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Exploring the Early Molecular Pathogenesis of Osteoarthritis Using Differential Network Analysis of Human Synovial Fluid

Rydén, Martin LU orcid ; Sjögren, Amanda LU orcid ; Önnerfjord, Patrik LU orcid ; Turkiewicz, Aleksandra LU ; Tjörnstrand, Jon LU ; Englund, Martin LU orcid and Ali, Neserin LU orcid (2024) In Molecular and Cellular Proteomics 23(6).
Abstract

The molecular mechanisms that drive the onset and development of osteoarthritis (OA) remain largely unknown. In this exploratory study, we used a proteomic platform (SOMAscan assay) to measure the relative abundance of more than 6000 proteins in synovial fluid (SF) from knees of human donors with healthy or mildly degenerated tissues, and knees with late-stage OA from patients undergoing knee replacement surgery. Using a linear mixed effects model, we estimated the differential abundance of 6251 proteins between the three groups. We found 583 proteins upregulated in the late-stage OA, including MMP1, collagenase 3 and interleukin-6. Further, we selected 760 proteins (800 aptamers) based on absolute fold changes between the healthy and... (More)

The molecular mechanisms that drive the onset and development of osteoarthritis (OA) remain largely unknown. In this exploratory study, we used a proteomic platform (SOMAscan assay) to measure the relative abundance of more than 6000 proteins in synovial fluid (SF) from knees of human donors with healthy or mildly degenerated tissues, and knees with late-stage OA from patients undergoing knee replacement surgery. Using a linear mixed effects model, we estimated the differential abundance of 6251 proteins between the three groups. We found 583 proteins upregulated in the late-stage OA, including MMP1, collagenase 3 and interleukin-6. Further, we selected 760 proteins (800 aptamers) based on absolute fold changes between the healthy and mild degeneration groups. To those, we applied Gaussian Graphical Models (GGMs) to analyze the conditional dependence of proteins and to identify key proteins and subnetworks involved in early OA pathogenesis. After regularization and stability selection, we identified 102 proteins involved in GGM networks. Notably, network complexity was lost in the protein graph for mild degeneration when compared to controls, suggesting a disruption in the regular protein interplay. Furthermore, among our main findings were several downregulated (in mild degeneration versus healthy) proteins with unique interactions in the healthy group, one of which, SLCO5A1, has not previously been associated with OA. Our results suggest that this protein is important for healthy joint function. Further, our data suggests that SF proteomics, combined with GGMs, can reveal novel insights into the molecular pathogenesis and identification of biomarker candidates for early-stage OA.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Molecular and Cellular Proteomics
volume
23
issue
6
article number
100785
publisher
American Society for Biochemistry and Molecular Biology
external identifiers
  • pmid:38750696
  • scopus:85197305023
ISSN
1535-9476
DOI
10.1016/j.mcpro.2024.100785
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 THE AUTHORS. Published by Elsevier Inc on behalf of American Society for Biochemistry and Molecular Biology.
id
658ac42a-9a7a-4ea7-8390-388647ff2ce6
date added to LUP
2024-08-11 19:09:39
date last changed
2025-07-01 12:40:38
@article{658ac42a-9a7a-4ea7-8390-388647ff2ce6,
  abstract     = {{<p>The molecular mechanisms that drive the onset and development of osteoarthritis (OA) remain largely unknown. In this exploratory study, we used a proteomic platform (SOMAscan assay) to measure the relative abundance of more than 6000 proteins in synovial fluid (SF) from knees of human donors with healthy or mildly degenerated tissues, and knees with late-stage OA from patients undergoing knee replacement surgery. Using a linear mixed effects model, we estimated the differential abundance of 6251 proteins between the three groups. We found 583 proteins upregulated in the late-stage OA, including MMP1, collagenase 3 and interleukin-6. Further, we selected 760 proteins (800 aptamers) based on absolute fold changes between the healthy and mild degeneration groups. To those, we applied Gaussian Graphical Models (GGMs) to analyze the conditional dependence of proteins and to identify key proteins and subnetworks involved in early OA pathogenesis. After regularization and stability selection, we identified 102 proteins involved in GGM networks. Notably, network complexity was lost in the protein graph for mild degeneration when compared to controls, suggesting a disruption in the regular protein interplay. Furthermore, among our main findings were several downregulated (in mild degeneration versus healthy) proteins with unique interactions in the healthy group, one of which, SLCO5A1, has not previously been associated with OA. Our results suggest that this protein is important for healthy joint function. Further, our data suggests that SF proteomics, combined with GGMs, can reveal novel insights into the molecular pathogenesis and identification of biomarker candidates for early-stage OA.</p>}},
  author       = {{Rydén, Martin and Sjögren, Amanda and Önnerfjord, Patrik and Turkiewicz, Aleksandra and Tjörnstrand, Jon and Englund, Martin and Ali, Neserin}},
  issn         = {{1535-9476}},
  language     = {{eng}},
  number       = {{6}},
  publisher    = {{American Society for Biochemistry and Molecular Biology}},
  series       = {{Molecular and Cellular Proteomics}},
  title        = {{Exploring the Early Molecular Pathogenesis of Osteoarthritis Using Differential Network Analysis of Human Synovial Fluid}},
  url          = {{http://dx.doi.org/10.1016/j.mcpro.2024.100785}},
  doi          = {{10.1016/j.mcpro.2024.100785}},
  volume       = {{23}},
  year         = {{2024}},
}