Literature DB >> 24583074

The dynamic microbiome.

Georg K Gerber1.   

Abstract

While our genomes are essentially static, our microbiomes are inherently dynamic. The microbial communities we harbor in our bodies change throughout our lives due to many factors, including maturation during childhood, alterations in our diets, travel, illnesses, and medical treatments. Moreover, there is mounting evidence that our microbiomes change us, by promoting health through their beneficial actions or by increasing our susceptibility to diseases through a process termed dysbiosis. Recent technological advances are enabling unprecedentedly detailed studies of the dynamics of the microbiota in animal models and human populations. This review will highlight key areas of investigation in the field, including establishment of the microbiota during early childhood, temporal variability of the microbiome in healthy adults, responses of the microbiota to intentional perturbations such as antibiotics and dietary changes, and prospective analyses linking changes in the microbiota to host disease status. Given the importance of computational methods in the field, this review will also discuss issues and pitfalls in the analysis of microbiome time-series data, and explore several promising new directions for mathematical model and algorithm development.
Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Computational model; Dynamic; Longitudinal; Microbiome; Time-series

Mesh:

Year:  2014        PMID: 24583074     DOI: 10.1016/j.febslet.2014.02.037

Source DB:  PubMed          Journal:  FEBS Lett        ISSN: 0014-5793            Impact factor:   4.124


  67 in total

Review 1.  Recurrent Clostridium difficile infection and the microbiome.

Authors:  Rowena Almeida; Teklu Gerbaba; Elaine O Petrof
Journal:  J Gastroenterol       Date:  2015-07-08       Impact factor: 7.527

Review 2.  Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference.

Authors:  Anders B Dohlman; Xiling Shen
Journal:  Exp Biol Med (Maywood)       Date:  2019-03-16

Review 3.  Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research.

Authors:  Johannes R Björk; Mauna Dasari; Laura Grieneisen; Elizabeth A Archie
Journal:  Am J Primatol       Date:  2019-04-02       Impact factor: 2.371

Review 4.  The intestinal microbiome and surgical disease.

Authors:  Monika A Krezalek; Kinga B Skowron; Kristina L Guyton; Baddr Shakhsheer; Sanjiv Hyoju; John C Alverdy
Journal:  Curr Probl Surg       Date:  2016-06-14       Impact factor: 1.909

5.  A two-part mixed-effects model for analyzing longitudinal microbiome compositional data.

Authors:  Eric Z Chen; Hongzhe Li
Journal:  Bioinformatics       Date:  2016-05-14       Impact factor: 6.937

6.  Correlation detection strategies in microbial data sets vary widely in sensitivity and precision.

Authors:  Sophie Weiss; Will Van Treuren; Catherine Lozupone; Karoline Faust; Jonathan Friedman; Ye Deng; Li Charlie Xia; Zhenjiang Zech Xu; Luke Ursell; Eric J Alm; Amanda Birmingham; Jacob A Cram; Jed A Fuhrman; Jeroen Raes; Fengzhu Sun; Jizhong Zhou; Rob Knight
Journal:  ISME J       Date:  2016-02-23       Impact factor: 10.302

Review 7.  Evolutionary ecology of Lyme Borrelia.

Authors:  Kayleigh R O'Keeffe; Zachary J Oppler; Dustin Brisson
Journal:  Infect Genet Evol       Date:  2020-09-28       Impact factor: 3.342

8.  Initial soil microbiome composition and functioning predetermine future plant health.

Authors:  Zhong Wei; Yian Gu; Ville-Petri Friman; George A Kowalchuk; Yangchun Xu; Qirong Shen; Alexandre Jousset
Journal:  Sci Adv       Date:  2019-09-25       Impact factor: 14.136

9.  Longitudinal Effects of Supplemental Forage on the Honey Bee (Apis mellifera) Microbiota and Inter- and Intra-Colony Variability.

Authors:  Jason A Rothman; Mark J Carroll; William G Meikle; Kirk E Anderson; Quinn S McFrederick
Journal:  Microb Ecol       Date:  2018-02-03       Impact factor: 4.552

10.  MODELING MICROBIAL ABUNDANCES AND DYSBIOSIS WITH BETA-BINOMIAL REGRESSION.

Authors:  Bryan D Martin; Daniela Witten; Amy D Willis
Journal:  Ann Appl Stat       Date:  2020-04-16       Impact factor: 2.083

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