ATtRACT-a database of RNA-binding proteins and associated motifs

Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing

It is estimated that 10%-30% of disease-associated genetic variants affect splicing. Splicing variants may generate deleteriously altered gene product and are potential therapeutic targets. However, systematic diagnosis or prediction of splicing variants is yet to be established, especially for the near-exon intronic splice region. The major challenge lies in the redundant and ill-defined branch sites and other splicing motifs therein. Here, we carried out unbiased massively parallel splicing assays on 5,307 disease-associated variants that overlapped with branch sites and collected 5,884 variants across the 5' splice region. We found that strong splice sites and exonic features preserve splicing from intronic sequence variation. Whereas the splice-altering mechanism of the 3' intronic variants is complex, that of the 5' is mainly splice-site destruction. Statistical learning combined with these molecular features allows precise prediction of altered splicing from an intronic variant. This statistical model provides the identity and ranking of biological features that determine splicing, which serves as transferable knowledge and out-performs the benchmarking predictive tool. Moreover, we demonstrated that intronic splicing variants may associate with disease risks in the human population. Our study elucidates the mechanism of splicing response of intronic variants, which classify disease-associated splicing variants for the promise of precision medicine.

HNRNPA2B1: RNA-Binding Protein That Orchestrates Smooth Muscle Cell Phenotype in Pulmonary Arterial Hypertension

BACKGROUND

RNA-binding proteins are master orchestrators of gene expression regulation. They regulate hundreds of transcripts at once by recognizing specific motifs. Thus, characterizing RNA-binding proteins targets is critical to harvest their full therapeutic potential. However, such investigation has often been restricted to a few RNA-binding protein targets, limiting our understanding of their function. In cancer, the RNA-binding protein HNRNPA2B1 (heterogeneous nuclear ribonucleoprotein A2B1; A2B1) promotes the pro-proliferative/anti-apoptotic phenotype. The same phenotype in pulmonary arterial smooth muscle cells (PASMCs) is responsible for the development of pulmonary arterial hypertension (PAH). However, A2B1 function has never been investigated in PAH.

METHOD

Through the integration of computational and experimental biology, the authors investigated the role of A2B1 in human PAH-PASMC. Bioinformatics and RNA sequencing allowed them to investigate the transcriptome-wide function of A2B1, and RNA immunoprecipitation and A2B1 silencing experiments allowed them to decipher the intricate molecular mechanism at play. In addition, they performed a preclinical trial in the monocrotaline-induced pulmonary hypertension rat model to investigate the relevance of A2B1 inhibition in mitigating pulmonary hypertension severity.

RESULTS

They found that A2B1 expression and its nuclear localization are increased in human PAH-PASMC. Using bioinformatics, they identified 3 known motifs of A2B1 and all mRNAs carrying them. In PAH-PASMC, they demonstrated the complementary nonredundant function of A2B1 motifs because all motifs are implicated in different aspects of the cell cycle. In addition, they showed that in PAH-PASMC, A2B1 promotes the expression of its targets. A2B1 silencing in PAH-PASMC led to a decrease of all tested mRNAs carrying an A2B1 motif and a concomitant decrease in proliferation and resistance to apoptosis. Last, in vivo A2B1 inhibition in the lungs rescued pulmonary hypertension in rats.

CONCLUSIONS

Through the integration of computational and experimental biology, the study revealed the role of A2B1 as a master orchestrator of the PAH-PASMC phenotype and its relevance as a therapeutic target in PAH.

A massively parallel reporter assay reveals focused and broadly encoded RNA localization signals in neurons

Asymmetric subcellular mRNA localization allows spatial regulation of gene expression and functional compartmentalization. In neurons, localization of specific mRNAs to neurites is essential for cellular functioning. However, it is largely unknown how transcript sorting works in a sequence-specific manner. Here, we combined subcellular transcriptomics and massively parallel reporter assays and tested ∼50 000 sequences for their ability to localize to neurites. Mapping the localization potential of >300 genes revealed two ways neurite targeting can be achieved: focused localization motifs and broadly encoded localization potential. We characterized the interplay between RNA stability and localization and identified motifs able to bias localization towards neurite or soma as well as the trans-acting factors required for their action. Based on our data, we devised machine learning models that were able to predict the localization behavior of novel reporter sequences. Testing this predictor on native mRNA sequencing data showed good agreement between predicted and observed localization potential, suggesting that the rules uncovered by our MPRA also apply to the localization of native full-length transcripts.

A universal model of RNA.DNA:DNA triplex formation accurately predicts genome-wide RNA-DNA interactions

RNA.DNA:DNA triple helix (triplex) formation is a form of RNA-DNA interaction which regulates gene expression but is difficult to study experimentally in vivo. This makes accurate computational prediction of such interactions highly important in the field of RNA research. Current predictive methods use canonical Hoogsteen base pairing rules, which whilst biophysically valid, may not reflect the plastic nature of cell biology. Here, we present the first optimization approach to learn a probabilistic model describing RNA-DNA interactions directly from motifs derived from triplex sequencing data. We find that there are several stable interaction codes, including Hoogsteen base pairing and novel RNA-DNA base pairings, which agree with in vitro measurements. We implemented these findings in TriplexAligner, a program that uses the determined interaction codes to predict triplex binding. TriplexAligner predicts RNA-DNA interactions identified in all-to-all sequencing data more accurately than all previously published tools in human and mouse and also predicts previously studied triplex interactions with known regulatory functions. We further validated a novel triplex interaction using biophysical experiments. Our work is an important step towards better understanding of triplex formation and allows genome-wide analyses of RNA-DNA interactions.

High-throughput mutagenesis identifies mutations and RNA-binding proteins controlling CD19 splicing and CART-19 therapy resistance

Following CART-19 immunotherapy for B-cell acute lymphoblastic leukaemia (B-ALL), many patients relapse due to loss of the cognate CD19 epitope. Since epitope loss can be caused by aberrant CD19 exon 2 processing, we herein investigate the regulatory code that controls CD19 splicing. We combine high-throughput mutagenesis with mathematical modelling to quantitatively disentangle the effects of all mutations in the region comprising CD19 exons 1-3. Thereupon, we identify ~200 single point mutations that alter CD19 splicing and thus could predispose B-ALL patients to developing CART-19 resistance. Furthermore, we report almost 100 previously unknown splice isoforms that emerge from cryptic splice sites and likely encode non-functional CD19 proteins. We further identify cis-regulatory elements and trans-acting RNA-binding proteins that control CD19 splicing (e.g., PTBP1 and SF3B4) and validate that loss of these factors leads to pervasive CD19 mis-splicing. Our dataset represents a comprehensive resource for identifying predictive biomarkers for CART-19 therapy.

Multiple alternative splicing events in CD19 mRNA have been associated with resistance/relapse to CD19 CAR-T therapy in patients with B cell malignancies. Here, by combining patient data and a high-throughput mutagenesis screen, the authors identify single point mutations and RNA-binding proteins that can control CD19 splicing and be associated with CD19 CAR-T therapy resistance.

