eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse

sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human

Identifying cell populations associated with risk variants is essential for uncovering cell-specific mechanisms that drive disease development and progression. Integrating genome-wide association studies (GWAS) with single-cell RNA sequencing (scRNA-seq) has become an effective strategy for detecting trait-cell relationships. The accumulation of trait-related single cell data has led to an urgent need for its comprehensively processing. To address this, we developed sc2GWAS (https://bio.liclab.net/sc2GWAS/), which aims to document large-scale GWAS trait-cell regulatory pairs at single-cell resolution and provide comprehensive annotations and enrichment analyses for these related pairs. The current version of sc2GWAS curates a total of 15 078 310 candidate trait-cell pairs from > 6 300 000 individual cells, offering a valuable resource for exploring complex regulatory relationships between traits and cells. We applied strict quality control measures on both scRNA-seq data and GWAS data, ensuring the reliability and accuracy of the datasets for the identification of trait-relevant cells and genes. In addition, sc2GWAS provides ranked lists of trait-relevant genes and extensive (epi) genetic annotations, making it a valuable resource for downstream analyses. We demonstrate the utility of the platform by investigating Alzheimer's disease, where we identified significant associations between the disease and microglial cells, with the APOE gene emerging as particularly significant. This platform facilitates detailed research into complex trait-cell and trait-gene interactions, we anticipate that sc2GWAS will become a comprehensive and valuable platform for exploring GWAS trait-cell regulatory mechanisms.

LnCeCell 2.0: an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data

We describe LnCeCell 2.0 (http://bio-bigdata.hrbmu.edu.cn/LnCeCell), an updated resource for lncRNA-associated competing endogenous RNA (ceRNA) networks and web tools based on single-cell and spatial transcriptomics sequencing (stRNA-seq) data. We have updated the LnCeCell 2.0 database with significantly expanded data and improved features, including (i) 257 single-cell RNA sequencing and stRNA-seq datasets across 86 diseases/phenotypes and 80 human normal tissues, (ii) 836 581 cell-specific and spatial spot-specific ceRNA interactions and functional networks for 1 002 988 cells and 367 971 spatial spots, (iii) 15 489 experimentally supported lncRNA biomarkers related to disease pathology, diagnosis and treatment, (iv) detailed annotation of cell type, cell state, subcellular and extracellular locations of ceRNAs through manual curation and (v) ceRNA expression profiles and follow-up clinical information of 20 326 cancer patients. Further, a panel of 24 flexible tools (including 8 comprehensive and 16 mini-analysis tools) was developed to investigate ceRNA-regulated mechanisms at single-cell/spot resolution. The CeCellTraject tool, for example, illustrates the detailed ceRNA distribution of different cell populations and explores the dynamic change of the ceRNA network along the developmental trajectory. LnCeCell 2.0 will facilitate the study of fine-tuned lncRNA-ceRNA networks with single-cell and spatial spot resolution, helping us to understand the regulatory mechanisms behind complex microbial ecosystems.

A Current Perspective of Medical Informatics Developments for a Clinical Translation of (Non-coding)RNAs and Single-Cell Technologies

The journey from laboratory research to clinical practice is marked by significant advancements in the fields of single-cell technologies and non-coding RNA (ncRNA) research. This convergence may reshape our approach to personalized medicine, offering groundbreaking insights and treatments in various clinical settings. This chapter discusses advancements in (nc)RNAs in the clinics, innovations in single-cell technologies and algorithms, and the impact on actual precision medicine, showing the integration of single-cell and ncRNA research can have a tangible impact on precision medicine. Case studies in Oncology, Immunology, and other fields demonstrate how these technologies can guide treatment decisions, tailor therapies to individual patients, and improve outcomes. This approach is particularly potent in addressing diseases with high inter- and intra-tumor heterogeneity. The final sections address standardization, data integration, and analysis challenges because the complexity and volume of data generated by single-cell and ncRNA research poses significant challenges. Medical Informatics is not just a support tool but could be seen as a pivotal component in advancing clinical applications of single-cell and ncRNA research by bridging the gap between bench and bedside. The future of personalized medicine depends on our ability to harness the power of these technologies, and Medical Informatics in combination with ncRNA and single-cell technologies may stand at the forefront of this endeavor.

STAT3 drives the expression of HIF1alpha in cancer cells through a novel super-enhancer

Aerobic glycolysis is one of the major hallmarks of malignant tumors. This metabolic reprogramming benefits the rapid proliferation of cancer cells, facilitates the formation of tumor microenvironment to support their growth and survival, and impairs the efficacy of various tumor therapies. Therefore, the elucidation of the mechanisms driving aerobic glycolysis in tumors represents a pivotal breakthrough in developing therapeutic strategies for solid tumors. HIF1α serves as a central regulator of aerobic glycolysis with elevated mRNA and protein expression across multiple tumor types. However, the mechanisms contributing to this upregulation remain elusive. This study reports the identification of a novel HIF1α super enhancer (HSE) in multiple cancer cells using bioinformatics analysis, chromosome conformation capture (3C), chromatin immunoprecipitation (ChIP), and CRISPR/Cas9 genome editing techniques. Deletion of HSE in cancer cells significantly reduces the expression of HIF1α, glycolysis, cell proliferation, colony and tumor formation ability, confirming the role of HSE as the enhancer of HIF1α in cancer cells. Particularly, we demonstrated that STAT3 promotes the expression of HIF1α by binding to HSE. The discovery of HSE will help elucidate the pathways driving tumor aerobic glycolysis, offering new therapeutic targets and potentially resolving the bottleneck in solid tumor treatment.

