Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae580.
Alterations in human microbial communities are intricately linked to the onset and progression of diseases. Identifying the key microbes driving these community changes is crucial, as they may serve as valuable biomarkers for disease prevention, diagnosis, and treatment. However, there remains a need for further research to develop effective methods for addressing this critical task. This is primarily because defining the driver microbe requires consideration not only of each microbe's individual contributions but also their interactions. This paper introduces a novel framework, called mbDriver, for identifying driver microbes based on microbiome abundance data collected at discrete time points. mbDriver comprises three main components: (i) data preprocessing of time-series abundance data using smoothing splines based on the negative binomial distribution, (ii) parameter estimation for the generalized Lotka-Volterra (gLV) model using regularized least squares, and (iii) quantification of each microbe's contribution to the community's steady state by manipulating the causal graph implied by gLV equations. The performance of nonparametric spline-based denoising and regularized least squares estimation is comprehensively evaluated on simulated datasets, demonstrating superiority over existing methods. Furthermore, the practical applicability and effectiveness of mbDriver are showcased using a dietary fiber intervention dataset and an ulcerative colitis dataset. Notably, driver microbes identified in the dietary fiber intervention dataset exhibit significant effects on the abundances of short-chain fatty acids, while those identified in the ulcerative colitis dataset show a significant correlation with metabolism-related pathways.
人类微生物群落的改变与疾病的发生和发展密切相关。确定驱动这些群落变化的关键微生物至关重要，因为它们可能成为疾病预防、诊断和治疗的有价值的生物标志物。然而，仍需要进一步的研究来开发有效的方法来解决这一关键任务。这主要是因为定义驱动微生物不仅需要考虑每个微生物的个体贡献，还需要考虑它们的相互作用。本文介绍了一种新的框架，称为 mbDriver，用于根据在离散时间点采集的微生物组丰度数据识别驱动微生物。mbDriver 由三个主要部分组成：(i) 使用基于负二项分布的平滑样条对时间序列丰度数据进行数据预处理，(ii) 使用正则化最小二乘法对广义 Lotka-Volterra（gLV）模型进行参数估计，以及 (iii) 通过操纵 gLV 方程隐含的因果图来量化每个微生物对群落稳态的贡献。非参数样条去噪和正则化最小二乘估计的性能在模拟数据集上进行了全面评估，证明优于现有方法。此外，使用膳食纤维干预数据集和溃疡性结肠炎数据集展示了 mbDriver 的实际适用性和有效性。值得注意的是，在膳食纤维干预数据集识别出的驱动微生物对短链脂肪酸的丰度有显著影响，而在溃疡性结肠炎数据集识别出的驱动微生物与代谢相关途径有显著相关性。
