An integrative multiomics analysis identifies putative causal genes for COVID-19 severity

GWAS-significant loci and severe COVID-19: analysis of associations, link with thromboinflammation syndrome, gene-gene, and gene-environmental interactions

Objective

The aim of this study was to replicate associations of GWAS-significant loci with severe COVID-19 in the population of Central Russia, to investigate associations of the SNPs with thromboinflammation parameters, to analyze gene-gene and gene-environmental interactions.

Materials and Methods

DNA samples from 798 unrelated Caucasian subjects from Central Russia (199 hospitalized COVID-19 patients and 599 controls with a mild or asymptomatic course of COVID-19) were genotyped using probe-based polymerase chain reaction for 10 GWAS-significant SNPs: rs143334143 CCHCR1, rs111837807 CCHCR1, rs17078346 SLC6A20-LLZTFL1, rs17713054 SLC6A20-LLZTFL1, rs7949972 ELF5, rs61882275 ELF5, rs12585036 ATP11A, rs67579710 THBS3, THBS3-AS1, rs12610495 DPP9, rs9636867 IFNAR2.

Results

SNP rs17713054 SLC6A20-LZTFL1 was associated with increased risk of severe COVID-19 in the entire group (risk allele A, OR = 1.78, 95% CI = 1.22-2.6, p = 0.003), obese individuals (OR = 2.31, 95% CI = 1.52-3.5, p = 0.0002, (p bonf = 0.0004)), patients with low fruit and vegetable intake (OR = 1.72, 95% CI = 1.15-2.58, p = 0.01, (p bonf = 0.02)), low physical activity (OR = 1.93, 95% CI = 1.26-2.94, p = 0.0035, (p bonf = 0.007)), and nonsmokers (OR = 1.65, 95% CI = 1.11-2.46, p = 0.02). This SNP correlated with increased BMI (p = 0.006) and worsened thrombodynamic parameters (maximum optical density of the formed clot, D (p = 0.02), delayed appearance of spontaneous clots, Tsp (p = 0.02), clot size 30 min after coagulation activation, CS (p = 0.036)). SNP rs17078346 SLC6A20-LZTFL1 was linked with increased BMI (p = 0.01) and severe COVID-19 in obese individuals (risk allele C, OR = 1.72, 95% CI = 1.15-2.58, p = 0.01, (p bonf = 0.02)). SNP rs12610495 DPP9 was associated with increased BMI (p = 0.01), severe COVID-19 in obese patients (risk allele G, OR = 1.48, 95% CI = 1.09-2.01, p = 0.01, (p bonf = 0.02)), and worsened thrombodynamic parameters (time to the start of clot growth, Tlag (p = 0.01)). For rs7949972 ELF5, a protective effect against severe COVID-19 was observed in non-obese patients (effect allele T, OR = 0.67, 95% CI = 0.47-0.95, p = 0.02, (p bonf = 0.04)), improving thrombodynamic parameters (CS (p = 0.02), stationary spatial clot growth rates, Vst (p = 0.02)). Finally, rs12585036 ATP11A exhibited a protective effect against severe COVID-19 in males (protective allele A, OR = 0.51, 95% CI = 0.32-0.83, p = 0.004). SNPs rs67579710 THBS3, THBS3-AS1, rs17713054 SLC6A20-LZTFL1, rs7949972 ELF5, rs9636867 IFNAR2-were involved in two or more of the most significant G×G interactions (p perm ≤ 0.01). The pairwise combination rs67579710 THBS3, THBS3-AS1 × rs17713054 SLC6A20-LZTFL1 was a priority in determining susceptibility to severe COVID-19 (it was included in four of the top five most significant SNP-SNP interaction models).

Conclusion

Overall, this study represents a comprehensive molecular-genetic and bioinformatics analysis of the involvement of GWAS-significant loci in the molecular mechanisms of severe COVID-19, gene-gene and gene-environmental interactions, and provides evidence of their relationship with thromboinflammation parameters in patients hospitalized in intensive care units.

