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

Host transcriptome response to Mycoplasma bovis and bovine viral diarrhea virus in bovine tissues

BACKGROUND

Mycoplasma bovis is a prominent pathogen associated with respiratory disease in livestock. Respiratory disease in cattle often involves co-infection, where a primary viral infection can weaken the host immune system and thus enhance subsequent bacterial infection. The objective of this study was to investigate changes in the host (cattle) transcriptome during bacterial-viral co-infection. RNA sequencing was done in whole blood cells (WBC), liver, mesenteric lymph node (MLN), tracheal-bronchial lymph node (TBLN), spleen, and thymus collected from Control animals (n = 2), animals infected with M. bovis (MB; n = 3), and animals infected with M. bovis and bovine viral diarrhea virus (BVDV) (Dual; n = 3).

RESULTS

Thymus and spleen had the greatest number of differentially expressed genes (DEGs) out of all tissues analyzed. In spleen, genes involved in maintenance of the extracellular matrix (ECM) including collagen type XV alpha 1 chain (COL15A1), collagen type IV alpha 2 chain (COL4A2), and heparan sulfate proteoglycan 2 (HSPG2) were the most significantly downregulated in Dual compared to Control and MB. In thymus, complement 3 (C3) was a highly significant DEG and upregulated in Dual compared to Control and MB. Interferon alpha inducible protein 6 (IFI6) and interferon-induced transmembrane proteins (IFITM1 and IFITM3), were significantly associated with infection status and upregulated in spleen and thymus of Dual compared to Control and MB.

CONCLUSION

Downregulation of ECM components may cause degradation of the ECM and contribute to increased viral spread due to co-infection. Hyperactivation of complement pathway genes may contribute to damage to the thymus and influence severity of co-infection. Co-expression of IFI6, IFITM1 and IFITM3 across lymphoid tissues may be connected to enhanced pathogenesis in co-infection. These findings suggest co-infection exacerbates disease severity through modulation of ECM components in spleen and complement and coagulation cascades in the thymus. These impacted pathways may underlie thymic atrophy and impaired pathogen clearance due to BVDV and M. bovis co-infection.

PEG-SeNPs as therapeutic agents inhibiting apoptosis and inflammation of cells infected with H1N1 influenza A virus

The rapid variation of influenza challenges vaccines and treatments, which makes an urgent task to develop the high-efficiency and low-toxicity new anti-influenza virus drugs. Selenium is one of the essential trace elements for the human body that possesses a good antiviral activity. In this study, we assessed anti-influenza A virus (H1N1) activity of polyethylene glycol (PEG)-modified gray selenium nanoparticles (PEG-SeNPs) on Madin-Darby Canine Kidney (MDCK) cells in vitro. CCK-8 assay showed that PEG-SeNPs had a protective effect on H1N1-infected MDCK cells. Moreover, PEG-SeNPs significantly reduced the mRNA level of H1N1. TUNEL-DAPI test showed that DNA damage reached a high level but effectively prevented after PEG-SeNPs treatment. Meanwhile, JC-1, Annexin V-FITC and cell cycle assay demonstrated the apoptosis induced by H1N1 was reduced greatly when treated with PEG-SeNPs. Furthermore, the downregulation of p-ATM, p-ATR and P53 protein, along with the upregualation of AKT protein indicated that PEG-SeNPs could inhibit H1N1-induced cell apoptosis through reactive oxygen species (ROS)-mediated related signaling pathways. Finally, Cytokine detection demonstrated PEG-SeNPs inhibited the production of pro-inflammatory factors after infection, including IL-1β, IL-5, IL-6, and TNF-α. To sum up, PEG-SeNPs might become a new potential anti-H1N1 influenza virus drug due to its antiviral and anti-inflammatory activity.

A causal multiomics study discriminates the early immune features of Ad5-vectored Ebola vaccine recipients

The vaccine-induced innate immune response is essential for the generation of an antibody response. To date, how Ad5-vectored vaccines are influenced by preexisting anti-Ad5 antibodies during activation of the early immune response remains unclear. Here, we investigated the specific alterations in GP1,2-specific IgG-related elements of the early immune response at the genetic, molecular, and cellular levels on days 0, 1, 3, and 7 after Ad5-EBOV vaccination. In a causal multiomics analysis, distinct early immune responses associated with GP1,2-specific IgG were observed in Ad5-EBOV recipients with a low level of preexisting anti-Ad5 antibodies. This study revealed the correlates of the Ad5-EBOV-induced IgG response and provided mechanistic evidence for overcoming preexisting Ad5 immunity during the administration of Ad5-vectored vaccines.

Bioinformatics and system biology approach to identify the influences among COVID-19, influenza, and HIV on the regulation of gene expression

Background

Coronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses.

Methods

Sequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes.

Results

The analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified.

Conclusion

This research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

Discovering common pathogenetic processes between COVID-19 and tuberculosis by bioinformatics and system biology approach

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, stemming from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has persistently threatened the global health system. Meanwhile, tuberculosis (TB) caused by Mycobacterium tuberculosis (M. tuberculosis) still continues to be endemic in various regions of the world. There is a certain degree of similarity between the clinical features of COVID-19 and TB, but the underlying common pathogenetic processes between COVID-19 and TB are not well understood.

Methods

To elucidate the common pathogenetic processes between COVID-19 and TB, we implemented bioinformatics and systematic research to obtain shared pathways and molecular biomarkers. Here, the RNA-seq datasets (GSE196822 and GSE126614) are used to extract shared differentially expressed genes (DEGs) of COVID-19 and TB. The common DEGs were used to identify common pathways, hub genes, transcriptional regulatory networks, and potential drugs.

Results

A total of 96 common DEGs were selected for subsequent analyses. Functional enrichment analyses showed that viral genome replication and immune-related pathways collectively contributed to the development and progression of TB and COVID-19. Based on the protein-protein interaction (PPI) network analysis, we identified 10 hub genes, including IFI44L, ISG15, MX1, IFI44, OASL, RSAD2, GBP1, OAS1, IFI6, and HERC5. Subsequently, the transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory network identified 61 TFs and 29 miRNAs. Notably, we identified 10 potential drugs to treat TB and COVID-19, namely suloctidil, prenylamine, acetohexamide, terfenadine, prochlorperazine, 3'-azido-3'-deoxythymidine, chlorophyllin, etoposide, clioquinol, and propofol.

Conclusion

This research provides novel strategies and valuable references for the treatment of tuberculosis and COVID-19.