Widespread autogenous mRNA-protein interactions detected by CLIP-seq

Autogenous interactions between mRNAs and the proteins they encode are implicated in cellular feedback-loop regulation, but their extent and mechanistic foundation are unclear. It was recently hypothesized that such interactions may be common, reflecting the role of intrinsic nucleobase-amino acid affinities in shaping the genetic code's structure. Here we analyze a comprehensive set of CLIP-seq experiments involving multiple protocols and report on widespread autogenous interactions across different organisms. Specifically, 230 of 341 (67%) studied RNA-binding proteins (RBPs) interact with their own mRNAs, with a heavy enrichment among high-confidence hits and a preference for coding sequence binding. We account for different confounding variables, including physical (overexpression and proximity during translation), methodological (difference in CLIP protocols, peak callers and cell types) and statistical (treatment of null backgrounds). In particular, we demonstrate a high statistical significance of autogenous interactions by sampling null distributions of fixed-margin interaction matrices. Furthermore, we study the dependence of autogenous binding on the presence of RNA-binding motifs and structured domains in RBPs. Finally, we show that intrinsic nucleobase-amino acid affinities favor co-aligned binding between mRNA coding regions and the proteins they encode. Our results suggest a central role for autogenous interactions in RBP regulation and support the possibility of a fundamental connection between coding and binding.

Single-cell transcriptome and translatome dual-omics reveals potential mechanisms of human oocyte maturation

The combined use of transcriptome and translatome as indicators of gene expression profiles is usually more accurate than the use of transcriptomes alone, especially in cell types governed by translational regulation, such as mammalian oocytes. Here, we developed a dual-omics methodology that includes both transcriptome and translatome sequencing (T&T-seq) of single-cell oocyte samples, and we used it to characterize the transcriptomes and translatomes during mouse and human oocyte maturation. T&T-seq analysis revealed distinct translational expression patterns between mouse and human oocytes and delineated a sequential gene expression regulation from the cytoplasm to the nucleus during human oocyte maturation. By these means, we also identified a functional role of OOSP2 inducing factor in human oocyte maturation, as human recombinant OOSP2 induced in vitro maturation of human oocytes, which was blocked by anti-OOSP2. Single-oocyte T&T-seq analyses further elucidated that OOSP2 induces specific signaling pathways, including small GTPases, through translational regulation.

Development of methods for simultaneous single cell analysis of transcription and translation is still underway. Here, Hu et al. develop single-cell transcriptome and translatome dual-omics on human oocytes, which enables them to identify OOSP2 as an induction factor during human oocyte maturation.

Structural Characteristics of the 5′-Terminal Region of Mouse p53 mRNA and Identification of Proteins That Bind to This mRNA Region

A mouse model has often been used in studies of p53 gene expression. Detailed interpretation of functional studies is, however, hampered by insufficient knowledge of the impact of mouse p53 mRNA’s structure and its interactions with proteins in the translation process. In particular, the 5′-terminal region of mouse p53 mRNA is an important region which takes part in the regulation of the synthesis of p53 protein and its N-truncated isoform Δ41p53. In this work, the spatial folding of the 5′-terminal region of mouse p53 mRNA and its selected sub-fragments was proposed based on the results of the SAXS method and the RNAComposer program. Subsequently, RNA-assisted affinity chromatography was used to identify proteins present in mouse fibroblast cell lysates that are able to bind the RNA oligomer, which corresponds to the 5′-terminal region of mouse p53 mRNA. Possible sites to which the selected, identified proteins can bind were proposed. Interestingly, most of these binding sites coincide with the sites determined as accessible to hybridization of complementary oligonucleotides. Finally, the high binding affinity of hnRNP K and PCBP2 to the 5′-terminal region of mouse p53 mRNA was confirmed and their possible binding sites were proposed.

Genomic variants disrupt miRNA-mRNA regulation

Micro RNA (miRNA) and its regulatory effect on messenger RNA (mRNA) gene expression are a major focus in cancer research. Disruption in the normal miRNA-mRNA regulation network can result in serious cascading biological repercussions. In this study, we curated miRNA-related variants from major genomic consortiums and thoroughly evaluated how these variants could exert their effects by cross-validating with independent functional knowledge bases. Nearly all known variants (more than 664 million) categorized by type (germline, somatic, epigenetic) were mapped to the genomic regions involved in miRNA-mRNA binding (miRNA seeds and miRNA-mRNA 3'-UTR binding sequence). Subsets of miRNA-related variants supported by additional functional evidence, such as expression Quantitative Trait Loci (eQTL) and Genome-Wide Association Study (GWAS), were identified and scrutinized. Our results show that variants in miRNA seeds can substantially alter the composition of an miRNA's target mRNA set. Various functional analyses converged to reveal a post-transcriptional complex regulatory network where miRNA, eQTL, and RNA-binding protein intertwined to disseminate the impact of genomic variants. These results may potentially explain how certain variants affect disease/trait risks in genome wide association studies.

DNA- and RNA-Binding Proteins Linked Transcriptional Control and Alternative Splicing Together in a Two-Layer Regulatory Network System of Chronic Myeloid Leukemia

DNA- and RNA-binding proteins (DRBPs) typically possess multiple functions to bind both DNA and RNA and regulate gene expression from more than one level. They are controllers for post-transcriptional processes, such as splicing, polyadenylation, transportation, translation, and degradation of RNA transcripts in eukaryotic organisms, as well as regulators on the transcriptional level. Although DRBPs are reported to play critical roles in various developmental processes and diseases, it is still unclear how they work with DNAs and RNAs simultaneously and regulate genes at the transcriptional and post-transcriptional levels. To investigate the functional mechanism of DRBPs, we collected data from a variety of databases and literature and identified 118 DRBPs, which function as both transcription factors (TFs) and splicing factors (SFs), thus called DRBP-SF. Extensive investigations were conducted on four DRBP-SFs that were highly expressed in chronic myeloid leukemia (CML), heterogeneous nuclear ribonucleoprotein K (HNRNPK), heterogeneous nuclear ribonucleoprotein L (HNRNPL), non-POU domain–containing octamer–binding protein (NONO), and TAR DNA-binding protein 43 (TARDBP). By integrating and analyzing ChIP-seq, CLIP-seq, RNA-seq, and shRNA-seq data in K562 using binding and expression target analysis and Statistical Utility for RBP Functions, we discovered a two-layer regulatory network system centered on these four DRBP-SFs and proposed three possible regulatory models where DRBP-SFs can connect transcriptional and alternative splicing regulatory networks cooperatively in CML. The exploration of the identified DRBP-SFs provides new ideas for studying DRBP and regulatory networks, holding promise for further mechanistic discoveries of the two-layer gene regulatory system that may play critical roles in the occurrence and development of CML.

RNA-Binding Proteins: Emerging Therapeutics for Vascular Dysfunction

Vascular diseases account for a significant number of deaths worldwide, with cardiovascular diseases remaining the leading cause of mortality. This ongoing, ever-increasing burden has made the need for an effective treatment strategy a global priority. Recent advances in regenerative medicine, largely the derivation and use of induced pluripotent stem cell (iPSC) technologies as disease models, have provided powerful tools to study the different cell types that comprise the vascular system, allowing for a greater understanding of the molecular mechanisms behind vascular health. iPSC disease models consequently offer an exciting strategy to deepen our understanding of disease as well as develop new therapeutic avenues with clinical translation. Both transcriptional and post-transcriptional mechanisms are widely accepted to have fundamental roles in orchestrating responses to vascular damage. Recently, iPSC technologies have increased our understanding of RNA-binding proteins (RBPs) in controlling gene expression and cellular functions, providing an insight into the onset and progression of vascular dysfunction. Revelations of such roles within vascular disease states have therefore allowed for a greater clarification of disease mechanisms, aiding the development of novel therapeutic interventions. Here, we discuss newly discovered roles of RBPs within the cardio-vasculature aided by iPSC technologies, as well as examine their therapeutic potential, with a particular focus on the Quaking family of isoforms.

