Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, New York, United States of America.
Department of Medicine, University at Buffalo, The State University of New York, Buffalo, New York, United States of America.
PLoS Comput Biol. 2022 Aug 4;18(8):e1010373. doi: 10.1371/journal.pcbi.1010373. eCollection 2022 Aug.
A microbial community is a dynamic system undergoing constant change in response to internal and external stimuli. These changes can have significant implications for human health. However, due to the difficulty in obtaining longitudinal samples, the study of the dynamic relationship between the microbiome and human health remains a challenge. Here, we introduce a novel computational strategy that uses massive cross-sectional sample data to model microbiome landscapes associated with chronic disease development. The strategy is based on the rationale that each static sample provides a snapshot of the disease process, and if the number of samples is sufficiently large, the footprints of individual samples populate progression trajectories, which enables us to recover disease progression paths along a microbiome landscape by using computational approaches. To demonstrate the validity of the proposed strategy, we developed a bioinformatics pipeline and applied it to a gut microbiome dataset available from a Crohn's disease study. Our analysis resulted in one of the first working models of microbial progression for Crohn's disease. We performed a series of interrogations to validate the constructed model. Our analysis suggested that the model recapitulated the longitudinal progression of microbial dysbiosis during the known clinical trajectory of Crohn's disease. By overcoming restrictions associated with complex longitudinal sampling, the proposed strategy can provide valuable insights into the role of the microbiome in the pathogenesis of chronic disease and facilitate the shift of the field from descriptive research to mechanistic studies.
微生物群落是一个动态系统，会对外界和内部刺激做出持续的变化。这些变化可能会对人类健康产生重大影响。然而，由于难以获得纵向样本，微生物组与人类健康之间的动态关系研究仍然具有挑战性。在这里，我们介绍了一种新的计算策略，该策略使用大量的横断面样本数据来对与慢性疾病发展相关的微生物组景观进行建模。该策略基于以下原理：每个静态样本都提供了疾病过程的一个快照，如果样本数量足够大，那么单个样本的痕迹就会填充进展轨迹，这使得我们能够通过计算方法来恢复微生物组景观上的疾病进展路径。为了证明所提出策略的有效性，我们开发了一个生物信息学管道，并将其应用于来自克罗恩病研究的肠道微生物组数据集。我们的分析得出了克罗恩病微生物进展的第一个工作模型之一。我们进行了一系列的询问来验证所构建的模型。我们的分析表明，该模型再现了克罗恩病已知临床轨迹中微生物失调的纵向进展。通过克服与复杂的纵向采样相关的限制，所提出的策略可以为微生物组在慢性疾病发病机制中的作用提供有价值的见解，并促进该领域从描述性研究向机制研究的转变。
