SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A first update on mapping the human genetic architecture of COVID-19

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Chemical modification of activated carbon surface with iron functional groups for efficient separation of vanadium: batch and column study

In this study, iron functional groups-impregnated activated carbon (IIAC) composite was prepared as a novel adsorbent for vanadium separation. Adsorption experiments were performed in batch and column systems, and the effects of various operating parameters, such as solution pH, initial concentration, contact time, and temperature, were evaluated. The kinetic data confirmed the validity of the pseudo-second-order kinetic model for vanadium adsorption on IIAC. The sorption equilibrium data were analyzed using Langmuir, Freundlich, and Dubinin–Radushkevich isotherm models. The results showed that IIAC has a vanadium ions adsorption capacity of 313 mg g −1 . The activation and thermodynamic parameters were determined using kinetics and equilibrium data. The experimental data of the column adsorption process were fitted by Thomas and BDST models. The results showed that Thomas model can well describe the breakthrough curves. The column experiments showed that IIAC composite has good adsorption performance for vanadium ions adsorption

Iron-activated carbon nanocomposite: Synthesis, characterization and application for lead removal from aqueous solution

The removal of Pb(ii) ions from aqueous solution by adsorption on an Iron-Activated Carbon (IAC) nanocomposite was investigated. Removal studies were carried out in a batch system, and the effects of various operating parameters, such as solution pH, solid to liquid ratio and initial concentration were evaluated. Experimental design was carried out using central composite design (CCD) with response surface methodology (RSM). According to the RSM results, the optimum adsorption conditions for Pb(ii) removal by IAC were pH = 6.5, solid to liquid ratio of 3 g L-1 and initial lead concentration of 10 mg L-1. Under these optimum operating conditions, 96.5% of Pb(ii) was removed by the IAC nanocomposite. The equilibrium adsorption data were well described by the Freundlich isotherm. The maximum adsorption capacity of IAC was 121.9 mg g-1 for Pb(ii). It was observed that the adsorption kinetics of Pb(ii) on the IAC could be well analyzed with a pseudo-second-order model

Intention to leave the nursing profession and its relation with work climate and demographic characteristics

Background: Because of the importance of staff shortage in health systems, considering the intention to leave the job and its related factors among nurses is very important. The aim of this study was to identify the association between the intention to leave the nursing profession and work climate and demographic characteristics. Materials and Methods: A cross-sectional survey was conducted among 206 nurses, by random sampling method from six hospitals (response rate = 92). A set of self-administered questionnaires were applied for the evaluation of intention to leave and work climate. Results: The high level of intention to leave the profession was expressed by 23.70 of the participants; 25.10 of the participants had the moderate intention. Data analysis revealed that work climate, type of employment, marital status, and overtime working were significant predictors of nurses' intention to leave after controlling other independent variables (R2 = 0.10, p < 0.001). Conclusions: It was found that work climate and some demographic characteristics can be seen as indicators for intention to leave among nurses; therefore, considering the so-called variables is required. Further studies are needed to identify other aspects of the issue. © 2019 Iranian Journal of Nursing and Midwifery Research. All rights reserved