Method of predicting splice sites based on signal interactions

The Influence of a Genetic Variant in CCDC78 on LMNA-Associated Skeletal Muscle Disease

Mutations in the LMNA gene-encoding A-type lamins can cause Limb-Girdle muscular dystrophy Type 1B (LGMD1B). This disease presents with weakness and wasting of the proximal skeletal muscles and has a variable age of onset and disease severity. This variability has been attributed to genetic background differences among individuals; however, such variants have not been well characterized. To identify such variants, we investigated a multigeneration family in which affected individuals are diagnosed with LGMD1B. The primary genetic cause of LGMD1B in this family is a dominant mutation that activates a cryptic splice site, leading to a five-nucleotide deletion in the mature mRNA. This results in a frame shift and a premature stop in translation. Skeletal muscle biopsies from the family members showed dystrophic features of variable severity, with the muscle fibers of some family members possessing cores, regions of sarcomeric disruption, and a paucity of mitochondria, not commonly associated with LGMD1B. Using whole genome sequencing (WGS), we identified 21 DNA sequence variants that segregate with the family members possessing more profound dystrophic features and muscle cores. These include a relatively common variant in coiled-coil domain containing protein 78 (CCDC78). This variant was given priority because another mutation in CCDC78 causes autosomal dominant centronuclear myopathy-4, which causes cores in addition to centrally positioned nuclei. Therefore, we analyzed muscle biopsies from family members and discovered that those with both the LMNA mutation and the CCDC78 variant contain muscle cores that accumulated both CCDC78 and RyR1. Muscle cores containing mislocalized CCDC78 and RyR1 were absent in the less profoundly affected family members possessing only the LMNA mutation. Taken together, our findings suggest that a relatively common variant in CCDC78 can impart profound muscle pathology in combination with a LMNA mutation and accounts for variability in skeletal muscle disease phenotypes.

Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup

The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1, PS3, PP3, BS3, BP4, and BP7. However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. We utilized empirically derived splicing evidence to (1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, (2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and (3) exemplify methodology to calibrate splice prediction tools. We propose repurposing the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely, BP7 may be used to capture RNA results demonstrating no splicing impact for intronic and synonymous variants. We propose that the PS3/BS3 codes are applied only for well-established assays that measure functional impact not directly captured by RNA-splicing assays. We recommend the application of PS1 based on similarity of predicted RNA-splicing effects for a variant under assessment in comparison with a known pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA-assay evidence described aim to help standardize variant pathogenicity classification processes when interpreting splicing-based evidence.

APPLICATION OF THE ACMG/AMP FRAMEWORK TO CAPTURE EVIDENCE RELEVANT TO PREDICTED AND OBSERVED IMPACT ON SPLICING: RECOMMENDATIONS FROM THE CLINGEN SVI SPLICING SUBGROUP

The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1 (null variant in a gene where loss-of-function is the mechanism of disease), PS3 (functional assays show damaging effect on splicing), PP3 (computational evidence supports a splicing effect), BS3 (functional assays show no damaging effect on splicing), BP4 (computational evidence suggests no splicing impact), and BP7 (silent change with no predicted impact on splicing). However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation (SVI) Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. Our study utilised empirically derived splicing evidence to: 1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, 2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and 3) exemplify methodology to calibrate bioinformatic splice prediction tools. We propose repurposing of the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely BP7 may be used to capture RNA results demonstrating no impact on splicing for both intronic and synonymous variants, and for missense variants if protein functional impact has been excluded. Furthermore, we propose that the PS3 and BS3 codes are applied only for well-established assays that measure functional impact that is not directly captured by RNA splicing assays. We recommend the application of PS1 based on similarity of predicted RNA splicing effects for a variant under assessment in comparison to a known Pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA assay evidence described aim to help standardise variant pathogenicity classification processes and result in greater consistency when interpreting splicing-based evidence.

Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability

Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.

