Outlier detection for improved differential splicing quantification from RNA-Seq experiments with replicates

Splicing control by PHF5A is crucial for melanoma cell survival

Abnormalities in alternative splicing are a hallmark of cancer formation. In this study, we investigated the role of the splicing factor PHD finger protein 5A (PHF5A) in melanoma. Malignant melanoma is the deadliest form of skin cancer, and patients with a high PHF5A expression show poor overall survival. Our data revealed that an siRNA-mediated downregulation of PHF5A in different melanoma cell lines leads to massive splicing defects of different tumour-relevant genes. The loss of PHF5A results in an increased rate of apoptosis by triggering Fas- and unfolded protein response (UPR)-mediated apoptosis pathways in melanoma cells. These findings are tumour-specific because we did not observe this regulation in fibroblasts. Our study identifies a crucial role of PHF5A as driver for melanoma malignancy and the described underlying splicing network provides an interesting basis for the development of new therapeutic targets for this aggressive form of skin cancer.

A defective splicing machinery promotes senescence through MDM4 alternative splicing

Defects in the splicing machinery are implicated in various diseases, including cancer. We observed a general reduction in the expression of spliceosome components and splicing regulators in human cell lines undergoing replicative, stress-induced, and telomere uncapping-induced senescence. Supporting the view that defective splicing contributes to senescence, splicing inhibitors herboxidiene, and pladienolide B induced senescence in normal and cancer cell lines. Furthermore, depleting individual spliceosome components also promoted senescence. All senescence types were associated with an alternative splicing transition from the MDM4-FL variant to MDM4-S. The MDM4 splicing shift was reproduced when splicing was inhibited, and spliceosome components were depleted. While decreasing the level of endogenous MDM4 promoted senescence and cell survival independently of the MDM4-S expression status, cell survival was also improved by increasing MDM4-S. Overall, our work establishes that splicing defects modulate the alternative splicing of MDM4 to promote senescence and cell survival.

Dysregulation of RNA splicing in early non-alcoholic fatty liver disease through hepatocellular carcinoma

While changes in RNA splicing have been extensively studied in hepatocellular carcinoma (HCC), no studies have systematically investigated changes in RNA splicing during earlier liver disease. Mouse studies have shown that disruption of RNA splicing can trigger liver disease and we have shown that the splicing factor SRSF3 is decreased in the diseased human liver, so we profiled RNA splicing in liver samples from twenty-nine individuals with no-history of liver disease or varying degrees of non-alcoholic fatty liver disease (NAFLD). We compared our results with three publicly available transcriptome datasets that we re-analyzed for splicing events (SEs). We found many changes in SEs occurred during early liver disease, with fewer events occurring with the onset of inflammation and fibrosis. Many of these early SEs were enriched for SRSF3-dependent events and were associated with SRSF3 binding sites. Mapping the early and late changes to gene ontologies and pathways showed that the genes harboring these early SEs were involved in normal liver metabolism, whereas those harboring late SEs were involved in inflammation, fibrosis and proliferation. We compared the SEs with HCC data from the TCGA and observed that many of these early disease SEs are found in HCC samples and, furthermore, are correlated with disease survival. Changes in splicing factor expression are also observed, which may be associated with distinct subsets of the SEs. The maintenance of these SEs through the multi-year oncogenic process suggests that they may be causative. Understanding the role of these splice variants in metabolic liver disease progression may shed light on the triggers of liver disease progression and the pathogenesis of HCC.

MAJIQlopedia: an encyclopedia of RNA splicing variations in human tissues and cancer

Quantification of RNA splicing variations based on RNA-Sequencing can reveal tissue- and disease-specific splicing patterns. To study such splicing variations, we introduce MAJIQlopedia, an encyclopedia of splicing variations that encompasses 86 human tissues and 41 cancer datasets. MAJIQlopedia reports annotated and unannotated splicing events for a total of 486 175 alternative splice junctions in normal tissues and 338 317 alternative splice junctions in cancer. This database, available at https://majiq.biociphers.org/majiqlopedia/, includes a user-friendly interface that provides graphical representations of junction usage quantification for each junction across all tissue or cancer types. To demonstrate case usage of MAJIQlopedia, we review splicing variations in genes WT1, MAPT and BIN1, which all have known tissue or cancer-specific splicing variations. We also use MAJIQlopedia to highlight novel splicing variations in FDX1 and MEGF9 in normal tissues, and we uncover a novel exon inclusion event in RPS6KA6 that only occurs in two cancer types. Users can download the database, request the addition of data to the webtool, or install a MAJIQlopedia server to integrate proprietary data. MAJIQlopedia can serve as a reference database for researchers seeking to understand what splicing variations exist in genes of interest, and those looking to understand tissue- or cancer-specific splice isoform usage.

