Analysis of the potential relationship between COVID-19 and Behcet's disease using transcriptome data.

To investigate the potential role of COVID-19 in relation to Behcet's disease (BD) and to search for relevant biomarkers. We used a bioinformatics approach to download transcriptomic data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and PBMCs of BD patients, screened the common differential genes between COVID-19 and BD, performed gene ontology (GO) and pathway analysis, and constructed the protein-protein interaction (PPI) network, screened the hub genes and performed co-expression analysis. In addition, we constructed the genes-transcription factors (TFs)-miRNAs network, the genes-diseases network and the genes-drugs network to gain insight into the interactions between the 2 diseases. We used the RNA-seq dataset from the GEO database (GSE152418, GSE198533). We used cross-analysis to obtain 461 up-regulated common differential genes and 509 down-regulated common differential genes, mapped the PPI network, and used Cytohubba to identify the 15 most strongly associated genes as hub genes (ACTB, BRCA1, RHOA, CCNB1, ASPM, CCNA2, TOP2A, PCNA, AURKA, KIF20A, MAD2L1, MCM4, BUB1, RFC4, and CENPE). We screened for statistically significant hub genes and found that ACTB was in low expression of both BD and COVID-19, and ASPM, CCNA2, CCNB1, and CENPE were in low expression of BD and high expression of COVID-19. GO analysis and pathway analysis was then performed to obtain common pathways and biological response processes, which suggested a common association between BD and COVID-19. The genes-TFs-miRNAs network, genes-diseases network and genes-drugs network also play important roles in the interaction between the 2 diseases. Interaction between COVID-19 and BD exists. ACTB, ASPM, CCNA2, CCNB1, and CENPE as potential biomarkers for 2 diseases.

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19.

Background: Corona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients. Methods: The four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockTools. Results: Firstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2. Conclusion: In the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2.

Association between mannose binding lectin gene polymorphisms and clinical severity of COVID-19 in children.

BACKGROUND: Mannose-binding lectin (MBL) is a member of innate immunity and acts with MASP (MBL-associated serine protease) to activate the lectin pathway of the complement system. MBL gene polymorphisms are associated with susceptibility to infectious diseases. This study investigated whether MBL2 genotype, serum MBL levels, and serum MASP-2 levels affect the course of SARS-CoV-2 infection. METHODS AND RESULTS: Pediatric patients diagnosed with COVID-19 by positive real-time polymerase chain reaction (PCR) were included in the study. Single nucleotide polymorphisms in the promoter and exon 1 in the MBL2 gene (rs11003125, rs7096206, rs1800450, rs1800451, rs5030737) were identified by a PCR and restriction fragment length polymorphisms analysis. Serum MBL and MASP-2 levels were measured by ELISA. COVID-19 patients were divided into asymptomatic and symptomatic. Variables were compared between these two groups. A total of 100 children were included in the study. The mean age of the patients was 130 ± 67.2 months. Of the patients, 68 (68%) were symptomatic, and 32 (32%) were asymptomatic. The polymorphisms in the - 221nt and - 550nt promoter regions did not differ between groups (p > 0.05). All codon 52 and codon 57 genotypes were determined as wild-type AA. AB genotypes were found 45.6% in symptomatic patients while 23.5% in asymptomatics. Moreover, BB genotype was detected 9.4% in symptomatic and 6.3% in asymptomatic patients (p < 0.001). B allele was more frequent in symptomatic patients (46.3%) compared to asymptomatic patients (10.9%). (p < 0.001). Serum MBL and MASP-2 levels did not differ statistically between the groups (p = 0.295, p = 0.073). CONCLUSION: These findings suggest that codon 54 polymorphism in the MBL2 gene exon-1 region can be associated with the symptomatic course of COVID-19.

Identification and Profiling of a Novel Bombyx mori latent virus Variant Acutely Infecting Helicoverpa armigera and Trichoplusia ni.

