Splicing Outcomes Research Articles

Non-invasive monitoring of alternative splicing outcomes to identify candidate therapies for myotonic dystrophy type 1

During drug development, tissue samples serve as indicators of disease activity and pharmacodynamic responses. Reliable non-invasive measures of drug target engagement will facilitate identification of promising new treatments. Here we develop and validate a novel bi-transgenic mouse model of myotonic dystrophy type 1 (DM1) in which expression of either DsRed or GFP is determined by alternative splicing of an upstream minigene that is mis-regulated in DM1. Using a novel in vivo fluorescence spectroscopy system, we show that quantitation of the DsRed/GFP ratio provides an accurate estimation of splicing outcomes in muscle tissue of live mice that nearly doubles throughput over conventional fluorescence imaging techniques. Serial in vivo spectroscopy measurements in mice treated with a C16 fatty acid ligand conjugated antisense (LICA) oligonucleotide reveal a dose-dependent therapeutic response within seven days, confirm a several-week duration of action, and demonstrate a two-fold greater target engagement as compared to the unconjugated parent oligonucleotide.

RUNX1 Deficiency Cooperates with SRSF2 Mutation to Further Disrupt RNA Splicing and Exacerbate Myelodysplastic Syndromes in Mouse Models

Myelodysplastic syndromes (MDS) and leukemias require the acquisition of multiple mutations during disease development resulting in clonal diversity and different responses. Splicing factors, transcription factors, epigenetic regulators, and cell signaling proteins are the common molecular events mutated during disease evolution and those events rarely occur alone. However, it remains unclear how the combinations of mutations in different categories may have cooperative effects in gene regulation and disease etiology. Mutations in the splicing factor SRSF2 and the transcription factor RUNX1 are closely associated in MDS patients, and their co-existence is linked to poor prognosis.To understand the functional contribution of the coexistence in vivo, we utilized Mx1-Cre based conditional knock-in Srsf2-P95H mutation (P95H/+) mice, and Mx1-Cre based Runx1 conditional knockout mice (Runx1 f/f). We crossed these two strains to establish a new mouse model with inducible double mutations (Srsf2 P95H/+ Runx1Δ/Δ). Double mutant mice showed pancytopenia with MDS features including severe leukopenia in multiple lineages, macrocytic anemia, thrombocytopenia, and dysplastic morphology in peripheral blood. Double mutant mice also displayed more dramatic skewing toward the myeloid lineage at the expense of the B cell lineage when compared to single mutant mice. In competitive bone marrow transplantation assays, SRSF2 P95H cooperated with RUNX1 deficiency to confer a competitive disadvantage in vivo.To investigate the mechanistic basis of this cooperation, differential splicing and gene expression were assessed by RNA sequencing of Lineage- c-kit+ cells isolated from WT, SRSF2 P95H, RUNX1 KO, and Double mutant bone marrow cells. Interestingly, deletion of the Runx1 gene alone resulted in significant changes to RNA splicing in 1120 genes, while the SRSF2 P95H mutation itself induced splicing changes in 935 genes. Furthermore, 2468 splice junctions in 1677 genes showed splicing changes in double mutant samples compared to wildtype controls. Among these altered splicing events, intriguingly, exon skipping was the major alteration in single and double mutants. Furthermore, the double mutant demonstrated increased aberrant splicing events when compared to the single mutants alone. We performed pathway analysis using the differentially spliced genes identified in double mutant cells. Pathways in cancer, DNA replication/repair, cell death and survival, hematological disease and inflammatory response were enriched. Splicing changes were detected in genes recurrently mutated in blood malignancies, including Fanca, Fance, Fancl, Ezh2, Atm, Gnas, Braf, Bcor, Fyn, and Wsb1 as well as in genes critical for splicing regulation, such as Srsf6, Fus, Hnrnpa2b1, and Srrm2. Gene expression analysis revealed 869 significantly differentially expressed genes in double mutant cells. Within the events in the double mutant population, 60% of the differentially expressed genes were also observed in RUNX1 single mutant cells, while only 2% of the differentially expressed genes were observed in SRSF2 single mutant cells, and 38% of the differentially expressed genes were uniquely presented in the double mutant cells. These results suggest that the gene expression program is heavily affected by loss of RUNX1 and the coexistence of an SRSF2 mutation contributes to certain synergistic effects in transcriptional regulation. Furthermore, we identified 101 genes that showed both differential splicing and expression, including Jak3, Jag2, Csf3r, Fcer1g, CD244 which are important in hematologic disorders.Together, these results suggest that the deficiency of compound RUNX1 and SRSF2 P95H mutations impairs multi-lineage hematopoiesis and exacerbates the disease phenotypes caused by single mutations alone. At the genome-wide level, loss of the transcription factor RUNX1 itself dysregulates splicing outcomes and cooperates with the splicing factor SRSF2 P95H mutation to further perturb the expression and splicing of key regulators involved in hematopoietic stem/progenitor cell development, inflammatory responses, DNA damage, and RNA splicing. DisclosuresNo relevant conflicts of interest to declare.

