The dynamic microbiome

FEBS Letters - Tập 588 - Trang 4131-4139 - 2014
Georg K. Gerber1
1Brigham and Women’s Hospital and Harvard Medical School, Department of Pathology, Center for Clinical and Translational Metagenomics, 221 Longwood Avenue, EBRC 422B, Boston, MA 02115, United States

Tóm tắt

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.

Tài liệu tham khảo

10.1186/1471-2164-14-81 10.1073/pnas.1000081107 10.1371/journal.pbio.0050177 10.4161/gmic.21008 10.1101/gr.142315.112 10.1371/journal.pone.0073465 10.1038/ismej.2009.96 10.1371/journal.pcbi.1003042 10.1038/nature12820 10.1371/journal.pbio.1001637 10.1126/scitranslmed.3000322 10.1126/science.1208344 10.1038/nature11053 10.1126/scitranslmed.3003605 10.1371/journal.pone.0080254 10.1371/journal.pone.0036298 10.1073/pnas.1002611107 10.1128/IAI.05496-11 10.1128/IAI.00493-09 10.1186/gb-2012-13-9-r79 10.1073/pnas.1000087107 10.1136/gutjnl-2012-303184 10.1371/journal.pone.0046966 10.1038/nature11209 10.1073/pnas.1311322111 10.1371/journal.pcbi.1003388 10.1371/journal.pcbi.1002624 10.1126/science.1227079 10.1128/mBio.00782-13 10.1186/gb-2011-12-5-r50 10.1126/science.1237439 10.1016/j.cell.2012.07.008 10.1073/pnas.1000097107 10.1073/pnas.1300833110 10.1056/NEJMoa1205037 10.1056/NEJMra0707500 10.1038/nature11234 10.1038/nrg1919 10.1126/science.1229000 10.1084/jem.20112408 10.1038/nrmicro3032 Chaloner K., 1999, Bayesian experimental design: a review, Stat. Sci., 3, 273 10.1186/1471-2288-13-100 W.W.S. Wei 2005 Pearson New Jersey J. Guckenheimer P. Holmes 2002 Springer Berlin 10.1038/nature03627 10.1038/238413a0 10.2307/1935352 10.1126/science.1220529 10.1890/0012-9658(1997)078[0653:ATRFMT]2.0.CO;2 10.1126/science.1188321 10.1146/annurev.es.04.110173.000245 10.1038/ismej.2008.36 10.1111/j.1467-9868.2007.00610.x Calderhead B Girolami M Lawrence ND (2009) Accelerating Bayesian inference over nonlinear differential equations with Gaussian processes. NIPS Vancouver Curran Associates Inc. Redhook NY USA. pp. 217–224. 10.1016/S0022-5193(89)80211-5 J.M. Wang D.J. Fleet A. Hertzman 2006 MIT Press NIPS Vancouver B. Oksendal 2003 Springer Berlin 10.1007/s00422-006-0098-0 J. Pearl The mathematics of causal inference in: Proceedings of the 27th AAAI Conference on Artificial Intelligence American Statistical Association Alexandria VA 2013 pp. 2515–2529. 10.2307/1912791 10.1016/j.tim.2013.07.001 10.1111/1574-6976.12015 10.1038/nrmicro2857 10.1038/nrg3542 10.1038/nmeth.2771