Wang Luman, Simopoulos Caitlin M A, Serrana Joeselle M, Ning Zhibin, Li Yutong, Sun Boyan, Yuan Jinhui, Figeys Daniel, Li Leyuan
Department of Health Informatics and Management, School of Health Humanities, Peking University, Beijing, 100191, China.
School of Pharmaceutical Sciences and Department of Biochemistry, Microbiology, and Immunology, Faculty of Medicine, Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, ON, K1H 8M5, Canada.
Microbiome. 2025 Feb 11;13(1):50. doi: 10.1186/s40168-024-02015-4.
Beta-diversity is a fundamental ecological metric for exploring dissimilarities between microbial communities. On the functional dimension, metaproteomics data can be used to quantify beta-diversity to understand how microbial community functional profiles vary under different environmental conditions. Conventional approaches to metaproteomic functional beta-diversity often treat protein functions as independent features, ignoring the evolutionary relationships among microbial taxa from which different proteins originate. A more informative functional distance metric that incorporates evolutionary relatedness is needed to better understand microbiome functional dissimilarities.
Here, we introduce PhyloFunc, a novel functional beta-diversity metric that incorporates microbiome phylogeny to inform on metaproteomic functional distance. Leveraging the phylogenetic framework of weighted UniFrac distance, PhyloFunc innovatively utilizes branch lengths to weigh between-sample functional distances for each taxon, rather than differences in taxonomic abundance as in weighted UniFrac. Proof of concept using a simulated toy dataset and a real dataset from mouse inoculated with a synthetic gut microbiome and fed different diets show that PhyloFunc successfully captured functional compensatory effects between phylogenetically related taxa. We further tested a third dataset of complex human gut microbiomes treated with five different drugs to compare PhyloFunc's performance with other traditional distance methods. PCoA and machine learning-based classification algorithms revealed higher sensitivity of PhyloFunc in microbiome responses to paracetamol. We provide PhyloFunc as an open-source Python package (available at https://pypi.org/project/phylofunc/ ), enabling efficient calculation of functional beta-diversity distances between a pair of samples or the generation of a distance matrix for all samples within a dataset.
Unlike traditional approaches that consider metaproteomics features as independent and unrelated, PhyloFunc acknowledges the role of phylogenetic context in shaping the functional landscape in metaproteomes. In particular, we report that PhyloFunc accounts for the functional compensatory effect of taxonomically related species. Its effectiveness, ecological relevance, and enhanced sensitivity in distinguishing group variations are demonstrated through the specific applications presented in this study. Video Abstract.
β多样性是探索微生物群落间差异的一项基本生态指标。在功能维度上,元蛋白质组学数据可用于量化β多样性,以了解微生物群落功能概况在不同环境条件下如何变化。传统的元蛋白质组学功能β多样性方法通常将蛋白质功能视为独立特征,忽略了不同蛋白质所源自的微生物分类群之间的进化关系。需要一种更具信息量的纳入进化相关性的功能距离度量方法,以更好地理解微生物组功能差异。
在此,我们引入了PhyloFunc,这是一种新颖的功能β多样性度量方法,它纳入微生物组系统发育以提供元蛋白质组功能距离信息。利用加权UniFrac距离的系统发育框架,PhyloFunc创新性地利用分支长度来权衡每个分类群的样本间功能距离,而不是像加权UniFrac那样基于分类丰度的差异。使用模拟玩具数据集以及来自接种合成肠道微生物群并喂食不同饮食的小鼠的真实数据集进行的概念验证表明,PhyloFunc成功捕获了系统发育相关分类群之间的功能补偿效应。我们进一步测试了用五种不同药物处理的复杂人类肠道微生物群的第三个数据集,以比较PhyloFunc与其他传统距离方法的性能。主坐标分析(PCoA)和基于机器学习的分类算法显示,PhyloFunc在微生物组对扑热息痛的反应中具有更高的敏感性。我们将PhyloFunc作为一个开源Python包提供(可在https://pypi.org/project/phylofunc/获取),能够高效计算一对样本之间的功能β多样性距离,或生成数据集中所有样本的距离矩阵。
与将元蛋白质组学特征视为独立且不相关的传统方法不同,PhyloFunc认识到系统发育背景在塑造元蛋白质组功能格局中的作用。特别是,我们报告PhyloFunc考虑了分类学相关物种的功能补偿效应。通过本研究中展示的具体应用,证明了其有效性、生态相关性以及在区分组间差异方面增强的敏感性。视频摘要。