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Integrating omic approaches to investigate the gut microbiota, School of Biosciences, Cardiff University, Julian R. Marchesi Copenhagenomics 2012
 

Integrating omic approaches to investigate the gut microbiota, School of Biosciences, Cardiff University, Julian R. Marchesi Copenhagenomics 2012

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Integrating omic approaches to investigate the gut microbiota

Integrating omic approaches to investigate the gut microbiota

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    Integrating omic approaches to investigate the gut microbiota, School of Biosciences, Cardiff University, Julian R. Marchesi Copenhagenomics 2012 Integrating omic approaches to investigate the gut microbiota, School of Biosciences, Cardiff University, Julian R. Marchesi Copenhagenomics 2012 Presentation Transcript

    • Integrating “omic”approaches to investigate the gut microbiota Julian R. Marchesi School of Biosciences
    • IBDMany claimshave been Fibromyalgia Colon Cancermade for gutmicrobiota anddiseases Diabetes T I & T II Driving force behind integrating the gut CVD microbiota into host biology is to understand how it maintains health and Obesity initiates or supports disease Depression Normal Fatty liver liver Non-alcoholic fatty liver disease Atopic disease Kidney stones
    • It is also politically important to integrate the microbiota into the host biology.The HGMP and associated claims – the search for SNPs withlinks to diseases - GWAS. NY Time 12 June 2010
    • “Omic” approaches available to investigate the gut Inventories of 16S rRNA genes
    • Using omic approaches was defended on the premise we can’t grow 20-30% of the bacteria.DIET PCoA of fecal samples from gnotobiotic mice, colonized with complete or cultured human fecal communities from two unrelated donors and sampled over time Cultured bacteria recapitulate total community functions
    • METAHIT consortiumAbstractHere we describe the Illumina-based metagenomic sequencing, assembly and characterization of3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, fromfaecal samples of 124 European individuals. The gene set, ~150 times larger than the humangene complement, contains an overwhelming majority of the prevalent (more frequent)microbial genes of the cohort and probably includes a large proportion of the prevalent humanintestinal microbial genes. The genes are largely shared among individuals of the cohort. Over99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and1,150 prevalent bacterial species and each individual at least 160 such species, which are alsolargely shared. We define and describe the minimal gut metagenome and the minimal gutbacterial genome in terms of functions present in all individuals and most bacteria, respectively.
    • One of their aims wasto define the coremicrobiome of the gutIn 85 healthyEuropeans (Danish to We want determineand Spanish). • What are the keystone functions? • Which ones are variable?A large proportion of • Whichunknowns and ones are druggable?fundamental bacterialfunctions indentified,but are these corefunctions of gutmicrobiota?
    • In this study when the supplementary data is searchedthere are no reported hits to genes involved in:-We need to re-define what can be• butyrate synthesis• bile catabolismcore function:considered a• glucuronidasesNeeds to which are not easily microbial host, butOr functions useful to the classified, but maybeimportant to the host show an interaction withat the same timethe eukaryotic host.• indole-3-propionic acid synthesis - depression• choline catabolism – cardiovascular disease• NF-κB modulators – innate immunity
    • How can we start to define the core microbiome/bacteriome?Change the strategy to a top-down rather than bottom-upinvestigation.Use metabonomics/metaproteomics to determine the core In the healthy host the dialogue betweenmetabonome and from this isolate, functions or species which areresponsible for these bioactive molecules. the microbiome and the karyome is via the proteome and metabonome. Studies providing links between bacterial species and molecules – from correlations to cause and effect.
    • Metabonomics 21 3 1 22 15 15 10 16 16 10 A 17 1H 7 NMR 2,4,5 8 11 8 B 6 13 12 20 1&3 1 9 19 19 C 14 8.0 7.5 ppm 4.0 3.5 3.0 2.5 2.0 1.5 ppm Chemical shift (ppm) UPLC-MSSample: Faecal water Faecal extracted Data reduction •Metabolic profiles Analysis •Biomarkers Collaboration with Prof. Elaine Holmes, Imperial College London
    • Marrying together “omic” datasets NMR/MS OTUsobservations Metabonomics Metataxonomics observations X Y Pearson’s correlation OTUs Correlation NMR/MS matrix
    • NMR/ OTU MS s X Y Observatio Observatio ns ns Pearson’s correlation OTU s NMR/MOr function e.g. BSH S genes
    • Bacteroides enterotype Enterotype ≠ metabotypeAre enterotypes real and biologically significant? Prevotella enterotype
    • PCA plot of genomes based on their functions (COGs) 30 Alistipes Atopobium Bacillus Bacteroides Bacteroides Bifidobacterium 20 Blautia Clostridium How can Prevotella Collinsella Desulfovibrio we move to Enterobacter 10 PC1 here? Enterococcus Escherichia Lactobacillus Archeae 0 Parabacteroides Porphyromonas Prevotella Escherichia/ Propionibacterium -10 Enterobacter -20 -15 -10 -5 0 5 10 PC2
    • RAT MODEL PCA of metabolite profiles PCA of Rat model profiles bacterial Metabolic Roux-en-Y Gastric Bypass Surgery (RYGB) and gut microbiota/metabolitesLi et al., (2011) Gut
    • Rat (Li et al. 2011 Gut) Human (Zhang et al. 2009 PNAS)2-week post 2-week post Normal Obese RYGB SHAM RYGB weight
    • p = 0 .0 1 Aerococcaceae Alcaligenaceae 0. 86 Alterom onadaceae Bacillaceae Bacteroidaceae Bifidobacteriaceae 0. 69 Carnobacteriaceae Clostridiaceae Coriobacteriaceae Corynebacteriaceae 0. 52 Deferribacteraceae Desulfov ibrionaceae Enterobacteriaceae 0. 35 Enterococcaceae Erysipelotrichaceae Eubacteriaceae Incertae Sedis X II 0. 18 Incertae Sedis X III Lachnospiraceae Lactobacillaceae Methylobacteriaceae 0. 01 Methylococcaceae Microbacteriaceae Micrococcaceae -0. 15 Moraxellaceae Pasteurellaceae Peptococcaceae Peptostreptococcaceae -0. 32 Planococcaceae Porphyrom onadaceae Prev otellaceae Pseudom onadaceae -0. 49 Rikenellaceae Rum inococcaceae Staphylococcaceae Streptococcaceae -0. 66 Veillonellaceae PG PAG creatine PS m ylam e p trescin eth in u e uracilCross correlation plots derived from selected urinary and fecal metabolites and 37bacterial families at the levels of p= 0.01.Key: PS, p-cresyl sulfate; PG, p-cresyl glucuronide; PAG, phenylacetylglycine.
    • Exploring the impact of GB on the host usingmetabonomic and metataxonomics
    • Thank you for listeningThanks to colleagues at University College Cork, Cardiff University, Nijmegen University, Liverpool University, and especially the metabonomics group at Imperial College London. Science Foundation Ireland, Enterprise Ireland, TheRoyal Society, ESF and BBSRC studentship for funding my work