SARS-CoV-2 variant with the spike protein mutation F306L in the southern border provinces of Thailand.

The southernmost part of Thailand is a unique and culturally diverse region that has been greatly affected by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak during the coronavirus disease-2019 pandemic. To gain insights into this situation, we analyzed 1942 whole-genome sequences of SARS-CoV-2 obtained from the five southernmost provinces of Thailand between April 2021 and March 2022, together with those publicly available in the Global Initiative on Sharing All Influenza Data database. Our analysis revealed evidence for transboundary transmissions of the virus in and out of the five southernmost provinces during the study period, from both domestic and international sources. The most prevalent viral variant in our sequence dataset was the Delta B.1.617.2.85 variant, also known as the Delta AY.85 variant, with many samples carrying a non-synonymous mutation F306L in their spike protein. Protein-protein docking and binding interface analyses suggested that the mutation may enhance the binding between the spike protein and host cell receptor protein angiotensin-converting enzyme 2, and we found that the mutation was significantly associated with an increased fatality rate. This mutation has also been observed in other SARS-CoV-2 variants, suggesting that it is of particular interest and should be monitored.

Reassessing human MHC-I genetic diversity in T cell studies.

The Major Histocompatibility Complex class I (MHC-I) system plays a vital role in immune responses by presenting antigens to T cells. Allele specific technologies, including recombinant MHC-I technologies, have been extensively used in T cell analyses for COVID-19 patients and are currently used in the development of immunotherapies for cancer. However, the immense diversity of MHC-I alleles presents challenges. The genetic diversity serves as the foundation of personalized medicine, yet it also poses a potential risk of exacerbating healthcare disparities based on MHC-I alleles. To assess potential biases, we analysed (pre)clinical publications focusing on COVID-19 studies and T cell receptor (TCR)-based clinical trials. Our findings reveal an underrepresentation of MHC-I alleles associated with Asian, Australian, and African descent. Ensuring diverse representation is vital for advancing personalized medicine and global healthcare equity, transcending genetic diversity. Addressing this disparity is essential to unlock the full potential of T cells for enhancing diagnosis and treatment across all individuals.

Metacell-based differential expression analysis identifies cell type specific temporal gene response programs in COVID-19 patient PBMCs.

By profiling gene expression in individual cells, single-cell RNA-sequencing (scRNA-seq) can resolve cellular heterogeneity and cell-type gene expression dynamics. Its application to time-series samples can identify temporal gene programs active in different cell types, for example, immune cells' responses to viral infection. However, current scRNA-seq analysis has limitations. One is the low number of genes detected per cell. The second is insufficient replicates (often 1-2) due to high experimental cost. The third lies in the data analysis-treating individual cells as independent measurements leads to inflated statistics. To address these, we explore a new computational framework, specifically whether "metacells" constructed to maintain cellular heterogeneity within individual cell types (or clusters) can be used as "replicates" for increasing statistical rigor. Toward this, we applied SEACells to a time-series scRNA-seq dataset from peripheral blood mononuclear cells (PBMCs) after SARS-CoV-2 infection to construct metacells, and used them in maSigPro for quadratic regression to find significantly differentially expressed genes (DEGs) over time, followed by clustering expression velocity trends. We showed that such metacells retained greater expression variances and produced more biologically meaningful DEGs compared to either metacells generated randomly or from simple pseudobulk methods. More specifically, this approach correctly identified the known ISG15 interferon response program in almost all PBMC cell types and many DEGs enriched in the previously defined SARS-CoV-2 infection response pathway. It also uncovered additional and more cell type-specific temporal gene expression programs. Overall, our results demonstrate that the metacell-pseudoreplicate strategy could potentially overcome the limitation of 1-2 replicates.

Properties and Mechanisms of Deletions, Insertions, and Substitutions in the Evolutionary History of SARS-CoV-2.

