IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1002150.html
   My bibliography  Save this article

A Detailed History of Intron-rich Eukaryotic Ancestors Inferred from a Global Survey of 100 Complete Genomes

Author

Listed:
  • Miklos Csuros
  • Igor B Rogozin
  • Eugene V Koonin

Abstract

Protein-coding genes in eukaryotes are interrupted by introns, but intron densities widely differ between eukaryotic lineages. Vertebrates, some invertebrates and green plants have intron-rich genes, with 6–7 introns per kilobase of coding sequence, whereas most of the other eukaryotes have intron-poor genes. We reconstructed the history of intron gain and loss using a probabilistic Markov model (Markov Chain Monte Carlo, MCMC) on 245 orthologous genes from 99 genomes representing the three of the five supergroups of eukaryotes for which multiple genome sequences are available. Intron-rich ancestors are confidently reconstructed for each major group, with 53 to 74% of the human intron density inferred with 95% confidence for the Last Eukaryotic Common Ancestor (LECA). The results of the MCMC reconstruction are compared with the reconstructions obtained using Maximum Likelihood (ML) and Dollo parsimony methods. An excellent agreement between the MCMC and ML inferences is demonstrated whereas Dollo parsimony introduces a noticeable bias in the estimations, typically yielding lower ancestral intron densities than MCMC and ML. Evolution of eukaryotic genes was dominated by intron loss, with substantial gain only at the bases of several major branches including plants and animals. The highest intron density, 120 to 130% of the human value, is inferred for the last common ancestor of animals. The reconstruction shows that the entire line of descent from LECA to mammals was intron-rich, a state conducive to the evolution of alternative splicing. Author Summary: In eukaryotes, protein-coding genes are interrupted by non-coding introns. The intron densities widely differ, from 6–7 introns per kilobase of coding sequence in vertebrates, some invertebrates and plants, to only a few introns across the entire genome in many unicellular forms. We applied a robust statistical methodology, Markov Chain Monte Carlo, to reconstruct the history of intron gain and loss throughout the evolution of eukaryotes using a set of 245 homologous genes from 99 genomes that represent the diversity of eukaryotes. Intron-rich ancestors were confidently inferred for each major eukaryotic group including 53% to 74% of the human intron density for the last eukaryotic common ancestor, and 120% to 130% of the human value for the last common ancestor of animals. Evolution of eukaryotic genes involved primarily intron loss, with substantial gain only at the bases of several major branches including plants and animals. Thus, the common ancestor of all extant eukaryotes was a complex organism with a gene architecture resembling those in multicellular organisms. The line of descent from the last common ancestor to mammals was an uninterrupted intron-rich state that, given the error-prone splicing in intron-rich organisms, was conducive to the elaboration of functional alternative splicing.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1002150
    DOI: 10.1371/journal.pcbi.1002150
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002150
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002150&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1002150?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. Hung D Nguyen & Maki Yoshihama & Naoya Kenmochi, 2005. "New Maximum Likelihood Estimators for Eukaryotic Intron Evolution," PLOS Computational Biology, Public Library of Science, vol. 1(7), pages 1-8, December.
    3. Alastair G. B. Simpson & Erin K. MacQuarrie & Andrew J. Roger, 2002. "Early origin of canonical introns," Nature, Nature, vol. 419(6904), pages 270-270, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maria E Gallegos & Sanjeev Balakrishnan & Priya Chandramouli & Shaily Arora & Aruna Azameera & Anitha Babushekar & Emilee Bargoma & Abdulmalik Bokhari & Siva Kumari Chava & Pranti Das & Meetali Desai , 2012. "The C. elegans Rab Family: Identification, Classification and Toolkit Construction," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-19, November.

    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. Jun Inamo & Akari Suzuki & Mahoko Takahashi Ueda & Kensuke Yamaguchi & Hiroshi Nishida & Katsuya Suzuki & Yuko Kaneko & Tsutomu Takeuchi & Hiroaki Hatano & Kazuyoshi Ishigaki & Yasushi Ishihama & Kazu, 2024. "Long-read sequencing for 29 immune cell subsets reveals disease-linked isoforms," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    4. 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.
    5. Elizabeth A. Werren & Geneva R. LaForce & Anshika Srivastava & Delia R. Perillo & Shaokun Li & Katherine Johnson & Safa Baris & Brandon Berger & Samantha L. Regan & Christian D. Pfennig & Sonja Munnik, 2024. "TREX tetramer disruption alters RNA processing necessary for corticogenesis in THOC6 Intellectual Disability Syndrome," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    6. 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.
    7. 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.
    8. Miklós Csűrös, 2006. "On the Estimation of Intron Evolution," PLOS Computational Biology, Public Library of Science, vol. 2(7), pages 1-1, July.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:pcbi00:1002150. 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.