Hypothesis: Is there a link between the immune response to Human Herpes Virus type 6Α (HHV-6Α) infection and the interaction network (interactome) of the genes encoding the CTSS, PTX3, CHI3L1, Mx1, CXCL16, BIRC3 and BST2 proteins?

Differing coronavirus genres alter shared host signaling pathways upon viral infection

Coronaviruses are important viral pathogens across a range of animal species including humans. They have a high potential for cross-species transmission as evidenced by the emergence of COVID-19 and may be the origin of future pandemics. There is therefore an urgent need to study coronaviruses in depth and to identify new therapeutic targets. This study shows that distant coronaviruses such as Alpha-, Beta-, and Deltacoronaviruses can share common host immune associated pathways and genes. Differentially expressed genes (DEGs) in the transcription profile of epithelial cell lines infected with swine acute diarrhea syndrome, severe acute respiratory syndrome coronavirus 2, or porcine deltacoronavirus, showed that DEGs within 10 common immune associated pathways were upregulated upon infection. Twenty Three pathways and 21 DEGs across 10 immune response associated pathways were shared by these viruses. These 21 DEGs can serve as focused targets for therapeutics against newly emerging coronaviruses. We were able to show that even though there is a positive correlation between PDCoV and SARS-CoV-2 infections, these viruses could be using different strategies for efficient replication in their cells from their natural hosts. To the best of our knowledge, this is the first report of comparative host transcriptome analysis across distant coronavirus genres.

Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is one of the most severe global pandemic due to its high pathogenicity and death rate starting from the end of 2019. Though there are some vaccines available against SAER-CoV-2 infections, we are worried about their effectiveness, due to its unstable sequence patterns. Therefore, beside vaccines, globally effective supporting drugs are also required for the treatment against SARS-CoV-2 infection. To explore commonly effective repurposable drugs for the treatment against different variants of coronavirus infections, in this article, an attempt was made to explore host genomic biomarkers guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. At first, we identified 138 differentially expressed genes (DEGs) between SARS-CoV-1 infected and control samples by analyzing high throughput gene-expression profiles to select drug target key receptors. Then we identified top-ranked 11 key DEGs (SMAD4, GSK3B, SIRT1, ATM, RIPK1, PRKACB, MED17, CCT2, BIRC3, ETS1 and TXN) as hub genes (HubGs) by protein-protein interaction (PPI) network analysis of DEGs highlighting their functions, pathways, regulators and linkage with other disease risks that may influence SARS-CoV-1 infections. The DEGs-set enrichment analysis significantly detected some crucial biological processes (immune response, regulation of angiogenesis, apoptotic process, cytokine production and programmed cell death, response to hypoxia and oxidative stress), molecular functions (transcription factor binding and oxidoreductase activity) and pathways (transcriptional mis-regulation in cancer, pathways in cancer, chemokine signaling pathway) that are associated with SARS-CoV-1 infections as well as SARS-CoV-2 infections by involving HubGs. The gene regulatory network (GRN) analysis detected some transcription factors (FOXC1, GATA2, YY1, FOXL1, TP53 and SRF) and micro-RNAs (hsa-mir-92a-3p, hsa-mir-155-5p, hsa-mir-106b-5p, hsa-mir-34a-5p and hsa-mir-19b-3p) as the key transcriptional and post- transcriptional regulators of HubGs, respectively. We also detected some chemicals (Valproic Acid, Cyclosporine, Copper Sulfate and arsenic trioxide) that may regulates HubGs. The disease-HubGs interaction analysis showed that our predicted HubGs are also associated with several other diseases including different types of lung diseases. Then we considered 11 HubGs mediated proteins and their regulatory 6 key TFs proteins as the drug target proteins (receptors) and performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 anti-viral drugs out of 3410. We found Rapamycin, Tacrolimus, Torin-2, Radotinib, Danoprevir, Ivermectin and Daclatasvir as the top-ranked 7 candidate-drugs with respect to our proposed target proteins for the treatment against SARS-CoV-1 infections. Then, we validated these 7 candidate-drugs against the already published top-ranked 11 target proteins associated with SARS-CoV-2 infections by molecular docking simulation and found their significant binding affinity scores with our proposed candidate-drugs. Finally, we validated all of our findings by the literature review. Therefore, the proposed candidate-drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections with comorbidities, since the proposed HubGs are also associated with several comorbidities.

The transcriptomic profiling of SARS-CoV-2 compared to SARS, MERS, EBOV, and H1N1