Insect cell expression systems are increasingly being used in the medical industry to develop vaccines against diseases such as COVID-19. However, viral infections are common in these systems, making it necessary to thoroughly characterize the viruses present. One such virus is Bombyx mori latent virus (BmLV), which is known to be specific to Bombyx mori and to have low pathogenicity. However, there has been little research on the tropism and virulence of BmLV. In this study, we examined the genomic diversity of BmLV and identified a variant that persistently infects Trichoplusia ni-derived High Five cells. We also assessed the pathogenicity of this variant and its effects on host responses using both in vivo and in vitro systems. Our results showed that this BmLV variant causes acute infections with strong cytopathic effects in both systems. Furthermore, we characterized the RNAi-based immune response in the T. ni cell line and in Helicoverpa armigera animals by assessing the regulation of RNAi-related genes and profiling the generated viral small RNAs. Overall, our findings shed light on the prevalence and infectious properties of BmLV. We also discuss the potential impact of virus genomic diversity on experimental outcomes, which can help interpret past and future research results.

Enhancing SARS-CoV-2 Surveillance through Regular Genomic Sequencing in Spain: The RELECOV Network.

Millions of SARS-CoV-2 whole genome sequences have been generated to date. However, good quality data and adequate surveillance systems are required to contribute to meaningful surveillance in public health. In this context, the network of Spanish laboratories for coronavirus (RELECOV) was created with the main goal of promoting actions to speed up the detection, analyses, and evaluation of SARS-CoV-2 at a national level, partially structured and financed by an ECDC-HERA-Incubator action (ECDC/GRANT/2021/024). A SARS-CoV-2 sequencing quality control assessment (QCA) was developed to evaluate the network's technical capacity. QCA full panel results showed a lower hit rate for lineage assignment compared to that obtained for variants. Genomic data comprising 48,578 viral genomes were studied and evaluated to monitor SARS-CoV-2. The developed network actions showed a 36% increase in sharing viral sequences. In addition, analysis of lineage/sublineage-defining mutations to track the virus showed characteristic mutation profiles for the Delta and Omicron variants. Further, phylogenetic analyses strongly correlated with different variant clusters, obtaining a robust reference tree. The RELECOV network has made it possible to improve and enhance the genomic surveillance of SARS-CoV-2 in Spain. It has provided and evaluated genomic tools for viral genome monitoring and characterization that make it possible to increase knowledge efficiently and quickly, promoting the genomic surveillance of SARS-CoV-2 in Spain.

Exploration of the potential common pathogenic mechanisms in COVID-19 and silicosis by using bioinformatics and system biology.

Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has provided daunting challenges to public healthcare systems globally. Although multiple studies have shown a close link between COVID-19 and other respiratory diseases, the inter-relational mechanisms between COVID-19 and silicosis remain unclear. This study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (budding uninhibited by benzimidazoles 1 [BUB1], protein regulator of cytokinesis 1 [PRC1], kinesin family member C1 [KIFC1], ribonucleotide reductase regulatory subunit M2 [RRM2], cyclin-dependent kinase inhibitor 3 [CDKN3], Cyclin B2 [CCNB2], and minichromosome maintenance complex component 6 [MCM6]) were involved in the interaction between COVID-19 and silicosis. We investigated how diverse microRNAs and transcription factors regulate these seven genes. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data from COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, molecular docking results reveal small molecular compounds that may improve COVID-19 and silicosis. The current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.

DYRK1A promotes viral entry of highly pathogenic human coronaviruses in a kinase-independent manner.

Identifying host genes essential for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has the potential to reveal novel drug targets and further our understanding of Coronavirus Disease 2019 (COVID-19). We previously performed a genome-wide CRISPR/Cas9 screen to identify proviral host factors for highly pathogenic human coronaviruses. Few host factors were required by diverse coronaviruses across multiple cell types, but DYRK1A was one such exception. Although its role in coronavirus infection was previously undescribed, DYRK1A encodes Dual Specificity Tyrosine Phosphorylation Regulated Kinase 1A and is known to regulate cell proliferation and neuronal development. Here, we demonstrate that DYRK1A regulates ACE2 and DPP4 transcription independent of its catalytic kinase function to support SARS-CoV, SARS-CoV-2, and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) entry. We show that DYRK1A promotes DNA accessibility at the ACE2 promoter and a putative distal enhancer, facilitating transcription and gene expression. Finally, we validate that the proviral activity of DYRK1A is conserved across species using cells of nonhuman primate and human origin. In summary, we report that DYRK1A is a novel regulator of ACE2 and DPP4 expression that may dictate susceptibility to multiple highly pathogenic human coronaviruses.

Spreading processes with mutations over multilayer networks.

