Greer Renee, Dong Xiaoxi, Morgun Andrey, Shulzhenko Natalia
a College of Veterinary Medicine, Oregon State University , Corvallis , OR , USA.
b College of Pharmacy, Oregon State University , Corvallis , OR , USA.
Gut Microbes. 2016;7(2):126-35. doi: 10.1080/19490976.2015.1128625. Epub 2016 Mar 16.
The scientific community has recently come to appreciate that, rather than existing as independent organisms, multicellular hosts and their microbiota comprise a complex evolving superorganism or metaorganism, termed a holobiont. This point of view leads to a re-evaluation of our understanding of different physiological processes and diseases. In this paper we focus on experimental and computational approaches which, when combined in one study, allowed us to dissect mechanisms (traditionally named host-microbiota interactions) regulating holobiont physiology. Specifically, we discuss several approaches for microbiota perturbation, such as use of antibiotics and germ-free animals, including advantages and potential caveats of their usage. We briefly review computational approaches to characterize the microbiota and, more importantly, methods to infer specific components of microbiota (such as microbes or their genes) affecting host functions. One such approach called transkingdom network analysis has been recently developed and applied in our study. (1) Finally, we also discuss common methods used to validate the computational predictions of host-microbiota interactions using in vitro and in vivo experimental systems.
科学界最近逐渐认识到,多细胞宿主及其微生物群并非作为独立的生物体存在,而是构成了一个复杂的、不断进化的超级生物体或元生物体,称为全生物。这种观点促使我们重新审视对不同生理过程和疾病的理解。在本文中,我们重点关注实验和计算方法,当这些方法在一项研究中结合使用时,能让我们剖析调节全生物生理学的机制(传统上称为宿主 - 微生物群相互作用)。具体而言,我们讨论了几种微生物群扰动方法,例如使用抗生素和无菌动物,包括其使用的优点和潜在注意事项。我们简要回顾了表征微生物群的计算方法,更重要的是,推断影响宿主功能的微生物群特定成分(如微生物或其基因)的方法。一种名为跨界网络分析的方法最近已被开发并应用于我们的研究。(1)最后,我们还讨论了使用体外和体内实验系统验证宿主 - 微生物群相互作用计算预测的常用方法。