Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis

Enhancer RNA Transcriptome-Wide Association Study Reveals a Distinctive Class of Pan-Cancer Susceptibility eRNAs

Many cancer risk variants are located within enhancer regions and lack sufficient molecular interpretation. Here, we constructed the first comprehensive atlas of enhancer RNA (eRNA)-mediated genetic effects from 28 033 RNA sequencing samples across 11 606 individuals, identifying 21 073 eRNA quantitative trait loci (eRNA-QTLs) significantly associated with eRNA expression. Mechanistically, eRNA-QTLs frequently altered binding motifs of transcription factors. In addition, 28.48% of cancer risk variants are strongly colocalized with eRNA-QTLs. A pan-cancer eRNA-based transcriptome-wide association study is conducted across 23 major cancer types, identifying 626 significant cancer susceptibility eRNAs predicted to modulate cancer risk via eRNA, from which 54.90% of the eRNA target genes are overlooked by traditional gene expression studies, and most are essential for cancer cell proliferation. As proof of principle validation, the enhancer functionality of two newly identified susceptibility eRNAs, CCND1e and SNAPC1e, is confirmed through CRISPR inhibition and shRNA-mediated knockdown, resulting in a marked decrease in the expression of their respective target genes, consequently suppressing the proliferation of prostate cancer cells. The study underscores the essential role of eRNA in unveiling new cancer susceptibility genes and establishes a strong framework for enhancing our understanding of human cancer etiology.

Modular organization of enhancer network provides transcriptional robustness in mammalian development

Enhancer clusters, pivotal in mammalian development and diseases, can organize as enhancer networks to control cell identity and disease genes; however, the underlying mechanism remains largely unexplored. Here, we introduce eNet 2.0, a comprehensive tool for enhancer networks analysis during development and diseases based on single-cell chromatin accessibility data. eNet 2.0 extends our previous work eNet 1.0 by adding network topology, comparison and dynamics analyses to its network construction function. We reveal modularly organized enhancer networks, where inter-module interactions synergistically affect gene expression. Moreover, network alterations correlate with abnormal and dynamic gene expression in disease and development. eNet 2.0 is robust across diverse datasets. To facilitate application, we introduce eNetDB (https://enetdb.huanglabxmu.com), an enhancer network database leveraging extensive scATAC-seq (single-cell assay for transposase-accessible chromatin sequencing) datasets from human and mouse tissues. Together, our work provides a powerful computational tool and reveals that modularly organized enhancer networks contribute to gene expression robustness in mammalian development and diseases.

Integrative approaches based on genomic techniques in the functional studies on enhancers

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.