Within the tumour microenvironment, cells exhibit different behaviours driven by fine-tuning of gene regulation. Identification of cellular-specific gene regulatory networks will deepen the understanding of disease pathology at single-cell resolution and contribute to the development of precision medicine. Here, we describe a database, LnCeCell (http://www.bio-bigdata.net/LnCeCell/ or http://bio-bigdata.hrbmu.edu.cn/LnCeCell/), which aims to document cellular-specific long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) networks for personalised characterisation of diseases based on the 'One Cell, One World' theory. LnCeCell is curated with cellular-specific ceRNA regulations from >94 000 cells across 25 types of cancers and provides >9000 experimentally supported lncRNA biomarkers, associated with tumour metastasis, recurrence, prognosis, circulation, drug resistance, etc. For each cell, LnCeCell illustrates a global map of ceRNA sub-cellular locations, which have been manually curated from the literature and related data sources, and portrays a functional state atlas for a single cancer cell. LnCeCell also provides several flexible tools to infer ceRNA functions based on a specific cellular background. LnCeCell serves as an important resource for investigating the gene regulatory networks within a single cell and can help researchers understand the regulatory mechanisms underlying complex microbial ecosystems and individual phenotypes.

The CCR4-associated factor CAF1, also called CNOT7, is a catalytic subunit of the CCR4-NOT complex, which has been implicated in all aspects of the mRNA life cycle, from mRNA synthesis in the nucleus to degradation in the cytoplasm. In human cells, alternative splicing of the CNOT7 gene yields a second CNOT7 transcript leading to the formation of a shorter protein, CNOT7 variant 2 (CNOT7v2). Biochemical characterization indicates that CNOT7v2 interacts with CCR4-NOT subunits, although it does not bind to BTG proteins. We report that CNOT7v2 displays a distinct expression profile in human tissues, as well as a nuclear sub-cellular localization compared to CNOT7v1. Despite a conserved DEDD nuclease domain, CNOT7v2 is unable to degrade a poly(A) tail in vitro and preferentially associates with the protein arginine methyltransferase PRMT1 to regulate its activity. Using both in vitro and in cellulo systems, we have also demonstrated that CNOT7v2 regulates the inclusion of CD44 variable exons. Altogether, our findings suggest a preferential involvement of CNOT7v2 in nuclear processes, such as arginine methylation and alternative splicing, rather than mRNA turnover. These observations illustrate how the integration of a splicing variant inside CCR4-NOT can diversify its cell- and tissue-specific functions.

An updated Lnc2Cancer 3.0 (http://www.bio-bigdata.net/lnc2cancer or http://bio-bigdata.hrbmu.edu.cn/lnc2cancer) database, which includes comprehensive data on experimentally supported long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) associated with human cancers. In addition, web tools for analyzing lncRNA expression by high-throughput RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) are described. Lnc2Cancer 3.0 was updated with several new features, including (i) Increased cancer-associated lncRNA entries over the previous version. The current release includes 9254 lncRNA-cancer associations, with 2659 lncRNAs and 216 cancer subtypes. (ii) Newly adding 1049 experimentally supported circRNA-cancer associations, with 743 circRNAs and 70 cancer subtypes. (iii) Experimentally supported regulatory mechanisms of cancer-related lncRNAs and circRNAs, involving microRNAs, transcription factors (TF), genetic variants, methylation and enhancers were included. (iv) Appending experimentally supported biological functions of cancer-related lncRNAs and circRNAs including cell growth, apoptosis, autophagy, epithelial mesenchymal transformation (EMT), immunity and coding ability. (v) Experimentally supported clinical relevance of cancer-related lncRNAs and circRNAs in metastasis, recurrence, circulation, drug resistance, and prognosis was included. Additionally, two flexible online tools, including RNA-seq and scRNA-seq web tools, were developed to enable fast and customizable analysis and visualization of lncRNAs in cancers. Lnc2Cancer 3.0 is a valuable resource for elucidating the associations between lncRNA, circRNA and cancer.

Exosomes, which are nanosized endocytic vesicles that are secreted by most cells, contain an abundant cargo of different RNA species that can modulate the behavior of recipient cells and may be used as circulating biomarkers for diseases. Here, we develop a web-accessible database (http://www.exoRBase.org), exoRBase, which is a repository of circular RNA (circRNA), long non-coding RNA (lncRNA) and messenger RNA (mRNA) derived from RNA-seq data analyses of human blood exosomes. Experimental validations from the published literature are also included. exoRBase features the integration and visualization of RNA expression profiles based on normalized RNA-seq data spanning both normal individuals and patients with different diseases. exoRBase aims to collect and characterize all long RNA species in human blood exosomes. The first release of exoRBase contains 58 330 circRNAs, 15 501 lncRNAs and 18 333 mRNAs. The annotation, expression level and possible original tissues are provided. exoRBase will aid researchers in identifying molecular signatures in blood exosomes and will trigger new exosomal biomarker discovery and functional implication for human diseases.