A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mutate in response to changing environments, especially caused by the increase in population immunity to existing strains, and the emergence of new pathogen strains poses a continued threat to public health. Further, in the light of differing transmission risks in different congregate settings (e.g., schools and offices), different mitigation strategies may need to be adopted to control the spread of infection. We analyze a multilayer multistrain model by simultaneously accounting for i) pathways for mutations in the pathogen leading to the emergence of new pathogen strains, and ii) differing transmission risks in different settings, modeled as network layers. Assuming complete cross-immunity among strains, namely, recovery from any infection prevents infection with any other (an assumption that will need to be relaxed to deal with COVID-19 or influenza), we derive the key epidemiological parameters for the multilayer multistrain framework. We demonstrate that reductions to existing models that discount heterogeneity in either the strain or the network layers may lead to incorrect predictions. Our results highlight that the impact of imposing/lifting mitigation measures concerning different contact network layers (e.g., school closures or work-from-home policies) should be evaluated in connection with their effect on the likelihood of the emergence of new strains.

Demographic, clinical and genetic factors associated with COVID-19 disease susceptibility and mortality in a Kurdish population.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a devastating pandemic that causes disease with a variability in susceptibility and mortality based on variants of various clinical and demographic factors, including particular genes among populations. OBJECTIVES: Determine associations of demographic, clinical, laboratory, and single nucleotide polymorphisms in the ACE2, TMPRSS2, TNF-α, and IFN-γ genes to the incidence of infection and mortality in COVID-19 patients. DESIGN: Prospective cohort study SETTINGS: Various cities in the Kurdistan Region of Iraq. PATIENTS AND METHODS: This prospective cohort study compared laboratory markers (D-dimer, tumor necrosis factor-alpha [TNF-α], interferon-gamma [IFN-γ], C-reactive protein [CRP], lymphocyte and neutrophil counts) between COVID-19 patients and healthy controls. DNA was extracted from blood, and genotypes were done by Sanger sequencing. MAIN OUTCOME MEASURES: Single nucleotide polymorphisms of the ACE2, TMPRSS2, TNF-α, and IFN-γ genes and demographic characteristics and laboratory markers for predicting mortality in COVID-19. SAMPLE SIZE: 203 (153 COVID-19 patients, 50 health control subjects). RESULTS: Forty-eight (31.4%) of the COVID-19 patients died. Age over 40 and comorbidities were risk factors for mortality, but the strongest associations were with serum IFN-γ, the neutrophil-to-lymphocyte ratio (NLR), and serum TNF-α. The AA genotype and A allele of TMPRSS2 rs2070788 decreased while the GA genotype and A allele of TNF-α increased susceptibility to COVID-19. Patients with the GA genotype of TNF-α rs1800629 had shorter survival times (9.9 days) than those carrying the GG genotype (18.3 days) (P<.0001 by log-rank test). The GA genotype versus the GG genotype was associated with higher levels of serum TNF-α. The GA genotype increased mortality rates by up to 3.8 fold. The survival rate for COVID-19 patients carrying the IFN-γ rs2430561 TT genotype (58.5%) was lower than in patients with the TA and AA genotypes (80.3%). The TT genotype increased the risk of death (HR=3.664, P<.0001) and was linked to high serum IFN-γ production. Olfactory dysfunction was a predictor of survival among COVID-19 patients. CONCLUSIONS: Age older than 40, comorbidities, the NLR and particular genotypes for and the IFN-γ and TNF-α genes were risk factors for death. Larger studies in different populations must be conducted to validate the possible role of particular SNPs as genetic markers for disease severity and mortality in COVID-19 disease. LIMITATIONS: Small sample size. CONFLICT OF INTEREST: None.

An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design.

Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.

CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning.

