tModBase: deciphering the landscape of tRNA modifications and their dynamic changes from epitranscriptome data.

tRNA molecules contain dense, abundant modifications that affect tRNA structure, stability, mRNA decoding and tsRNA formation. tRNA modifications and related enzymes are responsive to environmental cues and are associated with a range of physiological and pathological processes. However, there is a lack of resources that can be used to mine and analyse these dynamically changing tRNA modifications. In this study, we established tModBase (https://www.tmodbase.com/) for deciphering the landscape of tRNA modification profiles from epitranscriptome data. We analysed 103 datasets generated with second- and third-generation sequencing technologies and illustrated the misincorporation and termination signals of tRNA modification sites in ten species. We thus systematically demonstrate the modification profiles across different tissues/cell lines and summarize the characteristics of tRNA-associated human diseases. By integrating transcriptome data from 32 cancers, we developed novel tools for analysing the relationships between tRNA modifications and RNA modification enzymes, the expression of 1442 tRNA-derived small RNAs (tsRNAs), and 654 DNA variations. Our database will provide new insights into the features of tRNA modifications and the biological pathways in which they participate.

tsRFun: a comprehensive platform for decoding human tsRNA expression, functions and prognostic value by high-throughput small RNA-Seq and CLIP-Seq data.

tRNA-derived small RNA (tsRNA), a novel type of regulatory small noncoding RNA, plays an important role in physiological and pathological processes. However, the understanding of the functional mechanism of tsRNAs in cells and their role in the occurrence and development of diseases is limited. Here, we integrated multiomics data such as transcriptome, epitranscriptome, and targetome data, and developed novel computer tools to establish tsRFun, a comprehensive platform to facilitate tsRNA research (http://rna.sysu.edu.cn/tsRFun/ or http://biomed.nscc-gz.cn/DB/tsRFun/). tsRFun evaluated tsRNA expression profiles and the prognostic value of tsRNAs across 32 types of cancers, identified tsRNA target molecules utilizing high-throughput CLASH/CLEAR or CLIP sequencing data, and constructed the interaction networks among tsRNAs, microRNAs, and mRNAs. In addition to its data presentation capabilities, tsRFun offers multiple real-time online tools for tsRNA identification, target prediction, and functional enrichment analysis. In summary, tsRFun provides a valuable data resource and multiple analysis tools for tsRNA investigation.

ColorCells: a database of expression, classification and functions of lncRNAs in single cells.

Although long noncoding RNAs (lncRNAs) have significant tissue specificity, their expression and variability in single cells remain unclear. Here, we developed ColorCells (http://rna.sysu.edu.cn/colorcells/), a resource for comparative analysis of lncRNAs expression, classification and functions in single-cell RNA-Seq data. ColorCells was applied to 167 913 publicly available scRNA-Seq datasets from six species, and identified a batch of cell-specific lncRNAs. These lncRNAs show surprising levels of expression variability between different cell clusters, and has the comparable cell classification ability as known marker genes. Cell-specific lncRNAs have been identified and further validated by in vitro experiments. We found that lncRNAs are typically co-expressed with the mRNAs in the same cell cluster, which can be used to uncover lncRNAs' functions. Our study emphasizes the need to uncover lncRNAs in all cell types and shows the power of lncRNAs as novel marker genes at single cell resolution.

The cardiac translational landscape reveals that micropeptides are new players involved in cardiomyocyte hypertrophy.

