The dynamic interplay between microbiota and host during 1 ... · 9/25/2012 · HMI Host-Microbe Interactomics Focus on gut mucosal interactions between bacteria and their hosts

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HMIHost-MicrobeInteractomics

The dynamic interplay between microbiota and host during 1 month of conventionalisation of germ-free mice

Peter van Baarlen 25 September 2012 / MetagenomicsHost-Microbe Interactomics Group Wageningen University

E-mail:[email protected]


Focus on gut mucosal interactions between bacteria and their hosts We study pathogenic as well as commensal (symbiotic) bacteria including

the microbiota of the oral cavity, esophagus and intestinal tract We study cellular processes in epithelial cell lines and in mucosal tissues of

human, pig and mouse, and zebrafish The HMI research is multidisciplinary: cell biology, immunology, molecular

(micro-)biology, -omics and diverse microscopy

Research within the HMI group


(Confocal) microscopy (Immuno)histochemistry Cell culture of in vitro gut and tracheal epithelial cell lines (human and pig) Immune cell analysis (flow cytometry) Toll-like receptor (TLR) signal transduction assays Transfection of cell lines using Lentivirus to study gene expression

responses (including knock-down) to bioactive molecules and bacteria Infection models to study pathogens Molecular (micro)biology High-content HTP automated microscopy (BD Pathway platform) Cellular pathway reconstruction: visualisation, integration and analysis in

(functional) -omics Microbiota and microbiome analysis Clustering and stratification of individuals

Techniques used at HMI


the number of bacterial species in the intestine is larger than 1000

Gut bacteria produce factors that “train” our immune system and are essential for intestinal (immune) homeostasis

Presence of gut bacteria is necessary for proper intestinal development (nutrient resorbtion, immunity, ...)

Dysbalanced intestinal colonisation may lead to improper immune responses that can lead to immune, metabolic and auto-immune diseases

Only 20-30% of gut bacteria has ever been cultured

Why study the intestine? The intestine: one large microbial ecosystem


How to study effect of changes in microbiota on the host?o preferably, trace in

neonateso monitor changes in

microbiota diversity "at relevant stages"

o monitor corresponding host responses

Simplified model: Germ-free and gnotobiotic miceo well-studied modelo amenable to experimentingo possible to sample

microbiota and organs

The microbiota have a huge impact on human health throughout life


Gnotobiotic animals - not only of this millennium


compartmentalisation involves:o immune cellso metabolism: dietary

nutrientso microbiota (anaerobic

metabolism)

Experimental set-up: sample multiple regions throughout the guto jejunum o ileumo colon

Compartmentalisation of the animal intestine and microbiota

Carmody and Turnbaugh, CHM 2012


Time-series analysis of GF mouse colonisation by microbiota

+ =

Michiel Kleerebezem, Microbiology WUJerry Wells, HMI WUJoël Doré, INRASahar El-Aidy, Microbiology WU


Host model: germ-free mice (Joël Doré), mouse microbiota

D0 D2D1 D4 D8 D16

GF days colonisation by bacteria

sampling 3 intestinal regions: jejunum, ileum, & colonHistochemistryTranscriptomicsMetabolomics (urine & blood samples;collaboration with J. Nicholson lab)

GF: -♂ C57 BL/6 J, 8-10 weeks old- 6 mice/ day (D)- Diet: standard chow (sterilised)


Mouse intestinal colonisation: immunity meets metabolism

how are these transcriptomechanges coordinated across time?


Gene clusters that form time- or location-dependent modules


Enriched GO categories during mouse colonisation by microbiota

calculated global enriched GO gene sets and their up-downregulation

gene sets were further evaluated by network analysis

calculated global enriched GO gene sets and their up-downregulation

gene sets were further evaluated by network analysis


Annotating gene co-expression modules using gene ontologies


MITchip: phylogenetic arrayo 3.580 probes (detecting

OTUs)o Agilent platform (standard

feature extraction and data analysis)

MITchip: variant of HITchip (Rajilic-Stojanovic et al. Env. Microbiol. 2009)o good (>0.93) correlation

between HItchip and 454 pyrosequencing at phylum, class, and order level

o lower (0.77) correlation at family and variable at genus level

Analysis of mouse intestinal microbiota via MITchip analysis


Microbiota clustering across days


Microbiota diversity changes at different rates across GI regions


1 2 4 8 160

500000

1000000

1500000

2000000

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3000000

3500000

4000000

4500000

5000000Colon

Colonisation (days)

Prob

e in

tens

ityBacilli Bacteroidetes Clostridium cluster IV Clostridium cluster IX Clostridium cluster XIVa Proteobacteria

