RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease

SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice

Spinal muscular atrophy (SMA), which results from the loss of expression of the survival of motor neuron-1 (SMN1) gene, represents the most common genetic cause of pediatric mortality. A duplicate copy (SMN2) is inefficiently spliced, producing a truncated and unstable protein. We describe herein a potent, orally active, small-molecule enhancer of SMN2 splicing that elevates full-length SMN protein and extends survival in a severe SMA mouse model. We demonstrate that the molecular mechanism of action is via stabilization of the transient double-strand RNA structure formed by the SMN2 pre-mRNA and U1 small nuclear ribonucleic protein (snRNP) complex. The binding affinity of U1 snRNP to the 5' splice site is increased in a sequence-selective manner, discrete from constitutive recognition. This new mechanism demonstrates the feasibility of small molecule-mediated, sequence-selective splice modulation and the potential for leveraging this strategy in other splicing diseases.

Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome

Accurate prediction of the functional effect of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants, a class of variants expected to have profound effects on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitated tissue-specific and positional effects on nonsense-mediated transcript decay and present an improved predictive model for this decay. We directly measured the effect of variants both proximal and distal to splice junctions. Furthermore, we found that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants.

On simplicity and complexity in the brave new world of large-scale neuroscience

Technological advances have dramatically expanded our ability to probe multi-neuronal dynamics and connectivity in the brain. However, our ability to extract a simple conceptual understanding from complex data is increasingly hampered by the lack of theoretically principled data analytic procedures, as well as theoretical frameworks for how circuit connectivity and dynamics can conspire to generate emergent behavioral and cognitive functions. We review and outline potential avenues for progress, including new theories of high dimensional data analysis, the need to analyze complex artificial networks, and methods for analyzing entire spaces of circuit models, rather than one model at a time. Such interplay between experiments, data analysis and theory will be indispensable in catalyzing conceptual advances in the age of large-scale neuroscience.

Antisense oligonucleotide-mediated exon skipping of CHRNA1 pre-mRNA as potential therapy for Congenital Myasthenic Syndromes

CHRNA1 encodes the α subunit of nicotinic acetylcholine receptors (nAChRs) and is expressed at the neuromuscular junction. Moreover, it is one of the causative genes of Congenital Myasthenic Syndromes (CMS). CHRNA1 undergoes alternative splicing to produce two splice variants: P3A(-), without exon P3A, and P3A(+), with the exon P3A. Only P3A(-) forms functional nAChR. Aberrant alternative splicing caused by intronic or exonic point mutations in patients leads to an extraordinary increase in P3A(+) and a concomitant decrease in P3A(-). Consequently this resulted in a shortage of functional receptors. Aiming to restore the imbalance between the two splice products, antisense oligonucleotides (AONs) were employed to induce exon P3A skipping. Three AON sequences were designed to sterically block the putative binding sequences for splicing factors necessary for exon recognition. Herein, we show that AON complementary to the 5' splice site of the exon was the most effective at exon skipping of the minigene with causative mutations, as well as endogenous wild-type CHRNA1. We conclude that single administration of the AON against the 5' splice site is a promising therapeutic approach for patients based on the dose-dependent effect of the AON and the additive effect of combined AONs. This conclusion is favorable to patients with inherited diseases of uncertain etiology that arise from aberrant splicing leading to a subsequent loss of functional translation products because our findings encourage the option of AON treatment as a therapeutic for these prospectively identified diseases.

