Identification of a novel immune-inflammatory signature of COVID-19 infections, and evaluation of pharmacokinetics and therapeutic potential of RXn-02, a novel small-molecule derivative of quinolone

Lawal, Bashir; Kuo, Yi Chun; Sumitra, Maryam Rachmawati; Wu, Alexander T.H.; Huang, Hsu‐Shan

doi:10.1016/j.compbiomed.2022.105814

Cited by 9 publications

(9 citation statements)

References 98 publications

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“…Besides, expressions of Ki-67 in CD4 + or CD8 + T cells were signi cantly higher in the severe group than in the mild group(Kang, Han et al 2020). MKI67 also appeared to be the most pertinent infection signatures in an analysis of transcriptomic datasets(Lawal, Kuo et al 2022), which is in accordance with our results.TOP2A, also called DNA topoisomerase 2-α, is located on human chromosome 17 (17q21-22) and encodes DNA topoisomerase II α(Tsai-P ugfelder, Liu et al 1988). It has been shown to play an important role in the development and prognosis of a variety of cancers, especially hepatocellular carcinoma, but its contribution in circadian rhythms has not been clearly examined(Dong, Sun et al 2021, Meng, Wei et al…”

supporting

confidence: 92%

Identification of six genes associated with COVID-19-related Circadian rhythm dysfunction by integrated bioinformatic analysis

Zhang

Wang

et al. 2023

Preprint

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Background Patients with coronavirus disease 2019 (COVID-19) might cause long-term burden of insomnia, while the common pathogenic mechanisms are not elucidated.Methods The gene expression profiles of COVID-19 patients and healthy controls were retrieved from the GEO database, while gene set related with circadian rhythm were obtained from Genecards database. The weight gene co-expression network analysis (WGCNA) algorithms were conducted to identify the most correlated gene module with COVID-19. Functional enrichment analysis and protein-protein interaction network (PPI) were performed on shared genes between key module and circadian rhythm gene set. Hub genes were identified and gene regulatory networks, immune cell Infiltration evaluation and Drug–Gene interaction were constructed.Results 76 shared genes were screened and mainly enriched in cell cycle, cell division and cell proliferation, and 6 hub genes were found out including CCNA2, CCNB1, CDK1, CHEK1, MKI67 and TOP2A, with positive correlation to plasma cells. In the TF-gene regulatory network, NFYA, NFIC, MEF2A and FOXC1 showed high connectivity with hub genes.Conclusions This study established the co-expression network and identified six hub genes, which might provide new insights into pathogenic mechanisms and novel clinical management strategies.

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supporting

confidence: 92%

Identification of six genes associated with COVID-19-related Circadian rhythm dysfunction by integrated bioinformatic analysis

Zhang

Wang

et al. 2023

Preprint

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“…Interestingly, previous study also showed MKI67 could be a novel biomarker signature for SARS-CoV-2 pathogenesis ( Lawal et al., 2022 ). Also, MKI67 and BCL2 as a marker combination that enables the tracking of nascent memory cells within the effector phase ( Crauste et al., 2017 ).…”

Section: Discussionmentioning

confidence: 96%

Identifying key genes related to inflammasome in severe COVID-19 patients based on a joint model with random forest and artificial neural network

Fan

Guo

et al. 2023

Front. Cell. Infect. Microbiol.

