Biology subjects

Umarova, R.

Publications and source records attributed to Umarova, R..

3 recordsLinked to original sources

bioRxiv2026

P2Y14 receptor agonist impairs, and antagonist improves whole-body glucose homeostasis and liver steatosis

Knockout of the UDP-glucose-activated P2Y14 receptor (P2Y14R) in adipocytes or whole-body has been reported to provide metabolic benefits in obese mice. We hypothesized that selective P2Y14R activation would lead to metabolic impairments, whereas pharmacological antagonism would improve metabolic deficits in diet-induced obese (DIO) mice. Here, we investigated the metabolic effects of a synthetic P2Y14R agonist, UDP-like methylene-bridged MRS2905. Acute P2Y14R activation with MRS2905 triggered a robust and prolonged hyperglycemic effect in lean and obese mice with impaired glucose homeostasis. Moreover, MRS2905 treatment of obese mice lowered fasting plasma insulin and increased glucagon levels, along with upregulation of liver JNK phosphorylation and the expression of rate-limiting gluconeogenic genes. The MRS2905-induced hyperglycemic effect was blunted in whole-body P2Y14R knockout mice compared with wild-type control. In contrast, a potent P2Y14R antagonist mono-ester prodrug (MRS4779) partially reversed the agonist-induced hyperglycemia and restored proper glucose homeostasis after acute treatment. Chronic MRS4779 administration in DIO mice reduced fat mass and improved various metabolic parameters including liver steatosis. Additionally, we examined the roles of P2Y14R in hepatocytes of DIO mice. Here, we report that P2Y14R was upregulated in liver and hepatocytes from obese mice compared to lean mice. Overnight fasting also upregulated hepatic P2Y14R expression. P2Y14R deletion from hepatocytes in DIO mice improved fasting blood glucose level and lipid metabolism without improving glucose homeostasis. These results suggest a novel P2Y14R function in hepatic lipid metabolism, and P2Y14R antagonists may prove useful for the treatment of obesity and obesity-related metabolic disorders.

physiology↗

bioRxiv2024

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover optimal recipes for predicting each domain. The differences in these optimal recipes reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com

neuroscience↗

bioRxiv2024

Control without cause: How covariate control biases our insights into brain architecture and pathology

Inferential analysis of normal or pathological brain imaging data - as in brain mapping or the identification of neurological imaging markers - is often controlled for secondary variables. However, a rationale for covariate control is rarely given and formal criteria to identify appropriate covariates in such complex data are lacking. We investigated the impact and adequacy of covariate control in large-scale imaging data using the example of stroke lesion-deficit mapping. In 183 stroke patients, we evaluated control for age, sex, hypertension, or lesion volume when mapping real or simulated deficits. We found that the impact of covariate control varies and can be strong, but it does not necessarily improve the precision of results. Instead, it systematically shifts results towards the inversed associations between imaging features and the covariate. This effect of covariate control can bias results and, as shown in another experiment, can even create effects out of nothing. The widespread use of covariate control in the statistical analysis of clinical brain imaging data - and, likely, other biological high-dimensional data as well - may not generally improve statistical results, but it may just change them. Therefore, covariate control constitutes a problematic degree of freedom in the analysis of brain imaging data and may often not be justified at all.

neuroscience↗