Screening of BRCA1/2 deep intronic regions by targeted gene sequencing identifies the first germline BRCA1 variant causing pseudoexon activation in a patient with breast/ovarian cancer

BackgroundGenetic analysis of BRCA1 and BRCA2 for the diagnosis of hereditary breast and ovarian cancer (HBOC) is commonly restricted to coding regions and exon-intron boundaries. Although germline pathogenic variants in...

Splicing of long non-coding RNAs primarily depends on polypyrimidine tract and 5' splice-site sequences due to weak interactions with SR proteins.

Many nascent long non-coding RNAs (lncRNAs) undergo the same maturation steps as pre-mRNAs of protein-coding genes (PCGs), but they are often poorly spliced. To identify the underlying mechanisms for this phenomenon, we searched for putative splicing inhibitory sequences using the ncRNA-a2 as a model. Genome-wide analyses of intergenic lncRNAs (lincRNAs) revealed that lincRNA splicing efficiency positively correlates with 5′ss strength while no such correlation was identified for PCGs. In addition, efficiently spliced lincRNAs have higher thymidine content in the polypyrimidine tract (PPT) compared to efficiently spliced PCGs. Using model lincRNAs, we provide experimental evidence that strengthening the 5′ss and increasing the T content in PPT significantly enhances lincRNA splicing. We further showed that lincRNA exons contain less putative binding sites for SR proteins. To map binding of SR proteins to lincRNAs, we performed iCLIP with SRSF2, SRSF5 and SRSF6 and analyzed eCLIP data for SRSF1, SRSF7 and SRSF9. All examined SR proteins bind lincRNA exons to a much lower extent than expression-matched PCGs. We propose that lincRNAs lack the cooperative interaction network that enhances splicing, which renders their splicing outcome more dependent on the optimality of splice sites.

Mathematical modeling identifies potential gene structure determinants of co-transcriptional control of alternative pre-mRNA splicing.

The spliceosome catalyzes the removal of introns from pre-messenger RNA (mRNA) and subsequent pairing of exons with remarkable fidelity. Some exons are known to be skipped or included in the mature mRNA in a cell type- or context-dependent manner (cassette exons), thereby contributing to the diversification of the human proteome. Interestingly, splicing is initiated (and sometimes completed) co-transcriptionally. Here, we develop a kinetic mathematical modeling framework to investigate alternative co-transcriptional splicing (CTS) and, specifically, the control of cassette exons’ inclusion. We show that when splicing is co-transcriptional, default splice patterns of exon inclusion are more likely than when splicing is post-transcriptional, and that certain exons are more likely to be regulatable (i.e. cassette exons) than others, based on the exon–intron structure context. For such regulatable exons, transcriptional elongation rates may affect splicing outcomes. Within the CTS paradigm, we examine previously described hypotheses of co-operativity between splice sites of short introns (i.e. ‘intron definition’) or across short exons (i.e. ‘exon definition’), and find that models encoding these faithfully recapitulate observations in the fly and human genomes, respectively.

Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations?

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR, and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon–intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11, and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy, and NPV (99.44%, 99.44%, and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65%, and 84.44, respectively) for acceptor sites.We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high NPV obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors.

A comprehensive characterization of cis-acting splicing-associated variants in human cancer.

Although many driver mutations are thought to promote carcinogenesis via abnormal splicing, the landscape of splicing-associated variants (SAVs) remains unknown due to the complexity of splicing abnormalities. Here, we developed a statistical framework to systematically identify SAVs disrupting or newly creating splice site motifs and applied it to matched whole-exome and transcriptome sequencing data from 8976 samples across 31 cancer types, generating a catalog of 14,438 SAVs. Such a large collection of SAVs enabled us to characterize their genomic features, underlying mutational processes, and influence on cancer driver genes. In fact, ∼50% of SAVs identified were those disrupting noncanonical splice sites (non-GT-AG dinucleotides), including the third and fifth intronic bases of donor sites, or newly creating splice sites. Mutation signature analysis revealed that tobacco smoking is more strongly associated with SAVs, whereas ultraviolet exposure has less impact. SAVs showed remarkable enrichment of cancer-related genes, and as many as 14.7% of samples harbored at least one SAVs affecting them, particularly in tumor suppressors. In addition to intron retention, whose association with tumor suppressor inactivation has been previously reported, exon skipping and alternative splice site usage caused by SAVs frequently affected tumor suppressors. Finally, we described high-resolution distributions of SAVs along the gene and their splicing outcomes in commonly disrupted genes, including TP53, PIK3R1, GATA3, and CDKN2A, which offers genetic clues for understanding their functional properties. Collectively, our findings delineate a comprehensive portrait of SAVs, novel insights into transcriptional de-regulation in cancer.

Post-Translational Modifications in Polypyrimidine Tract Binding Proteins PTBP1 and PTBP2.

RNA binding proteins play an important role in regulating alternative pre-mRNA splicing and in turn cellular gene expression. Many of these RNA binding proteins occur as gene families with members sharing a high degree of primary structure identity and domain organization yet have tissue-specific expression patterns and regulate different sets of target exons. How highly similar members in a gene family can exert different splicing outcomes is not well understood. We conducted mass spectrometry analysis of post-translational phosphorylation and acetylation modifications for two paralogs of the polypyrimidine tract binding protein family, PTBP1 and PTBP2, to discover modifications that occur in splicing reaction mixtures and to identify discrete modifications that may direct their different splicing activities. We find that PTBP1 and PTBP2 have many distinct phosphate modifications located in the unstructured N-terminal, linker 1, and linker 2 regions. We find that the two proteins have many overlapping acetate modifications in the RNA recognition motifs (RRMs) with a few distinct sites in PTBP1 RRM2 and RRM3. Our data also reveal that lysine residues in the nuclear localization sequence of PTBP2 are acetylated. Collectively, our results highlight important differences in post-translational modifications between the paralogs and suggest a role for them in the differential splicing activity of PTBP1 and PTBP2.

In vitro iCLIP-based modeling uncovers how the splicing factor U2AF2 relies on regulation by cofactors.

Alternative splicing generates distinct mRNA isoforms and is crucial for proteome diversity in eukaryotes. The RNA-binding protein (RBP) U2AF2 is central to splicing decisions, as it recognizes 3′ splice sites and recruits the spliceosome. We establish “in vitro iCLIP” experiments, in which recombinant RBPs are incubated with long transcripts, to study how U2AF2 recognizes RNA sequences and how this is modulated by trans-acting RBPs. We measure U2AF2 affinities at hundreds of binding sites and compare in vitro and in vivo binding landscapes by mathematical modeling. We find that trans-acting RBPs extensively regulate U2AF2 binding in vivo, including enhanced recruitment to 3′ splice sites and clearance of introns. Using machine learning, we identify and experimentally validate novel trans-acting RBPs (including FUBP1, CELF6, and PCBP1) that modulate U2AF2 binding and affect splicing outcomes. Our study offers a blueprint for the high-throughput characterization of in vitro mRNP assembly and in vivo splicing regulation.