Hypertrophic growth of cardiomyocytes is one of the major compensatory responses in the heart after physiological or pathological stimulation. Protein synthesis enhancement, which is mediated by the translation of messenger RNAs, is one of the main features of cardiomyocyte hypertrophy. Although the transcriptome shift caused by cardiac hypertrophy induced by different stimuli has been extensively investigated, translatome dynamics in this cellular process has been less studied. Here, we generated a nucleotide-resolution translatome as well as transcriptome data from isolated primary cardiomyocytes undergoing hypertrophy. More than 10,000 open reading frames (ORFs) were detected from the deep sequencing of ribosome-protected fragments (Ribo-seq), which orchestrated the shift of the translatome in hypertrophied cardiomyocytes. Our data suggest that rather than increase the translational rate of ribosomes, the increased efficiency of protein synthesis in cardiomyocyte hypertrophy was attributable to an increased quantity of ribosomes. In addition, more than 100 uncharacterized short ORFs (sORFs) were detected in long noncoding RNA genes from Ribo-seq with potential of micropeptide coding. In a random test of 15 candidates, the coding potential of 11 sORFs was experimentally supported. Three micropeptides were identified to regulate cardiomyocyte hypertrophy by modulating the activities of oxidative phosphorylation, the calcium signaling pathway, and the mitogen-activated protein kinase (MAPK) pathway. Our study provides a genome-wide overview of the translational controls behind cardiomyocyte hypertrophy and demonstrates an unrecognized role of micropeptides in cardiomyocyte biology.

Novel organization of mitochondrial minicircles and guide RNAs in the zoonotic pathogen Trypanosoma lewisi.

Kinetoplastid flagellates are known for several unusual features, one of which is their complex mitochondrial genome, known as kinetoplast (k) DNA, composed of mutually catenated maxi- and minicircles. Trypanosoma lewisi is a member of the Stercorarian group of trypanosomes which is, based on human infections and experimental data, now considered a zoonotic pathogen. By assembling a total of 58 minicircle classes, which fall into two distinct categories, we describe a novel type of kDNA organization in T. lewisi. RNA-seq approaches allowed us to map the details of uridine insertion and deletion editing events upon the kDNA transcriptome. Moreover, sequencing of small RNA molecules enabled the identification of 169 unique guide (g) RNA genes, with two differently organized minicircle categories both encoding essential gRNAs. The unprecedented organization of minicircles and gRNAs in T. lewisi broadens our knowledge of the structure and expression of the mitochondrial genomes of these human and animal pathogens. Finally, a scenario describing the evolution of minicircles is presented.

Differential impacts of charcoal-stripped fetal bovine serum on c-Myc among distinct subtypes of breast cancer cell lines.

Charcoal-stripped fetal bovine serum (CS-FBS) is frequently used in studies on hormone-responsive cancers to provide hormone-free cell culture conditions. CS-FBS may influence the growth of cancer cells; however, the underlying mechanisms remain unclear. In this study, we aimed to clarify the effects of CS-FBS on distinct subtypes of breast cancer cells. We found that the crucial oncoprotein c-Myc was significantly inhibited in estrogen receptor alpha (ER-α)-positive breast cancer cells when cultured in CS-FBS-supplemented medium, but it was not suppressed in ER-α-negative cells. The addition of 17ß-estradiol (E2) to CS-FBS-supplemented medium rescued the CS-FBS-induced inhibition of c-Myc, while treatment with 5α-dihydrotestosterone (DHT) suppressed c-Myc expression. Our data demonstrated that CS-FBS may impede the growth of ER-α-positive breast cancer cells via c-Myc inhibition, and this was possibly due to the removal of estrogen. These results highlighted that the core drivers of c-Myc expression were subtype-specific depending on the distinct cell context and special caution should be exercised when using CS-FBS in studies of hormone-responsive cancer cells.

RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data.

