Controlling gene activation by enhancers through a drug-inducible topological insulator

Enhancer selectivity in space and time: from enhancer-promoter interactions to promoter activation

The primary regulators of metazoan gene expression are enhancers, originally functionally defined as DNA sequences that can activate transcription at promoters in an orientation-independent and distance-independent manner. Despite being crucial for gene regulation in animals, what mechanisms underlie enhancer selectivity for promoters, and more fundamentally, how enhancers interact with promoters and activate transcription, remain poorly understood. In this Review, we first discuss current models of enhancer-promoter interactions in space and time and how enhancers affect transcription activation. Next, we discuss different mechanisms that mediate enhancer selectivity, including repression, biochemical compatibility and regulation of 3D genome structure. Through 3D polymer simulations, we illustrate how the ability of 3D genome folding mechanisms to mediate enhancer selectivity strongly varies for different enhancer-promoter interaction mechanisms. Finally, we discuss how recent technical advances may provide new insights into mechanisms of enhancer-promoter interactions and how technical biases in methods such as Hi-C and Micro-C and imaging techniques may affect their interpretation.

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding

Enhancers, genomic DNA elements, regulate neighboring gene expression crucial for biological processes like cell differentiation and stress response. However, current machine learning methods for predicting DNA enhancers often underutilize hidden features in gene sequences, limiting model accuracy. Hence, this article proposes the PDCNN model, a deep learning-based enhancer prediction method. PDCNN extracts statistical nucleotide representations from gene sequences, discerning positional distribution information of nucleotides in modifier-like DNA sequences. With a convolutional neural network structure, PDCNN employs dual convolutional and fully connected layers. The cross-entropy loss function iteratively updates using a gradient descent algorithm, enhancing prediction accuracy. Model parameters are fine-tuned to select optimal combinations for training, achieving over 95% accuracy. Comparative analysis with traditional methods and existing models demonstrates PDCNN's robust feature extraction capability. It outperforms advanced machine learning methods in identifying DNA enhancers, presenting an effective method with broad implications for genomics, biology, and medical research.

Dissection of a CTCF topological boundary uncovers principles of enhancer-oncogene regulation

Enhancer-gene communication is dependent on topologically associating domains (TADs) and boundaries enforced by the CCCTC-binding factor (CTCF) insulator, but the underlying structures and mechanisms remain controversial. Here, we investigate a boundary that typically insulates fibroblast growth factor (FGF) oncogenes but is disrupted by DNA hypermethylation in gastrointestinal stromal tumors (GISTs). The boundary contains an array of CTCF sites that enforce adjacent TADs, one containing FGF genes and the other containing ANO1 and its putative enhancers, which are specifically active in GIST and its likely cell of origin. We show that coordinate disruption of four CTCF motifs in the boundary fuses the adjacent TADs, allows the ANO1 enhancer to contact FGF3, and causes its robust induction. High-resolution micro-C maps reveal specific contact between transcription initiation sites in the ANO1 enhancer and FGF3 promoter that quantitatively scales with FGF3 induction such that modest changes in contact frequency result in strong changes in expression, consistent with a causal relationship.

Improving Enhancer Identification with a Multi-Classifier Stacked Ensemble Model

Enhancers are DNA regions that are responsible for controlling the expression of genes. Enhancers are usually found upstream or downstream of a gene, or even inside a gene's intron region, but are normally located at a distant location from the genes they control. By integrating experimental and computational approaches, it is possible to uncover enhancers within DNA sequences, which possess regulatory properties. Experimental techniques such as ChIP-seq and ATAC-seq can identify genomic regions that are associated with transcription factors or accessible to regulatory proteins. On the other hand, computational techniques can predict enhancers based on sequence features and epigenetic modifications. In our study, we have developed a multi-classifier stacked ensemble (MCSE-enhancer) model that can accurately identify enhancers. We utilized feature descriptors from various physiochemical properties as input for our six baseline classifiers and built a stacked classifier, which outperformed previous enhancer classification techniques in terms of accuracy, specificity, sensitivity, and Mathew's correlation coefficient. Our model achieved an accuracy of 81.5%, representing a 2-3% improvement over existing models.

Contributions of 3D chromatin structure to cell-type-specific gene regulation

Eukaryotic genomes are organized in 3D in a multiscale manner, and different mechanisms acting at each of these scales can contribute to transcriptional regulation. However, the large single-cell variability in 3D chromatin structures represents a challenge to understand how transcription may be differentially regulated between cell types in a robust and efficient manner. Here, we describe the different mechanisms by which 3D chromatin structure was shown to contribute to cell-type-specific transcriptional regulation. Excitingly, several novel methodologies able to measure 3D chromatin conformation and transcription in single cells in their native tissue context, or to detect the dynamics of cis-regulatory interactions, are starting to allow quantitative dissection of chromatin structure noise and relate it to how transcription may be regulated between different cell types and cell states.