Investigating the Human Host-ssRNA Virus Interaction Landscape Using the SMEAGOL Toolbox

Viruses have evolved numerous mechanisms to exploit the molecular machinery of their host cells, including the broad spectrum of host RNA-binding proteins (RBPs). However, the RBP interactomes of most viruses are largely unknown. To shed light on the interaction landscape of RNA viruses with human host cell RBPs, we have analysed 197 single-stranded RNA (ssRNA) viral genome sequences and found that the majority of ssRNA virus genomes are significantly enriched or depleted in motifs for specific human RBPs, suggesting selection pressure on these interactions. To facilitate tailored investigations and the analysis of genomes sequenced in future, we have released our methodology as a fast and user-friendly computational toolbox named SMEAGOL. Our resources will contribute to future studies of specific ssRNA virus-host cell interactions and support the identification of antiviral drug targets.

PRES: a webserver for decoding the functional perturbations of RNA editing sites

Rapid progresses in RNA-Seq and computational methods have assisted in quantifying A-to-I RNA editing and altered RNA editing sites have been widely observed in various diseases. Nevertheless, functional characterization of the altered RNA editing sites still remains a challenge. Here, we developed perturbations of RNA editing sites (PRES; http://bio-bigdata.hrbmu.edu.cn/PRES/) as the webserver for decoding functional perturbations of RNA editing sites based on editome profiling. After uploading an editome profile among samples of different groups, PRES will first annotate the editing sites to various genomic elements and detect differential editing sites under the user-selected method and thresholds. Next, the downstream functional perturbations of differential editing sites will be characterized from gain or loss miRNA/RNA binding protein regulation, RNA and protein structure changes, and the perturbed biological pathways. A prioritization module was developed to rank genes based on their functional consequences of RNA editing events. PRES provides user-friendly functionalities, ultra-efficient calculation, intuitive table and figure visualization interface to display the annotated RNA editing events, filtering options and elaborate application notebooks. We anticipate PRES will provide an opportunity for better understanding the regulatory mechanisms of RNA editing in human complex diseases.

Data of RNA-seq transcriptomes of liver, bone, heart, kidney and blood in klotho mice at a pre-symptomatic state and the effect of a traditional Japanese multi-herbal medicine, juzentaihoto

We performed RNA-seq analyses of mRNA isolated from five organs, liver, bone, heart, kidney and blood at the pre-symptomatic state of klotho mice with/without administration of a Japanese traditional herbal medicine, juzentaihoto (JTT). Data of differentially expressed genes (DEG) with/without JTT was included. Intron retention (IR) is an important regulatory mechanism that affects gene expression and protein functions. We collected data in which retained-introns were accumulated in a particular set of genes of these organs, and showed that among these retained introns in the liver and bone a subset was recovered to the normal state by the medicine. All of the data present changes of molecular events on the levels of metabolites, proteins and gene expressions observed at the pre- symptomatic state of aging in klotho mice with/without JTT. The research article related to this Data in Brief is published in GENE entitled as “Intron retention as a new pre-symptomatic marker of aging and its recovery to the normal state by a traditional Japanese herbal medicine”.

Complementarity of the residue-level protein function and structure predictions in human proteins

Sequence-based predictors of the residue-level protein function and structure cover a broad spectrum of characteristics including intrinsic disorder, secondary structure, solvent accessibility and binding to nucleic acids. They were catalogued and evaluated in numerous surveys and assessments. However, methods focusing on a given characteristic are studied separately from predictors of other characteristics, while they are typically used on the same proteins. We fill this void by studying complementarity of a representative collection of methods that target different predictions using a large, taxonomically consistent, and low similarity dataset of human proteins. First, we bridge the gap between the communities that develop structure-trained vs. disorder-trained predictors of binding residues. Motivated by a recent study of the protein-binding residue predictions, we empirically find that combining the structure-trained and disorder-trained predictors of the DNA-binding and RNA-binding residues leads to substantial improvements in predictive quality. Second, we investigate whether diverse predictors generate results that accurately reproduce relations between secondary structure, solvent accessibility, interaction sites, and intrinsic disorder that are present in the experimental data. Our empirical analysis concludes that predictions accurately reflect all combinations of these relations. Altogether, this study provides unique insights that support combining results produced by diverse residue-level predictors of protein function and structure.

Leveraging omic features with F3UTER enables identification of unannotated 3'UTRs for synaptic genes

There is growing evidence for the importance of 3' untranslated region (3'UTR) dependent regulatory processes. However, our current human 3'UTR catalogue is incomplete. Here, we develop a machine learning-based framework, leveraging both genomic and tissue-specific transcriptomic features to predict previously unannotated 3'UTRs. We identify unannotated 3'UTRs associated with 1,563 genes across 39 human tissues, with the greatest abundance found in the brain. These unannotated 3'UTRs are significantly enriched for RNA binding protein (RBP) motifs and exhibit high human lineage-specificity. We find that brain-specific unannotated 3'UTRs are enriched for the binding motifs of important neuronal RBPs such as TARDBP and RBFOX1, and their associated genes are involved in synaptic function. Our data is shared through an online resource F3UTER ( https://astx.shinyapps.io/F3UTER/ ). Overall, our data improves 3'UTR annotation and provides additional insights into the mRNA-RBP interactome in the human brain, with implications for our understanding of neurological and neurodevelopmental diseases.

Tissue-specific alternative splicing separates the catalytic and cell signaling functions of human leucyl-tRNA synthetase

The aminoacyl-tRNA synthetases are an ancient and ubiquitous component of all life. Many eukaryotic synthetases balance their essential function, preparing aminoacyl-tRNA for use in mRNA translation, with diverse roles in cell signaling. Herein, we use long-read sequencing to discover a leukocyte-specific exon skipping event in human leucyl-tRNA synthetase (LARS). We show that this highly expressed splice variant, LSV3, is regulated by serine-arginine-rich splicing factor 1 (SRSF1) in a cell-type-specific manner. LSV3 has a 71 amino acid deletion in the catalytic domain and lacks any tRNA leucylation activity in vitro. However, we demonstrate that this LARS splice variant retains its role as a leucine sensor and signal transducer for the proliferation-promoting mTOR kinase. This is despite the exon deletion in LSV3 including a portion of the previously mapped Vps34-binding domain used for one of two distinct pathways from LARS to mTOR. In conclusion, alternative splicing of LARS has separated the ancient catalytic activity of this housekeeping enzyme from its more recent evolutionary role in cell signaling, providing an opportunity for functional specificity in human immune cells.

Comprehensive Analysis of Co-Mutations Identifies Cooperating Mechanisms of Tumorigenesis

Somatic mutations are one of the most important factors in tumorigenesis and are the focus of most cancer-sequencing efforts. The co-occurrence of multiple mutations in one tumor has gained increasing attention as a means of identifying cooperating mutations or pathways that contribute to cancer. Using multi-omics, phenotypical, and clinical data from 29,559 cancer subjects and 1747 cancer cell lines covering 78 distinct cancer types, we show that co-mutations are associated with prognosis, drug sensitivity, and disparities in sex, age, and race. Some co-mutation combinations displayed stronger effects than their corresponding single mutations. For example, co-mutation TP53:KRAS in pancreatic adenocarcinoma is significantly associated with disease specific survival (hazard ratio = 2.87, adjusted p-value = 0.0003) and its prognostic predictive power is greater than either TP53 or KRAS as individually mutated genes. Functional analyses revealed that co-mutations with higher prognostic values have higher potential impact and cause greater dysregulation of gene expression. Furthermore, many of the prognostically significant co-mutations caused gains or losses of binding sequences of RNA binding proteins or micro RNAs with known cancer associations. Thus, detailed analyses of co-mutations can identify mechanisms that cooperate in tumorigenesis.