Cancer whole-genome sequencing: present and future

Recent explosive advances in next-generation sequencing technology and computational approaches to massive data enable us to analyze a number of cancer genome profiles by whole-genome sequencing (WGS). To explore cancer genomic alterations and their diversity comprehensively, global and local cancer genome-sequencing projects, including ICGC and TCGA, have been analyzing many types of cancer genomes mainly by exome sequencing. However, there is limited information on somatic mutations in non-coding regions including untranslated regions, introns, regulatory elements and non-coding RNAs, and rearrangements, sometimes producing fusion genes, and pathogen detection in cancer genomes remain widely unexplored. WGS approaches can detect these unexplored mutations, as well as coding mutations and somatic copy number alterations, and help us to better understand the whole landscape of cancer genomes and elucidate functions of these unexplored genomic regions. Analysis of cancer genomes using the present WGS platforms is still primitive and there are substantial improvements to be made in sequencing technologies, informatics and computer resources. Taking account of the extreme diversity of cancer genomes and phenotype, it is also required to analyze much more WGS data and integrate these with multi-omics data, functional data and clinical-pathological data in a large number of sample sets to interpret them more fully and efficiently.

Rethinking gene regulatory networks in light of alternative splicing, intrinsically disordered protein domains, and post-translational modifications

Models for genetic regulation and cell fate specification characteristically assume that gene regulatory networks (GRNs) are essentially deterministic and exhibit multiple stable states specifying alternative, but pre-figured cell fates. Mounting evidence shows, however, that most eukaryotic precursor RNAs undergo alternative splicing (AS) and that the majority of transcription factors contain intrinsically disordered protein (IDP) domains whose functionalities are context dependent as well as subject to post-translational modification (PTM). Consequently, many transcription factors do not have fixed cis-acting regulatory targets, and developmental determination by GRNs alone is untenable. Modeling these phenomena requires a multi-scale approach to explain how GRNs operationally interact with the intra- and intercellular environments. Evidence shows that AS, IDP, and PTM complicate gene expression and act synergistically to facilitate and promote time- and cell-specific protein modifications involved in cell signaling and cell fate specification and thereby disrupt a strict deterministic GRN-phenotype mapping. The combined effects of AS, IDP, and PTM give proteomes physiological plasticity, adaptive responsiveness, and developmental versatility without inefficiently expanding genome size. They also help us understand how protein functionalities can undergo major evolutionary changes by buffering mutational consequences.

Homozygous missense mutation in STYXL1 associated with moderate intellectual disability, epilepsy and behavioural complexities

The introduction of massive parallel sequencing has led to the identification of multiple novel genes for intellectual disability (ID) as well as epilepsy. Whereas dominant de novo mutations have been proven to be a leading cause for these disorders, they do not apply to families suggestive of an autosomal recessive inheritance pattern. In this study, we combined the use of linkage analysis with exome sequencing to elucidate the cause of moderate non-syndromic ID, epilepsy and behavioural problems in a consanguineous Asian family. A founder missense mutation was identified in STYXL1. We propose this as a novel candidate gene involved in ID, accompanied by seizures and behavioural problems. Our findings further confirm the genetic heterogeneity of cognitive disorders and genetic epilepsy.

Whole-genome sequencing of quartet families with autism spectrum disorder

Autism spectrum disorder (ASD) is genetically heterogeneous, with evidence for hundreds of susceptibility loci. Previous microarray and exome-sequencing studies have examined portions of the genome in simplex families (parents and one ASD-affected child) having presumed sporadic forms of the disorder. We used whole-genome sequencing (WGS) of 85 quartet families (parents and two ASD-affected siblings), consisting of 170 individuals with ASD, to generate a comprehensive data resource encompassing all classes of genetic variation (including noncoding variants) and accompanying phenotypes, in apparently familial forms of ASD. By examining de novo and rare inherited single-nucleotide and structural variations in genes previously reported to be associated with ASD or other neurodevelopmental disorders, we found that some (69.4%) of the affected siblings carried different ASD-relevant mutations. These siblings with discordant mutations tended to demonstrate more clinical variability than those who shared a risk variant. Our study emphasizes that substantial genetic heterogeneity exists in ASD, necessitating the use of WGS to delineate all genic and non-genic susceptibility variants in research and in clinical diagnostics.

PredcircRNA: computational classification of circular RNA from other long non-coding RNA using hybrid features

PredcircRNA presents computational classification of circularRNA from other lncRNA using hybrid features based on multiple kernel learning.