Cited by 294RNA Research and SplicingRNA regulation and diseaseRNA and protein synthesis mechanisms

Integrative Modeling Defines the Nova Splicing-Regulatory Network and Its Combinatorial Controls

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Zhang, Chaolin, Frias, Maria A., Mele, Aldo, Ruggiu, Matteo, Eom, Taesun, Marney, Christina B., Wang, Huidong, Licatalosi, Donny D. +2 more·2010·Science

Abstract

Making the Final Cut: RNA splicing, which involves selectively cutting and pasting messenger RNA to generate different proteins, is critical in regulating human physiology and diseases. However, our knowledge of the underlying rules governing splicing regulation remains incomplete. The recent emergence of next-generation sequencing and other high-throughput technologies has provided an opportunity to transform our understanding of RNA regulation. Zhang et al. (p. 439 , published online 17 June) combined multiple data sets to generate a robust and relatively complete picture of splicing regulation by the mammalian neuronal splicing factor Nova in the brain. About 700 splicing events were identified, including many novel target exons, some likely to be involved in neurological disease. Combining genomic studies with computational biology also yielded insight into the regulation of alternative splicing.

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  • Pleurobrachia has more RNA-binding proteins, especially RRM/ELAV, KH and NOVAs, than any basal metazoan or choanoflagellate examined.

    “Matching the expansion of RNA regulatory mechanisms, Pleurobrachia has more RNA-binding proteins (especially RRM/ELAV, KH and NOVAs 26,27 , Supplementary Table 21) thananybasalmetazoanorchoanoflagellateexamined.”
  • Bioinformatically predicted Rbfox target transcripts based on conserved motif sites significantly overlap with autism-implicated genes.

    “We previously demonstrated that Rbfox target transcripts predicted bioinformatically based on conserved motif sites have significant overlap with genes implicated in autism, supporting the notion that disruption of either Rbfox1 itself or of its targets observed in autism patients is likely pathogenic (Zhang et al., 2010).”
  • The study used an integrative modeling approach that combines multiple complementary data types to make strong predictions of Rbfox targets.

    “The second critical aspect of this study is the use of an integrative modeling approach to combine multiple complementary types of data, so that individually weak bits of information can be integrated to make strong predictions of Rbfox targets (Zhang et al., 2010).”

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Authors
Zhang, C., Frias, M. A., Mele, A., Ruggiu, M., Eom, T., Marney, C. B., Wang, H., Licatalosi, D. D., Fak, J. J., & Darnell, R. B.
Affiliations
  • Howard Hughes Medical Institute (US)
  • Laboratory of Molecular Neuro-Oncology, Howard Hughes Medical Institute, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA.
Corresponding author
  • Zhang, Chaolin
  • Darnell, Robert B.
Venue
Science
Published
2010-07-23
Volume
329
Issue
5990
Pages
439-443
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2036583346
Cited by
294
References
27
Citations by year
2026: 9 · 2025: 11 · 2024: 15 · 2023: 15 · 2022: 8 · 2021: 8 · 2020: 10 · 2019: 15 · 2018: 16 · 2017: 17 · 2016: 25 · 2015: 21 · 2014: 34 · 2013: 28 · 2012: 26
FWCI
11.73
Type
journal article
Language
en
Open access
Locations
  • Science (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Howard Hughes Medical Institute
  • National Institute of General Medical Sciences (K99GM095713)
  • National Institute of Neurological Disorders and Stroke (NS34389, R01NS034389, R56NS034389)
  • National Center for Research Resources (UL1RR024143)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords
alternative splicing, nova, RNA-binding proteins, splicing regulatory network, Bayesian networks, combinatorial regulation, mouse brain, neurological disorders
MeSH terms
Neuro-Oncological Ventral Antigen, Animals, Antigens, Neoplasm, Artificial Intelligence, Bayes Theorem, Binding Sites, Brain, Cell Line, Exons, Humans, Introns, Models, Genetic, Nerve Tissue Proteins, Nervous System Diseases, Phosphorylation, Protein Binding, Proteins, RNA, Models, Statistical, RNA-Binding Proteins, Alternative Splicing, Evolution, Molecular, Computational Biology, Oligonucleotide Array Sequence Analysis, Mice, Gene Regulatory Networks

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