DARNED: a DAtabase of RNa EDiting in humans

Loss of ADAR1 protein induces changes in small RNA landscape in hepatocytes

In recent years, numerous evidence has been accumulated about the extent of A-to-I editing in human RNAs and the key role ADAR1 plays in the cellular editing machinery. It has been shown that A-to-I editing occurrence and frequency are tissue-specific and essential for some tissue development, such as the liver. To study the effect of ADAR1 function in hepatocytes, we have created Huh7.5 ADAR1 KO cell lines. Upon IFN treatment, the Huh7.5 ADAR1 KO cells show rapid arrest of growth and translation, from which they do not recover. We analyzed translatome changes by using a method based on sequencing of separate polysome profile RNA fractions. We found significant changes in the transcriptome and translatome of the Huh7.5 ADAR1 KO cells. The most prominent changes include negatively affected transcription by RNA polymerase III and the deregulation of snoRNA and Y RNA levels. Furthermore, we observed that ADAR1 KO polysomes are enriched in mRNAs coding for proteins pivotal in a wide range of biological processes such as RNA localization and RNA processing, whereas the unbound fraction is enriched mainly in mRNAs coding for ribosomal proteins and translational factors. This indicates that ADAR1 plays a more relevant role in small RNA metabolism and ribosome biogenesis.

RBPs: an RNA editor's choice

RNA-binding proteins (RBPs) play a key role in gene expression and post-transcriptional RNA regulation. As integral components of ribonucleoprotein complexes, RBPs are susceptible to genomic and RNA Editing derived amino acid substitutions, impacting functional interactions. This article explores the prevalent RNA Editing of RBPs, unravelling the complex interplay between RBPs and RNA Editing events. Emphasis is placed on their influence on single amino acid variants (SAAVs) and implications for disease development. The role of Proteogenomics in identifying SAAVs is briefly discussed, offering insights into the RBP landscape. RNA Editing within RBPs emerges as a promising target for precision medicine, reshaping our understanding of genetic and epigenetic variations in health and disease.

A-to-I Editing Is Subtype-Specific in Non-Hodgkin Lymphomas

Cancer is a complex and heterogeneous disease, in which a number of genetic and epigenetic changes occur in tumor onset and progression. Recent studies indicate that changes at the RNA level are also involved in tumorigenesis, such as adenosine-to-inosine (A-to-I) RNA editing. Here, we systematically investigate transcriptome-wide A-to-I editing events in a large number of samples from Non-Hodgkin lymphomas (NHLs). Using a computational pipeline that determines significant differences in editing level between NHL and normal samples at known A-to-I editing sites, we identify a number of differentially edited editing sites between NHL subtypes and normal samples. Most of the differentially edited sites are located in non-coding regions, and many such sites show a strong correlation between gene expression level and editing efficiency, indicating that RNA editing might have direct consequences for the cancer cell's aberrant gene regulation status in these cases. Moreover, we establish a strong link between RNA editing and NHL by demonstrating that NHL and normal samples and even NHL subtypes can be distinguished based on genome-wide RNA editing profiles alone. Our study establishes a strong link between RNA editing, cancer and aberrant gene regulation in NHL.

De novo detection of somatic mutations in high-throughput single-cell profiling data sets

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

Single cell analyses of cancer cells identified two regulatorily and functionally distinct categories in differentially expressed genes among tumor subclones

To explore the feature of cancer cells and tumor subclones, we analyzed 101,065 single-cell transcriptomes from 12 colorectal cancer (CRC) patients and 92 single cell genomes from one of these patients. We found cancer cells, endothelial cells and stromal cells in tumor tissue expressed much more genes and had stronger cell-cell interactions than their counterparts in normal tissue. We identified copy number variations (CNVs) in each cancer cell and found correlation between gene copy number and expression level in cancer cells at single cell resolution. Analysis of tumor subclones inferred by CNVs showed accumulation of mutations in each tumor subclone along lineage trajectories. We found differentially expressed genes (DEGs) between tumor subclones had two populations: DEGCNV and DEGreg. DEGCNV, showing high CNV-expression correlation and whose expression differences depend on the differences of CNV level, enriched in housekeeping genes and cell adhesion associated genes. DEGreg, showing low CNV-expression correlation and mainly in low CNV variation regions and regions without CNVs, enriched in cytokine signaling genes. Furthermore, cell-cell communication analyses showed that DEGCNV tends to involve in cell-cell contact while DEGreg tends to involve in secreted signaling, which further support that DEGCNV and DEGreg are two regulatorily and functionally distinct categories.

Malignant A-to-I RNA editing by ADAR1 drives T cell acute lymphoblastic leukemia relapse via attenuating dsRNA sensing

Leukemia-initiating cells (LICs) are regarded as the origin of leukemia relapse and therapeutic resistance. Identifying direct stemness determinants that fuel LIC self-renewal is critical for developing targeted approaches. Here, we show that the RNA-editing enzyme ADAR1 is a crucial stemness factor that promotes LIC self-renewal by attenuating aberrant double-stranded RNA (dsRNA) sensing. Elevated adenosine-to-inosine editing is a common attribute of relapsed T cell acute lymphoblastic leukemia (T-ALL) regardless of molecular subtype. Consequently, knockdown of ADAR1 severely inhibits LIC self-renewal capacity and prolongs survival in T-ALL patient-derived xenograft models. Mechanistically, ADAR1 directs hyper-editing of immunogenic dsRNA to avoid detection by the innate immune sensor melanoma differentiation-associated protein 5 (MDA5). Moreover, we uncover that the cell-intrinsic level of MDA5 dictates the dependency on the ADAR1-MDA5 axis in T-ALL. Collectively, our results show that ADAR1 functions as a self-renewal factor that limits the sensing of endogenous dsRNA. Thus, targeting ADAR1 presents an effective therapeutic strategy for eliminating T-ALL LICs.

REDH: A database of RNA editome in hematopoietic differentiation and malignancy

BACKGROUND

The conversion of adenosine (A) to inosine (I) through deamination is the prevailing form of RNA editing, impacting numerous nuclear and cytoplasmic transcripts across various eukaryotic species. Millions of high-confidence RNA editing sites have been identified and integrated into various RNA databases, providing a convenient platform for the rapid identification of key drivers of cancer and potential therapeutic targets. However, the available database for integration of RNA editing in hematopoietic cells and hematopoietic malignancies is still lacking.

METHODS

We downloaded RNA sequencing (RNA-seq) data of 29 leukemia patients and 19 healthy donors from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, and RNA-seq data of 12 mouse hematopoietic cell populations obtained from our previous research were also used. We performed sequence alignment, identified RNA editing sites, and obtained characteristic editing sites related to normal hematopoietic development and abnormal editing sites associated with hematologic diseases.

RESULTS

We established a new database, "REDH", represents RNA editome in hematopoietic differentiation and malignancy. REDH is a curated database of associations between RNA editome and hematopoiesis. REDH integrates 30,796 editing sites from 12 murine adult hematopoietic cell populations and systematically characterizes more than 400,000 edited events in malignant hematopoietic samples from 48 cohorts (human). Through the Differentiation, Disease, Enrichment, and knowledge modules, each A-to-I editing site is systematically integrated, including its distribution throughout the genome, its clinical information (human sample), and functional editing sites under physiological and pathological conditions. Furthermore, REDH compares the similarities and differences of editing sites between different hematologic malignancies and healthy control.

CONCLUSIONS

REDH is accessible at http://www.redhdatabase.com/ . This user-friendly database would aid in understanding the mechanisms of RNA editing in hematopoietic differentiation and malignancies. It provides a set of data related to the maintenance of hematopoietic homeostasis and identifying potential therapeutic targets in malignancies.

