Biology subjects

Janssens, R.

Publications and source records attributed to Janssens, R..

5 recordsLinked to original sources

bioRxiv2025

Dual Modes of Gene Regulation by CDK12

The process of transcription is driven forward by the activity of kinases including CDK7, CDK9 and CDK12. Accordingly, acute inhibition of any of these kinases results in profound downregulation of gene expression. Here, we discover that loss or inhibition of CDK12 also significantly upregulates a set of coding and non-coding loci, whose activation could contribute to the anti-proliferative effects of CDK12 inhibitors. Mechanistically, CDK12 inhibition impairs transcription elongation, leading to increased RNA polymerase II termination or arrest in long genes. However, short genes such as MYC and enhancer RNAs are highly transcribed in the absence of CDK12 activity. Indeed, in HER2+ breast cancer, a malignancy where CDK12 is co-amplified with HER2 and its expression correlates with disease status, CDK12 inhibition markedly elevates MYC expression to induce lethality. The dual effects of CDK12 inhibition elucidated herein clarify its role in transcriptional control and have significant translational implications.

cancer biology↗

bioRxiv2022

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts and clinical tumors using deep neural networks

Cell lines and patient-derived xenografts are essential to cancer research, however, the results derived from such models often lack clinical translatability, as these models do not fully recapitulate the complex cancer biology. It is critically important to better understand the systematic differences between cell lines, xenografts and clinical tumors, and to be able to identify pre-clinical models that sufficiently resemble the biological characteristics of clinical tumors across different cancers. On another side, direct comparison of transcriptional profiles from pre-clinical models and clinical tumors is infeasible due to the mixture of technical artifacts and inherent biological signals. To address these challenges, we developed MOBER, Multi-Origin Batch Effect Remover method, to simultaneously extract biologically meaningful embeddings and remove batch effects from transcriptomic datasets of different origin. MOBER consists of two neural networks: conditional variational autoencoder and source discriminator neural network that is trained in adversarial fashion. We applied MOBER on transcriptional profiles from 932 cancer cell lines, 434 patient-derived tumor xenografts and 11159 clinical tumors and identified pre-clinical models with greatest transcriptional fidelity to clinical tumors, and models that are transcriptionally unrepresentative of their respective clinical tumors. MOBER can conserve the biological signals from the original datasets, while generating embeddings that do not encode confounder information. In addition, it allows for transformation of transcriptional profiles of pre-clinical models to resemble the ones of clinical tumors, and therefore can be used to improve the clinical translation of insights gained from pre-clinical models. As a batch effect removal method, MOBER can be applied widely to transcriptomics datasets of different origin, allowing for integration of multiple datasets simultaneously.

bioinformatics↗

bioRxiv2022

High-content cellular screen image analysis benchmark study

Recent development of novel methods based on deep neural networks has transformed how high-content microscopy cellular images are analyzed. Nonetheless, it is still a challenge to identify cellular phenotypic changes caused by chemical or genetic treatments and to elucidate the relationships among treatments in an unsupervised manner, due to the large data volume, high phenotypic complexity and the presence of a priori unknown phenotypes. Here we benchmarked five deep neural network methods and two feature engineering methods on a well-characterized public data set. In contrast to previous benchmarking efforts, the manual annotations were not provided to the methods, but rather used as evaluation criteria afterwards. The seven methods individually performed feature extraction or representation learning from cellular images, and were consistently evaluated for downstream phenotype prediction and clustering tasks. We identified the strengths of individual methods across evaluation metrics, and further examined the biological concepts of features automatically learned by deep neural networks.

bioinformatics↗

bioRxiv2021

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↗

bioRxiv2020

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↗