The SARS-CoV-2 (COVID-19) pandemic is a global crisis that threatens our way of life. As of November 18, 2020, SARS-CoV-2 has claimed more than 1,342,709 lives, with a global mortality rate of ~2.4% and a recovery rate of ~69.6%. Understanding the interaction of cellular targets with the SARS-CoV-2 infection is crucial for therapeutic development. Therefore, the aim of this study was to perform a comparative analysis of transcriptomic signatures of infection of SARS-CoV-2 compared to other respiratory viruses (EBOV, H1N1, MERS-CoV, and SARS-CoV), to determine a unique anti-SARS-CoV-2 gene signature. We identified for the first time that molecular pathways for heparin-binding, RAGE, miRNA, and PLA2 inhibitors were associated with SARS-CoV-2 infection. The NRCAM and SAA2 genes, which are involved in severe inflammatory responses, and the FGF1 and FOXO1 genes, which are associated with immune regulation, were found to be associated with the cellular gene response to SARS-CoV-2 infection. Moreover, several cytokines, most significantly IL-8 and IL-6, demonstrated key associations with SARS-CoV-2 infection. Interestingly, the only response gene that was shared among the five viral infections was SERPINB1. The protein-protein interaction (PPI) analysis shed light on genes with high interaction activity that SARS-CoV-2 shares with other viral infections. The findings showed that the genetic pathways associated with rheumatoid arthritis, the AGE-RAGE signaling system, malaria, hepatitis B, and influenza A were of high significance. We found that the virogenomic transcriptome of infection, gene modulation of host antiviral responses, and GO terms of SARS-CoV-2 and EBOV were more similar than to SARS, H1N1, and MERS. This work compares the virogenomic signatures of highly pathogenic viruses and provides valid targets for potential therapy against SARS-CoV-2.

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection is a leading cause of pneumonia and death. The aim of this investigation is to identify the key genes in SARS-CoV-2 infection and uncover their potential functions. We downloaded the expression profiling by high throughput sequencing of GSE152075 from the Gene Expression Omnibus database. Normalization of the data from primary SARS-CoV-2 infected samples and negative control samples in the database was conducted using R software. Then, joint analysis of the data was performed. Pathway and Gene ontology (GO) enrichment analyses were performed, and the protein-protein interaction (PPI) network, target gene - miRNA regulatory network, target gene - TF regulatory network of the differentially expressed genes (DEGs) were constructed using Cytoscape software. Identification of diagnostic biomarkers was conducted using receiver operating characteristic (ROC) curve analysis. 994 DEGs (496 up regulated and 498 down regulated genes) were identified. Pathway and GO enrichment analysis showed up and down regulated genes mainly enriched in the NOD-like receptor signaling pathway, Ribosome, response to external biotic stimulus and viral transcription in SARS-CoV-2 infection. Down and up regulated genes were selected to establish the PPI network, modules, target gene - miRNA regulatory network, target gene - TF regulatory network revealed that these genes were involved in adaptive immune system, fluid shear stress and atherosclerosis, influenza A and protein processing in endoplasmic reticulum. In total, ten genes (CBL, ISG15, NEDD4, PML, REL, CTNNB1, ERBB2, JUN, RPS8 and STUB1) were identified as good diagnostic biomarkers. In conclusion, the identified DEGs, hub genes and target genes contribute to the understanding of the molecular mechanisms underlying the advancement of SARS-CoV-2 infection and they may be used as diagnostic and molecular targets for the treatment of patients with SARS-CoV-2 infection in the future.

Interactome networks between the human respiratory syncytial virus (HRSV), the human metapneumovirus (ΗMPV), and their host: In silico investigation and comparative functional enrichment analysis

BACKGROUND AND OBJECTIVES

Human respiratory syncytial virus (HRSV) and human metapneumovirus (HMPV) are leading causes of upper and lower respiratory tract infections in non-immunocompetent subjects, yet the mechanisms by which they induce their pathogenicity differ significantly and remain elusive. In this study we aimed at identifying the gene interaction networks between the HRSV, HMPV respiratory pathogens and their host along with the different cell-signaling pathways associated with the above interactomes.

MATERIALS AND METHODS

The Viruses STRING database (http://viruses.string-db.org/) was used for the identification of the host-viruses interaction networks. The two lists of the predicted functional partners were entered in the FunRich tool (http://www.funrich.org) for the construction of the Venn diagram and the comparative Funcional Enrichment Analysis (FEA) with respect to biological pathways. The sets of the common and unique human genes identified in the two networks were also analyzed. The computational predictions regarding the shared human genes in the host-HRSV and the host-HMPV interactomes were further evaluated via the analysis of the GSE111732 dataset. miRNA transcriptomics data were mapped to gene targets using the miRNomics pipeline of the GeneTrail2 database (https://genetrail2.bioinf.uni-sb.de/).

RESULTS

Eleven out of twenty predicted human genes were common in the two interactomes (TLR4, SOCS3, SFXN1, AKT1, SFXN3, LY96, SFXN2, SOCS7, CISH, SOCS6, SOCS1). FEA of these common genes identified the kit receptor and the GH receptor signaling pathways as the most significantly enriched annotations. The remaining nine genes of the host-HRSV and the host-HMPV interaction networks were the IFIH1, DDX58, NCL, IRF3, STAT2, HSPA4, CD209, KLF6, CHKA and the MYD88, SOCS4, SOCS2, SOCS5 AKT2, AKT3, SFXN4, SFXN5 and TLR3 respectively. Distinct cell-signaling pathways were enriched per interactome. The comparative FEA highlighted the association of the host-HRSV functional partners with the negative regulation of RIG-I/MDA5 signaling. The analysis with respect to miRNAs mapping to gene targets of the GSE111732 dataset indicated that nine out of the eleven common host genes are either enriched or depleted in the sample sets (HRSV or HMPV infected) as compared with the reference set (non-infected), although with no significant scores.

CONCLUSIONS

We have identified both shared and unique host genes as members of the HRSV and HMPV interaction networks. The disparate human genes likely contribute to distinct responses in airway epithelial cells.