Identification of key genes and pathways in regulating immune‑induced diseases of dendritic cells by bioinformatic analysis

The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: An in-silico quest

Introduction

The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes.

Methods

Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI's gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results.

Results

The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events.

Discussion

The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.

Genetic perturbation of IFN-α transcriptional modulators in human endothelial cells uncovers pivotal regulators of angiogenesis

Interferon-α (IFN-α) comprises a family of 13 cytokines involved in the modulation of antiviral, immune, and anticancer responses by orchestrating a complex transcriptional network. The activation of IFN-α signaling pathway in endothelial cells results in decreased proliferation and migration, ultimately leading to suppression of angiogenesis. In this study, we knocked-down the expression of seven established or candidate modulators of IFN-α response in endothelial cells to reconstruct a gene regulatory network and to investigate the antiangiogenic activity of IFN-α. This genetic perturbation approach, along with the analysis of interferon-induced gene expression dynamics, highlighted a complex and highly interconnected network, in which the angiostatic chemokine C-X-C Motif Chemokine Ligand 10 (CXCL10) was a central node targeted by multiple modulators. IFN-α-induced secretion of CXCL10 protein by endothelial cells was blunted by the silencing of Signal Transducer and Activator of Transcription 1 (STAT1) and of Interferon Regulatory Factor 1 (IRF1) and it was exacerbated by the silencing of Ubiquitin Specific Peptidase 18 (USP18). In vitro sprouting assay, which mimics in vivo angiogenesis, confirmed STAT1 as a positive modulator and USP18 as a negative modulator of IFN-α-mediated sprouting suppression. Our data reveal an unprecedented physiological regulation of angiogenesis in endothelial cells through a tonic IFN-α signaling, whose enhancement could represent a viable strategy to suppress tumor neoangiogenesis.

Construction and analysis of the lncRNA‑miRNA‑mRNA network based on competitive endogenous RNA reveals functional genes in heart failure

Heart failure (HF) is a principal cause of morbidity and mortality worldwide, affecting an estimated 38 million people. Although significant progress has been made with respect to the underlying molecular mechanisms, the role of the competing endogenous RNA (ceRNA) network in the pathogenesis of HF remains largely unknown. In this study, an HF-associated ceRNA network was constructed based on the differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs obtained, respectively, from the GSE77399, GSE104150 and GSE84796 datasets. The ceRNA network consisted of 12 lncRNA nodes, 43 miRNA nodes, 343 mRNA nodes and 530 edges. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that the ceRNA network was primarily enriched in the immune response, inflammatory response and T cell and B cell receptor signaling pathways. In addition, three lncRNAs (growth arrest specific 5, taurine upregulated 1 and HOX transcript antisense RNA) and three miRNAs [hsa-miRNA (miR)-26b-5p, hsa-miR-8485 and hsa-miR-940] with higher node degrees compared with other genes were selected as hub nodes. The expression of hub nodes in patients with HF was verified by reverse transcription-quantitative polymerase chain reaction analysis. The present study provided further insights into the important roles of the ceRNA network in HF development, and indicated the potential use of these hub nodes as diagnostic biomarkers and therapeutic targets.