| Title |
MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota
|
|---|---|
| Published in |
Microbiome, June 2016
|
| DOI | 10.1186/s40168-016-0176-z |
| Pubmed ID | |
| Authors |
Xu Zhang, Zhibin Ning, Janice Mayne, Jasmine I. Moore, Jennifer Li, James Butcher, Shelley Ann Deeke, Rui Chen, Cheng-Kang Chiang, Ming Wen, David Mack, Alain Stintzi, Daniel Figeys |
| Abstract |
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. 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. 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 14% |
| United Kingdom | 3 | 14% |
| Canada | 3 | 14% |
| China | 1 | 5% |
| Spain | 1 | 5% |
| Netherlands | 1 | 5% |
| Austria | 1 | 5% |
| Unknown | 9 | 41% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 59% |
| Scientists | 9 | 41% |
Mendeley readers
The data shown below were compiled from readership statistics for 217 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 1% |
| Brazil | 2 | <1% |
| United Kingdom | 1 | <1% |
| Japan | 1 | <1% |
| Spain | 1 | <1% |
| Unknown | 209 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 43 | 20% |
| Student > Ph. D. Student | 33 | 15% |
| Student > Master | 28 | 13% |
| Student > Bachelor | 25 | 12% |
| Student > Doctoral Student | 15 | 7% |
| Other | 36 | 17% |
| Unknown | 37 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 59 | 27% |
| Agricultural and Biological Sciences | 57 | 26% |
| Immunology and Microbiology | 12 | 6% |
| Medicine and Dentistry | 11 | 5% |
| Computer Science | 10 | 5% |
| Other | 22 | 10% |
| Unknown | 46 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 August 2020.
All research outputs
#1,794,368
of 24,885,505 outputs
Outputs from Microbiome
#688
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Outputs of similar age
#32,425
of 360,545 outputs
Outputs of similar age from Microbiome
#16
of 23 outputs
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