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

Model uncertainty in the ecosystem approach to fisheries

Fisheries scientists habitually consider uncertainty in parameter values, but often neglect uncertainty about model structure. The importance of this latter source of uncertainty is likely to increase with the greater emphasis on ecosystem models in the move to an ecosystem approach to fisheries (EAF). It is therefore necessary to increase awareness about pragmatic approaches with which fisheries modellers and managers can account for model uncertainty and so we review current ways of dealing with model uncertainty in fisheries and other disciplines. These all involve considering a set of alternative models representing different structural assumptions, but differ in how those models are used. The models can be used to identify bounds on possible outcomes, find management actions that will perform adequately irrespective of the true model, find management actions that best achieve one or more objectives given weights assigned to each model, or formalise hypotheses for evaluation through experimentation. Data availability is likely to limit the use of approaches that involve weighting alternative models in an ecosystem setting, and the cost of experimentation is likely to limit its use. Practical implementation of the EAF should therefore be based on management approaches that acknowledge the uncertainty inherent in model predictions and are robust to it. Model results must be presented in a way that represents the risks and trade-offs associated with alternative actions and the degree of uncertainty in predictions. This presentation should not disguise the fact that, in many cases, estimates of model uncertainty may be based on subjective criteria. The problem of model uncertainty is far from unique to fisheries, and coordination among fisheries modellers and modellers from other communities will therefore be useful

Modification of marine habitats by trawling activities: prognosis and solutions

Fishing affects the seabed habitat worldwide on the continental shelf. These impacts are patchily distributed according to the spatial and temporal variation in fishing effort that results from fishers' behaviour. As a consequence, the frequency and intensity of fishing disturbance varies among different habitat types. Different fishing methodologies vary in the degree to which they affect the seabed. Structurally complex habitats (e.g. seagrass meadows, biogenic reefs) and those that are relatively undisturbed by natural perturbations (e.g. deep-water mud substrata) are more adversely affected by fishing than unconsolidated sediment habitats that occur in shallow coastal waters. These habitats also have the longest recovery trajectories in terms of the recolonization of the habitat by the associated fauna. Comparative studies of areas of the seabed that have experienced different levels of fishing activity demonstrate that chronic fishing disturbance leads to the removal of high-biomass species that are composed mostly of emergent seabed organisms. Contrary to the belief of fishers that fishing enhances seabed production and generates food for target fish species, productivity is actually lowered as fishing intensity increases and high-biomass species are removed from the benthic habitat. These organisms also increase the topographic complexity of the seabed which has been shown to provide shelter for juvenile fishes, reducing their vulnerability to predation. Conversely, scavengers and small-bodied organisms, such as polychaete worms, dominate heavily fished areas. Major changes in habitat can lead to changes in the composition of the resident fish fauna. Fishing has indirect effects on habitat through the removal of predators that control bio-engineering organisms such as algal-grazing urchins. Fishing gear resuspend the upper layers of sedimentary seabed habitats and hence remobilize contaminants and fine particulate matter into the water column. The ecological significance of these fishing effects has not yet been determined but could have implications for eutrophication and biogeochemical cycling. Simulation results suggest that the effects of low levels of trawling disturbance will be similar to those of natural bioturbators. In contrast, high levels of trawling disturbance cause sediment systems to become unstable due to large carbon fluxes between oxic and anoxic carbon compartments. In low energy habitats, intensive trawling disturbance may destabilize benthic system chemical fluxes, which has the potential to propagate more widely through the marine ecosystem. Management regimes that aim to incorporate both fisheries and habitat conservation objectives can be achieved through the appropriate use of a number of approaches, including total and partial exclusion of towed bottom fishing gears, and seasonal and rotational closure techniques. However, the inappropriate use of closed areas may displace fishing activities into habitats that are more vulnerable to disturbance than those currently trawled by fishers. In many cases, the behaviour of fishers constrains the extent of the impact of their fishing activities. Management actions that force them to redistribute their effort may be more damaging in the longer term