MicrobioLink: An Integrated Computational Pipeline to Infer Functional Effects of Microbiome-Host Interactions

被引:23
|
作者
Andrighetti, Tahila
Bohar, Balazs
Lemke, Ney
Sudhakar, Padhmanand
Korcsmaros, Tamas
机构
[1] Earlham Institute, Norwich Research Park
[2] Institute of Biosciences, São Paulo University (UNESP)
[3] Department of Genetics, Eötvös Loránd University, 1117, Budapest
[4] Quadram Institute Bioscience, Norwich Research Park, Norwich
[5] Department of Chronic Diseases, Metabolism and Ageing
基金
英国生物技术与生命科学研究理事会; 欧洲研究理事会;
关键词
microbiota-host interactions; protein-protein interactions; systems biology; networks; network diffusion; computational pipeline; AUTOPHAGY; DATABASE; DISEASE; NETWORKS; RESOURCE; BIOINFORMATICS; DYSBIOSIS; BACTERIA; BIOLOGY; CANCER;
D O I
10.3390/cells9051278
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Microbiome-host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe-host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions-as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn's disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.
引用
收藏
页数:15
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