Biology subjects

Stephan, J.-P.

Publications and source records attributed to Stephan, J.-P..

2 recordsLinked to original sources

bioRxiv2021

Fatty acid binding proteins shape the cellular response to activation of the glucocorticoid receptor

Glucocorticoids are steroid hormones that are essential for life in mammals. Therapeutically, they are some of the most cost-effective drugs for the treatment of inflammatory diseases ranging from skin rashes to COVID-19, but their use is limited by adverse effects. Glucocorticoids exert their effects via the glucocorticoid receptor, a type I nuclear hormone receptor which modulates gene expression. The transcriptional activity of some related, but nuclear restricted, type II nuclear hormone receptors can be enhanced by a family of intracellular transport proteins, the fatty acid binding proteins (FABPs). We find that the transcriptional activity of the GR can be altered by a sub-set of FABP family members dependent on the GR-ligand. The ability of some FABPs to selectively promote or limit the transcriptional activity of the GR in a ligand-dependent manner could facilitate the discovery of drugs that narrow GR activity to only the desired subset of therapeutically relevant genes.

pharmacology and toxicology↗

bioRxiv2020

Visualising uncertainty in brightfield to fluorescent image inference with BFNet

Predicting fluorescently labelled cellular structures from brightfield images is a recent application of convolutional neural networks and has already proven a valuable tool in biological imaging studies. These methods reduce the need for time-consuming manual annotations for supervised datasets, potentially reduce the costs in high-throughput imaging screens, and open up a number of potentially novel analyses. However as with any prediction there can be sources of error and uncertainty. Here we present BFNet, a method to visualise the uncertainty in predicted images by applying Monte Carlo dropout during inference and calculating per-pixel variance as an uncertainty map. Our method demonstrates the ability to highlight regions of an image where prediction is difficult or impossible due to imaging artefacts such as occlusions or out-of-focus images, as well as more general uncertainty when a trained model is applied to new data from different imaging of experimental settings. We have provided a python implementation of the method which is available at github.com/swarchal/bfnet.

bioinformatics↗