Down the Penrose stairs, or how selection for fewer recombination hotspots maintains their existence

  1. Zachary Baker  Is a corresponding author
  2. Molly Przeworski
  3. Guy Sella
  1. Columbia University, United States

Abstract

In many species, meiotic recombination events tend to occur in narrow intervals of the genome, known as hotspots. In humans and mice, double strand break (DSB) hotspot locations are determined by the DNA-binding specificity of the zinc finger array of the PRDM9 protein, which is rapidly evolving at residues in contact with DNA. Previous models explained this rapid evolution in terms of the need to restore PRDM9 binding sites lost to gene conversion over time, under the assumption that more PRDM9 binding always leads to more DSBs. This assumption, however, does not align with current evidence. Recent experimental work indicates that PRDM9 binding on both homologs facilitates DSB repair, and that the absence of sufficient symmetric binding disrupts meiosis. We therefore consider an alternative hypothesis: that rapid PRDM9 evolution is driven by the need to restore symmetric binding because of its role in coupling DSB formation and efficient repair. To this end, we model the evolution of PRDM9 from first principles: from its binding dynamics to the population genetic processes that govern the evolution of the zinc finger array and its binding sites. We show that the loss of a small number of strong binding sites leads to the use of a greater number of weaker ones, resulting in a sharp reduction in symmetric binding and favoring new PRDM9 alleles that restore the use of a smaller set of strong binding sites. This decrease, in turn, drives rapid PRDM9 evolutionary turnover. Our results therefore suggest that the advantage of new PRDM9 alleles is in limiting the number of binding sites used effectively, rather than in increasing net PRDM9 binding. By extension, our model suggests that the evolutionary advantage of hotspots may have been to increase the efficiency of DSB repair and/or homolog pairing.

Data availability

All modeling code, as well as code used to generate the figures, is available at https://github.com/sellalab/PRDM9_model. Source Data files have been provided for Figures 2-6 and their associated figure supplements, as well as for Figures in appendices 4-5.

Article and author information

Author details

  1. Zachary Baker

    Department of Systems Biology, Columbia University, New York, United States
    For correspondence
    zb267@cam.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1540-0731
  2. Molly Przeworski

    Department of Systems Biology, Columbia University, New York, United States
    Competing interests
    Molly Przeworski, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5369-9009
  3. Guy Sella

    Department of Biological Sciences, Columbia University, New York, United States
    Competing interests
    Guy Sella, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5239-7930

Funding

National Institute of Health (R01 GM83098)

  • Molly Przeworski

National Institute of Health (R01 GM115889)

  • Guy Sella

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2023, Baker et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 966
    views
  • 170
    downloads
  • 11
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Zachary Baker
  2. Molly Przeworski
  3. Guy Sella
(2023)
Down the Penrose stairs, or how selection for fewer recombination hotspots maintains their existence
eLife 12:e83769.
https://doi.org/10.7554/eLife.83769

Share this article

https://doi.org/10.7554/eLife.83769

Further reading

    1. Evolutionary Biology
    Julia D Sigwart, Yunlong Li ... Jin Sun
    Research Article

    A major question in animal evolution is how genotypic and phenotypic changes are related, and another is when and whether ancient gene order is conserved in living clades. Chitons, the molluscan class Polyplacophora, retain a body plan and general morphology apparently little changed since the Palaeozoic. We present a comparative analysis of five reference quality genomes, including four de novo assemblies, covering all major chiton clades, and an updated phylogeny for the phylum. We constructed 20 ancient molluscan linkage groups (MLGs) and show that these are relatively conserved in bivalve karyotypes, but in chitons they are subject to re-ordering, rearrangement, fusion, or partial duplication and vary even between congeneric species. The largest number of novel fusions is in the most plesiomorphic clade Lepidopleurida, and the chitonid Liolophura japonica has a partial genome duplication, extending the occurrence of large-scale gene duplication within Mollusca. The extreme and dynamic genome rearrangements in this class stands in contrast to most other animals, demonstrating that chitons have overcome evolutionary constraints acting on other animal groups. The apparently conservative phenome of chitons belies rapid and extensive changes in genome.

    1. Evolutionary Biology
    Mauna R Dasari, Kimberly E Roche ... Elizabeth A Archie
    Research Article

    Mammalian gut microbiomes are highly dynamic communities that shape and are shaped by host aging, including age-related changes to host immunity, metabolism, and behavior. As such, gut microbial composition may provide valuable information on host biological age. Here, we test this idea by creating a microbiome-based age predictor using 13,563 gut microbial profiles from 479 wild baboons collected over 14 years. The resulting ‘microbiome clock’ predicts host chronological age. Deviations from the clock’s predictions are linked to some demographic and socio-environmental factors that predict baboon health and survival: animals who appear old-for-age tend to be male, sampled in the dry season (for females), and have high social status (both sexes). However, an individual’s ‘microbiome age’ does not predict the attainment of developmental milestones or lifespan. Hence, in our host population, gut microbiome age largely reflects current, as opposed to past, social and environmental conditions, and does not predict the pace of host development or host mortality risk. We add to a growing understanding of how age is reflected in different host phenotypes and what forces modify biological age in primates.