RMBase v3.0: decode the landscape, mechanisms and functions of RNA modifications

Although over 170 chemical modifications have been identified, their prevalence, mechanism and function remain largely unknown. To enable integrated analysis of diverse RNA modification profiles, we have developed RMBase v3.0 (http://bioinformaticsscience.cn/rmbase/), a comprehensive platform consisting of eight modules. These modules facilitate the exploration of transcriptome-wide landscape, biogenesis, interactome and functions of RNA modifications. By mining thousands of epitranscriptome datasets with novel pipelines, the 'RNA Modifications' module reveals the map of 73 RNA modifications of 62 species. the 'Genes' module allows to retrieve RNA modification profiles and clusters by gene and transcript. The 'Mechanisms' module explores 23 382 enzyme-catalyzed or snoRNA-guided modified sites to elucidate their biogenesis mechanisms. The 'Co-localization' module systematically formulates potential correlations between 14 histone modifications and 6 RNA modifications in various cell-lines. The 'RMP' module investigates the differential expression profiles of 146 RNA-modifying proteins (RMPs) in 18 types of cancers. The 'Interactome' integrates the interactional relationships between 73 RNA modifications with RBP binding events, miRNA targets and SNPs. The 'Motif' illuminates the enriched motifs for 11 types of RNA modifications identified from epitranscriptome datasets. The 'Tools' introduces a novel web-based 'modGeneTool' for annotating modifications. Overall, RMBase v3.0 provides various resources and tools for studying RNA modifications.

Identification and Interpretation of A-to-I RNA Editing Events in Insect Transcriptomes

Adenosine-to-inosine (A-to-I) RNA editing is the most prevalent RNA modification in the nervous systems of metazoans. To study the biological significance of RNA editing, we first have to accurately identify these editing events from the transcriptome. The genome-wide identification of RNA editing sites remains a challenging task. In this review, we will first introduce the occurrence, regulation, and importance of A-to-I RNA editing and then describe the established bioinformatic procedures and difficulties in the accurate identification of these sit esespecially in small sized non-model insects. In brief, (1) to obtain an accurate profile of RNA editing sites, a transcriptome coupled with the DNA resequencing of a matched sample is favorable; (2) the single-cell sequencing technique is ready to be applied to RNA editing studies, but there are a few limitations to overcome; (3) during mapping and variant calling steps, various issues, like mapping and base quality, soft-clipping, and the positions of mismatches on reads, should be carefully considered; (4) Sanger sequencing of both RNA and the matched DNA is the best verification of RNA editing sites, but other auxiliary evidence, like the nonsynonymous-to-synonymous ratio or the linkage information, is also helpful for judging the reliability of editing sites. We have systematically reviewed the understanding of the biological significance of RNA editing and summarized the methodology for identifying such editing events. We also raised several promising aspects and challenges in this field. With insightful perspectives on both scientific and technical issues, our review will benefit the researchers in the broader RNA editing community.

dsRID: in silico identification of dsRNA regions using long-read RNA-seq data

MOTIVATION

Double-stranded RNAs (dsRNAs) are potent triggers of innate immune responses upon recognition by cytosolic dsRNA sensor proteins. Identification of endogenous dsRNAs helps to better understand the dsRNAome and its relevance to innate immunity related to human diseases.

RESULTS

Here, we report dsRID (double-stranded RNA identifier), a machine-learning-based method to predict dsRNA regions in silico, leveraging the power of long-read RNA-sequencing (RNA-seq) and molecular traits of dsRNAs. Using models trained with PacBio long-read RNA-seq data derived from Alzheimer's disease (AD) brain, we show that our approach is highly accurate in predicting dsRNA regions in multiple datasets. Applied to an AD cohort sequenced by the ENCODE consortium, we characterize the global dsRNA profile with potentially distinct expression patterns between AD and controls. Together, we show that dsRID provides an effective approach to capture global dsRNA profiles using long-read RNA-seq data.

AVAILABILITY AND IMPLEMENTATION

Software implementation of dsRID, and genomic coordinates of regions predicted by dsRID in all samples are available at the GitHub repository: https://github.com/gxiaolab/dsRID.

Identification of microRNA editing sites in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor originating from the renal tubular epithelium. Although the microRNAs (miRNAs) transcriptome of ccRCC has been extensively studied, the role of miRNAs editing in ccRCC is largely unknown. By analyzing small RNA sequencing profiles of renal tissues of 154 ccRCC patients and 22 normal controls, we identified 1025 miRNA editing sites from 246 pre-miRNAs. There were 122 editing events with significantly different editing levels in ccRCC compared to normal samples, which include two A-to-I editing events in the seed regions of hsa-mir-376a-3p and hsa-mir-376c-3p, respectively, and one C-to-U editing event in the seed region of hsa-mir-29c-3p. After comparing the targets of the original and edited miRNAs, we found that hsa-mir-376a-1_49g, hsa-mir-376c_48g and hsa-mir-29c_59u had many new targets, respectively. Many of these new targets were deregulated in ccRCC, which might be related to the different editing levels of hsa-mir-376a-3p, hsa-mir-376c-3p, hsa-mir-29c-3p in ccRCC compared to normal controls. Our study sheds new light on miRNA editing events and their potential biological functions in ccRCC.

Transcriptome-wide profiling of A-to-I RNA editing by Slic-seq

Adenosine-to-inosine (A-to-I) RNA editing is a post-transcriptional processing event involved in diversifying the transcriptome and is responsible for various biological processes. In this context, we developed a new method based on the highly selective cleavage activity of Endonuclease V against Inosine and the universal activity of sodium periodate against all RNAs to enrich the inosine-containing RNA and accurately identify the editing sites. We validated the reliability of our method in human brain in both Alu and non-Alu elements. The conserved sites of A-to-I editing in human cells (HEK293T, HeLa, HepG2, K562 and MCF-7) primarily occurs in the 3'UTR of the RNA, which are highly correlated with RNA binding and protein binding. Analysis of the editing sites between the human brain and mouse brain revealed that the editing of exons is more conserved than that in other regions. This method was applied to three neurological diseases (Alzheimer's, epilepsy and ageing) of mouse brain, reflecting that A-to-I editing sites significantly decreased in neuronal activity genes.

Benchmarking RNA Editing Detection Tools

RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identified: (1) the most frequent, adenosine to inosine (A-to-I); and (2) the less frequent, cysteine to uracil (C-to-U) RNA editing. Unlike other epitranscriptomic marks, RNA editing can be readily detected from RNA sequencing (RNA-seq) data without any chemical conversions of RNA before sequencing library preparation. Furthermore, analyzing RNA editing patterns from transcriptomic data provides an additional layer of information about the epitranscriptome. As the significance of epitranscriptomics, particularly RNA editing, gains recognition in various fields of biology and medicine, there is a growing interest in detecting RNA editing sites (RES) by analyzing RNA-seq data. To cope with this increased interest, several bioinformatic tools are available. However, each tool has its advantages and disadvantages, which makes the choice of the most appropriate tool for bench scientists and clinicians difficult. Here, we have benchmarked bioinformatic tools to detect RES from RNA-seq data. We provide a comprehensive view of each tool and its performance using previously published RNA-seq data to suggest recommendations on the most appropriate for utilization in future studies.

