cBio Center, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, United States.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, United States.
Elife. 2018 Apr 17;7:e30916. doi: 10.7554/eLife.30916.
Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (T) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to T induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting T activation and rank them by the T Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured T. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics.
肠道微生物群的操纵为疾病的治疗带来了巨大的希望。然而，一个主要的挑战是确定具有治疗潜力的微生物群落，这些群落能够有效地定植宿主，同时最大限度地提高免疫效果。在这里，我们提出了一种从微生物组组成和免疫效应物测量中选择最佳免疫诱导群落的新工作流程。使用来自无菌小鼠的已发表和新生成的微生物和调节性 T 细胞（T）数据，我们估计了具有已知免疫调节作用的十二株梭状芽孢杆菌菌株对 T 诱导的贡献。将其与纵向数据约束生态模型相结合，我们预测了每个可实现和生态稳定的亚群落促进 T 激活的能力，并通过 T 诱导评分（TrIS）对它们进行排名。所选群落的实验验证表明，预测的 TrIS 与测量的 T 之间存在很强的统计学显著相关性。我们认为，计算指标，如 TrIS，是系统选择免疫调节细菌疗法的有价值的工具。
