Rare Variants in Inborn Errors of Immunity Genes Associated With Covid-19 Severity

Genomic Landscape of Susceptibility to Severe COVID-19 in the Slovenian Population

Determining the genetic contribution of susceptibility to severe SARS-CoV-2 infection outcomes is important for public health measures and individualized treatment. Through intense research on this topic, several hundred genes have been implicated as possibly contributing to the severe infection phenotype(s); however, the findings are complex and appear to be population-dependent. We aimed to determine the contribution of human rare genetic variants associated with a severe outcome of SARS-CoV-2 infections and their burden in the Slovenian population. A panel of 517 genes associated with severe SARS-CoV-2 infection were obtained by combining an extensive review of the literature, target genes identified by the COVID-19 Host Genetic Initiative, and the curated Research COVID-19 associated genes from PanelApp, England Genomics. Whole genome sequencing was performed using PCR-free WGS on DNA from 60 patients hospitalized due to severe COVID-19 disease, and the identified rare genomic variants were analyzed and classified according to the ACMG criteria. Background prevalence in the general Slovenian population was determined by comparison with sequencing data from 8025 individuals included in the Slovenian genomic database (SGDB). Results show that several rare pathogenic/likely pathogenic genomic variants in genes CFTR, MASP2, MEFV, TNFRSF13B, and RNASEL likely contribute to the severe infection outcomes in our patient cohort. These results represent an insight into the Slovenian genomic diversity associated with a severe COVID-19 outcome.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response

With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.

TLRs: Innate Immune Sentries against SARS-CoV-2 Infection

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been responsible for a devastating pandemic since March 2020. Toll-like receptors (TLRs), crucial components in the initiation of innate immune responses to different pathogens, trigger the downstream production of pro-inflammatory cytokines, interferons, and other mediators. It has been demonstrated that they contribute to the dysregulated immune response observed in patients with severe COVID-19. TLR2, TLR3, TLR4 and TLR7 have been associated with COVID-19 severity. Here, we review the role of TLRs in the etiology and pathogenesis of COVID-19, including TLR7 and TLR3 rare variants, the L412F polymorphism in TLR3 that negatively regulates anti-SARS-CoV-2 immune responses, the TLR3-related cellular senescence, the interaction of TLR2 and TLR4 with SARS-CoV-2 proteins and implication of TLR2 in NET formation by SARS-CoV-2. The activation of TLRs contributes to viral clearance and disease resolution. However, TLRs may represent a double-edged sword which may elicit dysregulated immune signaling, leading to the production of proinflammatory mediators, resulting in severe disease. TLR-dependent excessive inflammation and TLR-dependent antiviral response may tip the balance towards the former or the latter, altering the equilibrium that drives the severity of disease.

Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.

Real-life data on monoclonal antibodies and antiviral drugs in Italian inborn errors of immunity patients during COVID-19 pandemic

Background

Since the beginning of the COVID-19 pandemic, patients with Inborn Errors of Immunity have been infected by SARS-CoV-2 virus showing a spectrum of disease ranging from asymptomatic to severe COVID-19. A fair number of patients did not respond adequately to SARS-CoV-2 vaccinations, thus early therapeutic or prophylactic measures were needed to prevent severe or fatal course or COVID-19 and to reduce the burden of hospitalizations.

Methods

Longitudinal, multicentric study on patients with Inborn Errors of Immunity immunized with mRNA vaccines treated with monoclonal antibodies and/or antiviral agents at the first infection and at reinfection by SARS-CoV-2. Analyses of efficacy were performed according to the different circulating SARS-CoV-2 strains.

Results

The analysis of the cohort of 192 SARS-CoV-2 infected patients, across 26 months, showed the efficacy of antivirals on the risk of hospitalization, while mabs offered a positive effect on hospitalization, and COVID-19 severity. This protection was consistent across the alpha, delta and early omicron waves, although the emergence of BA.2 reduced the effect of available mabs. Hospitalized patients treated with mabs and antivirals had a lower risk of ICU admission. We reported 16 re-infections with a length of SARS-CoV-2 positivity at second infection shorter among patients treated with mabs. Treatment with antivirals and mabs was safe.

Conclusions

The widespread use of specific therapy, vaccination and better access to care might have contributed to mitigate risk of mortality, hospital admission, and severe disease. However, the rapid spread of new viral strains underlines that mabs and antiviral beneficial effects should be re- evaluated over time.