Mechanical Stress and Single Nucleotide Variants Regulate Alternative Splicing of the MYLK Gene

The nonmuscle (nm) myosin light-chain kinase isoform (MLCK), encoded by the MYLK gene, is a vital participant in regulating vascular barrier responses to mechanical and inflammatory stimuli. We determined that MYLK is alternatively spliced, yielding functionally distinct nmMLCK splice variants including nmMLCK2, a splice variant highly expressed in vascular endothelial cells (EC) and associated with reduced EC barrier integrity. We demonstrated previously that the nmMLCK2 variant lacks exon 11, which encodes a key regulatory region containing two differentially phosphorylated tyrosine residues (Y⁴⁶⁴ and Y⁴⁷¹) that influence vascular barrier function during inflammation. In this study, we used minigene constructs and RT-PCR to interrogate biophysical factors (mechanical stress) and genetic variants (MYLK single-nucleotide polymorphisms [SNPs]) that are potentially involved in regulating MYLK alternative splicing and nmMLCK2 generation. Human lung EC exposed to pathologic mechanical stress (18% cyclic stretch) produced increased nmMLCK2 expression relative to levels of nmMLCK1 with alternative splicing significantly influenced by MYLK SNPs rs77323602 and rs147245669. In silico analyses predicted that these variants would alter exon 11 donor and acceptor sites for alternative splicing, computational predictions that were confirmed by minigene studies. The introduction of rs77323602 favored wild-type nmMLCK expression, whereas rs147245669 favored alternative splicing and deletion of exon 11, yielding increased nmMLCK2 expression. Finally, lymphoblastoid cell lines selectively harboring these MYLK SNPs (rs77323602 and rs147245669) directly validated SNP-specific effects on MYLK alternative splicing and nmMLCK2 generation. Together, these studies demonstrate that mechanical stress and MYLK SNPs regulate MYLK alternative splicing and generation of a splice variant, nmMLCK2, that contributes to the severity of inflammatory injury.

Analysis of CACTA transposases reveals intron loss as major factor influencing their exon/intron structure in monocotyledonous and eudicotyledonous hosts

Background

CACTA elements are DNA transposons and are found in numerous organisms. Despite their low activity, several thousand copies can be identified in many genomes. CACTA elements transpose using a ‘cut-and-paste’ mechanism, which is facilitated by a DDE transposase. DDE transposases from CACTA elements contain, despite their conserved function, different exon numbers among various CACTA families. While earlier studies analyzed the ancestral history of the DDE transposases, no studies have examined exon loss and gain with a view of mechanisms that could drive the changes.

Results

We analyzed 64 transposases from different CACTA families among monocotyledonous and eudicotyledonous host species. The annotation of the exon/intron boundaries showed a range from one to six exons. A robust multiple sequence alignment of the 64 transposases based on their protein sequences was created and used for phylogenetic analysis, which revealed eight different clades. We observed that the exon numbers in CACTA transposases are not specific for a host genome. We found that ancient CACTA lineages diverged before the divergence of monocotyledons and eudicotyledons. Most exon/intron boundaries were found in three distinct regions among all the transposases, grouping 63 conserved intron/exon boundaries.

Conclusions

We propose a model for the ancestral CACTA transposase gene, which consists of four exons, that predates the divergence of the monocotyledons and eudicotyledons. Based on this model, we propose pathways of intron loss or gain to explain the observed variation in exon numbers. While intron loss appears to have prevailed, a putative case of intron gain was nevertheless observed.

Interpretation, stratification and evidence for sequence variants affecting mRNA splicing in complete human genome sequences

Information theory-based methods have been shown to be sensitive and specific for predicting and quantifying the effects of non-coding mutations in Mendelian diseases. We present the Shannon pipeline software for genome-scale mutation analysis and provide evidence that the software predicts variants affecting mRNA splicing. Individual information contents (in bits) of reference and variant splice sites are compared and significant differences are annotated and prioritized. The software has been implemented for CLC-Bio Genomics platform. Annotation indicates the context of novel mutations as well as common and rare SNPs with splicing effects. Potential natural and cryptic mRNA splicing variants are identified, and null mutations are distinguished from leaky mutations. Mutations and rare SNPs were predicted in genomes of three cancer cell lines (U2OS, U251 and A431), which were supported by expression analyses. After filtering, tractable numbers of potentially deleterious variants are predicted by the software, suitable for further laboratory investigation. In these cell lines, novel functional variants comprised 6–17 inactivating mutations, 1–5 leaky mutations and 6–13 cryptic splicing mutations. Predicted effects were validated by RNA-seq analysis of the three aforementioned cancer cell lines, and expression microarray analysis of SNPs in HapMap cell lines.

