Petersen, U. S. S. | bioRxiv Science

bioRxiv2023

Regulating PCCA gene expression by modulation of pseudoexon splicing patterns to rescue enzyme activity in propionic acidemia

Pseudoexons are nonfunctional intronic sequences that can be activated by deep intronic sequence variation. Activation increases pseudoexon inclusion in mRNA and interferes with normal gene expression. The PCCA c.1285-1416A>G variation activates a pseudoexon and causes the severe metabolic disorder, propionic acidemia, by deficiency of the propionyl-CoA carboxylase enzyme encoded by PCCA and PCCB. We characterized this pathogenic pseudoexon activation event in detail and identified hnRNP A1 to be important for normal repression. The PCCA c.1285-1416A>G variation disrupts an hnRNP A1-binding splicing silencer and simultaneously creates a splicing enhancer. We demonstrate that blocking this region of regulation with splice-switching antisense oligonucleotides restores normal splicing and rescues enzyme activity in patient fibroblasts and in a cellular model created by CRISPR gene editing. The PCCA pseudoexon can be exploited as a gene-regulatory switch, as healthy tissues show relatively high levels of inclusion. By blocking inclusion of the non-activated wild type pseudoexon, we increase both PCCA and PCCB protein levels, which increases the activity of the heterododecameric enzyme. Surprisingly, we can increase enzyme activity from residual levels not only in patient fibroblasts harboring PCCA missense variants, but also those harboring PCCB missense variants. This could be a potential treatment strategy for propionic acidemia.

molecular biology↗

bioRxiv2023

All exons are not created equal - Exon vulnerability determines the effect of exonic mutations on splicing.

It is now widely accepted that aberrant splicing of constitutive exons is often caused by mutations affecting cis-acting splicing regulatory elements (SREs), but there is a misconception that all exons have an equal dependency on SREs and thus a similar vulnerability to aberrant splicing. We demonstrate that some exons are more likely to be affected by exonic splicing mutations (ESM) due to an inherent vulnerability, which is context-dependent and influenced by the strength of exon definition. We have developed VulExMap, a tool which based on empirical data that can designate whether a constitutive exon is vulnerable. Using VulExMap, we find that only 27% of all exons can be categorized as vulnerable whereas two-thirds of 332 previously reported ESMs in 71 disease genes are located in vulnerable exons. Because VulExMap analysis is based on empirical data on splicing of exons in their endogenous context, it includes all features important in determining the vulnerability. We believe that VulExMap will be an important tool when assessing the effect of exonic mutations by pinpointing whether they are located in exons vulnerable to ESMs.

molecular biology↗

bioRxiv2019

DeepCLIP: Predicting the effect of mutations on protein-RNA binding with Deep Learning

Nucleotide variants can cause functional changes by altering protein-RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modelling of protein-RNA binding is critical when predicting the effects of sequence variations. Many RNA-binding proteins recognize a diverse set of motifs and binding is typically also dependent on the genomic context, making this task particularly challenging. Here, we present DeepCLIP, the first method for context-aware modeling and predicting protein binding to nucleic acids using exclusively sequence data as input. We show that DeepCLIP outperforms existing methods for modelling RNA-protein binding. Importantly, we demonstrate that DeepCLIP is able to reliably predict the functional effects of contextually dependent nucleotide variants in independent wet lab experiments. Furthermore, we show how DeepCLIP binding profiles can be used in the design of therapeutically relevant antisense oligonucleotides, and to uncover possible position-dependent regulation in a tissue-specific manner. DeepCLIP can be freely used at http://deepclip.compbio.sdu.dk.\n\nHighlightsO_LIWe have designed DeepCLIP as a simple neural network that requires only CLIP binding sites as input. The architecture and parameter settings of DeepCLIP makes it an efficient classifier and robust to train, making high performing models easy to train and recreate.\nC_LIO_LIUsing an extensive benchmark dataset, we demonstrate that DeepCLIP outperforms existing tools in classification. Furthermore, DeepCLIP provides direct information about the neural networks decision process through visualization of binding motifs and a binding profile that directly indicates sequence elements contributing to the classification.\nC_LIO_LITo show that DeepCLIP models generalize to different datasets we have demonstrated that predictions correlate with in vivo and in vitro experiments using quantitative binding assays and minigenes.\nC_LIO_LIIdentifying the binding sites for regulatory RNA-binding proteins is fundamental for efficient design of (therapeutic) antisense oligonucleotides. Employing a reported disease associated mutation, we demonstrate that DeepCLIP can be used for design of therapeutic antisense oligonucleotides that block regions important for binding of regulatory proteins and correct aberrant splicing.\nC_LIO_LIUsing DeepCLIP binding profiles, we uncovered a possible position-dependent mechanism behind the reported tissue-specificity of a group of TDP-43 repressed pseudoexons.\nC_LIO_LIWe have made DeepCLIP available as an online tool for training and application of proteinRNA binding deep learning models and prediction of the potential effects of clinically detected sequence variations (http://deepclip.compbio.sdu.dk/). We also provide DeepCLIP as a configurable stand-alone program (http://www.github.com/deepclip).\nC_LI

bioinformatics↗