Literature DB >> 22445903

Data mining the human gut microbiota for therapeutic targets.

Matthew Collison1, Robert P Hirt, Anil Wipat, Sirintra Nakjang, Philippe Sanseau, James R Brown.   

Abstract

It is well known that microbes have an intricate role in human health and disease. However, targeted strategies for modulating human health through the modification of either human-associated microbial communities or associated human-host targets have yet to be realized. New knowledge about the role of microbial communities in the microbiota of the gastrointestinal tract (GIT) and their collective genomes, the GIT microbiome, in chronic diseases opens new opportunities for therapeutic interventions. GIT microbiota participation in drug metabolism is a further pharmaceutical consideration. In this review, we discuss how computational methods could lead to a systems-level understanding of the global physiology of the host-microbiota superorganism in health and disease. Such knowledge will provide a platform for the identification and development of new therapeutic strategies for chronic diseases possibly involving microbial as well as human-host targets that improve upon existing probiotics, prebiotics or antibiotics. In addition, integrative bioinformatics analysis will further our understanding of the microbial biotransformation of exogenous compounds or xenobiotics, which could lead to safer and more efficacious drugs.

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Year:  2012        PMID: 22445903     DOI: 10.1093/bib/bbs002

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  8 in total

1.  Accurate genome relative abundance estimation for closely related species in a metagenomic sample.

Authors:  Michael B Sohn; Lingling An; Naruekamol Pookhao; Qike Li
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2.  HemI: a toolkit for illustrating heatmaps.

Authors:  Wankun Deng; Yongbo Wang; Zexian Liu; Han Cheng; Yu Xue
Journal:  PLoS One       Date:  2014-11-05       Impact factor: 3.240

Review 3.  Bile acids at the cross-roads of gut microbiome-host cardiometabolic interactions.

Authors:  Paul M Ryan; Catherine Stanton; Noel M Caplice
Journal:  Diabetol Metab Syndr       Date:  2017-12-28       Impact factor: 3.320

4.  Negative binomial mixed models for analyzing microbiome count data.

Authors:  Xinyan Zhang; Himel Mallick; Zaixiang Tang; Lei Zhang; Xiangqin Cui; Andrew K Benson; Nengjun Yi
Journal:  BMC Bioinformatics       Date:  2017-01-03       Impact factor: 3.169

5.  Negative Binomial Mixed Models for Analyzing Longitudinal Microbiome Data.

Authors:  Xinyan Zhang; Yu-Fang Pei; Lei Zhang; Boyi Guo; Amanda H Pendegraft; Wenzhuo Zhuang; Nengjun Yi
Journal:  Front Microbiol       Date:  2018-07-26       Impact factor: 5.640

Review 6.  Human microbiomes and their roles in dysbiosis, common diseases, and novel therapeutic approaches.

Authors:  José E Belizário; Mauro Napolitano
Journal:  Front Microbiol       Date:  2015-10-06       Impact factor: 5.640

Review 7.  Worms need microbes too: microbiota, health and aging in Caenorhabditis elegans.

Authors:  Filipe Cabreiro; David Gems
Journal:  EMBO Mol Med       Date:  2013-08-01       Impact factor: 12.137

8.  Functional environmental screening of a metagenomic library identifies stlA; a unique salt tolerance locus from the human gut microbiome.

Authors:  Eamonn P Culligan; Roy D Sleator; Julian R Marchesi; Colin Hill
Journal:  PLoS One       Date:  2013-12-12       Impact factor: 3.240

  8 in total

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