Aptamers' vast conformation ensemble consisting of interconverting substates severely impairs their performance and applications in biomedicine. Therefore, developing new chemistries stabilizing aptamer conformation and exploring the conformation-performance relationship are highly desired. Herein, we developed an 8-methoxypsoralen-based photochemically covalent lock to stabilize aptamer conformation via crosslinking the inter-stranded thymine nucleotides at TpA sites. Systematical studies and molecular dynamics simulations were performed to explore the conformation-performance relationship of aptamers, revealing that conformation-stabilized aptamers displayed better ability to bind targets, adapt to physiological environment, resist macrophage uptake, prolong circulation half-life, accumulate in and penetrate into tumor than their counterparts. As expected, conformation-stabilized aptamers efficiently improved the therapeutic efficacy of aptamer-drug conjugation on tumor-bearing mice. Collectively, our study has developed a general, simple and economic strategy to stabilize aptamer conformation and shed light on the conformation-performance relationship of aptamers, laying a basis for promoting their basic researches and applications in biomedicine.

Epithelial-to-mesenchymal transition (EMT) is a complex multistep process in which phenotype switches are mediated by a network of transcription factors (TFs). Systematic characterization of all dynamic TFs controlling EMT state transitions, especially for the intermediate partial-EMT state, represents a highly relevant yet largely unexplored task. Here, we performed a computational analysis that integrated time-course EMT transcriptomic data with public cistromic data and identified three synergistic master TFs (ETS2, HNF4A and JUNB) that regulate the transition through the partial-EMT state. Overexpression of these regulators predicted a poor clinical outcome, and their elimination readily abolished TGF-β-induced EMT. Importantly, these factors utilized a clique motif, physically interact and their cumulative binding generally characterized EMT-associated genes. Furthermore, analyses of H3K27ac ChIP-seq data revealed that ETS2, HNF4A and JUNB are associated with super-enhancers and the administration of BRD4 inhibitor readily abolished TGF-β-induced EMT. These findings have implications for systematic discovery of master EMT regulators and super-enhancers as novel targets for controlling metastasis.

Lnc2Meth (http://www.bio-bigdata.com/Lnc2Meth/), an interactive resource to identify regulatory relationships between human long non-coding RNAs (lncRNAs) and DNA methylation, is not only a manually curated collection and annotation of experimentally supported lncRNAs-DNA methylation associations but also a platform that effectively integrates tools for calculating and identifying the differentially methylated lncRNAs and protein-coding genes (PCGs) in diverse human diseases. The resource provides: (i) advanced search possibilities, e.g. retrieval of the database by searching the lncRNA symbol of interest, DNA methylation patterns, regulatory mechanisms and disease types; (ii) abundant computationally calculated DNA methylation array profiles for the lncRNAs and PCGs; (iii) the prognostic values for each hit transcript calculated from the patients clinical data; (iv) a genome browser to display the DNA methylation landscape of the lncRNA transcripts for a specific type of disease; (v) tools to re-annotate probes to lncRNA loci and identify the differential methylation patterns for lncRNAs and PCGs with user-supplied external datasets; (vi) an R package (LncDM) to complete the differentially methylated lncRNAs identification and visualization with local computers. Lnc2Meth provides a timely and valuable resource that can be applied to significantly expand our understanding of the regulatory relationships between lncRNAs and DNA methylation in various human diseases.

During the complex process of tumour development, the unique destiny of cells is driven by the fine-tuning of multilevel features such as gene expression, network regulation and pathway activation. The dynamic formation of the tumour microenvironment influences the therapeutic response and clinical outcome. Thus, characterizing the developmental landscape and identifying driver features at multiple levels will help us understand the pathological development of disease in individual cell populations and further contribute to precision medicine. Here, we describe a database, CellTracer (http://bio-bigdata.hrbmu.edu.cn/CellTracer), which aims to dissect the causative multilevel interplay contributing to cell development trajectories. CellTracer consists of the gene expression profiles of 1 941 552 cells from 222 single-cell datasets and provides the development trajectories of different cell populations exhibiting diverse behaviours. By using CellTracer, users can explore the significant alterations in molecular events and causative multilevel crosstalk among genes, biological contexts, cell characteristics and clinical treatments along distinct cell development trajectories. CellTracer also provides 12 flexible tools to retrieve and analyse gene expression, cell cluster distribution, cell development trajectories, cell-state variations and their relationship under different conditions. Collectively, CellTracer will provide comprehensive insights for investigating the causative multilevel interplay contributing to cell development trajectories and serve as a foundational resource for biomarker discovery and therapeutic exploration within the tumour microenvironment.

