An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with training of multiple supervised classifiers, to predict severity on the basis of screened features. Feature importance analysis from tree-based models allowed to identify a handful of 16 variants with highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with good accuracy (ACC=81.88%; ROC_AUC=96%; MCC=61.55%). Principal Component Analysis (PCA) and clustering of patients on important variants orthogonally identified two groups of individuals with a higher fraction of severe cases. Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response, such as JAK-STAT, Cytokine, Interleukin, and C-type lectin receptor signaling. It also identified additional processes cross-talking with immune pathways, such as GPCR signalling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, confirming their link with COVID-19 severity outcome. Taken together, our analysis suggests that curated genetic information can be effectively integrated along with other patient clinical covariates to forecast COVID-19 disease severity and dissect the underlying host genetic mechanisms for personalized medicine treatments.

Putative host-derived insertions in the genome of circulating SARS-CoV-2 variants

Insertions in the SARS-CoV-2 genome have the potential to drive viral evolution, but the source of the insertions is often unknown. Recent proposals have suggested that human RNAs could be a source of some insertions, but the small size of many insertions makes this difficult to confirm. Through an analysis of available direct RNA sequencing data from SARS-CoV-2 infected cells, we show that viral-host chimeric RNAs are formed through what are likely stochastic RNA-dependent RNA polymerase template switching events. Through an analysis of the publicly available GISAID SARS-CoV-2 genome collection, we then identified two genomic insertions in circulating SARS-CoV-2 variants that are identical to regions of the human 18S and 28S rRNAs. These results provide direct evidence of the formation of viral-host chimeric sequences and the integration of host genetic material into the SARS-CoV-2 genome, highlighting the potential importance of host-derived insertions in viral evolution.

Variation in Host Resistance to Blastomyces dermatitidis: Potential Use of Genetic Reference Panels and Advances in Immunophenotyping of Diverse Mouse Strains

Host genetic variation significantly impacts vulnerability to infectious diseases. While host variation in susceptibility to fungal infection with dimorphic fungi has long been recognized, genes that underpin this variation are poorly understood.

Prediction of the Occurrence of Leprosy Reactions Based on Bayesian Networks

Background: Leprosy reactions (LR) are severe episodes of intense activation of the host inflammatory response, of uncertain etiology, today the leading cause of permanent nerve damage in leprosy patients. Several genetic and non-genetic risk factors for LR have been described; however, there are limited attempts to combine this information in order to estimate the risk of a leprosy patient to develop LR. Here we present an artificial intelligence (AI)-based system able to estimate risk of LR using clinical, demographic and genetic data.Methods: The study includes four datasets from different regions of Brazil, totalizing 1,450 leprosy patients followed prospectively for at least two years to assess the occurrence of LR. Data mining using WEKA software was performed following a two-step protocol to select the variables included in the AI system, based on Bayesian Networks and developed using the NETICA software.Results: Analysis of the complete database resulted in a system able to estimate LR-risk with 82.7% accuracy, 79.3% sensitivity, and 86.2% specificity. When using only databases for which host genetic information associated with LR was included, the performance increased to up to 87.7% accuracy, 85.7% sensitivity, and 89.4% specificity.Conclusion: We produced an easy-to-use, online, free-access system that allows the identification of leprosy patients at high risk of developing LR. Risk assessment of LR for individual patients may detect candidates close monitoring, with potential positive impact upon the prevention of permanent disabilities, the quality of life of the patients, as well as upon leprosy control programs.

Molecular Investigation of Association Among Common IL-6 Polymorphism with Cytomegalovirus(CMV) Infection and Recurrent Miscarriage in Iranian Women

Background: Recurrent pregnancy loss (RPL) is described as two or more spontaneous abortions. Until now, although various factors such as genetic, endocrinology, anatomy, immunology, and microbiology have been distinguished that affect abortions, the precise basic etiology in up to 50% of RPL cases are not determined. Human cytomegalovirus (CMV) infection and host genetic background, like IL-6 SNP polymorphisms play important roles in RPL etiology. Objective: This study aimed to evaluate relationship among single nucleotide polymorphisms (-634C/G and -174 G/C) in the IL-6 gene with CMV infection and risk of RPL for early detection and treatment of RPL. Materials and methods: This case-control study was carried on 80 Iranian females with RPL and 80 healthy females as control group. The extraction of DNA from samples and detection of CMV and IL6 SNPs were determined by Tetra ARMS-PCR. Finally, the statistical analysis for detection CMV and two polymorphisms roles in RPL were analyzed by Epi Info TM software by X2 test. Results: Our results indicated an increased rate of CMV infection in RPL group (44%) versus the control group (25.45%). Also, the prevalence of IL-6 -634C/G genotype among RPL patients with CMV infection was 80%, while the frequency of this genotype among RPL patients without CMV infection was 50%. Furthermore, no substantial relation was found between IL-6 -174 G/C genotypes and RPL (P ≤0.0001). BesidesConclusion: This study not only indicated a significant role of CMV in RPL, but also showed CMV association with allele G in IL6 -634 among Iranian women. In addition, suggested the use of CMV and IL-6 -634 GG genotypes in RPL as diagnostic and prognostic biomarkers in Iranian population.

