IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1005761.html
   My bibliography  Save this article

Stochastic principles governing alternative splicing of RNA

Author

Listed:
  • Jianfei Hu
  • Eli Boritz
  • William Wylie
  • Daniel C Douek

Abstract

The dominance of the major transcript isoform relative to other isoforms from the same gene generated by alternative splicing (AS) is essential to the maintenance of normal cellular physiology. However, the underlying principles that determine such dominance remain unknown. Here, we analyzed the physical AS process and found that it can be modeled by a stochastic minimization process, which causes the scaled expression levels of all transcript isoforms to follow the same Weibull extreme value distribution. Surprisingly, we also found a simple equation to describe the median frequency of transcript isoforms of different dominance. This two-parameter Weibull model provides the statistical distribution of all isoforms of all transcribed genes, and reveals that previously unexplained observations concerning relative isoform expression derive from these principles.Author summary: Alternative RNA splicing within eukaryotic cells enables each gene to generate multiple different mature transcripts which further encode proteins with distinct or even opposing functions. The relative frequencies of the transcript isoforms generated by a particular gene are essential to the maintenance of normal cellular physiology; however, the underlying mechanisms and principles that govern these frequencies are unknown. We analyzed the frequency distribution of all transcript isoforms in highly purified human T cell subsets and built a simple mathematical model, based on the physical process of alternative splicing, which provides statistical principles that govern this process. This model matches very well with the observed distributions of expression levels and relative frequencies of all transcript isoforms from different tissues and cell lines. Notably, we used this model to elucidate many previously unexplained observations concerning transcript isoform expression. More importantly, this model reveals the existence of simple statistical principles that can be applied to understanding an essential and complex biological process such as alternative splicing.