Profiling A-to-I RNA editing during mouse somatic reprogramming at the single-cell level

Mouse somatic cells can be reprogrammed into induced pluripotent stem cells through a highly heterogeneous process regulated by numerous biological factors, including adenosine-to-inosine (A-to-I) RNA editing. In this study, we analyzed A-to-I RNA editing sites using a single-cell RNA sequencing (scRNA-seq) dataset with high-depth and full-length coverage. Our method revealed that A-to-I RNA editing frequency varied widely at the single-cell level and underwent dynamic changes. We also found that A-to-I RNA editing level was correlated with the expression of the RNA editing enzyme ADAR1. The analysis combined with gene ontology (GO) enrichment revealed that ADAR1-dependent A-to-I editing may downregulate the expression levels of Igtp, Irgm2, Mndal, Ifi202b, and Tapbp in the early stage, to inhibit the pathways of cellular response to interferon-beta and regulation of protein complex stability to promote mesenchymal-epithelial transition (MET). Notably, we identified a negative correlation between A-to-I RNA editing frequency and the expression of certain genes, such as Nras, Ube2l6, Zfp987, and Adsl.

Malignant A-to-I RNA editing by ADAR1 drives T-cell acute lymphoblastic leukemia relapse via attenuating dsRNA sensing

Leukemia initiating cells (LICs) are regarded as the origin of leukemia relapse and therapeutic resistance. Identifying direct stemness determinants that fuel LIC self-renewal is critical for developing targeted approaches to eliminate LICs and prevent relapse. Here, we show that the RNA editing enzyme ADAR1 is a crucial stemness factor that promotes LIC self-renewal by attenuating aberrant double-stranded RNA (dsRNA) sensing. Elevated adenosine-to-inosine (A-to-I) editing is a common attribute of relapsed T-ALL regardless of molecular subtypes. Consequently, knockdown of ADAR1 severely inhibits LIC self-renewal capacity and prolongs survival in T-ALL PDX models. Mechanistically, ADAR1 directs hyper-editing of immunogenic dsRNA and retains unedited nuclear dsRNA to avoid detection by the innate immune sensor MDA5. Moreover, we uncovered that the cell intrinsic level of MDA5 dictates the dependency on ADAR1-MDA5 axis in T-ALL. Collectively, our results show that ADAR1 functions as a self-renewal factor that limits the sensing of endogenous dsRNA. Thus, targeting ADAR1 presents a safe and effective therapeutic strategy for eliminating T-ALL LICs.

dsRID: Editing-free in silico identification of dsRNA region using long-read RNA-seq data

Double-stranded RNAs (dsRNAs) are potent triggers of innate immune responses upon recognition by cytosolic dsRNA sensor proteins. Identification of endogenous dsRNAs helps to better understand the dsRNAome and its relevance to innate immunity related to human diseases. Here, we report dsRID (double-stranded RNA identifier), a machine learning-based method to predict dsRNA regions in silico, leveraging the power of long-read RNA-sequencing (RNA-seq) and molecular traits of dsRNAs. Using models trained with PacBio long-read RNA-seq data derived from Alzheimer's disease (AD) brain, we show that our approach is highly accurate in predicting dsRNA regions in multiple datasets. Applied to an AD cohort sequenced by the ENCODE consortium, we characterize the global dsRNA profile with potentially distinct expression patterns between AD and controls. Together, we show that dsRID provides an effective approach to capture global dsRNA profiles using long-read RNA-seq data.

ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species

A-to-I editing is the most prevalent RNA editing event, which refers to the change of adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have revealed that A-to-I editing can regulate cellular processes and is associated with various human diseases. Therefore, accurate identification of A-to-I editing sites is crucial for understanding RNA-level (i.e. transcriptional) modifications and their potential roles in molecular functions. To date, various computational approaches for A-to-I editing site identification have been developed; however, their performance is still unsatisfactory and needs further improvement. In this study, we developed a novel stacked-ensemble learning model, ATTIC (A-To-I ediTing predICtor), to accurately identify A-to-I editing sites across three species, including Homo sapiens, Mus musculus and Drosophila melanogaster. We first comprehensively evaluated 37 RNA sequence-derived features combined with 14 popular machine learning algorithms. Then, we selected the optimal base models to build a series of stacked ensemble models. The final ATTIC framework was developed based on the optimal models improved by the feature selection strategy for specific species. Extensive cross-validation and independent tests illustrate that ATTIC outperforms state-of-the-art tools for predicting A-to-I editing sites. We also developed a web server for ATTIC, which is publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/ATTIC/. We anticipate that ATTIC can be utilized as a useful tool to accelerate the identification of A-to-I RNA editing events and help characterize their roles in post-transcriptional regulation.

Somatic A-to-I RNA-edited RHOA isoform 2 specific-R176G mutation promotes tumor progression in lung adenocarcinoma

Adenosine-to-inosine (A-to-I) RNA editing is the most common posttranscriptional editing to create somatic mutations and increase proteomic diversity. However, the functions of the edited mutations are largely underexplored. To identify novel targets in lung adenocarcinoma (LUAD), we conducted a genome-wide somatic A-to-I RNA editing analysis of 23 paired adjacent normal and LUAD transcriptomes and identified 26,280 events, including known nonsynonymous AZIN1-S367G and novel RHOAiso2 (RHOA isoform 2)-R176G, tubulin gamma complex associated protein 2 (TUBGCP2)-N211S, and RBMXL1-I40 M mutations. We validated the edited mutations in silico in multiple databases and in newly collected LUAD tissue pairs with the SEQUENOM MassARRAY® and TaqMan PCR Systems. We selected RHOAiso2-R176G due to its significant level, isoform-specificity, and being the most common somatic edited nonsynonymous mutation of RHOAiso2 to investigate its roles in LUAD tumorigenesis. RHOAiso2 is a ubiquitous but low-expression alternative spliced isoform received a unique Alu-rich exon at the 3' RHOA mRNA to become an editing RNA target, leading to somatic hypermutation and protein diversity. Interestingly, LUAD patients harboring the RHOAiso2-R176G mutation were associated with aberrant RHOA functions, cancer cell proliferation and migration, and poor clinical outcomes in transcriptome analysis. Mechanistically, RHOAiso2-R176G mutation-expressing LUAD cells potentiate RHOA-guanosine triphosphate (GTP) activity to phosphorylate ROCK1/2 effectors and enhance cell proliferation and migration in vitro and increase tumor growth in xenograft and systemic metastasis models in vivo. Taken together, the RHOAiso2-R176G mutation is a common somatic A-to-I edited mutation of the hypermutated RHOA isoform 2. It is an oncogenic and isoform-specific theranostic target that activates RHOA-GTP/p-ROCK1/2 signaling to promote tumor progression.

ADAR RNA editing on antisense RNAs results in apparent U-to-C base changes on overlapping sense transcripts

Despite hundreds of RNA modifications described to date, only RNA editing results in a change in the nucleotide sequence of RNA molecules compared to the genome. In mammals, two kinds of RNA editing have been described so far, adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. Recent improvements in RNA sequencing technologies have led to the discovery of a continuously growing number of editing sites. These methods are powerful but not error-free, making routine validation of newly-described editing sites necessary. During one of these validations on DDX58 mRNA, along with A-to-I RNA editing sites, we encountered putative U-to-C editing. These U-to-C edits were present in several cell lines and appeared regulated in response to specific environmental stimuli. The same findings were also observed for the human long intergenic non-coding RNA p21 (hLincRNA-p21). A more in-depth analysis revealed that putative U-to-C edits result from A-to-I editing on overlapping antisense RNAs that are transcribed from the same loci. Such editing events, occurring on overlapping genes transcribed in opposite directions, have recently been demonstrated to be immunogenic and have been linked with autoimmune and immune-related diseases. Our findings, also confirmed by deep transcriptome data, demonstrate that such loci can be recognized simply through the presence of A-to-I and U-to-C mismatches within the same locus, reflective A-to-I editing both in the sense-oriented transcript and in the cis-natural antisense transcript (cis-NAT), implying that such clusters could be a mark of functionally relevant ADAR1 editing events.