More than 100 distinct chemical modifications to RNA have been characterized so far. However, the prevalence, mechanisms and functions of various RNA modifications remain largely unknown. To provide transcriptome-wide landscapes of RNA modifications, we developed the RMBase v2.0 (http://rna.sysu.edu.cn/rmbase/), which is a comprehensive database that integrates epitranscriptome sequencing data for the exploration of post-transcriptional modifications of RNAs and their relationships with miRNA binding events, disease-related single-nucleotide polymorphisms (SNPs) and RNA-binding proteins (RBPs). RMBase v2.0 was expanded with ∼600 datasets and ∼1 397 000 modification sites from 47 studies among 13 species, which represents an approximately 10-fold expansion when compared with the previous release. It contains ∼1 373 000 N6-methyladenosines (m6A), ∼5400 N1-methyladenosines (m1A), ∼9600 pseudouridine (Ψ) modifications, ∼1000 5-methylcytosine (m5C) modifications, ∼5100 2'-O-methylations (2'-O-Me), and ∼2800 modifications of other modification types. Moreover, we built a new module called 'Motif' that provides the visualized logos and position weight matrices (PWMs) of the modification motifs. We also constructed a novel module termed 'modRBP' to study the relationships between RNA modifications and RBPs. Additionally, we developed a novel web-based tool named 'modMetagene' to plot the metagenes of RNA modification along a transcript model. This database will help researchers investigate the potential functions and mechanisms of RNA modifications.

dreamBase: DNA modification, RNA regulation and protein binding of expressed pseudogenes in human health and disease.

Although thousands of pseudogenes have been annotated in the human genome, their transcriptional regulation, expression profiles and functional mechanisms are largely unknown. In this study, we developed dreamBase (http://rna.sysu.edu.cn/dreamBase) to facilitate the investigation of DNA modification, RNA regulation and protein binding of potential expressed pseudogenes from multidimensional high-throughput sequencing data. Based on ∼5500 ChIP-seq and DNase-seq datasets, we identified genome-wide binding profiles of various transcription-associated factors around pseudogene loci. By integrating ∼18 000 RNA-seq data, we analysed the expression profiles of pseudogenes and explored their co-expression patterns with their parent genes in 32 cancers and 31 normal tissues. By combining microRNA binding sites, we demonstrated complex post-transcriptional regulation networks involving 275 microRNAs and 1201 pseudogenes. We generated ceRNA networks to illustrate the crosstalk between pseudogenes and their parent genes through competitive binding of microRNAs. In addition, we studied transcriptome-wide interactions between RNA binding proteins (RBPs) and pseudogenes based on 458 CLIP-seq datasets. In conjunction with epitranscriptome sequencing data, we also mapped 1039 RNA modification sites onto 635 pseudogenes. This database will provide insights into the transcriptional regulation, expression, functions and mechanisms of pseudogenes as well as their roles in biological processes and diseases.

ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data.

The abnormal transcriptional regulation of non-coding RNAs (ncRNAs) and protein-coding genes (PCGs) is contributed to various biological processes and linked with human diseases, but the underlying mechanisms remain elusive. In this study, we developed ChIPBase v2.0 (http://rna.sysu.edu.cn/chipbase/) to explore the transcriptional regulatory networks of ncRNAs and PCGs. ChIPBase v2.0 has been expanded with ∼10 200 curated ChIP-seq datasets, which represent about 20 times expansion when comparing to the previous released version. We identified thousands of binding motif matrices and their binding sites from ChIP-seq data of DNA-binding proteins and predicted millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. We constructed 'Regulator' module to predict hundreds of TFs and histone modifications that were involved in or affected transcription of ncRNAs and PCGs. Moreover, we built a web-based tool, Co-Expression, to explore the co-expression patterns between DNA-binding proteins and various types of genes by integrating the gene expression profiles of ∼10 000 tumor samples and ∼9100 normal tissues and cell lines. ChIPBase also provides a ChIP-Function tool and a genome browser to predict functions of diverse genes and visualize various ChIP-seq data. This study will greatly expand our understanding of the transcriptional regulations of ncRNAs and PCGs.

A group of tissue-specific microRNAs contribute to the silencing of CUX1 in different cell lineages during development.