SBSA: an online service for somatic binding sequence annotation

Efficient annotation of alterations in binding sequences of molecular regulators can help identify novel candidates for mechanisms study and offer original therapeutic hypotheses. In this work, we developed Somatic Binding Sequence Annotator (SBSA) as a full-capacity online tool to annotate altered binding motifs/sequences, addressing diverse types of genomic variants and molecular regulators. The genomic variants can be somatic mutation, single nucleotide polymorphism, RNA editing, etc. The binding motifs/sequences involve transcription factors (TFs), RNA-binding proteins, miRNA seeds, miRNA-mRNA 3'-UTR binding target, or can be any custom motifs/sequences. Compared to similar tools, SBSA is the first to support miRNA seeds and miRNA-mRNA 3'-UTR binding target, and it unprecedentedly implements a personalized genome approach that accommodates joint adjacent variants. SBSA is empowered to support an indefinite species, including preloaded reference genomes for SARS-Cov-2 and 25 other common organisms. We demonstrated SBSA by annotating multi-omics data from over 30,890 human subjects. Of the millions of somatic binding sequences identified, many are with known severe biological repercussions, such as the somatic mutation in TERT promoter region which causes a gained binding sequence for E26 transformation-specific factor (ETS1). We further validated the function of this TERT mutation using experimental data in cancer cells. Availability:http://innovebioinfo.com/Annotation/SBSA/SBSA.php.

Circular RNA, microRNA and Protein Profiles of the Longissimus Dorsi of Germany ZIKA and Sichuan White Rabbits

Due to the dietetic properties and remarkable nutritive value of rabbit meat, its industry is increasing rapidly. However, the association between circular RNAs, microRNAs, and proteins and muscle fiber type, and meat quality of rabbit is still unknown. Here, using deep sequencing and iTRAQ proteomics technologies we first identified 3159 circRNAs, 356 miRNAs, and 755 proteins in the longissimus dorsi tissues from Sichuan white (SCWrabs) and Germany great line ZIKA rabbits (ZIKArabs). Next, we identified 267 circRNAs, 3 miRNAs, and 29 proteins differentially expressed in the muscle tissues of SCWrabs and ZIKArabs. Interaction network analysis revealed some key regulation relationships between noncoding RNAs and proteins that might be associated with the muscle fiber type and meat quality of rabbit. Further, miRNA isoforms and gene variants identified in SCWrabs and ZIKArabs revealed some pathways and biological processes related to the muscle development. This is the first study of noncoding RNA and protein profiles for the two rabbit breeds. It provides a valuable resource for future studies in rabbits and will improve our understanding of the molecular regulation mechanisms in the muscle development of livestock. More importantly, the output of our study will benefit the researchers and producers in the rabbit breeding program.

Towards resolution of the intron retention paradox in breast cancer

BACKGROUND

After many years of neglect in the field of alternative splicing, the importance of intron retention (IR) in cancer has come into focus following landmark discoveries of aberrant IR patterns in cancer. Many solid and liquid tumours are associated with drastic increases in IR, and such patterns have been pursued as both biomarkers and therapeutic targets. Paradoxically, breast cancer (BrCa) is the only tumour type in which IR is reduced compared to adjacent normal breast tissue.

METHODS

In this study, we have conducted a pan-cancer analysis of IR with emphasis on BrCa and its subtypes. We explored mechanisms that could cause aberrant and pathological IR and clarified why normal breast tissue has unusually high IR.

RESULTS

Strikingly, we found that aberrantly decreasing IR in BrCa can be largely attributed to normal breast tissue having the highest occurrence of IR events compared to other healthy tissues. Our analyses suggest that low numbers of IR events in breast tumours are associated with poor prognosis, particularly in the luminal B subtype. Interestingly, we found that IR frequencies negatively correlate with cell proliferation in BrCa cells, i.e. rapidly dividing tumour cells have the lowest number of IR events. Aberrant RNA-binding protein expression and changes in tissue composition are among the causes of aberrantly decreasing IR in BrCa.

CONCLUSIONS

Our results suggest that IR should be considered for therapeutic manipulation in BrCa patients with aberrantly low IR levels and that further work is needed to understand the cause and impact of high IR in other tumour types.

Decoding the effects of synonymous variants

Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning

Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts interactions with nucleic acids was released, and recent assessments demonstrate that current predictors offer modest levels of accuracy. We have developed DeepDISOBind, an innovative deep multi-task architecture that accurately predicts deoxyribonucleic acid (DNA)-, ribonucleic acid (RNA)- and protein-binding IDRs from protein sequences. DeepDISOBind relies on an information-rich sequence profile that is processed by an innovative multi-task deep neural network, where subsequent layers are gradually specialized to predict interactions with specific partner types. The common input layer links to a layer that differentiates protein- and nucleic acid-binding, which further links to layers that discriminate between DNA and RNA interactions. Empirical tests show that this multi-task design provides statistically significant gains in predictive quality across the three partner types when compared to a single-task design and a representative selection of the existing methods that cover both disorder- and structure-trained tools. Analysis of the predictions on the human proteome reveals that DeepDISOBind predictions can be encoded into protein-level propensities that accurately predict DNA- and RNA-binding proteins and protein hubs. DeepDISOBind is available at https://www.csuligroup.com/DeepDISOBind/.

Genome-wide RNA structure changes during human neurogenesis modulate gene regulatory networks

The distribution, dynamics, and function of RNA structures in human development are under-explored. Here, we systematically assayed RNA structural dynamics and their relationship with gene expression, translation, and decay during human neurogenesis. We observed that the human ESC transcriptome is globally more structurally accessible than differentiated cells and undergoes extensive RNA structure changes, particularly in the 3' UTR. Additionally, RNA structure changes during differentiation are associated with translation and decay. We observed that RBP and miRNA binding is associated with RNA structural changes during early neuronal differentiation, and splicing is associated during later neuronal differentiation. Furthermore, our analysis suggests that RBPs are major factors in structure remodeling and co-regulate additional RBPs and miRNAs through structure. We demonstrated an example of this by showing that PUM2-induced structure changes on LIN28A enable miR-30 binding. This study deepens our understanding of the widespread and complex role of RNA-based gene regulation during human development.

The translatome of neuronal cell bodies, dendrites, and axons

To form synaptic connections and store information, neurons continuously remodel their proteomes. The impressive length of dendrites and axons imposes logistical challenges to maintain synaptic proteins at locations remote from the transcription source (the nucleus). The discovery of thousands of messenger RNAs (mRNAs) near synapses suggested that neurons overcome distance and gain autonomy by producing proteins locally. It is not generally known, however, if, how, and when localized mRNAs are translated into protein. To investigate the translational landscape in neuronal subregions, we performed simultaneous RNA sequencing (RNA-seq) and ribosome sequencing (Ribo-seq) from microdissected rodent brain slices to identify and quantify the transcriptome and translatome in cell bodies (somata) as well as dendrites and axons (neuropil). Thousands of transcripts were differentially translated between somatic and synaptic regions, with many scaffold and signaling molecules displaying increased translation levels in the neuropil. Most translational changes between compartments could be accounted for by differences in RNA abundance. Pervasive translational regulation was observed in both somata and neuropil influenced by specific mRNA features (e.g., untranslated region [UTR] length, RNA-binding protein [RBP] motifs, and upstream open reading frames [uORFs]). For over 800 mRNAs, the dominant source of translation was the neuropil. We constructed a searchable and interactive database for exploring mRNA transcripts and their translation levels in the somata and neuropil [MPI Brain Research, The mRNA translation landscape in the synaptic neuropil. https://public.brain.mpg.de/dashapps/localseq/ Accessed 5 October 2021]. Overall, our findings emphasize the substantial contribution of local translation to maintaining synaptic protein levels and indicate that on-site translational control is an important mechanism to control synaptic strength.

