CTCF Expression and Dynamic Motif Accessibility Modulates Epithelial–Mesenchymal Gene Expression

Epithelial–mesenchymal transition (EMT) and its reversal, mesenchymal–epithelial transition (MET) drive tissue reorganization critical for early development. In carcinomas, processing through EMT, MET, or partial states promotes migration, invasion, dormancy, and metastatic colonization. As a reversible process, EMT is inherently regulated at epigenetic and epigenomic levels. To understand the epigenomic nature of reversible EMT and its partial states, we characterized chromatin accessibility dynamics, transcriptomic output, protein expression, and cellular phenotypes during stepwise reversible EMT. We find that the chromatin insulating protein machinery, including CTCF, is suppressed and re-expressed, coincident with broad alterations in chromatin accessibility, during EMT/MET, and is lower in triple-negative breast cancer cell lines with EMT features. Through an analysis of chromatin accessibility using ATAC-seq, we identify that early phases of EMT are characterized by enrichment for AP-1 family member binding motifs, but also by a diminished enrichment for CTCF binding motifs. Through a loss-of-function analysis, we demonstrate that the suppression of CTCF alters cellular plasticity, strengthening the epithelial phenotype via the upregulation of epithelial markers E-cadherin/CDH1 and downregulation of N-cadherin/CDH2. Conversely, the upregulation of CTCF leads to the upregulation of EMT gene expression and an increase in mesenchymal traits. These findings are indicative of a role of CTCF in regulating epithelial–mesenchymal plasticity and gene expression.

Epithelial-mesenchymal plasticity through loss of CTCF motif accessibility and protein expression

Epithelial-mesenchymal transition (EMT) and its reversal, mesenchymal-epithelial transition (MET) drive tissue reorganization critical for early development. In carcinomas, processing through EMT, MET or partial states promotes migration, invasion, dormancy, and metastatic colonization. As a reversible process, EMT is inherently regulated at epigenetic and epigenomic levels. To understand the epigenomic nature of reversible EMT and its partial states, we characterized chromatin accessibility dynamics, transcriptomic output, protein expression, and cellular phenotypes during stepwise reversible EMT. We found that the chromatin insulating protein machinery, including CTCF, is suppressed and re-expressed, coincident with broad alterations in chromatin accessibility, during EMT/MET and is lower in triple-negative breast cancer cell lines with EMT features. Through analysis of chromatin accessibility using ATAC-seq, we identify that early phases of EMT are characterized by enrichment for AP-1 family member binding motifs but also by diminished enrichment for CTCF binding motifs. Through loss-of-function analysis we demonstrate that suppression of CTCF alters cellular plasticity, facilitating entrance into a partial EMT state. These findings are indicative of a role of CTCF and chromatin reorganization for epithelial-mesenchymal plasticity.

Research on predicting 2D-HP protein folding using reinforcement learning with full state space

Background Protein structure prediction has always been an important issue in bioinformatics. Prediction of the two-dimensional structure of proteins based on the hydrophobic polarity model is a typical non-deterministic polynomial hard problem. Currently reported hydrophobic polarity model optimization methods, greedy method, brute-force method, and genetic algorithm usually cannot converge robustly to the lowest energy conformations. Reinforcement learning with the advantages of continuous Markov optimal decision-making and maximizing global cumulative return is especially suitable for solving global optimization problems of biological sequences. Results In this study, we proposed a novel hydrophobic polarity model optimization method derived from reinforcement learning which structured the full state space, and designed an energy-based reward function and a rigid overlap detection rule. To validate the performance, sixteen sequences were selected from the classical data set. The results indicated that reinforcement learning with full states successfully converged to the lowest energy conformations against all sequences, while the reinforcement learning with partial states folded 50% sequences to the lowest energy conformations. Reinforcement learning with full states hits the lowest energy on an average 5 times, which is 40 and 100% higher than the three and zero hit by the greedy algorithm and reinforcement learning with partial states respectively in the last 100 episodes. Conclusions Our results indicate that reinforcement learning with full states is a powerful method for predicting two-dimensional hydrophobic-polarity protein structure. It has obvious competitive advantages compared with greedy algorithm and reinforcement learning with partial states.