Division of Animal Sciences, University of Missouri-Columbia, Columbia, MO, USA.
Department of Animal and Veterinary Science, University of Idaho, Moscow, ID, USA.
Microbiome. 2017 Jun 8;5(1):60. doi: 10.1186/s40168-017-0274-6.
Grazing mammals rely on their ruminal microbial symbionts to convert plant structural biomass into metabolites they can assimilate. To explore how this complex metabolic system adapts to the host animal's diet, we inferred a microbiome-level metabolic network from shotgun metagenomic data.
Using comparative genomics, we then linked this microbial network to that of the host animal using a set of interface metabolites likely to be transferred to the host. When the host sheep were fed a grain-based diet, the induced microbial metabolic network showed several critical differences from those seen on the evolved forage-based diet. Grain-based (e.g., concentrate) diets tend to be dominated by a smaller set of reactions that employ metabolites that are nearer in network space to the host's metabolism. In addition, these reactions are more central in the network and employ substrates with shorter carbon backbones. Despite this apparent lower complexity, the concentrate-associated metabolic networks are actually more dissimilar from each other than are those of forage-fed animals. Because both groups of animals were initially fed on a forage diet, we propose that the diet switch drove the appearance of a number of different microbial networks, including a degenerate network characterized by an inefficient use of dietary nutrients. We used network simulations to show that such disparate networks are not an unexpected result of a diet shift.
We argue that network approaches, particularly those that link the microbial network with that of the host, illuminate aspects of the structure of the microbiome not seen from a strictly taxonomic perspective. In particular, different diets induce predictable and significant differences in the enzymes used by the microbiome. Nonetheless, there are clearly a number of microbiomes of differing structure that show similar functional properties. Changes such as a diet shift uncover more of this type of diversity.
反刍哺乳动物依靠其瘤胃微生物共生体将植物结构性生物量转化为它们可以同化的代谢物。为了探索这个复杂的代谢系统如何适应宿主动物的饮食,我们从鸟枪法宏基因组数据中推断出微生物组水平的代谢网络。
然后,我们使用比较基因组学,通过一组可能被转移到宿主的接口代谢物将这个微生物网络与宿主动物的网络联系起来。当绵羊以谷物为基础的饮食时,诱导的微生物代谢网络显示出与进化的以草料为基础的饮食相比存在几个关键差异。以谷物为基础(例如浓缩物)的饮食往往由更小组的反应主导,这些反应使用的代谢物在网络空间上更接近宿主的新陈代谢。此外,这些反应在网络中更为核心,并且使用具有更短碳骨架的底物。尽管这种明显的低复杂性,但是浓缩物相关的代谢网络实际上彼此之间的差异比草料喂养动物的代谢网络更大。因为两组动物最初都以草料为食,我们提出饮食转变促使了许多不同微生物网络的出现,包括一种以低效利用膳食营养为特征的退化网络。我们使用网络模拟表明,这种不同的网络不是饮食转变的意外结果。
我们认为,网络方法,特别是将微生物网络与宿主网络联系起来的方法,阐明了微生物组结构的一些方面,这些方面从严格的分类学角度是看不到的。特别是,不同的饮食会导致微生物组使用的酶产生可预测且显著的差异。然而,显然有许多结构不同的微生物组具有相似的功能特性。像饮食转变这样的变化揭示了更多这种类型的多样性。