FUBP1 is a general splicing factor facilitating 3' splice site recognition and splicing of long introns

Splicing of pre-mRNAs critically contributes to gene regulation and proteome expansion in eukaryotes, but our understanding of the recognition and pairing of splice sites during spliceosome assembly lacks detail. Here, we identify the multidomain RNA-binding protein FUBP1 as a key splicing factor that binds to a hitherto unknown cis-regulatory motif. By collecting NMR, structural, and in vivo interaction data, we demonstrate that FUBP1 stabilizes U2AF2 and SF1, key components at the 3' splice site, through multivalent binding interfaces located within its disordered regions. Transcriptional profiling and kinetic modeling reveal that FUBP1 is required for efficient splicing of long introns, which is impaired in cancer patients harboring FUBP1 mutations. Notably, FUBP1 interacts with numerous U1 snRNP-associated proteins, suggesting a unique role for FUBP1 in splice site bridging for long introns. We propose a compelling model for 3' splice site recognition of long introns, which represent 80% of all human introns.

LIS1 RNA-binding orchestrates the mechanosensitive properties of embryonic stem cells in AGO2-dependent and independent ways

Lissencephaly-1 (LIS1) is associated with neurodevelopmental diseases and is known to regulate the molecular motor cytoplasmic dynein activity. Here we show that LIS1 is essential for the viability of mouse embryonic stem cells (mESCs), and it governs the physical properties of these cells. LIS1 dosage substantially affects gene expression, and we uncovered an unexpected interaction of LIS1 with RNA and RNA-binding proteins, most prominently the Argonaute complex. We demonstrate that LIS1 overexpression partially rescued the extracellular matrix (ECM) expression and mechanosensitive genes conferring stiffness to Argonaute null mESCs. Collectively, our data transforms the current perspective on the roles of LIS1 in post-transcriptional regulation underlying development and mechanosensitive processes.

RNA splicing analysis using heterogeneous and large RNA-seq datasets

The ubiquity of RNA-seq has led to many methods that use RNA-seq data to analyze variations in RNA splicing. However, available methods are not well suited for handling heterogeneous and large datasets. Such datasets scale to thousands of samples across dozens of experimental conditions, exhibit increased variability compared to biological replicates, and involve thousands of unannotated splice variants resulting in increased transcriptome complexity. We describe here a suite of algorithms and tools implemented in the MAJIQ v2 package to address challenges in detection, quantification, and visualization of splicing variations from such datasets. Using both large scale synthetic data and GTEx v8 as benchmark datasets, we assess the advantages of MAJIQ v2 compared to existing methods. We then apply MAJIQ v2 package to analyze differential splicing across 2,335 samples from 13 brain subregions, demonstrating its ability to offer insights into brain subregion-specific splicing regulation.

Heterogeneous nuclear ribonucleoprotein U (HNRNPU) safeguards the developing mouse cortex

HNRNPU encodes the heterogeneous nuclear ribonucleoprotein U, which participates in RNA splicing and chromatin organization. Microdeletions in the 1q44 locus encompassing HNRNPU and other genes and point mutations in HNRNPU cause brain disorders, including early-onset seizures and severe intellectual disability. We aimed to understand HNRNPU’s roles in the developing brain. Our work revealed that HNRNPU loss of function leads to rapid cell death of both postmitotic neurons and neural progenitors, with an apparent higher sensitivity of the latter. Further, expression and alternative splicing of multiple genes involved in cell survival, cell motility, and synapse formation are affected following Hnrnpu’s conditional truncation. Finally, we identified pharmaceutical and genetic agents that can partially reverse the loss of cortical structures in Hnrnpu mutated embryonic brains, ameliorate radial neuronal migration defects and rescue cultured neural progenitors’ cell death.

HNRNPU is an RNA splicing protein associated with brain disorders such as early onset seizures. Here they show that HNRNPU functions to maintain neural progenitors and their progeny by regulating splicing of key neuronal genes.

