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

peer reviewe

A MobileNet Neural Network Model for Fault Diagnosis in Roller Bearings

This work is focused on the realization of a low-complexity MobileNet neural network able to classify different bearing failure vibration signals. The network was designed to be deployed on an embedded microcontroller to be used in smart sensors in an IoT context, pursuing the paradigms of Embedded Artificial Intelligence and Edge Computing. Following these same concepts, data preprocessing as well has been kept as simple as possible, implementing a computationally low-cost strategy, allowing nonetheless for creating meaningful images (i.e. able to highlight the different bearing faults). Moreover, the network was trained with an ad hoc dataset created exploiting a test bench able to emulate different typologies of bearing failure vibration patterns, measured with different accelerometers. The study demonstrates, exploiting emulated data, how the MobileNet can generalize the learned features, exhibiting satisfactory performance when tested on data having different characteristics in terms of frequency components and acquired by sensors with different metrological behaviors, reaching an over-99% classification accuracy

4-Amino-1,2,4-triazole-3-thione-derived Schiff bases as metallo-β-lactamase inhibitors

Resistance to β-lactam antibiotics in Gram-negatives producing metallo-β-lactamases (MBLs) represents a major medical threat and there is an extremely urgent need to develop clinically useful inhibitors. We previously reported the original binding mode of 5-substituted-4-amino/H-1,2,4-triazole-3-thione compounds in the catalytic site of an MBL. Moreover, we showed that, although moderately potent, they represented a promising basis for the development of broad-spectrum MBL inhibitors. Here, we synthesized and characterized a large number of 4-amino-1,2,4-triazole-3-thione-derived Schiff bases. Compared to the previous series, the presence of an aryl moiety at position 4 afforded an average 10-fold increase in potency. Among 90 synthetic compounds, more than half inhibited at least one of the six tested MBLs (L1, VIM-4, VIM-2, NDM-1, IMP-1, CphA) with Ki values in the μM to sub-μM range. Several were broad-spectrum inhibitors, also inhibiting the most clinically relevant VIM-2 and NDM-1. Active compounds generally contained halogenated, bicyclic aryl or phenolic moieties at position 5, and one substituent among o-benzoic, 2,4-dihydroxyphenyl, p-benzyloxyphenyl or 3-(m-benzoyl)-phenyl at position 4. The crystallographic structure of VIM-2 in complex with an inhibitor showed the expected binding between the triazole-thione moiety and the dinuclear centre and also revealed a network of interactions involving Phe61, Tyr67, Trp87 and the conserved Asn233. Microbiological analysis suggested that the potentiation activity of the compounds was limited by poor outer membrane penetration or efflux. This was supported by the ability of one compound to restore the susceptibility of an NDM-1-producing E. coli clinical strain toward several β-lactams in the presence only of a sub-inhibitory concentration of colistin, a permeabilizing agent. Finally, some compounds were tested against the structurally similar di-zinc human glyoxalase II and found weaker inhibitors of the latter enzyme, thus showing a promising selectivity towards MBLs

4-Amino-1,2,4-triazole-3-thione-derived Schiff bases as metallo-beta-lactamase inhibitors

Resistance to β-lactam antibiotics in Gram-negatives producing metallo-β-lactamases (MBLs) represents a major medical threat and there is an extremely urgent need to develop clinically useful inhibitors. We previously reported the original binding mode of 5-substituted-4-amino/H-1,2,4-triazole-3-thione compounds in the catalytic site of an MBL. Moreover, we showed that, although moderately potent, they represented a promising basis for the development of broad-spectrum MBL inhibitors. Here, we synthesized and characterized a large number of 4-amino-1,2,4-triazole-3-thione-derived Schiff bases. Compared to the previous series, the presence of an aryl moiety at position 4 afforded an average 10-fold increase in potency. Among 90 synthetic compounds, more than half inhibited at least one of the six tested MBLs (L1, VIM-4, VIM-2, NDM-1, IMP-1, CphA) with Ki values in the microM to sub-microM range. Several were broad-spectrum inhibitors, also inhibiting the most clinically relevant VIM-2 and NDM-1. Active compounds generally contained halogenated, bicyclic aryl or phenolic moieties at position 5, and one substituent among o-benzoic, 2,4-dihydroxyphenyl, p-benzyloxyphenyl or 3-(m-benzoyl)-phenyl at position 4. The crystallographic structure of VIM-2 in complex with an inhibitor showed the expected binding between the triazole-thione moiety and the dinuclear centre and also revealed a network of interactions involving Phe61, Tyr67, Trp87 and the conserved Asn233. Microbiological analysis suggested that the compound antibacterial activity was limited by poor outer membrane penetration. This was supported by the ability of one compound to restore the susceptibility of an NDM-1-producing E. coli clinical strain toward several beta-lactams in the presence only of a sub-inhibitory concentration of colistin, a permeabilizing agent. Finally, some compounds were tested against the structurally similar di-zinc human glyoxalase II and found weaker inhibitors of the latter enzyme, thus showing a promising selectivity towards MBLs

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