Bioinformatics approach to identify the influences of SARS-COV2 infections on atherosclerosis

Zhang, Jiuchang; Zhang, Liming

doi:10.3389/fcvm.2022.907665

Cited by 7 publications

(5 citation statements)

References 44 publications

(47 reference statements)

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“…The characteristic gene VSIG4 identified by the three algorithms is mapped to the human X chromosome; the encoding product is a negative regulator of T-cell response, and the VSIG4 protein is a receptor for the complement component 3 fragments C3b and iC3b [64]. VSIG4 may serve as a pivot gene for atherosclerosis and COVID-19, which is consistent with the clinical severity of SARS-CoV-2 infection in patients with cardiovascular disease [65].…”

Section: Myeloid Cell Features Associated With the Severity Of Covid-19mentioning

confidence: 63%

Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes

Ren

Gao

Zhou

et al. 2023

Biology

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As COVID-19 develops, dynamic changes occur in the patient’s immune system. Changes in molecular levels in different immune cells can reflect the course of COVID-19. This study aims to uncover the molecular characteristics of different immune cell subpopulations at different stages of COVID-19. We designed a machine learning workflow to analyze scRNA-seq data of three immune cell types (B, T, and myeloid cells) in four levels of COVID-19 severity/outcome. The datasets for three cell types included 403,700 B-cell, 634,595 T-cell, and 346,547 myeloid cell samples. Each cell subtype was divided into four groups, control, convalescence, progression mild/moderate, and progression severe/critical, and each immune cell contained 27,943 gene features. A feature analysis procedure was applied to the data of each cell type. Irrelevant features were first excluded according to their relevance to the target variable measured by mutual information. Then, four ranking algorithms (last absolute shrinkage and selection operator, light gradient boosting machine, Monte Carlo feature selection, and max-relevance and min-redundancy) were adopted to analyze the remaining features, resulting in four feature lists. These lists were fed into the incremental feature selection, incorporating three classification algorithms (decision tree, k-nearest neighbor, and random forest) to extract key gene features and construct classifiers with superior performance. The results confirmed that genes such as PFN1, RPS26, and FTH1 played important roles in SARS-CoV-2 infection. These findings provide a useful reference for the understanding of the ongoing effect of COVID-19 development on the immune system.

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Section: Myeloid Cell Features Associated With the Severity Of Covid-19mentioning

confidence: 63%

Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes

Ren

Gao

Zhou

et al. 2023

Biology

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“…Therefore, patients with MUO precondition are more likely to suffer from severe SARS-COV-2 infection complication. Furthermore, the hub genes C1QA, C1QB, C1QC, VSIG4 and CD14 have been regarded as the main biomarkers for predicting macrophage and neutrophil mediated inflammation, especially in the respiratory and cardiovascular systems ( Zhang and Zhang, 2022 ). They are also the essential biomarkers that have been identified in the unfavorable metabolic profile of MUO patients ( Chang et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 on metabolic unhealthy obese patients

Huang,

Jiang,

Song

et al. 2023

Front. Mol. Biosci.

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Introduction: The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) has posed a significant challenge to individuals’ health. Increasing evidence shows that patients with metabolic unhealthy obesity (MUO) and COVID-19 have severer complications and higher mortality rate. However, the molecular mechanisms underlying the association between MUO and COVID-19 are poorly understood.Methods: We sought to reveal the relationship between MUO and COVID-19 using bioinformatics and systems biology analysis approaches. Here, two datasets (GSE196822 and GSE152991) were employed to extract differentially expressed genes (DEGs) to identify common hub genes, shared pathways, transcriptional regulatory networks, gene-disease relationship and candidate drugs.Results: Based on the identified 65 common DEGs, the complement-related pathways and neutrophil degranulation-related functions are found to be mainly affected. The hub genes, which included SPI1, CD163, C1QB, SIGLEC1, C1QA, ITGAM, CD14, FCGR1A, VSIG4 and C1QC, were identified. From the interaction network analysis, 65 transcription factors (TFs) were found to be the regulatory signals. Some infections, inflammation and liver diseases were found to be most coordinated with the hub genes. Importantly, Paricalcitol, 3,3′,4,4′,5-Pentachlorobiphenyl, PD 98059, Medroxyprogesterone acetate, Dexamethasone and Tretinoin HL60 UP have shown possibility as therapeutic agents against COVID-19 and MUO.Conclusion: This study provides new clues and references to treat both COVID-19 and MUO.

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“…Daniel Wendisch et al, described the CD163-expressing macrophage phenotype as the profibrotic transcriptional phenotype during COVID-19 infection [26], which is also the reason why obese patients develop metabolic diseases [27]. Furthermore, the hub genes C1QA, C1QB, C1QC, VSIG4 and CD14 have been regarded as the main biomarkers for predicting macrophage and neutrophil mediated inflammation, especially in the respiratory and cardiovascular systems [28]. They are also the essential biomarkers that have been identified in the unfavorable metabolic profile of MUO patients [29].…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics and system biology approach to identify the influences of COVID-19 on metabolic unhealthy obese patients

Huang

Jiang

Song

et al. 2023

Preprint

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Objective: The severe acute respiratory syndrome coronavirus 2 has posed a significant challenge to health of individual. Increasing evidence shows that patients with metabolic unhealthy obesity (MUO) and COVID19 have severer complications and higher mortality rate. However, the molecular mechanisms underlying the association between MUO and COVID19 are poorly understood. We sought to implement transcriptomic analysis using bioinformatics and systems biology analysis approaches. Methods: Here, two datasets (GSE196822 and GSE152991) were employed to extract differentially expressed genes (DEGs) to identify common hub genes, shared pathways and candidate drugs and construct a gene disease network. Results: Based on the identified 65 common DEGs, the results revealed hub genes and essential modules. Moreover, common associations between MUO and COVID-19 were found. Transcription factors (TFs) and genes interaction, protein and drug interactions, and DEGs and miRNAs coregulatory network were identified. Furthermore, the gene-disease association were obtained and constructed. Conclusions: The shared pathogenic pathways are noted worth paying attention to. Several genes are highlighted as critical targets for developing treatments for and investigating the complications of COVID19 and MUO. Additionally, multiple genes are identified as promising biomarkers. We think this result of the study may help in selecting and inventing future treatments that can combat COVID-19 and MUO.

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Bioinformatics approach to identify the influences of SARS-COV2 infections on atherosclerosis

Cited by 7 publications

References 44 publications

Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes

Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 on metabolic unhealthy obese patients

Bioinformatics and system biology approach to identify the influences of COVID-19 on metabolic unhealthy obese patients

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