A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2

Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

SARS-CoV-2 nucleocapsid protein promotes self-deacetylation by inducing HDAC6 to facilitate viral replication

BACKGROUND

The global outbreak of COVID-19 caused by the SARS-CoV-2 has led to millions of deaths. This unanticipated emergency has prompted virologists across the globe to delve deeper into the intricate dynamicity of the host-virus interface with an aim to identify antiviral targets and elucidate host and viral determinants of severe disease.

AIM

The present study was undertaken to analyse the role of histone deacetylase 6 (HDAC6) in regulating SARS-CoV-2 infection.

RESULTS

Gradual increase in HDAC6 expression was observed in different SARS-CoV-2-permissive cell lines following SARS-CoV-2 infection. The SARS-CoV-2 nucleocapsid protein (N protein) was identified as the primary viral factor responsible for upregulating HDAC6 expression. Downregulation of HDAC6 using shRNA or a specific inhibitor tubacin resulted in reduced viral replication suggesting proviral role of its deacetylase activity. Further investigations uncovered the interaction of HDAC6 with stress granule protein G3BP1 and N protein during infection. HDAC6-mediated deacetylation of SARS-CoV-2 N protein was found to be crucial for its association with G3BP1.

CONCLUSION

This study provides valuable insights into the molecular mechanisms underlying the disruption of cytoplasmic stress granules during SARS-CoV-2 infection and highlights the significance of HDAC6 in the process.

Identification of key regulatory genes in the pathogenesis of COVID-19 and sepsis: An observational study

Patients with severe COVID-19 and those with sepsis have similar clinical manifestations. We used bioinformatics methods to identify the common hub genes in these 2 diseases. Two RNA-seq datasets from the Gene Expression Omnibus were used to identify common differentially expressed genes (DEGs) in COVID-19 and sepsis. These common genes were used for analysis of functional enrichment; pathway analysis; identification of associated transcription factors, metabolites, and miRNAs; and mapping of protein-protein interaction networks. The major hub genes of COVID-19 and sepsis were identified, and validation datasets were used to assess the value of these hub genes using receiver operating characteristic (ROC) curves. Analysis of the 800 common DEGs for COVID-19 and sepsis, as well as common transcription factors, miRNAs, and metabolites, demonstrated that the immune response had a key role in both diseases. DLGAP5, BUB1, CDK1, CCNB1, and BUB1B were the most important common hub genes. Analysis of a validation cohort indicated these 5 genes had significantly higher expression in COVID-19 patients and sepsis patients than in corresponding controls, and the area under the ROC curves ranged from 0.832 to 0.981 for COVID-19 and 0.840 to 0.930 for sepsis. We used bioinformatics tools to identify common DEGs, miRNAs, and transcription factors for COVID-19 and sepsis. The 5 identified hub genes had higher expression in validation cohorts of COVID-19 and sepsis. These genes had good or excellent diagnostic performance based on ROC analysis, and therefore have potential use as novel markers or therapeutic targets.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

Systems-wide view of host-pathogen interactions across COVID-19 severities using integrated omics analysis

The mechanisms explaining the variability in COVID-19 clinical manifestations (mild, moderate, and severe) are not fully understood. To identify key gene expression markers linked to disease severity, we employed an integrated approach, combining host-pathogen protein-protein interaction data and viral-induced host gene expression data. We analyzed an RNA-seq dataset from peripheral blood mononuclear cells across 12 projects representing the spectrum of disease severity. We identified genes showing differential expression across mild, moderate, and severe conditions. Enrichment analysis of the pathways in host proteins targeted by each of the SARS-CoV-2 proteins revealed a strong association with processes related to ribosomal biogenesis, translation, and translocation. Interestingly, most of these pathways and associated cellular machinery, including ribosomal biogenesis, ribosomal proteins, and translation, were upregulated in mild conditions but downregulated in severe cases. This suggests that COVID-19 exhibits a paradoxical host response, boosting host/viral translation in mild cases but slowing it in severe cases.

Exploration of the potential common pathogenic mechanisms in COVID-19 and silicosis by using bioinformatics and system biology

Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has provided daunting challenges to public healthcare systems globally. Although multiple studies have shown a close link between COVID-19 and other respiratory diseases, the inter-relational mechanisms between COVID-19 and silicosis remain unclear. This study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (budding uninhibited by benzimidazoles 1 [BUB1], protein regulator of cytokinesis 1 [PRC1], kinesin family member C1 [KIFC1], ribonucleotide reductase regulatory subunit M2 [RRM2], cyclin-dependent kinase inhibitor 3 [CDKN3], Cyclin B2 [CCNB2], and minichromosome maintenance complex component 6 [MCM6]) were involved in the interaction between COVID-19 and silicosis. We investigated how diverse microRNAs and transcription factors regulate these seven genes. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data from COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, molecular docking results reveal small molecular compounds that may improve COVID-19 and silicosis. The current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in Triple Negative Breast Cancer

Breast cancers exhibit substantial transcriptional heterogeneity, posing a significant challenge to the prediction of treatment response and prognostication of outcomes. Especially, translation of TNBC subtypes to the clinic remains a work in progress, in part because of a lack of clear transcriptional signatures distinguishing the subtypes. Our recent network-based approach, PathExt, demonstrates that global transcriptional changes in a disease context are likely mediated by a small number of key genes, and these mediators may better reflect functional or translationally relevant heterogeneity. We apply PathExt to 1059 BRCA tumors and 112 healthy control samples across 4 subtypes to identify frequent, key-mediator genes in each BRCA subtype. Compared to conventional differential expression analysis, PathExt-identified genes (1) exhibit greater concordance across tumors, revealing shared as well as BRCA subtype-specific biological processes, (2) better recapitulate BRCA-associated genes in multiple benchmarks, and (3) exhibit greater dependency scores in BRCA subtype-specific cancer cell lines. Single cell transcriptomes of BRCA subtype tumors reveal a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified TNBC subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target top novel genes potentially mediating drug resistance. Overall, PathExt applied to breast cancer refines previous views of gene expression heterogeneity and identifies potential mediators of TNBC subtypes, including potential therapeutic targets.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Identification of novel antiviral drug candidates using an optimized SARS-CoV-2 phenotypic screening platform

Reliable, easy-to-handle phenotypic screening platforms are needed for the identification of anti-SARS-CoV-2 compounds. Here, we present caspase 3/7 activity as a readout for monitoring the replication of SARS-CoV-2 isolates from different variants, including a remdesivir-resistant strain, and of other coronaviruses in numerous cell culture models, independently of cytopathogenic effect formation. Compared to other models, the Caco-2 subline Caco-2-F03 displayed superior performance. It possesses a stable SARS-CoV-2 susceptibility phenotype and does not produce false-positive hits due to drug-induced phospholipidosis. A proof-of-concept screen of 1,796 kinase inhibitors identified known and novel antiviral drug candidates including inhibitors of phosphoglycerate dehydrogenase (PHGDH), CDC like kinase 1 (CLK-1), and colony stimulating factor 1 receptor (CSF1R). The activity of the PHGDH inhibitor NCT-503 was further increased in combination with the hexokinase II (HK2) inhibitor 2-deoxy-D-glucose, which is in clinical development for COVID-19. In conclusion, caspase 3/7 activity detection in SARS-CoV-2-infected Caco-2-F03 cells provides a simple phenotypic high-throughput screening platform for SARS-CoV-2 drug candidates that reduces false-positive hits.