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

North American Carbon Program (NACP) regional interim synthesis: Terrestrial biospheric model intercomparison

Understanding of carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding to much larger terrestrial regions. Although models vary in their specific goals and approaches, their central role within carbon cycle science is to provide a better understanding of the mechanisms currently controlling carbon exchange. Recently, the North American Carbon Program (NACP) organized several interim-synthesis activities to evaluate and inter-compare models and observations at local to continental scales for the years 2000–2005. Here, we compare the results from the TBMs collected as part of the regional and continental interim-synthesis (RCIS) activities. The primary objective of this work is to synthesize and compare the 19 participating TBMs to assess current understanding of the terrestrial carbon cycle in North America. Thus, the RCIS focuses on model simulations available from analyses that have been completed by ongoing NACP projects and other recently published studies. The TBM flux estimates are compared and evaluated over different spatial (1◦ × 1◦ and spatially aggregated to different regions) and temporal (monthly and annually) scales. The range in model estimates of net ecosystem productivity (NEP) for North America is much narrower than estimates of productivity or respiration, with estimates of NEP varying between −0.7 and 2.2 PgC yr−1, while gross primary productivity and heterotrophic respiration vary between 12.2 and 32.9 PgC yr−1 and 5.6 and 13.2 PgC yr−1, respectively. The range in estimates from the models appears to be driven by a combination of factors, including the representation of photosynthesis, the source and of environmental driver data and the tempora

Global solutions to regional problems: Collecting global expertise to address the problem of harmful cyanobacterial blooms. A Lake Erie case study

In early August 2014, the municipality of Toledo, OH (USA) issued a ‘do not drink’ advisory on their water supply directly affecting over 400,000 residential customers and hundreds of businesses (Wilson, 2014). This order was attributable to levels of microcystin, a potent liver toxin, which rose to 2.5 μg L−1 in finished drinking water. The Toledo crisis afforded an opportunity to bring together scientists from around the world to share ideas regarding factors that contribute to bloom formation and toxigenicity, bloom and toxin detection as well as prevention and remediation of bloom events. These discussions took place at an NSF- and NOAA-sponsored workshop at Bowling Green State University on April 13 and 14, 2015. In all, more than 100 attendees from six countries and 15 US states gathered together to share their perspectives. The purpose of this review is to present the consensus summary of these issues that emerged from discussions at the Workshop. As additional reports in this special issue provide detailed reviews on many major CHAB species, this paper focuses on the general themes common to all blooms, such as bloom detection, modeling, nutrient loading, and strategies to reduce nutrients

Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions

Terrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO2, and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections.This article is published as 7. Huntzinger, D., A. Michalak, C. Schwalm, P. Ciais, A. King, Y. Fang, K. Schefer, Y. Wei, R. Cook, J. Fisher, D. Hayes, M. Huang, A. Ito, A. Jain, H. Lei, C. Lu, F. Maignam, J. Mao, N. Parazoo, S. Peng, B. Poulter, D. Ricciuto, X. Shi, H. Tian, W. Wang, N. Zeng, and F. Zhao. 2017. Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions.Nature Scientific Reports, 7(2017); 4765. doi: 10.1038/s41598-017-03818-2 .</p

Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions

International audienceTerrestrial ecosystems play a vital role in regulating the accumulation of carbon (C) in the atmosphere. Understanding the factors controlling land C uptake is critical for reducing uncertainties in projections of future climate. The relative importance of changing climate, rising atmospheric CO 2 , and other factors, however, remains unclear despite decades of research. Here, we use an ensemble of land models to show that models disagree on the primary driver of cumulative C uptake for 85% of vegetated land area. Disagreement is largest in model sensitivity to rising atmospheric CO 2 which shows almost twice the variability in cumulative land uptake since 1901 (1 s.d. of 212.8 PgC vs. 138.5 PgC, respectively). We find that variability in CO 2 and temperature sensitivity is attributable, in part, to their compensatory effects on C uptake, whereby comparable estimates of C uptake can arise by invoking different sensitivities to key environmental conditions. Conversely, divergent estimates of C uptake can occur despite being based on the same environmental sensitivities. Together, these findings imply an important limitation to the predictability of C cycling and climate under unprecedented environmental conditions. We suggest that the carbon modeling community prioritize a probabilistic multi-model approach to generate more robust C cycle projections