PolyASite 2.0: a consolidated atlas of polyadenylation sites from 3' end sequencing

Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms

RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders; yet, the role of cell type-specific splicing and transcript-isoform diversity during human brain development has not been systematically investigated. In this work, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone and cortical plate regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 distinct isoforms, of which 72.6% were novel (not previously annotated in Gencode version 33), and uncovered a substantial contribution of transcript-isoform diversity-regulated by RNA binding proteins-in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to reprioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.

Quantifying 3'UTR length from scRNA-seq data reveals changes independent of gene expression

Although more than half of all genes generate transcripts that differ in 3'UTR length, current analysis pipelines only quantify the amount but not the length of mRNA transcripts. 3'UTR length is determined by 3' end cleavage sites (CS). We map CS in more than 200 primary human and mouse cell types and increase CS annotations relative to the GENCODE database by 40%. Approximately half of all CS are used in few cell types, revealing that most genes only have one or two major 3' ends. We incorporate the CS annotations into a computational pipeline, called scUTRquant, for rapid, accurate, and simultaneous quantification of gene and 3'UTR isoform expression from single-cell RNA sequencing (scRNA-seq) data. When applying scUTRquant to data from 474 cell types and 2134 perturbations, we discover extensive 3'UTR length changes across cell types that are as widespread and coordinately regulated as gene expression changes but affect mostly different genes. Our data indicate that mRNA abundance and mRNA length are two largely independent axes of gene regulation that together determine the amount and spatial organization of protein synthesis.

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.

InPACT: a computational method for accurate characterization of intronic polyadenylation from RNA sequencing data

Alternative polyadenylation can occur in introns, termed intronic polyadenylation (IPA), has been implicated in diverse biological processes and diseases, as it can produce noncoding transcripts or transcripts with truncated coding regions. However, a reliable method is required to accurately characterize IPA. Here, we propose a computational method called InPACT, which allows for the precise characterization of IPA from conventional RNA-seq data. InPACT successfully identifies numerous previously unannotated IPA transcripts in human cells, many of which are translated, as evidenced by ribosome profiling data. We have demonstrated that InPACT outperforms other methods in terms of IPA identification and quantification. Moreover, InPACT applied to monocyte activation reveals temporally coordinated IPA events. Further application on single-cell RNA-seq data of human fetal bone marrow reveals the expression of several IPA isoforms in a context-specific manner. Therefore, InPACT represents a powerful tool for the accurate characterization of IPA from RNA-seq data.

T helper cells exhibit a dynamic and reversible 3'-UTR landscape

3' untranslated regions (3' UTRs) are critical elements of messenger RNAs, as they contain binding sites for RNA-binding proteins (RBPs) and microRNAs that affect various aspects of the RNA life cycle including transcript stability and cellular localization. In response to T cell receptor activation, T cells undergo massive expansion during the effector phase of the immune response and dynamically modify their 3' UTRs. Whether this serves to directly regulate the abundance of specific mRNAs or is a secondary effect of proliferation remains unclear. To study 3'-UTR dynamics in T helper cells, we investigated division-dependent alternative polyadenylation (APA). In addition, we generated 3' end UTR sequencing data from naive, activated, memory, and regulatory CD4+ T cells. 3'-UTR length changes were estimated using a nonnegative matrix factorization approach and were compared with those inferred from long-read PacBio sequencing. We found that APA events were transient and reverted after effector phase expansion. Using an orthogonal bulk RNA-seq data set, we did not find evidence of APA association with differential gene expression or transcript usage, indicating that APA has only a marginal effect on transcript abundance. 3'-UTR sequence analysis revealed conserved binding sites for T cell-relevant microRNAs and RBPs in the alternative 3' UTRs. These results indicate that poly(A) site usage could play an important role in the control of cell fate decisions and homeostasis.

The pancancer overexpressed NFYC Antisense 1 controls cell cycle mitotic progression through in cis and in trans modes of action

Antisense RNAs (asRNAs) represent an underappreciated yet crucial layer of gene expression regulation. Generally thought to modulate their sense genes in cis through sequence complementarity or their act of transcription, asRNAs can also regulate different molecular targets in trans, in the nucleus or in the cytoplasm. Here, we performed an in-depth molecular characterization of NFYC Antisense 1 (NFYC-AS1), the asRNA transcribed head-to-head to NFYC subunit of the proliferation-associated NF-Y transcription factor. Our results show that NFYC-AS1 is a prevalently nuclear asRNA peaking early in the cell cycle. Comparative genomics suggests a narrow phylogenetic distribution, with a probable origin in the common ancestor of mammalian lineages. NFYC-AS1 is overexpressed pancancer, preferentially in association with RB1 mutations. Knockdown of NFYC-AS1 by antisense oligonucleotides impairs cell growth in lung squamous cell carcinoma and small cell lung cancer cells, a phenotype recapitulated by CRISPR/Cas9-deletion of its transcription start site. Surprisingly, expression of the sense gene is affected only when endogenous transcription of NFYC-AS1 is manipulated. This suggests that regulation of cell proliferation is at least in part independent of the in cis transcription-mediated effect on NFYC and is possibly exerted by RNA-dependent in trans effects converging on the regulation of G2/M cell cycle phase genes. Accordingly, NFYC-AS1-depleted cells are stuck in mitosis, indicating defects in mitotic progression. Overall, NFYC-AS1 emerged as a cell cycle-regulating asRNA with dual action, holding therapeutic potential in different cancer types, including the very aggressive RB1-mutated tumors.

TDP-43 loss induces extensive cryptic polyadenylation in ALS/FTD

Nuclear depletion and cytoplasmic aggregation of the RNA-binding protein TDP-43 is the hallmark of ALS, occurring in over 97% of cases. A key consequence of TDP-43 nuclear loss is the de-repression of cryptic exons. Whilst TDP-43 regulated cryptic splicing is increasingly well catalogued, cryptic alternative polyadenylation (APA) events, which define the 3' end of last exons, have been largely overlooked, especially when not associated with novel upstream splice junctions. We developed a novel bioinformatic approach to reliably identify distinct APA event types: alternative last exons (ALE), 3'UTR extensions (3'Ext) and intronic polyadenylation (IPA) events. We identified novel neuronal cryptic APA sites induced by TDP-43 loss of function by systematically applying our pipeline to a compendium of publicly available and in house datasets. We find that TDP-43 binding sites and target motifs are enriched at these cryptic events and that TDP-43 can have both repressive and enhancing action on APA. Importantly, all categories of cryptic APA can also be identified in ALS and FTD post mortem brain regions with TDP-43 proteinopathy underlining their potential disease relevance. RNA-seq and Ribo-seq analyses indicate that distinct cryptic APA categories have different downstream effects on transcript and translation. Intriguingly, cryptic 3'Exts occur in multiple transcription factors, such as ELK1, SIX3, and TLX1, and lead to an increase in wild-type protein levels and function. Finally, we show that an increase in RNA stability leading to a higher cytoplasmic localisation underlies these observations. In summary, we demonstrate that TDP-43 nuclear depletion induces a novel category of cryptic RNA processing events and we expand the palette of TDP-43 loss consequences by showing this can also lead to an increase in normal protein translation.

