Therapeutic Targeting of Transcription Factors to Control the Cytokine Release Syndrome in COVID-19

A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection

Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.

The use of bioinformatics methods to identify the effects of SARS-CoV-2 and influenza viruses on the regulation of gene expression in patients

Background

SARS-CoV-2 infection is a respiratory infectious disease similar to influenza virus infection. Numerous studies have reported similarities and differences in the clinical manifestations, laboratory tests, and mortality between these two infections. However, the genetic effects of coronavirus and influenza viruses on the host that lead to these characteristics have rarely been reported.

Methods

COVID-19 (GSE157103) and influenza (GSE111368, GSE101702) datasets were downloaded from the Gene Expression Ominbus (GEO) database. Differential gene, gene set enrichment, protein-protein interaction (PPI) network, gene regulatory network, and immune cell infiltration analyses were performed to identify the critical impact of COVID-19 and influenza viruses on the regulation of host gene expression.

Results

The number of differentially expressed genes in the COVID-19 patients was significantly higher than in the influenza patients. 22 common differentially expressed genes (DEGs) were identified between the COVID-19 and influenza datasets. The effects of the viruses on the regulation of host gene expression were determined using gene set enrichment and PPI network analyses. Five HUB genes were finally identified: IFI27, OASL, RSAD2, IFI6, and IFI44L.

Conclusion

We identified five HUB genes between COVID-19 and influenza virus infection, which might be helpful in the diagnosis and treatment of COVID-19 and influenza. This knowledge may also guide future mechanistic studies that aim to identify pathogen-specific interventions.

Single-Cell Gene Expression Analysis Revealed Immune Cell Signatures of Delta COVID-19

The coronavirus disease 2019 (COVID-19) is accompanied by a cytokine storm with the release of many proinflammatory factors and development of respiratory syndrome. Several SARS-CoV-2 lineages have been identified, and the Delta variant (B.1.617), linked with high mortality risk, has become dominant in many countries. Understanding the immune responses associated with COVID-19 lineages may therefore aid the development of therapeutic and diagnostic strategies. Multiple single-cell gene expression studies revealed innate and adaptive immunological factors and pathways correlated with COVID-19 severity. Additional investigations covering host–pathogen response characteristics for infection caused by different lineages are required. Here, we performed single-cell transcriptome profiling of blood mononuclear cells from the individuals with different severity of the COVID-19 and virus lineages to uncover variant specific molecular factors associated with immunity. We identified significant changes in lymphoid and myeloid cells. Our study highlights that an abundant population of monocytes with specific gene expression signatures accompanies Delta lineage of SARS-CoV-2 and contributes to COVID-19 pathogenesis inferring immune components for targeted therapy.

PLK4 inhibitor plus bortezomib exhibits a synergistic effect on treating multiple myeloma via inactivating PI3K/AKT signaling

OBJECTIVE

The anti-tumor effect of polo-like kinase 4 (PLK4) inhibitor has been explored in several neoplasms, while its synergy with bortezomib in multiple myeloma (MM) remains elusive. Hence, the present study aimed to investigate the effect of PLK4 inhibitor on the sensitivity of MM to bortezomib treatment and its underlying mechanism.

METHODS

MM cell lines (RPMI-8226 and U266) were cultured in different concentrations of CFI-400945 (PLK4 inhibitor), bortezomib, or their combination. Subsequently, 740 Y-P (PI3K activator) was added in the combination of CFI-400945 and bortezomib. Besides, cell viability and apoptosis were measured by CCK-8 reagent and TUNEL apoptosis kit, separately; meanwhile, western blot was carried out for detecting PLK4, p-PI3K, PI3K, p-AKT, and AKT.

RESULTS

CFI-400945 and bortezomib decreased the cell viability in dose-dependent manners in MM cell lines, respectively. The combination of different concentrations of CFI-400945 and bortezomib reduced cell viability compared with monotherapy in MM cell lines (all P < 0.05). Interestingly, 200 nM CFI-400945 and 4 nM bortezomib showed the maximum synergy in MM cell lines. Furthermore, 200 nM CFI-400945 plus 4 nM bortezomib showed a better effect on decreasing cell viability and promoting cell apoptosis than CFI-400945 or bortezomib monotherapy in MM cells cell lines (all P < 0.05). Moreover, 740 Y-P alleviated the effect of bortezomib and CFI-400945 on PI3K/AKT signaling, cell viability, and apoptosis in MM cell lines.

CONCLUSIONS

PLK4 inhibitor plus bortezomib shows synergy in decreasing cell viability and enhancing cell apoptosis via repressing PI3K/AKT signaling in MM.

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

Background

The 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.

Methods

RNA-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.

Results

Based 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.

Conclusion

This 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.

Co-Regulation of Protein Coding Genes by Transcription Factor and Long Non-Coding RNA in SARS-CoV-2 Infected Cells: An In Silico Analysis

Altered expression of protein coding gene (PCG) and long non-coding RNA (lncRNA) have been identified in SARS-CoV-2 infected cells and tissues from COVID-19 patients. The functional role and mechanism (s) of transcriptional regulation of deregulated genes in COVID-19 remain largely unknown. In the present communication, reanalyzing publicly available gene expression data, we observed that 66 lncRNA and 5491 PCG were deregulated in more than one experimental condition. Combining our earlier published results and using different publicly available resources, it was observed that 72 deregulated lncRNA interacted with 3228 genes/proteins. Many targets of deregulated lncRNA could also interact with SARS-CoV-2 coded proteins, modulated by IFN treatment and identified in CRISPR screening to modulate SARS-CoV-2 infection. The majority of the deregulated lncRNA and PCG were targets of at least one of the transcription factors (TFs), interferon responsive factors (IRFs), signal transducer, and activator of transcription (STATs), NFκB, MYC, and RELA/p65. Deregulated 1069 PCG was joint targets of lncRNA and TF. These joint targets are significantly enriched with pathways relevant for SARS-CoV-2 infection indicating that joint regulation of PCG could be one of the mechanisms for deregulation. Over all this manuscript showed possible involvement of lncRNA and mechanisms of deregulation of PCG in the pathogenesis of COVID-19.