A global screening identifies chromatin-enriched RNA-binding proteins and the transcriptional regulatory activity of QKI5 during monocytic differentiation

BACKGROUND

Cellular RNA-binding proteins (RBPs) have multiple roles in post-transcriptional control, and some are shown to bind DNA. However, the global localization and the general chromatin-binding ability of RBPs are not well-characterized and remain undefined in hematopoietic cells.

RESULTS

We first provide a full view of RBPs' distribution pattern in the nucleus and screen for chromatin-enriched RBPs (Che-RBPs) in different human cells. Subsequently, by generating ChIP-seq, CLIP-seq, and RNA-seq datasets and conducting combined analysis, the transcriptional regulatory potentials of certain hematopoietic Che-RBPs are predicted. From this analysis, quaking (QKI5) emerges as a potential transcriptional activator during monocytic differentiation. QKI5 is over-represented in gene promoter regions, independent of RNA or transcription factors. Furthermore, DNA-bound QKI5 activates the transcription of several critical monocytic differentiation-associated genes, including CXCL2, IL16, and PTPN6. Finally, we show that the differentiation-promoting activity of QKI5 is largely dependent on CXCL2, irrespective of its RNA-binding capacity.

CONCLUSIONS

Our study indicates that Che-RBPs are versatile factors that orchestrate gene expression in different cellular contexts, and identifies QKI5, a classic RBP regulating RNA processing, as a novel transcriptional activator during monocytic differentiation.

Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases

Parkinson's disease (PD), Parkinson's disease with dementia (PDD) and dementia with Lewy bodies (DLB) are three clinically, genetically and neuropathologically overlapping neurodegenerative diseases collectively known as the Lewy body diseases (LBDs). A variety of molecular mechanisms have been implicated in PD pathogenesis, but the mechanisms underlying PDD and DLB remain largely unknown, a knowledge gap that presents an impediment to the discovery of disease-modifying therapies. Transcriptomic profiling can contribute to addressing this gap, but remains limited in the LBDs. Here, we applied paired bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from 28 individuals, including healthy controls, PD, PDD and DLB cases (n = 7 per group), to transcriptomically profile the LBDs. Using this approach, we (i) found transcriptional alterations in multiple cell types across the LBDs; (ii) discovered evidence for widespread dysregulation of RNA splicing, particularly in PDD and DLB; (iii) identified potential splicing factors, with links to other dementia-related neurodegenerative diseases, coordinating this dysregulation; and (iv) identified transcriptomic commonalities and distinctions between the LBDs that inform understanding of the relationships between these three clinical disorders. Together, these findings have important implications for the design of RNA-targeted therapies for these diseases and highlight a potential molecular "window" of therapeutic opportunity between the initial onset of PD and subsequent development of Lewy body dementia.

RNA editing affects cis-regulatory elements and predicts adverse cancer survival

BACKGROUND

RNA editing exerts critical impacts on numerous biological processes and thus are implicated in crucial human phenotypes, including tumorigenesis and prognosis. While previous studies have analyzed aggregate RNA editing activity at the sample level and associated it with overall cancer survival, there is not yet a large-scale disease-specific survival study to examine genome-wide RNA editing sites' prognostic value taking into account the host gene expression and clinical variables.

METHODS

In this study, we solved comprehensive Cox proportional models of disease-specific survival on individual RNA-editing sites plus host gene expression and critical demographic covariates. This allowed us to interrogate the prognostic value of a large number of RNA-editing sites at single-nucleotide resolution.

RESULTS

As a result, we identified 402 gene-proximal RNA-editing sites that generally predict adverse cancer survival. For example, an RNA-editing site residing in ZNF264 indicates poor survival of uterine corpus endometrial carcinoma, with a hazard ratio of 2.13 and an adjusted p-value of 4.07 × 10-7 . Some of these prognostic RNA-editing sites mediate the binding of RNA binding proteins and microRNAs, thus propagating their impacts to extensive regulatory targets.

CONCLUSIONS

In conclusion, RNA editing affects cis-regulatory elements and predicts adverse cancer survival.

Translational control by DHX36 binding to 5'UTR G-quadruplex is essential for muscle stem-cell regenerative functions

Skeletal muscle has a remarkable ability to regenerate owing to its resident stem cells (also called satellite cells, SCs). SCs are normally quiescent; when stimulated by damage, they activate and expand to form new fibers. The mechanisms underlying SC proliferative progression remain poorly understood. Here we show that DHX36, a helicase that unwinds RNA G-quadruplex (rG4) structures, is essential for muscle regeneration by regulating SC expansion. DHX36 (initially named RHAU) is barely expressed at quiescence but is highly induced during SC activation and proliferation. Inducible deletion of Dhx36 in adult SCs causes defective proliferation and muscle regeneration after damage. System-wide mapping in proliferating SCs reveals DHX36 binding predominantly to rG4 structures at various regions of mRNAs, while integrated polysome profiling shows that DHX36 promotes mRNA translation via 5′-untranslated region (UTR) rG4 binding. Furthermore, we demonstrate that DHX36 specifically regulates the translation of Gnai2 mRNA by unwinding its 5′ UTR rG4 structures and identify GNAI2 as a downstream effector of DHX36 for SC expansion. Altogether, our findings uncover DHX36 as an indispensable post-transcriptional regulator of SC function and muscle regeneration acting through binding and unwinding rG4 structures at 5′ UTR of target mRNAs.

Skeletal muscle stem cells (or satellite cells, SCs) are normally quiescent but activate and expand in response to injury. Here the authors show that induction of DHX36 helicase during SC activation promotes mRNA translation by binding to 5′UTR mRNA G-quadruplexes (rG4) in targets including Gnai2 and unwinding them.

RNAProt: an efficient and feature-rich RNA binding protein binding site predictor

BACKGROUND

Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression, which can be highly variable between conditions and thus cannot provide a complete picture of the RBP binding landscape. This creates a demand for computational methods to predict missing binding sites. Although there exist various methods using traditional machine learning and lately also deep learning, we encountered several problems: many of these are not well documented or maintained, making them difficult to install and use, or are not even available. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features.

RESULTS

Here, we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. We compare RNAProt with 1 traditional machine learning approach and 2 deep-learning methods, demonstrating its state-of-the-art predictive performance and better run time efficiency. We further show that its implemented visualizations capture known binding preferences and thus can help to understand what is learned. Since RNAProt supports various additional features (including user-defined features, which no other tool offers), we also present their influence on benchmark set performance. Finally, we show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of an hairpin loop binding RBP.

CONCLUSIONS

RNAProt provides a complete framework for RBP binding site predictions, from data set generation over model training to the evaluation of binding preferences and prediction. It offers state-of-the-art predictive performance, as well as superior run time efficiency, while at the same time supporting more features and input types than any other tool available so far. RNAProt is easy to install and use, comes with comprehensive documentation, and is accompanied by informative statistics and visualizations. All this makes RNAProt a valuable tool to apply in future RBP binding site research.