TDP-43 nuclear loss in FTD/ALS causes widespread alternative polyadenylation changes

In frontotemporal dementia and amyotrophic lateral sclerosis, the RNA-binding protein TDP-43 is depleted from the nucleus. TDP-43 loss leads to cryptic exon inclusion but a role in other RNA processing events remains unresolved. Here, we show that loss of TDP-43 causes widespread changes in alternative polyadenylation, impacting expression of disease-relevant genes (e.g., ELP1, NEFL, and TMEM106B) and providing evidence that alternative polyadenylation is a new facet of TDP-43 pathology.

mRNA initiation and termination are spatially coordinated

The expression of a precise mRNA transcriptome is crucial for establishing cell identity and function, with dozens of alternative isoforms produced for a single gene sequence. The regulation of mRNA isoform usage occurs by the coordination of co-transcriptional mRNA processing mechanisms across a gene. Decisions involved in mRNA initiation and termination underlie the largest extent of mRNA isoform diversity, but little is known about any relationships between decisions at both ends of mRNA molecules. Here, we systematically profile the joint usage of mRNA transcription start sites (TSSs) and polyadenylation sites (PASs) across tissues and species. Using both short and long read RNA-seq data, we observe that mRNAs preferentially using upstream TSSs also tend to use upstream PASs, and congruently, the usage of downstream sites is similarly paired. This observation suggests that mRNA 5' end choice may directly influence mRNA 3' ends. Our results suggest a novel "Positional Initiation-Termination Axis" (PITA), in which the usage of alternative terminal sites are coupled based on the order in which they appear in the genome. PITA isoforms are more likely to encode alternative protein domains and use conserved sites. PITA is strongly associated with the length of genomic features, such that PITA is enriched in longer genes with more area devoted to regions that regulate alternative 5' or 3' ends. Strikingly, we found that PITA genes are more likely than non-PITA genes to have multiple, overlapping chromatin structural domains related to pairing of ordinally coupled start and end sites. In turn, PITA coupling is also associated with fast RNA Polymerase II (RNAPII) trafficking across these long gene regions. Our findings indicate that a combination of spatial and kinetic mechanisms couple transcription initiation and mRNA 3' end decisions based on ordinal position to define the expression mRNA isoforms.

The landscape of alternative polyadenylation during EMT and its regulation by the RNA-binding protein Quaking

Epithelial-mesenchymal transition (EMT) plays important roles in tumour progression and is orchestrated by dynamic changes in gene expression. While it is well established that post-transcriptional regulation plays a significant role in EMT, the extent of alternative polyadenylation (APA) during EMT has not yet been explored. Using 3' end anchored RNA sequencing, we mapped the alternative polyadenylation (APA) landscape following Transforming Growth Factor (TGF)-β-mediated induction of EMT in human mammary epithelial cells and found APA generally causes 3'UTR lengthening during this cell state transition. Investigation of potential mediators of APA indicated the RNA-binding protein Quaking (QKI), a splicing factor induced during EMT, regulates a subset of events including the length of its own transcript. Analysis of QKI crosslinked immunoprecipitation (CLIP)-sequencing data identified the binding of QKI within 3' untranslated regions (UTRs) was enriched near cleavage and polyadenylation sites. Following QKI knockdown, APA of many transcripts is altered to produce predominantly shorter 3'UTRs associated with reduced gene expression. These findings reveal the changes in APA that occur during EMT and identify a potential role for QKI in this process.

Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data

The tremendous rate with which data is generated and analysis methods emerge makes it increasingly difficult to keep track of their domain of applicability, assumptions, limitations, and consequently, of the efficacy and precision with which they solve specific tasks. Therefore, there is an increasing need for benchmarks, and for the provision of infrastructure for continuous method evaluation. APAeval is an international community effort, organized by the RNA Society in 2021, to benchmark tools for the identification and quantification of the usage of alternative polyadenylation (APA) sites from short-read, bulk RNA-sequencing (RNA-seq) data. Here, we reviewed 17 tools and benchmarked eight on their ability to perform APA identification and quantification, using a comprehensive set of RNA-seq experiments comprising real, synthetic, and matched 3'-end sequencing data. To support continuous benchmarking, we have incorporated the results into the OpenEBench online platform, which allows for continuous extension of the set of methods, metrics, and challenges. We envisage that our analyses will assist researchers in selecting the appropriate tools for their studies, while the containers and reproducible workflows could easily be deployed and extended to evaluate new methods or data sets.

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease

The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant proteins. To obtain mechanistic insights into polyA site optimization across the human genome, we develop deep/machine learning models to identify genome-wide putative polyA sites at unprecedented nucleotide-level resolution and calculate their strength and usage in the genomic context. Our models quantitatively measure position-specific motif importance and their crosstalk in polyA site formation and cleavage heterogeneity. The intronic site expression is governed by the surrounding splicing landscape. The usage of alternative polyA sites in terminal exons is modulated by their relative locations and distance to downstream genes. Finally, we apply our models to reveal thousands of disease- and trait-associated genetic variants altering polyadenylation activity. Altogether, our models represent a valuable resource to dissect molecular mechanisms mediating genome-wide polyA site expression and characterize their functional roles in human diseases.

Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms

RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders, yet the role of cell-type-specific splicing or transcript-isoform diversity during human brain development has not been systematically investigated. Here, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone (GZ) and cortical plate (CP) regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 unique isoforms, of which 72.6% are novel (unannotated in Gencode-v33), and uncovered a substantial contribution of transcript-isoform diversity, regulated by RNA binding proteins, in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to re-prioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.

One-Sentence Summary

A cell-specific atlas of gene isoform expression helps shape our understanding of brain development and disease.