Characterizing Relevant MicroRNA Editing Sites in Parkinson's Disease

MicroRNAs (miRNAs) are extensively edited in human brains. However, the functional relevance of the miRNA editome is largely unknown in Parkinson's disease (PD). By analyzing small RNA sequencing profiles of brain tissues of 43 PD patients and 88 normal controls, we found that the editing levels of five A-to-I and two C-to-U editing sites are significantly correlated with the ages of normal controls, which is disrupted in PD patients. We totally identified 362 miRNA editing sites with significantly different editing levels in prefrontal cortices of PD patients (PD-PC) compared to results of normal controls. We experimentally validated that A-to-I edited miR-497-5p, with significantly higher expression levels in PD-PC compared to normal controls, directly represses OPA1 and VAPB. Furthermore, overexpression of A-to-I edited miR-497-5p downregulates OPA1 and VAPB in two cell lines, and inhibits proliferation of glioma cells. These results suggest that the hyperediting of miR-497-5p in PD contributes to enhanced progressive neurodegeneration of PD patients. Our results provide new insights into the mechanistic understanding, novel diagnostics, and therapeutic clues of PD.

The RNA editing landscape in acute myeloid leukemia reveals associations with disease mutations and clinical outcome

Several studies have documented aberrant RNA editing patterns across multiple tumors across large patient cohorts from The Cancer Genome Atlas (TCGA). However, studies on understanding the role of RNA editing in acute myeloid leukemia (AML) have been limited to smaller sample sizes. Using high throughput transcriptomic data from the TCGA, we demonstrated higher levels of editing as a predictor of poor outcome within the AML patient samples. Moreover, differential editing patterns were observed across individual AML genotypes. We also could demonstrate a negative association between the degree of editing and mRNA abundance for some transcripts, identifying the potential regulatory potential of RNA-editing in altering gene expression in AML. Further edQTL analysis suggests potential cis-regulatory mechanisms in RNA editing variation. Our work suggests a functional and regulatory role of RNA editing in the pathogenesis of AML and we extended our analysis to gain insight into the factors influencing altered levels of editing.

RNA Epigenetics in Chronic Lung Diseases

Chronic lung diseases are highly prevalent worldwide and cause significant mortality. Lung cancer is the end stage of many chronic lung diseases. RNA epigenetics can dynamically modulate gene expression and decide cell fate. Recently, studies have confirmed that RNA epigenetics plays a crucial role in the developing of chronic lung diseases. Further exploration of the underlying mechanisms of RNA epigenetics in chronic lung diseases, including lung cancer, may lead to a better understanding of the diseases and promote the development of new biomarkers and therapeutic strategies. This article reviews basic information on RNA modifications, including N⁶ methylation of adenosine (m⁶A), N¹ methylation of adenosine (m¹A), N⁷-methylguanosine (m⁷G), 5-methylcytosine (m⁵C), 2'O-methylation (2'-O-Me or Nm), pseudouridine (5-ribosyl uracil or Ψ), and adenosine to inosine RNA editing (A-to-I editing). We then show how they relate to different types of lung disease. This paper hopes to summarize the mechanisms of RNA modification in chronic lung disease and finds a new way to develop early diagnosis and treatment of chronic lung disease.

The A-to-I editing of KPC1 promotes intrahepatic cholangiocarcinoma by attenuating proteasomal processing of NF-κB1 p105 to p50

BACKGROUND

Aberrant RNA editing of adenosine-to-inosine (A-to-I) has been linked to multiple human cancers, but its role in intrahepatic cholangiocarcinoma (iCCA) remains unknown. We conducted an exome-wide investigation to search for dysregulated RNA editing that drive iCCA pathogenesis.

METHODS

An integrative whole-exome and transcriptome sequencing analysis was performed to elucidate the RNA editing landscape in iCCAs. Putative RNA editing sites were validated by Sanger sequencing. In vitro and in vivo experiments were used to assess the effects of an exemplary target gene Kip1 ubiquitination-promoting complex 1 (KPC1) and its editing on iCCA cells growth and metastasis. Crosstalk between KPC1 RNA editing and NF-κB signaling was analyzed by molecular methods.

RESULTS

Through integrative omics analyses, we revealed an adenosine deaminases acting on RNA 1A (ADAR1)-mediated over-editing pattern in iCCAs. ADAR1 is frequently amplified and overexpressed in iCCAs and plays oncogenic roles. Notably, we identified a novel ADAR1-mediated A-to-I editing of KPC1 transcript, which results in substitution of methionine with valine at residue 8 (p.M8V). KPC1 p.M8V editing confers loss-of-function phenotypes through blunting the tumor-suppressive role of wild-type KPC1. Mechanistically, KPC1 p.M8V weakens the affinity of KPC1 to its substrate NF-κB1 p105, thereby reducing the ubiquitinating and proteasomal processing of p105 to p50, which in turn enhances the activity of oncogenic NF-κB signaling.

CONCLUSIONS

Our findings established that amplification-driven ADAR1 overexpression results in overediting of KPC1 p.M8V in iCCAs, leading to progression via activation of the NF-κB signaling pathway, and suggested ADAR1-KPC1-NF-κB axis as a potential therapeutic target for iCCA.

Identification of microRNA editing sites in three subtypes of leukemia

Leukemia is an aberrant hyper-proliferation of immature blood cells that do not form solid tumors. The transcriptomes of microRNAs (miRNAs) of leukemia have been intensively explored. However, miRNA editing of leukemia has not been extensively studied. To identify miRNA editing patterns and explore their functional relevance in leukemia, we analyzed 200 small RNA sequencing profiles of three subtypes of leukemia and identified hundreds of miRNA editing sites in three subtypes of leukemia. Then, we compared the editing levels of identified miRNA editing sites in leukemia and normal controls. Many miRNAs were differential edited in different subtypes of leukemia. We also found the editing levels of 3'-A editing sites of hsa-mir-21-5p and hsa-mir-155-5p decreased in chronic lymphocytic leukemia patients with radiation treatments. By integrating PAR-CLIP sequencing profiles, we predicted the targets of original and edited miRNAs. One of the edited miRNA, hsa-let-7b_5c, with an additional cytosine at 5' end of hsa-let-7b-5p, potentially targeted VBP1 and CTDSP1. CTDSP1 was significantly downregulated in T-ALL compared to normal controls, which might be originated from the hyperediting of hsa-let-7b-5p in T-ALL. Our study provides a comprehensive view of miRNA editing in three different subtypes of leukemia.

The snoRNA-like lncRNA LNC-SNO49AB drives leukemia by activating the RNA-editing enzyme ADAR1

Long noncoding RNAs (lncRNAs) are usually 5' capped and 3' polyadenylated, similar to most typical mRNAs. However, recent studies revealed a type of snoRNA-related lncRNA with unique structures, leading to questions on how they are processed and how they work. Here, we identify a novel snoRNA-related lncRNA named LNC-SNO49AB containing two C/D box snoRNA sequences, SNORD49A and SNORD49B; and show that LNC-SNO49AB represents an unreported type of lncRNA with a 5'-end m7G and a 3'-end snoRNA structure. LNC-SNO49AB was found highly expressed in leukemia patient samples, and silencing LNC-SNO49AB dramatically suppressed leukemia progression in vitro and in vivo. Subcellular location indicated that the LNC-SNO49AB is mainly located in nucleolus and interacted with the nucleolar protein fibrillarin. However, we found that LNC-SNO49AB does not play a role in 2'-O-methylation regulation, a classical function of snoRNA; instead, its snoRNA structure affected the lncRNA stability. We further demonstrated that LNC-SNO49AB could directly bind to the adenosine deaminase acting on RNA 1(ADAR1) and promoted its homodimerization followed by a high RNA A-to-I editing activity. Transcriptome profiling shows that LNC-SNO49AB and ADAR1 knockdown respectively share very similar patterns of RNA modification change in downstream signaling pathways, especially in cell cycle pathways. These findings suggest a previously unknown class of snoRNA-related lncRNAs, which function via a manner in nucleolus independently on snoRNA-guide rRNA modification. This is the first report that a lncRNA regulates genome-wide RNA A-to-I editing by enhancing ADAR1 dimerization to facilitate hematopoietic malignancy, suggesting that LNC-SNO49AB may be a novel target in therapy directed to leukemia.

