dSCOPE: a software to detect sequences critical for liquid-liquid phase separation.

Membrane-based cells are the fundamental structural and functional units of organisms, while evidences demonstrate that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation (PS), but these studies lacked an effective tool to identify the sequence segments that critical for LLPS. In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the PS-driving regions. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted PS-driving regions enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted PS-driving regions enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS-related research.

Histone H3 trimethylation at lysine 36 guides m6A RNA modification co-transcriptionally.

DNA and histone modifications have notable effects on gene expression1. Being the most prevalent internal modification in mRNA, the N6-methyladenosine (m6A) mRNA modification is as an important post-transcriptional mechanism of gene regulation2-4 and has crucial roles in various normal and pathological processes5-12. However, it is unclear how m6A is specifically and dynamically deposited in the transcriptome. Here we report that histone H3 trimethylation at Lys36 (H3K36me3), a marker for transcription elongation, guides m6A deposition globally. We show that m6A modifications are enriched in the vicinity of H3K36me3 peaks, and are reduced globally when cellular H3K36me3 is depleted. Mechanistically, H3K36me3 is recognized and bound directly by METTL14, a crucial component of the m6A methyltransferase complex (MTC), which in turn facilitates the binding of the m6A MTC to adjacent RNA polymerase II, thereby delivering the m6A MTC to actively transcribed nascent RNAs to deposit m6A co-transcriptionally. In mouse embryonic stem cells, phenocopying METTL14 knockdown, H3K36me3 depletion also markedly reduces m6A abundance transcriptome-wide and in pluripotency transcripts, resulting in increased cell stemness. Collectively, our studies reveal the important roles of H3K36me3 and METTL14 in determining specific and dynamic deposition of m6A in mRNA, and uncover another layer of gene expression regulation that involves crosstalk between histone modification and RNA methylation.

RM2Target: a comprehensive database for targets of writers, erasers and readers of RNA modifications.

RNA modification is a dynamic and reversible process regulated by a series of writers, erasers and readers (WERs). Abnormal changes of WERs will disrupt the RNA modification homeostasis of their target genes, leading to the dysregulation of RNA metabolisms such as RNA stability and translation, and consequently to diseases such as cancer. A public repository hosting the regulatory relationships between WERs and their target genes will help in understanding the roles of RNA modifications in various physiological and pathological conditions. Previously, we developed a database named 'm6A2Target' to host targets of WERs in m6A, one of the most prevalent RNA modifications in eukaryotic cells. To host all RNA modification (RM)-related WER-target associations, we hereby present an updated database, named 'RM2Target' (http://rm2target.canceromics.org/). In this update, RM2Target encompasses 1 619 653 WER-target associations for nine RNA modifications in human and mouse, including m6A, m6Am, m5C, m5U, m1A, m7G, pseudouridine, 2'-O-Me and A-to-I. Extensive annotations of target genes are available in RM2Target, including but not limited to basic gene information, RNA modifications, RNA-RNA/RNA-protein interactions and related diseases. Altogether, we expect that RM2Target will facilitate further downstream functional and mechanistic studies in the field of RNA modification research.

Single-cell transcriptomic analysis deciphers heterogenous cancer stem-like cells in colorectal cancer and their organ-specific metastasis.

OBJECTIVE: Metastasis is the major cause of cancer death. However, what types of heterogenous cancer cells in primary tumour and how they metastasise to the target organs remain largely undiscovered. DESIGN: We performed single-cell RNA sequencing and spatial transcriptomic analysis in primary colorectal cancer (CRC) and metastases in the liver (lCRC) or ovary (oCRC). We also conducted immunofluorescence staining and functional experiments to examine the mechanism. RESULTS: Integrative analyses of epithelial cells reveal a stem-like cell cluster with high protein tyrosine phosphatase receptor type O (PTPRO) and achaete scute-like 2 (ASCL2) expression as the metastatic culprit. This cell cluster comprising distinct subpopulations shows distinct liver or ovary metastatic preference. Population 1 (P1) cells with high delta-like ligand 4 (DLL4) and MAF bZIP transcription factor A (MAFA) expression are enriched in primary CRC and oCRC, thus may be associated with ovarian metastasis. P3 cells having a similar expression pattern as cholangiocytes are found mainly in primary CRC and lCRC, presuming to be likely the culprits that specifically metastasise to the liver. Stem-like cells interacted with cancer-associated fibroblasts and endothelial cells via the DLL4-NOTCH signalling pathway to metastasise from primary CRC to the ovary. In the oCRC microenvironment, myofibroblasts provide cancer cells with glutamine and perform a metabolic reprogramming, which may be essential for cancer cells to localise and develop in the ovary. CONCLUSION: We uncover a mechanism for organ-specific CRC metastasis.