Structured Abstract

INTRODUCTION: The development of the human brain is regulated by precise molecular and genetic mechanisms driving spatio-temporal and cell-type-specific transcript expression programs. Alternative splicing, a major mechanism increasing transcript diversity, is highly prevalent in the human brain, influences many aspects of brain development, and has strong links to neuropsychiatric disorders. Despite this, the cell-type-specific transcript-isoform diversity of the developing human brain has not been systematically investigated.RATIONALE: Understanding splicing patterns and isoform diversity across the developing neocortex has translational relevance and can elucidate genetic risk mechanisms in neurodevelopmental disorders. However, short-read sequencing, the prevalent technology for transcriptome profiling, is not well suited to capturing alternative splicing and isoform diversity. To address this, we employed third-generation long-read sequencing, which enables capture and sequencing of complete individual RNA molecules, to deeply profile the full-length transcriptome of the germinal zone (GZ) and cortical plate (CP) regions of the developing human neocortex at tissue and single-cell resolution.RESULTS: We profiled microdissected GZ and CP regions of post-conception week (PCW) 15-17 human neocortex in bulk and at single-cell resolution across six subjects using high-fidelity long-read sequencing (PacBio IsoSeq). We identified 214,516 unique isoforms, of which 72.6% were novel (unannotated in Gencode), and >7,000 novel exons, expanding the proteome by 92,422 putative proteoforms. We uncovered thousands of isoform switches during cortical neurogenesis predicted to impact RNA regulatory domains or protein structure and implicating previously uncharacterized RNA-binding proteins in cellular identity and neuropsychiatric disease. At the single-cell level, early-stage excitatory neurons exhibited the greatest isoform diversity, and isoform-centric single-cell clustering led to the identification of previously uncharacterized cell states. We systematically assessed the contribution of transcriptomic features, and localized cell and spatio-temporal transcript expression signatures across neuropsychiatric disorders, revealing predominant enrichments in dynamic isoform expression and utilization patterns and that the number and complexity of isoforms per gene is strongly predictive of disease. Leveraging this resource, we re-prioritized thousands of rare de novo risk variants associated with autism spectrum disorders (ASD), intellectual disability (ID), and neurodevelopmental disorders (NDDs), more broadly, to potentially more severe consequences and revealed a larger proportion of cryptic splice variants with the expanded transcriptome annotation provided in this study.CONCLUSION: Our study offers a comprehensive landscape of isoform diversity in the human neocortex during development. This extensive cataloging of novel isoforms and splicing events sheds light on the underlying mechanisms of neurodevelopmental disorders and presents an opportunity to explore rare genetic variants linked to these conditions. The implications of our findings extend beyond fundamental neuroscience, as they provide crucial insights into the molecular basis of developmental brain disorders and pave the way for targeted therapeutic interventions. To facilitate exploration of this dataset we developed an online portal ( https://sciso.gandallab.org/ ).

Infernape uncovers cell type-specific and spatially resolved alternative polyadenylation in the brain

Differential polyadenylation sites (PAs) critically regulate gene expression, but their cell type-specific usage and spatial distribution in the brain have not been systematically characterized. Here, we present Infernape, which infers and quantifies PA usage from single-cell and spatial transcriptomic data and show its application in the mouse brain. Infernape uncovers alternative intronic PAs and 3'-UTR lengthening during cortical neurogenesis. Progenitor-neuron comparisons in the excitatory and inhibitory neuron lineages show overlapping PA changes in embryonic brains, suggesting that the neural proliferation-differentiation axis plays a prominent role. In the adult mouse brain, we uncover cell type-specific PAs and visualize such events using spatial transcriptomic data. Over two dozen neurodevelopmental disorder-associated genes such as Csnk2a1 and Mecp2 show differential PAs during brain development. This study presents Infernape to identify PAs from scRNA-seq and spatial data, and highlights the role of alternative PAs in neuronal gene regulation.

Pervasive transcriptome interactions of protein-targeted drugs

The off-target toxicity of drugs targeted to proteins imparts substantial health and economic costs. Proteome interaction studies can reveal off-target effects with unintended proteins; however, little attention has been paid to intracellular RNAs as potential off-targets that may contribute to toxicity. To begin to assess this, we developed a reactivity-based RNA profiling methodology and applied it to uncover transcriptome interactions of a set of Food and Drug Administration-approved small-molecule drugs in vivo. We show that these protein-targeted drugs pervasively interact with the human transcriptome and can exert unintended biological effects on RNA functions. In addition, we show that many off-target interactions occur at RNA loci associated with protein binding and structural changes, allowing us to generate hypotheses to infer the biological consequences of RNA off-target binding. The results suggest that rigorous characterization of drugs' transcriptome interactions may help assess target specificity and potentially avoid toxicity and clinical failures.

Challenges in identifying mRNA transcript starts and ends from long-read sequencing data

Long-read sequencing (LRS) technologies have the potential to revolutionize scientific discoveries in RNA biology, especially by enabling the comprehensive identification and quantification of full length mRNA isoforms. However, inherently high error rates make the analysis of long-read sequencing data challenging. While these error rates have been characterized for sequence and splice site identification, it is still unclear how accurately LRS reads represent transcript start and end sites. Here, we systematically assess the variability and accuracy of mRNA terminal ends identified by LRS reads across multiple sequencing platforms. We find substantial inconsistencies in both the start and end coordinates of LRS reads spanning a gene, such that LRS reads often fail to accurately recapitulate annotated or empirically derived terminal ends of mRNA molecules. To address this challenge, we introduce an approach to condition reads based on empirically derived terminal ends and identified a subset of reads that are more likely to represent full-length transcripts. Our approach can improve transcriptome analyses by enhancing the fidelity of transcript terminal end identification, but may result in lower power to quantify genes or discover novel isoforms. Thus, it is necessary to be cautious when selecting sequencing approaches and/or interpreting data from long-read RNA sequencing.

Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq data

The tremendous rate with which data is generated and analysis methods emerge makes it increasingly difficult to keep track of their domain of applicability, assumptions, and limitations and consequently, of the efficacy and precision with which they solve specific tasks. Therefore, there is an increasing need for benchmarks, and for the provision of infrastructure for continuous method evaluation. APAeval is an international community effort, organized by the RNA Society in 2021, to benchmark tools for the identification and quantification of the usage of alternative polyadenylation (APA) sites from short-read, bulk RNA-sequencing (RNA-seq) data. Here, we reviewed 17 tools and benchmarked eight on their ability to perform APA identification and quantification, using a comprehensive set of RNA-seq experiments comprising real, synthetic, and matched 3'-end sequencing data. To support continuous benchmarking, we have incorporated the results into the OpenEBench online platform, which allows for seamless extension of the set of methods, metrics, and challenges. We envisage that our analyses will assist researchers in selecting the appropriate tools for their studies. Furthermore, the containers and reproducible workflows generated in the course of this project can be seamlessly deployed and extended in the future to evaluate new methods or datasets.

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms

The emergence of long-read RNA sequencing (lrRNA-seq) has provided an unprecedented opportunity to analyze transcriptomes at isoform resolution. However, the technology is not free from biases, and transcript models inferred from these data require quality control and curation. In this study, we introduce SQANTI3, a tool specifically designed to perform quality analysis on transcriptomes constructed using lrRNA-seq data. SQANTI3 provides an extensive naming framework to describe transcript model diversity in comparison to the reference transcriptome. Additionally, the tool incorporates a wide range of metrics to characterize various structural properties of transcript models, such as transcription start and end sites, splice junctions, and other structural features. These metrics can be utilized to filter out potential artifacts. Moreover, SQANTI3 includes a Rescue module that prevents the loss of known genes and transcripts exhibiting evidence of expression but displaying low-quality features. Lastly, SQANTI3 incorporates IsoAnnotLite, which enables functional annotation at the isoform level and facilitates functional iso-transcriptomics analyses. We demonstrate the versatility of SQANTI3 in analyzing different data types, isoform reconstruction pipelines, and sequencing platforms, and how it provides novel biological insights into isoform biology. The SQANTI3 software is available at https://github.com/ConesaLab/SQANTI3 .

