Trosvik Pål, de Muinck Eric Jacques, Stenseth Nils Christian
Department of Biosciences, Centre for Ecological and Evolutionary Synthesis, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
ISME J. 2015 Mar;9(3):533-41. doi: 10.1038/ismej.2014.147. Epub 2014 Aug 22.
The human gastrointestinal (GI) microbiota is important to human health and imbalances or shifts in the gut microbial community have been linked to many diseases. Most studies of the GI microbiota only capture snapshots of this dynamic community at one or a few time points. Although this is valuable in terms of providing knowledge of community composition and variability between individuals, it does not provide the foundation for going beyond descriptive studies and toward truly predictive ecological models. In order to achieve this goal, we need longitudinal data of appropriate temporal and taxonomic resolution, so that established time series analysis tools for identifying and quantifying putative interactions among community members can be used. Here, we present new analyses of existing data to illustrate the potential usefulness of this approach. We discuss challenges related to sampling and data processing, as well as analytical approaches and considerations for future studies of the GI microbiota and other complex microbial systems.
人类胃肠道(GI)微生物群对人类健康至关重要,肠道微生物群落的失衡或变化与许多疾病有关。大多数关于胃肠道微生物群的研究仅在一个或几个时间点捕捉这个动态群落的快照。虽然这在提供群落组成知识和个体间变异性方面很有价值,但它并没有为超越描述性研究并建立真正的预测生态模型提供基础。为了实现这一目标,我们需要具有适当时间和分类分辨率的纵向数据,以便能够使用已建立的时间序列分析工具来识别和量化群落成员之间的假定相互作用。在这里,我们对现有数据进行了新的分析,以说明这种方法的潜在用途。我们讨论了与采样和数据处理相关的挑战,以及胃肠道微生物群和其他复杂微生物系统未来研究的分析方法和注意事项。