Cargando…

Metabolic networks of plasma and joint fluid base on differential correlation

Whether osteoarthritis (OA) is a systemic metabolic disorder remains controversial. The aim of this study was to investigate the metabolic characteristics between plasma and knee joint fluid (JF) of patients with advanced OA using a differential correlation metabolic (DCM) networks approach. Plasma...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Bingyong, Su, Hong, Wang, Ruya, Wang, Yixiao, Zhang, Weidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899361/
https://www.ncbi.nlm.nih.gov/pubmed/33617578
http://dx.doi.org/10.1371/journal.pone.0247191
_version_ 1783654038776053760
author Xu, Bingyong
Su, Hong
Wang, Ruya
Wang, Yixiao
Zhang, Weidong
author_facet Xu, Bingyong
Su, Hong
Wang, Ruya
Wang, Yixiao
Zhang, Weidong
author_sort Xu, Bingyong
collection PubMed
description Whether osteoarthritis (OA) is a systemic metabolic disorder remains controversial. The aim of this study was to investigate the metabolic characteristics between plasma and knee joint fluid (JF) of patients with advanced OA using a differential correlation metabolic (DCM) networks approach. Plasma and JF were collected during the joint replacement surgery of patients with knee OA. The biological samples were pretreated with standard procedures for metabolite analysis. The metabolic profiling was conducted by means of liquid mass spectrometry coupled with a AbsoluteIDQ kit. A DCM network approach was adopted for analyzing the metabolomics data between the plasma and JF. The variation in the correlation of the pairwise metabolites was quantified across the plasma and JF samples, and networks analysis was used to characterize the difference in the correlations of the metabolites from the two sample types. Core metabolites that played an important role in the DCM networks were identified via topological analysis. One hundred advanced OA patients (50 men and 50 women) were included in this study, with an average age of 65.0 ± 7.6 years (65.6 ± 7.1 years for females and 64.4 ± 8.1 years for males) and a mean BMI of 32.6 ± 5.8 kg/m(2) (33.4 ± 6.3 kg/m(2) for females and 31.7 ± 5.3 kg/m(2) for males). Age and BMI matched between the male and female groups. One hundred and forty-five nodes, 567 edges, and 131 nodes, 407 edges were found in the DCM networks (p < 0.05) of the female and male groups, respectively. Six metabolites in the female group and 5 metabolites in the male group were identified as key nodes in the network. There was a significant difference in the differential correlation metabolism networks of plasma and JF that may be related to local joint metabolism. Focusing on these key metabolites may help uncover the pathogenesis of knee OA. In addition, the differential metabolic correlation between plasma and JF mostly overlapped, indicating that these common correlations of pairwise metabolites may be a reflection of systemic characteristics of JF and that most significant correlation variations were just a result of "housekeeping” biological reactions.
format Online
Article
Text
id pubmed-7899361
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-78993612021-03-02 Metabolic networks of plasma and joint fluid base on differential correlation Xu, Bingyong Su, Hong Wang, Ruya Wang, Yixiao Zhang, Weidong PLoS One Research Article Whether osteoarthritis (OA) is a systemic metabolic disorder remains controversial. The aim of this study was to investigate the metabolic characteristics between plasma and knee joint fluid (JF) of patients with advanced OA using a differential correlation metabolic (DCM) networks approach. Plasma and JF were collected during the joint replacement surgery of patients with knee OA. The biological samples were pretreated with standard procedures for metabolite analysis. The metabolic profiling was conducted by means of liquid mass spectrometry coupled with a AbsoluteIDQ kit. A DCM network approach was adopted for analyzing the metabolomics data between the plasma and JF. The variation in the correlation of the pairwise metabolites was quantified across the plasma and JF samples, and networks analysis was used to characterize the difference in the correlations of the metabolites from the two sample types. Core metabolites that played an important role in the DCM networks were identified via topological analysis. One hundred advanced OA patients (50 men and 50 women) were included in this study, with an average age of 65.0 ± 7.6 years (65.6 ± 7.1 years for females and 64.4 ± 8.1 years for males) and a mean BMI of 32.6 ± 5.8 kg/m(2) (33.4 ± 6.3 kg/m(2) for females and 31.7 ± 5.3 kg/m(2) for males). Age and BMI matched between the male and female groups. One hundred and forty-five nodes, 567 edges, and 131 nodes, 407 edges were found in the DCM networks (p < 0.05) of the female and male groups, respectively. Six metabolites in the female group and 5 metabolites in the male group were identified as key nodes in the network. There was a significant difference in the differential correlation metabolism networks of plasma and JF that may be related to local joint metabolism. Focusing on these key metabolites may help uncover the pathogenesis of knee OA. In addition, the differential metabolic correlation between plasma and JF mostly overlapped, indicating that these common correlations of pairwise metabolites may be a reflection of systemic characteristics of JF and that most significant correlation variations were just a result of "housekeeping” biological reactions. Public Library of Science 2021-02-22 /pmc/articles/PMC7899361/ /pubmed/33617578 http://dx.doi.org/10.1371/journal.pone.0247191 Text en © 2021 Xu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Xu, Bingyong
Su, Hong
Wang, Ruya
Wang, Yixiao
Zhang, Weidong
Metabolic networks of plasma and joint fluid base on differential correlation
title Metabolic networks of plasma and joint fluid base on differential correlation
title_full Metabolic networks of plasma and joint fluid base on differential correlation
title_fullStr Metabolic networks of plasma and joint fluid base on differential correlation
title_full_unstemmed Metabolic networks of plasma and joint fluid base on differential correlation
title_short Metabolic networks of plasma and joint fluid base on differential correlation
title_sort metabolic networks of plasma and joint fluid base on differential correlation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899361/
https://www.ncbi.nlm.nih.gov/pubmed/33617578
http://dx.doi.org/10.1371/journal.pone.0247191
work_keys_str_mv AT xubingyong metabolicnetworksofplasmaandjointfluidbaseondifferentialcorrelation
AT suhong metabolicnetworksofplasmaandjointfluidbaseondifferentialcorrelation
AT wangruya metabolicnetworksofplasmaandjointfluidbaseondifferentialcorrelation
AT wangyixiao metabolicnetworksofplasmaandjointfluidbaseondifferentialcorrelation
AT zhangweidong metabolicnetworksofplasmaandjointfluidbaseondifferentialcorrelation