BACKGROUND: Literature about SARS-CoV-2 widely discusses the effects of variations that have spread in the past 3 years. Such information is dispersed in the texts of several research articles, hindering the possibility of practically integrating it with related datasets (e.g., millions of SARS-CoV-2 sequences available to the community). We aim to fill this gap, by mining literature abstracts to extract-for each variant/mutation-its related effects (in epidemiological, immunological, clinical, or viral kinetics terms) with labeled higher/lower levels in relation to the nonmutated virus. RESULTS: The proposed framework comprises (i) the provisioning of abstracts from a COVID-19-related big data corpus (CORD-19) and (ii) the identification of mutation/variant effects in abstracts using a GPT2-based prediction model. The above techniques enable the prediction of mutations/variants with their effects and levels in 2 distinct scenarios: (i) the batch annotation of the most relevant CORD-19 abstracts and (ii) the on-demand annotation of any user-selected CORD-19 abstract through the CoVEffect web application (http://gmql.eu/coveffect), which assists expert users with semiautomated data labeling. On the interface, users can inspect the predictions and correct them; user inputs can then extend the training dataset used by the prediction model. Our prototype model was trained through a carefully designed process, using a minimal and highly diversified pool of samples. CONCLUSIONS: The CoVEffect interface serves for the assisted annotation of abstracts, allowing the download of curated datasets for further use in data integration or analysis pipelines. The overall framework can be adapted to resolve similar unstructured-to-structured text translation tasks, which are typical of biomedical domains.

Genes Involved in miRNA Biogenesis Are Not Downregulated in SARS-CoV-2 Infection.

miRNAs, small non-coding RNAs that regulate gene expression, are involved in various pathological processes, including viral infections. Virus infections may interfere with the miRNA pathway through the inhibition of genes involved in miRNA biogenesis. A reduction in the number and the levels of miRNAs expressed in nasopharyngeal swabs of patients with severe COVID-19 was lately observed by us, pointing towards the potential of miRNAs as possible diagnostic or prognostic biomarkers for predicting outcomes among patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. The objective of the present study was to investigate whether SARS-CoV-2 infection influences the expression levels of messenger RNAs (mRNAs) of key genes involved in miRNA biogenesis. mRNA levels of AGO2, DICER1, DGCR8, DROSHA, and Exportin-5 (XPO5) were measured by quantitative reverse-transcription polymerase chain reaction (RT-qPCR) in nasopharyngeal swab specimens from patients with COVID-19 and controls, as well as in cells infected with SARS-CoV-2 in vitro. Our data showed that the mRNA expression levels of AGO2, DICER1, DGCR8, DROSHA, and XPO5 were not significantly different in patients with severe COVID-19 when compared to patients with non-severe COVID-19 and controls. Similarly, the mRNA expression of these genes was not affected by SARS-CoV-2 infection in NHBE and Calu-3 cells. However, in Vero E6 cells, AGO2, DICER1, DGCR8, and XPO5 mRNA levels were slightly upregulated 24 h after infection with SARS-CoV-2. In conclusion, we did not find evidence for downregulation of mRNA levels of miRNA biogenesis genes during SARS-CoV-2 infection, neither ex vivo nor in vitro.

Association of IFIH1 and DDX58 genes polymorphism with susceptibility to COVID-19.

Pattern recognition receptors of the innate immune system, such as RIG-I and MDA5, are responsible for recognizing viruses and inducing interferon production. Genetic polymorphisms in the coding regions of RLR may be associated with the severity of COVID-19. Considering the contribution of the RLR signaling in immune-mediated reactions, this study investigated the association between three SNP in the coding region of IFIH1 and DDX58 genes with the susceptibility to COVID-19 in the Kermanshah population, Iran. 177 patients with severe and 182 with mild COVID-19 were admitted for this study. Genomic DNA was extracted from peripheral blood leukocytes of patients to determine the genotypes of two SNPs, rs1990760(C>T) and rs3747517(T>C) IFIH1 gene and rs10813831(G>A) DDX58 gene using PCR-RFLP method. Our results showed that the frequency of the AA genotype of rs10813831(G>A) was associated with susceptibility to COVID-19 compared to the GG genotype (p = 0.017, OR = 2.593, 95% CI 1.173-5.736). We also observed a statistically significant difference in the recessive model for SNPs rs10813831 variant (AA versus GG + GA, p = 0.003, OR = 2.901, 95% CI 1.405-6.103). Furthermore, No significant association was found between rs1990760 (C>T) and rs3747517(T>C) of IFIH1 gene polymorphisms with COVID-19. Our findings suggest that DDX58 rs10813831(A>G) polymorphism may be associated with COVID-19 severity in the Kermanshah population, Iran.

Identification of Evolutionary Trajectories Shared across Human Betacoronaviruses.

