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

Determining the optimal size of small molecule mixtures for high throughput NMR screening

High-throughput screening (HTS) using NMR spectroscopy has become a common component of the drug discovery effort and is widely used throughout the pharmaceutical industry. NMR provides additional information about the nature of small molecule-protein interactions compared to traditional HTS methods. In order to achieve comparable efficiency, small molecules are often screened as mixtures in NMR-based assays. Nevertheless, an analysis of the efficiency of mixtures and a corresponding determination of the optimum mixture size (OMS) that minimizes the amount of material and instrumentation time required for an NMR screen has been lacking. A model for calculating OMS based on the application of the hypergeometric distribution function to determine the probability of a \u27hit\u27 for various mixture sizes and hit rates is presented. An alternative method for the deconvolution of large screening mixtures is also discussed. These methods have been applied in a high-throughput NMR screening assay using a small, directed library

The Biology of the Presenilin Complexes

APP proteolytic processing Alzheimer's Disease (AD) is characterized by the deposition of two kinds of abnormal protein aggregates, senile plaques and neurofibrillary tangles, and by neuronal dysfunction and cell loss in the brain. Senile plaques are primarily composed of extracellular deposits of hydrophobic 37-43 amino acid Aβ peptides. Aβ peptides are derived by successive enzymatic cleavages of the type I membrane protein, β-amyloid precursor protein (APP) (Haass and Selkoe 1993). APP is first cleaved close to the membrane in the extracellular domain by either α-or β-secretase, resulting in a release of soluble APP ectodomains, and residual membrane-tethered C-terminal protein stubs, termed C83 or C99, respectively (The numbers indicate the length of each carboxylterminal fragment). C83 and C99 are substrates for γ-secretase, an activity that generates p3 and Aβ peptides, respectively. γ-Secretase processes substrates at different positions within the membrane domain and thus, both Aβ and p3 have "ragged" termini. Aβ has been best studied in this regard and species between 37 and 43 amino acid residues have been identified. γ-Secretase cleavage of APP also releases the intracellular carboxy-terminal "APP intracellular domain" or "AICD". The function of both Aβ and AICD is the subject of intense investigations. Because Aβ42 is the primary constituent of the amyloid fibrils deposited in the AD brains, and mutations in APP and presenilin enhance the production of this peptide, γ-secretase cleavage of APP is a pivotal step in AD pathogenesis. It is striking that this proteolytic reaction occurs within the highly hydrophobic environment of the membrane. Identification of presenilin Genetic studies in familial AD (FAD) cases have identified disease-linked mutations in three genes that contribute to AD. The first pathogenic mutations in early-onset FAD families were found in the APP gene on chromosome 21 (Chartier-Harlin et al. 1991; Goate et al. 1991; Murrell et al. 1991). However, subsequent studies indicated that mutations in APP account only for a small fraction of FAD cases. Several genetic studies indicated a major locus for FAD on chromosome 14 in early onset autosomal dominant AD, and in 1995, the Presenilin1 (PS1) gene on chromosome 14 (14q24.3) was identified by positional cloning (Sherrington et al. 1995). Shortly thereafter, it was shown that mutations in the closely related PS2 gene on chromosome 1 (1q42.2) could cause FAD as well (Levy-Lahad et al. 1995; Rogaev et al. 1995). Studies in transgenic mice (Borchelt et al. 1996; Duff et al. 1996) and cultured cells (Citron et al. 1997; Scheuner et al. 1996; Tomita et al. 1997) have revealed that expression of FAD-linked PS variants elevates Aβ42/Aβ40 ratios. Moreover, transgenic mice that co-express FAD-mutant PS1 and APP develop amyloid plaques much earlier than age-matched mutant APP mice (Borchelt et al. 1997). Therefore, PS mutations cause a change in the Aβ42/40 ratio, but whether PS is directly involved in γ-secretase processing of APP was unclear. However, in PS-deficient neurons and fibroblasts, APP processing was greatly impaired, leading to the accumulation of the C83 and C99 APP fragments, the direct substrates of γ-secretase, and inhibition of Aβ (and p3) generation (De Strooper et al. 1998; Xia et al. 1998). Thus, PS are directly required for γ-secretase cleavage of APP. Overall, the findings imply that mutations in the substrate (APP) or in the proteolytic machinery (PS) result in similar changes in Aβ42 generation (Scheuner et al. 1996). This provides very strong support for the "amyloid cascade hypothesis". © 2007 Springer Science+Business Media, LLC. All rights reserved

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

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization 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