Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data
Hartman, Erik LU ; Forsberg, Fredrik LU ; Kjellström, Sven LU ; Petrlova, Jitka LU ; Luo, Congyu LU ; Scott, Aaron LU ; Puthia, Manoj LU ; Malmström, Johan LU
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.
(Less)
- author
- Hartman, Erik
LU
; Forsberg, Fredrik
LU
; Kjellström, Sven
LU
; Petrlova, Jitka
LU
; Luo, Congyu
LU
; Scott, Aaron
LU
; Puthia, Manoj
LU
; Malmström, Johan
LU
and Schmidtchen, Artur
LU
- organization
-
- Infection Medicine (BMC)
- Dermatology and Venereology (Lund)
- BioMS (research group)
- Mass Spectrometry
- Schmidtchen Lab (research group)
- Infection Medicine Proteomics (research group)
- epIgG (research group)
- LTH Profile Area: Engineering Health
- SEBRA Sepsis and Bacterial Resistance Alliance (research group)
- LU Profile Area: Light and Materials
- publishing date
- 2024-08-20
- 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
- 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
- 2024-08-30 04:01:20
@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}},
}