Comparing the evolution of distantly related viruses can provide insights into common adaptive processes related to shared ecological niches. Phylogenetic approaches, coupled with other molecular evolution tools, can help identify mutations informative on adaptation, although the structural contextualization of these to functional sites of proteins may help gain insight into their biological properties. Two zoonotic betacoronaviruses capable of sustained human-to-human transmission have caused pandemics in recent times (SARS-CoV-1 and SARS-CoV-2), although a third virus (MERS-CoV) is responsible for sporadic outbreaks linked to animal infections. Moreover, two other betacoronaviruses have circulated endemically in humans for decades (HKU1 and OC43). To search for evidence of adaptive convergence between established and emerging betacoronaviruses capable of sustained human-to-human transmission (HKU1, OC43, SARS-CoV-1, and SARS-CoV-2), we developed a methodological pipeline to classify shared nonsynonymous mutations as putatively denoting homoplasy (repeated mutations that do not share direct common ancestry) or stepwise evolution (sequential mutations leading towards a novel genotype). In parallel, we look for evidence of positive selection and draw upon protein structure data to identify potential biological implications. We find 30 candidate mutations, from which 4 (codon sites 18121 [nsp14/residue 28], 21623 [spike/21], 21635 [spike/25], and 23948 [spike/796]; SARS-CoV-2 genome numbering) further display evolution under positive selection and proximity to functional protein regions. Our findings shed light on potential mechanisms underlying betacoronavirus adaptation to the human host and pinpoint common mutational pathways that may occur during establishment of human endemicity.

Transcription Factor Driven Gene Regulation in COVID-19 Patients.

SARS-CoV-2 and its many variants have caused a worldwide emergency. Host cells colonised by SARS-CoV-2 present a significantly different gene expression landscape. As expected, this is particularly true for genes that directly interact with virus proteins. Thus, understanding the role that transcription factors can play in driving differential regulation in patients affected by COVID-19 is a focal point to unveil virus infection. In this regard, we have identified 19 transcription factors which are predicted to target human proteins interacting with Spike glycoprotein of SARS-CoV-2. Transcriptomics RNA-Seq data derived from 13 human organs are used to analyse expression correlation between identified transcription factors and related target genes in both COVID-19 patients and healthy individuals. This resulted in the identification of transcription factors showing the most relevant impact in terms of most evident differential correlation between COVID-19 patients and healthy individuals. This analysis has also identified five organs such as the blood, heart, lung, nasopharynx and respiratory tract in which a major effect of differential regulation mediated by transcription factors is observed. These organs are also known to be affected by COVID-19, thereby providing consistency to our analysis. Furthermore, 31 key human genes differentially regulated by the transcription factors in the five organs are identified and the corresponding KEGG pathways and GO enrichment are also reported. Finally, the drugs targeting those 31 genes are also put forth. This in silico study explores the effects of transcription factors on human genes interacting with Spike glycoprotein of SARS-CoV-2 and intends to provide new insights to inhibit the virus infection.

The Mutational Landscape of SARS-CoV-2.

Mutation research is crucial for detecting and treating SARS-CoV-2 and developing vaccines. Using over 5,300,000 sequences from SARS-CoV-2 genomes and custom Python programs, we analyzed the mutational landscape of SARS-CoV-2. Although almost every nucleotide in the SARS-CoV-2 genome has mutated at some time, the substantial differences in the frequency and regularity of mutations warrant further examination. C>U mutations are the most common. They are found in the largest number of variants, pangolin lineages, and countries, which indicates that they are a driving force behind the evolution of SARS-CoV-2. Not all SARS-CoV-2 genes have mutated in the same way. Fewer non-synonymous single nucleotide variations are found in genes that encode proteins with a critical role in virus replication than in genes with ancillary roles. Some genes, such as spike (S) and nucleocapsid (N), show more non-synonymous mutations than others. Although the prevalence of mutations in the target regions of COVID-19 diagnostic RT-qPCR tests is generally low, in some cases, such as for some primers that bind to the N gene, it is significant. Therefore, ongoing monitoring of SARS-CoV-2 mutations is crucial. The SARS-CoV-2 Mutation Portal provides access to a database of SARS-CoV-2 mutations.

Association of Polymorphisms of IL-6 Pathway Genes (IL6, IL6R and IL6ST) with COVID-19 Severity in an Amazonian Population.

