Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Tyrosine phosphorylation is critical for ACLY activity in lipid metabolism and cancer

A fundamental requirement for growth of rapidly proliferating cells is metabolic adaptation to promote synthesis of biomass 1 . ATP citrate lyase (ACLY) is a critical enzyme responsible for synthesis of cytosolic acetyl-CoA, the key building component for de novo fatty acid synthesis and links vital pathways such as carbohydrate and lipid metabolism 2 . The mechanisms of ACLY regulation are not completely understood and the regulation of ACLY function by tyrosine phosphorylation is unknown. Here we show using mass-spectrometry-driven phosphoproteomics and metabolomics that ACLY is phosphorylated and functionally regulated at an evolutionary conserved residue, Y682. Physiologic signals promoting rapid cell growth such as epidermal growth factor stimulation in epithelial cells and T-cell receptor activation in primary human T-cells result in rapid phosphorylation of ACLY at Y682. In vitro kinase assays demonstrate that Y682 is directly phosphorylated by multiple tyrosine kinases, including ALK, ROS1, SRC, JAK2 and LTK. Oncogenically activating structural alterations such as gene-fusions, amplification or point mutations of ALK tyrosine kinase result in constitutive phosphorylation of ACLY in diverse forms of primary human cancer such as lung cancer, anaplastic large cell lymphoma (ALCL) and neuroblastoma. Expression of a phosphorylation-defective ACLY-Y682F mutant in NPM-ALK+ ALCL decreases ACLY activity and attenuates lipid synthesis. Metabolomic analyses reveal that ACLY-Y682F expression results in increased β-oxidation of 13 C-oleic acid-labeled fatty acid with increased labeling of +2-citrate (p<0.01) and +18-oleyol carnitine (p<0.001). Similarly, oxygen consumption rate (OCR) is significantly increased in cells expressing ACLY-Y682F (p<0.001). Moreover, expression of ACLY-Y682F dramatically decreases cell proliferation, impairs clonogenicity and abrogates tumor growth in vivo . Our results reveal a novel mechanism for direct ACLY regulation that is subverted by multiple oncogenically-activated tyrosine kinases in diverse human cancers. These findings have significant implications for novel therapies targeting ACLY in cancer and metabolism

SLC2A9 influences uric acid concentrations with pronounced sex-specific effects

Serum uric acid concentrations are correlated with gout and clinical entities such as cardiovascular disease and diabetes. In the genome-wide association study KORA (Kooperative Gesundheitsforschung in der Region Augsburg) F3 500K (n = 1,644), the most significant SNPs associated with uric acid concentrations mapped within introns 4 and 6 of SLC2A9, a gene encoding a putative hexose transporter (effects: -0.23 to -0.36 mg/dl per copy of the minor allele). We replicated these findings in three independent samples from Germany (KORA S4 and SHIP (Study of Health in Pomerania)) and Austria (SAPHIR; Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk), with P values ranging from 1.2 × 10-8 to 1.0 × 10 -32. Analysis of whole blood RNA expression profiles from a KORA F3 500K subgroup (n = 117) showed a significant association between the SLC2A9 isoform 2 and urate concentrations. The SLC2A9 genotypes also showed significant association with self-reported gout. The proportion of the variance of serum uric acid concentrations explained by genotypes was about 1.2% in men and 6% in women, and the percentage accounted for by expression levels was 3.5% in men and 15% in women