Capsid Proteins Are Necessary for Replication of a Parvovirus

Despite tight genetic compression, viral genomes are often organized into functional gene clusters, a modular structure that might favor their evolvability. This has greatly facilitated biotechnological developments such as the recombinant adeno-associated virus (AAV) systems for gene therapy. Following this lead, we endeavored to engineer the related insect parvovirus Junonia coenia densovirus (JcDV) to create addressable vectors for insect pest biocontrol. To enable safer manipulation of capsid mutants, we translocated the nonstructural (ns) gene cluster outside the viral genome. To our dismay, this yielded a virtually nonreplicable clone. We linked the replication defect to an unexpected modularity breach, as ns translocation truncated the overlapping 3' untranslated region (UTR) of the capsid transcript (vp). We found that the native vp 3' UTR is necessary for high-level VP production but that decreased expression does not adversely impact the expression of NS proteins, which are known replication effectors. As nonsense vp mutations recapitulate the replication defect, VP proteins appear to be directly implicated in the replication process. Our findings suggest intricate replication-encapsidation couplings that favor the maintenance of genetic integrity. We discuss possible connections with an intriguing cis-packaging phenomenon previously observed in parvoviruses whereby capsids preferentially package the genome from which they were expressed. IMPORTANCE Densoviruses could be used as biological control agents to manage insect pests. Such applications require an in-depth biological understanding and associated molecular tools. However, the genomes of these viruses remain difficult to manipulate due to poorly tractable secondary structures at their extremities. We devised a construction strategy that enables precise and efficient molecular modifications. Using this approach, we endeavored to create a split clone of Junonia coenia densovirus (JcDV) that can be used to safely study the impact of capsid mutations on host specificity. Our original construct proved to be nonfunctional. Fixing this defect led us to uncover that capsid proteins and their correct expression are essential for continued rolling-hairpin replication. This points to an intriguing link between replication and packaging, which might be shared with related viruses. This serendipitous discovery illustrates the power of synthetic biology approaches to advance our knowledge of biological systems.

Matrix-screening reveals a vast potential for direct protein-protein interactions among RNA binding proteins

RNA-binding proteins (RBPs) are crucial factors of post-transcriptional gene regulation and their modes of action are intensely investigated. At the center of attention are RNA motifs that guide where RBPs bind. However, sequence motifs are often poor predictors of RBP-RNA interactions in vivo. It is hence believed that many RBPs recognize RNAs as complexes, to increase specificity and regulatory possibilities. To probe the potential for complex formation among RBPs, we assembled a library of 978 mammalian RBPs and used rec-Y2H matrix screening to detect direct interactions between RBPs, sampling > 600 K interactions. We discovered 1994 new interactions and demonstrate that interacting RBPs bind RNAs adjacently in vivo. We further find that the mRNA binding region and motif preferences of RBPs deviate, depending on their adjacently binding interaction partners. Finally, we reveal novel RBP interaction networks among major RNA processing steps and show that splicing impairing RBP mutations observed in cancer rewire spliceosomal interaction networks. The dataset we provide will be a valuable resource for understanding the combinatorial interactions of RBPs with RNAs and the resulting regulatory outcomes.

Dynamic changes in RNA-protein interactions and RNA secondary structure in mammalian erythropoiesis

Two features of eukaryotic RNA molecules that regulate their post-transcriptional fates are RNA secondary structure and RNA-binding protein (RBP) interaction sites. However, a comprehensive global overview of the dynamic nature of these sequence features during erythropoiesis has never been obtained. Here, we use our ribonuclease-mediated structure and RBP-binding site mapping approach to reveal the global landscape of RNA secondary structure and RBP-RNA interaction sites and the dynamics of these features during this important developmental process. We identify dynamic patterns of RNA secondary structure and RBP binding throughout the process and determine a set of corresponding protein-bound sequence motifs along with their dynamic structural and RBP-binding contexts. Finally, using these dynamically bound sequences, we identify a number of RBPs that have known and putative key functions in post-transcriptional regulation during mammalian erythropoiesis. In total, this global analysis reveals new post-transcriptional regulators of mammalian blood cell development.

Engineering and standardization of posttranscriptional biocircuitry in Saccharomyces cerevisiae

This short review considers to what extent posttranscriptional steps of gene expression can provide the basis for novel control mechanisms and procedures in synthetic biology and biotechnology. The term biocircuitry is used here to refer to functionally connected components comprising DNA, RNA or proteins. The review begins with an overview of the diversity of devices being developed and then considers the challenges presented by trying to engineer more scaled-up systems. While the engineering of RNA-based and protein-based circuitry poses new challenges, the resulting 'toolsets' of components and novel mechanisms of operation will open up multiple new opportunities for synthetic biology. However, agreed procedures for standardization will need to be placed at the heart of this expanding field if the full potential benefits are to be realized.

catRAPID omics v2.0: going deeper and wider in the prediction of protein-RNA interactions

Prediction of protein-RNA interactions is important to understand post-transcriptional events taking place in the cell. Here we introduce catRAPID omics v2.0, an update of our web server dedicated to the computation of protein-RNA interaction propensities at the transcriptome- and RNA-binding proteome-level in 8 model organisms. The server accepts multiple input protein or RNA sequences and computes their catRAPID interaction scores on updated precompiled libraries. Additionally, it is now possible to predict the interactions between a custom protein set and a custom RNA set. Considerable effort has been put into the generation of a new database of RNA-binding motifs that are searched within the predicted RNA targets of proteins. In this update, the sequence fragmentation scheme of the catRAPID fragment module has been included, which allows the server to handle long linear RNAs and to analyse circular RNAs. For the top-scoring protein-RNA pairs, the web server shows the predicted binding sites in both protein and RNA sequences and reports whether the predicted interactions are conserved in orthologous protein-RNA pairs. The catRAPID omics v2.0 web server is a powerful tool for the characterization and classification of RNA-protein interactions and is freely available at http://service.tartaglialab.com/page/catrapid_omics2_group along with documentation and tutorial.

A Survey of Current Resources to Study lncRNA-Protein Interactions

Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein-DNA interactions such as histone and transcription factor binding are well studied, along with RNA-RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.

Nuclear compartmentalization of TERT mRNA and TUG1 lncRNA is driven by intron retention

The spatial partitioning of the transcriptome in the cell is an important form of gene-expression regulation. Here, we address how intron retention influences the spatio-temporal dynamics of transcripts from two clinically relevant genes: TERT (Telomerase Reverse Transcriptase) pre-mRNA and TUG1 (Taurine-Upregulated Gene 1) lncRNA. Single molecule RNA FISH reveals that nuclear TERT transcripts uniformly and robustly retain specific introns. Our data suggest that the splicing of TERT retained introns occurs during mitosis. In contrast, TUG1 has a bimodal distribution of fully spliced cytoplasmic and intron-retained nuclear transcripts. We further test the functionality of intron-retention events using RNA-targeting thiomorpholino antisense oligonucleotides to block intron excision. We show that intron retention is the driving force for the nuclear compartmentalization of these RNAs. For both RNAs, altering this splicing-driven subcellular distribution has significant effects on cell viability. Together, these findings show that stable retention of specific introns can orchestrate spatial compartmentalization of these RNAs within the cell. This process reveals that modulating RNA localization via targeted intron retention can be utilized for RNA-based therapies.

Large scale RNA-binding proteins/LncRNAs interaction analysis to uncover lncRNA nuclear localization mechanisms

Long non-coding RNAs (lncRNAs) are key regulators of major biological processes and their functional modes are dictated by their subcellular localization. Relative nuclear enrichment of lncRNAs compared to mRNAs is a prevalent phenomenon but the molecular mechanisms governing their nuclear retention in cells remain largely unknown. Here in this study, we harness the recently released eCLIP data for a large number of RNA-binding proteins (RBPs) in K562 and HepG2 cells and utilize multiple bioinformatics methods to comprehensively survey the roles of RBPs in lncRNA nuclear retention. We identify an array of splicing RBPs that bind to nuclear-enriched lincRNAs (large intergenic non-coding RNAs) thus may act as trans-factors regulating their nuclear retention. Further analyses reveal that these RBPs may bind with distinct core motifs, flanking sequence compositions, or secondary structures to drive lincRNA nuclear retention. Moreover, network analyses uncover potential co-regulatory RBP clusters and the physical interaction between HNRNPU and SAFB2 proteins in K562 cells is further experimentally verified. Altogether, our analyses reveal previously unknown factors and mechanisms that govern lincRNA nuclear localization in cells.