Interleukin-6 has been recognized as a major role player in COVID-19 severity, being an important regulator of the cytokine storm. Hence, the evaluation of the influence of polymorphisms in key genes of the IL-6 pathway, namely IL6, IL6R, and IL6ST, may provide valuable prognostic/predictive markers for COVID-19. The present cross-sectional study genotyped three SNPs (rs1800795, rs2228145, and rs7730934) at IL6. IL6R and IL6ST genes, respectively, in 227 COVID-19 patients (132 hospitalized and 95 non-hospitalized). Genotype frequencies were compared between these groups. As a control group, published data on gene and genotype frequencies were gathered from published studies before the pandemic started. Our major results point to an association of the IL6 C allele with COVID-19 severity. Moreover, IL-6 plasmatic levels were higher among IL6 CC genotype carriers. Additionally, the frequency of symptoms was higher at IL6 CC and IL6R CC genotypes. In conclusion, the data suggest an important role of IL6 C allele and IL6R CC genotype on COVID-19 severity, in agreement with indirect evidence from the literature about the association of these genotypes with mortality rates, pneumonia, and heightening of protein plasmatic levels pro-inflammatory driven effects.

SARS-CoV-2 ORF8 Mediates Signals in Macrophages and Monocytes through MyD88 Independently of the IL-17 Receptor.

SARS-CoV-2 has caused an estimated 7 million deaths worldwide to date. A secreted SARS-CoV-2 accessory protein, known as open reading frame 8 (ORF8), elicits inflammatory pulmonary cytokine responses and is associated with disease severity in COVID-19 patients. Recent reports proposed that ORF8 mediates downstream signals in macrophages and monocytes through the IL-17 receptor complex (IL-17RA, IL-17RC). However, generally IL-17 signals are found to be restricted to the nonhematopoietic compartment, thought to be due to rate-limiting expression of IL-17RC. Accordingly, we revisited the capacity of IL-17 and ORF8 to induce cytokine gene expression in mouse and human macrophages and monocytes. In SARS-CoV-2-infected human and mouse lungs, IL17RC mRNA was undetectable in monocyte/macrophage populations. In cultured mouse and human monocytes and macrophages, ORF8 but not IL-17 led to elevated expression of target cytokines. ORF8-induced signaling was fully preserved in the presence of anti-IL-17RA/RC neutralizing Abs and in Il17ra-/- cells. ORF8 signaling was also operative in Il1r1-/- bone marrow-derived macrophages. However, the TLR/IL-1R family adaptor MyD88, which is dispensable for IL-17R signaling, was required for ORF8 activity yet MyD88 is not required for IL-17 signaling. Thus, we conclude that ORF8 transduces inflammatory signaling in monocytes and macrophages via MyD88 independently of the IL-17R.

Dicoumarol is an effective post-exposure prophylactic for SARS-CoV-2 Omicron infection in human airway epithelium.

Repurposing existing drugs to inhibit SARS-CoV-2 infection in airway epithelial cells (AECs) is a quick way to find novel treatments for COVID-19. Computational screening has found dicoumarol (DCM), a natural anticoagulant, to be a potential SARS-CoV-2 inhibitor, but its inhibitory effects and possible working mechanisms remain unknown. Using air-liquid interface culture of primary human AECs, we demonstrated that DCM has potent antiviral activity against the infection of multiple Omicron variants (including BA.1, BQ.1 and XBB.1). Time-of-addition and drug withdrawal assays revealed that early treatment (continuously incubated after viral absorption) of DCM could markedly inhibit Omicron replication in AECs, but DCM did not affect the absorption, exocytosis and spread of viruses or directly eliminate viruses. Mechanistically, we performed single-cell sequencing analysis (a database of 77,969 cells from different airway locations from 10 healthy volunteers) and immunofluorescence staining, and showed that the expression of NAD(P)H quinone oxidoreductase 1 (NQO1), one of the known DCM targets, was predominantly localised in ciliated AECs. We further found that the NQO1 expression level was positively correlated with both the disease severity of COVID-19 patients and virus copy levels in cultured AECs. In addition, DCM treatment downregulated NQO1 expression and disrupted signalling pathways associated with SARS-CoV-2 disease outcomes (e.g., Endocytosis and COVID-19 signalling pathways) in cultured AECs. Collectively, we demonstrated that DCM is an effective post-exposure prophylactic for SARS-CoV-2 infection in the human AECs, and these findings could help physicians formulate novel treatment strategies for COVID-19.