Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data.

Autor: Hartman E; Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden. erik.hartman@med.lu.se., Forsberg F; Division of Dermatology and Venereology, Department of Clinical Sciences, Lund University, Lund, Sweden., Kjellström S; Division of Mass Spectrometry, Department of Clinical Sciences, Lund University, Lund, Sweden., Petrlova J; Division of Dermatology and Venereology, Department of Clinical Sciences, Lund University, Lund, Sweden., Luo C; Division of Dermatology and Venereology, Department of Clinical Sciences, Lund University, Lund, Sweden., Scott A; Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden., Puthia M; Division of Dermatology and Venereology, Department of Clinical Sciences, Lund University, Lund, Sweden., Malmström J; Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden., Schmidtchen A; Division of Dermatology and Venereology, Department of Clinical Sciences, Lund University, Lund, Sweden.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 Aug 20; Vol. 15 (1), pp. 7128. Date of Electronic Publication: 2024 Aug 20.
DOI: 10.1038/s41467-024-51589-y
Abstrakt: Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: