Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses

Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (β per SD increase = 0.37%, 95% confidence interval: 0.12–0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.     

Orphan nuclear receptor COUP‐TFII enhances myofibroblast glycolysis leading to kidney fibrosis

Recent studies demonstrate that metabolic disturbance, such as augmented glycolysis, contributes to fibrosis. The molecular regulation of this metabolic perturbation in fibrosis, however, has been elusive. COUP‐TFII (also known as NR2F2) is an important regulator of glucose and lipid metabolism. Its contribution to organ fibrosis is undefined. Here, we found increased COUP‐TFII expression in myofibroblasts in human fibrotic kidneys, lungs, kidney organoids, and mouse kidneys after injury. Genetic ablation of COUP‐TFII in mice resulted in attenuation of injury‐induced kidney fibrosis. A non‐biased proteomic study revealed the suppression of fatty acid oxidation and the enhancement of glycolysis pathways in COUP‐TFII overexpressing fibroblasts. Overexpression of COUP‐TFII in fibroblasts also induced production of alpha‐smooth muscle actin (αSMA) and collagen 1. Knockout of COUP‐TFII decreased glycolysis and collagen 1 levels in fibroblasts. Chip‐qPCR revealed the binding of COUP‐TFII on the promoter of PGC1α. Overexpression of COUP‐TFII reduced the cellular level of PGC1α. Targeting COUP‐TFII serves as a novel treatment approach for mitigating fibrosis in chronic kidney disease and potentially fibrosis in other organs.     

RAD sequencing of common whelk,Buccinum undatum,reveals fine‐scale population structuring in Europe and cryptic speciation within the North Atlantic

Buccinum undatum is a subtidal gastropod that exhibits clear spatial variation in several phenotypic shell traits (color, shape, and thickness) across its North Atlantic distribution. Studies of spatial phenotypic variation exist for the species; however, population genetic studies have thus far relied on a limited set of mitochondrial and microsatellite markers. Here, we greatly expand on previous work by characterizing population genetic structure in B. undatum across the North Atlantic from SNP variation obtained by RAD sequencing. There was a high degree of genetic differentiation between Canadian and European populations (Iceland, Faroe Islands, and England) consistent with the divergence of populations in allopatry (F _ST > 0.57 for all pairwise comparisons). In addition, B. undatum populations within Iceland, the Faroe Islands, and England are typified by weak but significant genetic structuring following an isolation‐by‐distance model. Finally, we established a significant correlation between genetic structuring in Iceland and two phenotypic traits: shell shape and color frequency. The works detailed here enhance our understanding of genetic structuring in B. undatum and establish the species as an intriguing model for future genome‐wide association studies.     

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single‐species time series

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single‐species time series.