Origin and evolution of spliceosomal introns

Evolution of exon-intron structure of eukaryotic genes has been a matter of long-standing, intensive debate. The introns-early concept, later rebranded ‘introns first’ held that protein-coding genes were interrupted by numerous introns even at the earliest stages of life's evolution and that introns played a major role in the origin of proteins by facilitating recombination of sequences coding for small protein/peptide modules. The introns-late concept held that introns emerged only in eukaryotes and new introns have been accumulating continuously throughout eukaryotic evolution. Analysis of orthologous genes from completely sequenced eukaryotic genomes revealed numerous shared intron positions in orthologous genes from animals and plants and even between animals, plants and protists, suggesting that many ancestral introns have persisted since the last eukaryotic common ancestor (LECA). Reconstructions of intron gain and loss using the growing collection of genomes of diverse eukaryotes and increasingly advanced probabilistic models convincingly show that the LECA and the ancestors of each eukaryotic supergroup had intron-rich genes, with intron densities comparable to those in the most intron-rich modern genomes such as those of vertebrates. The subsequent evolution in most lineages of eukaryotes involved primarily loss of introns, with only a few episodes of substantial intron gain that might have accompanied major evolutionary innovations such as the origin of metazoa. The original invasion of self-splicing Group II introns, presumably originating from the mitochondrial endosymbiont, into the genome of the emerging eukaryote might have been a key factor of eukaryogenesis that in particular triggered the origin of endomembranes and the nucleus. Conversely, splicing errors gave rise to alternative splicing, a major contribution to the biological complexity of multicellular eukaryotes. There is no indication that any prokaryote has ever possessed a spliceosome or introns in protein-coding genes, other than relatively rare mobile self-splicing introns. Thus, the introns-first scenario is not supported by any evidence but exon-intron structure of protein-coding genes appears to have evolved concomitantly with the eukaryotic cell, and introns were a major factor of evolution throughout the history of eukaryotes. This article was reviewed by I. King Jordan, Manuel Irimia (nominated by Anthony Poole), Tobias Mourier (nominated by Anthony Poole), and Fyodor Kondrashov. For the complete reports, see the Reviewers’ Reports section.

A method of predicting changes in human gene splicing induced by genetic variants in context of cis-acting elements

Background

Polymorphic variants and mutations disrupting canonical splicing isoforms are among the leading causes of human hereditary disorders. While there is a substantial evidence of aberrant splicing causing Mendelian diseases, the implication of such events in multi-genic disorders is yet to be well understood. We have developed a new tool (SpliceScan II) for predicting the effects of genetic variants on splicing and cis-regulatory elements. The novel Bayesian non-canonical 5'GC splice site (SS) sensor used in our tool allows inference on non-canonical exons.

Results

Our tool performed favorably when compared with the existing methods in the context of genes linked to the Autism Spectrum Disorder (ASD). SpliceScan II was able to predict more aberrant splicing isoforms triggered by the mutations, as documented in DBASS5 and DBASS3 aberrant splicing databases, than other existing methods. Detrimental effects behind some of the polymorphic variations previously associated with Alzheimer's and breast cancer could be explained by changes in predicted splicing patterns.

Conclusions

We have developed SpliceScan II, an effective and sensitive tool for predicting the detrimental effects of genomic variants on splicing leading to Mendelian and complex hereditary disorders. The method could potentially be used to screen resequenced patient DNA to identify de novo mutations and polymorphic variants that could contribute to a genetic disorder.

Computational prediction of splicing regulatory elements shared by Tetrapoda organisms

Background

Auxiliary splicing sequences play an important role in ensuring accurate and efficient splicing by promoting or repressing recognition of authentic splice sites. These cis-acting motifs have been termed splicing enhancers and silencers and are located both in introns and exons. They co-evolved into an intricate splicing code together with additional functional constraints, such as tissue-specific and alternative splicing patterns. We used orthologous exons extracted from the University of California Santa Cruz multiple genome alignments of human and 22 Tetrapoda organisms to predict candidate enhancers and silencers that have reproducible and statistically significant bias towards annotated exonic boundaries.

