Biology subjects

Onuchic, J.

Publications and source records attributed to Onuchic, J..

3 recordsLinked to original sources

bioRxiv2024

Epigenetics is all you need: A Transformer to decode chromatin structural compartments from the epigenome

Chromatin within the nucleus adopts complex three-dimensional structures that are crucial for gene regulation and cellular function. Recent studies have revealed the presence of distinct chromatin subcompartments beyond the traditional A/B compartments (eu- and hetero-chromatin), each exhibiting unique structural and functional properties. Here, we introduce TECSAS (Transformer of Epigenetics to Chromatin Structural AnnotationS), a deep learning model based on the Transformer architecture, designed to predict chromatin subcompartment annotations directly from epigenomic data. TECSAS leverages information from histone modifications, transcription factor binding profiles, and RNA-Seq data to decode the relationship between the biochemical composition of chromatin and its 3D structural behavior. TECSAS achieves high accuracy in predicting subcompartment annotations and reveals the influence of long-range epigenomic context on chromatin organization. Furthermore, we demonstrate the models capability to predict the association of loci with nuclear bodies, such as the lamina, nucleoli, and speckles, providing insights into the role of these structures in shaping the 3D genome organization. This study highlights the potential of deep learning models for deciphering the complex interplay between epigenomic features and 3D genome organization, allowing us to better understand genome structure and function.

genomics↗

bioRxiv2022

Decoding the coupled decision-making of the epithelial-mesenchymal transition and metabolic reprogramming in cancer

Cancer metastasis relies on an orchestration of multiple traits driven by different functional interacting modules including metabolism and epithelial-mesenchymal transition (EMT). Cancer cells can adjust their metabolism during metastasis by increasing oxidative phosphorylation without compromising glycolysis, acquiring a hybrid metabolic phenotype (W/O). Often required by metastasis, cancer cells engage EMT and can acquire a hybrid epithelial/mesenchymal (E/M) phenotype. Both the W/O and E/M states are associated with high metastatic potentials. Many regulatory links coupling metabolism and EMT have been identified, but how these two modules regulate each other remains largely unexplored. Here, we investigate the coupled decision-making networks of metabolism and EMT, and systematically analyzed the effect of their crosstalk. This crosstalk can exhibits synergistic or antagonistic effects on the acquisition and stability of different coupled metabolism-EMT states. Strikingly, the aggressive E/M-W/O state can be enabled and stabilized by the crosstalk irrespective of these hybrid states availability in individual metabolism or EMT modules. To acquire an E/M-W/O state, the W/O state emerges first, followed by the E/M state, suggesting metabolism can drive EMT. Our work emphasizes the mutual activation between metabolism and EMT, providing an important step towards understanding the multi-faceted nature of cancer metastasis.

cancer biology↗

bioRxiv2016

Interrogating the Topological Robustness of Gene Regulatory Circuits

One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally-verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.

systems biology↗