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

Hartman, Erik LU orcid ; Forsberg, Fredrik LU orcid ; Kjellström, Sven LU ; Petrlova, Jitka LU ; Luo, Congyu LU ; Scott, Aaron LU ; Puthia, Manoj LU ; Malmström, Johan LU orcid and Schmidtchen, Artur LU (2024) In Nature Communications 15(1).
Abstract

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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Animals, Humans, Proteolysis, Peptides/metabolism, Swine, Algorithms, Proteomics/methods, Mass Spectrometry/methods, Diabetes Mellitus, Type 1/metabolism, Wound Infection/microbiology, Cluster Analysis
in
Nature Communications
volume
15
issue
1
article number
7128
publisher
Nature Publishing Group
external identifiers
  • pmid:39164298
  • scopus:85201627257
ISSN
2041-1723
DOI
10.1038/s41467-024-51589-y
project
Computational methods for understanding the role of protein degradation in immunity
language
English
LU publication?
yes
additional info
© 2024. The Author(s).
id
dc98a0a0-ec26-4297-bf39-b6ad81efac80
date added to LUP
2024-08-29 07:54:41
date last changed
2025-07-05 23:44:13
@article{dc98a0a0-ec26-4297-bf39-b6ad81efac80,
  abstract     = {{<p>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.</p>}},
  author       = {{Hartman, Erik and Forsberg, Fredrik and Kjellström, Sven and Petrlova, Jitka and Luo, Congyu and Scott, Aaron and Puthia, Manoj and Malmström, Johan and Schmidtchen, Artur}},
  issn         = {{2041-1723}},
  keywords     = {{Animals; Humans; Proteolysis; Peptides/metabolism; Swine; Algorithms; Proteomics/methods; Mass Spectrometry/methods; Diabetes Mellitus, Type 1/metabolism; Wound Infection/microbiology; Cluster Analysis}},
  language     = {{eng}},
  month        = {{08}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data}},
  url          = {{http://dx.doi.org/10.1038/s41467-024-51589-y}},
  doi          = {{10.1038/s41467-024-51589-y}},
  volume       = {{15}},
  year         = {{2024}},
}