Integrative machine learning framework for the identification of cell-specific enhancers from the human genome

Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription factors enhance the transcription of related genes. Due to its sporadic distribution and similar fractions, identification of enhancers from the human genome seems a daunting task. Compared to the traditional experimental approaches, computational methods with easy-to-use platforms could be efficiently applied to annotate enhancers' functions and physiological roles. In this aspect, several bioinformatics tools have been developed to identify enhancers. Despite their spectacular performances, existing methods have certain drawbacks and limitations, including fixed length of sequences being utilized for model development and cell-specificity negligence. A novel predictor would be beneficial in the context of genome-wide enhancer prediction by addressing the above-mentioned issues. In this study, we constructed new datasets for eight different cell types. Utilizing these data, we proposed an integrative machine learning (ML)-based framework called Enhancer-IF for identifying cell-specific enhancers. Enhancer-IF comprehensively explores a wide range of heterogeneous features with five commonly used ML methods (random forest, extremely randomized tree, multilayer perceptron, support vector machine and extreme gradient boosting). Specifically, these five classifiers were trained with seven encodings and obtained 35 baseline models. The output of these baseline models was integrated and again inputted to five classifiers for the construction of five meta-models. Finally, the integration of five meta-models through ensemble learning improved the model robustness. Our proposed approach showed an excellent prediction performance compared to the baseline models on both training and independent datasets in different cell types, thus highlighting the superiority of our approach in the identification of the enhancers. We assume that Enhancer-IF will be a valuable tool for screening and identifying potential enhancers from the human DNA sequences.

Tumor DNA methylation profiles correlate with response to anti-PD-1 immune checkpoint inhibitor monotherapy in sarcoma patients

BACKGROUND

Some sarcomas respond to immune checkpoint inhibition, but predictive biomarkers are unknown. We analyzed tumor DNA methylation profiles in relation to immunological parameters and response to anti-programmed cell death 1 (anti-PD-1) immune checkpoint inhibitor (ICI) therapy in patients with sarcoma.

PATIENTS AND METHODS

We retrospectively identified adult patients who had received anti-PD-1 ICI therapy for recurrent sarcoma in two independent centers. We performed (1) blinded radiological response evaluation according to immune response evaluation criteria in solid tumors (iRECIST) ; (2) tumor DNA methylation profiling of >850,000 probes using Infinium MethylationEPIC microarrays; (3) analysis of tumor-infiltrating immune cell subsets (CD3, CD8, CD45RO, FOXP3) and intratumoral expression of immune checkpoint molecules (PD-L1, PD-1, LAG-3) using immunohistochemistry; and (4) evaluation of blood-based systemic inflammation scores (neutrophil-to-lymphocyte ratio, leucocyte-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio). Response to anti-PD-1 ICI therapy was bioinformatically and statistically correlated with DNA methylation profiles and immunological data.

RESULTS

35 patients (median age of 50 (23-81) years; 18 females, 17 males; 27 soft tissue sarcomas; 8 osteosarcomas) were included in this study. The objective response rate to anti-PD-1 ICI therapy was 22.9% with complete responses in 3 out of 35 and partial responses in 5 out of 35 patients. Adjustment of DNA methylation data for tumor-infiltrating immune cells resulted in identification of methylation differences between responders and non-responders to anti-PD-1 ICI. 2453 differentially methylated CpG sites (DMPs; 2043 with decreased and 410 with increased methylation) were identified. Clustering of sarcoma samples based on these DMPs revealed two main clusters: methylation cluster 1 (MC1) consisted of 73% responders and methylation cluster 2 (MC2) contained only non-responders to anti-PD-1 ICI. Median progression-free survival from anti-PD-1 therapy start of MC1 and MC2 patients was 16.5 and 1.9 months, respectively (p=0.001). Median overall survival of these patients was 34.4 and 8.0 months, respectively (p=0.029). The most prominent DNA methylation differences were found in pathways implicated in Rap1 signaling, focal adhesion, adherens junction Phosphoinositide 3-kinase (PI3K)-Akt signaling and extracellular matrix (ECM)-receptor interaction.

CONCLUSIONS

Our data demonstrate that tumor DNA methylation profiles may serve as a predictive marker for response to anti-PD-1 ICI therapy in sarcoma.