
http://bitbucket.org/CibioCM/panphlan
Metagenomes from [Loman et al., 2013]
StrainPhlAn: metagenomic
strain identification and tracking
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Thanks!
http://huttenhower.sph.harvard.edu
Human Microbiome Project 2
Alex
Ayshwarya
Xochitl
Kostic Subramanian Morgan
Casey
DuLong
Daniela
Boernigen
Lauren
McIver
Ramnik Xavier
Lita Procter
Bruce Birren
Jon Braun
Chad Nusbaum
Dermot McGovern
Clary Clish
Subra Kugathasan
Joe Petrosino
Ted Denson
Thad Stappenbeck
Janet Jansson
Human Microbiome Project
George
Weingart
Emma
Schwager
Eric
Franzosa
Boyu
Ren
Tiffany
Hsu
Ali
Rahnavard
Joseph
Moon
Jim
Kaminski
Tommi
Vatanen
Koji
Yasuda
Siyuan
Ma
Galeb
Abu-Ali
Bahar
Sayoldin
Randall
Schwager
Melanie
Schirmer
Himel
Mallick
Moran
Yassour
Alexandra
Sirota-Madi
Hera Vlamakis
Dirk Gevers
Jane Peterson
Sarah Highlander
Barbara Methe
Nicola Segata
Clary Clish
Justin Scott
Karen Nelson
George Weinstock
Owen White
Levi
Waldron
Wendy Garrett