CYP11B1 variants influence skeletal maturation via alternative splicing

We performed genome-wide association study meta-analysis to identify genetic determinants of skeletal age (SA) deviating in multiple growth disorders. The joint meta-analysis (N = 4557) in two multiethnic cohorts of school-aged children identified one locus, CYP11B1 (expression confined to the adrenal gland), robustly associated with SA (rs6471570-A; β = 0.14; P = 6.2 × 10-12). rs6410 (a synonymous variant in the first exon of CYP11B1 in high LD with rs6471570), was prioritized for functional follow-up being second most significant and the one closest to the first intron-exon boundary. In 208 adrenal RNA-seq samples from GTEx, C-allele of rs6410 was associated with intron 3 retention (P = 8.11 × 10-40), exon 4 inclusion (P = 4.29 × 10-34), and decreased exon 3 and 5 splicing (P = 7.85 × 10-43), replicated using RT-PCR in 15 adrenal samples. As CYP11B1 encodes 11-β-hydroxylase, involved in adrenal glucocorticoid and mineralocorticoid biosynthesis, our findings highlight the role of adrenal steroidogenesis in SA in healthy children, suggesting alternative splicing as a likely underlying mechanism.

CAMPAREE: a robust and configurable RNA expression simulator

Background

The accurate interpretation of RNA-Seq data presents a moving target as scientists continue to introduce new experimental techniques and analysis algorithms. Simulated datasets are an invaluable tool to accurately assess the performance of RNA-Seq analysis methods. However, existing RNA-Seq simulators focus on modeling the technical biases and artifacts of sequencing, rather than on simulating the original RNA samples. A first step in simulating RNA-Seq is to simulate RNA.

Results

To fill this need, we developed the Configurable And Modular Program Allowing RNA Expression Emulation (CAMPAREE), a simulator using empirical data to simulate diploid RNA samples at the level of individual molecules. We demonstrated CAMPAREE’s use for generating idealized coverage plots from real data, and for adding the ability to generate allele-specific data to existing RNA-Seq simulators that do not natively support this feature.

Conclusions

Separating input sample modeling from library preparation/sequencing offers added flexibility for both users and developers to mix-and-match different sample and sequencing simulators to suit their specific needs. Furthermore, the ability to maintain sample and sequencing simulators independently provides greater agility to incorporate new biological findings about transcriptomics and new developments in sequencing technologies. Additionally, by simulating at the level of individual molecules, CAMPAREE has the potential to model molecules transcribed from the same genes as a heterogeneous population of transcripts with different states of degradation and processing (splicing, editing, etc.). CAMPAREE was developed in Python, is open source, and freely available at https://github.com/itmat/CAMPAREE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07934-2.

Chromatin-mediated alternative splicing regulates cocaine-reward behavior

Neuronal alternative splicing is a key gene regulatory mechanism in the brain. However, the spliceosome machinery is insufficient to fully specify splicing complexity. In considering the role of the epigenome in activity-dependent alternative splicing, we and others find the histone modification H3K36me3 to be a putative splicing regulator. In this study, we found that mouse cocaine self-administration caused widespread differential alternative splicing, concomitant with the enrichment of H3K36me3 at differentially spliced junctions. Importantly, only targeted epigenetic editing can distinguish between a direct role of H3K36me3 in splicing and an indirect role via regulation of splice factor expression elsewhere on the genome. We targeted Srsf11, which was both alternatively spliced and H3K36me3 enriched in the brain following cocaine self-administration. Epigenetic editing of H3K36me3 at Srsf11 was sufficient to drive its alternative splicing and enhanced cocaine self-administration, establishing the direct causal relevance of H3K36me3 to alternative splicing of Srsf11 and to reward behavior.

MOCCASIN: a method for correcting for known and unknown confounders in RNA splicing analysis

The effects of confounding factors on gene expression analysis have been extensively studied following the introduction of high-throughput microarrays and subsequently RNA sequencing. In contrast, there is a lack of equivalent analysis and tools for RNA splicing. Here we first assess the effect of confounders on both expression and splicing quantifications in two large public RNA-Seq datasets (TARGET, ENCODE). We show quantification of splicing variations are affected at least as much as those of gene expression, revealing unwanted sources of variations in both datasets. Next, we develop MOCCASIN, a method to correct the effect of both known and unknown confounders on RNA splicing quantification and demonstrate MOCCASIN's effectiveness on both synthetic and real data. Code, synthetic and corrected datasets are all made available as resources.