Understanding the Role of Post‐Translational Modifications on the Splicing Activity of RNA Binding Proteins

The Polypyrimidine Tract Binding Protein 1 (PTBP1) is an RNA binding protein that controls the alternative splicing of many gene transcripts. PTBP1 belongs to a gene family with paralogs expressed in varying cell types and stages of cell growth. PTPB1 is expressed in most cell types but is absent in differentiating neurons and muscle. Paralog PTBP2 is highly expressed in differentiating neurons. The amino acid sequence of PTBP1 is 74% identical to PTBP2 and the two proteins have a similar domain organization with 4 RNA binding domains (RBDs) connected by 3 linker regions and an N‐terminal region. The two proteins have over‐lapping and non‐overlapping target exons; PTBP1 functions as a splicing repressor of many neuronal exons while PTBP2 does not. PTBP1 is expressed in neuronal progenitor cells, but is down‐regulated during neuronal differentiation while the level of PTBP2 is up‐regulated. The switch in the levels of the two proteins effects the splicing of many exons that are sensitive to PTBP1, thus causing a change in the neuronal splicing program that is critical for development and maturation of neurons. How the two proteins exert different splicing outcomes is not well understood. Recent studies using PTBP1‐PTBP2 chimeric proteins highlighted regions of PTBP1 that change the splicing activity of PTBP2, and revealed the two proteins have different protein‐protein interactions. Thus, we hypothesize that additional factors such as post‐translational modifications (PTMs) dictate PTBP splicing activity. To test this, we used in vitro splicing reaction conditions, in which PTBP1 and 2 showed different splicing activity. To capture splicing relevant PTM's bacterial expressed recombinant PTB proteins were incubated in splicing reaction mixtures containing HeLa nuclear extract, pull‐down using Ni2+‐NTA beads, boiled and separated on SDS‐PAGE. The corresponding PTBP gel bands were analyzed by mass spectrometry for PTM's. Our Results highlight that PTBP1 and 2 have many PTM's including phosphate, acetate and ubiquitin groups. The N‐terminal and linker 1 region are modified primarily by phosphate groups while the RRM domains are modified by acetate and ubiquitin groups. We also note that some residues contain more than one type of modification such as acetate and ubiquitin. Percent modified for each residue‐modification was calculated by counting the modified residue and the total number of peptide fragments containing that residue. Our results highlight that the type and degree of modification vary between the two proteins, in regions including the N‐terminal, linker 1 and linker 2. These regions were identified by the chimera study to alter PTBP2 activity. Currently, we are conducting a mutational analysis of these modified residues that are different between the two proteins, to determine the role of PTM's in PTBP splicing activity.Support or Funding InformationThis work was supported by a Maximizing Access to Research Careers grant to CSUF from the National Institutes of Health [5T34GM008612‐21]This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

A Bioinformatics Approach to Discover the Evolutionary Origin of the PTBP Splicing Regulators

