Showing papers by "Julia Zeitlinger published in 2022"
TL;DR: In this article , the authors leveraged the MITOMI microfluidic platform to measure equilibrium binding and kinetics of basic-helix-looploop,helix TFs Pho4 and MAX to their consensus binding sites, surrounded by various random or repetitive sequences.
3 citations
TL;DR: In this article , a hierarchical relationship between the pioneer TF Zelda and the TFs involved in axis patterning was uncovered, and it was shown that Zelda consistently pioneers chromatin accessibility proportional to motif affinity, while patterning TFs augment chromatin access in sequence contexts in which they mediate enhancer activation.
Abstract: Chromatin accessibility is integral to the process by which transcription factors (TFs) read out cis-regulatory DNA sequences, but it is difficult to differentiate between TFs that drive accessibility and those that do not. Deep learning models that learn complex sequence rules provide an unprecedented opportunity to dissect this problem. Using zygotic genome activation in the Drosophila embryo as a model, we generated high-resolution TF binding and chromatin accessibility data, analyzed the data with interpretable deep learning, and performed genetic experiments for validation. We uncover a clear hierarchical relationship between the pioneer TF Zelda and the TFs involved in axis patterning. Zelda consistently pioneers chromatin accessibility proportional to motif affinity, while patterning TFs augment chromatin accessibility in sequence contexts in which they mediate enhancer activation. We conclude that chromatin accessibility occurs in two phases: one through pioneering, which makes enhancers accessible but not necessarily active, and a second when the correct combination of transcription factors leads to enhancer activation.
2 citations
TL;DR: It is shown that Lola-I, a Drosophila zinc finger transcription factor, is ubiquitously expressed at the end of embryogenesis and causes its target promoters to become accessible and acquire paused Pol II throughout the embryo.
Abstract: While enhancers are often regulated at the level of accessibility by pioneer factors, promoters tend to be constitutively accessible and poised for activation by paused Pol II — thus are often not considered as sites of developmental regulation. Here we show that the accessibility of promoters and the acquisition of paused Pol II can be subject to developmental regulation by pioneer factors. We show that Lola-I, a Drosophila zinc finger transcription factor, is ubiquitously expressed at the end of embryogenesis and causes its target promoters to become accessible and acquire paused Pol II throughout the embryo. This promoter transition is required but not sufficient for tissue-specific target gene expression. Lola-I mediates this function by binding to the edges of the promoter nucleosomes, which leads to their depletion, similar to the action of pioneer factors at enhancers. These results uncover a level of regulation for promoters that is normally found at enhancers, providing further evidence that promoters and enhancers display unexpectedly similar characteristics.
2 citations
TL;DR: DeepSTarr as discussed by the authors is a machine learning method that uses neural networks to learn complex rules that make predictions about diverse types of data, and their high prediction accuracy allows the identification of impactful genetic variants within and across species.
19 May 2022
TL;DR: A model of mRNA production governed by the dynamics of a gene that exists in three possible states – inactive, poised and active is considered and the distribution of mRNA number is derived and compared to the known result for the two-state gene model.
Abstract: We consider a model of mRNA production governed by the dynamics of a gene that exists in three possible states – inactive, poised and active. The transitions between the adjacent states are controlled by stochastic processes characterized by corresponding on/off rates. mRNA is produced only when the gene is in active state and we also consider mRNA denaturation leading to its death. We derive the distribution of mRNA number and compare it to the known result for the two-state gene model.