H/ACA RNA-guided ribonucleoprotein particle (RNP), the most complicated RNA pseudouridylase so far known, uses H/ACA guide RNA for substrate capture and four proteins (Cbf5, Nop10, L7Ae and Gar1) for pseudouridylation. Although it was shown that Gar1 not only facilitates the product release, but also enhances the catalytic activity, the chemical role that Gar1 plays in this complicated machinery is largely unknown. Kinetics measurement on Pyrococcus furiosus RNPs at different temperatures making use of fluorescence anisotropy showed that Gar1 reduces the catalytic barrier through affecting the activation entropy instead of enthalpy. Site-directed mutagenesis combined with molecular dynamics simulations demonstrated that V149 in the thumb loop of Cbf5 is critical in placing the target uridine to the right position toward catalytic D85 of Cbf5. The enzyme elegantly aligns the position of uridine in the catalytic site with the help of Gar1. In addition, conversion of uridine to pseudouridine results in a rigid syn configuration of the target nucleotide in the active site and causes Gar1 to pull out the thumb. Both factors guarantee the efficient release of the product.

Recent studies have suggested that long non-coding RNAs (lncRNAs) can interact with microRNAs (miRNAs) and indirectly regulate miRNA targets though competing interactions. However, the molecular mechanisms underlying these interactions are still largely unknown. In this study, these lncRNA-miRNA-gene interactions were defined as lncRNA-associated competing triplets (LncACTs), and an integrated pipeline was developed to identify lncACTs that are active in cancer. Competing lncRNAs had sponge features distinct from non-competing lncRNAs. In the lncACT cross-talk network, disease-associated lncRNAs, miRNAs and coding-genes showed specific topological patterns indicative of their competence and control of communication within the network. The construction of global competing activity profiles revealed that lncACTs had high activity specific to cancers. Analyses of clustered lncACTs revealed that they were enriched in various cancer-related biological processes. Based on the global cross-talk network and cluster analyses, nine cancer-specific sub-networks were constructed. H19- and BRCA1/2-associated lncACTs were able to discriminate between two groups of patients with different clinical outcomes. Disease-associated lncACTs also showed variable competing patterns across normal and cancer patient samples. In summary, this study uncovered and systematically characterized global properties of human lncACTs that may have prognostic value for predicting clinical outcome in cancer patients.

Polycomb repression complex 2 (PRC2) component EZH2 tri-methylates H3K27 and exerts epigenetic repression on target gene expression. EZH2-mediated epigenetic control of RNA polymerase II (Pol II) transcribed coding gene transcription has been well established. However, little is known about EZH2-mediated epigenetic regulation of RNA polymerase III (Pol III) transcription. Here we present a paradigm that EZH2 is involved in the repression of Pol III transcription via interaction with transcriptional factor complex IIIC (TFIIIC). EZH2 and H3K27me3 co-occupy the promoter of tRNA(Tyr), 5S rRNA and 7SL RNA genes. Depletion of EZH2 or inhibition of EZH2 methyltransferase activity led to upregulation of Pol III target gene transcription. EZH2-mediated repression of Pol III transcribed gene expression requires presence of SUZ12. SUZ12 was able to interact with TFIIIC complex and knockdown of SUZ12 decreased occupancy of EZH2 and H3K27me3 at the promoter of Pol III target genes. Our findings pointed out a previously unidentified role of PRC2 complex in suppressing transcription of Pol III transcribed non-translated RNA genes, putting Pol III on a new layer of epigenetic regulation.

LncACTdb 3.0 is a comprehensive database of experimentally supported interactions among competing endogenous RNA (ceRNA) and the corresponding personalized networks contributing to precision medicine. LncACTdb 3.0 is freely available at http://bio-bigdata.hrbmu.edu.cn/LncACTdb or http://www.bio-bigdata.net/LncACTdb. We have updated the LncACTdb 3.0 database with several new features, including (i) 5669 experimentally validated ceRNA interactions across 25 species and 537 diseases/phenotypes through manual curation of published literature, (ii) personalized ceRNA interactions and networks for 16 228 patients from 62 datasets in TCGA and GEO, (iii) sub-cellular and extracellular vesicle locations of ceRNA manually curated from literature and data sources, (iv) more than 10 000 experimentally supported long noncoding RNA (lncRNA) biomarkers associated with tumor diagnosis and therapy, and (v) lncRNA/mRNA/miRNA expression profiles with clinical and pathological information of thousands of cancer patients. A panel of improved tools has been developed to explore the effects of ceRNA on individuals with specific pathological backgrounds. For example, a network tool provides a comprehensive view of lncRNA-related, patient-specific, and custom-designed ceRNA networks. LncACTdb 3.0 will provide novel insights for further studies of complex diseases at the individual level and will facilitate the development of precision medicine to treat such diseases.

