Biology subjects

Price, E.

Publications and source records attributed to Price, E..

2 recordsLinked to original sources

bioRxiv2024

CTCF binding landscape is established by the epigenetic status of the nucleosome, well-positioned relative to CTCF motif orientation

CTCF binding sites serve as anchors for the 3D chromatin architecture in vertebrates. The functionality of these anchors is influenced by the residence time of CTCF on chromatin, which is determined by its binding affinity and its interactions with nucleosomes and other chromatin-associated factors. In this study, we demonstrate that CTCF occupancy is driven by CTCF motifs, strategically positioned at the entry sides of a well-positioned nucleosome, such that, upon binding, the N-terminus of CTCF is oriented towards the nucleosome. We refer to this nucleosome as the CTCF priming nucleosome (CPN). CTCF recognizes its binding sites if they are not methylated. It can then displace the CPN, provided the nucleosome is not marked by CpG methylation or repressive histone modifications. Under these permissive conditions, the N-terminus of CTCF recruits SMARCA5 to reposition the CPN downstream, thereby creating nucleosome-free regions that enhance CTCF occupancy and cohesin stalling. In contrast, when CPNs carry repressive epigenetic marks, CTCF binding is transient, without nucleosome displacement or chromatin opening. In such cases, cohesin is not effectively retained at CTCF binding sites. We propose that the epigenetic status of CPNs shapes cell-specific CTCF binding patterns, ensuring the maintenance of chromatin architecture throughout the cell cycle. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/614770v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@105f34eorg.highwire.dtl.DTLVardef@1a88a82org.highwire.dtl.DTLVardef@1d90f2org.highwire.dtl.DTLVardef@1170e49_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

A Framework for Fast, Large-scale, Semi-Automatic Inference of Animal Behavior from Monocular Videos

An automatic, quick, accurate, and scalable method for animal behavior inference using only videos of animals offers unprecedented opportunities to understand complex biological phenomena and answer challenging ecological questions. The advent of sophisticated machine learning techniques now allows the development and implementation of such a method. However, apart from developing a network model that infers animal behavior from video inputs, the key challenge is to obtain sufficient labeled (annotated) data to successfully train that network - a laborious task that needs to be repeated for every species and/or animal system. Here, we propose solutions for both problems, i) a novel methodology for rapidly generating large amounts of annotated data of animals from videos and ii) using it to reliably train deep neural network models to infer the different behavioral states of every animal in each frame of the video. Our methods workflow is bootstrapped with a relatively small amount of manually-labeled video frames. We develop and implement this novel method by building upon the open-source tool Smarter-LabelMe, leveraging deep convolutional visual detection and tracking in combination with our behavior inference model to quickly produce large amounts of reliable training data. We demonstrate the effectiveness of our method on aerial videos of plains and Grevys Zebras (Equus quagga and Equus grevyi). We fully open-source the code1 of our method as well as provide large amounts of accurately-annotated video datasets2 of zebra behavior using our method. A video abstract of this paper is available here3.

animal behavior and cognition↗