AStruct: detection of allele-specific RNA secondary structure in structuromic probing data.

BACKGROUND: Uncovering functional genetic variants from an allele-specific perspective is of paramount importance in advancing our understanding of gene regulation and genetic diseases. Recently, various allele-specific events, such as allele-specific gene expression, allele-specific methylation, and allele-specific binding, have been explored on a genome-wide scale due to the development of high-throughput sequencing methods. RNA secondary structure, which plays a crucial role in multiple RNA-associated processes like RNA modification, translation and splicing, has emerged as an essential focus of relevant research. However, tools to identify genetic variants associated with allele-specific RNA secondary structures are still lacking. RESULTS: Here, we develop a computational tool called 'AStruct' that enables us to detect allele-specific RNA secondary structure (ASRS) from RT-stop based structuromic probing data. AStruct shows robust performance in both simulated datasets and public icSHAPE datasets. We reveal that single nucleotide polymorphisms (SNPs) with higher AStruct scores are enriched in coding regions and tend to be functional. These SNPs are highly conservative, have the potential to disrupt sites involved in m6A modification or protein binding, and are frequently associated with disease. CONCLUSIONS: AStruct is a tool dedicated to invoke allele-specific RNA secondary structure events at heterozygous SNPs in RT-stop based structuromic probing data. It utilizes allelic variants, base pairing and RT-stop information under different cell conditions to detect dynamic and functional ASRS. Compared to sequence-based tools, AStruct considers dynamic cell conditions and outperforms in detecting functional variants. AStruct is implemented in JAVA and is freely accessible at: https://github.com/canceromics/AStruct .

FTO-mediated DSP m6A demethylation promotes an aggressive subtype of growth hormone-secreting pituitary neuroendocrine tumors.

BACKGROUND: Growth hormone-secreting pituitary neuroendocrine tumors can be pathologically classified into densely granulated (DGGH) and sparsely granulated types (SGGH). SGGH is more aggressive and associated with a poorer prognosis. While epigenetic regulation is vital in tumorigenesis and progression, the role of N6-methyladenosine (m6A) in aggressive behavior has yet to be elucidated. METHODS: We performed m6A-sequencing on tumor samples from 8 DGGH and 8 SGGH patients, complemented by a suite of assays including ELISA, immuno-histochemistry, -blotting and -fluorescence, qPCR, MeRIP, RIP, and RNA stability experiments, aiming to delineate the influence of m6A on tumor behavior. We further assessed the therapeutic potential of targeted drugs using cell cultures, organoid models, and animal studies. RESULTS: We discovered a significant reduction of m6A levels in SGGH compared to DGGH, with an elevated expression of fat mass and obesity-associated protein (FTO), an m6A demethylase, in SGGH subtype. Series of in vivo and in vitro experiments demonstrated that FTO inhibition in tumor cells robustly diminishes hypoxia resistance, attenuates growth hormone secretion, and augments responsiveness to octreotide. Mechanically, FTO-mediated m6A demethylation destabilizes desmoplakin (DSP) mRNA, mediated by the m6A reader FMR1, leading to prohibited desmosome integrity and enhanced tumor hypoxia tolerance. Targeting the FTO-DSP-SSTR2 axis curtailed growth hormone secretion, therefor sensitizing tumors to octreotide therapy. CONCLUSION: Our study reveals the critical role of FTO in the aggressive growth hormone-secreting pituitary neuroendocrine tumors subtype and suggests FTO may represent a new therapeutic target for refractory/persistent SGGH.