A-to-I nonsynonymous RNA editing was significantly enriched in the ubiquitination site and correlated with clinical features and immune response

RNA editing is a post-transcriptional process that alters RNA sequence in a site-specific manner. A-to-I editing is the most abundant as well as the most well-studied type of RNA editing. About 0.5% of A-to-I editing sites were located in the coding regions. Despite of thousands of identified A-to-I nonsynonymous editing sites, the function of nonsynonymous editing was poorly studied. Here, we found that the nonsynonymous editing was significantly enriched in the ubiquitination site, compared to the synonymous editing. This enrichment was also in a modification type dependent manner, since it was not significantly enriched in other modification types. This observation was consistent with previous study that the codons for lysine (AAG and AAA) were enriched in the preferred deamination site for RNA editing. The peptides from proteomic data in CPTAC supported that mRNAs harboring edited ubiquitination sites can be translated into protein in cells. We identified the editing sites on ubiquitination site were significantly differential edited between tumor and para-tumor samples as well as among different subtypes in TCGA datasets and also correlated with clinical outcome, especially for the nonsynonymous editing sites on GSTM5, WDR1, SSR4 and PSMC4. Finally, the enrichment analysis revealed that the function of these above genes was specifically enriched in the immune response pathway. Our study shed a light on understanding the functions of nonsynonymous editing in tumorigenesis and provided nonsynonymous editing targets for potential cancer diagnosis and therapy.

Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample

Detection of somatic mutations using patients sequencing data has many clinical applications, including the identification of cancer driver genes, detection of mutational signatures, and estimation of tumor mutational burden (TMB). We have previously developed a tool for detection of somatic mutations using tumor RNA and a matched-normal DNA. Here, we further extend it to detect somatic mutations from RNA sequencing data without a matched-normal sample. This is accomplished via a machine-learning approach that classifies mutations as either somatic or germline based on various features. When applied to RNA-sequencing of >450 melanoma samples high precision and recall are achieved, and both mutational signatures and driver genes are correctly identified. Finally, we show that RNA-based TMB is significantly associated with patient survival, showing similar or higher significance level as compared to DNA-based TMB. Our pipeline can be utilized in many future applications, analyzing novel and existing datasets where only RNA is available.

Increase in ADAR1p110 activates the canonical Wnt signaling pathway associated with aggressive phenotype in triple negative breast cancer cells

Triple-negative breast cancer (TNBC) represents a challenge in the search for new therapeutic targets. TNBCs are aggressive and generate resistance to chemotherapy. Tumors of TNBC patients with poor prognosis present a high level of adenosine deaminase acting on RNA1 (ADAR1). We explore the connection of ADAR1 with the canonical Wnt signaling pathway and the effect of modulation of its expression in TNBC. Expression data from cell line sequencing (DepMap) and TCGA samples were downloaded and analyzed. We lentivirally generated an MDA-MB-231 breast cancer cell line that overexpress (OE) ADAR1p110 or an ADAR knockdown. Abundance of different proteins related to Wnt/β-catenin pathway and activity of nuclear β-catenin were analyzed by Western blot and luciferase TOP/FOP reporter assay, respectively. Cell invasion was analyzed by matrigel assay. In mice, we study the behavior of tumors generated from ADAR1p110 (OE) cells and tumor vascularization immunostaining were analyzed. ADAR1 connects to the canonical Wnt pathway in TNBC. ADAR1p110 overexpression decreased GSK-3β, while increasing active β-catenin. It also increased the activity of nuclear β-catenin and increased its target levels. ADAR1 knockdown has the opposite effect. MDA-MB-231 ADAR1 (OE) cells showed increased capacity of invasion. Subsequently, we observed that tumors derived from ADAR1p110 (OE) cells showed increased invasion towards the epithelium, and increased levels of Survivin and CD-31 expressed in vascular endothelial cells. These results indicate that ADAR1 overexpression alters the expression of some key components of the canonical Wnt pathway, favoring invasion and neovascularization, possibly through activation of the β-catenin, which suggests an unknown role of ADAR1p110 in aggressiveness of TNBC tumors.

Current Status of Epitranscriptomic Marks Affecting lncRNA Structures and Functions

Long non-coding RNAs (lncRNAs) belong to a class of non-protein-coding RNAs with their lengths longer than 200 nucleotides. Most of the mammalian genome is transcribed as RNA, yet only a small percent of the transcribed RNA corresponds to exons of protein-coding genes. Thus, the number of lncRNAs is predicted to be several times higher than that of protein-coding genes. Because of sheer number of lncRNAs, it is often difficult to elucidate the functions of all lncRNAs, especially those arising from their relationship to their binding partners, such as DNA, RNA, and proteins. Due to their binding to other macromolecules, it has become evident that the structures of lncRNAs influence their functions. In this regard, the recent development of epitranscriptomics (the field of study to investigate RNA modifications) has become important to further elucidate the structures and functions of lncRNAs. In this review, the current status of lncRNA structures and functions influenced by epitranscriptomic marks is discussed.

A Systematic Review of Common and Brain-Disease-Specific RNA Editing Alterations Providing Novel Insights into Neurological and Neurodegenerative Disease Manifestations

RNA editing contributes to transcriptome diversification through RNA modifications in relation to genome-encoded information (RNA–DNA differences, RDDs). The deamination of Adenosine (A) to Inosine (I) or Cytidine (C) to Uridine (U) is the most common type of mammalian RNA editing. It occurs as a nuclear co- and/or post-transcriptional event catalyzed by ADARs (Adenosine deaminases acting on RNA) and APOBECs (apolipoprotein B mRNA editing enzyme catalytic polypeptide-like genes). RNA editing may modify the structure, stability, and processing of a transcript. This review focuses on RNA editing in psychiatric, neurological, neurodegenerative (NDs), and autoimmune brain disorders in humans and rodent models. We discuss targeted studies that focus on RNA editing in specific neuron-enriched transcripts with well-established functions in neuronal activity, and transcriptome-wide studies, enabled by recent technological advances. We provide comparative editome analyses between human disease and corresponding animal models. Data suggest RNA editing to be an emerging mechanism in disease development, displaying common and disease-specific patterns. Commonly edited RNAs represent potential disease-associated targets for therapeutic and diagnostic values. Currently available data are primarily descriptive, calling for additional research to expand global editing profiles and to provide disease mechanistic insights. The potential use of RNA editing events as disease biomarkers and available tools for RNA editing identification, classification, ranking, and functional characterization that are being developed will enable comprehensive analyses for a better understanding of disease(s) pathogenesis and potential cures.

