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Our study on predicting expression variation is out – and on the cover of Mol. Sys. Biol.!

Using gene-specific regulatory features we could predict how genes vary across individuals in fly and humans. Read the full paper here or a short lay summary in the EMBL news.

Conserved genomic features predictive of gene expression variation across individuals are identified in Drosophila and human using a machine-learning approach. The same features predict differential expression upon perturbation revealing a link between variation and differential expression. Cover concept by the authors. (Scientific image by Scistories LLC, www.scistories.com)

Our multiomics study describing ESC to neuron differentiation is out!

EMBL news coverage of our article

Genomic Rewiring of SOX2 Chromatin Interaction Network during Differentiation of ESCs to Postmitotic Neurons
Cellular differentiation requires dramatic changes in chromatin organization, transcriptional regulation, and protein production. To understand the regulatory connections between these processes, we generated proteomic, transcriptomic, and chromatin accessibility data during differentiation of mouse embryonic stem cells (ESCs) into postmitotic neurons and found extensive associations between different molecular layers within and across differentiation time points. We observed that SOX2, as a regulator of pluripotency and neuronal genes, redistributes from pluripotency enhancers to neuronal promoters during differentiation, likely driven by changes in its protein interaction network. We identified ATRX as a major SOX2 partner in neurons, whose co-localization correlated with an increase in active enhancer marks and increased expression of nearby genes, which we experimentally confirmed for three loci. Collectively, our data provide key insights into the regulatory transformation of SOX2 during neuronal differentiation, and we highlight the significance of multi-omic approaches in understanding gene regulation in complex systems.

You can check out the paper here: https://doi.org/10.1016/j.cels.2020.05.003

Our paper on gene regulatory networks to understand disease mechanism of pulmonary arterial hypertension (PAH) is out!

This work results from a collaboration with 2 groups at Stanford. Check it out here: https://www.nature.com/articles/s41467-020-15463-x


Our main take-home messages from the study:
1) Chromatin is more sensitive than RNA to capture a cell’s regulatory state
2) Chromatin changes are best interpreted in the context of a gene regulatory network
3) The PAH phenotype in pulmonary arterial endothelial cells involves a complex network of different processes that are reflected in epigenetic marks

Overview of the workflow

A striking part of this study was that we didn’t find any detectable difference in gene expression, yet huge changes in H3K27ac marks, which recapitulated differential activity in almost all known PAH TFs (using diffTF) and suggested a complete rewiring of the gene regulatory network. The changes in the regulatory network converge on endothelial to mesenchymal transition and response to signaling. And follow-up experiments confirmed that genes with differential H3K27ac in enhancers show a differential response to endogenous stimuli despite the no difference at baseline expression.

Our paper on TF cooperative binding is out!

Check out here how we combine high-throughput genomics data and statistical learning to gain mechanistic insights and functional consequences of cooperative transcription factor binding:

Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions

App, data and code

Recent high-throughput transcription factor (TF) binding assays revealed that TF cooperativity is a widespread phenomenon. However, a global mechanistic and functional understanding of TF cooperativity is still lacking. To address this, here we introduce a statistical learning framework that provides structural insight into TF cooperativity and its functional consequences based on next generation sequencing data. We identify DNA shape as driver for cooperativity, with a particularly strong effect for Forkhead-Ets pairs. Follow-up experiments reveal a local shape preference at the Ets-DNA-Forkhead interface and decreased cooperativity upon loss of the interaction. Additionally, we discover many functional associations for cooperatively bound TFs. Examination of the link between FOXO1:ETV6 and lymphomas reveals that their joint expression levels improve patient clinical outcome stratification. Altogether, our results demonstrate that inter-family cooperative TF binding is driven by position-specific DNA readout mechanisms, which provides an additional regulatory layer for downstream biological functions.

Our diffTF paper is out!

Interested in quantifying differential TF activity and classifying TFs into activators and repressors? Then you should check out our recent manuscript and associated tool diffTF:

Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF.

Transcription factors (TFs) regulate many cellular processes and can therefore serve as readouts of the signaling and regulatory state. Yet for many TFs, the mode of action-repressing or activating transcription of target genes-is unclear. Here, we present diffTF (https://git.embl.de/grp-zaugg/diffTF) to calculate differential TF activity (basic mode) and classify TFs into putative transcriptional activators or repressors (classification mode). In basic mode, it combines genome-wide chromatin accessibility/activity with putative TF binding sites that, in classification mode, are integrated with RNA-seq. We apply diffTF to compare (1) mutated and unmutated chronic lymphocytic leukemia patients and (2) two hematopoietic progenitor cell types. In both datasets, diffTF recovers most known biology and finds many previously unreported TFs. It classifies almost 40% of TFs based on their mode of action, which we validate experimentally. Overall, we demonstrate that diffTF recovers known biology, identifies less well-characterized TFs, and classifies TFs into transcriptional activators or repressors.

https://www.ncbi.nlm.nih.gov/pubmed/31801079