Evolutionarily Dynamic Alternative Splicing of
            <i>GPR56</i>
            Regulates Regional Cerebral Cortical Patterning

Objective: To identify the genetic cause of pontocerebellar hypoplasia type III (PCH3). Methods:We studied the original reported pedigree of PCH3 and performed genetic analysis including genome-wide single nucleotide polymorphism genotyping, linkage analysis, wholeexome sequencing, and Sanger sequencing. Human fetal brain RNA sequencing data were then analyzed for the identified candidate gene. Results:The affected individuals presented with severe global developmental delay and seizures starting in the first year of life. Brain MRI of an affected individual showed diffuse atrophy of the cerebrum, cerebellum, and brainstem. Genome-wide single nucleotide polymorphism analysis confirmed the linkage to chromosome 7q we previously reported, and showed no other genomic areas of linkage. Whole-exome sequencing of 2 affected individuals identified a shared homozygous, nonsense variant in the PCLO (piccolo) gene. This variant segregated with the disease phenotype in the pedigree was rare in the population and was predicted to eliminate the PDZ and C2 domains in the C-terminus of the protein. RNA sequencing data of human fetal brain showed that PCLO was moderately expressed in the developing cerebral cortex.Conclusions: Here, we show that a homozygous, nonsense PCLO mutation underlies the autosomal recessive neurodegenerative disorder, PCH3. PCLO is a component of the presynaptic cytoskeletal matrix, and is thought to be involved in regulation of presynaptic proteins and synaptic vesicles. Our findings suggest that PCLO is crucial for the development and survival of a wide range of neuronal types in the human brain. Neurology ® 2015;84:1745-1750 GLOSSARY dbSNP 5 Single Nucleotide Polymorphism Database; ExAC 5 Exome Aggregation Consortium; FPKM 5 fragments per kilobase of exon per million fragments mapped; GATK 5 Genome Analysis Toolkit; indel 5 insertion-deletion; LOD 5 logarithm of odds; NHLBI 5 National Heart, Lung, and Blood Institute; PCH 5 pontocerebellar hypoplasia; PCH3 5 pontocerebellar hypoplasia type III.

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“…Messenger RNA sequencing data from human fetal (9 weeks postconception) cerebral cortex was obtained as previously described. 7 …”

Section: Resultsmentioning

confidence: 99%

Loss of PCLO function underlies pontocerebellar hypoplasia type III

et al. 2015

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“…A 15-bp deletion of a highly conserved segment deletes one of the binding sites, resulting in abnormal cortical development 40 . DeepBind analysis discovered a third RFX3 binding site located on the opposite strand, overlapping both of the known tandem binding sites (Fig.…”

Section: A N a Ly S I Smentioning

confidence: 99%

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

et al. 2015

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Knowing the sequence specificities of DNA-and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.DNA-and RNA-binding proteins play a central role in gene regulation, including transcription and alternative splicing. The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques 2-5 . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 . We adapted deep learning methods to the task of predicting sequence specificities and found that they compete favorably with the state of the art. Our approach, called DeepBind, is based on deep convolutional neural networks and can discover new patterns even when the locations of patterns within sequences are unknown-a task for which traditional neural networks require an exorbitant amount of training data.There are several challenging aspects in learning models of sequence specificity using modern high-throughput technologies. First, the data come in qualitatively different forms. Protein binding microarrays (PBMs) 8 and RNAcompete assays 9 provide a specificity coefficient for each probe sequence, whereas chromatin immunoprecipitation (ChIP)-seq 10 provides a ranked list of putatively bound sequences of varying length, and HT-SELEX 11 generates a set of very high affinity sequences. Second, the quantity of data is large. A typical high-throughput experiment measures between 10,000 and 100,000 sequences, and it is computationally demanding to incorporate them all. Third, each data acquisition technology has its own artifacts, biases and limitations, and we must discover the pertinent specificities despite these unwanted effects. For example, ChIP-seq reads often localize to "hyper-ChIPable" regions of the genome near highly expressed genes 12 .DeepBind (Fig. 1) addresses the above challenges. (i) It can be applied to both microarray and sequencing data; (ii) it can learn from millions of...

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“…Recently it has been demonstrated that GPR56 expression is strictly regionalized and regulated in a very complex manner, and new subtle mutations in its enhancer regions have been recently identified as causative of patterned cortical malformations in humans. Most interestingly, there exist species‐specific variations in the GPR56 regulatory regions that drive gene expression in distinct patterns in the developing neocortex, strongly suggesting this may be a mechanism contributing to define the species‐specific pattern of cortical folding across mammals (Bae et al, 2014). …”

Section: Beyond Cortical Expansion: Gyrificationmentioning

confidence: 99%

Coevolution of radial glial cells and the cerebral cortex

Romero

Borrell

2015

Glia

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Radial glia cells play fundamental roles in the development of the cerebral cortex, acting both as the primary stem and progenitor cells, as well as the guides for neuronal migration and lamination. These critical functions of radial glia cells in cortical development have been discovered mostly during the last 15 years and, more recently, seminal studies have demonstrated the existence of a remarkable diversity of additional cortical progenitor cell types, including a variety of basal radial glia cells with key roles in cortical expansion and folding, both in ontogeny and phylogeny. In this review, we summarize the main cellular and molecular mechanisms known to be involved in cerebral cortex development in mouse, as the currently preferred animal model, and then compare these with known mechanisms in other vertebrates, both mammal and nonmammal, including human. This allows us to present a global picture of how radial glia cells and the cerebral cortex seem to have coevolved, from reptiles to primates, leading to the remarkable diversity of vertebrate cortical phenotypes. GLIA 2015;63:1303–1319

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Evolutionarily Dynamic Alternative Splicing of GPR56 Regulates Regional Cerebral Cortical Patterning

Cited by 208 publications

References 41 publications

Loss of PCLO function underlies pontocerebellar hypoplasia type III

Loss of PCLO function underlies pontocerebellar hypoplasia type III

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Coevolution of radial glial cells and the cerebral cortex

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