Polypyrimidine binding tract proteins (PTBPs) are members of the hnRNP family and are involved in the modulation of alternative splicing, serving as both repressors and activators of cassette exon inclusion in mature mRNA (Kafasla et al, 2012). The human genome encodes for three different PTBP paralogs, PTBP1, PTBP2 and PTPB3. These homologous proteins are highly similar in primary structure and domain organization, which is characterized by a N‐terminal domain and four RNA recognition motifs (RRMs) connected via linker regions. PTBPs share >70% sequence identity and differ in their tissue expression with PTBP1 being expressed nearly ubiquitously while PTBP2 and PTBP3 show more tissue specific expression (Keppetipola et al, 2012; Tan et al, 2015). Changes in the expression of PTBP1 and PTBP2 have been found to be critical in the process of neuronal differentiation and maturation (Li et al, 2014). PTBP1 has been observed to be overexpressed in ovarian cancer cells (He et al, 2007) and PTBP1 and PTBP2 have been implicated in the splicing of transcripts that promote proliferation of glioma (Cheung et al, 2009). Inhibition of PTBP3 expression in gastric cancer cells induced apoptosis (Liang et al, 2017). Therefore, further understanding of PTBPs may provide insight to how related proteins exert differential splicing outcomes and may provide therapeutic targets for drug design. In this study, we sought to characterize PTBP by elucidating its evolutionary history via sequence and phylogenetic analyses. Homologous sequences, including vertebrate, invertebrate, plant and fungi sequences, were identified by using BLAST search tools across various species databases. Multiple sequencing alignments (MSAs) and phylogenetic analyses were performed using different methods in MEGA6. Our current results reveal that the three human PTBP paralogs have orthologs found ubiquitously amongst jawed vertebrate species. In contrast, jawless vertebrate and invertebrate species contain only one homologous protein. This finding suggests that the gene duplication events that produced the three human PTBPs occurred within the ancestor of all jawed vertebrates. Plant species also have two or three PTBP homologs; however, these do not cluster with their animal homologs. This suggests that in both animal and plant species the genes multiplied independently. Fungi, which are unicellular organisms, contain one or no PTBP homologs. MSAs have also revealed a variety of residues within the RRMs that are highly conserved across the various PTBP orthologs surveyed, which suggests their functional and structural importance. We are conducting analysis of intron and synteny conservation as well as 3D structure comparisons to further develop the tentative conclusions posited.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Roles of alternative splicing in modulating transcriptional regulation

BackgroundThe ability of a transcription factor to regulate its targets is modulated by a variety of genetic and epigenetic mechanisms. Alternative splicing can modulate gene function by adding or removing certain protein domains, and therefore affect the activity of protein. Reverse engineering of gene regulatory networks using gene expression profiles has proven valuable in dissecting the logical relationships among multiple proteins during the transcriptional regulation. However, it is unclear whether alternative splicing of certain proteins affects the activity of other transcription factors.ResultsIn order to investigate the roles of alternative splicing during transcriptional regulation, we constructed a statistical model to infer whether the alternative splicing events of modulator proteins can affect the ability of key transcription factors in regulating the expression levels of their transcriptional targets. We tested our strategy in KIRC (Kidney Renal Clear Cell Carcinoma) using the RNA-seq data downloaded from TCGA (the Cancer Genomic Atlas). We identified 828of modulation relationships between the splicing levels of modulator proteins and activity levels of transcription factors. For instance, we found that the activity levels of GR (glucocorticoid receptor) protein, a key transcription factor in kidney, can be influenced by the splicing status of multiple proteins, including TP53, MDM2 (mouse double minute 2 homolog), RBM14 (RNA-binding protein 14) and SLK (STE20 like kinase). The influenced GR-targets are enriched by key cancer-related pathways, including p53 signaling pathway, TR/RXR activation, CAR/RXR activation, G1/S checkpoint regulation pathway, and G2/M DNA damage checkpoint regulation pathway.ConclusionsOur analysis suggests, for the first time, that exon inclusion levels of certain regulatory proteins can affect the activities of many transcription factors. Such analysis can potentially unravel a novel mechanism of how splicing variation influences the cellular function and provide important insights for how dysregulation of splicing outcome can lead to various diseases.

Alternative Splicing in Genetic Diseases: Improved Diagnosis and Novel Treatment Options.