Deciphering clonal dynamics and metastatic routines in a rare patient of synchronous triple-primary tumors and multiple metastases with MPTevol.

Multiple primary tumor (MPT) is a special and rare cancer type, defined as more than two primary tumors presenting at the diagnosis in a single patient. The molecular characteristics and tumorigenesis of MPT remain unclear due to insufficient approaches. Here, we present MPTevol, a practical computational framework for comprehensively exploring the MPT from multiregion sequencing (MRS) experiments. To verify the utility of MPTevol, we performed whole-exome MRS for 33 samples of a rare patient with triple-primary tumors and three metastatic sites and systematically investigated clonal dynamics and metastatic routines. MPTevol assists in comparing genomic profiles across samples, detecting clonal evolutionary history and metastatic routines and quantifying the metastatic history. All triple-primary tumors were independent origins and their genomic characteristics were consistent with corresponding sporadic tumors, strongly supporting their independent tumorigenesis. We further showed two independent early monoclonal seeding events for the metastases in the ovary and uterus. We revealed that two ovarian metastases were disseminated from the same subclone of the primary tumor through undergoing whole-genome doubling processes, suggesting metastases-to-metastases seeding occurred when tumors had similar microenvironments. Surprisingly, according to the metastasis timing model of MPTevol, we found that primary tumors of about 0.058-0.124 cm diameter have been disseminating to distant organs, which is much earlier than conventional clinical views. We developed MPT-specialized analysis framework MPTevol and demonstrated its utility in explicitly resolving clonal evolutionary history and metastatic seeding routines with a rare MPT case. MPTevol is implemented in R and is available at https://github.com/qingjian1991/MPTevol under the GPL v3 license.

SPENCER: a comprehensive database for small peptides encoded by noncoding RNAs in cancer patients.

As an increasing number of noncoding RNAs (ncRNAs) have been suggested to encode short bioactive peptides in cancer, the exploration of ncRNA-encoded small peptides (ncPEPs) is emerging as a fascinating field in cancer research. To assist in studies on the regulatory mechanisms of ncPEPs, we describe here a database called SPENCER (http://spencer.renlab.org). Currently, SPENCER has collected a total of 2806 mass spectrometry (MS) data points from 55 studies, covering 1007 tumor samples and 719 normal samples. Using an MS-based proteomics analysis pipeline, SPENCER identified 29 526 ncPEPs across 15 different cancer types. Specifically, 22 060 of these ncPEPs were experimentally validated in other studies. By comparing tumor and normal samples, the identified ncPEPs were divided into four expression groups: tumor-specific, upregulated in cancer, downregulated in cancer, and others. Additionally, since ncPEPs are potential targets for neoantigen-based cancer immunotherapy, SPENCER also predicted the immunogenicity of all the identified ncPEPs by assessing their MHC-I binding affinity, stability, and TCR recognition probability. As a result, 4497 ncPEPs curated in SPENCER were predicted to be immunogenic. Overall, SPENCER will be a useful resource for investigating cancer-associated ncPEPs and may boost further research in cancer.

RPS: a comprehensive database of RNAs involved in liquid-liquid phase separation.

Liquid-liquid phase separation (LLPS) is critical for assembling membraneless organelles (MLOs) such as nucleoli, P-bodies, and stress granules, which are involved in various physiological processes and pathological conditions. While the critical role of RNA in the formation and the maintenance of MLOs is increasingly appreciated, there is still a lack of specific resources for LLPS-related RNAs. Here, we presented RPS (http://rps.renlab.org), a comprehensive database of LLPS-related RNAs in 20 distinct biomolecular condensates from eukaryotes and viruses. Currently, RPS contains 21,613 LLPS-related RNAs with three different evidence types, including 'Reviewed', 'High-throughput' and 'Predicted'. RPS provides extensive annotations of LLPS-associated RNA properties, including sequence features, RNA structures, RNA-protein/RNA-RNA interactions, and RNA modifications. Moreover, RPS also provides comprehensive disease annotations to help users to explore the relationship between LLPS and disease. The user-friendly web interface of RPS allows users to access the data efficiently. In summary, we believe that RPS will serve as a valuable platform to study the role of RNA in LLPS and further improve our understanding of the biological functions of LLPS.

