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Biology subjects

Janssens, R.

Publications and source records attributed to Janssens, R..

2 recordsLinked to original sources

Elongation factor ELOF1 drives transcription-coupled repair and prevents genome instability

Correct transcription is crucial for life. However, DNA damage severely impedes elongating RNA Polymerase II (Pol II), causing transcription inhibition and transcription-replication conflicts. Cells are equipped with intricate mechanisms to counteract the severe consequence of these transcription-blocking lesions (TBLs). However, the exact mechanism and factors involved remain largely unknown. Here, using a genome-wide CRISPR/cas9 screen, we identified elongation factor ELOF1 as an important new factor in the transcription stress response upon DNA damage. We show that ELOF1 has an evolutionary conserved role in Transcription-Coupled Nucleotide Excision Repair (TC-NER), where it promotes recruitment of the TC-NER factors UVSSA and TFIIH to efficiently repair TBLs and resume transcription. Additionally, ELOF1 modulates transcription to protect cells from transcription-mediated replication stress, thereby preserving genome stability. Thus, ELOF1 protects the transcription machinery from DNA damage by two distinct mechanisms.

cell biology↗

Fully unsupervised deep mode of action learning for phenotyping high-content cellular images

The identification and discovery of phenotypes from high content screening (HCS) images is a challenging task. Earlier works use image analysis pipelines to extract biological features, supervised training methods or generate features with neural networks pretrained on non-cellular images. We introduce a novel fully unsupervised deep learning algorithm to cluster cellular images with similar Mode-of-Action together using only the images pixel intensity values as input. The method outperforms existing approaches on the labelled subset of the BBBC021 dataset and achieves an accuracy of 97.09% for correctly classifying the Mode-of-Action (MOA) by nearest neighbors matching. One unique aspect of the approach is that it is able to perform training on the entire unannotated dataset, to correctly cluster similar treatments beyond the annotated subset of the dataset and can be used for novel MOA discovery.

bioinformatics↗