Sun Zhongzhi, Ning Zhibin, Wu Qing, Li Leyuan, Doxey Andrew C, Figeys Daniel
School of Pharmaceutical Sciences, Ottawa Institute of Systems Biology, and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
NPJ Biofilms Microbiomes. 2025 Aug 19;11(1):166. doi: 10.1038/s41522-025-00801-y.
Mass spectrometry (MS)-based proteomics is widely used for quantitative protein profiling and protein interaction studies. However, most current research focuses on single-species proteomics, while protein interactions within complex microbiomes, composed of hundreds of bacterial species, remain largely unexplored. In this study, we analyzed peptide abundance correlations within a metaproteomics dataset derived from in vitro cultured human gut microbiomes subjected to various drug treatments. Our analysis revealed that peptides from the same protein or taxon exhibited correlated abundance changes. By using t-SNE for visualization, we generated a peptide correlation map in which peptides from the same taxon formed distinct clusters. Furthermore, peptide abundance correlations enabled genome-level taxonomic assignments for a greater number of peptides. For instance, 1880 (48.9%) of the 3845 peptides initially assigned only to the family Bacteroidaceae could now be assigned to a specific genome. In species representative genome subsets, peptide correlation networks based on taxon-normalized peptide abundance (TNPA) linked functionally related peptides and provided insights into uncharacterized proteins. Altogether, our study demonstrates that analyzing peptide abundance correlations enhances both taxonomic and functional analyses in human gut metaproteomics research.
基于质谱（MS）的蛋白质组学被广泛用于定量蛋白质谱分析和蛋白质相互作用研究。然而，目前大多数研究集中在单物种蛋白质组学，而由数百种细菌组成的复杂微生物群落中的蛋白质相互作用在很大程度上仍未得到探索。在本研究中，我们分析了来自体外培养的、经各种药物处理的人类肠道微生物群落的宏蛋白质组学数据集中肽丰度的相关性。我们的分析表明，来自同一蛋白质或分类单元的肽表现出相关的丰度变化。通过使用t-SNE进行可视化，我们生成了一个肽相关图谱，其中来自同一分类单元的肽形成了不同的簇。此外，肽丰度相关性能够对更多肽进行基因组水平的分类归属。例如，最初仅被归属到拟杆菌科的3845个肽中的1880个（48.9%）现在可以被归属到特定的基因组。在物种代表性基因组子集中，基于分类单元标准化肽丰度（TNPA）的肽相关网络连接了功能相关的肽，并为未表征的蛋白质提供了见解。总之，我们的研究表明，分析肽丰度相关性可增强人类肠道宏蛋白质组学研究中的分类和功能分析。