Jejunum

Colonisation (days)1 2 4

Prob

e in

tens

ity

0

100000

200000

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Dynamics of microbiota - abundance of specific taxa

Development of microbiota diversity starts between Day 2 & 4 in the jejunum Development of microbiota diversity starts between Day 4 & 8 in the colon


Jejunum: composition of microbiota diversity shows expansions of specific taxa

Colon: Similar diversity across timepoints, dominated by Clostridia XIVa and Bacteroidetes

1J 2J 4J 8J 16J

Jejunum Colon

0%

20%

40%

60%

80%

100%

Clostridia XIVa

Bacteroidetes

Clostridia IV

Proteobacteria

Bacilli

Clostridia I, II

1C 2C 4C 8C 16C

Dynamics of microbiota - relative abundance


Gross changes in Firmicutes populations across time in colono loss of Bacilli, increase of

Clostridia

largest effect on variation: SI vs colono total diversity of microbial

taxa in SI lowero jejunum appears to have a

lower diversity than ileumo biological cause?

Microbiota per intestinal region differ substantially

PC1-% variance explained (40.2%)


PCA of taxa strongly changing in abundance in MITchip analysis

colon


Firmicutes decrease slightly, Bacteroidetes increaseo Firmicutes: largest proportion of jejunal microbiota

at day 1o all Bacteroidetes taxa increase similarly to 40% of

final microbiomeo jejunal communities are distinct from those in ileum

and colon

Jejunal communities are less diverse and present from day 1


Days 1-2o Proteobacteria increaseo Bacteroidetes increaseo Lactobacilli, Enterococci decrease

Day 4o Sphingomonas decreaseo Helicobacter decreaseo Desulfovibrio decrease

Days 8-16-30o Firmicutes dominateo Bacteroidetes decrease

Loss of typical (pathobiont) genera at day 4 in jejunum


Day 4 marks transition during which immunity is strongly stimulated


increased sialylation of mucins at day 4o increases adhesion between epithelial

cells

increased emptying of mucins by goblet cells in jejunum and colon

strong induction of interferon-gamma at day 4o IFN-gamma: critical cell signalling

protein that is essential for innate and adaptive immunity

Changes in numbers of specific microbial taxa at day 4 in jejunum appears to be a result of a stronger immune response

Strong induction of innate immunity at day 4


Mouse metabolomics has shown great promise in earlier studies

Claus et al. MSB 2008


Challenges in studying metabolomes - complexity vs. knowledge


1H-NMR spectroscopyo Fourier transformationo spectra calibrated to lactate

Statistics via linear (un)supervised partial least squares models in Matlabo O-PLS models to identify

differentially accumulating metabolites

o O2-PLS models to integrate metabolome and transcriptome data

Metabolic profiling in lumen, urine, blood and tissues

In collaborations with Nicholson Lab, UK and NMC (Leiden)


significant changes in plasma metabolite concentrations at day 4o ky/trp ratio: exemplifies trp degradation

via ky pathway via Ido; hallmark of actively growing (immune) cells

o citrullin/L-glutamine ratio: L-glut can be converted to citrulline, a precursor of arginine and NO (a.o. via Nos2/iNos) that are involved in enhanced macrophage activity

microbiota composition and tissue transcriptomes correlate with systemic (blood) metaboliteso changes in microbiota correlate with

increased immune activationo changes in microbiota correlated with

changes of important blood plasma metabolites

Plasma accumulation of metabolites at day 4 correlate with -omes


significant changes in plasma inflammation-associated cytokine concentrations at day 4o cytokines measured via Luminex

mouse MAP multiplex immunoassay (left panel)

intestinal gene expression correlates with plasma cytokine concentrationso corresponding gene expression

measured via QPCR in individual mice

Day4 plasma immune cell cytokines correlate with plasma metabolites


Exploring correlations between transcriptomes and metabolomes

jejunum


Correlations between microbiota taxa and host metabolic pathways


Changes in microbiota composition impact on jejunal metabolism


Major microbiota-induced colon metabolomic changes during conv.


Microbiota-induced changes in host immune system during conv.


Integrating -omics, microbiota changes and histology


Acknowledgements

Wageningen UniversityMichiel KleerebezemJerry WellsSahar El AidyMuriel DerrienNMC

RU Groningen MCBert GroenDirk-Jan Reijngoud

Imperial College, UKElaine HolmesJeremy NicholsonSandrine ClausClaire Merrifield

[email protected]

Documents

The dynamic interplay between microbiota and host during 1 ... · 9/25/2012 · HMI Host-Microbe Interactomics Focus on gut mucosal interactions between bacteria and their hosts