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BackgroundThe coronavirus disease 2019 (COVID-19) has been spreading astonishingly and caused catastrophic losses worldwide. The high mortality of severe COVID-19 patients is an serious problem that needs to be solved urgently. However, the biomarkers and fundamental pathological mechanisms of severe COVID-19 are poorly understood. The aims of this study was to explore key genes related to inflammasome in severe COVID-19 and their potential molecular mechanisms using random forest and artificial neural network modeling.MethodsDifferentially expressed genes (DEGs) in severe COVID-19 were screened from GSE151764 and GSE183533 via comprehensive transcriptome Meta-analysis. Protein-protein interaction (PPI) networks and functional analyses were conducted to identify molecular mechanisms related to DEGs or DEGs associated with inflammasome (IADEGs), respectively. Five the most important IADEGs in severe COVID-19 were explored using random forest. Then, we put these five IADEGs into an artificial neural network to construct a novel diagnostic model for severe COVID-19 and verified its diagnostic efficacy in GSE205099.ResultsUsing combining P value < 0.05, we obtained 192 DEGs, 40 of which are IADEGs. The GO enrichment analysis results indicated that 192 DEGs were mainly involved in T cell activation, MHC protein complex and immune receptor activity. The KEGG enrichment analysis results indicated that 192 GEGs were mainly involved in Th17 cell differentiation, IL-17 signaling pathway, mTOR signaling pathway and NOD-like receptor signaling pathway. In addition, the top GO terms of 40 IADEGs were involved in T cell activation, immune response-activating signal transduction, external side of plasma membrane and phosphatase binding. The KEGG enrichment analysis results indicated that IADEGs were mainly involved in FoxO signaling pathway, Toll-like receptor, JAK-STAT signaling pathway and Apoptosis. Then, five important IADEGs (AXL, MKI67, CDKN3, BCL2 and PTGS2) for severe COVID-19 were screened by random forest analysis. By building an artificial neural network model, we found that the AUC values of 5 important IADEGs were 0.972 and 0.844 in the train group (GSE151764 and GSE183533) and test group (GSE205099), respectively.ConclusionThe five genes related to inflammasome, including AXL, MKI67, CDKN3, BCL2 and PTGS2, are important for severe COVID-19 patients, and these molecules are related to the activation of NLRP3 inflammasome. Furthermore, AXL, MKI67, CDKN3, BCL2 and PTGS2 as a marker combination could be used as potential markers to identify severe COVID-19 patients.

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“…The immunopathological changes in the spleen of patients with COVID-19 are also worthy of attention as they involve the functions of plasma cells and monocytes/macrophages and a decrease in CD8A abundance ( 78 ). Through the analysis of transcriptome data, PTPRC was shown to be an important inflammatory and immunomodulatory signature in COVID-19, and that it has high binding efficiency with related drugs in clinical transformation research ( 79 ). There have been some studies on CCL2, CD8A, and PTPRC in cardiovascular diseases; however, their roles in pericarditis require further research.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive bioinformatics analysis and systems biology approaches to identify the interplay between COVID-19 and pericarditis

Li,

Chen,

Huang

et al. 2024

Front. Immunol.

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BackgroundIncreasing evidence indicating that coronavirus disease 2019 (COVID-19) increased the incidence and related risks of pericarditis and whether COVID-19 vaccine is related to pericarditis has triggered research and discussion. However, mechanisms behind the link between COVID-19 and pericarditis are still unknown. The objective of this study was to further elucidate the molecular mechanisms of COVID-19 with pericarditis at the gene level using bioinformatics analysis.MethodsGenes associated with COVID-19 and pericarditis were collected from databases using limited screening criteria and intersected to identify the common genes of COVID-19 and pericarditis. Subsequently, gene ontology, pathway enrichment, protein–protein interaction, and immune infiltration analyses were conducted. Finally, TF–gene, gene–miRNA, gene–disease, protein–chemical, and protein–drug interaction networks were constructed based on hub gene identification.ResultsA total of 313 common genes were selected, and enrichment analyses were performed to determine their biological functions and signaling pathways. Eight hub genes (IL-1β, CD8A, IL-10, CD4, IL-6, TLR4, CCL2, and PTPRC) were identified using the protein–protein interaction network, and immune infiltration analysis was then carried out to examine the functional relationship between the eight hub genes and immune cells as well as changes in immune cells in disease. Transcription factors, miRNAs, diseases, chemicals, and drugs with high correlation with hub genes were predicted using bioinformatics analysis.ConclusionsThis study revealed a common gene interaction network between COVID-19 and pericarditis. The screened functional pathways, hub genes, potential compounds, and drugs provided new insights for further research on COVID-19 associated with pericarditis.

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Identification of a novel immune-inflammatory signature of COVID-19 infections, and evaluation of pharmacokinetics and therapeutic potential of RXn-02, a novel small-molecule derivative of quinolone

Cited by 9 publications

References 98 publications

Identification of six genes associated with COVID-19-related Circadian rhythm dysfunction by integrated bioinformatic analysis

Identification of six genes associated with COVID-19-related Circadian rhythm dysfunction by integrated bioinformatic analysis

Identifying key genes related to inflammasome in severe COVID-19 patients based on a joint model with random forest and artificial neural network

Comprehensive bioinformatics analysis and systems biology approaches to identify the interplay between COVID-19 and pericarditis

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