Critically-ill COVID-19 susceptibility gene CCR3 shows natural selection in sub-Saharan Africans

The prevalence of COVID-19 critical illness varies across ethnicities, with recent studies suggesting that genetic factors may contribute to this variation. The aim of this study was to investigate natural selection signals of genes associated with critically-ill COVID-19 in sub-Saharan Africans. Severe COVID-19 SNPs were obtained from the HGI website. Selection signals were assessed in 661 sub-Sahara Africans from 1000 Genomes Project using integrated haplotype score (iHS), cross-population extended haplotype homozygosity (XP-EHH), and fixation index (Fst). Allele frequency trajectory analysis of ancient DNA samples were used to validate the existing of selection in sub-Sahara Africans. We also used Mendelian randomization to decipher the correlation between natural selection and critically-ill COVID-19. We identified that CCR3 exhibited significant natural selection signals in sub-Sahara Africans. Within the CCR3 gene, rs17217831-A showed both high iHS (Standardized iHS = 2) and high XP-EHH (Standardized XP-EHH = 2.5) in sub-Sahara Africans. Allele frequency trajectory of CCR3 rs17217831-A revealed natural selection occurring in the recent 1,500 years. Natural selection resulted in increased CCR3 expression in sub-Sahara Africans. Mendelian Randomization provided evidence that increased blood CCR3 expression and eosinophil counts lowered the risk of critically ill COVID-19. Our findings suggest that sub-Saharan Africans are resistant to critically ill COVID-19 due to natural selection and identify CCR3 as a potential novel therapeutic target.

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.

SARS-CoV-2 rapidly evolves lineage-specific phenotypic differences when passaged repeatedly in immune-naïve mice

The persistence of SARS-CoV-2 despite the development of vaccines and a degree of herd immunity is partly due to viral evolution reducing vaccine and treatment efficacy. Serial infections of wild-type (WT) SARS-CoV-2 in Balb/c mice yield mouse-adapted strains with greater infectivity and mortality. We investigate if passaging unmodified B.1.351 (Beta) and B.1.617.2 (Delta) 20 times in K18-ACE2 mice, expressing the human ACE2 receptor, in a BSL-3 laboratory without selective pressures, drives human health-relevant evolution and if evolution is lineage-dependent. Late-passage virus causes more severe disease, at organism and lung tissue scales, with late-passage Delta demonstrating antibody resistance and interferon suppression. This resistance co-occurs with a de novo spike S371F mutation, linked with both traits. S371F, an Omicron-characteristic mutation, is co-inherited at times with spike E1182G per Nanopore sequencing, existing in different within-sample viral variants at others. Both S371F and E1182G are linked to mammalian GOLGA7 and ZDHHC5 interactions, which mediate viral-cell entry and antiviral response. This study demonstrates SARS-CoV-2's tendency to evolve with phenotypic consequences, its evolution varying by lineage, and suggests non-dominant quasi-species contribution.

Integrating population-level and cell-based signatures for drug repositioning

Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.

Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs

Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

A Clinical Qualification Protocol Highlights Overlapping Genomic Influences and Neuro-Autonomic Mechanisms in Ehlers-Danlos and Long COVID-19 Syndromes

A substantial fraction of the 15% with double-jointedness or hypermobility have the traditionally ascertained joint-skeletal, cutaneous, and cardiovascular symptoms of connective tissue dysplasia and its particular manifestation as Ehlers-Danlos syndrome (EDS). The holistic ascertainment of 120 findings in 1261 EDS patients added neuro-autonomic symptoms like headaches, muscle weakness, brain fog, chronic fatigue, dyspnea, and bowel irregularity to those of arthralgia and skin laxity, 15 of these symptoms shared with those of post-infectious SARS-CoV-2 (long COVID-19). Underlying articulo-autonomic mechanisms guided a clinical qualification protocol that qualified DNA variants in 317 genes as having diagnostic utility for EDS, six of them identical (F2-LIFR-NLRP3-STAT1-T1CAM1-TNFRSF13B) and eighteen similar to those modifying COVID-19 severity/EDS, including ADAMTS13/ADAMTS2-C3/C1R-IKBKG/IKBKAP-PIK3C3/PIK3R1-POLD4/POLG-TMPRSS2/TMPRSS6-WNT3/WNT10A. Also, contributing to EDS and COVID-19 severity were forty and three genes, respectively, impacting mitochondrial functions as well as parts of an overlapping gene network, or entome, that are hypothesized to mediate the cognitive-behavioral, neuro-autonomic, and immune-inflammatory alterations of connective tissue in these conditions. The further characterization of long COVID-19 natural history and genetic predisposition will be necessary before these parallels to EDS can be carefully delineated and translated into therapies.

Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis

Introduction

The comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection).

Methods

In this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis.

Results

We have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037-3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021-1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction.

Discussion

Our findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19.

