Mechanism of Splicing Regulation of Spinal Muscular Atrophy Genes

Spinal muscular atrophy (SMA) is one of the major genetic disorders associated with infant mortality. More than 90% cases of SMA result from deletions or mutations of Survival Motor Neuron 1 (SMN1) gene. SMN2, a nearly identical copy of SMN1, does not compensate for the loss of SMN1due to predominant skipping of exon 7. However, correction of SMN2 exon 7 splicing has proven to confer therapeutic benefits in SMA patients. The only approved drug for SMA is an antisense oligonucleotide (Spinraza™/Nusinersen), which corrects SMN2 exon 7 splicing by blocking intronic splicing silencer N1 (ISS-N1) located immediately downstream of exon 7. ISS-N1 is a complex regulatory element encompassing overlapping negative motifs and sequestering a cryptic splice site. More than 40 protein factors have been implicated in the regulation of SMN exon 7 splicing. There is evidence to support that multiple exons of SMN are alternatively spliced during oxidative stress, which is associated with a growing number of pathological conditions. Here, we provide the most up to date account of the mechanism of splicing regulation of the SMN genes

A quantitative profiling tool for diverse genomic data types reveals potential associations between chromatin and Pre-mRNA processing

High-throughput sequencing, and genome-based datasets in general, are often represented as profiles centered at reference points to study the association of protein binding and other signals to particular regulatory mechanisms. Although these profiles often provide compelling evidence of these associations, they do not provide a quantitative assessment of the enrichment, which makes the comparison between signals and conditions difficult. In addition, a number of biases can confound profiles, but are rarely accounted for in the tools currently available. We present a novel computational method, ProfileSeq, for the quantitative assessment of biological profiles to provide an exact, nonparametric test that specific regions of the test profile have higher or lower signal densities than a control set. The method is applicable to high-throughput sequencing data (ChIP-Seq, GRO-Seq, CLIP-Seq, etc.) and to genome-based datasets (motifs, etc.). We validate ProfileSeq by recovering and providing a quantitative assessment of several results reported before in the literature using independent datasets. We show that input signal and mappability have confounding effects on the profile results, but that normalizing the signal by input reads can eliminate these biases while preserving the biological signal. Moreover, we apply ProfileSeq to ChIP-Seq data for transcription factors, as well as for motif and CLIP-Seq data for splicing factors. In all examples considered, the profiles were robust to biases in mappability of sequencing reads. Furthermore, analyses performed with ProfileSeq reveal a number of putative relationships between transcription factor binding to DNA and splicing factor binding to pre-mRNA, adding to the growing body of evidence relating chromatin and pre-mRNA processing. ProfileSeq provides a robust way to quantify genome-wide coordinate-based signal. Software and documentation are freely available for academic use at https://bitbucket.org/regulatorygenomicsupf/profileseq/.Support was provided by an Initial Training Network grant RNPNet (289007) from the European/nCommission [http://ec.europa.eu/research/mariecurieactions/]; grants BIO2011-23920 and/nCSD2009-00080 from Ministerio de Economía y Competitividad (MINECO) from the Spanish/nGovernment [http://www.mineco.gob.es/portal/site/mineco/]; and Fundación Sandra Ibarra de/nSolidaridad Frente al Cáncer [http://www.fundacionsandraibarra.org/