Microbial Adaptations of the Microbiome. Damian R. Plichta, PhD Director of Bioinformatics
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1 Microbial Adaptations of the Microbiome Damian R. Plichta, PhD Director of Bioinformatics
2 Tailored shotgun metagenomics
3 Microbiome model for biomarker discovery microbiome incoming species conditional colonisation probability transient species stable species conditional persistence probability extinct species Examples of conditions: drugs pre- and probiotics species co-existence
4 Metagenomic Species (MGS)
5 MGS: de-novo species discovery Microbiome samples Deep sequencing and de-novo gene assembly Co-abundance binning Metagenomic species High resolution microbiomics 100 % 100 % MGS? 80 % Gene catalogue CAG? 73 % Metagenomic species MGS MGS 20 % Reference genomes Non-redundant gene catalogue Nielsen et al., Nature Biotechnology 2014
6 The abundance profile of one gene Abundance Fecal samples
7 4099 The abundance profile sof one genes Bacteroides caccae (MGS:igc24) Abundance Fecal samples
8 Adult gut gene catalogue decomposition into MGS es 1 MGS:34 MGS:34 Correlation to MGS:34 profile MGS:279 MGS:301 MGS:387 MGS: MGS:314 MGS:216 MGS:232 MGS:164 MGS:183 MGS:474 MGS:172 MGS:5 MGS:74 MGS:23 MGS:120 MGS:6 MGS:77 MGS:14 MGS:607 MGS:21 MGS:10 MGS:20 MGS: Correlation to MGS:20 profile
9 Dedicated MGS databases for various guts Adult human Infant* Mouse** Samples used Genes 9.8M 6.5M 7M MGS es MGS es with reference genome 23 % 20 % 4 % * meconium and stool samples from children < 3 years old ** cecum and stool samples
10 Ultrahigh-resolution microbiomics SNVs called de novo from metagenomics assemblies
11 Unknown part of the microbiome is large MGS richness (MGS per sample) gene richness Taxonomy unknown Spirochaetes Synergistetes Euryarchaeota Fusobacteria Lentisphaerae Verrucomicrobia Proteobacteria Actinobacteria Bacteroidetes Firmicutes Fecal samples (n=396) Crohn s disease: Metabolic markers and inflammation* * insulin resistance, adiposity, dyslipidaemia and inflammation markers (Chatelier et al. Nature, 2013)
12 Microbial survival and persistence probability
13 Persistence in longitudinal sampling Kaplan-Meyer estimator
14 Persistence probability Survival (%) Persistence (%) !!!!!!!!!!!!!!!!!!! Prevotella copri (CAG:164)!!!!!!! 16 individuals sampled at 2 time points with With CAG:2457 MGU:2457 without Without CAG: MGU: Sampling time (days after 1st sampling)
15 Persistence probabilities Probability density B. animalis mean APP = F. prausnitzii mean APP = Annual persistence probability MGS:igc689 MGS:igc701 (APP) Persistence probability Survival rate (%) (% per year) Prevalence in humans B. vulgatus Observation frequency across 316 independent samples (%)
16 Conditional persistence
17 The little difference Survival (%) Bifidobacterium adolescentis With CAG:2298 MGU:2298 Without MGU:2298 CAG: Sampling time (days after 1st sampling) Across different species, the optional gene set that enhance survival was enriched for genes with important for resistance towards free-radicals
18 Competition may result in extinction C. bolteae Persistance (%) C. bolteae Ruminococcus Time (days)
19 Competition may result in extinction C. bolteae Persistance (%) C. bolteae Ruminococcus 0 Persistance (%) Ruminococcus Ruminococcus C. bolteae Time (days) Time (days)
20 Division of labour In vitro evidence for interactions between co-existing species: gene expression changes cross-feeding Mahowald, M. A. et al., PNAS (2009)
21 Division of labour In vitro evidence for interactions between co-existing species: gene expression changes cross-feeding Mahowald, M. A. et al., PNAS (2009) Gut microbiome consists of species with overlapping genetic potential. Metabolic modelling suggest that its assembly is driven by habitat filtering. Levy, R. & Borenstein, E., PNAS (2013)
22 Metagenomics and metatranscriptomics integration
23 Transcriptional adaptations ANOVA, FDR<0.1 Plichta et al., Nature Microbiology 2016
24 Transcriptional adaptations ANOVA, FDR<0.1 Plichta et al., Nature Microbiology 2016
25 53 species adapt to the presence of other species Fisher s exact test, P<0.05, Bonferroni corrected
26 Wood-Ljungdahl pathway in Blautia hydrogenotrophica
27 Wood-Ljungdahl pathway in Blautia hydrogenotrophica
28 Wood-Ljungdahl pathway in Blautia hydrogenotrophica
29 Wood-Ljungdahl pathway in Blautia hydrogenotrophica
30 Therapeutic target discovery
31 Host phenotype modulation by microbiome Pedersen et al., Nature 2016
32 Host phenotype modulation by microbiome 366 individuals 291 non-diabetics 75 type 2 diabetics 325 metabolites 876 lipids (many unknowns) 56 diabetes relevant variables 7M microbial genes Pedersen et al., Nature 2016
33 Integrating three domains of data
34 Integrating three domains of data Driver species identification
35 Functional species groups Clinical parameter Example: BMI Species with overlapping functions Species 1 Species 2 Species 3 Weak association Weak association Weak association Group of functional overlapping species Samples Strong association
36 The difference between the gut microbiome potential for BCAA biosynthesis and transport correlates with fasting serum BCAA levels and insulin resistance SCC = 0.30, P = SCC = -0.17, P = SCC: Valine = 0.49 Leucine = 0.40 Isoleucine = 0.44 P <
37 The difference between the gut microbiome potential for BCAA biosynthesis and transport correlates with fasting serum BCAA levels and insulin resistance SCC = 0.30, P = SCC = -0.17, P = SCC: Valine = 0.49 Leucine = 0.40 Isoleucine = 0.44 P <
38 The association between BCAA biosynthesis/transport and insulin resistance can be attributed to a few driver species BCAA biosynthesis BCAA transport
39 The association between BCAA biosynthesis/transport and insulin resistance can be attributed to a few driver species BCAA biosynthesis BCAA transport
40 The association between BCAA biosynthesis/transport and insulin resistance can be attributed to a few driver species BCAA biosynthesis BCAA transport
41 P. copri induces diabetic phenotype in mice
42 P. copri induces diabetic phenotype in mice * P < 0.05; ** P < 0.01
43 P. copri induces diabetic phenotype in mice * P < 0.05; ** P < 0.01 P = 0.032, RM two-way ANOVA
44 P. copri induces diabetic phenotype in mice * P < 0.05; ** P < 0.01 P = 0.032, RM two-way ANOVA SCC = 0.46, P =
45 Proposed model for the interplay between gut microbiome and host phenotype mediated through metabolites
46 Tailored shotgun metagenomics
47 Thank you for the attention
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