MYC as a driver of stochastic chromatin networks: implications for the fitness of cancer cells

Single-molecule RNA-FISH analysis reveals stochasticity in reactivation of latent HIV-1 regulated by Nuclear Orphan Receptors NR4A and cMYC

HIV-1 eradication strategies require complete reactivation of HIV-1 latent cells by Latency Reversing Agents (LRA). Current methods lack effectiveness due to incomplete proviral reactivation. We employed a single-molecule RNA-FISH (smRNA-FISH) and FISH-Quant analysis and found that proviral reactivation is highly variable from cell-to-cell, stochastic, and occurs in bursts and waves, with different kinetics in response to diverse LRAs. Approximately 1-5% of latent cells exhibited stochastic reactivation without LRAs. Through single-cell RNA-seq analysis, we identified NR4A3 and cMYC as extrinsic factors associated with stochastic HIV-1 reactivation. Concomitant with HIV-1 reactivation cMYC was downregulated and NR4A3 was upregulated in both latent cell lines and primary CD4 + T-cells from aviremic patients. By inhibiting cMYC using SN-38, an active metabolite of irinotecan, we induced NR4A3 and HIV-1 expression. Our results suggest that inherent stochasticity in proviral reactivation contributes to cell-to-cell variability, which could potentially be modulated by drugs targeting cMYC and NR4A3.

Network models of chromatin structure

Increasing numbers of datasets and experimental assays that capture the organization of chromatin inside the nucleus warrant an effort to develop tools to visualize and analyze these structures. Alongside polymer physics or constraint-based modeling, network theory approaches to describe 3D epigenome organization have gained in popularity. Representing genomic regions as nodes in a network enables visualization of 1D epigenomics datasets in the context of chromatin structure maps, while network theory metrics can be used to describe 3D epigenome organization and dynamics. In this review, we summarize the most salient applications of network theory to the study of chromatin contact maps, demonstrating its potential in revealing epigenomic patterns and relating them to cellular phenotypes.

The Nodewalk assay to quantitate chromatin fiber interactomes in very small cell populations

The chromosome conformation capture method and its derivatives, such as circularized chromosome conformation capture, carbon copy chromosome conformation capture, high-throughput chromosome conformation capture and capture high-throughput chromosome conformation capture, have pioneered our understanding of the principles of chromosome folding in the nucleus. These technical advances, however, cannot precisely quantitate interaction frequency in very small input samples. Here we describe a protocol for the Nodewalk assay, which is based on converting chromosome conformation capture DNA samples to RNA and subsequently to cDNA using strategically placed primers. This pipeline enables the quantitative analyses of chromatin fiber interactions without compromising its sensitivity down to <300 cells, making it suitable for MiSeq analyses of higher-order chromatin structures in biopsies, circulating tumor cells and transitional cell states, for example. Importantly, the quality of the Nodewalk sample can be assessed before sequencing to avoid unnecessary costs. Moreover, it enables analyses from hundreds of different restriction enzyme fragment viewpoints within the same initial small input sample to uncover complex, genome-wide networks. Following optimization of the different steps, the entire protocol can be completed within 2 weeks.

Chromatin Hubs: A biological and computational outlook

This review discusses our current understanding of chromatin biology and bioinformatics under the unifying concept of “chromatin hubs.” The first part reviews the biology of chromatin hubs, including chromatin–chromatin interaction hubs, chromatin hubs at the nuclear periphery, hubs around macromolecules such as RNA polymerase or lncRNAs, and hubs around nuclear bodies such as the nucleolus or nuclear speckles. The second part reviews existing computational methods, including enhancer–promoter interaction prediction, network analysis, chromatin domain callers, transcription factory predictors, and multi-way interaction analysis. We introduce an integrated model that makes sense of the existing evidence. Understanding chromatin hubs may allow us (i) to explain long-unsolved biological questions such as interaction specificity and redundancy of mechanisms, (ii) to develop more realistic kinetic and functional predictions, and (iii) to explain the etiology of genomic disease.

Canonical WNT signaling-dependent gating of MYC requires a noncanonical CTCF function at a distal binding site

Abnormal WNT signaling increases MYC expression in colon cancer cells in part via oncogenic super-enhancer-(OSE)-mediated gating of the active MYC to the nuclear pore in a poorly understood process. We show here that the principal tenet of the WNT-regulated MYC gating, facilitating nuclear export of the MYC mRNA, is regulated by a CTCF binding site (CTCFBS) within the OSE to confer growth advantage in HCT-116 cells. To achieve this, the CTCFBS directs the WNT-dependent trafficking of the OSE to the nuclear pore from intra-nucleoplasmic positions in a stepwise manner. Once the OSE reaches a peripheral position, which is triggered by a CTCFBS-mediated CCAT1 eRNA activation, its final stretch (≤0.7 μm) to the nuclear pore requires the recruitment of AHCTF1, a key nucleoporin, to the CTCFBS. Thus, a WNT/ß-catenin-AHCTF1-CTCF-eRNA circuit enables the OSE to promote pathological cell growth by coordinating the trafficking of the active MYC gene within the 3D nuclear architecture.

Gene-gating of a MYC oncogenic super-enhancer (OSE) increases its expression in colon cancer cells in a poorly understood process. Here the authors show that MYC gating requires a CTCF binding site (CTCFBS) within the OSE that directs the stepwise trafficking of the OSE to the nuclear pore to facilitate increased nuclear export of MYC mRNA, which results in a growth advantage.