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Westermark, P. O.

Publications and source records attributed to Westermark, P. O..

2 recordsLinked to original sources

Transcriptomic, proteomic and phosphoproteomic underpinnings of daily exercise performance and Zeitgeber activity of endurance training

Timed physical activity might potentiate the health benefits of training. The underlying signaling events triggered by exercise at different times of the day are, however, poorly understood. Here, we found that time-dependent variations in maximal treadmill exercise capacity of naive mice were associated with energy stores, mostly hepatic glycogen levels. Importantly, running at different times of the day resulted in a vastly different activation of signaling pathways, e.g., related to stress response, vesicular trafficking, repair, and regeneration. Second, voluntary wheel running at the opposite phase of the dark, feeding period surprisingly revealed minimal Zeitgeber (i.e., synchronizing) activity of training. This integrated study provides important insights into the circadian regulation of endurance performance and the control of the circadian clock by exercise. These results are of high importance to understand circadian aspects of training design in athletes and the application of chrono-exercise-based interventions in patients. HighlightsO_LIMaximal endurance performance is greater in the early morning C_LIO_LITimed exercise differentially alters the muscle transcriptome and (phospho)-proteome C_LIO_LIMorning exercise triggers energy provisioning and tissue regeneration C_LIO_LIEvening exercise activates stress-related and catabolic pathways C_LIO_LITraining exerts poor Zeitgeber activity on the muscle and liver clocks C_LI

physiology↗

seagull: lasso, group lasso and sparse-group lasso regularisation for linear regression models via proximal gradient descent

Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalisation approaches are often the methods of choice. They are especially useful in case of multicollinearity which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalised by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realised by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, the package produces complete regularisation paths. Availability and implementationseagull is an R package that is freely available on the Comprehensive R Archive Network (CRAN; https://CRAN.R-project.org/package=seagull; vignette included). The source code is available on https://github.com/jklosa/seagull. Contactwittenburg@fbn-dummerstorf.de

bioinformatics↗