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Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular
Yaochen Xu1, Qinglan Ma2, Jingxin Ren2
1Department of Mathematics, School of Sciences, Shanghai University, Shanghai 200444, China.
Insights
COVID-19 (Coronavirus Disease 2019) significantly impacts heart cells. This study used machine learning to identify key genes like MALAT1, MT-CO1, and CD36 in cardiac cells affected by SARS-CoV-2 infection.
Area of Science:
- Cardiovascular Biology
- Infectious Disease Research
- Genomics
Background:
- COVID-19 (Coronavirus Disease 2019) presents significant cardiovascular risks beyond respiratory symptoms.
- Vascular endothelial cells and cardiomyocytes are crucial for cardiac function and are implicated in COVID-19 pathogenesis.
- Aberrant gene expression in these cardiac cells can lead to cardiovascular diseases.
Purpose of the Study:
- To investigate the influence of SARS-CoV-2 (Respiratory Syndrome Coronavirus 2) infection on gene expression in cardiac vascular endothelial cells and cardiomyocytes.
- To identify specific genes and regulatory patterns altered by COVID-19 in heart cells.
- To uncover potential therapeutic targets for COVID-19-related cardiovascular complications.
Main Methods:
- Utilized an advanced machine learning workflow to analyze single-cell RNA sequencing data.
- Employed incremental feature selection with a decision tree for classifier building and gene identification.
- Analyzed gene expression profiles from a large dataset of cardiomyocytes and vascular endothelial cells from COVID-19 patients and healthy controls.
Main Results:
- Identified key genes, including MALAT1, MT-CO1, and CD36, with altered expression in cardiac cells of COVID-19 patients.
- Quantified differential gene expression patterns in 12,007 cardiomyocytes and 10,812 vascular endothelial cells from COVID-19 patients compared to controls.
- Established classification rules based on gene expression for distinguishing between COVID-19 affected and healthy cardiac cells.
Conclusions:
- SARS-CoV-2 infection alters gene expression profiles in cardiomyocytes and vascular endothelial cells, contributing to cardiovascular strain.
- The identified genes (MALAT1, MT-CO1, CD36) are critical players in the cardiac pathology of COVID-19.
- These findings offer insights into COVID-19's cardiovascular pathogenesis and suggest potential targets for therapeutic intervention.
Abstract:
Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets.
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