Genome‐wide screening of SARS‐CoV‐2 infection‐related genes based on the blood leukocytes sequencing data set of patients with COVID‐19

Gào, Xīn; Liu, Yuan; Zou, Shao-hui; Liu, Pengqin; Zhao, Jing; Yang, Changshun; Liang, Mingxing; Yang, Jian

doi:10.1002/jmv.27093

Cited by 26 publications

(21 citation statements)

References 38 publications

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“…5B). We observed a substantial overlap in DEGs/DAGs in wbRN cfRNA when comparing MIS-C to controls (n=494) and COVID- 19 [34][35][36][37][38] . Similar to the gene abundance profiles, differential pathway analysis revealed minimal overlap in top enriched pathways between wbRNA and cfRNA for each subgroup comparison (Supplemental Fig.…”

Section: Comparative Analysis Of Plasma Cell-free Rna and Whole Blood...mentioning

confidence: 90%

“…C to moderate-to-severe COVID-19 (n=9). Of the 9 DEGs/DAGs that overlap when comparing MIS-C to moderate-to-severe COVID-19, seven had been previously reported in association with COVID-19 (IFI6, IFI44L, RSAD2, LY6E, EPSTI1, XAF1, MX1)[34][35][36][37][38] . Similar to the gene abundance profiles, differential pathway analysis revealed minimal overlap in top enriched pathways between wbRNA and cfRNA for each subgroup comparison (Supplemental Fig.5C and D).…”

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confidence: 99%

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WITHDRAWN: Nucleic acid biomarkers of immune response and cell and tissue damage in COVID-19 and MIS-C

Loy

Sotomayor-González

Servellita

et al. 2022

Preprint

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Differential host responses in coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C) remain poorly characterized. Here we use next-generation sequencing to longitudinally analyze blood samples from pediatric patients with acute COVID-19 (n=70) or MIS-C (n=141) across three hospitals. Profiling of plasma cell-free nucleic acids uncovers distinct signatures of cell injury and death between these two disease states, with increased heterogeneity and multi-organ involvement in MIS-C encompassing diverse cell types such as endothelial and neuronal Schwann cells. Whole blood RNA profiling reveals upregulation of similar pro-inflammatory signaling pathways in COVID-19 and MIS-C, but also MIS-C specific downregulation of T cell-associated pathways. Profiling of plasma cell-free RNA and whole blood RNA in paired samples yields different yet complementary signatures for each disease state. Our work provides a systems-level, multi-analyte view of immune responses and tissue damage in COVID-19 and MIS-C and informs the future development of new disease biomarkers.

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Section: Comparative Analysis Of Plasma Cell-free Rna and Whole Blood...mentioning

confidence: 90%

mentioning

confidence: 99%

WITHDRAWN: Nucleic acid biomarkers of immune response and cell and tissue damage in COVID-19 and MIS-C

Loy

Sotomayor-González

Servellita

et al. 2022

Preprint

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“…Therefore, early intervention to prevent overexpression of CCL5 can restore immune balance in COVID-19 patients and prevent disease progression ( 311 , 314 ). Moreover, 2′–5′‐oligoadenylate synthetase‐like ( OASL ), the other hub-high traffic gene of midnightblue module, is an interferon-stimulating gene with a key role in antiviral defense mechanisms ( 326 ) and with potential as a diagnostic biomarker for COVID-19 ( 327 ).…”

Section: Discussionmentioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

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BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

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“…The optimal 24 feature genes, which were further analyzed by consulting the retrieved literature, we found that four genes (XAF1, OAS2, CES1, RPS8) have been reported in COVID-19. Gao et al ( 23 ) found that XAF1 was abnormally strongly expressed in COVID-19 patients and positively correlated with the expression of ARS-CoV-2 invasion-related genes (ACE2, TMPRSS2, CTSB, and CTSL). In contrast, XAF1 was found to be associated with SARS infection by Park and Harris ( 24 ).…”

Section: Discussionmentioning

confidence: 99%

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers

Song

Zhu

Tan

et al. 2022

Front. Public Health

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In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19.

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Genome‐wide screening of SARS‐CoV‐2 infection‐related genes based on the blood leukocytes sequencing data set of patients with COVID‐19

Cited by 26 publications

References 38 publications

WITHDRAWN: Nucleic acid biomarkers of immune response and cell and tissue damage in COVID-19 and MIS-C

WITHDRAWN: Nucleic acid biomarkers of immune response and cell and tissue damage in COVID-19 and MIS-C

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers

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