Alternative splicing is an important mechanism to regulate gene expression and to expand the repertoire of gene products in order to accommodate an increase in complexity of multicellular organisms. It needs to be precisely regulated, which is achieved via RNA structure, splicing factors, transcriptional regulation, and chromatin. Changes in any of these factors can lead to disease. These may include the core spliceosome, splicing enhancer/repressor sequences and their interacting proteins, the speed of transcription by RNA polymerase II, and histone modifications. While the basic principle of splicing is well understood, it is still very difficult to predict splicing outcome, due to the multiple levels of regulation. Current molecular diagnostics mainly uses Sanger sequencing of exons, or next-generation sequencing of gene panels or the whole exome. Functional analysis of potential splicing variants is scarce, and intronic variants are often not considered. This likely results in underestimation of the percentage of splicing variants. Understanding how sequence variants may affect splicing is not only crucial for confirmation of diagnosis and for genetic counseling, but also for the development of novel treatment options. These include small molecules, transsplicing, antisense oligonucleotides, and gene therapy. Here we review the current state of molecular mechanisms of splicing regulation and how deregulation can lead to human disease, diagnostics to detect splicing variants, and novel treatment options based on splicing correction.

Integrative deep models for alternative splicing.

MotivationAdvancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The first involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work, we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors.ResultsWe perform a detailed comparison of two previous modeling approaches, Bayesian and Deep Neural networks, dissecting the confounding effects of datasets and target functions. We then develop a new target function for AS prediction in exon skipping events and show it significantly improves model accuracy. Next, we develop a modeling framework that leverages transfer learning to incorporate CLIP-Seq, knockdown and over expression experiments, which are inherently noisy and suffer from missing values. Using several datasets involving key splice factors in mouse brain, muscle and heart we demonstrate both the prediction improvements and biological insights offered by our new models. Overall, the framework we propose offers a scalable integrative solution to improve splicing code modeling as vast amounts of relevant genomic data become available.Availability and implementationCode and data available at: majiq.biociphers.org/jha_et_al_2017/Supplementary information Supplementary data are available at Bioinformatics online.

Ancient antagonism between CELF and RBFOX families tunes mRNA splicing outcomes.

Over 95% of human multi-exon genes undergo alternative splicing, a process important in normal development and often dysregulated in disease. We sought to analyze the global splicing regulatory network of CELF2 in human T cells, a well-studied splicing regulator critical to T cell development and function. By integrating high-throughput sequencing data for binding and splicing quantification with sequence features and probabilistic splicing code models, we find evidence of splicing antagonism between CELF2 and the RBFOX family of splicing factors. We validate this functional antagonism through knockdown and overexpression experiments in human cells and find CELF2 represses RBFOX2 mRNA and protein levels. Because both families of proteins have been implicated in the development and maintenance of neuronal, muscle, and heart tissues, we analyzed publicly available data in these systems. Our analysis suggests global, antagonistic coregulation of splicing by the CELF and RBFOX proteins in mouse muscle and heart in several physiologically relevant targets, including proteins involved in calcium signaling and members of the MEF2 family of transcription factors. Importantly, a number of these coregulated events are aberrantly spliced in mouse models and human patients with diseases that affect these tissues, including heart failure, diabetes, or myotonic dystrophy. Finally, analysis of exons regulated by ancient CELF family homologs in chicken, Drosophila, and Caenorhabditis elegans suggests this antagonism is conserved throughout evolution.

