Environmental DNA captures elasmobranch diversity in a temperate marine ecosystem

Abstract: Many sharks, skates, and rays (elasmobranchs) are highly threatened by the activities of commercial fisheries, and a clear understanding of their distributions, diversity, and abundance can guide protective measures. However, surveying and monitoring elasmobranch species can be highly invasive or resource‐intensive, and utilization of non‐invasive environmental DNA‐based methods may overcome these problems. Here, we studied spatial and seasonal variation in the elasmobranch community of the Western English Channel using environmental DNA (eDNA) collected from surface and bottom waters periodically over an annual cycle (2017–2018). In total we recovered 13 elasmobranch species within eDNA samples, and the number of transformed eDNA reads was positively associated with species (hourly) catch data resolved from 105‐year time series trawl data (1914–2018). These results demonstrate the ability of eDNA to detect and semi‐quantitatively reflect the prevalence of historically dominant and rare elasmobranch species in this region. Notably, eDNA recorded a greater number of species per sampling event than a conventional trawl survey in the same area over the same sampling years (2017–2018). Several threatened species were recovered within the eDNA, including undulate ray, porbeagle shark, and thresher shark. Using eDNA, we found differences in elasmobranch communities among sampling stations and between seasons, but not between sampling depths. Collectively, our results suggest that non‐invasive eDNA‐based methods can be used to study the spatial and seasonal changes in the diversity and abundance of whole elasmobranch communities within temperate shelf habitats. Given the threatened status of many elasmobranchs in human‐impacted marine environments, eDNA analysis is poised to provide key information on their diversity and distributions to inform conservation‐focused monitoring and management

In Vitro Assay Development and HTS of Small-Molecule Human ABAD/17β-HSD10 Inhibitors as Therapeutics in Alzheimer's Disease

This research was funded by the Scottish Universities Life Science Alliance (SULSA) assay development fund. This research was also kindly supported by The Rosetrees Trust and The Alzheimer’s Society, specifically The Barcopel Foundation, and partly funded by the MSD Scottish Life Sciences fund. As part of an ongoing contribution to Scottish life sciences, MSD Limited, a global health care leader, has given substantial monetary funding to the Scottish Funding Council for distribution via SULSA to develop and deliver a high-quality drug discovery research and training program.A major hallmark of Alzheimer’s disease (AD) is the formation of neurotoxic aggregates composed of the amyloid-β peptide (Aβ). Aβ has been recognized to interact with numerous proteins, resulting in pathological changes to the metabolism of patients with AD. One such mitochondrial metabolic enzyme is amyloid-binding alcohol dehydrogenase (ABAD), where altered enzyme function caused by the Aβ-ABAD interaction is known to cause mitochondrial distress and cytotoxic effects, providing a feasible therapeutic target for AD drug development. Here we have established a high-throughput screening platform for the identification of modulators to the ABAD enzyme. A pilot screen with a total of 6759 compounds from the NIH Clinical Collections (NCC) and SelleckChem libraries and a selection of compounds from the BioAscent diversity collection have allowed validation and robustness to be optimized. The pilot screen revealed 16 potential inhibitors in the low µM range against ABAD with favorable physicochemical properties for blood-brain barrier penetration.PostprintPeer reviewe

Sets of coregulated serum lipids are associated with Alzheimer's disease pathophysiology

Introduction: Comorbidity with metabolic diseases indicates that lipid metabolism plays a role in the etiology of Alzheimer's disease (AD). Comprehensive lipidomic analysis can provide new insights into the altered lipid metabolism in AD. Method: In this study, a total 349 serum lipids were measured in 806 participants enrolled in the Alzheimer's Disease Neuroimaging Initiative Phase 1 cohort and analyzed using lipid-set enrichment statistics, a data mining method to find coregulated lipid sets. Results: We found that sets of blood lipids were associated with current AD biomarkers and with AD clinical symptoms. AD diagnosis was associated with 7 of 28 lipid sets of which four also correlated with cognitive decline, including polyunsaturated fatty acids. Cerebrospinal fluid amyloid beta (Aβ1-42) correlated with glucosylceramides, lysophosphatidylcholines and unsaturated triacylglycerides; cerebrospinal fluid total tau and brain atrophy correlated with monounsaturated sphingomyelins and ceramides, in addition to EPA-containing lipids. Discussion: AD-associated lipid sets indicated that lipid desaturation, elongation, and acyl chain remodeling processes are disturbed in AD subjects. Monounsaturated lipid metabolism was important in early stages of AD, whereas the polyunsaturated lipid metabolism was associated with later stages of AD. Our study provides several new hypotheses for studying the role of lipid metabolism in AD

Patient emergency health-care use before hospital admission for COVID-19 and long-term outcomes in Scotland: a national cohort study