SARS-CoV-2 has accumulated many mutations since its emergence in late 2019. Nucleotide substitutions leading to amino acid replacements constitute the primary material for natural selection. Insertions, deletions, and substitutions appear to be critical for coronavirus's macro- and microevolution. Understanding the molecular mechanisms of mutations in the mutational hotspots (positions, loci with recurrent mutations, and nucleotide context) is important for disentangling roles of mutagenesis and selection. In the SARS-CoV-2 genome, deletions and insertions are frequently associated with repetitive sequences, whereas C>U substitutions are often surrounded by nucleotides resembling the APOBEC mutable motifs. We describe various approaches to mutation spectra analyses, including the context features of RNAs that are likely to be involved in the generation of recurrent mutations. We also discuss the interplay between mutations and natural selection as a complex evolutionary trend. The substantial variability and complexity of pipelines for the reconstruction of mutations and the huge number of genomic sequences are major problems for the analyses of mutations in the SARS-CoV-2 genome. As a solution, we advocate for the development of a centralized database of predicted mutations, which needs to be updated on a regular basis.

Inflammasome-Related Genetic Polymorphisms as Severity Biomarkers of COVID-19.

The most critical forms of coronavirus disease 2019 (COVID-19) are associated with excessive activation of the inflammasome. Despite the COVID-19 impact on public health, we still do not fully understand the mechanisms by which the inflammatory response influences disease prognosis. Accordingly, we aimed to elucidate the role of polymorphisms in the key genes of the formation and signaling of the inflammasome as biomarkers of COVID-19 severity. For this purpose, a large and well-defined cohort of 377 COVID-19 patients with mild (n = 72), moderate (n = 84), severe (n = 100), and critical (n = 121) infections were included. A total of 24 polymorphisms located in inflammasome-related genes (NLRP3, NLRC4, NLRP1, CARD8, CASP1, IL1B, IL18, NFKB1, ATG16L1, and MIF) were genotyped in all of the patients and in the 192 healthy controls (HCs) (who were without COVID-19 at the time of and before the study) by RT-qPCR. Our results showed that patients with mild, moderate, severe, and critical COVID-19 presented similar allelic and genotypic distribution in all the variants studied. No statistically significant differences in the haplotypic distribution of NLRP3, NLRC4, NLRP1, CARD8, CASP1, IL1B, and ATG16L1 were observed between COVID-19 patients, who were stratified by disease severity. Each stratified group of patients presented a similar genetic distribution to the HCs. In conclusion, our results suggest that the inflammasome polymorphisms studied are not associated with the worsening of COVID-19.

Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19.

The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.

The fatal contribution of serine protease-related genetic variants to COVID-19 outcomes.

Introduction: Serine proteases play a critical role during SARS-CoV-2 infection. Therefore, polymorphisms of transmembrane protease serine 2 (TMPRSS2) and serpine family E member 1 (SERPINE1) could help to elucidate the contribution of variability to COVID-19 outcomes. Methods: To evaluate the genetic variants of the genes previously associated with COVID-19 outcomes, we performed a cross-sectional study in which 1536 SARS-CoV-2-positive participants were enrolled. TMPRSS2 (rs2070788, rs75603675, rs12329760) and SERPINE1 (rs2227631, rs2227667, rs2070682, rs2227692) were genotyped using the Open Array Platform. The association of polymorphisms with disease outcomes was determined by logistic regression analysis adjusted for covariates (age, sex, hypertension, type 2 diabetes, and obesity). Results: According to our codominant model, the GA genotype of rs2227667 (OR=0.55; 95% CI = 0.36-0.84; p=0.006) and the AG genotype of rs2227667 (OR=0.59; 95% CI = 0.38-0.91; p=0.02) of SERPINE1 played a protective role against disease. However, the rs2227692 T allele and TT genotype SERPINE1 (OR=1.45; 95% CI = 1.11-1.91; p=0.006; OR=2.08; 95% CI = 1.22-3.57; p=0.007; respectively) were associated with a decreased risk of death. Similarly, the rs75603675 AA genotype TMPRSS2 had an OR of 1.97 (95% CI = 1.07-3.6; p=0.03) for deceased patients. Finally, the rs2227692 T allele SERPINE1 was associated with increased D-dimer levels (OR=1.24; 95% CI = 1.03-1.48; p=0.02). Discussion: Our data suggest that the rs75603675 TMPRSS2 and rs2227692 SERPINE1 polymorphisms are associated with a poor outcome. Additionally, rs2227692 SERPINE1 could participate in hypercoagulable conditions in critical COVID-19 patients, and this genetic variant could contribute to the identification of new pharmacological targets and treatment strategies to block the inhibition of TMPRSS2 entry into SARS-CoV-2.

