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Identification of TGF-β-related genes in cardiac hypertrophy and heart failure based on single cell RNA sequencing

Background: Heart failure (HF) remains a huge medical burden worldwide. Pathological cardiac hypertrophy is one of the most significant phenotypes of HF. Several studies have reported that the TGF-β pathway plays a double-sided role in HF. Therefore, TGF-β–related genes (TRGs) may be potential thera...

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Autores principales: Huang, Kai, Wu, Hao, Xu, Xiangyang, Wu, Lujia, Li, Qin, Han, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415570/
https://www.ncbi.nlm.nih.gov/pubmed/37498303
http://dx.doi.org/10.18632/aging.204901
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author Huang, Kai
Wu, Hao
Xu, Xiangyang
Wu, Lujia
Li, Qin
Han, Lin
author_facet Huang, Kai
Wu, Hao
Xu, Xiangyang
Wu, Lujia
Li, Qin
Han, Lin
author_sort Huang, Kai
collection PubMed
description Background: Heart failure (HF) remains a huge medical burden worldwide. Pathological cardiac hypertrophy is one of the most significant phenotypes of HF. Several studies have reported that the TGF-β pathway plays a double-sided role in HF. Therefore, TGF-β–related genes (TRGs) may be potential therapeutic targets for cardiac hypertrophy and HF. However, the roles of TRGs in HF at the single-cell level remain unclear. Method: In this study, to analyze the expression pattern of TRGs during the progress of cardiac hypertrophy and HF, we used three public single-cell RNA sequencing datasets for HF (GSE161470, GSE145154, and GSE161153), one HF transcriptome data (GSE57338), and one hypertrophic cardiomyopathy transcriptome data (GSE141910). Weighted gene co-expression network analysis (WGCNA), functional enrichment analysis and machine learning algorithms were used to filter hub genes. Transverse aortic constriction mice model, CCK-8, wound healing assay, quantitative real-time PCR and western blotting were used to validate bioinformatics results. Results: We observed that cardiac fibroblasts (CFs) and endothelial cells showed high TGF-β activity during the progress of HF. Three modules (royalblue, brown4, and darkturquoize) were identified to be significantly associated with TRGs in HF. Six hub genes (TANC2, ADAMTS2, DYNLL1, MRC2, EGR1, and OTUD1) showed anomaly trend in cardiac hypertrophy. We further validated the regulation of the TGF-β-MYC-ADAMTS2 axis on CFs activation in vitro. Conclusions: This study identified six hub genes (TANC2, ADAMTS2, DYNLL1, MRC2, EGR1, and OTUD1) by integrating scRNA and transcriptome data. These six hub genes might be therapeutic targets for cardiac hypertrophy and HF.
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spelling pubmed-104155702023-08-12 Identification of TGF-β-related genes in cardiac hypertrophy and heart failure based on single cell RNA sequencing Huang, Kai Wu, Hao Xu, Xiangyang Wu, Lujia Li, Qin Han, Lin Aging (Albany NY) Research Paper Background: Heart failure (HF) remains a huge medical burden worldwide. Pathological cardiac hypertrophy is one of the most significant phenotypes of HF. Several studies have reported that the TGF-β pathway plays a double-sided role in HF. Therefore, TGF-β–related genes (TRGs) may be potential therapeutic targets for cardiac hypertrophy and HF. However, the roles of TRGs in HF at the single-cell level remain unclear. Method: In this study, to analyze the expression pattern of TRGs during the progress of cardiac hypertrophy and HF, we used three public single-cell RNA sequencing datasets for HF (GSE161470, GSE145154, and GSE161153), one HF transcriptome data (GSE57338), and one hypertrophic cardiomyopathy transcriptome data (GSE141910). Weighted gene co-expression network analysis (WGCNA), functional enrichment analysis and machine learning algorithms were used to filter hub genes. Transverse aortic constriction mice model, CCK-8, wound healing assay, quantitative real-time PCR and western blotting were used to validate bioinformatics results. Results: We observed that cardiac fibroblasts (CFs) and endothelial cells showed high TGF-β activity during the progress of HF. Three modules (royalblue, brown4, and darkturquoize) were identified to be significantly associated with TRGs in HF. Six hub genes (TANC2, ADAMTS2, DYNLL1, MRC2, EGR1, and OTUD1) showed anomaly trend in cardiac hypertrophy. We further validated the regulation of the TGF-β-MYC-ADAMTS2 axis on CFs activation in vitro. Conclusions: This study identified six hub genes (TANC2, ADAMTS2, DYNLL1, MRC2, EGR1, and OTUD1) by integrating scRNA and transcriptome data. These six hub genes might be therapeutic targets for cardiac hypertrophy and HF. Impact Journals 2023-07-26 /pmc/articles/PMC10415570/ /pubmed/37498303 http://dx.doi.org/10.18632/aging.204901 Text en Copyright: © 2023 Huang et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Huang, Kai
Wu, Hao
Xu, Xiangyang
Wu, Lujia
Li, Qin
Han, Lin
Identification of TGF-β-related genes in cardiac hypertrophy and heart failure based on single cell RNA sequencing
title Identification of TGF-β-related genes in cardiac hypertrophy and heart failure based on single cell RNA sequencing
title_full Identification of TGF-β-related genes in cardiac hypertrophy and heart failure based on single cell RNA sequencing
title_fullStr Identification of TGF-β-related genes in cardiac hypertrophy and heart failure based on single cell RNA sequencing
title_full_unstemmed Identification of TGF-β-related genes in cardiac hypertrophy and heart failure based on single cell RNA sequencing
title_short Identification of TGF-β-related genes in cardiac hypertrophy and heart failure based on single cell RNA sequencing
title_sort identification of tgf-β-related genes in cardiac hypertrophy and heart failure based on single cell rna sequencing
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415570/
https://www.ncbi.nlm.nih.gov/pubmed/37498303
http://dx.doi.org/10.18632/aging.204901
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