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

Bandstructure approach to near edge structure

We review the current state of the art in EELS fingerprinting by computer simulation, focusing on the bandstructure approach to the problem. Currently calculations are made using a one electron theory, but we describe in principle the way to go beyond this to include final state effects. We include these effects within the one electron framework using the Slater transition state formula and assess the errors involved. Two examples are then given which illustrate the use of the one electron approximation within density functional theory. Our approach is to combine predicted atomic structure with predicted electronic structure to assist in fingerprinting of complex crystal structures

Atomistic calculations on interfaces: Bridging the length and time scales

Phase field simulations suitable to describe interfacial phenomena at the mesoscale require as input parameters accurate interfacial energies as well as the interface mobilities. However, this information is not directly accessible by experiment. Furthermore, phenomena such as impurity segregation cannot be decoupled and their independent role in interfacial cohesion and mobility cannot be deduced. On the other hand ab-initio calculations and/or classical interatomic potentials are suitable tools which can provide an on-atomic-scale description of the interfaces. However, there are a number of challenges that one encounters: multidimensional phase space of the interfacial misorientation degrees of freedom, suitable driving forces, and large length and time scales just to mention a few. In the present report we provide an extended review on the atomistic calculations and the simulation strategies proposed to tackle the corresponding problems