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Microbiome. 2016 Jun 24;4(1):31. doi: 10.1186/s40168-016-0176-z.

MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota.

Author information

1
Department of Biochemistry, Ottawa Institute of Systems Biology, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
2
Department of Paediatrics, CHEO Inflammatory Bowel Disease Centre and Research Institute, University of Ottawa, Ottawa, ON, Canada. DMack@cheo.on.ca.
3
Department of Biochemistry, Ottawa Institute of Systems Biology, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada. astintzi@uottawa.ca.
4
Department of Biochemistry, Ottawa Institute of Systems Biology, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada. dfigeys@uottawa.ca.

Abstract

BACKGROUND:

The gut microbiota has been shown to be closely associated with human health and disease. While next-generation sequencing can be readily used to profile the microbiota taxonomy and metabolic potential, metaproteomics is better suited for deciphering microbial biological activities. However, the application of gut metaproteomics has largely been limited due to the low efficiency of protein identification. Thus, a high-performance and easy-to-implement gut metaproteomic approach is required.

RESULTS:

In this study, we developed a high-performance and universal workflow for gut metaproteome identification and quantification (named MetaPro-IQ) by using the close-to-complete human or mouse gut microbial gene catalog as database and an iterative database search strategy. An average of 38 and 33 % of the acquired tandem mass spectrometry (MS) spectra was confidently identified for the studied mouse stool and human mucosal-luminal interface samples, respectively. In total, we accurately quantified 30,749 protein groups for the mouse metaproteome and 19,011 protein groups for the human metaproteome. Moreover, the MetaPro-IQ approach enabled comparable identifications with the matched metagenome database search strategy that is widely used but needs prior metagenomic sequencing. The response of gut microbiota to high-fat diet in mice was then assessed, which showed distinct metaproteome patterns for high-fat-fed mice and identified 849 proteins as significant responders to high-fat feeding in comparison to low-fat feeding.

CONCLUSIONS:

We present MetaPro-IQ, a metaproteomic approach for highly efficient intestinal microbial protein identification and quantification, which functions as a universal workflow for metaproteomic studies, and will thus facilitate the application of metaproteomics for better understanding the functions of gut microbiota in health and disease.

KEYWORDS:

Gene catalog; Gut microbiota; Metagenomics; Metaproteomics; Protein identification; Quantification

PMID:
27343061
PMCID:
PMC4919841
DOI:
10.1186/s40168-016-0176-z
[Indexed for MEDLINE]
Free PMC Article

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