SELP Asp603Asn and severe thrombosis in COVID-19 males.

Thromboembolism is a frequent cause of severity and mortality in COVID-19. However, the etiology of this phenomenon is not well understood. A cohort of 1186 subjects, from the GEN-COVID consortium, infected by SARS-CoV-2 with different severity was stratified by sex and adjusted by age. Then, common coding variants from whole exome sequencing were mined by LASSO logistic regression. The homozygosity of the cell adhesion molecule P-selectin gene (SELP) rs6127 (c.1807G > A; p.Asp603Asn) which has been already associated with thrombotic risk is found to be associated with severity in the male subcohort of 513 subjects (odds ratio = 2.27, 95% Confidence Interval 1.54-3.36). As the SELP gene is downregulated by testosterone, the odd ratio is increased in males older than 50 (OR 2.42, 95% CI 1.53-3.82). Asn/Asn homozygotes have increased D-dimers values especially when associated with poly Q ≥ 23 in the androgen receptor (OR 3.26, 95% CI 1.41-7.52). These results provide a rationale for the repurposing of antibodies against P-selectin as adjuvant therapy in rs6127 male homozygotes especially if older than 50 or with an impaired androgen receptor.

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 the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the 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 high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). 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. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, 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", supporting their link with COVID-19 severity outcome.

COVID-19 mortality in Italy varies by patient age, sex and pandemic wave.

SARS-CoV-2 has caused a worldwide epidemic of enormous proportions, which resulted in different mortality rates in different countries for unknown reasons. We analyzed factors associated with mortality using data from the Italian national database of more than 4 million SARS-CoV-2-positive cases diagnosed between January 2020 and July 2021, including > 415 thousand hospitalized for coronavirus disease-19 (COVID-19) and > 127 thousand deceased. For patients for whom age, sex and date of infection detection were available, we determined the impact of these variables on mortality 30 days after the date of diagnosis or hospitalization. Multivariable weighted Cox analysis showed that each of the analyzed variables independently affected COVID-19 mortality. Specifically, in the overall series, age was the main risk factor for mortality, with HR > 100 in the age groups older than 65 years compared with a reference group of 15-44 years. Male sex presented a two-fold higher risk of death than female sex. Patients infected after the first pandemic wave (i.e. after 30 June 2020) had an approximately threefold lower risk of death than those infected during the first wave. Thus, in a series of all confirmed SARS-CoV-2-infected cases in an entire European nation, elderly age was by far the most significant risk factor for COVID-19 mortality, confirming that protecting the elderly should be a priority in pandemic management. Male sex and being infected during the first wave were additional risk factors associated with COVID-19 mortality.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

CovidNeuroOnc: A UK multicenter, prospective cohort study of the impact of the COVID-19 pandemic on the neuro-oncology service.

BACKGROUND: The COVID-19 pandemic has profoundly affected cancer services. Our objective was to determine the effect of the COVID-19 pandemic on decision making and the resulting outcomes for patients with newly diagnosed or recurrent intracranial tumors. METHODS: We performed a multicenter prospective study of all adult patients discussed in weekly neuro-oncology and skull base multidisciplinary team meetings who had a newly diagnosed or recurrent intracranial (excluding pituitary) tumor between 01 April and 31 May 2020. All patients had at least 30-day follow-up data. Descriptive statistical reporting was used. RESULTS: There were 1357 referrals for newly diagnosed or recurrent intracranial tumors across 15 neuro-oncology centers. Of centers with all intracranial tumors, a change in initial management was reported in 8.6% of cases (n = 104/1210). Decisions to change the management plan reduced over time from a peak of 19% referrals at the start of the study to 0% by the end of the study period. Changes in management were reported in 16% (n = 75/466) of cases previously recommended for surgery and 28% of cases previously recommended for chemotherapy (n = 20/72). The reported SARS-CoV-2 infection rate was similar in surgical and non-surgical patients (2.6% vs. 2.4%, P > .9). CONCLUSIONS: Disruption to neuro-oncology services in the UK caused by the COVID-19 pandemic was most marked in the first month, affecting all diagnoses. Patients considered for chemotherapy were most affected. In those recommended surgical treatment this was successfully completed. Longer-term outcome data will evaluate oncological treatments received by these patients and overall survival.

Institutional and behaviour-change interventions to support COVID-19 public health measures: a review by the Lancet Commission Task Force on public health measures to suppress the pandemic.

The Lancet COVID-19 Commission Task Force for Public Health Measures to Suppress the Pandemic was launched to identify critical points for consideration by governments on public health interventions to control coronavirus disease 2019 (COVID-19). Drawing on our review of published studies of data analytics and modelling, evidence synthesis and contextualisation, and behavioural science evidence and theory on public health interventions from a range of sources, we outline evidence for a range of institutional measures and behaviour-change measures. We cite examples of measures adopted by a range of countries, but especially jurisdictions that have, thus far, achieved low numbers of COVID-19 deaths and limited community transmission of severe acute respiratory syndrome coronavirus 2. Finally, we highlight gaps in knowledge where research should be undertaken. As countries consider long-term measures, there is an opportunity to learn, improve the response and prepare for future pandemics.

Lung expression of genes putatively involved in SARS-CoV-2 infection is modulated in cis by germline variants.

Germline variants in genes involved in SARS-CoV-2 cell entry and in host innate immune responses to viruses may influence the susceptibility to infection. This study used whole-genome analyses of lung tissue to identify polymorphisms acting as expression quantitative trait loci (eQTLs) for 60 genes of relevance to SARS-CoV-2 infection susceptibility. The expression of genes with confirmed or possible roles in viral entry-replication and in host antiviral responses was studied in the non-diseased lung tissue of 408 lung adenocarcinoma patients. No gene was differently expressed by sex, but APOBEC3H levels were higher and PARP12 levels lower in older individuals. A total of 125 cis-eQTLs (false discovery rate < 0.05) was found to modulate mRNA expression of 15 genes (ABO, ANPEP, AP2A2, APOBEC3D, APOBEC3G, BSG, CLEC4G, DDX58, DPP4, FURIN, FYCO1, RAB14, SERINC3, TRIM5, ZCRB1). eQTLs regulating ABO and FYCO1 were found in COVID-19 susceptibility loci. No trans-eQTLs were identified. Genetic control of the expression of these 15 genes, which encode putative virus receptors, proteins required for vesicle trafficking, enzymes that interfere with viral replication, and other restriction factors, may underlie interindividual differences in risk or severity of infection with SARS-CoV-2 or other viruses.