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.

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.

Background: Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined. Methods: We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD. Results: Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion. Conclusion: Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

Next-generation sequencing of host genetics risk factors associated with COVID-19 severity and long-COVID in Colombian population.

Coronavirus disease 2019 (COVID-19) was considered a major public health burden worldwide. Multiple studies have shown that susceptibility to severe infections and the development of long-term symptoms is significantly influenced by viral and host factors. These findings have highlighted the potential of host genetic markers to identify high-risk individuals and develop target interventions to reduce morbimortality. Despite its importance, genetic host factors remain largely understudied in Latin-American populations. Using a case-control design and a custom next-generation sequencing (NGS) panel encompassing 81 genetic variants and 74 genes previously associated with COVID-19 severity and long-COVID, we analyzed 56 individuals with asymptomatic or mild COVID-19 and 56 severe and critical cases. In agreement with previous studies, our results support the association between several clinical variables, including male sex, obesity and common symptoms like cough and dyspnea, and severe COVID-19. Remarkably, thirteen genetic variants showed an association with COVID-19 severity. Among these variants, rs11385942 (p < 0.01; OR = 10.88; 95% CI = 1.36-86.51) located in the LZTFL1 gene, and rs35775079 (p = 0.02; OR = 8.53; 95% CI = 1.05-69.45) located in CCR3 showed the strongest associations. Various respiratory and systemic symptoms, along with the rs8178521 variant (p < 0.01; OR = 2.51; 95% CI = 1.27-4.94) in the IL10RB gene, were significantly associated with the presence of long-COVID. The results of the predictive model comparison showed that the mixed model, which incorporates genetic and non-genetic variables, outperforms clinical and genetic models. To our knowledge, this is the first study in Colombia and Latin-America proposing a predictive model for COVID-19 severity and long-COVID based on genomic analysis. Our study highlights the usefulness of genomic approaches to studying host genetic risk factors in specific populations. The methodology used allowed us to validate several genetic variants previously associated with COVID-19 severity and long-COVID. Finally, the integrated model illustrates the importance of considering genetic factors in precision medicine of infectious diseases.

Ancestral allele of DNA polymerase gamma modifies antiviral tolerance.

Mitochondria are critical modulators of antiviral tolerance through the release of mitochondrial RNA and DNA (mtDNA and mtRNA) fragments into the cytoplasm after infection, activating virus sensors and type-I interferon (IFN-I) response1-4. The relevance of these mechanisms for mitochondrial diseases remains understudied. Here we investigated mitochondrial recessive ataxia syndrome (MIRAS), which is caused by a common European founder mutation in DNA polymerase gamma (POLG1)5. Patients homozygous for the MIRAS variant p.W748S show exceptionally variable ages of onset and symptoms5, indicating that unknown modifying factors contribute to disease manifestation. We report that the mtDNA replicase POLG1 has a role in antiviral defence mechanisms to double-stranded DNA and positive-strand RNA virus infections (HSV-1, TBEV and SARS-CoV-2), and its p.W748S variant dampens innate immune responses. Our patient and knock-in mouse data show that p.W748S compromises mtDNA replisome stability, causing mtDNA depletion, aggravated by virus infection. Low mtDNA and mtRNA release into the cytoplasm and a slow IFN response in MIRAS offer viruses an early replicative advantage, leading to an augmented pro-inflammatory response, a subacute loss of GABAergic neurons and liver inflammation and necrosis. A population databank of around 300,000 Finnish individuals6 demonstrates enrichment of immunodeficient traits in carriers of the POLG1 p.W748S mutation. Our evidence suggests that POLG1 defects compromise antiviral tolerance, triggering epilepsy and liver disease. The finding has important implications for the mitochondrial disease spectrum, including epilepsy, ataxia and parkinsonism.