Inosine and its methyl derivatives: Occurrence, biogenesis, and function in RNA

Inosine is one of the most common post-transcriptional modifications. Since its discovery, it has been noted for its ability to contribute to non-Watson-Crick interactions within RNA. Rapidly accumulating evidence points to the widespread generation of inosine through hydrolytic deamination of adenosine to inosine by different classes of adenosine deaminases. Three naturally occurring methyl derivatives of inosine, i.e., 1-methylinosine, 2'-O-methylinosine and 1,2'-O-dimethylinosine are currently reported in RNA modification databases. These modifications are expected to lead to changes in the structure, folding, dynamics, stability and functions of RNA. The importance of the modifications is indicated by the strong conservation of the modifying enzymes across organisms. The structure, binding and catalytic mechanism of the adenosine deaminases have been well-studied, but the underlying mechanism of the catalytic reaction is not very clear yet. Here we extensively review the existing data on the occurrence, biogenesis and functions of inosine and its methyl derivatives in RNA. We also included the structural and thermodynamic aspects of these modifications in our review to provide a detailed and integrated discussion on the consequences of A-to-I editing in RNA and the contribution of different structural and thermodynamic studies in understanding its role in RNA. We also highlight the importance of further studies for a better understanding of the mechanisms of the different classes of deamination reactions. Further investigation of the structural and thermodynamic consequences and functions of these modifications in RNA should provide more useful information about their role in different diseases.

MicroRNA editing patterns in Huntington's disease

Huntington's disease (HD) is a neurodegenerative disease. MicroRNAs (miRNAs) are small non-coding RNAs that mediate post-transcriptional regulation of target genes. Although miRNAs are extensively edited in human brains, the editome of miRNAs in brains of HD patients is largely unknown. By analyzing the small RNA sequencing profiles of brain tissues of 28 HD patients and 83 normal controls, 1182 miRNA editing sites with significant editing levels were identified. In addition to 27 A-to-I editing sites, we identified 3 conserved C-to-U editing sites in miRNAs of HD patients. 30 SNPs in the miRNAs of HD patients were also identified. Furthermore, 129 miRNA editing events demonstrated significantly different editing levels in prefrontal cortex samples of HD patients (HD-PC) when compared to those of healthy controls. We found that hsa-mir-10b-5p was edited to have an additional cytosine at 5'-end in HD-PC, and the edited hsa-mir-10b repressed GTPBP10 that was often downregulated in HD. The down-regulation of GTPBP10 might contribute to the progression of HD by causing gradual loss of function of mitochondrial. These results provide the first endeavor to characterize the miRNA editing events in HD and their potential functions.

Penguin: A Tool for Predicting Pseudouridine Sites in Direct RNA Nanopore Sequencing Data

Pseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and has been reported to have application in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies have enabled direct detection of RNA modifications on the molecule being sequenced. In this study, we introduce a tool called Penguin that integrates several machine learning (ML) models to identify RNA Pseudouridine sites on Nanopore direct RNA sequencing reads. Pseudouridine sites were identified on single molecule sequencing data collected from direct RNA sequencing resulting in 723K reads in Hek293 and 500K reads in Hela cell lines. Penguin extracts a set of features from the raw signal measured by the Oxford Nanopore and the corresponding basecalled k-mer. Those features are used to train the predictors included in Penguin, which in turn, can predict whether the signal is modified by the presence of Pseudouridine sites in the testing phase. We have included various predictors in Penguin, including Support vector machines (SVM), Random Forest (RF), and Neural network (NN). The results on the two benchmark data sets for Hek293 and Hela cell lines show outstanding performance of Penguin either in random split testing or in independent validation testing. In random split testing, Penguin has been able to identify Pseudouridine sites with a high accuracy of 93.38% by applying SVM to Hek293 benchmark dataset. In independent validation testing, Penguin achieves an accuracy of 92.61% by training SVM with Hek293 benchmark dataset and testing it for identifying Pseudouridine sites on Hela benchmark dataset. Thus, Penguin outperforms the existing Pseudouridine predictors in the literature by 16 % higher accuracy than those predictors using independent validation testing. Employing penguin to predict Pseudouridine revealed a significant enrichment of "regulation of mRNA 3'-end processing" in Hek293 cell line and positive regulation of transcription from RNA polymerase II promoter involved in cellular response to chemical stimulus in Hela cell line. Penguin software and models are available on GitHub at https://github.com/Janga-Lab/Penguin and can be readily employed for predicting Ψ sites from Nanopore direct RNA-sequencing datasets.

ViRBase v3.0: a virus and host ncRNA-associated interaction repository with increased coverage and annotation

As a means to aid in the investigation of viral infection mechanisms and identification of more effective antivirus targets, the availability of a source which continually collects and updates information on the virus and host ncRNA-associated interaction resources is essential. Here, we update the ViRBase database to version 3.0 (http://www.virbase.org/ or http://www.rna-society.org/virbase/). This update represents a major revision: (i) the total number of interaction entries is now greater than 820,000, an approximately 70-fold increment, involving 116 virus and 36 host organisms, (ii) it supplements and provides more details on RNA annotations (including RNA editing, RNA localization and RNA modification), ncRNA SNP and ncRNA-drug related information and (iii) it provides two additional tools for predicting binding sites (IntaRNA and PRIdictor), a visual plug-in to display interactions and a website which is optimized for more practical and user-friendly operation. Overall, ViRBase v3.0 provides a more comprehensive resource for virus and host ncRNA-associated interactions enabling researchers a more effective means for investigation of viral infections.

The landscape of coding RNA editing events in pediatric cancer

Background

RNA editing leads to post-transcriptional variation in protein sequences and has important biological implications. We sought to elucidate the landscape of RNA editing events across pediatric cancers.

Methods

Using RNA-Seq data mapped by a pipeline designed to minimize mapping ambiguity, we investigated RNA editing in 711 pediatric cancers from the St. Jude/Washington University Pediatric Cancer Genome Project focusing on coding variants which can potentially increase protein sequence diversity. We combined de novo detection using paired tumor DNA-RNA data with analysis of known RNA editing sites.

Results

We identified 722 unique RNA editing sites in coding regions across pediatric cancers, 70% of which were nonsynonymous recoding variants. Nearly all editing sites represented the canonical A-to-I (n = 706) or C-to-U sites (n = 14). RNA editing was enriched in brain tumors compared to other cancers, including editing of glutamate receptors and ion channels involved in neurotransmitter signaling. RNA editing profiles of each pediatric cancer subtype resembled those of the corresponding normal tissue profiled by the Genotype-Tissue Expression (GTEx) project.

Conclusions

In this first comprehensive analysis of RNA editing events in pediatric cancer, we found that the RNA editing profile of each cancer subtype is similar to its normal tissue of origin. Tumor-specific RNA editing events were not identified indicating that successful immunotherapeutic targeting of RNA-edited peptides in pediatric cancer should rely on increased antigen presentation on tumor cells compared to normal but not on tumor-specific RNA editing per se.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08956-5.

EditPredict: Prediction of RNA editable sites with convolutional neural network

RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to predict human RNA editing events in both Alu regions and non-Alu regions. With a validation dataset resulting from CRISPR/Cas9 knockout of the ADAR1 enzyme, the validation accuracies reached 99.5% and 93.6% for Alu and non-Alu regions, respectively. We ported our CNN models in a web service named EditPredict. EditPredict not only works on reference genome sequences but can also take into consideration single nucleotide variants in personal genomes. In addition to the human genome, EditPredict tackles other model organisms including bumblebee, fruitfly, mouse, and squid genomes. EditPredict can be used stand-alone to predict novel RNA editing and it can be used to assist in filtering for candidate RNA editing detected from RNA-Seq data.

RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility

Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.

