Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Reconciling conflicting selection pressures in the plant collaborative non-self recognition self-incompatibility system

Amit Jangid1, Keren Erez1, Ohad Noy Feldheim2, and Tamar Friedlander1,*

  • *Contact author: tamar.friedlander@mail.huji.ac.il

Phys. Rev. Research 7, 033232 – Published 9 September, 2025

DOI: https://doi.org/10.1103/z2qq-t55b

Abstract

Complex biological systems should often reconcile conflicting selection pressures. In systems based on molecular recognition, molecules must specifically identify certain partners while excluding others. Here, we study how such selection pressures shape the evolution of the self-incompatibility system in plants. This system inhibits self-fertilization using specific molecular recognition between proteins, expressed in the plant female and male reproductive organs. We study the impact of these opposing selection pressures on the amino acid frequencies in these proteins’ recognition domain. We build on a theoretical framework enabling promiscuous recognition between proteins, as found empirically, and employ stochastic simulations to study their evolution. We find that selection exerts asymmetric responses of amino acid frequencies, affecting female proteins considerably, but hardly the male. Using large deviations theory, we well approximate the simulated frequencies and find agreement with genomic data. Our work offers a general theoretical framework to study the impact of multiple selection pressures, applicable to additional biological systems.

View figure in article

Physics Subject Headings (PhySH)

Article Text

Supplemental Material

References (59)

