Biology subjects

Barth, D.

Publications and source records attributed to Barth, D..

3 recordsLinked to original sources

bioRxiv2026

Combinatorial transcription factor interactions drive modular gene regulatory networks

Transcription factors (TFs) cooperatively drive gene regulatory networks (GRNs) to establish transcriptional states. Forced induction of TFs in combination can reprogram cell state by supplanting existing GRNs. Thus, TFs and GRNs are the building blocks to engineering transcriptional state. However, one key challenge is that the relationship between TF combinations and GRNs remains largely uncharacterized and difficult to accurately predict. Here, we apply single-cell overexpression screens to map the combinatorial activities of [~]100 TFs to gene expression states. Our analysis identifies diverse TF combinations driving cell-type specific regulatory programs. Notably, different TF combinations induce shared gene sets with cell-type specific functions, suggesting a modular regulatory architecture of the transcriptome. Furthermore, we define pairwise TF interactions and show that cooperative interactions improve transcriptional reprogramming. Finally, we developed tools to predict combinatorial TF phenotypes. These findings improve our understanding of cell state and how to manipulate it for biomedical applications. HIGHLIGHTSO_LICombinatorial over-expression screens for [~]100 transcription factors (TFs). C_LIO_LIDiverse TF combinations drive cell-type specific regulatory programs. C_LIO_LITF regulatory networks reveal a modular regulatory architecture of the transcriptome. C_LIO_LITF-TF interactions and predictive models enhance reprogramming cocktails. C_LI

genomics↗

bioRxiv2023

Machine learning of enhancer-promoter specificity based on enhancer perturbation studies reveals a distinct class of enhancers.

MotivationUnderstanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to learn to predict enhancer promoter relationships in a data driven manner. ResultsWe applied machine learning to one of the largest enhancer perturbation studies integrated with transcription factor and histone modification ChIP-seq. Based on the learned model, we confirmed previously reported rules governing enhancer driven transcription, and we gained some insights that generated new hypotheses, such as a novel role for protecting against replication-transcription conflict at the active enhancers in CHAMP1. We also identified a distinct class of enhancers that drives target promoter transcription, but is not in strong contact with the promoters. There were two clusters of such enhancers that regulated ATG2A and the histone 1 cluster respectively. These enhancers were different from other typical enhancers, in that they had other strong enhancers nearby, and they also had strong H3K4me3 marks at the target promoters, both patterns that typically predict reduced enhancer influence, but here contributing in the opposite way. In summary, we find that integrating genomic assays with enhancer perturbation studies increases the accuracy of the model, and provides novel insights into the understanding of enhancer driven transcription. Availabilitythe trained models and the source code are available at https://github.com/HanLabUNLV/abic. Contactmira.han@unlv.edu

bioinformatics↗

bioRxiv2017

Mechanical activation of epithelial Na+ channel relies on an interdependent activity of the extracellular matrix and extracellular N-glycans of αENaC

Mechanotransduction describes how cells perceive their mechanical environment and mechanosensitive ion channels are important for this process. ENaC (epithelial Na+ channel)/DEG (degenerin) proteins form mechanosensitive ion channels and it is hypothesized their interaction with the extracellular matrix (ECM) via tethers is required for mechanotransduction. Channels formed by vertebrate , {beta} and {gamma} ENaC proteins are activated by shear force (SF) and mediate electrolyte/fluid-homeostasis and blood pressure regulation. Here, we report an interdependent activity of ENaC and the ECM that mediates SF effects in murine arteries and heterologously expressed channels. Furthermore, replacement of conserved extracellular N-glycosylated asparagines of ENaC decreased the SF response indicating that the attached N-glycans provide a connection to the ECM. Insertion of N-glycosylation sites into a channel subunit, innately lacking these motifs, increased its SF response. These experiments confirm an interdependent channel/ECM activity of mechanosensitive ENaC channel and highlight the role of channel N-glycans as new constituents for the translation of mechanical force into cellular signals.

cell biology↗