PDF(Pubmed)
Abstract：
Understanding how microbes interact with each other is key to revealing the underlying role that microorganisms play in the host or environment and to identifying microorganisms as an agent that can potentially alter the host or environment. For example, understanding how the microbial interactions associate with parasitic infection can help resolve potential drug or diagnostic test for parasitic infection. To unravel the microbial interactions, existing tools often rely on graphical models to infer the conditional dependence of microbial abundances to represent their interactions. However, current methods do not simultaneously account for the discreteness, compositionality, and heterogeneity inherent to microbiome data. Thus, we build a new approach called \"compositional graphical lasso\" upon existing tools by incorporating the above characteristics into the graphical model explicitly. We illustrate the advantage of compositional graphical lasso over current methods under a variety of simulation scenarios and on a benchmark study, the Tara Oceans Project. Moreover, we present our results from the analysis of a dataset from the Zebrafish Parasite Infection Study, which aims to gain insight into how the gut microbiome and parasite burden covary during infection, thus uncovering novel putative methods of disrupting parasite success. Our approach identifies changes in interaction degree between infected and uninfected individuals for three taxa, Photobacterium, Gemmobacter, and Paucibacter, which are inversely predicted by other methods. Further investigation of these method-specific taxa interaction changes reveals their biological plausibility. In particular, we speculate on the potential pathobiotic roles of Photobacterium and Gemmobacter in the zebrafish gut, and the potential probiotic role of Paucibacter. Collectively, our analyses demonstrate that compositional graphical lasso provides a powerful means of accurately resolving interactions between microbiota and can thus drive novel biological discovery.
摘要：
了解微生物如何相互作用是揭示微生物在宿主或环境中发挥的潜在作用以及将微生物识别为可能改变宿主或环境的试剂的关键。例如,了解微生物相互作用如何与寄生虫感染相关，可以帮助解决寄生虫感染的潜在药物或诊断测试。为了解开微生物的相互作用，现有的工具通常依赖于图形模型来推断微生物丰度的条件依赖性，以表示它们的相互作用。然而,目前的方法不能同时考虑离散性，组合性，和微生物组数据固有的异质性。因此，我们建立了一个新的方法称为“组合图形套索”在现有的工具，通过结合上述特征到图形模型明确。我们在各种模拟场景和基准研究中说明了组合图形套索相对于当前方法的优势，塔拉海洋项目。此外，我们从斑马鱼寄生虫感染研究的数据集的分析结果，旨在深入了解肠道微生物组和寄生虫在感染过程中如何负担互变，从而揭示了破坏寄生虫成功的新的推定方法。我们的方法确定了三个分类群的感染和未感染个体之间相互作用程度的变化，光细菌,Gemmobacter,和Paucibacter,用其他方法反向预测。对这些方法特异性分类群相互作用变化的进一步研究揭示了它们的生物学合理性。特别是,我们推测了斑马鱼肠道中光细菌和Gemmobacter的潜在致病作用，以及Paucibacter的潜在益生菌作用。总的来说，我们的分析表明，组成图形套索提供了一个准确解决微生物群之间相互作用的强大手段，因此可以推动新的生物学发现。