Comparative evaluation of full-length isoform quantification from RNA-Seq

Background

Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short.

Results

Here we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control.

Conclusions

Salmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04198-1.

Robust principal component analysis for accurate outlier sample detection in RNA-Seq data

BACKGROUND

High throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here we apply robust statistics on RNA-seq data analysis.

RESULTS

We report the use of two robust principal component analysis (rPCA) methods, PcaHubert and PcaGrid, to detect outlier samples in multiple simulated and real biological RNA-seq data sets with positive control outlier samples. PcaGrid achieved 100% sensitivity and 100% specificity in all the tests using positive control outliers with varying degrees of divergence. We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granule layer in the cerebellum of control and conditional SnoN knockout mice. Both rPCA methods detected the same two outlier samples but cPCA failed to detect any. We performed differentially expressed gene detection before and after outlier removal as well as with and without batch effect modeling. We validated gene expression changes using quantitative reverse transcription PCR and used the result as reference to compare the performance of eight different data analysis strategies. Removing outliers without batch effect modeling performed the best in term of detecting biologically relevant differentially expressed genes.

CONCLUSIONS

rPCA implemented in the PcaGrid function is an accurate and objective method to detect outlier samples. It is well suited for high-dimensional data with small sample sizes like RNA-seq data. Outlier removal can significantly improve the performance of differential gene detection and downstream functional analysis.

SURF: integrative analysis of a compendium of RNA-seq and CLIP-seq datasets highlights complex governing of alternative transcriptional regulation by RNA-binding proteins

Advances in high-throughput profiling of RNA-binding proteins (RBPs) have resulted inCLIP-seq datasets coupled with transcriptome profiling by RNA-seq. However, analysis methods that integrate both types of data are lacking. We describe SURF, Statistical Utility for RBP Functions, for integrative analysis of large collections of CLIP-seq and RNA-seq data. We demonstrate SURF's ability to accurately detect differential alternative transcriptional regulation events and associate them to local protein-RNA interactions. We apply SURF to ENCODE RBP compendium and carry out downstream analysis with additional reference datasets. The results of this application are browsable at http://www.statlab.wisc.edu/shiny/surf/.

The changing paradigm of intron retention: regulation, ramifications and recipes

Intron retention (IR) is a form of alternative splicing that has long been neglected in mammalian systems although it has been studied for decades in non-mammalian species such as plants, fungi, insects and viruses. It was generally assumed that mis-splicing, leading to the retention of introns, would have no physiological consequence other than reducing gene expression by nonsense-mediated decay. Relatively recent landmark discoveries have highlighted the pivotal role that IR serves in normal and disease-related human biology. Significant technical hurdles have been overcome, thereby enabling the robust detection and quantification of IR. Still, relatively little is known about the cis- and trans-acting modulators controlling this phenomenon. The fate of an intron to be, or not to be, retained in the mature transcript is the direct result of the influence exerted by numerous intrinsic and extrinsic factors at multiple levels of regulation. These factors have altered current biological paradigms and provided unexpected insights into the transcriptional landscape. In this review, we discuss the regulators of IR and methods to identify them. Our focus is primarily on mammals, however, we broaden the scope to non-mammalian organisms in which IR has been shown to be biologically relevant.

Molecular networks in Network Medicine: Development and applications

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.

Mapping RNA splicing variations in clinically accessible and nonaccessible tissues to facilitate Mendelian disease diagnosis using RNA-seq

PURPOSE

RNA-seq is a promising approach to improve diagnoses by detecting pathogenic aberrations in RNA splicing that are missed by DNA sequencing. RNA-seq is typically performed on clinically accessible tissues (CATs) from blood and skin. RNA tissue specificity makes it difficult to identify aberrations in relevant but nonaccessible tissues (non-CATs). We determined how RNA-seq from CATs represent splicing in and across genes and non-CATs.

METHODS

We quantified RNA splicing in 801 RNA-seq samples from 56 different adult and fetal tissues from Genotype-Tissue Expression Project (GTEx) and ArrayExpress. We identified genes and splicing events in each non-CAT and determined when RNA-seq in each CAT would inadequately represent them. We developed an online resource, MAJIQ-CAT, for exploring our analysis for specific genes and tissues.