Suggested Citation

  • Jianfei Hu & Eli Boritz & William Wylie & Daniel C Douek, 2017. "Stochastic principles governing alternative splicing of RNA," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-20, September.
  • Handle: RePEc:plo:pcbi00:1005761
    DOI: 10.1371/journal.pcbi.1005761
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005761
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005761&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1005761?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Eric T. Wang & Rickard Sandberg & Shujun Luo & Irina Khrebtukova & Lu Zhang & Christine Mayr & Stephen F. Kingsmore & Gary P. Schroth & Christopher B. Burge, 2008. "Alternative isoform regulation in human tissue transcriptomes," Nature, Nature, vol. 456(7221), pages 470-476, November.
    2. Daisuke Hattori & Yi Chen & Benjamin J. Matthews & Lukasz Salwinski & Chiara Sabatti & Wesley B. Grueber & S. Lawrence Zipursky, 2009. "Robust discrimination between self and non-self neurites requires thousands of Dscam1 isoforms," Nature, Nature, vol. 461(7264), pages 644-648, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gustavo Glusman & Juan Caballero & Max Robinson & Burak Kutlu & Leroy Hood, 2013. "Optimal Scaling of Digital Transcriptomes," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-12, November.
    2. Xiaohong Li & Guy N Brock & Eric C Rouchka & Nigel G F Cooper & Dongfeng Wu & Timothy E O’Toole & Ryan S Gill & Abdallah M Eteleeb & Liz O’Brien & Shesh N Rai, 2017. "A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
    3. Feng Wang & Yang Xu & Robert Wang & Beatrice Zhang & Noah Smith & Amber Notaro & Samantha Gaerlan & Eric Kutschera & Kathryn E. Kadash-Edmondson & Yi Xing & Lan Lin, 2023. "TEQUILA-seq: a versatile and low-cost method for targeted long-read RNA sequencing," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Patricia González-Rodríguez & Daniel J. Klionsky & Bertrand Joseph, 2022. "Autophagy regulation by RNA alternative splicing and implications in human diseases," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    5. Miklos Csuros & Igor B Rogozin & Eugene V Koonin, 2011. "A Detailed History of Intron-rich Eukaryotic Ancestors Inferred from a Global Survey of 100 Complete Genomes," PLOS Computational Biology, Public Library of Science, vol. 7(9), pages 1-9, September.
    6. Nysia I George & John F Bowyer & Nathaniel M Crabtree & Ching-Wei Chang, 2015. "An Iterative Leave-One-Out Approach to Outlier Detection in RNA-Seq Data," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-10, June.
    7. Ilias Georgakopoulos-Soares & Guillermo E. Parada & Hei Yuen Wong & Ragini Medhi & Giulia Furlan & Roberto Munita & Eric A. Miska & Chun Kit Kwok & Martin Hemberg, 2022. "Alternative splicing modulation by G-quadruplexes," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    8. Areum Han & Peter Stoilov & Anthony J Linares & Yu Zhou & Xiang-Dong Fu & Douglas L Black, 2014. "De Novo Prediction of PTBP1 Binding and Splicing Targets Reveals Unexpected Features of Its RNA Recognition and Function," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-18, January.
    9. Judith A Potashkin & Jose A Santiago & Bernard M Ravina & Arthur Watts & Alexey A Leontovich, 2012. "Biosignatures for Parkinson’s Disease and Atypical Parkinsonian Disorders Patients," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-13, August.
    10. Jiang Lin & Jing Yang & Xiang-mei Wen & Lei Yang & Zhao-qun Deng & Zhen Qian & Ji-chun Ma & Hong Guo & Ying-ying Zhang & Wei Qian & Jun Qian, 2014. "Detection of SRSF2-P95 Mutation by High-Resolution Melting Curve Analysis and Its Effect on Prognosis in Myelodysplastic Syndrome," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-12, December.
    11. Wei Hu & Yangjun Wu & Qili Shi & Jingni Wu & Deping Kong & Xiaohua Wu & Xianghuo He & Teng Liu & Shengli Li, 2022. "Systematic characterization of cancer transcriptome at transcript resolution," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    12. Hillary M. Heiling & Douglas R. Wilson & Naim U. Rashid & Wei Sun & Joseph G. Ibrahim, 2023. "Estimating cell type composition using isoform expression one gene at a time," Biometrics, The International Biometric Society, vol. 79(2), pages 854-865, June.
    13. Zhiyi Qin & Xuegong Zhang, 2017. "The identification of switch-like alternative splicing exons among multiple samples with RNA-Seq data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-12, May.
    14. Marine Pesson & Alain Volant & Arnaud Uguen & Kilian Trillet & Pierre De La Grange & Marc Aubry & Mélanie Daoulas & Michel Robaszkiewicz & Gérald Le Gac & Alain Morel & Brigitte Simon & Laurent Corcos, 2014. "A Gene Expression and Pre-mRNA Splicing Signature That Marks the Adenoma-Adenocarcinoma Progression in Colorectal Cancer," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-13, February.
    15. Donna K Slonim & Itai Yanai, 2009. "Getting Started in Gene Expression Microarray Analysis," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-4, October.
    16. Jie Cheng & Yamei Yu & Xingyu Wang & Xi Zheng & Ting Liu & Daojun Hu & Yongfeng Jin & Ying Lai & Tian-Min Fu & Qiang Chen, 2023. "Structural basis for the self-recognition of sDSCAM in Chelicerata," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    17. Arashdeep Singh & Arati Rajeevan & Vishaka Gopalan & Piyush Agrawal & Chi-Ping Day & Sridhar Hannenhalli, 2022. "Broad misappropriation of developmental splicing profile by cancer in multiple organs," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    18. Seungjae Lee & Yen-Chung Chen & Austin E. Gillen & J. Matthew Taliaferro & Bart Deplancke & Hongjie Li & Eric C. Lai, 2022. "Diverse cell-specific patterns of alternative polyadenylation in Drosophila," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    19. Wei Sun & Yufeng Liu & James J. Crowley & Ting-Huei Chen & Hua Zhou & Haitao Chu & Shunping Huang & Pei-Fen Kuan & Yuan Li & Darla Miller & Ginger Shaw & Yichao Wu & Vasyl Zhabotynsky & Leonard McMill, 2015. "IsoDOT Detects Differential RNA-Isoform Expression/Usage With Respect to a Categorical or Continuous Covariate With High Sensitivity and Specificity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 975-986, September.
    20. Michelle M. Kameda-Smith & Helen Zhu & En-Ching Luo & Yujin Suk & Agata Xella & Brian Yee & Chirayu Chokshi & Sansi Xing & Frederick Tan & Raymond G. Fox & Ashley A. Adile & David Bakhshinyan & Kevin , 2022. "Characterization of an RNA binding protein interactome reveals a context-specific post-transcriptional landscape of MYC-amplified medulloblastoma," Nature Communications, Nature, vol. 13(1), pages 1-19, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1005761. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.