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

Quasi-centralized limit order books

A quasi-centralized limit order book (QCLOB) is a limit order book (LOB) in which financial institutions can only access the trading opportunities offered by counterparties with whom they possess sufficient bilateral credit. We perform an empirical analysis of a recent, high-quality data set from a large electronic trading platform that utilizes QCLOBs to facilitate trade. We find many significant differences between our results and those widely reported for other LOBs. We also uncover a remarkable empirical universality: although the distributions describing order flow and market state vary considerably across days, a simple, linear rescaling causes them to collapse onto a single curve. Motivated by this finding, we propose a semi-parametric model of order flow and market state in a QCLOB on a single trading day. Our model provides similar performance to that of parametric curve-fitting techniques, while being simpler to compute and faster to implement.Comment: 43 pages, 18 figure

Targeting OGG1 arrests cancer cell proliferation by inducing replication stress

Altered oncogene expression in cancer cells causes loss of redox homeostasis resulting in oxidative DNA damage, e.g. 8-oxoguanine (8-oxoG), repaired by base excision repair (BER). PARP1 coordinates BER and relies on the upstream 8-oxoguanine-DNA glycosylase (OGG1) to recognise and excise 8-oxoG. Here we hypothesize that OGG1 may represent an attractive target to exploit reactive oxygen species (ROS) elevation in cancer. Although OGG1 depletion is well tolerated in non-transformed cells, we report here that OGG1 depletion obstructs A3 T-cell lymphoblastic acute leukemia growth in vitro and in vivo, validating OGG1 as a potential anti-cancer target. In line with this hypothesis, we show that OGG1 inhibitors (OGG1i) target a wide range of cancer cells, with a favourable therapeutic index compared to non-transformed cells. Mechanistically, OGG1i and shRNA depletion cause S-phase DNA damage, replication stress and proliferation arrest or cell death, representing a novel mechanistic approach to target cancer. This study adds OGG1 to the list of BER factors, e.g. PARP1, as potential targets for cancer treatment