Machine learning applications in RNA modification sites prediction

Ribonucleic acid (RNA) modifications are post-transcriptional chemical composition changes that have a fundamental role in regulating the main aspect of RNA function. Recently, large datasets have become available thanks to the recent development in deep sequencing and large-scale profiling. This availability of transcriptomic datasets has led to increased use of machine learning based approaches in epitranscriptomics, particularly in identifying RNA modifications. In this review, we comprehensively explore machine learning based approaches used for the prediction of 11 RNA modification types, namely,m 1 A ,m 6 A ,m 5 C , 5 hmC , ψ , 2 ' - O - Me , ac 4 C ,m 7 G , A - to - I ,m 2 G , and D . This review covers the life cycle of machine learning methods to predict RNA modification sites including available benchmark datasets, feature extraction, and classification algorithms. We compare available methods in terms of datasets, target species, approach, and accuracy for each RNA modification type. Finally, we discuss the advantages and limitations of the reviewed approaches and suggest future perspectives.

MiREDiBase, a manually curated database of validated and putative editing events in microRNAs

MicroRNAs (miRNAs) are regulatory small non-coding RNAs that function as translational repressors. MiRNAs are involved in most cellular processes, and their expression and function are presided by several factors. Amongst, miRNA editing is an epitranscriptional modification that alters the original nucleotide sequence of selected miRNAs, possibly influencing their biogenesis and target-binding ability. A-to-I and C-to-U RNA editing are recognized as the canonical types, with the A-to-I type being the predominant one. Albeit some bioinformatics resources have been implemented to collect RNA editing data, it still lacks a comprehensive resource explicitly dedicated to miRNA editing. Here, we present MiREDiBase, a manually curated catalog of editing events in miRNAs. The current version includes 3,059 unique validated and putative editing sites from 626 pre-miRNAs in humans and three primates. Editing events in mature human miRNAs are supplied with miRNA-target predictions and enrichment analysis, while minimum free energy structures are inferred for edited pre-miRNAs. MiREDiBase represents a valuable tool for cell biology and biomedical research and will be continuously updated and expanded at https://ncrnaome.osumc.edu/miredibase .

Understanding small ORF diversity through a comprehensive transcription feature classification

Small open reading frames (small ORFs/sORFs/smORFs) are potentially coding sequences smaller than 100 codons that have historically been considered junk DNA by gene prediction software and in annotation screening; however, the advent of next-generation sequencing has contributed to the deeper investigation of junk DNA regions and their transcription products, resulting in the emergence of smORFs as a new focus of interest in systems biology. Several smORF peptides were recently reported in noncanonical mRNAs as new players in numerous biological contexts; however, their relevance is still overlooked in coding potential analysis. Hence, this review proposes a smORF classification based on transcriptional features, discussing the most promising approaches to investigate smORFs based on their different characteristics. First, smORFs were divided into nonexpressed (intergenic) and expressed (genic) smORFs. Second, genic smORFs were classified as smORFs located in noncoding RNAs (ncRNAs) or canonical mRNAs. Finally, smORFs in ncRNAs were further subdivided into sequences located in small or long RNAs, whereas smORFs located in canonical mRNAs were subdivided into several specific classes depending on their localization along the gene. We hope that this review provides new insights into large-scale annotations and reinforces the role of smORFs as essential components of a hidden coding DNA world.

Genome-wide expression changes mediated by A-to-I RNA editing correlate with hepatic oncogenesis

Background

Adenosine-to-inosine (A-to-I) RNA editing is one of the most prevalent RNA modifications in the animal kingdom. Since inosine is recognized as guanosines, the A-to-I process mimics A-to-G DNA mutations but can be controlled in a more flexible manner compared to DNA alterations.

Methods

We parsed the transcriptomes and translatomes of liver cancer and normal tissues from ten patients. We profiled the landscape of the A-to-I RNA editome in these samples and interrogated whether the A-to-I processes participated in the gene expression regulation in oncogenesis.

Results

Globally, editing activity was enhanced in all tumor samples compared to that in normal samples. Accordingly, expression of the gene encoding the RNA editing enzyme ADAR (adenosine deaminase acting on RNA) was elevated. Two intronic self-editing sites in ADAR mRNAs controlled its splicing pattern and may regulate its translation efficiency (TE). Moreover, the expression of oncogenes was generally upregulated in tumors, whereas tumor suppressor genes (TSG) were downregulated, possibly due to alterations to microRNA binding sites or RNA splicing defects caused by A-to-I editing.

Conclusions

A-to-I RNA editing plays a crucial role in the oncogenesis of liver cancer. ADAR regulates its own expression via self-editing, and it also affects global transcriptomes and translatomes involving cancer-related genes by RNA editing and changing their expression patterns.

EndoVIPER-seq for Improved Detection of A-to-I Editing Sites in Cellular RNA

Adenosine to-inosine (A-to-I) RNA editing is a conserved post-transcriptional modification that is critical for a variety of cellular processes. A-to-I editing is widespread in nearly all types of RNA, directly imparting significant global changes in cellular function and behavior. Dysfunctional RNA editing is also implicated in a number of diseases, and A-to-I editing activity is rapidly becoming an important biomarker for early detection of cancer, immune disorders, and neurodegeneration. While millions of sites have been identified, the biological function of the majority of these sites is unknown, and the regulatory mechanisms for controlling editing activity at individual sites is not well understood. Robust detection and mapping of A-to-I editing activity throughout the transcriptome is vital for understanding these properties and how editing affects cellular behavior. However, accurately identifying A-to-I editing sites is challenging because of inherent sampling errors present in RNA-seq. We recently developed Endonuclease V immunoprecipitation enrichment sequencing (EndoVIPER-seq) to directly address this challenge by enrichment of A-to-I edited RNAs prior to sequencing. This protocol outlines how to process cellular RNA, enrich for A-to-I edited transcripts with EndoVIPER pulldown, and prepare libraries suitable for generating RNA-seq data. © 2020 Wiley Periodicals LLC. Basic Protocol 1: mRNA fragmentation and glyoxalation Basic Protocol 2: EndoVIPER pulldown Basic Protocol 3: RNA-seq library preparation and data analysis.

A brief review of RNA modification related database resources

To date, over 150 different RNA modifications have been identified, playing crucial roles in biological processes and disease pathogenesis. Thanks to the advancement of high-throughput sequencing technologies employed for transcriptome-wide mapping, a bunch of RNA modification databases have emerged as an exciting area, which promotes further investigation of the mechanisms and functions of these modified ribonucleotides. This article introduces the high-throughput sequencing technique developed for transcriptome-wide mapping of RNA modifications, as well as the procedures and main techniques of building these databases from the developers' perspective. It also reviews existing RNA modification databases in terms of their main functions, species, the number of sites they collected, the annotations, and the tools they provided. From the view of users, we further analyze and compare these databases in terms of their functions. For instance, these databases can be applied to record chemical structures and biosynthetic pathways, or unravel the epi-transcriptome comprehensively, or only investigate specific features of RNA modifications. Additionally, the limitations of the existing approaches are discussed, and some future suggestions are offered.

Proteome Diversification by RNA Editing

RNA editing is an RNA modification that alters the RNA sequence relative to its genomic blueprint. The most common type of RNA editing is A-to-I editing by double-stranded RNA-specific adenosine deaminase (ADAR) enzymes. Editing of a protein-coding region within the RNA molecule may result in non-synonymous substitutions, leading to a modified protein product. These editing sites, also known as "recoding" sites, contribute to the complexity and diversification of the proteome. Recent computational transcriptomic studies have identified thousands of recoding sites in multiple species, many of which are conserved within (but not usually across) lineages and have functional and evolutionary importance. In this chapter we describe the recoding phenomenon across species, consider its potential utility for diversity and adaptation, and discuss its evolution.