BackgroundIt is unclear what effect the pattern of health-care use before admission to hospital with COVID-19 (index admission) has on the long-term outcomes for patients. We sought to describe mortality and emergency readmission to hospital after discharge following the index admission (index discharge), and to assess associations between these outcomes and patterns of health-care use before such admissions.MethodsWe did a national, retrospective, complete cohort study by extracting data from several national databases and linking the databases for all adult patients admitted to hospital in Scotland with COVID-19. We used latent class trajectory modelling to identify distinct clusters of patients on the basis of their emergency admissions to hospital in the 2 years before the index admission. The primary outcomes were mortality and emergency readmission up to 1 year after index admission. We used multivariable regression models to explore associations between these outcomes and patient demographics, vaccination status, level of care received in hospital, and previous emergency hospital use.FindingsBetween March 1, 2020, and Oct 25, 2021, 33 580 patients were admitted to hospital with COVID-19 in Scotland. Overall, the Kaplan-Meier estimate of mortality within 1 year of index admission was 29·6% (95% CI 29·1-30·2). The cumulative incidence of emergency hospital readmission within 30 days of index discharge was 14·4% (95% CI 14·0-14·8), with the number increasing to 35·6% (34·9-36·3) patients at 1 year. Among the 33 580 patients, we identified four distinct patterns of previous emergency hospital use: no admissions (n=18 772 [55·9%]); minimal admissions (n=12 057 [35·9%]); recently high admissions (n=1931 [5·8%]), and persistently high admissions (n=820 [2·4%]). Patients with recently or persistently high admissions were older, more multimorbid, and more likely to have hospital-acquired COVID-19 than patients with no or minimal admissions. People in the minimal, recently high, and persistently high admissions groups had an increased risk of mortality and hospital readmission compared with those in the no admissions group. Compared with the no admissions group, mortality was highest in the recently high admissions group (post-hospital mortality HR 2·70 [95% CI 2·35-2·81]; pInterpretationLong-term mortality and readmission rates for patients hospitalised with COVID-19 were high; within 1 year, one in three patients had died and a third had been readmitted as an emergency. Patterns of hospital use before index admission were strongly predictive of mortality and readmission risk, independent of age, pre-existing comorbidities, and COVID-19 vaccination status. This increasingly precise identification of individuals at high risk of poor outcomes from COVID-19 will enable targeted support.FundingChief Scientist Office Scotland, UK National Institute for Health Research, and UK Research and Innovation

Combined Mesenchymal Stromal Cell Therapy and ECMO in ARDS:A Controlled Experimental Study in Sheep

Rationale: Mesenchymal stromal cell (MSC) therapy is a promising intervention for acute respiratory distress syndrome (ARDS), although trials to date have not investigated its use alongside extracorporeal membrane oxygenation (ECMO). Recent preclinical studies have suggested that combining these interventions may attenuate the efficacy of ECMO. Objectives: To determine the safety and efficacy of MSC therapy in a model of ARDS and ECMO. Methods: ARDS was induced in 14 sheep, after which they were established on venovenous ECMO. Subsequently, they received either endobronchial induced pluripotent stem cell-derived human MSCs (hMSCs) (n = 7) or cell-free carrier vehicle (vehicle control; n = 7). During ECMO, a low VT ventilation strategy was employed in addition to protocolized hemodynamic support. Animals were monitored and supported for 24 hours. Lung tissue, bronchoalveolar fluid, and plasma were analyzed, in addition to continuous respiratory and hemodynamic monitoring. Measurements and Main Results: The administration of hMSCs did not improve oxygenation (PaO2/FIO2 mean difference =2146mmHg; P= 0.076) or pulmonary function.However, histological evidence of lung injury(lung injuryscoremeandifference=20.07;P=0.04) and BALIL-8 were reduced. In addition, hMSC-treated animals had a significantly lower cumulative requirement for vasopressor. Despite endobronchial administration, animals treated with hMSCs had a significant elevation in transmembrane oxygenator pressure gradients. Thiswas accompanied by more pulmonary artery thromboses and adherent hMSCs found on explanted oxygenator fibers. Conclusions: Endobronchial hMSC therapy in an ovine model of ARDS and ECMO can impair membrane oxygenator function and does not improve oxygenation. These data do not recommend the safe use of hMSCs during venovenous ECMO. </p

Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits

To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk

Defining the scope for altering rice leaf anatomy to improve photosynthesis: a modelling approach.

Leaf structure plays an important role in photosynthesis. However, the causal relationship and the quantitative importance of any single structural parameter to the overall photosynthetic performance of a leaf remains open to debate. In this paper, we report on a mechanistic model, eLeaf, which successfully captures rice leaf photosynthetic performance under varying environmental conditions of light and CO2. We developed a 3D reaction-diffusion model for leaf photosynthesis parameterised using a range of imaging data and biochemical measurements from plants grown under ambient and elevated CO2 and then interrogated the model to quantify the importance of these elements. The model successfully captured leaf-level photosynthetic performance in rice. Photosynthetic metabolism underpinned the majority of the increased carbon assimilation rate observed under elevated CO2 levels, with a range of structural elements making positive and negative contributions. Mesophyll porosity could be varied without any major outcome on photosynthetic performance, providing a theoretical underpinning for experimental data. eLeaf allows quantitative analysis of the influence of morphological and biochemical properties on leaf photosynthesis. The analysis highlights a degree of leaf structural plasticity with respect to photosynthesis of significance in the context of attempts to improve crop photosynthesis