Effects of pH, eelgrass, and settlement substrate on the growth of juvenile magallana (crassostrea) gigas, a commercially important oyster species

Worsening ocean acidification (OA), resulting from ongoing absorption of atmospheric carbon dioxide (CO2) by the oceans, threatens marine life globally. Calcifying organisms, especially their early life stages, are particularly vulnerable; this includes the economically important Pacific oyster, Magallana (Crassostrea) gigas. Uptake of dissolved CO2 through photosynthesis by seagrasses, like eelgrass (Zostera marina), may benefit calcifying organisms by increasing pH and carbonate availability. I conducted laboratory and field experiments to quantify carbonate chemistry modification by eelgrass and potential mitigation of OA impacts on growth in juvenile Pacific oysters. In the laboratory experiment, daytime net photosynthesis by eelgrass increased seawater pH, while nighttime net respiration reduced pH though to a lesser extent; both effects grew stronger as the pH of incoming seawater decreased. This is consistent with the expectation that eelgrass will benefit from increased aqueous CO2 levels and suggests that the importance of carbonate chemistry modification by eelgrass and its role as a refugium may increase as OA proceeds. Under the conditions tested, however, eelgrass effects on pH were modest and did not affect oyster growth in the lab or field. In the lab, oysters settled on shell flour grew faster than those on shell chunks, but unlike those on chunks, the growth rate of oysters on flour decreased significantly in low pH treatments. One hypothesis consistent with these results is that the boundary layer around shell chunks may have slowed oyster growth by limiting food availability but that it also reduced sensitivity to low pH via enhanced carbonate saturation

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Probabilistic hazard assessment for pyroclastic density currents at Tungurahua volcano, Ecuador

We assess the volcanic hazard derived from pyroclastic density currents (PDCs) at Tungurahua volcano, Ecuador, using a probabilistic approach based on the analysis of calibrated numerical simulations. We address the expected variability of explosive eruptions at Tungurahua volcano by adopting a scenario-based strategy, where we consider three cases: small magnitude violent Strombolian to Vulcanian eruption (VEI 2), intermediate magnitude sub-Plinian eruption (VEI 3), and large magnitude sub-Plinian to Plinian eruption (VEI 4–5). PDCs are modeled using the branching energy cone model and the branching box model, considering reproducible calibration procedures based on the geological record of Tungurahua volcano. The use of different calibration procedures and reference PDC deposits allows us to define uncertainty ranges for the inundation probability of each scenario. Numerical results indicate that PDCs at Tungurahua volcano propagate preferentially toward W and NW, where a series of catchment ravines can be recognized. Two additional valleys of channelization are observed in the N and NE flanks of the volcano, which may affect the city of Baños. The mean inundation probability calculated for Baños is small (6 ± 3%) for PDCs similar to those emplaced during the VEI 2 eruptions of July 2006, February 2008, May 2010, July 2013, February 2014 and February 2016, and on the order of 13 ± 4% for a PDC similar to that produced during the sub-Plinian phase of the August 2006 eruption (VEI 3). The highest energy scenario (VEI 4–5), for which we present and implement a novel calibration procedure based on a few control points, produces inundation areas that nearly always include inhabited centers such as Baños, Puela and Cotaló, among others. This calibration method is well suited for eruptive scenarios that lack detailed field information, and could be replicated for poorly-known active volcanoes around the world

Conformational Characterization of Intrinsically Disordered Proteins and Its Biological Significance

International audienc