Cut-like homeobox 1 (CUX1) is a highly conserved homeoprotein that functions as a transcriptional repressor of genes specifying terminal differentiation. We previously showed that liver-specific microRNA-122 (miR-122) regulates the timing of liver development by silencing CUX1 post-transcriptionally. Since the CUX1 protein is expressed in a subset of embryonic tissues, we hypothesized that it is regulated by specific microRNAs (miRNAs) in each cell type during development. Using a large-scale screening method, we identified ten tissue-specific miRNAs from different cell lineages that directly targeted CUX1. An analysis of the interaction between heart-specific microRNA-208a (miR-208a) and CUX1 in the hearts of developing mouse embryos and in P19CL6 cells undergoing cardiac differentiation indicated that CUX1 is regulated by miR-208a during heart development and cardiomyocyte differentiation. Functional analysis of miR-208a in P19CL6 cells using lentiviral-mediated over-expression showed that it regulates the transition between cellular proliferation and differentiation. These results suggest that these tissue-specific miRNAs might play a common role in timing the progression of terminal differentiation of different cell lineages, possibly by silencing the differentiation repressor CUX1.

RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data.

Although more than 100 different types of RNA modifications have been characterized across all living organisms, surprisingly little is known about the modified positions and their functions. Recently, various high-throughput modification sequencing methods have been developed to identify diverse post-transcriptional modifications of RNA molecules. In this study, we developed a novel resource, RMBase (RNA Modification Base, http://mirlab.sysu.edu.cn/rmbase/), to decode the genome-wide landscape of RNA modifications identified from high-throughput modification data generated by 18 independent studies. The current release of RMBase includes ∼ 9500 pseudouridine (Ψ) modifications generated from Pseudo-seq and CeU-seq sequencing data, ∼ 1000 5-methylcytosines (m(5)C) predicted from Aza-IP data, ∼ 124 200 N6-Methyladenosine (m(6)A) modifications discovered from m(6)A-seq and ∼ 1210 2'-O-methylations (2'-O-Me) identified from RiboMeth-seq data and public resources. Moreover, RMBase provides a comprehensive listing of other experimentally supported types of RNA modifications by integrating various resources. It provides web interfaces to show thousands of relationships between RNA modification sites and microRNA target sites. It can also be used to illustrate the disease-related SNPs residing in the modification sites/regions. RMBase provides a genome browser and a web-based modTool to query, annotate and visualize various RNA modifications. This database will help expand our understanding of potential functions of RNA modifications.

deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data.

Small non-coding RNAs (e.g. miRNAs) and long non-coding RNAs (e.g. lincRNAs and circRNAs) are emerging as key regulators of various cellular processes. However, only a very small fraction of these enigmatic RNAs have been well functionally characterized. In this study, we describe deepBase v2.0 (http://biocenter.sysu.edu.cn/deepBase/), an updated platform, to decode evolution, expression patterns and functions of diverse ncRNAs across 19 species. deepBase v2.0 has been updated to provide the most comprehensive collection of ncRNA-derived small RNAs generated from 588 sRNA-Seq datasets. Moreover, we developed a pipeline named lncSeeker to identify 176 680 high-confidence lncRNAs from 14 species. Temporal and spatial expression patterns of various ncRNAs were profiled. We identified approximately 24 280 primate-specific, 5193 rodent-specific lncRNAs, and 55 highly conserved lncRNA orthologs between human and zebrafish. We annotated 14 867 human circRNAs, 1260 of which are orthologous to mouse circRNAs. By combining expression profiles and functional genomic annotations, we developed lncFunction web-server to predict the function of lncRNAs based on protein-lncRNA co-expression networks. This study is expected to provide considerable resources to facilitate future experimental studies and to uncover ncRNA functions.

tRF2Cancer: A web server to detect tRNA-derived small RNA fragments (tRFs) and their expression in multiple cancers.