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification

Genes with moderate to low expression heritability may explain a large proportion of complex trait etiology, but such genes cannot be sufficiently captured in conventional transcriptome-wide association studies (TWASs), partly due to the relatively small available reference datasets for developing expression genetic prediction models to capture the moderate to low genetically regulated components of gene expression. Here, we introduce a method, the Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction model accuracy and the power of TWAS by using a large expression quantitative trait loci (eQTL) summary-level dataset. We apply SUMMIT to the eQTL summary-level data provided by the eQTLGen consortium. Through simulation studies and analyses of genome-wide association study summary statistics for 24 complex traits, we show that SUMMIT improves the accuracy of expression prediction in blood, successfully builds expression prediction models for genes with low expression heritability, and achieves higher statistical power than several benchmark methods. Finally, we conduct a case study of COVID-19 severity with SUMMIT and identify 11 likely causal genes associated with COVID-19 severity.

An interactive analysis of the mouse oviductal miRNA profiles

MicroRNAs are small non-coding molecules that control several cellular functions and act as negative post-transcriptional regulators of the mRNA. While their implication in several biological functions is already known, an important role as regulators of different physiological and pathological processes in fertilization and embryo development is currently emerging. Indeed, miRNAs have been found in the oviductal fluid packaged within the extracellular vesicles, which might act as natural nanoshuttles by transporting lipids, proteins, RNA molecules and miRNAs from the oviduct to the gametes or embryos. Here, an exhaustive bibliography search was carried out, followed by the construction of a computational model based on the networks theory in an attempt to recreate and elucidate the pathways potentially activated by the oviductal miRNA. The omics data published to date were gathered to create the Oviductal MiRNome, in which the miRNA target genes and their interactions are represented by using stringApp and the Network analyzer from Cytoscape 3.7.2. Then, the hyperlinked nodes were identified to investigate the pathways in which they are involved using the gene ontology enrichment analysis. To study the phenotypical effects after the removal of key genes on the reproductive system and embryo, knockout mouse lines for every protein-coding gene were investigated by using the International Mouse Phenotyping Consortium database. The creation of the Oviductal MiRNome revealed the presence of important genes and their interactions within the network. The functional enrichment analysis revealed that the hyperlinked nodes are involved in fundamental cellular functions, both structural and regulatory/signaling, suggesting their implication in fertilization and early embryo development. This fact was as well evidenced by the effects of the gene deletion in KO mice on the reproductive system and embryo development. The present study highlights the importance of studying the miRNA profiles and their enormous potential as tools to improve the assisted reproductive techniques currently used in human and animal reproduction.

Genetics of COVID-19 and myalgic encephalomyelitis/chronic fatigue syndrome: a systematic review

COVID‐19 and ME/CFS present with some similar symptoms, especially physical and mental fatigue. In order to understand the basis of these similarities and the possibility of underlying common genetic components, we performed a systematic review of all published genetic association and cohort studies regarding COVID‐19 and ME/CFS and extracted the genes along with the genetic variants investigated. We then performed gene ontology and pathway analysis of those genes that gave significant results in the individual studies to yield functional annotations of the studied genes using protein analysis through evolutionary relationships (PANTHER) VERSION 17.0 software. Finally, we identified the common genetic components of these two conditions. Seventy‐one studies for COVID‐19 and 26 studies for ME/CFS were included in the systematic review in which the expression of 97 genes for COVID‐19 and 429 genes for ME/CFS were significantly affected. We found that ACE, HLA‐A, HLA‐C, HLA‐DQA1, HLA‐DRB1, and TYK2 are the common genes that gave significant results. The findings of the pathway analysis highlight the contribution of inflammation mediated by chemokine and cytokine signaling pathways, and the T cell activation and Toll receptor signaling pathways. Protein class analysis revealed the contribution of defense/immunity proteins, as well as protein‐modifying enzymes. Our results suggest that the pathogenesis of both syndromes could involve some immune dysfunction.

Increased mRNA Levels of ADAM17, IFITM3, and IFNE in Peripheral Blood Cells Are Present in Patients with Obesity and May Predict Severe COVID-19 Evolution

Gene expression patterns in blood cells from SARS-CoV-2 infected individuals with different clinical phenotypes and body mass index (BMI) could help to identify possible early prognosis factors for COVID-19. We recruited patients with COVID-19 admitted in Hospital Universitari Son Espases (HUSE) between March 2020 and November 2021, and control subjects. Peripheral blood cells (PBCs) and plasma samples were obtained on hospital admission. Gene expression of candidate transcriptomic biomarkers in PBCs were compared based on the patients’ clinical status (mild, severe and critical) and BMI range (normal weight, overweight, and obesity). mRNA levels of ADAM17, IFITM3, IL6, CXCL10, CXCL11, IFNG and TYK2 were increased in PBCs of COVID-19 patients (n = 73) compared with controls (n = 47), independently of sex. Increased expression of IFNE was observed in the male patients only. PBC mRNA levels of ADAM17, IFITM3, CXCL11, and CCR2 were higher in those patients that experienced a more serious evolution during hospitalization. ADAM17, IFITM3, IL6 and IFNE were more highly expressed in PBCs of patients with obesity. Interestingly, the expression pattern of ADAM17, IFITM3 and IFNE in PBCs was related to both the severity of COVID-19 evolution and obesity status, especially in the male patients. In conclusion, gene expression in PBCs can be useful for the prognosis of COVID-19 evolution.