Probing molecular pathways: Illuminating the connection between COVID-19 and Alzheimer's disease through the endocannabinoid system dynamics.

Our study investigates the molecular link between COVID-19 and Alzheimer's disease (AD). We aim to elucidate the mechanisms by which COVID-19 may influence the onset or progression of AD. Using bioinformatic tools, we analyzed gene expression datasets from the Gene Expression Omnibus (GEO) database, including GSE147507, GSE12685, and GSE26927. Intersection analysis was utilized to identify common differentially expressed genes (CDEGs) and their shared biological pathways. Consensus clustering was conducted to group AD patients based on gene expression, followed by an analysis of the immune microenvironment and variations in shared pathway activities between clusters. Additionally, we identified transcription factor-binding sites shared by CDEGs and genes in the common pathway. The activity of the pathway and the expression levels of the CDEGs were validated using GSE164805 and GSE48350 datasets. Six CDEGs (MAL2, NECAB1, SH3GL2, EPB41L3, MEF2C, and NRGN) were identified, along with a downregulated pathway, the endocannabinoid (ECS) signaling pathway, common to both AD and COVID-19. These CDEGs showed a significant correlation with ECS activity (p < 0.05) and immune functions. The ECS pathway was enriched in healthy individuals' brains and downregulated in AD patients. Validation using GSE164805 and GSE48350 datasets confirmed the differential expression of these genes in COVID-19 and AD tissues. Our findings reveal a potential pathogenetic link between COVID-19 and AD, mediated by CDEGs and the ECS pathway. However, further research and multicenter evidence are needed to translate these findings into clinical applications.

Prevalence of the protective OAS1 rs10774671-G allele against severe COVID-19 in Moroccans: implications for a North African Neanderthal connection.

The clinical presentation of COVID-19 shows high variability among individuals, which is partly due to genetic factors. The OAS1/2/3 cluster has been found to be strongly associated with COVID-19 severity. We examined this locus in the Moroccan population for the occurrence of the critical variant rs10774671 and its respective haplotype blocks. The frequency of single-nucleotide polymorphisms (SNPs) in the cluster of OAS immunity genes in 157 unrelated individuals of Moroccan origin was determined using an in-house exome database. OAS1 exon 6 of 71 SARS-CoV-2-positive individuals with asymptomatic/mild disease and 74 with moderate/severe disease was sequenced by the Sanger method. The genotypic, allelic, and haplotype frequencies of three SNPs were compared between these two groups. Finally, males in our COVID-19 series were genotyped for the Berber-specific marker E-M81. The prevalence of the OAS1 rs10774671-G allele in present-day Moroccans was found to be 40.4%, which is similar to that found in Europeans. However, it was found equally in both the Neanderthal GGG haplotype and the African GAC haplotype, with a frequency of 20% each. These two haplotypes, and hence the rs10774671-G allele, were significantly associated with protection against severe COVID-19 (p = 0.034, p = 0.041, and p = 0.008, respectively). Surprisingly, in men with the Berber-specific uniparental markers, the African haplotype was absent, while the prevalence of the Neanderthal haplotype was similar to that in Europeans. The protective rs10774671-G allele of OAS1 was found only in the Neanderthal haplotype in Berbers, the indigenous people of North Africa, suggesting that this region may have served as a stepping-stone for the passage of hominids to other continents.

Potential therapeutic targets for COVID-19 complicated with pulmonary hypertension: a bioinformatics and early validation study.

Coronavirus disease (COVID-19) and pulmonary hypertension (PH) are closely correlated. However, the mechanism is still poorly understood. In this article, we analyzed the molecular action network driving the emergence of this event. Two datasets (GSE113439 and GSE147507) from the GEO database were used for the identification of differentially expressed genes (DEGs).Common DEGs were selected by VennDiagram and their enrichment in biological pathways was analyzed. Candidate gene biomarkers were selected using three different machine-learning algorithms (SVM-RFE, LASSO, RF).The diagnostic efficacy of these foundational genes was validated using independent datasets. Eventually, we validated molecular docking and medication prediction. We found 62 common DEGs, including several ones that could be enriched for Immune Response and Inflammation. Two DEGs (SELE and CCL20) could be identified by machine-learning algorithms. They performed well in diagnostic tests on independent datasets. In particular, we observed an upregulation of functions associated with the adaptive immune response, the leukocyte-lymphocyte-driven immunological response, and the proinflammatory response. Moreover, by ssGSEA, natural killer T cells, activated dendritic cells, activated CD4 T cells, neutrophils, and plasmacytoid dendritic cells were correlated with COVID-19 and PH, with SELE and CCL20 showing the strongest correlation with dendritic cells. Potential therapeutic compounds like FENRETI-NIDE, AFLATOXIN B1 and 1-nitropyrene were predicted. Further molecular docking and molecular dynamics simulations showed that 1-nitropyrene had the most stable binding with SELE and CCL20.The findings indicated that SELE and CCL20 were identified as novel diagnostic biomarkers for COVID-19 complicated with PH, and the target of these two key genes, FENRETI-NIDE and 1-nitropyrene, was predicted to be a potential therapeutic target, thus providing new insights into the prediction and treatment of COVID-19 complicated with PH in clinical practice.

FGF7 enhances the expression of ACE2 in human islet organoids aggravating SARS-CoV-2 infection.

The angiotensin-converting enzyme 2 (ACE2) is a primary cell surface viral binding receptor for SARS-CoV-2, so finding new regulatory molecules to modulate ACE2 expression levels is a promising strategy against COVID-19. In the current study, we utilized islet organoids derived from human embryonic stem cells (hESCs), animal models and COVID-19 patients to discover that fibroblast growth factor 7 (FGF7) enhances ACE2 expression within the islets, facilitating SARS-CoV-2 infection and resulting in impaired insulin secretion. Using hESC-derived islet organoids, we demonstrated that FGF7 interacts with FGF receptor 2 (FGFR2) and FGFR1 to upregulate ACE2 expression predominantly in ß cells. This upregulation increases both insulin secretion and susceptibility of ß cells to SARS-CoV-2 infection. Inhibiting FGFR counteracts the FGF7-induced ACE2 upregulation, subsequently reducing viral infection and replication in the islets. Furthermore, retrospective clinical data revealed that diabetic patients with severe COVID-19 symptoms exhibited elevated serum FGF7 levels compared to those with mild symptoms. Finally, animal experiments indicated that SARS-CoV-2 infection increased pancreatic FGF7 levels, resulting in a reduction of insulin concentrations in situ. Taken together, our research offers a potential regulatory strategy for ACE2 by controlling FGF7, thereby protecting islets from SARS-CoV-2 infection and preventing the progression of diabetes in the context of COVID-19.

Utilizing noncatalytic ACE2 protein mutant as a competitive inhibitor to treat SARS-CoV-2 infection.

