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Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome

Objectives: Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder with heterogeneous manifestations and complex etiology. We used quantitative proteomics analysis based on mass spectrometry to identify the differences in proteomics profiles for follicular fluid obtained from...

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Autores principales: Wang, Wenqi, Jiang, Qi, Niu, Yue, Ding, Qiaoqiao, Yang, Xiao, Zheng, Yanjun, Hao, Jing, Wei, Daimin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441494/
https://www.ncbi.nlm.nih.gov/pubmed/36072434
http://dx.doi.org/10.3389/fmolb.2022.956406
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author Wang, Wenqi
Jiang, Qi
Niu, Yue
Ding, Qiaoqiao
Yang, Xiao
Zheng, Yanjun
Hao, Jing
Wei, Daimin
author_facet Wang, Wenqi
Jiang, Qi
Niu, Yue
Ding, Qiaoqiao
Yang, Xiao
Zheng, Yanjun
Hao, Jing
Wei, Daimin
author_sort Wang, Wenqi
collection PubMed
description Objectives: Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder with heterogeneous manifestations and complex etiology. We used quantitative proteomics analysis based on mass spectrometry to identify the differences in proteomics profiles for follicular fluid obtained from patients with or without PCOS and explore possible mechanisms underlying PCOS. Methods: Follicular fluid samples were collected from infertile patients with (n = 9) or without (n = 9) PCOS. Total protein was extracted, quantitatively labeled with a tandem mass tag (TMT), and analyzed using liquid chromatography-mass spectrometry (LC‐MS). TMT-based proteomics and bioinformatics analysis were used to determine the differentially expressed proteins (DEPs) and understand the protein networks. The analysis included protein annotation, unsupervised hierarchical clustering, functional classification, functional enrichment and clustering, and protein-protein interaction analysis. Selected DEPs were confirmed by ELISA, and correlation analysis was performed between these DEPs and the clinical characteristics. Results: In this study, we have identified 1,216 proteins, including 70 DEPs (32 upregulated proteins, 38 downregulated proteins). Bioinformatics analysis revealed that the inflammatory response, complement and coagulation cascades, activation of the immune response, lipid transport, and regulation of protein metabolic processes were co-enriched in patients with PCOS. Based on ELISA results, insulin-like growth factor binding protein 1 (IGFBP1) and apolipoprotein C2 (APOC2) were differentially expressed between patients with and without PCOS. Follicular IGFBP1 showed a positive correlation with the serum levels of high-density lipoprotein cholesterol (HDL-C) (r = 0.3046, p = 0.0419), but negatively correlated with the serum levels of anti-Müllerian hormone (AMH) (r = –0.2924, p = 0.0354) and triglycerides (r = –0.3177, p = 0.0246). Follicular APOC2 was negatively correlated with the serum apolipoprotein A1 (APOA1) levels (r = 0.4509, p = 0.0002). Conclusion: Our study identified DEPs in the follicular fluid of patients with PCOS. Inflammatory response, complement and coagulation cascades, activation of the immune response, lipid transport, and regulation of protein metabolic process were deregulated in PCOS, which may play essential roles in the pathogenesis of PCOS.
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spelling pubmed-94414942022-09-06 Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome Wang, Wenqi Jiang, Qi Niu, Yue Ding, Qiaoqiao Yang, Xiao Zheng, Yanjun Hao, Jing Wei, Daimin Front Mol Biosci Molecular Biosciences Objectives: Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder with heterogeneous manifestations and complex etiology. We used quantitative proteomics analysis based on mass spectrometry to identify the differences in proteomics profiles for follicular fluid obtained from patients with or without PCOS and explore possible mechanisms underlying PCOS. Methods: Follicular fluid samples were collected from infertile patients with (n = 9) or without (n = 9) PCOS. Total protein was extracted, quantitatively labeled with a tandem mass tag (TMT), and analyzed using liquid chromatography-mass spectrometry (LC‐MS). TMT-based proteomics and bioinformatics analysis were used to determine the differentially expressed proteins (DEPs) and understand the protein networks. The analysis included protein annotation, unsupervised hierarchical clustering, functional classification, functional enrichment and clustering, and protein-protein interaction analysis. Selected DEPs were confirmed by ELISA, and correlation analysis was performed between these DEPs and the clinical characteristics. Results: In this study, we have identified 1,216 proteins, including 70 DEPs (32 upregulated proteins, 38 downregulated proteins). Bioinformatics analysis revealed that the inflammatory response, complement and coagulation cascades, activation of the immune response, lipid transport, and regulation of protein metabolic processes were co-enriched in patients with PCOS. Based on ELISA results, insulin-like growth factor binding protein 1 (IGFBP1) and apolipoprotein C2 (APOC2) were differentially expressed between patients with and without PCOS. Follicular IGFBP1 showed a positive correlation with the serum levels of high-density lipoprotein cholesterol (HDL-C) (r = 0.3046, p = 0.0419), but negatively correlated with the serum levels of anti-Müllerian hormone (AMH) (r = –0.2924, p = 0.0354) and triglycerides (r = –0.3177, p = 0.0246). Follicular APOC2 was negatively correlated with the serum apolipoprotein A1 (APOA1) levels (r = 0.4509, p = 0.0002). Conclusion: Our study identified DEPs in the follicular fluid of patients with PCOS. Inflammatory response, complement and coagulation cascades, activation of the immune response, lipid transport, and regulation of protein metabolic process were deregulated in PCOS, which may play essential roles in the pathogenesis of PCOS. Frontiers Media S.A. 2022-08-22 /pmc/articles/PMC9441494/ /pubmed/36072434 http://dx.doi.org/10.3389/fmolb.2022.956406 Text en Copyright © 2022 Wang, Jiang, Niu, Ding, Yang, Zheng, Hao and Wei. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Wang, Wenqi
Jiang, Qi
Niu, Yue
Ding, Qiaoqiao
Yang, Xiao
Zheng, Yanjun
Hao, Jing
Wei, Daimin
Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome
title Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome
title_full Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome
title_fullStr Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome
title_full_unstemmed Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome
title_short Proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome
title_sort proteomics and bioinformatics analysis of follicular fluid from patients with polycystic ovary syndrome
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441494/
https://www.ncbi.nlm.nih.gov/pubmed/36072434
http://dx.doi.org/10.3389/fmolb.2022.956406
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