  1. E. M. East and A. J. Mangelsdorf, A new interpretation of the hereditary behavior of self-sterile plants, Proc. Natl. Acad. Sci. USA 11, 166 (1925).
  2. S. Wright, The distribution of self-sterility alleles in populations, Genetics 24, 538 (1939).
  3. D. de Nettancourt, The genetic basis of self-incompatibility, in Incompatibility in Angiosperms, Monographs on Theoretical and Applied Genetics, edited by D. de Nettancourt (Springer, Berlin, 1977), pp. 28–57.
  4. B. Igic, R. Lande and J. Kohn, Loss of self-incompatibility and its evolutionary consequences, Int. J. Plant Sci. 169, 93 (2008).
  5. S. Takayama and A. Isogai, Self-incompatibility in plants, Annu. Rev. Plant Biol. 56, 467 (2005).
  6. S. Fujii, K.-I. Kubo and S. Takayama, Non-self- and self-recognition models in plant self-incompatibility, Nat. Plants 2, 16130 (2016).
  7. K. K. Shimizu and T. Tsuchimatsu, Evolution of selfing: Recurrent patterns in molecular adaptation, Annu. Rev. Ecol. Evol. Syst. 46, 593 (2015).
  8. M. Iwano and S. Takayama, Self/non-self discrimination in angiosperm self-incompatibility, Curr. Opin. Plant Biol. 15, 78 (2012).
  9. C. Gervais, D. A. Awad, D. Roze, V. Castric and S. Billiard, Genetic architecture of inbreeding depression and the maintenance of gametophytic self-incompatibility, Evolution 68, 3317 (2014).
  10. J. S. Williams, L. Wu, S. Li, P. Sun and T.-H. Kao, Insight into S-RNase-based self-incompatibility in Petunia: Recent findings and future directions, Front. Plant Sci. 6 (2015).
  11. B. A. McClure et al., Style self-incompatibility gene products of Nicotlana alata are ribonucleases, Nature (London) 342, 955 (1989).
  12. Z. Lai et al., An F-box gene linked to the self-incompatibility (S) locus of Antirrhinum is expressed specifically in pollen and tapetum, Plant Mol. Biol. 50, 29 (2002).
  13. P. Sijacic, X. Wang, A. L. Skirpan, Y. Wang, P. E. Dowd, A. G. McCubbin, S. Huang, and T. Kao, Identification of the pollen determinant of S-RNase-mediated self-incompatibility, Nature (London) 429, 302 (2004).
  14. K.-I. Kubo et al., Collaborative non-self recognition system in S-RNase–based self-incompatibility, Science 330, 796 (2010).
  15. K.-I. Kubo et al., Gene duplication and genetic exchange drive the evolution of S-RNase-based self-incompatibility in Petunia, Nat. Plants 1, 14005 (2015).
  16. J. Vieira, R. Morales-Hojas, R. A. M. Santos and C. P. Vieira, Different positively selected sites at the gametophytic self-incompatibility pistil S-RNase gene in the Solanaceae and Rosaceae (Prunus, Pyrus, and Malus), J. Mol. Evol. 65, 175 (2007).
  17. J. Vieira, P. G. Ferreira, B. Aguiar, N. A. Fonseca and C. P. Vieira, Evolutionary patterns at the RNase based gametophytic self-incompatibility system in two divergent Rosaceae groups (Maloideae and Prunus), BMC Evol. Biol. 10, 200 (2010).
  18. M. I. Pratas et al., Inferences on specificity recognition at the Malus × domestica gametophytic self-incompatibility system, Sci. Rep. 8, 1717 (2018).
  19. J. Vieira et al., Predicting specificities under the non-self gametophytic self-incompatibility recognition model, Front. Plant Sci. 10, (2019).
  20. B. Aguiar et al., Patterns of evolution at the gametophytic self-incompatibility Sorbus aucuparia (Pyrinae) S pollen genes support the non-self recognition by multiple factors model, J. Exp. Bot. 64, 2423 (2013).
  21. D. P. Matton et al., Hypervariable domains of self-incompatibility RNases mediate allele-specific pollen recognition, Plant Cell 9, 1757 (1997).
  22. D. P. Matton et al., Production of an S RNase with dual specificity suggests a novel hypothesis for the generation of new S alleles, Plant Cell 11, 2087 (1999).
  23. K. Bod’ová, T. Priklopil, D. L. Field, N. H. Barton and M. Pickup, Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system, Genetics 209, 861 (2018).
  24. A. Harkness, E. E. Goldberg and Y. Brandvain, Diversification or collapse of self-incompatibility haplotypes as a rescue process, Am. Nat. 197, E89 (2020).
  25. W. Fontana and P. Schuster, Shaping space: The Possible and the Attainable in RNA genotype–phenotype mapping, J. Theor. Biol. 194, 491 (1998).
  26. L. W. Ancel and W. Fontana, Plasticity, evolvability, and modularity in RNA, J. Exp. Zool. 288, 242 (2000).
  27. A. Wagner, Robustness and evolvability: A paradox resolved, Proc. R. Soc. B 275, 91 (2008).
  28. D. Lancet, E. Sadovsky and E. Seidemann, Probability model for molecular recognition in biological receptor repertoires: Significance to the olfactory system, Proc. Natl. Acad. Sci. USA 90, 3715 (1993).
  29. M. Lässig, From biophysics to evolutionary genetics: Statistical aspects of gene regulation, BMC Bioinform. 8, S7 (2007).