Transcriptome sequencing suggests that pre-mRNA splicing counteracts widespread intronic cleavage and polyadenylation

Alternative splicing (AS) and alternative polyadenylation (APA) are two crucial steps in the post-transcriptional regulation of eukaryotic gene expression. Protocols capturing and sequencing RNA 3'-ends have uncovered widespread intronic polyadenylation (IPA) in normal and disease conditions, where it is currently attributed to stochastic variations in the pre-mRNA processing. Here, we took advantage of the massive amount of RNA-seq data generated by the Genotype Tissue Expression project (GTEx) to simultaneously identify and match tissue-specific expression of intronic polyadenylation sites with tissue-specific splicing. A combination of computational methods including the analysis of short reads with non-templated adenines revealed that APA events are more abundant in introns than in exons. While the rate of IPA in composite terminal exons and skipped terminal exons expectedly correlates with splicing, we observed a considerable fraction of IPA events that lack AS support and attributed them to spliced polyadenylated introns (SPI). We hypothesize that SPIs represent transient byproducts of a dynamic coupling between APA and AS, in which the spliceosome removes the intron while it is being cleaved and polyadenylated. These findings indicate that cotranscriptional pre-mRNA splicing could serve as a rescue mechanism to suppress premature transcription termination at intronic polyadenylation sites.

Cochlear transcript diversity and its role in auditory functions implied by an otoferlin short isoform

Isoforms of a gene may contribute to diverse biological functions. In the cochlea, the repertoire of alternative isoforms remains unexplored. We integrated single-cell short-read and long-read RNA sequencing techniques and identified 236,012 transcripts, 126,612 of which were unannotated in the GENCODE database. Then we analyzed and verified the unannotated transcripts using RNA-seq, RT-PCR, Sanger sequencing, and MS-based proteomics approaches. To illustrate the importance of identifying spliced isoforms, we investigated otoferlin, a key protein involved in synaptic transmission in inner hair cells (IHCs). Upon deletion of the canonical otoferlin isoform, the identified short isoform is able to support normal hearing thresholds but with reduced sustained exocytosis of IHCs, and further revealed otoferlin functions in endocytic membrane retrieval that was not well-addressed previously. Furthermore, we found that otoferlin isoforms are associated with IHC functions and auditory phenotypes. This work expands our mechanistic understanding of auditory functions at the level of isoform resolution.

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity

The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3' end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3' processing are deployed across human tissues, with nearly half of multi-transcript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

Systematic screening identifies ABCG2 as critical factor underlying synergy of kinase inhibitors with transcriptional CDK inhibitors

BACKGROUND

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with limited treatment options and poor clinical prognosis. Inhibitors of transcriptional CDKs are currently under thorough investigation for application in the treatment of multiple cancer types, including breast cancer. These studies have raised interest in combining these inhibitors, including CDK12/13 inhibitor THZ531, with a variety of other anti-cancer agents. However, the full scope of these potential synergistic interactions of transcriptional CDK inhibitors with kinase inhibitors has not been systematically investigated. Moreover, the mechanisms behind these previously described synergistic interactions remain largely elusive.

METHODS

Kinase inhibitor combination screenings were performed to identify kinase inhibitors that synergize with CDK7 inhibitor THZ1 and CDK12/13 inhibitor THZ531 in TNBC cell lines. CRISPR-Cas9 knockout screening and transcriptomic evaluation of resistant versus sensitive cell lines were performed to identify genes critical for THZ531 resistance. RNA sequencing analysis after treatment with individual and combined synergistic treatments was performed to gain further insights into the mechanism of this synergy. Kinase inhibitor screening in combination with visualization of ABCG2-substrate pheophorbide A was used to identify kinase inhibitors that inhibit ABCG2. Multiple transcriptional CDK inhibitors were evaluated to extend the significance of the found mechanism to other transcriptional CDK inhibitors.

RESULTS

We show that a very high number of tyrosine kinase inhibitors synergize with the CDK12/13 inhibitor THZ531. Yet, we identified the multidrug transporter ABCG2 as key determinant of THZ531 resistance in TNBC cells. Mechanistically, we demonstrate that most synergistic kinase inhibitors block ABCG2 function, thereby sensitizing cells to transcriptional CDK inhibitors, including THZ531. Accordingly, these kinase inhibitors potentiate the effects of THZ531, disrupting gene expression and increasing intronic polyadenylation.

CONCLUSION

Overall, this study demonstrates the critical role of ABCG2 in limiting the efficacy of transcriptional CDK inhibitors and identifies multiple kinase inhibitors that disrupt ABCG2 transporter function and thereby synergize with these CDK inhibitors. These findings therefore further facilitate the development of new (combination) therapies targeting transcriptional CDKs and highlight the importance of evaluating the role of ABC transporters in synergistic drug-drug interactions in general.

Genome-wide screening for genetic variants in polyadenylation signal (PAS) sites in mouse selection lines for fatness and leanness

Alternative polyadenylation (APA) determines mRNA stability, localisation, translation and protein function. Several diseases, including obesity, have been linked to APA. Studies have shown that single nucleotide polymorphisms in polyadenylation signals (PAS-SNPs) can influence APA and affect phenotype and disease susceptibility. However, these studies focussed on associations between single PAS-SNP alleles with very large effects and phenotype. Therefore, we performed a genome-wide screening for PAS-SNPs in the polygenic mouse selection lines for fatness and leanness by whole-genome sequencing. The genetic variants identified in the two lines were overlapped with locations of PAS sites obtained from the PolyASite 2.0 database. Expression data for selected genes were extracted from the microarray expression experiment performed on multiple tissue samples. In total, 682 PAS-SNPs were identified within 583 genes involved in various biological processes, including transport, protein modifications and degradation, cell adhesion and immune response. Moreover, 63 of the 583 orthologous genes in human have been previously associated with human diseases, such as nervous system and physical disorders, and immune, endocrine, and metabolic diseases. In both lines, PAS-SNPs have also been identified in genes broadly involved in APA, such as Polr2c, Eif3e and Ints11. Five PAS-SNPs within 5 genes (Car, Col4a1, Itga7, Lat, Nmnat1) were prioritised as potential functional variants and could contribute to the phenotypic disparity between the two selection lines. The developed PAS-SNPs catalogue presents a key resource for planning functional studies to uncover the role of PAS-SNPs in APA, disease susceptibility and fat deposition.

Exon architecture controls mRNA m6A suppression and gene expression

N6-methyladenosine (m6A) is the most abundant messenger RNA (mRNA) modification and plays crucial roles in diverse physiological processes. Using a massively parallel assay for m6A (MPm6A), we discover that m6A specificity is globally regulated by suppressors that prevent m6A deposition in unmethylated transcriptome regions. We identify exon junction complexes (EJCs) as m6A suppressors that protect exon junction-proximal RNA within coding sequences from methylation and regulate mRNA stability through m6A suppression. EJC suppression of m6A underlies multiple global characteristics of mRNA m6A specificity, with the local range of EJC protection sufficient to suppress m6A deposition in average-length internal exons but not in long internal and terminal exons. EJC-suppressed methylation sites colocalize with EJC-suppressed splice sites, which suggests that exon architecture broadly determines local mRNA accessibility to regulatory complexes.

Genetic Variation in ATXN3 (Ataxin-3) 3'UTR: Insights into the Downstream Regulatory Elements of the Causative Gene of Machado-Joseph Disease/Spinocerebellar Ataxia Type 3

Untranslated regions are involved in the regulation of transcriptional and post-transcriptional processes. Characterization of these regions remains poorly explored for ATXN3, the causative gene of Machado-Joseph disease (MJD). Although a few genetic modifiers have been identified for MJD age at onset (AO), they only explain a small fraction of the AO variance. Our aim was to analyse variation at the 3'UTR of ATXN3 in MJD patients, analyse its impact on AO and attempt to build haplotypes that might discriminate between normal and expanded alleles.After assessing ATXN3 3'UTR variants in molecularly confirmed MJD patients, an in silico analysis was conducted to predict their functional impact (e.g. their effect on miRNA-binding sites). Alleles in cis with the expanded (CAG)_n were inferred from family data, and haplotypes were built. The effect of the alternative alleles on the AO and on SARA and NESSCA ataxia scales was tested.Nine variants, all previously described, were found. For eight variants, in silico analyses predicted (a) deleterious effects (rs10151135; rs55966267); (b) changes on miRNA-binding sites (rs11628764; rs55966267; rs709930) and (c) alterations of RNA-binding protein (RBP)-binding sites (rs1055996; rs910369; rs709930; rs10151135; rs3092822; rs7158733). Patients harbouring the alternative allele at rs10151135 had significantly higher SARA Axial subscores (p = 0.023), comparatively with those homozygous for the reference allele. Ten different haplotypes were obtained, one of which was exclusively found in cis with the expanded and four with the normal allele. These findings, which are relevant for the design of allele-specific therapies, warrant further investigation in independent MJD cohorts.

3'UTR Diversity: Expanding Repertoire of RNA Alterations in Human mRNAs

Genomic information stored in the DNA is transcribed to the mRNA and translated to proteins. The 3' untranslated regions (3'UTRs) of the mRNA serve pivotal roles in posttranscriptional gene expression, regulating mRNA stability, translation, and localization. Similar to DNA mutations producing aberrant proteins, RNA alterations expand the transcriptome landscape and change the cellular proteome. Recent global analyses reveal that many genes express various forms of altered RNAs, including 3'UTR length variants. Alternative polyadenylation and alternative splicing are involved in diversifying 3'UTRs, which could act as a hidden layer of eukaryotic gene expression control. In this review, we summarize the functions and regulations of 3'UTRs and elaborate on the generation and functional consequences of 3'UTR diversity. Given that dynamic 3'UTR length control contributes to phenotypic complexity, dysregulated 3'UTR diversity might be relevant to disease development, including cancers. Thus, 3'UTR diversity in cancer could open exciting new research areas and provide avenues for novel cancer theragnostics.

Macrophage differentiation is marked by increased abundance of the mRNA 3' end processing machinery, altered poly(A) site usage, and sensitivity to the level of CstF64

Regulation of mRNA polyadenylation is important for response to external signals and differentiation in several cell types, and results in mRNA isoforms that vary in the amount of coding sequence or 3' UTR regulatory elements. However, its role in differentiation of monocytes to macrophages has not been investigated. Macrophages are key effectors of the innate immune system that help control infection and promote tissue-repair. However, overactivity of macrophages contributes to pathogenesis of many diseases. In this study, we show that macrophage differentiation is characterized by shortening and lengthening of mRNAs in relevant cellular pathways. The cleavage/polyadenylation (C/P) proteins increase during differentiation, suggesting a possible mechanism for the observed changes in poly(A) site usage. This was surprising since higher C/P protein levels correlate with higher proliferation rates in other systems, but monocytes stop dividing after induction of differentiation. Depletion of CstF64, a C/P protein and known regulator of polyadenylation efficiency, delayed macrophage marker expression, cell cycle exit, attachment, and acquisition of structural complexity, and impeded shortening of mRNAs with functions relevant to macrophage biology. Conversely, CstF64 overexpression increased use of promoter-proximal poly(A) sites and caused the appearance of differentiated phenotypes in the absence of induction. Our findings indicate that regulation of polyadenylation plays an important role in macrophage differentiation.

PolyAMiner-Bulk: A Machine Learning Based Bioinformatics Algorithm to Infer and Decode Alternative Polyadenylation Dynamics from bulk RNA-seq data

More than half of human genes exercise alternative polyadenylation (APA) and generate mRNA transcripts with varying 3' untranslated regions (UTR). However, current computational approaches for identifying cleavage and polyadenylation sites (C/PASs) and quantifying 3'UTR length changes from bulk RNA-seq data fail to unravel tissue- and disease-specific APA dynamics. Here, we developed a next-generation bioinformatics algorithm and application, PolyAMiner-Bulk, that utilizes an attention-based machine learning architecture and an improved vector projection-based engine to infer differential APA dynamics accurately. When applied to earlier studies, PolyAMiner-Bulk accurately identified more than twice the number of APA changes in an RBM17 knockdown bulk RNA-seq dataset compared to current generation tools. Moreover, on a separate dataset, PolyAMiner-Bulk revealed novel APA dynamics and pathways in scleroderma pathology and identified differential APA in a gene that was identified as being involved in scleroderma pathogenesis in an independent study. Lastly, we used PolyAMiner-Bulk to analyze the RNA-seq data of post-mortem prefrontal cortexes from the ROSMAP data consortium and unraveled novel APA dynamics in Alzheimer's Disease. Our method, PolyAMiner-Bulk, creates a paradigm for future alternative polyadenylation analysis from bulk RNA-seq data.

LAFITE Reveals the Complexity of Transcript Isoforms in Subcellular Fractions

Characterization of the subcellular distribution of RNA is essential for understanding the molecular basis of biological processes. Here, the subcellular nanopore direct RNA-sequencing (DRS) of four lung cancer cell lines (A549, H1975, H358, and HCC4006) is performed, coupled with a computational pipeline, Low-abundance Aware Full-length Isoform clusTEr (LAFITE), to comprehensively analyze the full-length cytoplasmic and nuclear transcriptome. Using additional DRS and orthogonal data sets, it is shown that LAFITE outperforms current methods for detecting full-length transcripts, particularly for low-abundance isoforms that are usually overlooked due to poor read coverage. Experimental validation of six novel isoforms exclusively identified by LAFITE further confirms the reliability of this pipeline. By applying LAFITE to subcellular DRS data, the complexity of the nuclear transcriptome is revealed in terms of isoform diversity, 3'-UTR usage, m6A modification patterns, and intron retention. Overall, LAFITE provides enhanced full-length isoform identification and enables a high-resolution view of the RNA landscape at the isoform level.

Evolution and function of developmentally dynamic pseudogenes in mammals

BACKGROUND

Pseudogenes are excellent markers for genome evolution, which are emerging as crucial regulators of development and disease, especially cancer. However, systematic functional characterization and evolution of pseudogenes remain largely unexplored.

RESULTS

To systematically characterize pseudogenes, we date the origin of human and mouse pseudogenes across vertebrates and observe a burst of pseudogene gain in these two lineages. Based on a hybrid sequencing dataset combining full-length PacBio sequencing, sample-matched Illumina sequencing, and public time-course transcriptome data, we observe that abundant mammalian pseudogenes could be transcribed, which contribute to the establishment of organ identity. Our analyses reveal that developmentally dynamic pseudogenes are evolutionarily conserved and show an increasing weight during development. Besides, they are involved in complex transcriptional and post-transcriptional modulation, exhibiting the signatures of functional enrichment. Coding potential evaluation suggests that 19% of human pseudogenes could be translated, thus serving as a new way for protein innovation. Moreover, pseudogenes carry disease-associated SNPs and conduce to cancer transcriptome perturbation.

CONCLUSIONS

Our discovery reveals an unexpectedly high abundance of mammalian pseudogenes that can be transcribed and translated, and these pseudogenes represent a novel regulatory layer. Our study also prioritizes developmentally dynamic pseudogenes with signatures of functional enrichment and provides a hybrid sequencing dataset for further unraveling their biological mechanisms in organ development and carcinogenesis in the future.

Navigating the Multiverse of Antisense RNAs: The Transcription- and RNA-Dependent Dimension

Evidence accumulated over the past decades shows that the number of identified antisense transcripts is continuously increasing, promoting them from transcriptional noise to real genes with specific functions. Indeed, recent studies have begun to unravel the complexity of the antisense RNA (asRNA) world, starting from the multidimensional mechanisms that they can exert in physiological and pathological conditions. In this review, we discuss the multiverse of the molecular functions of asRNAs, describing their action through transcription-dependent and RNA-dependent mechanisms. Then, we report the workflow and methodologies to study and functionally characterize single asRNA candidates.

News from around the RNA world: new avenues in RNA biology, biotechnology and therapeutics from the 2022 SIBBM meeting

Since the formalization of the Central Dogma of molecular biology, the relevance of RNA in modulating the flow of information from DNA to proteins has been clear. More recently, the discovery of a vast set of non-coding transcripts involved in crucial aspects of cellular biology has renewed the enthusiasm of the RNA community. Moreover, the remarkable impact of RNA therapies in facing the COVID19 pandemics has bolstered interest in the translational opportunities provided by this incredible molecule. For all these reasons, the Italian Society of Biophysics and Molecular Biology (SIBBM) decided to dedicate its 17th yearly meeting, held in June 2022 in Rome, to the many fascinating aspects of RNA biology. More than thirty national and international speakers covered the properties, modes of action and applications of RNA, from its role in the control of development and cell differentiation to its involvement in disease. Here, we summarize the scientific content of the conference, highlighting the take-home message of each presentation, and we stress the directions the community is currently exploring to push forward our comprehension of the RNA World 3.0.

Regulating the regulator: a survey of mechanisms from transcription to translation controlling expression of mammalian cell cycle kinase Aurora A

Aurora Kinase A (AURKA) is a positive regulator of mitosis with a strict cell cycle-dependent expression pattern. Recently, novel oncogenic roles of AURKA have been uncovered that are independent of the kinase activity and act within multiple signalling pathways, including cell proliferation, survival and cancer stem cell phenotypes. For this, cellular abundance of AURKA protein is per se crucial and must be tightly fine-tuned. Indeed, AURKA is found overexpressed in different cancers, typically as a result of gene amplification or enhanced transcription. It has however become clear that impaired processing, decay and translation of AURKA mRNA can also offer the basis for altered AURKA levels. Accordingly, the involvement of gene expression mechanisms controlling AURKA expression in human diseases is increasingly recognized and calls for much more research. Here, we explore and create an integrated view of the molecular processes regulating AURKA expression at the level of transcription, post-transcription and translation, intercalating discussion on how impaired regulation underlies disease. Given that targeting AURKA levels might affect more functions compared to inhibiting the kinase activity, deeper understanding of its gene expression may aid the design of alternative and therapeutically more successful ways of suppressing the AURKA oncogene.

Expansion of the RNAStructuromeDB to include secondary structural data spanning the human protein-coding transcriptome

RNA plays vital functional roles in almost every component of biology, and these functional roles are often influenced by its folding into secondary and tertiary structures. An important role of RNA secondary structure is in maintaining proper gene regulation; therefore, making accurate predictions of the structures involved in these processes is important. In this study, we have expanded on our previous work that led to the creation of the RNAStructuromeDB. Unlike this previous study that analyzed the human genome at low resolution, we have now scanned the protein-coding human transcriptome at high (single nt) resolution. This provides more robust structure predictions for over 100,000 isoforms of known protein-coding genes. Notably, we also utilize the motif identification tool, ScanFold, to model structures with high propensity for ordered/evolved stability. All data have been uploaded to the RNAStructuromeDB, allowing for easy searching of transcripts, visualization of data tracks (via the Integrative Genomics Viewer or IGV), and download of ScanFold data—including unique highly-ordered motifs. Herein, we provide an example analysis of MAT2A to demonstrate the utility of ScanFold at finding known and novel secondary structures, highlighting regions of potential functionality, and guiding generation of functional hypotheses through use of the data.

Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci

Splicing quantitative trait loci (sQTLs) are one of the major causal mechanisms in genome-wide association study (GWAS) loci, but their role in disease pathogenesis is poorly understood. One reason is the complexity of alternative splicing events producing many unknown isoforms. Here, we propose two approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrate isoforms with the same coding sequence (CDS) and identify 369-601 integrated-isoform ratio QTLs (i²-rQTLs), which altered protein-structure, in six immune subsets. Second, we select CDS incomplete isoforms annotated in GENCODE and identify 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-sequencing among these incomplete isoforms, we reveal 29 full-length isoforms with unannotated CDSs associated with GWAS traits. Furthermore, we show that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases.

Splicing QTL (sQTL), genetic variants regulating alternative splicing, can be biologically important, but complex to detect and interpret. Here, the authors identify sQTL by focusing on protein coding sequences, as an alternative to junction-based approaches.

APAview: A web-based platform for alternative polyadenylation analyses in hematological cancers

Background: Hematologic malignancies, such as acute promyelocytic leukemia (APL) and acute myeloid leukemia (AML), are cancers that start in blood-forming tissues and can affect the blood, bone marrow, and lymph nodes. They are often caused by genetic and molecular alterations such as mutations and gene expression changes. Alternative polyadenylation (APA) is a post-transcriptional process that regulates gene expression, and dysregulation of APA contributes to hematological malignancies. RNA-sequencing-based bioinformatic methods can identify APA sites and quantify APA usages as molecular indexes to study APA roles in disease development, diagnosis, and treatment. Unfortunately, APA data pre-processing, analysis, and visualization are time-consuming, inconsistent, and laborious. A comprehensive, user-friendly tool will greatly simplify processes for APA feature screening and mining.

Results: Here, we present APAview, a web-based platform to explore APA features in hematological cancers and perform APA statistical analysis. APAview server runs on Python3 with a Flask framework and a Jinja2 templating engine. For visualization, APAview client is built on Bootstrap and Plotly. Multimodal data, such as APA quantified by QAPA/DaPars, gene expression data, and clinical information, can be uploaded to APAview and analyzed interactively. Correlation, survival, and differential analyses among user-defined groups can be performed via the web interface. Using APAview, we explored APA features in two hematological cancers, APL and AML. APAview can also be applied to other diseases by uploading different experimental data.