IDENTIFYING PLANT GENES SHAPING MICROBIOTA COMPOSITION IN THE BARLEY RHIZOSPHERE

A prerequisite to exploiting soil microbes for sustainable crop production is the identification of the plant genes shaping microbiota composition in the rhizosphere, the interface between roots and soil. Here we used metagenomics information as an external quantitative phenotype to map the host genetic determinants of the rhizosphere microbiota in wild and domesticated genotypes of barley, the fourth most cultivated cereal globally. We identified a small number of loci with a major effect on the composition of rhizosphere communities. One of those, designated the QRMC-3HS locus, emerged as a major determinant of microbiota composition. We then subjected soil-grown sibling lines harbouring contrasting alleles at QRMC-3HS and hosting contrasting microbiotas to comparative root RNA-seq profiling. This allowed us to identify three primary candidate genes, including a Nucleotide-Binding-Leucine-Rich-Repeat (NLR) gene in a region of structural variation of the barley genome. Our results provide novel insights into the footprint of crop improvement on the plants capacity of shaping rhizosphere microbes.

FcγR Genetic Variation and HIV-1 Vaccine Efficacy: Context And Considerations

Receptors for the crystallisable fragment (Fc) of immunoglobulin (Ig) G, Fcγ receptors (FcγRs), link the humoral and cellular arms of the immune response, providing a diverse armamentarium of antimicrobial effector functions. Findings from HIV-1 vaccine efficacy trials highlight the need for further study of Fc-FcR interactions in understanding what may constitute vaccine-induced protective immunity. These include host genetic correlates identified within the low affinity Fcγ-receptor locus in three HIV-1 efficacy trials – VAX004, RV144, and HVTN 505. This perspective summarizes our present knowledge of FcγR genetics in the context of findings from HIV-1 efficacy trials, and draws on genetic variation described in other contexts, such as mother-to-child HIV-1 transmission and HIV-1 disease progression, to explore the potential contribution of FcγR variability in modulating different HIV-1 vaccine efficacy outcomes. Appreciating the complexity and the importance of the collective contribution of variation within the FCGR gene locus is important for understanding the role of FcγRs in protection against HIV-1 acquisition.

Host Genetic Liability for Severe COVID-19 Associates with Alcohol Drinking Behavior and Diabetic Outcomes in Participants of European Descent

Risk factors and long-term consequences of COVID-19 infection are unclear but can be investigated with large-scale genomic data. To distinguish correlation from causation, we performed in-silico analyses of three COVID-19 outcomes (N > 1,000,000). We show genetic correlation and putative causality with depressive symptoms, metformin use (genetic causality proportion (gĉp) with severe respiratory COVID-19 = 0.576, p = 1.07 × 10−5 and hospitalized COVID-19 = 0.713, p = 0.003), and alcohol drinking status (gĉp with severe respiratory COVID-19 = 0.633, p = 7.04 × 10−5 and hospitalized COVID-19 = 0.848, p = 4.13 × 10−13). COVID-19 risk loci associated with several hematologic biomarkers. Comprehensive findings inform genetic contributions to COVID-19 epidemiology, molecular mechanisms, and risk factors and potential long-term health effects of severe response to infection.

GWAS in Mice Maps Susceptibility to HIV-Associated Nephropathy to the Ssbp2 Locus

BackgroundTo gain insight into the pathogenesis of collapsing glomerulopathy, a rare form of FSGS that often arises in the setting of viral infections, we performed a genome-wide association study (GWAS) among inbred mouse strains using a murine model of HIV-1 associated nephropathy (HIVAN).MethodsWe first generated F1 hybrids between HIV-1 transgenic mice on the FVB/NJ background and 20 inbred laboratory strains. Analysis of histology, BUN, and urinary NGAL demonstrated marked phenotypic variation among the transgenic F1 hybrids, providing strong evidence for host genetic factors in the predisposition to nephropathy. A GWAS in 365 transgenic F1 hybrids generated from these 20 inbred strains was performed.ResultsWe identified a genome-wide significant locus on chromosome 13-C3 and multiple additional suggestive loci. Crossannotation of the Chr. 13 locus, including single-cell transcriptomic analysis of wildtype and HIV-1 transgenic mouse kidneys, nominated Ssbp2 as the most likely candidate gene. Ssbp2 is highly expressed in podocytes, encodes a transcriptional cofactor that interacts with LDB1 and LMX1B, which are both previously implicated in FSGS. Consistent with these data, older Ssbp2 null mice spontaneously develop glomerulosclerosis, tubular casts, interstitial fibrosis, and inflammation, similar to the HIVAN mouse model.ConclusionsThese findings demonstrate the utility of GWAS in mice to uncover host genetic factors for rare kidney traits and suggest Ssbp2 as susceptibility gene for HIVAN, potentially acting via the LDB1-LMX1B transcriptional network.