Evolutionary remodelling of N-terminal domain loops fine-tunes SARS-CoV-2 spike

The emergence of SARS-CoV-2 variants has exacerbated the COVID-19 global health crisis. Thus far, all variants carry mutations in the spike glycoprotein, which is a critical determinant of viral transmission being responsible for attachment, receptor engagement and membrane fusion, and an important target of immunity. Variants frequently bear truncations of flexible loops in the N-terminal domain (NTD) of spike; the functional importance of these modifications has remained poorly characterised. We demonstrate that NTD deletions are important for efficient entry by the Alpha and Omicron variants and that this correlates with spike stability. Phylogenetic analysis reveals extensive NTD loop length polymorphisms across the sarbecoviruses, setting an evolutionary precedent for loop remodelling. Guided by these analyses, we demonstrate that variations in NTD loop length, alone, are sufficient to modulate virus entry. We propose that variations in NTD loop length act to fine-tune spike; this may provide a mechanism for SARS-CoV-2 to navigate a complex selection landscape encompassing optimisation of essential functionality, immune-driven antigenic variation and ongoing adaptation to a new host

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

Detail suppression of stress analysis models

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN014961 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Rates of assay success and genotyping error when single nucleotide polymorphism genotyping in non-model organisms: a case study in the Antarctic fur seal

Although single nucleotide polymorphisms (SNPs) are increasingly being recognized as powerful molecular markers, their application to non-model organisms can bring significant challenges. Among these are imperfect conversion rates of assays designed from in silico resources and the enhanced potential for genotyping error relative to pre-validated, highly optimized human SNPs. To explore these issues, we used Illumina’s GoldenGate assay to genotype 480 Antarctic fur seal (Arctocephalus gazella) individuals at 144 putative SNPs derived from a 454 transcriptome assembly. One hundred and thirty-five polymorphic SNPs (93.8%) were automatically validated by the program GenomeStudio, and the initial genotyping error rate, estimated from nine replicate samples, was 0.004 per reaction. However, an almost tenfold further reduction in the error rate was achieved by excluding 31 loci (21.5%) that exhibited unclear clustering patterns, manually editing clusters to allow rescoring of ambiguous or incorrect genotypes, and excluding 18 samples (3.8%) with unreliable genotypes. After stringent quality filtering, we also found a counter-intuitive negative relationship between in silico minor allele frequency and the conversion rate, suggesting that some of our assays may have been designed from paralogous loci. Nevertheless, we obtained over 45 000 individual SNP genotypes with a final error rate of 0.0005, indicating that the GoldenGate assay is eminently capable of generating large, high-quality data sets for non-model organisms. This has positive implications for future studies of the evolutionary, behavioural and conservation genetics of natural populations

Medials For Meshing And More

INTRODUCTION The goal of an automated FE modelling system is to accept a general problem definition as input and to return results of prescribed accuracy. A general problem definition will include the geometric model of the component to be analysed as well as all the required attributes such as loading, restraints and material properties. Automatic, adaptive mesh generation is an essential prerequisite for generating analysis results of prescribed accuracy for a given computational domain. However for many problems, the geometric design model is too complex a domain to analyse in a realistic timeframe. The purpose here is to argue that the medial axis transform of a geometric domain is a powerful tool for recognising features which are significant in the derivation of appropriate analysis models from design geometry. THE MEDIAL AXIS TRANSFORM The medial axis of a 2D region is the locus of the centre of an inscribed disc of maximal diameter as it rolls around t

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