Topological constraints in early multicellularity favor reproductive division of labor

Abstract

Reproductive division of labor (e.g., germ-soma specialization) is a hallmark of the evolution of multicellularity, signifying the emergence of a new type of individual and facilitating the evolution of increased organismal complexity. A large body of work from evolutionary biology, economics, and ecology has shown that specialization is beneficial when further division of labor produces an accelerating increase in absolute productivity (i.e., productivity is a convex function of specialization). Here we show that reproductive specialization is qualitatively different from classical models of resource sharing, and can evolve even when the benefits of specialization are saturating (i.e., productivity is a concave function of specialization). Through analytical theory and evolutionary individual-based simulations, we demonstrate that reproductive specialization is strongly favored in sparse networks of cellular interactions that reflect the morphology of early, simple multicellular organisms, highlighting the importance of restricted social interactions in the evolution of reproductive specialization.

Data availability

All evolutionary simulations and other computations associated with this work are available at github.com/dyanni3/topologicalConstraintsSpecialization; all parameters used in the current study are specified so all simulations can be repeated exactly.

Article and author information

Author details

  1. David Yanni

    School of Physics, Georgia Institute of Technology, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Shane Jacobeen

    School of Physics, Georgia Institute of Technology, Atlanta, United States
    For correspondence
    shane.jacobeen@gatech.edu
    Competing interests
    The authors declare that no competing interests exist.
  3. Pedro Márquez-Zacarías

    School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Joshua S Weitz

    School of Physics, Georgia Institute of Technology, Atlanta, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. William C Ratcliff

    School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States
    For correspondence
    william.ratcliff@gatech.edu
    Competing interests
    The authors declare that no competing interests exist.
  6. Peter J Yunker

    School of Physics, Georgia Institute of Technology, Atlanta, United States
    For correspondence
    peter.yunker@gatech.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8471-4171

Funding

National Science Foundation (IOS-1656549)

  • William C Ratcliff
  • Peter J Yunker

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

Copyright

© 2020, Yanni 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

  • 3,250
    views
  • 430
    downloads
  • 36
    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. David Yanni
  2. Shane Jacobeen
  3. Pedro Márquez-Zacarías
  4. Joshua S Weitz
  5. William C Ratcliff
  6. Peter J Yunker
(2020)
Topological constraints in early multicellularity favor reproductive division of labor
eLife 9:e54348.
https://doi.org/10.7554/eLife.54348

Share this article

https://doi.org/10.7554/eLife.54348

Further reading

    1. Evolutionary Biology
    Thibaut Sellinger, Frank Johannes, Aurélien Tellier
    Research Article

    With the availability of high-quality full genome polymorphism (SNPs) data, it becomes feasible to study the past demographic and selective history of populations in exquisite detail. However, such inferences still suffer from a lack of statistical resolution for recent, for example bottlenecks, events, and/or for populations with small nucleotide diversity. Additional heritable (epi)genetic markers, such as indels, transposable elements, microsatellites, or cytosine methylation, may provide further, yet untapped, information on the recent past population history. We extend the Sequential Markovian Coalescent (SMC) framework to jointly use SNPs and other hyper-mutable markers. We are able to (1) improve the accuracy of demographic inference in recent times, (2) uncover past demographic events hidden to SNP-based inference methods, and (3) infer the hyper-mutable marker mutation rates under a finite site model. As a proof of principle, we focus on demographic inference in Arabidopsis thaliana using DNA methylation diversity data from 10 European natural accessions. We demonstrate that segregating single methylated polymorphisms (SMPs) satisfy the modeling assumptions of the SMC framework, while differentially methylated regions (DMRs) are not suitable as their length exceeds that of the genomic distance between two recombination events. Combining SNPs and SMPs while accounting for site- and region-level epimutation processes, we provide new estimates of the glacial age bottleneck and post-glacial population expansion of the European A. thaliana population. Our SMC framework readily accounts for a wide range of heritable genomic markers, thus paving the way for next-generation inference of evolutionary history by combining information from several genetic and epigenetic markers.

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Kara Schmidlin, Sam Apodaca ... Kerry Geiler-Samerotte
    Research Article

    There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.