  30. T. Friedlander, R. Prizak, N. H. Barton and G. Tkačik, Evolution of new regulatory functions on biophysically realistic fitness landscapes, Nat. Commun. 8, 216 (2017).
  31. P. C. Wainwright, M. E. Alfaro, D. I. Bolnick and C. D. Hulsey, Many-to-one mapping of form to function: A general principle in organismal design? Integr. Comp. Biol. 45, 256 (2005).
  32. Y. Elhanati, A. Murugan, C. G. Callan, T. Mora and A. M. Walczak, Quantifying selection in immune receptor repertoires, Proc. Natl. Acad. Sci. USA 111, 9875 (2014).
  33. K. Erez, A. Jangid, O. N. Feldheim and T. Friedlander, The role of promiscuous molecular recognition in the evolution of RNase-based self-incompatibility in plants, Nat. Commun. 15, 4864 (2024).
  34. See Supplemental Material at http://link.aps.org/supplemental/10.1103/z2qq-t55b for details of supplementary notes and figures. This contains classification of amino acids, calculation of female frequencies used in the stochastic simulation, differences between diploid and haploid models, empirical data, and figures related to the main text.
  35. UniProt Consortium, Update on activities at the Universal Protein Resource (UniProt) in 2013, Nucleic Acids Res. 41, D43 (2013).
  36. O. Noivirt-Brik, R. Unger and A. Horovitz, Analysing the origin of long-range interactions in proteins using lattice models, BMC Struct. Biol. 9, 4 (2009).
  37. T. M. Cover and J. A. Thomas, Elements of Information Theory (John Wiley & Sons, 2012).
  38. A. Dembo and O. Zeitouni, Large Deviations Techniques and Applications. Stochastic Modelling and Applied Probability, Vol. 38 (Springer, Berlin, 2010).
  39. J. Berg, S. Willmann and M. Lässig, Adaptive evolution of transcription factor binding sites, BMC Evol. Biol. 4, 42 (2004).
  40. G. Sella and A. E. Hirsh, The application of statistical physics to evolutionary biology, Proc. Natl. Acad. Sci. USA 102, 9541 (2005).
  41. H. Zhao et al., Origin, loss, and regain of self-incompatibility in angiosperms, Plant Cell 34, 579 (2022).
  42. D. B. Lukatsky, B. E. Shakhnovich, J. Mintseris and E. I. Shakhnovich, Structural similarity enhances interaction propensity of proteins, J. Mol. Biol. 365, 1596 (2007).
  43. O. Peleg, J.-M. Choi and E. I. Shakhnovich, Evolution of specificity in protein-protein interactions, Biophys. J. 107, 1686 (2014).
  44. E. J. Deeds and M. A. Rowland, The evolution of crosstalk in signaling networks, Biophys. J. 106, 643a (2014).
  45. C. Aakre et al., Evolving new protein-protein interaction specificity through promiscuous intermediates, Cell 163, 594 (2015).
  46. M. Łuksza and M. Lässig, A predictive fitness model for influenza, Nature (London) 507, 57 (2014).
  47. M. L. Holding, D. H. Drabeck, S. A. Jansa and H. L. Gibbs, Venom resistance as a model for understanding the molecular basis of complex coevolutionary adaptations, Integr. Comp. Biol. 56, 1032 (2016).
  48. K. Arbuckle, R. C. Rodríguez de la Vega and N. R. Casewell, Coevolution takes the sting out of it: Evolutionary biology and mechanisms of toxin resistance in animals, Toxicon 140, 118 (2017).
  49. C. C. Nguyen and M. H. Saier, Phylogenetic, structural and functional analyses of the LacI-GalR family of bacterial transcription factors, FEBS Lett. 377, 98 (1995).
  50. R. P. Sear, Highly specific protein–protein interactions, evolution and negative design, Phys. Biol. 1, 166 (2004).
  51. R. P. Sear, Specific protein–protein binding in many-component mixtures of proteins, Phys. Biol. 1, 53 (2004).
  52. J. T. George, D. A. Kessler and H. Levine, Effects of thymic selection on T cell recognition of foreign and tumor antigenic peptides, Proc. Natl. Acad. Sci. USA 114, E7875 (2017).
  53. T. Friedlander, R. Prizak, C. C. Guet, N. H. Barton and G. Tkačik, Intrinsic limits to gene regulation by global crosstalk, Nat. Commun. 7, 12307 (2016).
  54. C. J. McClune and M. T. Laub, Constraints on the expansion of paralogous protein families, Curr. Biol. 30, R460 (2020).
  55. S. J. Maerkl and S. R. Quake, A systems approach to measuring the binding energy landscapes of transcription factors, Science 315, 233 (2007).
  56. K. Pougach et al., Duplication of a promiscuous transcription factor drives the emergence of a new regulatory network, Nat. Commun. 5, 4868 (2014).
  57. R. K. Shultzaberger, S. J. Maerkl, J. F. Kirsch and M. B. Eisen, Probing the informational and regulatory plasticity of a transcription factor DNA–binding domain, PLoS Genet. 8, e1002614 (2012).
  58. R. M. Adams, T. Mora, A. M. Walczak and J. B. Kinney, Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves, eLife 5, e23156 (2016).
  59. A. Mayer, V. Balasubramanian, T. Mora and A. M. Walczak, How a well-adapted immune system is organized, Proc. Natl. Acad. Sci. USA 112, 5950 (2015).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation