Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America.
Life Sciences, Ben Gurion University, Be'er Sheva, Israel.
PLoS Comput Biol. 2019 Jun 27;15(6):e1006960. doi: 10.1371/journal.pcbi.1006960. eCollection 2019 Jun.
Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.
鉴于人类肠道微生物群落高度动态和复杂的性质，能够识别和预测微生物随时间变化的组成模式对于我们理解该生态系统的结构和功能至关重要。影响这种时间依赖性模式的一个因素是微生物相互作用，其中特定时间点的群落组成会影响稍后时间点的微生物组成。然而，该领域尚未确定这种影响的程度。具体来说，最近有人认为，只有少数分类单元依赖于早期的微生物组成。为了解决识别和预测时间微生物模式的问题，我们开发了一种新模型，即 MTV-LMM（微生物时间变化线性混合模型），这是一种用于预测微生物群落时间动态的线性混合模型。MTV-LMM 可以识别纵向研究中依赖时间的微生物（即，其丰度可以基于先前的微生物组成进行预测的微生物），然后可以用于分析微生物组随时间的轨迹。我们在真实和合成时间序列数据集上评估了 MTV-LMM 的性能，发现 MTV-LMM 优于常用的微生物时间序列建模方法。特别是，我们证明了当应用先前的方法时，先前时间点的微生物组成对后来时间点分类单元丰度的影响被低估了至少 10 倍。使用 MTV-LMM，我们证明了人类肠道微生物组的相当一部分，无论是在婴儿还是成年人中，都具有显著的时间依赖性成分，这些成分可以基于早期时间点的微生物组成进行预测。这表明在特定时间点的微生物组成是定义未来微生物组成的主要因素，并且这种现象比以前报道的人类肠道微生物组更为常见。
