A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface

采用宏蛋白质组学方法研究粘膜腔内界面的人体-微生物生态系统

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作者:Xiaoxiao Li, James LeBlanc, Allison Truong, Ravi Vuthoori, Sharon S Chen, Jonathan L Lustgarten, Bennett Roth, Jeff Allard, Andrew Ippoliti, Laura L Presley, James Borneman, William L Bigbee, Vanathi Gopalakrishnan, Thomas G Graeber, David Elashoff, Jonathan Braun, Lee Goodglick

Abstract

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI.

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