Association of Severe Influenza Virus Infections With CD226 (DNAM-1) Variants

PRRSV-1 induced lung lesion is associated with an imbalance between costimulatory and coinhibitory immune checkpoints

Porcine reproductive and respiratory syndrome virus (PRRSV) induces a dysregulation on the innate and adaptive immune responses. T-cell activation requires a proper interaction and precise balance between costimulatory and coinhibitory molecules, commonly known as immune checkpoints. This study aims to evaluate the expression of immune checkpoints in lung and tracheobronchial lymph node from piglets infected with two PRRSV-1 strains of different virulence during the early stage of infection. Seventy 4-week-old piglets were grouped into three experimental groups: (i) control, (ii) 3249-infected group (low virulent strain), and (iii) Lena-infected group (virulent strain) and were euthanized at 1, 3, 6, 8, and 13 days post-infection (dpi). Lung and tracheobronchial lymph node were collected to evaluate histopathological findings, PRRSV viral load and mRNA expression of costimulatory (CD28, CD226, TNFRSF9, SELL, ICOS, and CD40) and coinhibitory (CTLA4, TIGIT, PD1/PDL1, TIM3, LAG3, and IDO1) molecules through RT-qPCR. Our findings highlight a mild increase of costimulatory molecules together with an earlier and stronger up-regulation of coinhibitory molecules in both organs from PRRSV-1-infected animals, especially in the lung from virulent Lena-infected animals. The simultaneous expression of coinhibitory immune checkpoints could work in synergy to control and limit the inflammation-induced tissue damage. Further studies should be addressed to determine the role of these molecules in later stages of PRRSV infection.

Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins

Whether risk genes of severe coronavirus disease 2019 (COVID-19) from genome-wide association study could play their regulatory roles by interacting with host genes that were interacted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins was worthy of exploration. In this study, we implemented a network-based approach by developing a user-friendly software Network Calculator (https://github.com/Haoxiang-Qi/Network-Calculator.git). By using Network Calculator, we identified a network composed of 13 risk genes and 28 SARS-CoV-2 interacted host genes that had the highest network proximity with each other, with a hub gene HNRNPK identified. Among these genes, 14 of them were identified to be differentially expressed in RNA-seq data from severe COVID-19 cases. Besides, by expression enrichment analysis in single-cell RNA-seq data, compared with mild COVID-19, these genes were significantly enriched in macrophage, T cell and epithelial cell for severe COVID-19. Meanwhile, 74 pathways were significantly enriched. Our analysis provided insights for the underlying genetic etiology of severe COVID-19 from the perspective of network biology.

De-coding genetic risk variants in type 1 diabetes

The conceptual basis for a genetic predisposition underlying the risk for developing type 1 diabetes (T1D) predates modern human molecular genetics. Over half of the genetic risk has been attributed to the human leukocyte antigen (HLA) class II gene region and to the insulin (INS) gene locus - both thought to confer direction of autoreactivity and tissue specificity. Notwithstanding, questions still remain regarding the functional contributions of a vast array of minor polygenic risk variants scattered throughout the genome that likely influence disease heterogeneity and clinical outcomes. Herein, we summarize the available literature related to the T1D-associated coding variants defined at the time of this review, for the genes PTPN22, IFIH1, SH2B3, CD226, TYK2, FUT2, SIRPG, CTLA4, CTSH and UBASH3A. Data from genotype-selected human cohorts are summarized, and studies from the non-obese diabetic (NOD) mouse are presented to describe the functional impact of these variants in relation to innate and adaptive immunity as well as to β-cell fragility, with expression profiles in tissues and peripheral blood highlighted. The contribution of each variant to progression through T1D staging, including environmental interactions, are discussed with consideration of how their respective protein products may serve as attractive targets for precision medicine-based therapeutics to prevent or suspend the development of T1D.

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.

Intoxication With Endogenous Angiotensin II: A COVID-19 Hypothesis

Severe acute respiratory syndrome coronavirus 2 has spread rapidly around the globe. However, despite its high pathogenicity and transmissibility, the severity of the associated disease, COVID-19, varies widely. While the prognosis is favorable in most patients, critical illness, manifested by respiratory distress, thromboembolism, shock, and multi-organ failure, has been reported in about 5% of cases. Several studies have associated poor COVID-19 outcomes with the exhaustion of natural killer cells and cytotoxic T cells, lymphopenia, and elevated serum levels of D-dimer. In this article, we propose a common pathophysiological denominator for these negative prognostic markers, endogenous, angiotensin II toxicity. We hypothesize that, like in avian influenza, the outlook of COVID-19 is negatively correlated with the intracellular accumulation of angiotensin II promoted by the viral blockade of its degrading enzyme receptors. In this model, upregulated angiotensin II causes premature vascular senescence, leading to dysfunctional coagulation, and immunity. We further hypothesize that angiotensin II blockers and immune checkpoint inhibitors may be salutary for COVID-19 patients with critical illness by reversing both the clotting and immune defects (Graphical Abstract).