Targeting ACSL1 promotes cardiomyocyte proliferation and cardiac regeneration

Li, Yuanlong; Yang, Ming; Tan, Jing; Shen, Conghui; Deng, Shijie; Fu, Xinlu; Gao, Saifei; Li, Hui; Zhang, Xiaoxue; Cai, Weibin

doi:10.1016/j.lfs.2022.120371

Cited by 15 publications

(11 citation statements)

References 45 publications

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“…ACSL1 can regulate lipid metabolism, and Zhaohui Hu et al found that ACSL1 is a potential marker for MI, which is potentially valuable for the diagnosis of MI [29]. Mouse experiments showed that ACSL1 knockdown promotes cardiomyocyte proliferation and cardiac regeneration [30]. FCER1G is also a potential biomarker for MI, and FCER1G expression was signi cantly elevated in MI patients [31].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Performance and Potential Molecular Mechanisms of IGF-1-related genes in Myocardial Infarction

Wang,

Shi,

Xue

et al. 2024

Preprint

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Backgrounds Myocardial infarction (MI) is ischemic necrosis of the myocardium due to the formation of plaque on the inner walls of the arteries. Insulin-like growth factor 1 (IGF-1) plays a prominent role in maintaining cardiac homeostasis. Investigating the molecular mechanism of the genes associated with IGF-1 in MI and the identification of diagnostic markers and therapeutic drugs could assist in treating MI. Methods Two datasets in Gene Expression Omnibus (GEO), GSE66360 and GSE60993, were subjected into this study. Primarily, the candidate genes were analyzed by differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The machine learning algorithms were used to select hub genes from candidate genes. In addition, the signaling pathway of hub genes were explored by Gene Set Enrichment Analysis (GSEA). The databases Diana-microT, miRNet, StarBase, and GeneMANIA were used to predict miRNAs, lncRNAs, transcription factors (TFs) and related genes, respectively. Eventually, the Comparative Toxicogenomics Database (CTD) was analyzed to investigate the relationship between hub genes and MI. Small-molecule drugs prediction through the utilization of the Drug-Gene Interaction Database (DGIdb). Results 5 hub genes associated with IGF-1 (ALDH2, NLRP3, CLEC4E, ACSL1, FCER1G) have been discovered in MI. These genes exhibited promising diagnostic potential and were implicated in the occurrence of MI. The regulatory network disclosed that ACSL1 predicted 7 miRNAs, 372 lncRNAs and 1 TF. Furthermore, 14 small-molecule drugs targeting hub genes were predicted. Conclusion Integrating bioinformatics approaches, we found 5 hub genes with diagnostic efficacy for MI. Moreover, the study discovered signaling pathways connected to MI, offering novel perspectives on the molecular causes of MI. Additionally, drugs targeting hub genes were predicted, which also able to supply new treatment options for MI in clinical settings.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Performance and Potential Molecular Mechanisms of IGF-1-related genes in Myocardial Infarction

Wang,

Shi,

Xue

et al. 2024

Preprint

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“…In addition, except for participating in lipid metabolism, Yuanlong Li et al’s study also found that the high regenerative activity of the myocardium in neonatal mice was also regulated by ACSL1 within 7 days. In the neonatal mouse MI model, mice knocked out of this gene showed more good recovery ( 27 ). IL1R2 is considered to mediate the anti-inflammatory response in the traditional inflammatory response ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients

Sun

et al. 2023

Front. Cardiovasc. Med.

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Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.

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“…Previous studies have shown that heart-specific overexpression of ACSL1 in mice increases triglyceride accumulation in cardiomyocytes [ 28 ]. Li et al demonstrated that inhibiting ACSL1 expression in the heart can reduce lipid metabolism and promote the regeneration of cardiomyocytes [ 29 ]. A cohort study has shown that the expression level of ACSL1 in peripheral blood leukocytes of AMI patients was higher than that of healthy controls, and this high expression was a risk factor for AMI [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

ACSL1, CH25H, GPCPD1, and PLA2G12A as the potential lipid-related diagnostic biomarkers of acute myocardial infarction

Liu

Cao

et al. 2023

Aging

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Lipid metabolism plays an essential role in the genesis and progress of acute myocardial infarction (AMI).Herein, we identified and verified latent lipid-related genes involved in AMI by bioinformatic analysis. Lipidrelated differentially expressed genes (DEGs) involved in AMI were identified using the GSE66360 dataset from the Gene Expression Omnibus (GEO) database and R software packages. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to analyze lipidrelated DEGs. Lipid-related genes were identified by two machine learning techniques: least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE). The receiver operating characteristic (ROC) curves were used to descript diagnostic accuracy. Furthermore, blood samples were collected from AMI patients and healthy individuals, and real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the RNA levels of four lipid-related DEGs. Fifty lipid-related DEGs were identified, 28 upregulated and 22 downregulated. Several enrichment terms related to lipid metabolism were found by GO and KEGG enrichment analyses. After LASSO and SVM-RFE screening, four genes (ACSL1, CH25H, GPCPD1, and PLA2G12A) were identified as potential diagnostic biomarkers for AMI. Moreover, the RT-qPCR analysis indicated that the expression levels of four DEGs in AMI patients and healthy individuals were consistent with bioinformatics analysis results. The validation of clinical samples suggested that 4 lipid-related DEGs are expected to be diagnostic markers for AMI and provide new targets for lipid therapy of AMI.

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Targeting ACSL1 promotes cardiomyocyte proliferation and cardiac regeneration

Cited by 15 publications

References 45 publications

Diagnostic Performance and Potential Molecular Mechanisms of IGF-1-related genes in Myocardial Infarction

Diagnostic Performance and Potential Molecular Mechanisms of IGF-1-related genes in Myocardial Infarction

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients

ACSL1, CH25H, GPCPD1, and PLA2G12A as the potential lipid-related diagnostic biomarkers of acute myocardial infarction

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