The effects of RNA editing in cancer tissue at different stages in carcinogenesis

RNA editing is one of the most prevalent and abundant forms of post-transcriptional RNA modification observed in normal physiological processes and often aberrant in diseases including cancer. RNA editing changes the sequences of mRNAs, making them different from the source DNA sequence. Edited mRNAs can produce editing-recoded protein isoforms that are functionally different from the corresponding genome-encoded protein isoforms. The major type of RNA editing in mammals occurs by enzymatic deamination of adenosine to inosine (A-to-I) within double-stranded RNAs (dsRNAs) or hairpins in pre-mRNA transcripts. Enzymes that catalyse these processes belong to the adenosine deaminase acting on RNA (ADAR) family. The vast majority of knowledge on the RNA editing landscape relevant to human disease has been acquired using in vitro cancer cell culture models. The limitation of such in vitro models, however, is that the physiological or disease relevance of results obtained is not necessarily obvious. In this review we focus on discussing in vivo occurring RNA editing events that have been identified in human cancer tissue using samples surgically resected or clinically retrieved from patients. We discuss how RNA editing events occurring in tumours in vivo can identify pathological signalling mechanisms relevant to human cancer physiology which is linked to the different stages of cancer progression including initiation, promotion, survival, proliferation, immune escape and metastasis.

A-to-I RNA Editing in Cancer: From Evaluating the Editing Level to Exploring the Editing Effects

As an important regulatory mechanism at the posttranscriptional level in metazoans, adenosine deaminase acting on RNA (ADAR)-induced A-to-I RNA editing modification of double-stranded RNA has been widely detected and reported. Editing may lead to non-synonymous amino acid mutations, RNA secondary structure alterations, pre-mRNA processing changes, and microRNA-mRNA redirection, thereby affecting multiple cellular processes and functions. In recent years, researchers have successfully developed several bioinformatics software tools and pipelines to identify RNA editing sites. However, there are still no widely accepted editing site standards due to the variety of parallel optimization and RNA high-seq protocols and programs. It is also challenging to identify RNA editing by normal protocols in tumor samples due to the high DNA mutation rate. Numerous RNA editing sites have been reported to be located in non-coding regions and can affect the biosynthesis of ncRNAs, including miRNAs and circular RNAs. Predicting the function of RNA editing sites located in non-coding regions and ncRNAs is significantly difficult. In this review, we aim to provide a better understanding of bioinformatics strategies for human cancer A-to-I RNA editing identification and briefly discuss recent advances in related areas, such as the oncogenic and tumor suppressive effects of RNA editing.

LincSNP 3.0: an updated database for linking functional variants to human long non-coding RNAs, circular RNAs and their regulatory elements

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.

Inflammation-driven deaminase deregulation fuels human pre-leukemia stem cell evolution

Inflammation-dependent base deaminases promote therapeutic resistance in many malignancies. However, their roles in human pre-leukemia stem cell (pre-LSC) evolution to acute myeloid leukemia stem cells (LSCs) had not been elucidated. Comparative whole-genome and whole-transcriptome sequencing analyses of FACS-purified pre-LSCs from myeloproliferative neoplasm (MPN) patients reveal APOBEC3C upregulation, an increased C-to-T mutational burden, and hematopoietic stem and progenitor cell (HSPC) proliferation during progression, which can be recapitulated by lentiviral APOBEC3C overexpression. In pre-LSCs, inflammatory splice isoform overexpression coincides with APOBEC3C upregulation and ADAR1p150-induced A-to-I RNA hyper-editing. Pre-LSC evolution to LSCs is marked by STAT3 editing, STAT3β isoform switching, elevated phospho-STAT3, and increased ADAR1p150 expression, which can be prevented by JAK2/STAT3 inhibition with ruxolitinib or fedratinib or lentiviral ADAR1 shRNA knockdown. Conversely, lentiviral ADAR1p150 expression enhances pre-LSC replating and STAT3 splice isoform switching. Thus, pre-LSC evolution to LSCs is fueled by primate-specific APOBEC3C-induced pre-LSC proliferation and ADAR1-mediated splicing deregulation.

Genome-Wide Characterization of RNA Editing Sites in Primary Gastric Adenocarcinoma through RNA-seq Data Analysis

RNA editing is a posttranscriptional nucleotide modification in humans. Of the various types of RNA editing, the adenosine to inosine substitution is the most widespread in higher eukaryotes, which is mediated by the ADAR family enzymes. Inosine is recognized by the biological machinery as guanosine; therefore, editing could have substantial functional effects throughout the genome. RNA editing could contribute to cancer either by exclusive editing of tumor suppressor/promoting genes or by introducing transcriptomic diversity to promote cancer progression. Here, we provided a comprehensive overview of the RNA editing sites in gastric adenocarcinoma and highlighted some of their possible contributions to gastric cancer. RNA-seq data corresponding to 8 gastric adenocarcinoma and their paired nontumor counterparts were retrieved from the GEO database. After preprocessing and variant calling steps, a stringent filtering pipeline was employed to distinguish potential RNA editing sites from SNPs. The identified potential editing sites were annotated and compared with those in the DARNED database. Totally, 12362 high-confidence adenosine to inosine RNA editing sites were detected across all samples. Of these, 12105 and 257 were known and novel editing events, respectively. These editing sites were unevenly distributed across genomic regions, and nearly half of them were located in 3′UTR. Our results revealed that 4868 editing sites were common in both normal and cancer tissues. From the remaining sites, 3985 and 3509 were exclusive to normal and cancer tissues, respectively. Further analysis revealed a significant number of differentially edited events among these sites, which were located in protein coding genes and microRNAs. Given the distinct pattern of RNA editing in gastric adenocarcinoma and adjacent normal tissue, edited sites have the potential to serve as the diagnostic biomarkers and therapeutic targets in gastric cancer.

Features of increased malignancy in eosinophilic clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common form of renal cancer. Due to inactivation of the von Hippel-Lindau tumour suppressor, the hypoxia-inducible transcription factors (HIFs) are constitutively activated in these tumours, resulting in a pseudo-hypoxic phenotype. The HIFs induce the expression of genes involved in angiogenesis and cell survival, but they also reset the cellular metabolism to protect cells from oxygen and nutrient deprivation. ccRCC tumours are highly vascularized and the cytoplasm of the cancer cells is filled with lipid droplets and glycogen, resulting in the histologically distinctive pale (clear) cytoplasm. Intratumoural heterogeneity may occur, and in some tumours, areas with granular, eosinophilic cytoplasm are found. Little is known regarding these traits and how they relate to the coexistent clear cell component, yet eosinophilic ccRCC is associated with higher grade and clinically more aggressive tumours. In this study, we have for the first time performed RNA sequencing comparing histologically verified clear cell and eosinophilic areas from ccRCC tissue, aiming to analyse the characteristics of these cell types. Findings from RNA sequencing were confirmed by immunohistochemical staining of biphasic ccRCC. We found that the eosinophilic phenotype displayed a higher proliferative drive and lower differentiation, and we confirmed a correlation to tumours of higher stage. We further identified mutations of the tumour suppressor p53 (TP53) exclusively in the eosinophilic ccRCC component, where mTORC1 activity was also elevated. Also, eosinophilic areas were less vascularized, yet harboured more abundant infiltrating immune cells. The cytoplasm of clear cell ccRCC cells was filled with lipids but had very low mitochondrial content, while the reverse was found in eosinophilic tissue. We herein suggest possible transcriptional mechanisms behind these phenomena. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.