Introduction: Angiotensin converting-enzyme 2 (ACE2) is an enzyme catalyzing the conversion of angiotensin 2 into angiotensin 1-7. ACE2 also serves as the receptor of several coronaviruses, including SARS-CoV-1 and SARS-CoV-2. Therefore, ACE2 could be utilized as a therapeutic target for treating these coronaviruses, ideally lacking enzymatic function. Methods: Based on structural analysis, specific mutations were introduced to generate mutants of ACE2 and ACE2-Fc (fusion protein of ACE2 and Fc region of IgG1). The enzyme activity, binding affinity, and neutralization abilities were measured. Results and discussion: As predicted, five mutants (AMI081, AMI082, AMI083, AMI084, AMI090) have completely depleted ACE2 enzymatic activities. More importantly, enzyme-linked receptor-ligand assay (ELRLA) and surface plasmon resonance (SPR) results showed that 2 mutants (AMI082, AMI090) maintained binding activity to the viral spike proteins of SARS-CoV-1 and SARS-CoV-2. In An in vitro neutralization experiment using a pseudovirus, SARS-CoV-2 S1 spike protein-packed lentivirus particles, was also performed, showing that AMI082 and AMI090 significantly reduced GFP transgene expression. Further, in vitro virulent neutralization assays using SARS-CoV-2 (strain name: USA-WA1/2020) showed that AMI082 and AMI090 had remarkable inhibitory effects, indicated by comparable IC50 to wildtype ACE2 (5.33 µg/mL). In addition to the direct administration of mutant proteins, an alternative strategy for treating COVID-19 is through AAV delivery to achieve long-lasting effects. Therefore, AAV5 encoding AMI082 and AMI090 were packaged and transgene expression was assessed. In summary, these ACE2 mutants represent a novel approach to prevent or treat COVID-19 and other viruses with the same spike protein.

Identification and characterization of endogenous retroviruses upon SARS-CoV-2 infection.

Endogenous retroviruses (ERVs) derived from the long terminal repeat (LTR) family of transposons constitute a significant portion of the mammalian genome, with origins tracing back to ancient viral infections. Despite comprising approximately 8% of the human genome, the specific role of ERVs in the pathogenesis of COVID-19 remains unclear. In this study, we conducted a genome-wide identification of ERVs in human peripheral blood mononuclear cells (hPBMCs) and primary lung epithelial cells from monkeys and mice, both infected and uninfected with SARS-CoV-2. We identified 405, 283, and 206 significantly up-regulated transposable elements (TEs) in hPBMCs, monkeys, and mice, respectively. This included 254, 119, 68, and 28 ERVs found in hPBMCs from severe and mild COVID-19 patients, monkeys, and transgenic mice expressing the human ACE2 receptor (hACE2) and infected with SARS-CoV-2. Furthermore, analysis using the Genomic Regions Enrichment of Annotations Tool (GREAT) revealed certain parental genomic sequences of these up-regulated ERVs in COVID-19 patients may be involved in various biological processes, including histone modification and viral replication. Of particular interest, we identified 210 ERVs specifically up-regulated in the severe COVID-19 group. The genes associated with these differentially expressed ERVs were enriched in processes such as immune response activation and histone modification. HERV1_I-int: ERV1:LTR and LTR7Y: ERV1:LTR were highlighted as potential biomarkers for evaluating the severity of COVID-19. Additionally, validation of our findings using RT-qPCR in Bone Marrow-Derived Macrophages (BMDMs) from mice infected by HSV-1 and VSV provided further support to our results. This study offers insights into the expression patterns and potential roles of ERVs following viral infection, providing a valuable resource for future studies on ERVs and their interaction with SARS-CoV-2.

Using genetics and proteomics data to identify proteins causally related to COVID-19, healthspan and lifespan: a Mendelian randomization study.

