Colonic metaproteomic signatures of active bacteria and the host in obesity

C.A. Kolmeder, Jarmo Ritari, F.J. Verdam, W.M. de Vos

Research output: Contribution to journalArticleAcademicpeer-review

35 Citations (Scopus)

Abstract

Obesity is associated with the intestinal microbiota in humans but the underlying mechanisms are yet to be fully understood. Our previous phylogenetic study showed that the faecal microbiota profiles of nonobese versus obese and morbidly obese individuals differed. Here, we have extended this analysis with a characterization of the faecal metaproteome, in order to detect differences at a functional level. Proteins were extracted from crude faecal samples of 29 subjects, separated by 1D gel electrophoresis and characterized using RP LC-MS/MS. The peptide data were analyzed in database searches with two complementary algorithms, OMSSA and X!Tandem, to increase the number of identifications. Evolutionary genealogy of genes: nonsupervised orthologous groups (EggNOG) database searches resulted in the functional annotation of over 90% of the identified microbial and human proteins. Based on both bacterial and human proteins, a clear clustering of obese and nonobese samples was obtained that exceeded the phylogenetic separation in dimension. Moreover, integration of the metaproteomics and phylogenetic datasets revealed notably that the phylum Bacteroidetes was metabolically more active in the obese than nonobese subjects. Finally, significant correlations between clinical measurements and bacterial gene functions were identified. This study emphasizes the importance of integrating data of the host and microbiota to understand their interactions.

Original languageEnglish
Pages (from-to)3544-3552
JournalProteomics
Volume15
Issue number20
DOIs
Publication statusPublished - 2015

Fingerprint

Bacteria
Obesity
Microbiota
Genes
Databases
Bacteroidetes
Genealogy and Heraldry
Bacterial Genes
Proteins
Bacterial Proteins
Electrophoresis
Cluster Analysis
Gels
Peptides
Gastrointestinal Microbiome
Datasets

Keywords

  • Composition
  • Intestinal microbiota
  • Metaproteomics
  • Microbiology
  • Obesity

Cite this

Kolmeder, C.A. ; Ritari, Jarmo ; Verdam, F.J. ; de Vos, W.M. / Colonic metaproteomic signatures of active bacteria and the host in obesity. In: Proteomics. 2015 ; Vol. 15, No. 20. pp. 3544-3552.
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Colonic metaproteomic signatures of active bacteria and the host in obesity. / Kolmeder, C.A.; Ritari, Jarmo; Verdam, F.J.; de Vos, W.M.

In: Proteomics, Vol. 15, No. 20, 2015, p. 3544-3552.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Colonic metaproteomic signatures of active bacteria and the host in obesity

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AU - Ritari, Jarmo

AU - Verdam, F.J.

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AB - Obesity is associated with the intestinal microbiota in humans but the underlying mechanisms are yet to be fully understood. Our previous phylogenetic study showed that the faecal microbiota profiles of nonobese versus obese and morbidly obese individuals differed. Here, we have extended this analysis with a characterization of the faecal metaproteome, in order to detect differences at a functional level. Proteins were extracted from crude faecal samples of 29 subjects, separated by 1D gel electrophoresis and characterized using RP LC-MS/MS. The peptide data were analyzed in database searches with two complementary algorithms, OMSSA and X!Tandem, to increase the number of identifications. Evolutionary genealogy of genes: nonsupervised orthologous groups (EggNOG) database searches resulted in the functional annotation of over 90% of the identified microbial and human proteins. Based on both bacterial and human proteins, a clear clustering of obese and nonobese samples was obtained that exceeded the phylogenetic separation in dimension. Moreover, integration of the metaproteomics and phylogenetic datasets revealed notably that the phylum Bacteroidetes was metabolically more active in the obese than nonobese subjects. Finally, significant correlations between clinical measurements and bacterial gene functions were identified. This study emphasizes the importance of integrating data of the host and microbiota to understand their interactions.

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