Integrating Long-Range Regulatory Interactions to Predict Gene Expression Using Graph Convolutional Networks.

Long-range regulatory interactions among genomic regions are critical for controlling gene expression, and their disruption has been associated with a host of diseases. However, when modeling the effects of regulatory factors, most deep learning models either neglect long-range interactions or fail to capture the inherent 3D structure of the underlying genomic organization. To address these limitations, we present a Graph Convolutional Model for Epigenetic Regulation of Gene Expression (GC-MERGE). Using a graph-based framework, the model incorporates important information about long-range interactions via a natural encoding of genomic spatial interactions into the graph representation. It integrates measurements of both the global genomic organization and the local regulatory factors, specifically histone modifications, to not only predict the expression of a given gene of interest but also quantify the importance of its regulatory factors. We apply GC-MERGE to data sets for three cell lines-GM12878 (lymphoblastoid), K562 (myelogenous leukemia), and HUVEC (human umbilical vein endothelial)-and demonstrate its state-of-the-art predictive performance. Crucially, we show that our model is interpretable in terms of the observed biological regulatory factors, highlighting both the histone modifications and the interacting genomic regions contributing to a gene's predicted expression. We provide model explanations for multiple exemplar genes and validate them with evidence from the literature. Our model presents a novel setup for predicting gene expression by integrating multimodal data sets in a graph convolutional framework. More importantly, it enables interpretation of the biological mechanisms driving the model's predictions.

G1/S transcription factors assemble in increasing numbers of discrete clusters through G1 phase.

In budding yeast, the transcription factors SBF and MBF activate a large program of gene expression in late G1 phase that underlies commitment to cell division, termed Start. SBF/MBF are limiting with respect to target promoters in small G1 phase cells and accumulate as cells grow, raising the questions of how SBF/MBF are dynamically distributed across the G1/S regulon and how this impacts the Start transition. Super-resolution Photo-Activatable Localization Microscopy (PALM) mapping of the static positions of SBF/MBF subunits in fixed cells revealed each transcription factor was organized into discrete clusters containing approximately eight copies regardless of cell size and that the total number of clusters increased as cells grew through G1 phase. Stochastic modeling using reasonable biophysical parameters recapitulated growth-dependent SBF/MBF clustering and predicted TF dynamics that were confirmed in live cell PALM experiments. This spatio-temporal organization of SBF/MBF may help coordinate activation of G1/S regulon and the Start transition.