Cargando…

Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques

Background: Sepsis-induced myocardial dysfunction (SIMD) is the most common and severe sepsis-related organ dysfunction. We aimed to investigate the metabolic changes occurring in the hearts of patients suffering from SIMD. Methods: An animal SIMD model was constructed by injecting lipopolysaccharid...

Descripción completa

Detalles Bibliográficos
Autores principales: Jia, Xiaonan, Peng, Yahui, Ma, Xiaohui, Liu, Xiaowei, Yu, Kaijiang, Wang, Changsong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9421372/
https://www.ncbi.nlm.nih.gov/pubmed/36046606
http://dx.doi.org/10.3389/fmolb.2022.967397
_version_ 1784777578531258368
author Jia, Xiaonan
Peng, Yahui
Ma, Xiaohui
Liu, Xiaowei
Yu, Kaijiang
Wang, Changsong
author_facet Jia, Xiaonan
Peng, Yahui
Ma, Xiaohui
Liu, Xiaowei
Yu, Kaijiang
Wang, Changsong
author_sort Jia, Xiaonan
collection PubMed
description Background: Sepsis-induced myocardial dysfunction (SIMD) is the most common and severe sepsis-related organ dysfunction. We aimed to investigate the metabolic changes occurring in the hearts of patients suffering from SIMD. Methods: An animal SIMD model was constructed by injecting lipopolysaccharide (LPS) into mice intraperitoneally. Metabolites and transcripts present in the cardiac tissues of mice in the experimental and control groups were extracted, and the samples were studied following the untargeted metabolomics–transcriptomics high-throughput sequencing method. SIMD-related metabolites were screened following univariate and multi-dimensional analyses methods. Additionally, differential analysis of gene expression was performed using the DESeq package. Finally, metabolites and their associated transcripts were mapped to the relevant metabolic pathways after extracting transcripts corresponding to relevant enzymes. The process was conducted based on the metabolite information present in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results: One hundred and eighteen significant differentially expressed metabolites (DEMs) (58 under the cationic mode and 60 under the anionic mode) were identified by studying the SIMD and control groups. Additionally, 3,081 significantly differentially expressed genes (DEGs) (1,364 were down-regulated and 1717 were up-regulated DEGs) were identified in the transcriptomes. The comparison was made between the two groups. The metabolomics–transcriptomics combination analysis of metabolites and their associated transcripts helped identify five metabolites (d-mannose, d-glucosamine 6-phosphate, maltose, alpha-linolenic acid, and adenosine 5′-diphosphate). Moreover, irregular and unusual events were observed during the processes of mannose metabolism, amino sugar metabolism, starch metabolism, unsaturated fatty acid biosynthesis, platelet activation, and purine metabolism. The AMP-activated protein kinase (AMPK) signaling pathways were also accompanied by aberrant events. Conclusion: Severe metabolic disturbances occur in the cardiac tissues of model mice with SIMD. This can potentially help in developing the SIMD treatment methods.
format Online
Article
Text
id pubmed-9421372
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-94213722022-08-30 Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques Jia, Xiaonan Peng, Yahui Ma, Xiaohui Liu, Xiaowei Yu, Kaijiang Wang, Changsong Front Mol Biosci Molecular Biosciences Background: Sepsis-induced myocardial dysfunction (SIMD) is the most common and severe sepsis-related organ dysfunction. We aimed to investigate the metabolic changes occurring in the hearts of patients suffering from SIMD. Methods: An animal SIMD model was constructed by injecting lipopolysaccharide (LPS) into mice intraperitoneally. Metabolites and transcripts present in the cardiac tissues of mice in the experimental and control groups were extracted, and the samples were studied following the untargeted metabolomics–transcriptomics high-throughput sequencing method. SIMD-related metabolites were screened following univariate and multi-dimensional analyses methods. Additionally, differential analysis of gene expression was performed using the DESeq package. Finally, metabolites and their associated transcripts were mapped to the relevant metabolic pathways after extracting transcripts corresponding to relevant enzymes. The process was conducted based on the metabolite information present in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results: One hundred and eighteen significant differentially expressed metabolites (DEMs) (58 under the cationic mode and 60 under the anionic mode) were identified by studying the SIMD and control groups. Additionally, 3,081 significantly differentially expressed genes (DEGs) (1,364 were down-regulated and 1717 were up-regulated DEGs) were identified in the transcriptomes. The comparison was made between the two groups. The metabolomics–transcriptomics combination analysis of metabolites and their associated transcripts helped identify five metabolites (d-mannose, d-glucosamine 6-phosphate, maltose, alpha-linolenic acid, and adenosine 5′-diphosphate). Moreover, irregular and unusual events were observed during the processes of mannose metabolism, amino sugar metabolism, starch metabolism, unsaturated fatty acid biosynthesis, platelet activation, and purine metabolism. The AMP-activated protein kinase (AMPK) signaling pathways were also accompanied by aberrant events. Conclusion: Severe metabolic disturbances occur in the cardiac tissues of model mice with SIMD. This can potentially help in developing the SIMD treatment methods. Frontiers Media S.A. 2022-08-15 /pmc/articles/PMC9421372/ /pubmed/36046606 http://dx.doi.org/10.3389/fmolb.2022.967397 Text en Copyright © 2022 Jia, Peng, Ma, Liu, Yu and Wang. 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
Jia, Xiaonan
Peng, Yahui
Ma, Xiaohui
Liu, Xiaowei
Yu, Kaijiang
Wang, Changsong
Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques
title Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques
title_full Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques
title_fullStr Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques
title_full_unstemmed Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques
title_short Analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques
title_sort analysis of metabolic disturbances attributable to sepsis-induced myocardial dysfunction using metabolomics and transcriptomics techniques
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9421372/
https://www.ncbi.nlm.nih.gov/pubmed/36046606
http://dx.doi.org/10.3389/fmolb.2022.967397
work_keys_str_mv AT jiaxiaonan analysisofmetabolicdisturbancesattributabletosepsisinducedmyocardialdysfunctionusingmetabolomicsandtranscriptomicstechniques
AT pengyahui analysisofmetabolicdisturbancesattributabletosepsisinducedmyocardialdysfunctionusingmetabolomicsandtranscriptomicstechniques
AT maxiaohui analysisofmetabolicdisturbancesattributabletosepsisinducedmyocardialdysfunctionusingmetabolomicsandtranscriptomicstechniques
AT liuxiaowei analysisofmetabolicdisturbancesattributabletosepsisinducedmyocardialdysfunctionusingmetabolomicsandtranscriptomicstechniques
AT yukaijiang analysisofmetabolicdisturbancesattributabletosepsisinducedmyocardialdysfunctionusingmetabolomicsandtranscriptomicstechniques
AT wangchangsong analysisofmetabolicdisturbancesattributabletosepsisinducedmyocardialdysfunctionusingmetabolomicsandtranscriptomicstechniques