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Transcriptome transfer provides a model for understanding the phenotype of cardiomyocytes

ABSTRACT: We show that the transfer of the adult ventricular myocyte (AVM) transcriptome into either a fibroblast or an astrocyte converts the host cell into a cardiomyocyte. Transcriptome-effected cardiomyocytes (tCardiomyocytes) display morphologies, immunocytochemical properties, and expression profiles of postnatal cardiomyocytes. Cell morphology analysis shows that tCardiomyoctes are elongated and have a similar length-to-width ratio as AVMs. These global phenotypic changes occur in a time-dependent manner and confer electroexcitability to the tCardiomyocytes. tCardiomyocyte generation does not require continuous overexpression of specific transcription factors; for example, the expression level of transcription factor Mef2c is higher in tCardiomyocytes than in fibroblasts, but similar in tCardiomyocytes and AVMs. These data highlight the dominant role of the gene expression profile in developing and maintaining cellular phenotype. The transcriptomeinduced phenotype remodeling–generated tCardiomyocyte has significant implications for understanding and modulating cardiac disease development.

ORGANISM(S): Mus musculus

PROVIDER: GSE30164 | GEO | 2011/06/23

SECONDARY ACCESSION(S): PRJNA143813

REPOSITORIES: GEO

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Project description:OBJECTIVE: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM).METHOD: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve.RESULT: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism and protein translation. Through machine learning algorithms, 9 metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid and dihydrojasmonic acid.CONCLUSION: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.

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Transcriptome transfer provides a model for understanding the phenotype of cardiomyocytes

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