Compartment-specific and ELAVL1-coordinated regulation of intronic polyadenylation isoforms by doxorubicin

Intronic polyadenylation (IPA) isoforms, which contain alternative last exons, are widely regulated in various biological processes and by many factors. However, little is known about their cytoplasmic regulation and translational status. In this study, we provide the first evidence that the genome-wide patterns of IPA isoform regulation during a biological process can be very distinct between the transcriptome and translatome, and between the nucleus and cytosol. Indeed, by 3'-seq analyses on breast cancer cells, we show that the genotoxic anticancer drug, doxorubicin, preferentially down-regulates the IPA to the last-exon (IPA:LE) isoform ratio in whole cells (as previously reported) but preferentially up-regulates it in polysomes. We further show that in nuclei, doxorubicin almost exclusively down-regulates the IPA:LE ratio, whereas in the cytosol, it preferentially up-regulates the isoform ratio, as in polysomes. Then, focusing on IPA isoforms that are up-regulated by doxorubicin in the cytosol and highly translated (up-regulated and/or abundant in polysomes), we identify several IPA isoforms that promote cell survival to doxorubicin. Mechanistically, by using an original approach of condition- and compartment-specific CLIP-seq (CCS-iCLIP) to analyze ELAVL1-RNA interactions in the nucleus and cytosol in the presence and absence of doxorubicin, as well as 3'-seq analyses upon ELAVL1 depletion, we show that the RNA-binding protein ELAVL1 mediates both nuclear down-regulation and cytosolic up-regulation of the IPA:LE isoform ratio in distinct sets of genes in response to doxorubicin. Altogether, these findings reveal differential regulation of the IPA:LE isoform ratio across subcellular compartments during drug response and its coordination by an RNA-binding protein.

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.

Sequencing of individual barcoded cDNAs using Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns

Long-read transcriptomics require understanding error sources inherent to technologies. Current approaches cannot compare methods for an individual RNA molecule. Here, we present a novel platform-comparison method that combines barcoding strategies and long-read sequencing to sequence cDNA copies representing an individual RNA molecule on both Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). We compare these long-read pairs in terms of sequence content and isoform patterns. Although individual read pairs show high similarity, we find differences in (1) aligned length, (2) transcription start site (TSS), (3) polyadenylation site (poly(A)-site) assignment, and (4) exon–intron structures. Overall, 25% of read pairs disagree on either TSS, poly(A)-site, or splice site. Intron-chain disagreement typically arises from alignment errors of microexons and complicated splice sites. Our single-molecule technology comparison reveals that inconsistencies are often caused by sequencing error–induced inaccurate ONT alignments, especially to downstream GUNNGU donor motifs. However, annotation-disagreeing upstream shifts in NAGNAG acceptors in ONT are often confirmed by PacBio and are thus likely real. In both barcoded and nonbarcoded ONT reads, we find that intron number and proximity of GU/AGs better predict inconsistencies with the annotation than read quality alone. We summarize these findings in an annotation-based algorithm for spliced alignment correction that improves subsequent transcript construction with ONT reads.

Differential Effects on the Translation of Immune-Related Alternatively Polyadenylated mRNAs in Melanoma and T Cells by eIF4A Inhibition

Targeting the translation initiation complex eIF4F, which binds the 5' cap of mRNAs, is a promising anti-cancer approach. Silvestrol, a small molecule inhibitor of eIF4A, the RNA helicase component of eIF4F, inhibits the translation of the mRNA encoding the signal transducer and activator of transcription 1 (STAT1) transcription factor, which, in turn, reduces the transcription of the gene encoding one of the major immune checkpoint proteins, i.e., programmed death ligand-1 (PD-L1) in melanoma cells. A large proportion of human genes produce multiple mRNAs differing in their 3'-ends through the use of alternative polyadenylation (APA) sites, which, when located in alternative last exons, can generate protein isoforms, as in the STAT1 gene. Here, we provide evidence that the STAT1α, but not STAT1β protein isoform generated by APA, is required for silvestrol-dependent inhibition of PD-L1 expression in interferon-γ-treated melanoma cells. Using polysome profiling in activated T cells we find that, beyond STAT1, eIF4A inhibition downregulates the translation of some important immune-related mRNAs, such as the ones encoding TIM-3, LAG-3, IDO1, CD27 or CD137, but with little effect on the ones for BTLA and ADAR-1 and no effect on the ones encoding CTLA-4, PD-1 and CD40-L. We next apply RT-qPCR and 3'-seq (RNA-seq focused on mRNA 3' ends) on polysomal RNAs to analyze in a high throughput manner the effect of eIF4A inhibition on the translation of APA isoforms. We identify about 150 genes, including TIM-3, LAG-3, AHNAK and SEMA4D, for which silvestrol differentially inhibits the translation of APA isoforms in T cells. It is therefore crucial to consider 3'-end mRNA heterogeneity in the understanding of the anti-tumor activities of eIF4A inhibitors.

Implications of Poly(A) Tail Processing in Repeat Expansion Diseases

Repeat expansion diseases are a group of more than 40 disorders that affect mainly the nervous and/or muscular system and include myotonic dystrophies, Huntington’s disease, and fragile X syndrome. The mutation-driven expanded repeat tract occurs in specific genes and is composed of tri- to dodeca-nucleotide-long units. Mutant mRNA is a pathogenic factor or important contributor to the disease and has great potential as a therapeutic target. Although repeat expansion diseases are quite well known, there are limited studies concerning polyadenylation events for implicated transcripts that could have profound effects on transcript stability, localization, and translation efficiency. In this review, we briefly present polyadenylation and alternative polyadenylation (APA) mechanisms and discuss their role in the pathogenesis of selected diseases. We also discuss several methods for poly(A) tail measurement (both transcript-specific and transcriptome-wide analyses) and APA site identification—the further development and use of which may contribute to a better understanding of the correlation between APA events and repeat expansion diseases. Finally, we point out some future perspectives on the research into repeat expansion diseases, as well as APA studies.

Identification of high-confidence human poly(A) RNA isoform scaffolds using nanopore sequencing

Nanopore sequencing devices read individual RNA strands directly. This facilitates identification of exon linkages and nucleotide modifications; however, using conventional direct RNA nanopore sequencing, the 5' and 3' ends of poly(A) RNA cannot be identified unambiguously. This is due in part to RNA degradation in vivo and in vitro that can obscure transcription start and end sites. In this study, we aimed to identify individual full-length human RNA isoforms among ∼4 million nanopore poly(A)-selected RNA reads. First, to identify RNA strands bearing 5' m⁷G caps, we exchanged the biological cap for a modified cap attached to a 45-nt oligomer. This oligomer adaptation method improved 5' end sequencing and ensured correct identification of the 5' m⁷G capped ends. Second, among these 5'-capped nanopore reads, we screened for features consistent with a 3' polyadenylation site. Combining these two steps, we identified 294,107 individual high-confidence full-length RNA scaffolds from human GM12878 cells, most of which (257,721) aligned to protein-coding genes. Of these, 4876 scaffolds indicated unannotated isoforms that were often internal to longer, previously identified RNA isoforms. Orthogonal data for m⁷G caps and open chromatin, such as CAGE and DNase-HS seq, confirmed the validity of these high-confidence RNA scaffolds.