IBS 2.0: an upgraded illustrator for the visualization of biological sequences.

The visualization of biological sequences with various functional elements is fundamental for the publication of scientific achievements in the field of molecular and cellular biology. However, due to the limitations of the currently used applications, there are still considerable challenges in the preparation of biological schematic diagrams. Here, we present a professional tool called IBS 2.0 for illustrating the organization of both protein and nucleotide sequences. With the abundant graphical elements provided in IBS 2.0, biological sequences can be easily represented in a concise and clear way. Moreover, we implemented a database visualization module in IBS 2.0, enabling batch visualization of biological sequences from the UniProt and the NCBI RefSeq databases. Furthermore, to increase the design efficiency, a resource platform that allows uploading, retrieval, and browsing of existing biological sequence diagrams has been integrated into IBS 2.0. In addition, a lightweight JS library was developed in IBS 2.0 to assist the visualization of biological sequences in customized web services. To obtain the latest version of IBS 2.0, please visit https://ibs.renlab.org.

TIRSF: a web server for screening gene signatures to predict Tumor immunotherapy response.

Immune checkpoint blockade (ICB) therapy has been successfully applied to clinically therapeutics in multiple cancers, but its efficacy varies greatly among different patients and cancer types. Therefore, the construction of gene signatures to identify patients who could benefit from ICB therapy is particularly important for precision cancer treatment. However, due to the lack of a user-friendly platform, the construction of such gene signatures is a great challenge for clinical investigators who have limited programming skills. In light of this challenge, we developed a web server called Tumor Immunotherapy Response Signature Finder(TIRSF) for the construction of gene signatures to predict ICB therapy response in cancer patients. TIRSF consists of three functional modules. The first module is the Signature Discovery module which provides signature construction and performance evaluation functionalities. The second is a module for response prediction based on the TIRSF signatures, which enables response prediction and prognostic analysis of immunotherapy samples. The last is a module for response prediction based on existing signatures. This module currently integrates 24 published signatures for ICB therapy response prediction. Together, all of above features can be freely accessed at http://tirsf.renlab.org/.

M6A2Target: a comprehensive database for targets of m6A writers, erasers and readers.

N6-methyladenosine (m6A) is the most abundant posttranscriptional modification in mammalian mRNA molecules and has a crucial function in the regulation of many fundamental biological processes. The m6A modification is a dynamic and reversible process regulated by a series of writers, erasers and readers (WERs). Different WERs might have different functions, and even the same WER might function differently in different conditions, which are mostly due to different downstream genes being targeted by the WERs. Therefore, identification of the targets of WERs is particularly important for elucidating this dynamic modification. However, there is still no public repository to host the known targets of WERs. Therefore, we developed the m6A WER target gene database (m6A2Target) to provide a comprehensive resource of the targets of m6A WERs. M6A2Target provides a user-friendly interface to present WER targets in two different modules: 'Validated Targets', referred to as WER targets identified from low-throughput studies, and 'Potential Targets', including WER targets analyzed from high-throughput studies. Compared to other existing m6A-associated databases, m6A2Target is the first specific resource for m6A WER target genes. M6A2Target is freely accessible at http://m6a2target.canceromics.org.

MeRIPseqPipe: an integrated analysis pipeline for MeRIP-seq data based on Nextflow.

SUMMARY: MeRIPseqPipe is an integrated and automatic pipeline that can provide users a friendly solution to perform in-depth mining of MeRIP-seq data. It integrates many functional analysis modules, range from basic processing to downstream analysis. All the processes are embedded in Nextflow with Docker support, which ensures high reproducibility and scalability of the analysis. MeRIPseqPipe is particularly suitable for analyzing a large number of samples at once with a simple command. The final output directory is structured based on each step and tool. And visualization reports containing various tables and plots are provided as HTML files. AVAILABILITY AND IMPLEMENTATION: MeRIPseqPipe is freely available at https://github.com/canceromics/MeRIPseqPipe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

N(6)-methyladenosine-binding protein YTHDF1 suppresses EBV replication and promotes EBV RNA decay.