IFNAR2 relevance in the clinical outcome of individuals with severe COVID-19

Interferons (IFNs) are a group of cytokines with antiviral, antiproliferative, antiangiogenic, and immunomodulatory activities. Type I IFNs amplify and propagate the antiviral response by interacting with their receptors, IFNAR1 and IFNAR2. In COVID-19, the IFNAR2 (interferon alpha and beta receptor subunit 2) gene has been associated with the severity of the disease, but the soluble receptor (sIFNAR2) levels have not been investigated. We aimed to evaluate the association of IFNAR2 variants (rs2236757, rs1051393, rs3153, rs2834158, and rs2229207) with COVID-19 mortality and to assess if there was a relation between the genetic variants and/or the clinical outcome, with the levels of sIFNAR2 in plasma samples from hospitalized individuals with severe COVID-19. We included 1,202 subjects with severe COVID-19. The genetic variants were determined by employing Taqman® assays. The levels of sIFNAR2 were determined with ELISA in plasma samples from a subgroup of 351 individuals. The rs2236757, rs3153, rs1051393, and rs2834158 variants were associated with mortality risk among patients with severe COVID-19. Higher levels of sIFNAR2 were observed in survivors of COVID-19 compared to the group of non-survivors, which was not related to the studied IFNAR2 genetic variants. IFNAR2, both gene, and soluble protein, are relevant in the clinical outcome of patients hospitalized with severe COVID-19.

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12 488 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ~0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative including non-Caucasian individuals, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.

Effect of Chemokine Gene Variants on Covid-19 Disease Severity

Patients immune phenotype/genotype data may be useful to understand the molecular mechanisms involved in SARS-CoV-2 infection and can contribute to the identify the different levels of disease severity. The roles of chemokines have been reported in the coronavirus-related diseases SARS and MERS and they may likewise play a critical role in the development of the symptoms of COVID-19 disease. We analyzed the association of the MCP-1-A2518 G, SDF-1-3'A, CCR5-delta32, CCR5-A55029 G, CXCR4-C138T and CCR2-V64I gene polymorphisms with COVID-19 severity to further unveil the immunological pathways leading to disease severity and death. Polymerase chain reaction(PCR)/Sanger sequencing analysis was performed for detection of the variations in 60 asymptomatic and 119 severe COVID-19 patients. In our study, we found that the frequencies of MCP-1 of GA genotype and G allele carriers were significantly higher in severe COVID-19 patients than the asymptomatic COVID-19 patients (p < .0001 and p: .005, respectively). However, no significant association was found for any of the other polymorphisms with the severity of COVID-19. Our findings suggest that there is a positive association between MCP-1-A2518 G gene variants with the severity of COVID-19. However, larger studies in different population which will focus on gene expression levels will help us to understand the capability of the mechanism role.

Mechanism of COVID-19-Related Proteins in Spinal Tuberculosis: Immune Dysregulation

Purpose

The purpose of this article was to investigate the mechanism of immune dysregulation of COVID-19-related proteins in spinal tuberculosis (STB).

Methods

Clinical data were collected to construct a nomogram model. C-index, calibration curve, ROC curve, and DCA curve were used to assess the predictive ability and accuracy of the model. Additionally, 10 intervertebral disc samples were collected for protein identification. Bioinformatics was used to analyze differentially expressed proteins (DEPs), including immune cells analysis, Gene Ontology (GO) and KEGG pathway enrichment analysis, and protein-protein interaction networks (PPI).

Results

The nomogram predicted risk of STB ranging from 0.01 to 0.994. The C-index and AUC in the training set were 0.872 and 0.862, respectively. The results in the external validation set were consistent with the training set. Immune cells scores indicated that B cells naive in STB tissues were significantly lower than non-TB spinal tissues. Hub proteins were calculated by Degree, Closeness, and MCC methods. The main KEGG pathway included Coronavirus disease-COVID-19. There were 9 key proteins in the intersection of COVID-19-related proteins and hub proteins. There was a negative correlation between B cells naive and RPL19. COVID-19-related proteins were associated with immune genes.

