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Type 2 diabetes influences bacterial tissue compartmentalisation in human obesity

Nature Metabolism volume 2, pages 233–242 (2020)Cite this article

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Abstract

Visceral obesity is a key risk factor for type 2 diabetes (T2D). Whereas gut dysbiosis appears to be instrumental for this relationship, whether gut-associated signatures translocate to extra-intestinal tissues and how this affects host metabolism remain elusive. Here we provide a comparative analysis of the microbial profile found in plasma, liver and in three distinct adipose tissues of individuals with morbid obesity. We explored how these tissue microbial signatures vary between individuals with normoglycaemia and those with T2D that were matched for body mass index. We identified tissue-specific signatures with higher bacterial load in the liver and omental adipose tissue. Gut commensals, but also environmental bacteria, showed tissue- and T2D-specific compartmentalisation. T2D signatures were most evident in mesenteric adipose tissue, in which individuals with diabetes displayed reduced bacterial diversity concomitant with fewer Gram-positive bacteria, such as Faecalibacterium, as opposed to enhanced levels of typically opportunistic Gram-negative Enterobacteriaceae. Plasma samples of individuals with diabetes were similarly enriched in Enterobacteriaceae, including the pathobiont Escherichia–Shigella. Our work provides evidence for the presence of selective plasma and tissue microbial signatures in individuals with severe obesity and identifies new potential microbial targets and biomarkers of T2D.

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Fig. 1: Workflow overview.
Fig. 2: Bacterial distribution across body sites.
Fig. 3: Tissue-specific bacterial signatures.
Fig. 4: Tissue bacterial profile in participants with normoglycaemia or type 2 diabetes.

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Data availability

Sequencing data was deposited to the European Nucleotide Archive, https://www.ebi.ac.uk/ena, with accession number: PRJEB36477. Secondary accession: ERP119674.

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Acknowledgements

This study was supported by a bariatric care team grant (TB2-138776) and a Canadian Microbiome Initiative team grant (MRT-168045) from the Canadian Institutes of Health Research (CIHR) and by a CIHR Foundation Scheme grant to A.M. (FDN-143247). A.M. was supported by a CIHR and Pfizer research chair in the pathogenesis of insulin resistance and cardiovascular diseases. F.F.A. holds a CIHR postdoctoral fellowship and Diabetes Canada incentive funding. B.A.H.J. was supported by awards from the Lundbeck Foundation (R232-2016-2425) and Novo Nordisk Foundation (NNF17OC0026698).

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Author notes

  1. These authors contributed equally: Fernando F. Anhê, Benjamin Anderschou Holbech Jensen.

Authors and Affiliations

  1. Québec Heart and Lung Research Institute, Laval University, Québec, Québec, Canada

    Fernando F. Anhê, Benjamin Anderschou Holbech Jensen, Thibault V. Varin, Denis Richard, Simon Marceau, Laurent Biertho, André Tchernof & André Marette

  2. Department of Biochemistry and Biomedical Sciences, Farncombe Family Digestive Health Research Institute and Centre for Metabolism Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada

    Fernando F. Anhê, Michael Surette & Jonathan D. Schertzer

  3. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Benjamin Anderschou Holbech Jensen

  4. Vaiomer, Labège, France

    Florence Servant, Sebastian Van Blerk & Benjamin Lelouvier

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Contributions

A.M., A.T., F.F.A. and B.A.H.J. conceived and planned the study. A.T., B.A.H.J. and F.F.A. identified and selected the patient cohort. S.M. and L.B. conducted tissue biopsies. B.L. led tissue 16S rRNA gene quantification and sequencing. F.S., S.V.B. and T.V.V. carried out bioinformatic analysis. F.F.A. and T.V.V. generated the figures. F.F.A. and B.A.H.J. integrated the data and wrote the manuscript. F.F.A, B.A.H.J., T.V.V., F.S., S.V.B., D.R., S.M., M.S., L.B., B.L., J.D.S., A.T. and A.M. contributed to data analysis and discussion and agreed upon the submitted manuscript.

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Correspondence to André Marette.

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The authors declare no competing interests.

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Peer review information Primary Handling Editors: Christoph Schmitt; Pooja Jha.

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Extended data

Extended Data Fig. 1 Genera distribution in the liver, plasma and mesenteric, omental and subcutaneous adipose tissue of obese subjects.

Filtered genera were plotted in a heatmap whereby genus abundance is depicted for each sample within each tissue analyzed. Dendograms on the left of heatmaps are based on correlations of abundance profile.

Extended Data Fig. 2 Principal Coordinate Analysis on generalized UniFrac distances of 16S sequences from negative controls.

Permutational multivariate analysis of variance (PERMANOVA), with subsequent Bonferroni-Holm P adjustment, was used to assign statistical significance to the differences between clusters of 16S sequences. The number of independent biological samples tested was: Air-Liver (n=3), Air-OAT (n=2), Air-SAT (n=3). The number of technical replicates tested was: Air-Lab (n=3), Air-Biobank (n=3), Swab-Biobank (n=3), Ext-Wa (n=6). Each dot represents a sample. All statistical testes were two-sided, and differences were considered statistically significant at P<0.05.

Extended Data Fig. 3 Validation with negative controls of tissue-specific taxa different between participants who were normoglycemic or type 2 diabetic.

Tissue-specific bacterial targets found to discriminate between disease state were identified by LefSe analysis. The relative abundance of these taxa (at family and genus level) in liver (a, b), mesenteric (MAT – c, d), omental (OAT – e, f) and subcutaneous (SAT – g, h) adipose tissue and plasma (i, j) was analyzed, without accounting for disease state distribution, against negative controls (NCs) using Mann-Whitney U test. P values are indicated at the top of each graph. Left side panels show the relative abundance of taxa, whereas right side panels depict relative abundance normalized by 16S rRNA gene count (that is, relative abundance x 16S count). Box plots depict the first and the third quartile with the median represented by a vertical line within the box; the whiskers extend from the first and third quartiles to the highest and lowest observation, respectively, not exceeding 1.5 x IQR. Each circle (Non-diabetic, ND) and triangle (Type 2 Diabetic, T2D) represents a sample, and their tissue-specific dispersion is presented using a log10 scale. The number of independent biological replicates tested was: Liver (n=39), MAT (n=40), OAT (n=40), SAT (n=40), Plasma (n=39), NC (n=23). All statistical tests were two-sided, and differences were considered statistically significant at P<0.05.

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Anhê, F.F., Jensen, B.A.H., Varin, T.V. et al. Type 2 diabetes influences bacterial tissue compartmentalisation in human obesity. Nat Metab 2, 233–242 (2020). https://doi.org/10.1038/s42255-020-0178-9

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