Prediction and analysis of functional RNA structures within the integrative genomics viewer

In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results—in conjunction with probing data and other annotations—in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast ‘omics’ resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

Widespread occurrence of hybrid internal-terminal exons in human transcriptomes

Messenger RNA isoform differences are predominantly driven by alternative first, internal, and last exons. Despite the importance of classifying exons to understand isoform structure, few tools examine isoform-specific exon usage. We recently observed that alternative transcription start sites often arise near internal exons, often creating “hybrid” first/internal exons. To systematically detect hybrid exons, we built the hybrid-internal-terminal (HIT) pipeline to classify exons depending on their isoform-specific usage. On the basis of splice junction reads in RNA sequencing data and probabilistic modeling, the HIT index identified thousands of previously misclassified hybrid first-internal and internal-last exons. Hybrid exons are enriched in long genes and genes involved in RNA splicing and have longer flanking introns and strong splice sites. Their usage varies considerably across human tissues. By developing the first method to classify exons according to isoform contexts, our findings document the occurrence of hybrid exons, a common quirk of the human transcriptome.

Analysis Polyadenylation Signal Usage in Sus scrofa

RNA polyadenylation is an important step in the messenger RNA (mRNA) maturation process, and the first step is recognizing the polyadenylation signal (PAS). The PAS type and distribution is a key determinant of post-transcriptional mRNA modification and gene expression. However, little is known about PAS usage and alternative polyadenylation (APA) regulation in livestock species. Recently, sequencing technology has enabled the generation of a large amount of sequencing data revealing variation in poly(A) signals and APA regulation in Sus scrofa. We identified 62,491 polyadenylation signals in Sus scrofa using expressed sequence tag (EST) sequences combined with RNA-seq analysis. The composition and usage frequency of polyadenylation signal in Sus scrofa is similar with that of human and mouse. The most highly conserved polyadenylation signals are AAUAAA and AUUAAA, used for over 63.35% of genes. In addition, we also analyzed the U/GU-rich downstream sequence (DSE) element, located downstream of the cleavage site. Our results indicate that APA regulation was widely occurred in Sus scrofa, as in other organisms. Our result was useful for the accurate annotation of RNA 3' ends in Sus scrofa and the analysis of polyadenylation signal usage in Sus scrofa would give the new insights into the mechanisms of transcriptional regulation.

High-throughput total RNA sequencing in single cells using VASA-seq

Most methods for single-cell transcriptome sequencing amplify the termini of polyadenylated transcripts, capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts and hinders alternative splicing analysis. We, therefore, developed VASA-seq to detect the total transcriptome in single cells, which is enabled by fragmenting and tailing all RNA molecules subsequent to cell lysis. The method is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to more than 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. Analyzing the dynamics of the total single-cell transcriptome, we discovered cell type markers, many based on non-coding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes. RNA velocity characterization was improved, accurately retracing blood maturation trajectories. Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial rearrangements during blood development and heart morphogenesis.

Benchmarking sequencing methods and tools that facilitate the study of alternative polyadenylation

BACKGROUND

Alternative cleavage and polyadenylation (APA), an RNA processing event, occurs in over 70% of human protein-coding genes. APA results in mRNA transcripts with distinct 3' ends. Most APA occurs within 3' UTRs, which harbor regulatory elements that can impact mRNA stability, translation, and localization.

RESULTS

APA can be profiled using a number of established computational tools that infer polyadenylation sites from standard, short-read RNA-seq datasets. Here, we benchmarked a number of such tools-TAPAS, QAPA, DaPars2, GETUTR, and APATrap- against 3'-Seq, a specialized RNA-seq protocol that enriches for reads at the 3' ends of genes, and Iso-Seq, a Pacific Biosciences (PacBio) single-molecule full-length RNA-seq method in their ability to identify polyadenylation sites and quantify polyadenylation site usage. We demonstrate that 3'-Seq and Iso-Seq are able to identify and quantify the usage of polyadenylation sites more reliably than computational tools that take short-read RNA-seq as input. However, we find that running one such tool, QAPA, with a set of polyadenylation site annotations derived from small quantities of 3'-Seq or Iso-Seq can reliably quantify variation in APA across conditions, such asacross genotypes, as demonstrated by the successful mapping of alternative polyadenylation quantitative trait loci (apaQTL).

CONCLUSIONS

We envisage that our analyses will shed light on the advantages of studying APA with more specialized sequencing protocols, such as 3'-Seq or Iso-Seq, and the limitations of studying APA with short-read RNA-seq. We provide a computational pipeline to aid in the identification of polyadenylation sites and quantification of polyadenylation site usages using Iso-Seq data as input.

Comprehensive characterization of somatic variants associated with intronic polyadenylation in human cancers

Somatic single nucleotide variants (SNVs) in cancer genome affect gene expression through various mechanisms depending on their genomic location. While somatic SNVs near canonical splice sites have been reported to cause abnormal splicing of cancer-related genes, whether these SNVs can affect gene expression through other mechanisms remains an open question. Here, we analyzed RNA sequencing and exome data from 4,998 cancer patients covering ten cancer types and identified 152 somatic SNVs near splice sites that were associated with abnormal intronic polyadenylation (IPA). IPA-associated somatic variants favored the localization near the donor splice sites compared to the acceptor splice sites. A proportion of SNV-associated IPA events overlapped with premature cleavage and polyadenylation events triggered by U1 small nuclear ribonucleoproteins (snRNP) inhibition. GC content, intron length and polyadenylation signal were three genomic features that differentiated between SNV-associated IPA and intron retention. Notably, IPA-associated SNVs were enriched in tumor suppressor genes (TSGs), including the well-known TSGs such as PTEN and CDH1 with recurrent SNV-associated IPA events. Minigene assay confirmed that SNVs from PTEN, CDH1, VEGFA, GRHL2, CUL3 and WWC2 could lead to IPA. This work reveals that IPA acts as a novel mechanism explaining the functional consequence of somatic SNVs in human cancer.

Cancer-associated dynamics and potential regulators of intronic polyadenylation revealed by IPAFinder using standard RNA-seq data

Intronic polyadenylation (IpA) usually leads to changes in the coding region of an mRNA, and its implication in diseases has been recognized, although at its very beginning status. Conveniently and accurately identifying IpA is of great importance for further evaluating its biological significance. Here, we developed IPAFinder, a bioinformatic method for the de novo identification of intronic poly(A) sites and their dynamic changes from standard RNA-seq data. Applying IPAFinder to 256 pan-cancer tumor/normal pairs across six tumor types, we discovered 490 recurrent dynamically changed IpA events, some of which are novel and derived from cancer-associated genes such as TSC1, SPERD2, and CCND2. Furthermore, IPAFinder revealed that IpA could be regulated by factors related to splicing and m⁶A modification. In summary, IPAFinder enables the global discovery and characterization of biologically regulated IpA with standard RNA-seq data and should reveal the biological significance of IpA in various processes.