Results

A total of 2,546 Tetrapoda enhancers and silencers were clustered into 15 putative core motifs based on their Markov properties. Most of these elements have been identified previously, but 118 putative silencers and 260 enhancers (~15%) were novel. Examination of previously published experimental data for the presence of predicted elements showed that their mutations in 21/23 (91.3%) cases altered the splicing pattern as expected. Predicted intronic motifs flanking 3' and 5' splice sites had higher evolutionary conservation than other sequences within intronic flanks and the intronic enhancers were markedly differed between 3' and 5' intronic flanks.

Conclusion

Difference in intronic enhancers supporting 5' and 3' splice sites suggests an independent splicing commitment for neighboring exons. Increased evolutionary conservation for ISEs/ISSs within intronic flanks and effect of modulation of predicted elements on splicing suggest functional significance of found elements in splicing regulation. Most of the elements identified were shown to have direct implications in human splicing and therefore could be useful for building computational splicing models in biomedical research.

An exon-based comparative variant analysis pipeline to study the scale and role of frameshift and nonsense mutation in the human-chimpanzee divergence

Chimpanzees and humans are closely related but differ in many deadly human diseases and other characteristics in physiology, anatomy, and pathology. In spite of decades of extensive research, crucial questions about the molecular mechanisms behind the differences are yet to be understood. Here I report ExonVar, a novel computational pipeline for Exon-based human-chimpanzee comparative Variant analysis. The objective is to comparatively analyze mutations specifically those that caused the frameshift and nonsense mutations and to assess their scale and potential impacts on human-chimpanzee divergence. Genomewide analysis of human and chimpanzee exons with ExonVar identified a number of species-specific, exon-disrupting mutations in chimpanzees but much fewer in humans. Many were found on genes involved in important biological processes such as T cell lineage development, the pathogenesis of inflammatory diseases, and antigen induced cell death. A “less-is-more” model was previously established to illustrate the role of the gene inactivation and disruptions during human evolution. Here this analysis suggested a different model where the chimpanzee-specific exon-disrupting mutations may act as additional evolutionary force that drove the human-chimpanzee divergence. Finally, the analysis revealed a number of sequencing errors in the chimpanzee and human genome sequences and further illustrated that they could be corrected without resequencing.

SpliceIT: a hybrid method for splice signal identification based on probabilistic and biological inference

Splice sites define the boundaries of exonic regions and dictate protein synthesis and function. The splicing mechanism involves complex interactions among positional and compositional features of different lengths. Computational modeling of the underlying constructive information is especially challenging, in order to decipher splicing-inducing elements and alternative splicing factors. SpliceIT (Splice Identification Technique) introduces a hybrid method for splice site prediction that couples probabilistic modeling with discriminative computational or experimental features inferred from published studies in two subsequent classification steps. The first step is undertaken by a Gaussian support vector machine (SVM) trained on the probabilistic profile that is extracted using two alternative position-dependent feature selection methods. In the second step, the extracted predictions are combined with known species-specific regulatory elements, in order to induce a tree-based modeling. The performance evaluation on human and Arabidopsis thaliana splice site datasets shows that SpliceIT is highly accurate compared to current state-of-the-art predictors in terms of the maximum sensitivity, specificity tradeoff without compromising space complexity and in a time-effective way. The source code and supplementary material are available at: http://www.med.auth.gr/research/spliceit/.

Aberrant RNA splicing and its functional consequences in cancer cells

Among the myriad of alterations present in cancer cells are an abundance of aberrant mRNA transcripts. Whether abnormal gene transcription is a by-product of cellular transformation or whether it represents an inherent element that contributes to the properties of cancer cells is not yet clear. Here, we present growing evidence that in many cases, aberrant mRNA transcripts contribute to essential phenotypes associated with transformed cells, suggesting that alterations in the splicing machinery are common and functionally important for cancer development. The proteins encoded by these abnormal transcripts are often truncated or missing domains, thereby altering protein function or conferring new functions altogether. Thus, aberrant splicing regulation has genome-wide effects, potentially altering gene expression in many cancer-associated pathways.

