Sakib, S. | bioRxiv Science

bioRxiv2024

Both the transcriptional activator, Bcd, and transcriptional repressor, Cic, form small mobile oligomeric clusters in early fly embryo nuclei

Transcription factors play an essential role in pattern formation during early embryo development, generating a strikingly fast and precise transcriptional response that results in sharp gene expression boundaries. To characterize the steps leading up to transcription, we performed a side-by-side comparison of the nuclear dynamics of two morphogens, a transcriptional activator, Bicoid (Bcd), and a transcriptional repressor, Capicua (Cic), both involved in body patterning along the anterior-posterior axis of the early Drosophila embryo. We used a combination of fluorescence recovery after photobleaching, fluorescence correlation spectroscopy, and single particle tracking to access a wide range of dynamical timescales. Despite their opposite effects on gene transcription, we find that Bcd and Cic have very similar nuclear dynamics, characterized by the co-existence of a freely diffusing monomer population with a number of oligomeric clusters, which range from low stoichiometry and high mobility clusters to larger, DNA-bound hubs. Our observations are consistent with the inclusion of both Bcd and Cic into transcriptional hubs or condensates, while putting constraints on the mechanism by which these form. These results fit in with the recent proposal that many transcription factors might share a common search strategy for target genes regulatory regions that makes use of their large unstructured regions, and may eventually help explain how the transcriptional response they elicit can be at the same time so fast and so precise. SIGNIFICANCEBy conducting a comparative study of the nuclear dynamics of Bicoid (a transcriptional activator) and Capicua (a transcriptional repressor) in the Drosophila embryo, we have uncovered a striking similarity in their behaviours. Despite their divergent roles in transcription, both proteins have a propensity to form oligomeric species ranging from highly mobile, low stoichiometry clusters to larger, DNA-bound hubs. Such findings impose new constraints on the existing models of gene regulation by transcription factors, particularly in aspects related to target search and oligomeric binding to gene regulatory regions needed to explain the rapid and precise transcriptional response observed in developmental processes.

biophysics↗

bioRxiv2022

DeepBend: An Interpretable Model of DNA Bendability

The bendability of genomic DNA impacts chromatin packaging and protein-DNA binding. However, beyond a handful of known sequence motifs, such as certain dinucleotides and poly(A)/poly(T) sequences, we do not have a comprehensive understanding of the motifs influencing DNA bendability. Recent high-throughput technologies like Loop-Seq offer an opportunity to address this gap but the lack of accurate and interpretable machine learning models still poses a significant challenge. Here we introduce DeepBend, a convolutional neural network model built as a visible neural network where we designed the convolutions to directly capture the motifs underlying DNA bendability and how their periodic occurrences or relative arrangements modulate bendability. Through extensive benchmarking on Loop-Seq data, we show that DeepBend consistently performs on par with alternative machine learning and deep learning models while giving an extra edge through mechanistic interpretations. Besides confirming the known motifs of DNA bendability, DeepBend also revealed several novel motifs and showed how the spatial patterns of motif occurrences influence bendability. DeepBends genome-wide prediction of bendability further showed how bendability is linked to chromatin conformation and revealed the motifs controlling bendability of topologically associated domains and their boundaries.

bioinformatics↗