Intron retention as a new pre-symptomatic marker of aging and its recovery to the normal state by a traditional Japanese multi-herbal medicine

Intron retention (IR) is an important regulatory mechanism that affects gene expression and protein functions. Using klotho mice at the pre-symptomatic state, we discovered that retained-introns accumulated in several organs including the liver and that among these retained introns in the liver a subset was recovered to the normal state by a Japanese traditional herbal medicine. This is the first report of IR recovery by a medicine. IR-recovered genes fell into two categories: those involved in liver-specific metabolism and in splicing. Metabolome analysis of the liver showed that the klotho mice were under starvation stress. In addition, our differentially expressed gene analysis showed that liver metabolism was actually recovered by the herbal medicine at the transcriptional level. By analogy with the widespread accumulation of intron-retained pre-mRNAs induced by heat shock stress, we propose a model in which retained-introns in klotho mice were induced by an aging stress and in which this medicine-related IR recovery is indicative of the actual recovery of liver-specific metabolic function to the healthy state. Accumulation of retained-introns was also observed at the pre-symptomatic state of aging in wild-type mice and may be an excellent marker for this state in general.

Conserved DNA sequence features underlie pervasive RNA polymerase pausing

Pausing of transcribing RNA polymerase is regulated and creates opportunities to control gene expression. Research in metazoans has so far mainly focused on RNA polymerase II (Pol II) promoter-proximal pausing leaving the pervasive nature of pausing and its regulatory potential in mammalian cells unclear. Here, we developed a pause detecting algorithm (PDA) for nucleotide-resolution occupancy data and a new native elongating transcript sequencing approach, termed nested NET-seq, that strongly reduces artifactual peaks commonly misinterpreted as pausing sites. Leveraging PDA and nested NET-seq reveal widespread genome-wide Pol II pausing at single-nucleotide resolution in human cells. Notably, the majority of Pol II pauses occur outside of promoter-proximal gene regions primarily along the gene-body of transcribed genes. Sequence analysis combined with machine learning modeling reveals DNA sequence properties underlying widespread transcriptional pausing including a new pause motif. Interestingly, key sequence determinants of RNA polymerase pausing are conserved between human cells and bacteria. These studies indicate pervasive sequence-induced transcriptional pausing in human cells and the knowledge of exact pause locations implies potential functional roles in gene expression.

Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis

The novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after emerging in Wuhan, China. Here we analyzed public host and viral RNA sequencing data to better understand how SARS-CoV-2 interacts with human respiratory cells. We identified genes, isoforms and transposable element families that are specifically altered in SARS-CoV-2-infected respiratory cells. Well-known immunoregulatory genes including CSF2, IL32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were upregulated. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) and eukaryotic initiation factor 4 (eIF4b). We also identified a viral sequence variant with a statistically significant skew associated with age of infection, that may contribute to intracellular host-pathogen interactions. These findings can help identify host mechanisms that can be targeted by prophylactics and/or therapeutics to reduce the severity of COVID-19.

Emerging Classes of Small Non-Coding RNAs With Potential Implications in Diabetes and Associated Metabolic Disorders

Most of the sequences in the human genome do not code for proteins but generate thousands of non-coding RNAs (ncRNAs) with regulatory functions. High-throughput sequencing technologies and bioinformatic tools significantly expanded our knowledge about ncRNAs, highlighting their key role in gene regulatory networks, through their capacity to interact with coding and non-coding RNAs, DNAs and proteins. NcRNAs comprise diverse RNA species, including amongst others PIWI-interacting RNAs (piRNAs), involved in transposon silencing, and small nucleolar RNAs (snoRNAs), which participate in the modification of other RNAs such as ribosomal RNAs and transfer RNAs. Recently, a novel class of small ncRNAs generated from the cleavage of tRNAs or pre-tRNAs, called tRNA-derived small RNAs (tRFs) has been identified. tRFs have been suggested to regulate protein translation, RNA silencing and cell survival. While for other ncRNAs an implication in several pathologies is now well established, the potential involvement of piRNAs, snoRNAs and tRFs in human diseases, including diabetes, is only beginning to emerge. In this review, we summarize fundamental aspects of piRNAs, snoRNAs and tRFs biology. We discuss their biogenesis while emphasizing on novel sequencing technologies that allow ncRNA discovery and annotation. Moreover, we give an overview of genomic approaches to decrypt their mechanisms of action and to study their functional relevance. The review will provide a comprehensive landscape of the regulatory roles of these three types of ncRNAs in metabolic disorders by reporting their differential expression in endocrine pancreatic tissue as well as their contribution to diabetes incidence and diabetes-underlying conditions such as inflammation. Based on these discoveries we discuss the potential use of piRNAs, snoRNAs and tRFs as promising therapeutic targets in metabolic disorders.

Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures

Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions. PrismNet results for 168 RBPs support its utility for both understanding CLIP-seq results and largely extending such interaction data to accurately analyze additional cell types. Further, PrismNet employs an "attention" strategy to computationally identify exact RBP-binding nucleotides, and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants (riboSNitches), which can link genetic diseases with dysregulated RBP bindings. Our rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions, with clear utility for understanding and treating human diseases.

Modeling multi-species RNA modification through multi-task curriculum learning

N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, various experimental techniques and computational methods have been developed to characterize the transcriptome-wide landscapes of m6A modification for understanding its underlying mechanisms and functions in mRNA regulation. However, the experimental techniques are generally costly and time-consuming, while the existing computational models are usually designed only for m6A site prediction in a single-species and have significant limitations in accuracy, interpretability and generalizability. Here, we propose a highly interpretable computational framework, called MASS, based on a multi-task curriculum learning strategy to capture m6A features across multiple species simultaneously. Extensive computational experiments demonstrate the superior performances of MASS when compared to the state-of-the-art prediction methods. Furthermore, the contextual sequence features of m6A captured by MASS can be explained by the known critical binding motifs of the related RNA-binding proteins, which also help elucidate the similarity and difference among m6A features across species. In addition, based on the predicted m6A profiles, we further delineate the relationships between m6A and various properties of gene regulation, including gene expression, RNA stability, translation, RNA structure and histone modification. In summary, MASS may serve as a useful tool for characterizing m6A modification and studying its regulatory code. The source code of MASS can be downloaded from https://github.com/mlcb-thu/MASS.

Genome-wide analysis of lncRNA stability in human

Transcript stability is associated with many biological processes, and the factors affecting mRNA stability have been extensively studied. However, little is known about the features related to human long noncoding RNA (lncRNA) stability. By inhibiting transcription and collecting samples in 10 time points, genome-wide RNA-seq studies was performed in human lung adenocarcinoma cells (A549) and RNA half-life datasets were constructed. The following observations were obtained. First, the half-life distributions of both lncRNAs and messanger RNAs (mRNAs) with one exon (lnc-human1 and m-human1) were significantly different from those of both lncRNAs and mRNAs with more than one exon (lnc-human2 and m-human2). Furthermore, some factors such as full-length transcript secondary structures played a contrary role in lnc-human1 and m-human2. Second, through the half-life comparisons of nucleus- and cytoplasm-specific and common lncRNAs and mRNAs, lncRNAs (mRNAs) in the nucleus were found to be less stable than those in the cytoplasm, which was derived from transcripts themselves rather than cellular location. Third, kmers-based protein−RNA or RNA−RNA interactions promoted lncRNA stability from lnc-human1 and decreased mRNA stability from m-human2 with high probability. Finally, through applying deep learning−based regression, a non-linear relationship was found to exist between the half-lives of lncRNAs (mRNAs) and related factors. The present study established lncRNA and mRNA half-life regulation networks in the A549 cell line and shed new light on the degradation behaviors of both lncRNAs and mRNAs.

