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Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers
BACKGROUND: Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This stu...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9125906/ https://www.ncbi.nlm.nih.gov/pubmed/35606786 http://dx.doi.org/10.1186/s13075-022-02801-1 |
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author | Tardif, Ginette Paré, Frédéric Gotti, Clarisse Roux-Dalvai, Florence Droit, Arnaud Zhai, Guangju Sun, Guang Fahmi, Hassan Pelletier, Jean-Pierre Martel-Pelletier, Johanne |
author_facet | Tardif, Ginette Paré, Frédéric Gotti, Clarisse Roux-Dalvai, Florence Droit, Arnaud Zhai, Guangju Sun, Guang Fahmi, Hassan Pelletier, Jean-Pierre Martel-Pelletier, Johanne |
author_sort | Tardif, Ginette |
collection | PubMed |
description | BACKGROUND: Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers. METHODS: Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography–tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis. RESULTS: In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064). CONCLUSION: Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-022-02801-1. |
format | Online Article Text |
id | pubmed-9125906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-91259062022-05-24 Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers Tardif, Ginette Paré, Frédéric Gotti, Clarisse Roux-Dalvai, Florence Droit, Arnaud Zhai, Guangju Sun, Guang Fahmi, Hassan Pelletier, Jean-Pierre Martel-Pelletier, Johanne Arthritis Res Ther Research Article BACKGROUND: Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers. METHODS: Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography–tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis. RESULTS: In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064). CONCLUSION: Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-022-02801-1. BioMed Central 2022-05-23 2022 /pmc/articles/PMC9125906/ /pubmed/35606786 http://dx.doi.org/10.1186/s13075-022-02801-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Tardif, Ginette Paré, Frédéric Gotti, Clarisse Roux-Dalvai, Florence Droit, Arnaud Zhai, Guangju Sun, Guang Fahmi, Hassan Pelletier, Jean-Pierre Martel-Pelletier, Johanne Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers |
title | Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers |
title_full | Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers |
title_fullStr | Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers |
title_full_unstemmed | Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers |
title_short | Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers |
title_sort | mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9125906/ https://www.ncbi.nlm.nih.gov/pubmed/35606786 http://dx.doi.org/10.1186/s13075-022-02801-1 |
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