Claussen Jens Christian, Skiecevičienė Jurgita, Wang Jun, Rausch Philipp, Karlsen Tom H, Lieb Wolfgang, Baines John F, Franke Andre, Hütt Marc-Thorsten
Computational Systems Biology, Department of Life Sciences and Chemistry, Jacobs University Bremen, Campus Ring 1, D-28759 Bremen, Germany.
Institute for Digestive Research, Lithuanian University of Health Sciences, Kaunas, Lithuania.
PLoS Comput Biol. 2017 Jun 22;13(6):e1005361. doi: 10.1371/journal.pcbi.1005361. eCollection 2017 Jun.
The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions.
由于数据和功能数据库的更广泛可用性以及数据分析方法的实质性进展，同时也由于微生物群在人类健康和疾病中的高度相关性，对人类肠道微生物群组成的分析越来越受到关注。虽然大多数分析推断高丰度物种之间的相互作用，但大量低丰度物种受到的关注较少。在这里，我们提出了一种基于布尔运算应用于微生物共现模式的新型分析方法。我们使用基于动态布尔网络模型的模拟数据校准我们的方法，从中我们将吸引子状态的统计数据解释为微生物群组成的理论代理。我们表明，对于模型中给定比例的协同和竞争相互作用，我们的布尔丰度分析可以可靠地检测到这些相互作用。分析一个包含822个人类肠道微生物群组成的新数据集，我们发现这些低丰度物种之间存在大量高度显著的协同相互作用，形成一个连通网络，以及一些孤立的竞争相互作用。