We describe an updated comprehensive database, LincSNP 3.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), which aims to document and annotate disease or phenotype-associated variants in human long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) or their regulatory elements. LincSNP 3.0 has updated with several novel features, including (i) more types of variants including single nucleotide polymorphisms (SNPs), linkage disequilibrium SNPs (LD SNPs), somatic mutations and RNA editing sites have been expanded; (ii) more regulatory elements including transcription factor binding sites (TFBSs), enhancers, DNase I hypersensitive sites (DHSs), topologically associated domains (TADs), footprintss, methylations and open chromatin regions have been added; (iii) the associations among circRNAs, regulatory elements and variants have been identified; (iv) more experimentally supported variant-lncRNA/circRNA-disease/phenotype associations have been manually collected; (v) the sources of lncRNAs, circRNAs, SNPs, somatic mutations and RNA editing sites have been updated. Moreover, four flexible online tools including Genome Browser, Variant Mapper, Circos Plotter and Functional Annotation have been developed to retrieve, visualize and analyze the data. Collectively, LincSNP 3.0 provides associations among functional variants, regulatory elements, lncRNAs and circRNAs in diseases. It will serve as an important and continually updated resource for investigating functions and mechanisms of lncRNAs and circRNAs in diseases.

We describe LincSNP 2.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), an updated database that is used specifically to store and annotate disease-associated single nucleotide polymorphisms (SNPs) in human long non-coding RNAs (lncRNAs) and their transcription factor binding sites (TFBSs). In LincSNP 2.0, we have updated the database with more data and several new features, including (i) expanding disease-associated SNPs in human lncRNAs; (ii) identifying disease-associated SNPs in lncRNA TFBSs; (iii) updating LD-SNPs from the 1000 Genomes Project; and (iv) collecting more experimentally supported SNP-lncRNA-disease associations. Furthermore, we developed three flexible online tools to retrieve and analyze the data. Linc-Mart is a convenient way for users to customize their own data. Linc-Browse is a tool for all data visualization. Linc-Score predicts the associations between lncRNA and disease. In addition, we provided users a newly designed, user-friendly interface to search and download all the data in LincSNP 2.0 and we also provided an interface to submit novel data into the database. LincSNP 2.0 is a continually updated database and will serve as an important resource for investigating the functions and mechanisms of lncRNAs in human diseases.

CellMarker 2.0 (http://bio-bigdata.hrbmu.edu.cn/CellMarker or http://117.50.127.228/CellMarker/) is an updated database that provides a manually curated collection of experimentally supported markers of various cell types in different tissues of human and mouse. In addition, web tools for analyzing single cell sequencing data are described. We have updated CellMarker 2.0 with more data and several new features, including (i) Appending 36 300 tissue-cell type-maker entries, 474 tissues, 1901 cell types and 4566 markers over the previous version. The current release recruits 26 915 cell markers, 2578 cell types and 656 tissues, resulting in a total of 83 361 tissue-cell type-maker entries. (ii) There is new marker information from 48 sequencing technology sources, including 10X Chromium, Smart-Seq2 and Drop-seq, etc. (iii) Adding 29 types of cell markers, including protein-coding gene lncRNA and processed pseudogene, etc. Additionally, six flexible web tools, including cell annotation, cell clustering, cell malignancy, cell differentiation, cell feature and cell communication, were developed to analysis and visualization of single cell sequencing data. CellMarker 2.0 is a valuable resource for exploring markers of various cell types in different tissues of human and mouse.

Splicing is an essential step of RNA processing for multi-exon genes, in which introns are removed from a precursor RNA, thereby producing mature RNAs containing splice junctions. Here, we develope the RJunBase (www.RJunBase.org), a web-accessible database of three types of RNA splice junctions (linear, back-splice, and fusion junctions) that are derived from RNA-seq data of non-cancerous and cancerous tissues. The RJunBase aims to integrate and characterize all RNA splice junctions of both healthy or pathological human cells and tissues. This new database facilitates the visualization of the gene-level splicing pattern and the junction-level expression profile, as well as the demonstration of unannotated and tumor-specific junctions. The first release of RJunBase contains 682 017 linear junctions, 225 949 back-splice junctions and 34 733 fusion junctions across 18 084 non-cancerous and 11 540 cancerous samples. RJunBase can aid researchers in discovering new splicing-associated targets and provide insights into the identification and assessment of potential neoepitopes for cancer treatment.