RBFOX1 regulates both splicing and transcriptional networks in human neuronal development

Fogel, Brent L.; Wexler, Eric; Wahnich, Amanda; Friedrich, Tara; Chandran, Vijayendran; Gao, Fuying; Parikshak, Neelroop N.; Konopka, Genevieve; Geschwind, Daniel H.

doi:10.1093/hmg/dds240

Cited by 181 publications

(198 citation statements)

References 84 publications

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“…S3). A2BP1 is an alternative splicing factor that functions in neural development, and its genomic alterations have been implicated with several neurodevelopmental and neuropsychiatric disorders including autism spectrum disorder (ASD) (21). A2BP1 encodes both cytoplasmic and nuclear isoforms ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Neutralization of terminal differentiation in gliomagenesis

Yuan

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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An immature state of cellular differentiation-characterized by stem cell-like tendencies and impaired differentiation-is a hallmark of cancer. Using glioblastoma multiforme (GBM) as a model system, we sought to determine whether molecular determinants that drive cells toward terminal differentiation are also genetically targeted in carcinogenesis and whether neutralizing such genes also plays an active role to reinforce the impaired differentiation state and promote malignancy. To that end, we screened 71 genes with known roles in promoting nervous system development that also sustain copy number loss in GBM through antineoplastic assay and identified A2BP1 (ataxin 2 binding protein 1, Rbfox1), an RNAbinding and splicing regulator that is deleted in 10% of GBM cases. Integrated in silico analysis of GBM profiles to elucidate the A2BP1 pathway and its role in glioma identified myelin transcription factor 1-like (Myt1L) as a direct transcriptional regulator of A2BP1. Reintroduction of A2BP1 or Myt1L in GBM cell lines and glioma stem cells profoundly inhibited tumorigenesis in multiple assays, and conversely, shRNA-mediated knockdown of A2BP1 or Myt1L in premalignant neural stem cells compromised neuronal lineage differentiation and promoted orthotopic tumor formation. On the mechanistic level, with the top-represented downstream target TPM1 as an illustrative example, we demonstrated that, among its multiple functions, A2BP1 serves to regulate TPM1's alternative splicing to promote cytoskeletal organization and terminal differentiation and suppress malignancy. Thus, in addition to the activation of self-renewal pathways, the neutralization of genetic programs that drive cells toward terminal differentiation may also promote immature and highly plastic developmental states that contribute to the aggressive malignant properties of GBM.oncogenomics | cancer stem cells

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Section: Resultsmentioning

confidence: 99%

Neutralization of terminal differentiation in gliomagenesis

Yuan

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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“…RBPs play a crucial role in regulating splicing, having an impact on a wide variety of developmental stages such as stem cell differentiation 46 and tissue development 47 . We predicted binding scores at junctions near exons that are putatively regulated by known splicing regulators that exhibit large changes in splicing when knocked down, including: Nova (neuro-oncological ventral antigen 1 and 2), PTBP1 (polypyrimidine tract binding protein 1), Mbnl1/2 (muscleblind-like protein), hnRNP C (heterogeneous nuclear ribonucleoprotein C) and cytotoxic TIA (T-cell intracellular antigen-like 1).…”

Section: Deepbind Models Identify Deleterious Genomic Variantsmentioning

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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“…Intriguingly, some proteins act to regulate alternative splicing in neurons (Grabowski 2011), and disruptions of these processes have been associated with human neurodevelopmental disorders. One notable example is RNA-binding protein, fox-1 homolog (encoded by the gene RBFOX1), which modulates splicing events implicated in neuronal development, maturation, and excitation (Fogel et al 2012). RBFOX1 has been independently implicated in autism spectrum disorders and in epilepsy (Gehman et al 2011).…”

Section: Epigeneticsmentioning

confidence: 99%

“…As discussed above, some proteins also act to regulate gene transcription and splicing of genes, and such proteins are, of course, themselves encoded by genes. Thus, genes and proteins interact with each other to form complicated networks, which can be carefully traced out and studied through a variety of experimental methods (Fisher 2006, Vernes & Fisher 2009, Fogel et al 2012, CarrionCastillo et al 2013, Rodenas-Cuadrado et al 2014. But how might defined functions of genes and proteins at cellular levels affect the building of brain circuitry, or influence neural processing?…”

Section: From Genes To Proteinsmentioning

confidence: 99%

Genetics and the Language Sciences

Fisher

Vernes

2015

Annu. Rev. Linguist.

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Theories addressing the biological basis of language must be built on an appreciation of the ways that molecular and neurobiological substrates can contribute to aspects of human cognition. Here, we lay out the principles by which a genome could potentially encode the necessary information to produce a language-ready brain. We describe what genes are; how they are regulated; and how they affect the formation, function, and plasticity of neuronal circuits. At each step, we give examples of molecules implicated in pathways that are important for speech and language. Finally, we discuss technological advances in genomics that are revealing considerable genotypic variation in the human population, from rare mutations to common polymorphisms, with the potential to relate this variation to natural variability in speech and language skills. Moving forward, an interdisciplinary approach to the language sciences, integrating genetics, neurobiology, psychology, and linguistics, will be essential for a complete understanding of our unique human capacities. 289

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RBFOX1 regulates both splicing and transcriptional networks in human neuronal development

Cited by 181 publications

References 84 publications

Neutralization of terminal differentiation in gliomagenesis

Neutralization of terminal differentiation in gliomagenesis

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

Genetics and the Language Sciences

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