DNA Methylation in the CYP3A Distal Regulatory Region (DRR) Is Associated with the Expression of CYP3A5 and CYP3A7 in Human Liver Samples

CYP3As are important drug-metabolizing enzymes in the liver. The causes for large inter-person variability in CYP3A expression/activity remain poorly understood. DNA methylation broadly regulates gene expression and the developmental transition from fetal CYP3A7 to adult CYP3A4, and CpG methylation upstream of the CYP3A4 promoter is associated with its expression. However, because non-promoter CYP3A regulatory regions remain largely uncharacterized, how DNA methylation influences CYP3A expression has yet to be fully explored. We recently identified a distal regulatory region (DRR) that controls the expression of CYP3A4, CYP3A5, and CYP3A7. Here, we investigated the relationship between CYP3A expression and the methylation status of 16 CpG sites within the DRR in 70 liver samples. We found significant associations between DRR methylation and the expression of CYP3A5 and CYP3A7 but not CYP3A4, indicating differential CYP3A regulation by the DRR. Also, we observed a dynamic reduction in DRR DNA methylation during the differentiation of induced pluripotent stem cells to hepatocytes, which correlated with increased CYP3A expression. We then evaluated the relative contribution of genetic variants, TFs, and DRR DNA methylation on CYP3A expression in liver samples. Our results reinforce the DRR as a CYP3A regulator and suggest that DNA methylation may impact CYP3A-mediated drug metabolism.

eRNA-IDO: A One-stop Platform for Identification, Interactome Discovery, and Functional Annotation of Enhancer RNAs

Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novo assembled transcriptomes. eRNA-ID includes eight kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNAs and protein-coding genes. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/.

Cut from the same cloth: RNAs transcribed from regulatory elements

A certain degree of chromatin openness is necessary for the activity of transcription-regulating regions within the genome, facilitating accessibility to RNA polymerases and subsequent synthesis of regulatory element RNAs (regRNAs) from these regions. The rapidly increasing number of studies underscores the significance of regRNAs across diverse cellular processes and diseases, challenging the paradigm that these transcripts are non-functional transcriptional noise. This review explores the multifaceted roles of regRNAs in human cells, encompassing rather well-studied entities such as promoter RNAs and enhancer RNAs (eRNAs), while also providing insights into overshadowed silencer RNAs and insulator RNAs. Furthermore, we assess notable examples of shorter regRNAs, like miRNAs, snRNAs, and snoRNAs, playing important roles. Expanding our discourse, we deliberate on the potential usage of regRNAs as biomarkers and novel targets for cancer and other human diseases.

The dynamic landscape of enhancer-derived RNA during mouse early embryo development

Enhancer-derived RNAs (eRNAs) play critical roles in diverse biological processes by facilitating their target gene expression. However, the abundance and function of eRNAs in early embryos are not clear. Here, we present a comprehensive eRNA atlas by systematically integrating publicly available datasets of mouse early embryos. We characterize the transcriptional and regulatory network of eRNAs and show that different embryo developmental stages have distinct eRNA expression and regulatory profiles. Paternal eRNAs are activated asymmetrically during zygotic genome activation (ZGA). Moreover, we identify an eRNA, MZGAe1, which plays an important function in regulating mouse ZGA and early embryo development. MZGAe1 knockdown leads to a developmental block from 2-cell embryo to blastocyst. We create an online data portal, M2ED2, to query and visualize eRNA expression and regulation. Our study thus provides a systematic landscape of eRNA and reveals the important role of eRNAs in regulating mouse early embryo development.

Comprehensive Pan-Cancer Mutation Density Patterns in Enhancer RNA

Significant advances have been achieved in understanding the critical role of enhancer RNAs (eRNAs) in the complex field of gene regulation. However, notable uncertainty remains concerning the biology of eRNAs, highlighting the need for continued research to uncover their exact functions in cellular processes and diseases. We present a comprehensive study to scrutinize mutation density patterns, mutation strand bias, and mutation burden in eRNAs across multiple cancer types. Our findings reveal that eRNAs exhibit mutation strand bias akin to that observed in protein-coding RNAs. We also identified a novel pattern, in which mutation density is notably diminished around the central region of the eRNA, but conspicuously elevated towards both the beginning and end. This pattern can be potentially explained by a mechanism involving heightened transcriptional activity and the activation of transcription-coupled repair. The central regions of the eRNAs appear to be more conserved, hinting at a potential mechanism preserving their structural and functional integrity, while the extremities may be more susceptible to mutations due to increased exposure. The evolutionary trajectory of this mutational pattern suggests a nuanced adaptation in eRNAs, where stability at their core coexists with flexibility at their extremities, potentially facilitating their diverse interactions with other genetic entities.