N6 -methyladenosine (m6 A) modification of mRNA mediates diverse cellular and viral functions. Infection with Epstein-Barr virus (EBV) is causally associated with nasopharyngeal carcinoma (NPC), 10% of gastric carcinoma, and various B-cell lymphomas, in which the viral latent and lytic phases both play vital roles. Here, we show that EBV transcripts exhibit differential m6 A modification in human NPC biopsies, patient-derived xenograft tissues, and cells at different EBV infection stages. m6 A-modified EBV transcripts are recognized and destabilized by the YTHDF1 protein, which leads to the m6 A-dependent suppression of EBV infection and replication. Mechanistically, YTHDF1 hastens viral RNA decapping and mediates RNA decay by recruiting RNA degradation complexes, including ZAP, DDX17, and DCP2, thereby post-transcriptionally downregulating the expression of EBV genes. Taken together, our results reveal the critical roles of m6 A modifications and their reader YTHDF1 in EBV replication. These findings contribute novel targets for the treatment of EBV-associated cancers.

Advances in detecting N6-methyladenosine modification in circRNAs.

Circular RNAs (circRNAs) are a class of noncoding RNAs with covalently single-stranded closed loop structures derived from back-splicing event of linear precursor mRNAs (pre-mRNAs). N6-methyladenosine (m6A), the most abundant epigenetic modification in eukaryotic RNAs, has been shown to play a crucial role in regulating the fate and biological function of circRNAs, and thus affecting various physiological and pathological processes. Accurate identification of m6A modification in circRNAs is an essential step to fully elucidate the crosstalk between m6A and circRNAs. In recent years, the rapid development of high-throughput sequencing technology and bioinformatic methodology has propelled the establishment of a multitude of approaches to detect circRNAs and m6A modification, including in vitro-based and in silico methods. Based on this, the research community has started on a new journey to develop methods for identification of m6A modification in circRNAs. In this review, we provide a comprehensive review and evaluation of the existing methods responsible for detecting circRNAs, m6A modification, and especially, m6A modification in circRNAs, which mainly focused on those developed based on high-throughput technologies and methodology of bioinformatics. This handy reference can help researchers figure out towards which direction this field will go.

ATXN2-mediated translation of TNFR1 promotes esophageal squamous cell carcinoma via m6A-dependent manner.

N6-methyladenosine (m6A) is the most prevalent RNA modification, and the effect of its dysregulation on esophageal squamous cell carcinoma (ESCC) development remains unclear. Here, by performing transcriptome-wide m6A sequencing in 16 ESCC tissue samples, we identified the key roles of m6A in TNFRSF1A (also known as TNFR1)-mediated MAPK and NF-κB activation in ESCC. Mechanistically, a functional protein involved in m6A methylation, ATXN2, is identified that augments the translation of TNFRSF1A by binding to m6A-modified TNFRSF1A mRNA. Upregulation of the TNFRSF1A protein level, a vital upstream switch for TNFRSF1A-mediated signaling events, activates the NF-κB and MAPK pathways and thus promotes ESCC development. Furthermore, TNFRSF1A m6A modifications and protein levels are upregulated in ESCC, and high levels of TNFRSF1A m6A and protein are correlated with poor ESCC patient survival. These results collectively indicate that the m6A-TNFRSF1A axis is critical for ESCC development and thus may serve as a potential druggable target.

RMVar: an updated database of functional variants involved in RNA modifications.