Transcript stability is important for many biological processes. However, little is known about the features related to human lncRNA stability. Through quantitative analysis between the half-lives of lncRNAs (mRNAs) and various factors, we found a nonlinear relationship between the half-lives of lncRNAs (mRNAs) and the related factors and their combinations. Our research provided a comprehensive understanding of lncRNA stability. Further efforts are needed to develop an accurate quantitative prediction model for the half-lives of lncRNA (mRNA).

On the function and relevance of alternative 3'-UTRs in gene expression regulation

Messanger RNA (mRNA) isoforms with alternative 3'-untranslated regions (3'-UTRs) are produced by alternative polyadenylation (APA), which occurs during transcription in most eukaryotic genes. APA fine-tunes gene expression in a cell-type- and cellular state-dependent manner. Selection of an APA site entails the binding of core cleavage and polyadenylation factors to a particular polyadenylation site localized in the pre-mRNA and is controlled by multiple regulatory determinants, including transcription, pre-mRNA cis-regulatory sequences, and protein factors. Alternative 3'-UTRs serve as platforms for specific RNA binding proteins and microRNAs, which regulate gene expression in a coordinated manner by controlling mRNA fate and function in the cell. Genome-wide studies illustrated the full extent of APA prevalence and revealed that specific 3'-UTR profiles are associated with particular cellular states and diseases. Generally, short 3'-UTRs are associated with proliferative and cancer cells, and long 3'-UTRs are mostly found in polarized and differentiated cells. Fundamental new insights on the physiological consequences of this widespread event and the molecular mechanisms involved have been revealed through single-cell studies. Publicly available comprehensive databases that cover all APA mRNA isoforms identified in many cellular states and diseases reveal specific APA signatures. Therapies tackling APA mRNA isoforms or APA regulators may be regarded as innovative and attractive tools for diagnostics or treatment of several pathologies. We highlight the function of APA and alternative 3'-UTRs in gene expression regulation, the control of these mechanisms, their physiological consequences, and their potential use as new biomarkers and therapeutic tools. This article is categorized under: RNA Processing > 3' End Processing RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA in Disease and Development > RNA in Disease.

Shifts in Ribosome Engagement Impact Key Gene Sets in Neurodevelopment and Ubiquitination in Rett Syndrome

Regulation of translation during human development is poorly understood, and its dysregulation is associated with Rett syndrome (RTT). To discover shifts in mRNA ribosomal engagement (RE) during human neurodevelopment, we use parallel translating ribosome affinity purification sequencing (TRAP-seq) and RNA sequencing (RNA-seq) on control and RTT human induced pluripotent stem cells, neural progenitor cells, and cortical neurons. We find that 30% of transcribed genes are translationally regulated, including key gene sets (neurodevelopment, transcription and translation factors, and glycolysis). Approximately 35% of abundant intergenic long noncoding RNAs (lncRNAs) are ribosome engaged. Neurons translate mRNAs more efficiently and have longer 3' UTRs, and RE correlates with elements for RNA-binding proteins. RTT neurons have reduced global translation and compromised mTOR signaling, and >2,100 genes are translationally dysregulated. NEDD4L E3-ubiquitin ligase is translationally impaired, ubiquitinated protein levels are reduced, and protein targets accumulate in RTT neurons. Overall, the dynamic translatome in neurodevelopment is disturbed in RTT and provides insight into altered ubiquitination that may have therapeutic implications.

MTSplice predicts effects of genetic variants on tissue-specific splicing

We develop the free and open-source model Multi-tissue Splicing (MTSplice) to predict the effects of genetic variants on splicing of cassette exons in 56 human tissues. MTSplice combines MMSplice, which models constitutive regulatory sequences, with a new neural network that models tissue-specific regulatory sequences. MTSplice outperforms MMSplice on predicting tissue-specific variations associated with genetic variants in most tissues of the GTEx dataset, with largest improvements on brain tissues. Furthermore, MTSplice predicts that autism-associated de novo mutations are enriched for variants affecting splicing specifically in the brain. We foresee that MTSplice will aid interpreting variants associated with tissue-specific disorders.

Quaking 5 suppresses TGF-β-induced EMT and cell invasion in lung adenocarcinoma

Quaking (QKI) proteins belong to the signal transduction and activation of RNA (STAR) family of RNA-binding proteins that have multiple functions in RNA biology. Here, we show that QKI-5 is dramatically decreased in metastatic lung adenocarcinoma (LUAD). QKI-5 overexpression inhibits TGF-β-induced epithelial-mesenchymal transition (EMT) and invasion, whereas QKI-5 knockdown has the opposite effect. QKI-5 overexpression and silencing suppresses and promotes TGF-β-stimulated metastasis in vivo, respectively. QKI-5 inhibits TGF-β-induced EMT and invasion in a TGFβR1-dependent manner. KLF6 knockdown increases TGFβR1 expression and promotes TGF-β-induced EMT, which is partly abrogated by QKI-5 overexpression. Mechanistically, QKI-5 directly interacts with the TGFβR1 3' UTR and causes post-transcriptional degradation of TGFβR1 mRNA, thereby inhibiting TGF-β-induced SMAD3 phosphorylation and TGF-β/SMAD signaling. QKI-5 is positively regulated by KLF6 at the transcriptional level. In LUAD tissues, KLF6 is lowly expressed and positively correlated with QKI-5 expression, while TGFβR1 expression is up-regulated and inversely correlated with QKI-5 expression. We reveal a novel mechanism by which KLF6 transcriptionally regulates QKI-5 and suggest that targeting the KLF6/QKI-5/TGFβR1 axis is a promising targeting strategy for metastatic LUAD.

Human cell-dependent, directional, time-dependent changes in the mono- and oligonucleotide compositions of SARS-CoV-2 genomes

Background

When a virus that has grown in a nonhuman host starts an epidemic in the human population, human cells may not provide growth conditions ideal for the virus. Therefore, the invasion of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which is usually prevalent in the bat population, into the human population is thought to have necessitated changes in the viral genome for efficient growth in the new environment. In the present study, to understand host-dependent changes in coronavirus genomes, we focused on the mono- and oligonucleotide compositions of SARS-CoV-2 genomes and investigated how these compositions changed time-dependently in the human cellular environment. We also compared the oligonucleotide compositions of SARS-CoV-2 and other coronaviruses prevalent in humans or bats to investigate the causes of changes in the host environment.

Results

Time-series analyses of changes in the nucleotide compositions of SARS-CoV-2 genomes revealed a group of mono- and oligonucleotides whose compositions changed in a common direction for all clades, even though viruses belonging to different clades should evolve independently. Interestingly, the compositions of these oligonucleotides changed towards those of coronaviruses that have been prevalent in humans for a long period and away from those of bat coronaviruses.

Conclusions

Clade-independent, time-dependent changes are thought to have biological significance and should relate to viral adaptation to a new host environment, providing important clues for understanding viral host adaptation mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02158-6.