Cargando…

Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics

Septic cardiomyopathy (SCM) is a common and severe complication of sepsis, characterized by left ventricular dilation and reduced ejection fraction leading to heart failure. The pathogenesis of SCM remains unclear. Understanding the SCM pathogenesis is essential in the search for effective therapeut...

Descripción completa

Detalles Bibliográficos
Autores principales: Feng, Shaodan, Cai, Kexin, Lin, Siming, Chen, Xiaojun, Luo, Yuqing, Wang, Jing, Lian, Guili, Lin, Zhihong, Xie, Liangdi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667505/
https://www.ncbi.nlm.nih.gov/pubmed/37996554
http://dx.doi.org/10.1038/s41598-023-47699-0
_version_ 1785149047830478848
author Feng, Shaodan
Cai, Kexin
Lin, Siming
Chen, Xiaojun
Luo, Yuqing
Wang, Jing
Lian, Guili
Lin, Zhihong
Xie, Liangdi
author_facet Feng, Shaodan
Cai, Kexin
Lin, Siming
Chen, Xiaojun
Luo, Yuqing
Wang, Jing
Lian, Guili
Lin, Zhihong
Xie, Liangdi
author_sort Feng, Shaodan
collection PubMed
description Septic cardiomyopathy (SCM) is a common and severe complication of sepsis, characterized by left ventricular dilation and reduced ejection fraction leading to heart failure. The pathogenesis of SCM remains unclear. Understanding the SCM pathogenesis is essential in the search for effective therapeutic agents for SCM. This study was to investigate the pathophysiology of SCM and explore new therapeutic drugs by bioinformatics. An SCM rat model was established by injection of 10 mg/kg lipopolysaccharide (LPS) for 24 h, and the myocardial tissues were collected for RNA sequencing. The differentially expressed genes (DEGs) between LPS rats and control (Ctrl) with the thresholds of |log2(fold change)|≥ 1 and P < 0.05. A protein–protein interaction (PPI) network was constructed based on the DEGs. The hub genes were identified using five algorithms of Cytoscape in the PPI networks and validated in the GSE185754 dataset and by RT-qPCR. The hub genes were analyzed by Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG), as well as Gene set enrichment analyses (GSEA). In addition, the miRNAs of hub genes were predicted through miRWalk, and the candidate therapeutic drugs were identified using the Connectivity Map (CMAP) database. This study revealed the identified hub genes (Itgb1, Il1b, Rac2, Vegfa) and key miRNAs (rno-miR-541-5p, rno-miR-487b-3p, rno-miR-1224, rno-miR-378a-5p, rno-miR-6334, and rno-miR-466b-5p), which were potential biological targets and biomarkers of SCM. Anomalies in cytokine-cytokine receptor interactions, complement and coagulation cascades, chemokine signaling pathways, and MAPK signaling pathways also played vital roles in SCM pathogenesis. Two high-confidence candidate compounds (KU-0063794 and dasatinib) were identified from the CMAP database as new therapeutic drugs for SCM. In summary, these four identified hub genes and enrichment pathways may hold promise for diagnosing and treating SCM.
format Online
Article
Text
id pubmed-10667505
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-106675052023-11-23 Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics Feng, Shaodan Cai, Kexin Lin, Siming Chen, Xiaojun Luo, Yuqing Wang, Jing Lian, Guili Lin, Zhihong Xie, Liangdi Sci Rep Article Septic cardiomyopathy (SCM) is a common and severe complication of sepsis, characterized by left ventricular dilation and reduced ejection fraction leading to heart failure. The pathogenesis of SCM remains unclear. Understanding the SCM pathogenesis is essential in the search for effective therapeutic agents for SCM. This study was to investigate the pathophysiology of SCM and explore new therapeutic drugs by bioinformatics. An SCM rat model was established by injection of 10 mg/kg lipopolysaccharide (LPS) for 24 h, and the myocardial tissues were collected for RNA sequencing. The differentially expressed genes (DEGs) between LPS rats and control (Ctrl) with the thresholds of |log2(fold change)|≥ 1 and P < 0.05. A protein–protein interaction (PPI) network was constructed based on the DEGs. The hub genes were identified using five algorithms of Cytoscape in the PPI networks and validated in the GSE185754 dataset and by RT-qPCR. The hub genes were analyzed by Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG), as well as Gene set enrichment analyses (GSEA). In addition, the miRNAs of hub genes were predicted through miRWalk, and the candidate therapeutic drugs were identified using the Connectivity Map (CMAP) database. This study revealed the identified hub genes (Itgb1, Il1b, Rac2, Vegfa) and key miRNAs (rno-miR-541-5p, rno-miR-487b-3p, rno-miR-1224, rno-miR-378a-5p, rno-miR-6334, and rno-miR-466b-5p), which were potential biological targets and biomarkers of SCM. Anomalies in cytokine-cytokine receptor interactions, complement and coagulation cascades, chemokine signaling pathways, and MAPK signaling pathways also played vital roles in SCM pathogenesis. Two high-confidence candidate compounds (KU-0063794 and dasatinib) were identified from the CMAP database as new therapeutic drugs for SCM. In summary, these four identified hub genes and enrichment pathways may hold promise for diagnosing and treating SCM. Nature Publishing Group UK 2023-11-23 /pmc/articles/PMC10667505/ /pubmed/37996554 http://dx.doi.org/10.1038/s41598-023-47699-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Article
Feng, Shaodan
Cai, Kexin
Lin, Siming
Chen, Xiaojun
Luo, Yuqing
Wang, Jing
Lian, Guili
Lin, Zhihong
Xie, Liangdi
Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics
title Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics
title_full Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics
title_fullStr Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics
title_full_unstemmed Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics
title_short Exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics
title_sort exploring potential therapeutic agents for lipopolysaccharide-induced septic cardiomyopathy based on transcriptomics using bioinformatics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667505/
https://www.ncbi.nlm.nih.gov/pubmed/37996554
http://dx.doi.org/10.1038/s41598-023-47699-0
work_keys_str_mv AT fengshaodan exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics
AT caikexin exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics
AT linsiming exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics
AT chenxiaojun exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics
AT luoyuqing exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics
AT wangjing exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics
AT lianguili exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics
AT linzhihong exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics
AT xieliangdi exploringpotentialtherapeuticagentsforlipopolysaccharideinducedsepticcardiomyopathybasedontranscriptomicsusingbioinformatics