tRNA-derived small RNA fragments (tRFs) are one class of small non-coding RNAs derived from transfer RNAs (tRNAs). tRFs play important roles in cellular processes and are involved in multiple cancers. High-throughput small RNA (sRNA) sequencing experiments can detect all the cellular expressed sRNAs, including tRFs. However, distinguishing genuine tRFs from RNA fragments generated by random degradation remains a major challenge. In this study, we developed an integrated web-based computing system, tRF2Cancer, to accurately identify tRFs from sRNA deep-sequencing data and evaluate their expression in multiple cancers. The binomial test was introduced to evaluate whether reads from a small RNA-seq data set represent tRFs or degraded fragments. A classification method was then used to annotate the types of tRFs based on their sites of origin in pre-tRNA or mature tRNA. We applied the pipeline to analyze 10 991 data sets from 32 types of cancers and identified thousands of expressed tRFs. A tool called 'tRFinCancer' was developed to facilitate the users to inspect the expression of tRFs across different types of cancers. Another tool called 'tRFBrowser' shows both the sites of origin and the distribution of chemical modification sites in tRFs on their source tRNA. The tRF2Cancer web server is available at http://rna.sysu.edu.cn/tRFfinder/.

Cancer in the parasitic protozoans Trypanosoma brucei and Toxoplasma gondii.

Cancer is a general name for more than 100 malignant diseases. It is postulated that all cancers start from a single abnormal cell that grows out of control. Untreated cancers can cause serious consequences and deaths. Great progress has been made in cancer research that has significantly improved our knowledge and understanding of the nature and mechanisms of the disease, but the origins of cancer are far from being well understood due to the limitations of suitable model systems and to the complexities of the disease. In view of the fact that cancers are found in various species of vertebrates and other metazoa, here, we suggest that cancer also occurs in parasitic protozoans such as Trypanosoma brucei, a blood parasite, and Toxoplasma gondii, an obligate intracellular pathogen. Without treatment, these protozoan cancers may cause severe disease and death in mammals, including humans. The simpler genomes of these single-cell organisms, in combination with their complex life cycles and fascinating life cycle differentiation processes, may help us to better understand the origins of cancers and, in particular, leukemias.

StarScan: a web server for scanning small RNA targets from degradome sequencing data.

Endogenous small non-coding RNAs (sRNAs), including microRNAs, PIWI-interacting RNAs and small interfering RNAs, play important gene regulatory roles in animals and plants by pairing to the protein-coding and non-coding transcripts. However, computationally assigning these various sRNAs to their regulatory target genes remains technically challenging. Recently, a high-throughput degradome sequencing method was applied to identify biologically relevant sRNA cleavage sites. In this study, an integrated web-based tool, StarScan (sRNA target Scan), was developed for scanning sRNA targets using degradome sequencing data from 20 species. Given a sRNA sequence from plants or animals, our web server performs an ultrafast and exhaustive search for potential sRNA-target interactions in annotated and unannotated genomic regions. The interactions between small RNAs and target transcripts were further evaluated using a novel tool, alignScore. A novel tool, degradomeBinomTest, was developed to quantify the abundance of degradome fragments located at the 9-11th nucleotide from the sRNA 5' end. This is the first web server for discovering potential sRNA-mediated RNA cleavage events in plants and animals, which affords mechanistic insights into the regulatory roles of sRNAs. The StarScan web server is available at http://mirlab.sysu.edu.cn/starscan/.

Both endo-siRNAs and tRNA-derived small RNAs are involved in the differentiation of primitive eukaryote Giardia lamblia.