Dissecting the Mechanism of H3K36 Methylation in Regulating Pre‐mRNA Splicing

Eukaryotic genes contain introns that are removed during pre‐mRNA splicing by the spliceosome, a large, dynamic RNA‐protein complex composed of five ribonucleoprotein complexes and many associated protein cofactors. Although the detailed mechanism of spliceosome assembly onto pre‐mRNA has been elucidated using elegant biochemistry, in vivo pre‐mRNA splicing is tightly coordinated with gene transcription. Splicing and transcription appear to be coupled not only in time, but also functionally. Spliceosome assembly and the subsequent catalytic steps take place in the context of a dynamic chromatin environment, which suggests that the state of the chromatin may affect splicing. Nonetheless, the precise role of histone modifications in contributing to splicing have remained unclear. Here, we investigate the role of the histone methyltransferase Set2 and its associated chromatin mark, H3K36 methylation, in determining splicing outcomes. Through high‐throughput RNA‐sequencing, we identify discreet classes of lowly expressed transcripts whose splicing is strongly dependent on an intact histone H3K36 methyltransferase complex. Moreover, this same subset of genes shows defective splicing when the H3K36 residue is mutated to prevent its methylation, reinforcing that it is histone methylation per se that is important for splicing these introns. To understand the mechanism by which H3K36 affects splicing, we have analyzed molecular events downstream of H3 methylation, and have identified the NuA4 complex and its chromodomain‐containing protein Eaf3, as a potential mediator of the interaction between the splicing machinery and H3K36 methylation. Here we report our detailed analysis of the mechanism by which this histone modification directly affects spliceosome assembly. Moreover, these studies suggest that subsets of introns may be “tuned” to respond to specific changes in the chromatin environment.Support or Funding InformationNIGMS grant number GM‐085474Ruth L. Kirschstein National Research Service Award GM007185

Functional classification of DNA variants by hybrid minigenes: Identification of 30 spliceogenic variants of BRCA2 exons 17 and 18.

Mutation screening of the breast cancer genes BRCA1 and BRCA2 identifies a large fraction of variants of uncertain clinical significance (VUS) whose functional and clinical interpretations pose a challenge for genomic medicine. Likewise, an increasing amount of evidence indicates that genetic variants can have deleterious effects on pre-mRNA splicing. Our goal was to investigate the impact on splicing of a set of reported variants of BRCA2 exons 17 and 18 to assess their role in hereditary breast cancer and to identify critical regulatory elements that may constitute hotspots for spliceogenic variants. A splicing reporter minigene with BRCA2 exons 14 to-20 (MGBR2_ex14-20) was constructed in the pSAD vector. Fifty-two candidate variants were selected with splicing prediction programs, introduced in MGBR2_ex14-20 by site-directed mutagenesis and assayed in triplicate in MCF-7 cells. Wild type MGBR2_ex14-20 produced a stable transcript of the expected size (1,806 nucleotides) and structure (V1-[BRCA2_exons_14–20]–V2). Functional mapping by microdeletions revealed essential sequences for exon recognition on the 3’ end of exon 17 (c.7944-7973) and the 5’ end of exon 18 (c.7979-7988, c.7999-8013). Thirty out of the 52 selected variants induced anomalous splicing in minigene assays with >16 different aberrant transcripts, where exon skipping was the most common event. A wide range of splicing motifs were affected including the canonical splice sites (15 variants), novel alternative sites (3 variants), the polypyrimidine tract (3 variants) and enhancers/silencers (9 variants). According to the guidelines of the American College of Medical Genetics and Genomics (ACMG), 20 variants could be classified as pathogenic (c.7806-2A>G, c.7806-1G>A, c.7806-1G>T, c.7806-1_7806-2dup, c.7976+1G>A, c.7977-3_7978del, c.7977-2A>T, c.7977-1G>T, c.7977-1G>C, c.8009C>A, c.8331+1G>T and c.8331+2T>C) or likely pathogenic (c.7806-9T>G, c.7976G>C, c.7976G>A, c.7977-7C>G, c.7985C>G, c.8023A>G, c.8035G>T and c.8331G>A), accounting for 30.8% of all pathogenic/likely pathogenic variants of exons 17–18 at the BRCA Share database. The remaining 8 variants (c.7975A>G, c.7977-6T>G, c.7988A>T, c.7992T>A, c.8007A>G, c.8009C>T, c.8009C>G, and c.8072C>T) induced partial splicing anomalies with important ratios of the full-length transcript (≥70%), so that they remained classified as VUS. Aberrant splicing is therefore especially prevalent in BRCA2 exons 17 and 18 due to the presence of active ESEs involved in exon recognition. Splicing functional assays with minigenes are a valuable strategy for the initial characterization of the splicing outcomes and the subsequent clinical interpretation of variants of any disease-gene, although these results should be checked, whenever possible, against patient RNA.

