IDEAS home Printed from https://ideas.repec.org/a/eee/thpobi/v150y2023icp1-13.html
   My bibliography  Save this article

A population genetics theory for piRNA-regulated transposable elements

Author

Listed:
  • Tomar, Siddharth S.
  • Hua-Van, Aurélie
  • Le Rouzic, Arnaud

Abstract

Transposable elements (TEs) are self-reproducing selfish DNA sequences that can invade the genome of virtually all living species. Population genetics models have shown that TE copy numbers generally reach a limit, either because the transposition rate decreases with the number of copies (transposition regulation) or because TE copies are deleterious, and thus purged by natural selection. Yet, recent empirical discoveries suggest that TE regulation may mostly rely on piRNAs, which require a specific mutational event (the insertion of a TE copy in a piRNA cluster) to be activated — the so-called TE regulation “trap model†. We derived new population genetics models accounting for this trap mechanism, and showed that the resulting equilibria differ substantially from previous expectations based on a transposition–selection equilibrium. We proposed three sub-models, depending on whether or not genomic TE copies and piRNA cluster TE copies are selectively neutral or deleterious, and we provide analytical expressions for maximum and equilibrium copy numbers, as well as cluster frequencies for all of them. In the full neutral model, the equilibrium is achieved when transposition is completely silenced, and this equilibrium does not depend on the transposition rate. When genomic TE copies are deleterious but not cluster TE copies, no long-term equilibrium is possible, and active TEs are eventually eliminated after an active incomplete invasion stage. When all TE copies are deleterious, a transposition–selection equilibrium exists, but the invasion dynamics is not monotonic, and the copy number peaks before decreasing. Mathematical predictions were in good agreement with numerical simulations, except when genetic drift and/or linkage disequilibrium dominates. Overall, the trap-model dynamics appeared to be substantially more stochastic and less repeatable than traditional regulation models.

Suggested Citation

  • Tomar, Siddharth S. & Hua-Van, Aurélie & Le Rouzic, Arnaud, 2023. "A population genetics theory for piRNA-regulated transposable elements," Theoretical Population Biology, Elsevier, vol. 150(C), pages 1-13.
  • Handle: RePEc:eee:thpobi:v:150:y:2023:i:c:p:1-13
    DOI: 10.1016/j.tpb.2023.02.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040580923000096
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tpb.2023.02.001?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    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. Fatima-Zahra Jaouimaa & Daniel Dempsey & Suzanne Van Osch & Stephen Kinsella & Kevin Burke & Jason Wyse & James Sweeney, 2021. "An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-25, December.
    2. Overstall, Antony M. & Woods, David C. & Martin, Kieran J., 2019. "Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 126-142.
    3. Serrouya, R. & Dickie, M. & DeMars, C. & Wittmann, M.J. & Boutin, S., 2020. "Predicting the effects of restoring linear features on woodland caribou populations," Ecological Modelling, Elsevier, vol. 416(C).
    4. Zadoki Tabo & Chester Kalinda & Lutz Breuer & Christian Albrecht, 2023. "Adapting Strategies for Effective Schistosomiasis Prevention: A Mathematical Modeling Approach," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
    5. Moore, Christopher M. & Catella, Samantha A. & Abbott, Karen C., 2018. "Population dynamics of mutualism and intraspecific density dependence: How θ-logistic density dependence affects mutualistic positive feedback," Ecological Modelling, Elsevier, vol. 368(C), pages 191-197.
    6. Yan, Chuan & Zhang, Zhibin, 2018. "Dome-shaped transition between positive and negative interactions maintains higher persistence and biomass in more complex ecological networks," Ecological Modelling, Elsevier, vol. 370(C), pages 14-21.
    7. Cécile Cathalot & Erwan G. Roussel & Antoine Perhirin & Vanessa Creff & Jean-Pierre Donval & Vivien Guyader & Guillaume Roullet & Jonathan Gula & Christian Tamburini & Marc Garel & Anne Godfroy & Pier, 2021. "Hydrothermal plumes as hotspots for deep-ocean heterotrophic microbial biomass production," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    8. Lamonica, Dominique & Herbach, Ulysse & Orias, Frédéric & Clément, Bernard & Charles, Sandrine & Lopes, Christelle, 2016. "Mechanistic modelling of daphnid-algae dynamics within a laboratory microcosm," Ecological Modelling, Elsevier, vol. 320(C), pages 213-230.
    9. Stahl, Gerhard & Wang, Shaohui & Wendt, Markus, 2011. "Validate Correlation of an ESG: Treasury Yields across," Hannover Economic Papers (HEP) dp-476, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    10. Alex Root, 2019. "Mathematical Modeling of The Challenge to Detect Pancreatic Adenocarcinoma Early with Biomarkers," Challenges, MDPI, vol. 10(1), pages 1-15, April.
    11. Chevallier, Damien & Mourrain, Baptiste & Girondot, Marc, 2020. "Modelling leatherback biphasic indeterminate growth using a modified Gompertz equation," Ecological Modelling, Elsevier, vol. 426(C).
    12. Diane Lefaudeux & Supriya Sen & Kevin Jiang & Alexander Hoffmann, 2022. "Kinetics of mRNA nuclear export regulate innate immune response gene expression," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    13. Jessica A Lee & Siavash Riazi & Shahla Nemati & Jannell V Bazurto & Andreas E Vasdekis & Benjamin J Ridenhour & Christopher H Remien & Christopher J Marx, 2019. "Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations," PLOS Genetics, Public Library of Science, vol. 15(11), pages 1-38, November.
    14. Turner, Rolf & Banerjee, Pradeep & Shahlori, Rayomand, 2014. "Optimal Asset Pricing," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i11).
    15. Carturan, Bruno S. & Siewe, Nourridine & Cobbold, Christina A. & Tyson, Rebecca C., 2023. "Bumble bee pollination and the wildflower/crop trade-off: When do wildflower enhancements improve crop yield?," Ecological Modelling, Elsevier, vol. 484(C).
    16. Yerin Jung & Yoonsub Kim & Hwi-Soo Seol & Jong-Hyeon Lee & Jung-Hwan Kwon, 2021. "Spatial Uncertainty in Modeling Inhalation Exposure to Volatile Organic Compounds in Response to the Application of Consumer Spray Products," IJERPH, MDPI, vol. 18(10), pages 1-11, May.
    17. Gregory L Watson & Di Xiong & Lu Zhang & Joseph A Zoller & John Shamshoian & Phillip Sundin & Teresa Bufford & Anne W Rimoin & Marc A Suchard & Christina M Ramirez, 2021. "Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-20, March.
    18. Venolia, Celeste T. & Lavaud, Romain & Green-Gavrielidis, Lindsay A. & Thornber, Carol & Humphries, Austin T., 2020. "Modeling the Growth of Sugar Kelp (Saccharina latissima) in Aquaculture Systems using Dynamic Energy Budget Theory," Ecological Modelling, Elsevier, vol. 430(C).
    19. Karim, Md Aktar Ul & Bhagat, Supriya Ramdas & Bhowmick, Amiya Ranjan, 2022. "Empirical detection of parameter variation in growth curve models using interval specific estimators," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    20. Bahi, Aya & Sauvage, Sabine & Payraudeau, Sylvain & Tournebize, Julien, 2023. "PESTIPOND: A descriptive model of pesticide fate in artificial ponds: I. Model development," Ecological Modelling, Elsevier, vol. 485(C).

    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:eee:thpobi:v:150:y:2023:i:c:p:1-13. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    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.