Proteomic interrogation of the gut microbiota: potential clinical impact

AbstractThe human gut microbiome possesses a high intra- and inter-subject variability with a remarkable high dynamic range of microbial species primarily from the four dominant phyla of Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria. The gold standard of 16S rRNA gene sequencing provides only a blueprint of the gut microbiome but misses the functionally important information. Potentially, metaproteomics is at the level of analysis closest to reveal true function especially when trying to link distinct functions to taxa. Personalized proteomics approaches have started to be implemented for clinical applications, however, to be applicable in routine analysis, the costs have to be lowered, the time-consuming data analyses accelerated and LC-MS-based approaches improved.

[1]  P. Lepage,et al.  The human gut microbiome and its dysfunctions through the meta‐omics prism , 2016, Annals of the New York Academy of Sciences.

[2]  I. Martínez,et al.  Challenges of metabolomics in human gut microbiota research. , 2016, International journal of medical microbiology : IJMM.

[3]  Martin von Bergen,et al.  Dysbiotic gut microbiota causes transmissible Crohn's disease-like ileitis independent of failure in antimicrobial defence , 2015, Gut.

[4]  F. Hugenholtz,et al.  Metaproteome analysis and molecular genetics of rat intestinal microbiota reveals section and localization resolved species distribution and enzymatic functionalities. , 2012, Journal of proteome research.

[5]  Shusen Zheng,et al.  Application of metagenomics in the human gut microbiome. , 2015, World journal of gastroenterology.

[6]  Joshua E. Elias,et al.  Host-centric Proteomics of Stool: A Novel Strategy Focused on intestinal Responses to the Gut Microbiota* , 2013, Molecular & Cellular Proteomics.

[7]  Chongle Pan,et al.  Microbial metaproteomics for characterizing the range of metabolic functions and activities of human gut microbiota , 2015, Proteomics.

[8]  M. Ferrer,et al.  Microbiota from the distal guts of lean and obese adolescents exhibit partial functional redundancy besides clear differences in community structure. , 2013, Environmental microbiology.

[9]  M. Blaut,et al.  Clostridium ramosum Promotes High-Fat Diet-Induced Obesity in Gnotobiotic Mouse Models , 2014, mBio.

[10]  R. Aebersold,et al.  The quantitative and condition-dependent Escherichia coli proteome , 2015, Nature Biotechnology.

[11]  C. Huttenhower,et al.  Meta'omic analytic techniques for studying the intestinal microbiome. , 2014, Gastroenterology.

[12]  Adam Godzik,et al.  Shotgun metaproteomics of the human distal gut microbiota , 2008, The ISME Journal.

[13]  Jesse R. Zaneveld,et al.  Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences , 2013, Nature Biotechnology.

[14]  A. Gjesing,et al.  ‘Omics’‐driven discoveries in prevention and treatment of type 2 diabetes , 2012, European journal of clinical investigation.

[15]  Hugo Y. K. Lam,et al.  Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes , 2012, Cell.

[16]  E. Allen-Vercoe,et al.  Optimization of metabolomics of defined in vitro gut microbial ecosystems. , 2016, International journal of medical microbiology : IJMM.

[17]  Ralf Rabus,et al.  Proteomic tools for environmental microbiology—A roadmap from sample preparation to protein identification and quantification , 2013, Proteomics.

[18]  J. Doré,et al.  Association of germ-free mice with a simplified human intestinal microbiota results in a shortened intestine , 2014, Gut microbes.

[19]  H. Tun,et al.  High Molecular Weight Barley β-Glucan Alters Gut Microbiota Toward Reduced Cardiovascular Disease Risk , 2016, Front. Microbiol..

[20]  I. Nookaew,et al.  Insights from 20 years of bacterial genome sequencing , 2015, Functional & Integrative Genomics.