RESULTS

In non-CATs, 40.2% of genes have splicing that is inadequately represented by at least one CAT; 6.3% of genes have splicing inadequately represented by all CATs. A majority (52.1%) of inadequately represented genes are lowly expressed in CATs (transcripts per million (TPM) < 1), but 5.8% are inadequately represented despite being well expressed (TPM > 10).

CONCLUSION

Many splicing events in non-CATs are inadequately evaluated using RNA-seq from CATs. MAJIQ-CAT allows users to explore which accessible tissues, if any, best represent splicing in genes and tissues of interest.

Steroid enzyme and receptor expression and regulations in breast tumor samples - A statistical evaluation of public data

In spite of the significant progress of estrogen-dependent breast cancer (BC) treatment, aromatase inhibitor resistance is a major problem limiting the clinical benefit of this frontier endocrine-therapy. The aim of this study was to determine the differential expression of steroid-converting enzymes between tumor and adjacent normal tissues, as well as their correlation in modulating intratumoral steroid-hormone levels in post-menopausal estrogen-dependent BC. RNA sequencing dataset (n = 1097) of The-Cancer-Genome-Atlas (Breast Invasive Carcinoma) retrieved through the data portal of Genomic Data Commons was used for differential expressions and expression correlation analyses by Mann-Whitney U and Spearman's rank test, respectively. The results showed significant up-regulation of 17β-HSD7 (2.50-fold, p < 0.0001) in BC, supporting its effect in sex-hormone control. Besides, suppression of 11β-HSD1 expression (-8.29-fold, p < 0.0001) and elevation of 11β-HSD2 expression (2.04-fold, p < 0.0001) provide a low glucocorticoid environment diminishing BC anti-proliferation. Furthermore, 3α-HSDs were down-regulated (-1.59-fold, p < 0.01; -8.18-fold, p < 0.0001; -33.96-fold, p < 0.0001; -31.85-fold, p < 0.0001 for type 1-4, respectively), while 5α-reductases were up-regulated (1.41-fold, p < 0.0001; 2.85-fold, p < 0.0001; 1.70-fold, p < 0.0001 for type 1-3, respectively) in BC, reducing cell proliferation suppressers 4-pregnenes, increasing cell proliferation stimulators 5α-pregnanes. Expression analysis indicates significant correlations between 11β-HSD1 with 3α-HSD4 (r = 0.605, p < 0.0001) and 3α-HSD3 (r = 0.537, p < 0.0001). Significant expression correlations between 3α-HSDs were also observed. Our results systematically present the regulation of steroid-converting enzymes and their roles in modulating the intratumoral steroid-hormone levels in BC with a vivid 3D-schema, supporting novel therapy targeting the reductive 17β-HSD7 and proposing a new combined therapy targeting 11β-HSD2 and 17β-HSD7.

Analysis of RNA-Seq datasets reveals enrichment of tissue-specific splice variants for nuclear envelope proteins

Laminopathies yield tissue-specific pathologies, yet arise from mutation of ubiquitously-expressed genes. A little investigated hypothesis to explain this is that the mutated proteins or their partners have tissue-specific splice variants. To test this, we analyzed RNA-Seq datasets, finding novel isoforms or isoform tissue-specificity for: Lap2, linked to cardiomyopathy; Nesprin 2, linked to Emery-Dreifuss muscular dystrophy and Lmo7, that regulates the Emery-Dreifuss muscular dystrophy linked emerin gene. Interestingly, the muscle-specific Lmo7 exon is rich in serine phosphorylation motifs, suggesting regulatory function. Muscle-specific splice variants in non-nuclear envelope proteins linked to other muscular dystrophies were also found. Nucleoporins tissue-specific variants were found for Nup54, Nup133, Nup153 and Nup358/RanBP2. RT-PCR confirmed novel Lmo7 and RanBP2 variants and specific knockdown of the Lmo7 variantreduced myogenic index. Nuclear envelope proteins were enriched for tissue-specific splice variants compared to the rest of the genome, suggesting that splice variants contribute to its tissue-specific functions.