Human Splicing Finder: an online bioinformatics tool to predict splicing signals

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5′ and 3′ splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.

Clustering ionic flow blockade toggles with a mixture of HMMs

BACKGROUND

Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties with potential implications for DNA sequencing. The alpha-Hemolysin transmembrane channel interacts with a translocating molecule in a nontrivial way, frequently evidenced by a complex ionic flow blockade pattern with readily distinguishable modes of toggling. Effective processing of such signals requires developing machine learning methods capable of learning the various blockade modes for classification and knowledge discovery purposes. Here we propose a method aimed to improve our stochastic analysis capabilities to better understand the discriminatory capabilities of the observed the nanopore channel interactions with analyte.

RESULTS

We tailored our memory-sparse distributed implementation of a Mixture of Hidden Markov Models (MHMMs) to the problem of channel current blockade clustering and associated analyte classification. By using probabilistic fully connected HMM profiles as mixture components we were able to cluster the various 9 base-pair hairpin channel blockades. We obtained very high Maximum a Posteriori (MAP) classification with a mixture of 12 different channel blockade profiles, each with 4 levels, a configuration that can be computed with sufficient speed for real-time experimental feedback. MAP classification performance depends on several factors such as the number of mixture components, the number of levels in each profile, and the duration of a channel blockade event. We distribute Baum-Welch Expectation Maximization (EM) algorithms running on our model in two ways. A distributed implementation of the MHMM data processing accelerates data clustering efforts. The second, simultanteous, strategy uses an EM checkpointing algorithm to lower the memory use and efficiently distribute the bulk of EM processing in processing large data sequences (such as for the progressive sums used in the HMM parameter estimates).

CONCLUSION

The proposed distributed MHMM method has many appealing properties, such as precise classification of analyte in real-time scenarios, and the ability to incorporate new domain knowledge into a flexible, easily distributable, architecture. The distributed HMM provides a feature extraction that is equivalent to that of the sequential HMM with a speedup factor approximately equal to the number of independent CPUs operating on the data. The MHMM topology learns clusters existing within data samples via distributed HMM EM learning. A Java implementation of the MHMM algorithm is available at http://logos.cs.uno.edu/~achurban.

A luteinizing hormone receptor intronic variant is significantly associated with decreased risk of Alzheimer's disease in males carrying an apolipoprotein E epsilon4 allele

Genetic and biochemical studies support the apolipoprotein E (APOE) ε4 allele as a major risk factor for late-onset Alzheimer's disease (AD), though ~50% of AD patients do not carry the allele. APOE transports cholesterol for luteinizing hormone (LH)-regulated steroidogenesis, and both LH and neurosteroids have been implicated in the etiology of AD. Since polymorphisms of LH beta-subunit (LHB) and its receptor (LHCGR) have not been tested for their association with AD, we scored AD and age-matched control samples for APOE genotype and 14 polymorphisms of LHB and LHCGR. Thirteen gene-gene interactions between the loci of LHB, LHCGR, and APOE were associated with AD. The most strongly supported of these interactions was between an LHCGR intronic polymorphism (rs4073366; lhcgr2) and APOE in males, which was detected using all three interaction analyses: linkage disequilibrium, multi-dimensionality reduction, and logistic regression. While the APOE ε4 allele carried significant risk of AD in males [p = 0.007, odds ratio (OR) = 3.08(95%confidence interval: 1.37, 6.91)], ε4-positive males carrying 1 or 2 C-alleles at lhcgr2 exhibited significantly decreased risk of AD [OR = 0.06(0.01, 0.38); p = 0.003]. This suggests that the lhcgr2 C-allele or a closely linked locus greatly reduces the risk of AD in males carrying an APOE ε4 allele. The reversal of risk embodied in this interaction powerfully supports the importance of considering the role gene-gene interactions play in the etiology of complex biological diseases and demonstrates the importance of using multiple analytic methods to detect well-supported gene-gene interactions.