Cryptic splice activation but not exon skipping is observed in minigene assays of dystrophin c.9361+1G>A mutation identified by NGS

Next-generation sequencing (NGS) discloses nucleotide changes in the genome. Mutations at splicing regulatory elements are expected to cause splicing errors, such as exon skipping, cryptic splice site activation, partial exon loss or intron retention. In dystrophinopathy patients, prediction of splicing outcomes is essential to determine the phenotype: either severe Duchenne or mild Becker muscular dystrophy, based on the reading frame rule. In a Vietnamese patient, NGS identified a c.9361+1G>A mutation in the dystrophin gene and an additional DNA variation of A>G at +117 bases in intron 64. To ascertain the consequences of these DNA changes on dystrophin splicing, minigene constructs were prepared inserting dystrophin exon 64 plus various lengths of intron 64. Exon 64 skipping was observed in the minigene construct with 160 nucleotide (nt) of intron 64 sequence with both c.9361+1A and +117G. In contrast, minigene constructs with larger flanking intronic domains resulted in cryptic splice site activation rather than exon skipping. Meanwhile, the cryptic splice site activation was induced even in +117G when intron 64 was elongated to 272 nt and longer. It was expected that cryptic splice site activation is an in vivo splicing outcome.

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics.

There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.

SF3B1 Interactions with Chromatin Are Dynamic and Regulated in a Cell Cycle-Dependent Manner

Splicing factor 3B1 (SF3B1) is a member of the U2 snRNP complex that is a key regulator of RNA splicing. RNA splicing begins with the recognition of splice sites (ss) at the 5' and 3' ends of introns and ends with the removal of introns and joining of exons flanking them. SF3B1 plays an important role in this process by facilitating the recognition of the 3'ss. SF3B1 is frequently mutated in numerous cancers as well as the myelodysplastic syndromes (MDS). Mutations within the HEAT domain of the protein potentially contribute to disease pathogenesis. In addition to influencing splicing by binding to pre-mRNA, SF3B1 has been shown to affect splicing of exons by associating with them directly on chromatin via histone/nucleosome interactions. However, it is not understood if or how SF3B1 association with chromatin is regulated. Given that N-terminal serine and threonine residues on SF3B1 are known substrates of cyclin E-Cdk2, which phosphorylates histone subunits and other chromatin associated proteins, we hypothesized that CDK2 activity regulates SF3B1-nucleosome interactions. Although SF3B1 is phosphorylated during splicing catalysis, the function of this phosphorylation has remained unknown. We have now discovered, using nucleosome preparations and histone subunit co-immunoprecipitation assays in synchronized cells, that endogenous SF3B1 interacts with nucleosomes in a highly cell-cycle dependent manner, while total cellular abundance of SF3B1 remains invariant during cell cycle progression. In human and mouse cells, including hematopoietic cell lines, SF3B1 is excluded from chromatin during both G0 (quiescence) and G2/M phases of cell cycle. Notably, SF3B1 is enriched within chromatin maximally during S-phase. Unexpectedly, we found that the inhibition of Cdc2 (Cdk1) during G2/M enforces the SF3B1-chromatin interaction, pointing to a direct role for Cdc2 in restraining this interaction during mitosis. Further, SF3B1 loading onto chromatin during early cell cycle progression from G0 to S-phase is inhibited by Cdk2 inhibition. Thus, Cdk2 and Cdc2 appear to have antagonistic roles in controlling SF3B1-chromatin interactions during the cell cycle. Our findings suggest that Cdk activity may regulate the recruitment of the spliceosome machinery in order to coordinate splicing of particular transcripts with cell cycle progression. Current studies are focusing on how disease-associated mutations in the HEAT domain of SF3B1 affect the dynamics of its cell cycle-dependent interaction with nucleosomes and corresponding alterations to splicing outcomes. DisclosuresNo relevant conflicts of interest to declare.