Degradation of splicing factor SRSF3 contributes to progressive liver disease

Serine rich splicing factor 3 (SRSF3) plays a critical role in liver function and its loss promotes chronic liver damage and regeneration. As a consequence, genetic deletion of SRSF3 in hepatocytes caused progressive liver disease and ultimately led to hepatocellular carcinoma. Here we show that SRSF3 is decreased in human liver samples with non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), or cirrhosis that was associated with alterations in RNA splicing of known SRSF3 target genes. Hepatic SRSF3 expression was similarly decreased and RNA splicing dysregulated in mouse models of NAFLD and NASH. We showed that palmitic acid-induced oxidative stress caused conjugation of the ubiquitin like NEDD8 protein to SRSF3 and proteasome mediated degradation. SRSF3 was selectively neddylated at lysine11 and mutation of this residue (SRSF3-K11R) was sufficient to prevent both SRSF3 degradation and alterations in RNA splicing. Finally prevention of SRSF3 degradation in vivo partially protected mice from hepatic steatosis, fibrosis and inflammation. These results highlight a neddylation-dependent mechanism regulating gene expression in the liver that is disrupted in early metabolic liver disease and may contribute to the progression to NASH, cirrhosis and ultimately hepatocellular carcinoma.

Aberrant splicing in B-cell acute lymphoblastic leukemia

Aberrant splicing is a hallmark of leukemias with mutations in splicing factor (SF)-encoding genes. Here we investigated its prevalence in pediatric B-cell acute lymphoblastic leukemias (B-ALL), where SFs are not mutated. By comparing these samples to normal pro-B cells, we found thousands of aberrant local splice variations (LSVs) per sample, with 279 LSVs in 241 genes present in every comparison. These genes were enriched in RNA processing pathways and encoded ∼100 SFs, e.g. hnRNPA1. HNRNPA1 3'UTR was most pervasively mis-spliced, yielding the transcript subject to nonsense-mediated decay. To mimic this event, we knocked it down in B-lymphoblastoid cells and identified 213 hnRNPA1-regulated exon usage events comprising the hnRNPA1 splicing signature in pediatric leukemia. Some of its elements were LSVs in DICER1 and NT5C2, known cancer drivers. We searched for LSVs in other leukemia and lymphoma drivers and discovered 81 LSVs in 41 additional genes. Seventy-seven LSVs out of 81 were confirmed using two large independent B-ALL RNA-seq datasets, and the twenty most common B-ALL drivers, including NT5C2, showed higher prevalence of aberrant splicing than of somatic mutations. Thus, post-transcriptional deregulation of SF can drive widespread changes in B-ALL splicing and likely contributes to disease pathogenesis.

Using equivalence class counts for fast and accurate testing of differential transcript usage

Background: RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantification estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis compared to exon-level analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Recent work has proposed performing a variety of RNA-seq analysis directly on equivalence class counts (ECCs). Methods: Here we demonstrate that ECCs can be used effectively with existing count-based methods for detecting DTU. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. Results: We find that ECCs have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. Conclusions: We posit that equivalence class read counts are a natural unit on which to perform differential transcript usage analysis.

Pre-mRNA modifications and their role in nuclear processing

Background

Cellular non-coding RNAs are extensively modified post-transcriptionally, with more than 100 chemically distinct nucleotides identified to date. In the past five years, new sequencing based methods have revealed widespread decoration of eukaryotic messenger RNA with diverse RNA modifications whose functions in mRNA metabolism are only beginning to be known.

Results

Since most of the identified mRNA modifying enzymes are present in the nucleus, these modifications have the potential to function in nuclear pre-mRNA processing including alternative splicing. Here we review recent progress towards illuminating the role of pre-mRNA modifications in splicing and highlight key areas for future investigation in this rapidly growing field.

Conclusions

Future studies to identify which modifications are added to nascent pre-mRNA and to interrogate the direct effects of individual modifications are likely to reveal new mechanisms by which nuclear pre-mRNA processing is regulated.

Dynamic transcriptomic m6A decoration: writers, erasers, readers and functions in RNA metabolism

N⁶-methyladenosine (m⁶A) is a chemical modification present in multiple RNA species, being most abundant in mRNAs. Studies on enzymes or factors that catalyze, recognize, and remove m⁶A have revealed its comprehensive roles in almost every aspect of mRNA metabolism, as well as in a variety of physiological processes. This review describes the current understanding of the m⁶A modification, particularly the functions of its writers, erasers, readers in RNA metabolism, with an emphasis on its role in regulating the isoform dosage of mRNAs.