Distinguishing the few disease-related variants from a massive number of passenger variants is a major challenge. Variants affecting RNA modifications that play critical roles in many aspects of RNA metabolism have recently been linked to many human diseases, such as cancers. Evaluating the effect of genetic variants on RNA modifications will provide a new perspective for understanding the pathogenic mechanism of human diseases. Previously, we developed a database called 'm6AVar' to host variants associated with m6A, one of the most prevalent RNA modifications in eukaryotes. To host all RNA modification (RM)-associated variants, here we present an updated version of m6AVar renamed RMVar (http://rmvar.renlab.org). In this update, RMVar contains 1 678 126 RM-associated variants for 9 kinds of RNA modifications, namely m6A, m6Am, m1A, pseudouridine, m5C, m5U, 2'-O-Me, A-to-I and m7G, at three confidence levels. Moreover, RBP binding regions, miRNA targets, splicing events and circRNAs were integrated to assist investigations of the effects of RM-associated variants on posttranscriptional regulation. In addition, disease-related information was integrated from ClinVar and other genome-wide association studies (GWAS) to investigate the relationship between RM-associated variants and diseases. We expect that RMVar may boost further functional studies on genetic variants affecting RNA modifications.

Identification of an N6-methyladenosine-mediated positive feedback loop that promotes Epstein-Barr virus infection.

N6-methyladenosine (m6A) is among the most abundant mRNA modifications, particularly in eukaryotes, and is found in mammals, plants, and even some viruses. Although essential for the regulation of many biological processes, the exact role of m6A modification in virus-host interaction remains largely unknown. Here, using m6A -immunoprecipitation and sequencing, we find that Epstein-Barr virus (EBV) infection decreases the m6A modification of transcriptional factor KLF4 mRNA and subsequently increases its protein level. Mechanistically, EBV immediate-early protein BZLF1 interacts with the promoter of m6A methyltransferase METTL3, inhibiting its expression. Subsequently, the decrease of METTL3 reduces the level of KLF4 mRNA m6A modification, preventing its decay by the m6A reader protein YTHDF2. As a result, KLF4 protein level is upregulated and, in turn, promotes EBV infection of nasopharyngeal epithelial cells. Thus, our results suggest the existence of a positive feedback loop formed between EBV and host molecules via cellular mRNA m6A levels, and this feedback loop acts to facilitate viral infection. This mechanism contains multiple potential targets for controlling viral infectious diseases.

CrossICC: iterative consensus clustering of cross-platform gene expression data without adjusting batch effect.

Unsupervised clustering of high-throughput gene expression data is widely adopted for cancer subtyping. However, cancer subtypes derived from a single dataset are usually not applicable across multiple datasets from different platforms. Merging different datasets is necessary to determine accurate and applicable cancer subtypes but is still embarrassing due to the batch effect. CrossICC is an R package designed for the unsupervised clustering of gene expression data from multiple datasets/platforms without the requirement of batch effect adjustment. CrossICC utilizes an iterative strategy to derive the optimal gene signature and cluster numbers from a consensus similarity matrix generated by consensus clustering. This package also provides abundant functions to visualize the identified subtypes and evaluate subtyping performance. We expected that CrossICC could be used to discover the robust cancer subtypes with significant translational implications in personalized care for cancer patients. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available at GitHub (https://github.com/bioinformatist/CrossICC) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/CrossICC.html) under the GPL v3 License.

BBCancer: an expression atlas of blood-based biomarkers in the early diagnosis of cancers.

The early detection of cancer holds the key to combat and control the increasing global burden of cancer morbidity and mortality. Blood-based screenings using circulating DNAs (ctDNAs), circulating RNA (ctRNAs), circulating tumor cells (CTCs) and extracellular vesicles (EVs) have shown promising prospects in the early detection of cancer. Recent high-throughput gene expression profiling of blood samples from cancer patients has provided a valuable resource for developing new biomarkers for the early detection of cancer. However, a well-organized online repository for these blood-based high-throughput gene expression data is still not available. Here, we present BBCancer (http://bbcancer.renlab.org/), a web-accessible and comprehensive open resource for providing the expression landscape of six types of RNAs, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), circular RNAs (circRNAs), tRNA-derived fragments (tRFRNAs) and Piwi-interacting RNAs (piRNAs) in blood samples, including plasma, CTCs and EVs, from cancer patients with various cancer types. Currently, BBCancer contains expression data of the six RNA types from 5040 normal and tumor blood samples across 15 cancer types. We believe this database will serve as a powerful platform for developing blood biomarkers.