Conclusion

Lymphocytes were predictive factors for the diagnosis of STB. Immune cells showed low expression in STB. Nine COVID-19-related proteins were involved in STB mechanisms. These nine key proteins may suppress the immune mechanism of STB by regulating the expression of immune genes.

Shared genetic etiology and causality between COVID-19 and venous thromboembolism: evidence from genome-wide cross trait analysis and bi-directional Mendelian randomization study

Venous thromboembolism (VTE) occurs in up to one third patients with COVID-19. VTE and COVID-19 may share a common genetic architecture, which has not been clarified yet. To fill this gap, we leveraged summary-level genetic data from the latest COVID-19 host genetics consortium and UK Biobank and examined the shared genetic etiology and causal relationship between COVID-19 and VTE. The cross-trait analysis identified 8, 11, and 7 shared loci between VTE and severe COVID-19, COVID-19 hospitalization, SARS-CoV-2 infection respectively, in 13 genes involved in coagulation and immune function and enriched in the lung. Co-localization analysis identified eight shared loci in ABO, ADAMTS13 and FUT2 genes. Bi-direction Mendelian randomization suggested that VTE was associated with higher risks of all COVID-19 related traits, and SARS-CoV-2 infection was associated with higher risk of VTE. Our study provided timely evidence and novel insights into the genetic etiology between COVID-19 and VTE.

An overview of human proteins and genes involved in SARS-CoV-2 infection

As of July 2021, the outbreak of coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has led to more than 200 million infections and more than 4.2 million deaths globally. Complications of severe COVID-19 include acute kidney injury, liver dysfunction, cardiomyopathy, and coagulation dysfunction. Thus, there is an urgent need to identify proteins and genetic factors associated with COVID-19 susceptibility and outcome. We comprehensively reviewed recent findings of host-SARS-CoV-2 interactome analyses. To identify genetic variants associated with COVID-19, we focused on the findings from genome and transcriptome wide association studies (GWAS and TWAS) and bioinformatics analysis. We described established human proteins including ACE2, TMPRSS2, 40S ribosomal subunit, ApoA1, TOM70, HLA-A, and PALS1 interacting with SARS-CoV-2 based on cryo-electron microscopy results. Furthermore, we described approximately 1000 human proteins showing evidence of interaction with SARS-CoV-2 and highlighted host cellular processes such as innate immune pathways affected by infection. We summarized the evidence on more than 20 identified candidate genes in COVID-19 severity. Predicted deleterious and disruptive genetic variants with possible effects on COVID-19 infectivity have been also summarized. These findings provide novel insights into SARS-CoV-2 biology and infection as well as potential strategies for development of novel COVID therapeutic targets and drug repurposing.

Mechanisms underlying host defense and disease pathology in response to severe acute respiratory syndrome (SARS)-CoV2 infection: insights from inborn errors of immunity

PURPOSE OF REVIEW

The severe acute respiratory syndrome (SARS)-coronavirus 2 (CoV2)/COVID-19 pandemic has reminded us of the fundamental and nonredundant role played by the innate and adaptive immune systems in host defense against emerging pathogens. The study of rare 'experiments of nature' in the setting of inborn errors of immunity (IEI) caused by monogenic germline variants has revealed key insights into the molecular and cellular requirements for immune-mediated protection against infectious diseases. This review will provide an overview of the discoveries obtained from investigating severe COVID-19 in patients with defined IEI or otherwise healthy individuals.

RECENT FINDINGS

Genetic, serological and cohort studies have provided key findings regarding host defense against SARS-CoV2 infection, and mechanisms of disease pathogenesis. Remarkably, the risk factors, severity of disease, and case fatality rate following SARS-CoV2 infection in patients with IEI were not too dissimilar to that observed for the general population. However, the type I interferon (IFN) signaling pathway - activated in innate immune cells in response to viral sensing - is critical for anti-SARS-CoV2 immunity. Indeed, genetic variants or autoAbs affecting type I IFN function account for up to 20% of all cases of life-threatening COVID-19.

SUMMARY

The analysis of rare cases of severe COVID-19, coupled with assessing the impact of SARS-CoV2 infection in individuals with previously diagnosed IEI, has revealed fundamental aspects of human immunology, disease pathogenesis and immunopathology in the context of exposure to and infection with a novel pathogen. These findings can be leveraged to improve therapies for treating for emerging and established infectious diseases.