BACKGROUND: COVID-19 pandemic poses a heavy burden on public health and accounts for substantial mortality and morbidity. Proteins are building blocks of life, but specific proteins causally related to COVID-19, healthspan and lifespan have not been systematically examined. METHODS: We conducted a Mendelian randomization study to assess the effects of 1,361 plasma proteins on COVID-19, healthspan and lifespan, using large GWAS of severe COVID-19 (up to 13,769 cases and 1,072,442 controls), COVID-19 hospitalization (32,519 cases and 2,062,805 controls) and SARS-COV2 infection (122,616 cases and 2,475,240 controls), healthspan (n = 300,477) and parental lifespan (~0.8 million of European ancestry). RESULTS: We identified 35, 43, and 63 proteins for severe COVID, COVID-19 hospitalization, and SARS-COV2 infection, and 4, 32, and 19 proteins for healthspan, father's attained age, and mother's attained age. In addition to some proteins reported previously, such as SFTPD related to severe COVID-19, we identified novel proteins involved in inflammation and immunity (such as ICAM-2 and ICAM-5 which affect COVID-19 risk, CXCL9, HLA-DRA and LILRB4 for healthspan and lifespan), apoptosis (such as FGFR2 and ERBB4 which affect COVID-19 risk and FOXO3 which affect lifespan) and metabolism (such as PCSK9 which lowers lifespan). We found 2, 2 and 3 proteins shared between COVID-19 and healthspan/lifespan, such as CXADR and LEFTY2, shared between severe COVID-19 and healthspan/lifespan. Three proteins affecting COVID-19 and seven proteins affecting healthspan/lifespan are targeted by existing drugs. CONCLUSIONS: Our study provided novel insights into protein targets affecting COVID-19, healthspan and lifespan, with implications for developing new treatment and drug repurposing.

Novel genetic association of the Furin gene polymorphism rs1981458 with COVID-19 severity among Indian populations.

SARS CoV-2, the causative agent for the ongoing COVID-19 pandemic, it enters the host cell by activating the ACE2 receptor with the help of two proteasesi.e., Furin and TMPRSS2. Therefore, variations in these genes may account for differential susceptibility and severity between populations. Previous studies have shown that the role of ACE2 and TMPRSS2 gene variants in understanding COVID-19 susceptibility among Indian populations. Nevertheless, a knowledge gap exists concerning the COVID-19 susceptibility of Furin gene variants among diverse South Asian ethnic groups. Investigating the role of Furin gene variants and their global phylogeographic structure is essential to comprehensively understanding COVID-19 susceptibility in these populations. We have used 450 samples from diverse Indian states and performed linear regression to analyse the Furin gene variant's with COVID-19 Case Fatality Rate (CFR) that could be epidemiologically associated with disease severity outcomes. Associated genetic variants were further evaluated for their expression and regulatory potential through various Insilco analyses. Additionally, we examined the Furin gene using next-generation sequencing (NGS) data from 393 diverse global samples, with a particular emphasis on South Asia, to investigate its Phylogeographic structure among diverse world populations. We found a significant positive association for the SNP rs1981458 with COVID-19 CFR (p < 0.05) among diverse Indian populations at different timelines of the first and second waves. Further, QTL and other regulatory analyses showed various significant associations for positive regulatory roles of rs1981458 and Furin gene, mainly in Immune cells and virus infection process, highlighting their role in host immunity and viral assembly and processing. The Furin protein-protein interaction suggested that COVID-19 may contribute to Pulmonary arterial hypertension via a typical inflammation mechanism. The phylogeographic architecture of the Furin gene demonstrated a closer genetic affinity of South Asia with West Eurasian populations. Therefore, it is worth proposing that for the Furin gene, the COVID-19 susceptibility of South Asians will be more similar to the West Eurasian population. Our previous studies on the ACE2 and TMPRSS2 genes showed genetic affinity of South Asian with East Eurasians and West Eurasians, respectively. Therefore, with the collective information from these three important genes (ACE2, TMPRSS2 and Furin) we modelled COVID-19 susceptibilityof South Asia in between these two major ancestries with an inclination towards West Eurasia. In conclusion, this study, for the first time, concluded the role of rs1981458 in COVID-19 severity among the Indian population and outlined its regulatory potential.This study also highlights that the genetic structure for COVID-19 susceptibilityof South Asia is distinct, however, inclined to the West Eurasian population. We believe this insight may be utilised as a genetic biomarker to identify vulnerable populations, which might be directly relevant for developing policies and allocating resources more effectively during an epidemic.