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

Thresholds of potential concern as benchmarks in the management of African savannahs

In the Kruger National Park (KNP), South Africa, ecosystem managers use a series of monitoring endpoints, known as thresholds of potential concern (TPCs), to define the upper and the lower levels of accepted variation in ecosystems. For woody vegetation, the current TPC suggests that woody cover should not drop by more than 80% of its ‘highest ever’ value. In this paper, we explore the utility of palaeoecological data in informing TPCs. We use calibrated fossil pollen data to explore variability in vegetation at two sites over the past 5000 years, to provide a long-term record of changes in woody vegetation cover and a context for interpreting more recent vegetation change. The fossil pollen data are calibrated using studies of modern pollen and vegetation from KNP; arboreal pollen percentage was simulated using pollen–landscape modelling software for savannah landscapes of varying woody vegetation cover, and the relationship between vegetation and pollen data was quantified using nonlinear regression. This quadratic equation was then applied to fossil pollen data in order to estimate woody vegetation cover from arboreal pollen percentages. Our results suggest that the TPCs have not been exceeded during the period represented in the pollen record, because estimated woody vegetation cover has remained above 20% of its highest ever value. By comparing the fossil pollen data with TPCs, our study demonstrates how palaeoecological data can be presented in a form that is directly relevant to management objectives

The lagrange approach to infinite linear programs

Infinite linear programs, convex problems, Lagrange approach, strong duality, 90C46, 90C25, 90C08,

A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes

Chronic inflammation is postulated to be involved in the development of end-stage renal disease in diabetes, but which specific circulating inflammatory proteins contribute to this risk remain unknown. To study this, we examined 194 circulating inflammatory proteins in subjects from three independent cohorts with type 1 and type 2 diabetes. In each cohort, we identified an extremely robust kidney risk inflammatory signature (KRIS), consisting of 17 proteins enriched in tumor necrosis factor-receptor superfamily members, that was associated with a 10-year risk of end-stage renal disease. All these proteins had a systemic, non-kidney source. Our prospective study findings provide strong evidence that KRIS proteins contribute to the inflammatory process underlying end-stage renal disease development in both types of diabetes. These proteins point to new therapeutic targets and new prognostic tests to identify subjects at risk of end-stage renal disease, as well as biomarkers to measure responses to treatment of diabetic kidney disease

Cryptic species within the cosmopolitan desiccation-tolerant moss Grimmia laevigata

The common cushion moss Grimmia laevigata (Bridel) Bridel grows on bare rock in a broad range of environments on every continent except Antarctica. As such, it must harbor adaptations to a remarkably broad set of environmental stresses, the extremes of which can include very high temperatures, prolonged nearly complete desiccation, and high ultraviolet B (UVB) exposure. Yet, like many mosses, G. laevigata shows very little morphological variability across its cosmopolitan range. This presents an evolutionary puzzle, the solution to which lies in understanding the phylogeographic structure of this morphologically simple organism. Here we report the results of an analysis of amplified fragment length polymorphisms (AFLPs) in G. laevigata, focusing on individuals from the California Floristic Province. We found evidence that populations within California constitute two distinct geographically overlapping cryptic species. Each clade harbors multiple private alleles, indicating they have been genetically isolated for some time. We suggest that the existence of cryptic species within G. laevigata, in combination with its life history, growth habits, and extreme desiccation tolerance, makes this moss an ideal research tool and a candidate for a biological indicator of climate change and pollution