Small RNAs (sRNAs), including microRNAs and endogenous siRNAs (endo-siRNAs), regulate most important biologic processes in eukaryotes, such as cell division and differentiation. Although sRNAs have been extensively studied in various eukaryotes, the role of sRNAs in the early emergence of eukaryotes is unclear. To address these questions, we deep sequenced the sRNA transcriptome of four different stages in the differentiation of Giardia lamblia, one of the most primitive eukaryotes. We identified a large number of endo-siRNAs in this fascinating parasitic protozoan and found that they were produced from live telomeric retrotransposons and three genomic regions (i.e., endo-siRNA generating regions [eSGRs]). eSGR-derived endo-siRNAs were proven to target mRNAs in trans. Gradual up-regulation of endo-siRNAs in the differentiation of Giardia suggested that they might be involved in the regulation of this process. This hypothesis was supported by the impairment of the differentiation ability of Giardia when GLDICER, essential for the biogenesis of endo-siRNAs, was knocked down. Endo-siRNAs are not the only sRNA regulators in Giardia differentiation, because a great number of tRNAs-derived sRNAs showed more dramatic expression changes than endo-siRNAs in this process. We totally identified five novel kinds of tRNAs-derived sRNAs and found that the biogenesis in four of them might be correlated with that of stress-induced tRNA-derived RNA (sitRNA), which was discovered in our previous studies. Our studies reveal an unexpected complex panorama of sRNA in G. lamblia and shed light on the origin and functional evolution of eukaryotic sRNAs.

27-Hydroxycholesterol increases Myc protein stability via suppressing PP2A, SCP1 and FBW7 transcription in MCF-7 breast cancer cells.

27-hydroxycholesterol (27-HC), the most abundant metabolite of cholesterol, is a risk factor for breast cancer. It can increase the proliferation of breast cancer cells and promote the metastasis of breast tumours in mouse models. Myc is a critical oncoprotein overexpressed in breast cancer. However, whether 27-HC affects Myc expression has not been reported. In the current study, we aimed to investigate the effects of 27-HC on Myc and the underlying mechanisms in MCF-7 breast cancer cells. Our data demonstrated that 27-HC activated Myc via increasing its protein stability. Three key negative modulators of Myc protein stability, PP2A, SCP1 and FBW7, were suppressed by 27-HC at the transcriptional level. We performed a data-mining analysis of the chromatin immunoprecipitation with next-generation DNA sequencing (ChIP-Seq) data in the ChIPBase, and discovered that a number of putative transcription factors (TFs), including Myc itself, were involved in the transcriptional regulation of PP2A, SCP1 and FBW7. Our results provide a novel mechanistic insight into the activation of Myc by 27-HC via transcriptional repression of PP2A, SCP1 and FBW7 to increase Myc protein stability in breast cancer cells.

Conservation and divergence of transcriptional coregulations between box C/D snoRNA and ribosomal protein genes in Ascomycota.

Coordinated assembly of the ribosome is essential for proper translational activity in eukaryotic cells. It is therefore critical to coordinate the expression of components of ribosomal programs with the cell's nutritional status. However, coordinating expression of these components is poorly understood. Here, by combining experimental and computational approaches, we systematically identified box C/D snoRNAs in four fission yeasts and found that the expression of box C/D snoRNA and ribosomal protein (RP) genes were orchestrated by a common Homol-D box, thereby ensuring a constant balance of these two genetic components. Interestingly, such transcriptional coregulations could be observed in most Ascomycota species and were mediated by different cis-regulatory elements. Via the reservation of cis elements, changes in spatial configuration, the substitution of cis elements, and gain or loss of cis elements, the regulatory networks of box C/D snoRNAs evolved to correspond with those of the RP genes, maintaining transcriptional coregulation between box C/D snoRNAs and RP genes. Our results indicate that coregulation via common cis elements is an important mechanism to coordinate expression of the RP and snoRNA genes, which ensures a constant balance of these two components.

starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data.

Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA-RNA and protein-RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285,000 protein-RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10,000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

[Application of Bioinformatics in Long Non-coding RNAs].

Long non-coding RNAs (lncRNAs)are non-coding RNA molecules larger than 200 nt.They play a key regulatory role in crucial biological processes.Recently,lncRNA researches have been devel-oped rapidly and a set of bioinformatic tools and databases about lncRNA identification,quantification, structural analysis and function prediction have been emerged.This review introduces the resources for lncRNA studies.