A framework for studying behavioral evolution by reconstructing ancestral repertoires

  1. Damián G Hernández
  2. Catalina Rivera
  3. Jessica Cande
  4. Baohua Zhou
  5. David Stern
  6. Gordon J Berman  Is a corresponding author
  1. Centro Atómico Bariloche and Instituto Balseiro, Argentina
  2. Emory University, United States
  3. Janelia Research Campus, Howard Hughes Medical Institute, United States

Abstract

Although different animal species often exhibit extensive variation in many behaviors, typically scientists examine one or a small number of behaviors in any single study. Here, we propose a new framework to simultaneously study the evolution of many behaviors. We measured the behavioral repertoire of individuals from six species of fruit flies using unsupervised techniques and identified all stereotyped movements exhibited by each species. We then fit a Generalized Linear Mixed Model to estimate the intra- and inter-species behavioral covariances, and, by using the known phylogenetic relationships among species, we estimated the (unobserved) behaviors exhibited by ancestral species. We found that much of intra-specific behavioral variation has a similar covariance structure to previously described long-time scale variation in an individual’s behavior, suggesting that much of the measured variation between individuals of a single species in our assay reflects differences in the status of neural networks, rather than genetic or developmental differences between individuals. We then propose a method to identify groups of behaviors that appear to have evolved in a correlated manner, illustrating how sets of behaviors, rather than individual behaviors, likely evolved. Our approach provides a new framework for identifying co-evolving behaviors and may provide new opportunities to study the mechanistic basis of behavioral evolution.

Data availability

All behavioral region information is submitted with the article and will be posted publically, if accepted, on GitHub (https://github.com/bermanlabemory/behavioral-evolution). The original video data are too large to post (tens of TB), but will be made available upon request.

Article and author information

Author details

  1. Damián G Hernández

    Department of Medical Physics, Centro Atómico Bariloche and Instituto Balseiro, Bariloche, Argentina
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8995-7495
  2. Catalina Rivera

    Department of Physics, Emory University, Atlanta, United States
    Competing interests
    No competing interests declared.
  3. Jessica Cande

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    No competing interests declared.
  4. Baohua Zhou

    Department of Physics, Emory University, Atlanta, United States
    Competing interests
    No competing interests declared.
  5. David Stern

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1847-6483
  6. Gordon J Berman

    Department of Biology, Emory University, Atlanta, United States
    For correspondence
    gordon.berman@emory.edu
    Competing interests
    Gordon J Berman, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3588-7820

Funding

National Institute of Mental Health (MH115831-01)

  • Gordon J Berman

Human Frontiers Science Program (RGY0076/2018)

  • Gordon J Berman

Howard Hughes Medical Institute

  • Jessica Cande
  • David Stern
  • Gordon J Berman

Research Corporation for Science Advancement (25999)

  • Gordon J Berman

National Science Foundation (1806833)

  • Catalina Rivera

Ministerio de Ciencia y Tecnología, Gobierno de la Provincia de Córdoba

  • Damián G Hernández

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

Reviewing Editor

  1. Jesse H Goldberg, Cornell University, United States

Publication history

  1. Preprint posted: July 17, 2020 (view preprint)
  2. Received: August 5, 2020
  3. Accepted: September 1, 2021
  4. Accepted Manuscript published: September 2, 2021 (version 1)
  5. Version of Record published: September 16, 2021 (version 2)

Copyright

© 2021, Hernández 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

  • 1,845
    Page views
  • 259
    Downloads
  • 5
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

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. Damián G Hernández
  2. Catalina Rivera
  3. Jessica Cande
  4. Baohua Zhou
  5. David Stern
  6. Gordon J Berman
(2021)
A framework for studying behavioral evolution by reconstructing ancestral repertoires
eLife 10:e61806.
https://doi.org/10.7554/eLife.61806

Further reading

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Mihaly Badonyi, Joseph A Marsh
    Research Article Updated

    Assembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this efficiency is by seeding assembly pathways during translation via the cotranslational assembly of subunits. While recent evidence suggests that such cotranslational assembly is widespread, little is known about the properties of protein complexes associated with the phenomenon. Here, using a combination of proteome-specific protein complex structures and publicly available ribosome profiling data, we show that cotranslational assembly is particularly common between subunits that form large intermolecular interfaces. To test whether large interfaces have evolved to promote cotranslational assembly, as opposed to cotranslational assembly being a non-adaptive consequence of large interfaces, we compared the sizes of first and last translated interfaces of heteromeric subunits in bacterial, yeast, and human complexes. When considering all together, we observe the N-terminal interface to be larger than the C-terminal interface 54% of the time, increasing to 64% when we exclude subunits with only small interfaces, which are unlikely to cotranslationally assemble. This strongly suggests that large interfaces have evolved as a means to maximise the chance of successful cotranslational subunit binding.

    1. Evolutionary Biology
    2. Microbiology and Infectious Disease
    Pramod K Jangir et al.
    Research Article

    Bacterial pathogens show high levels of chromosomal genetic diversity, but the influence of this diversity on the evolution of antibiotic resistance by plasmid acquisition remains unclear. Here, we address this problem in the context of colistin, a ‘last line of defence’ antibiotic. Using experimental evolution, we show that a plasmid carrying the MCR-1 colistin resistance gene dramatically increases the ability of Escherichia coli to evolve high-level colistin resistance by acquiring mutations in lpxC, an essential chromosomal gene involved in lipopolysaccharide biosynthesis. Crucially, lpxC mutations increase colistin resistance in the presence of the MCR-1 gene, but decrease the resistance of wild-type cells, revealing positive sign epistasis for antibiotic resistance between the chromosomal mutations and a mobile resistance gene. Analysis of public genomic datasets shows that lpxC polymorphisms are common in pathogenic E. coli, including those carrying MCR-1, highlighting the clinical relevance of this interaction. Importantly, lpxC diversity is high in pathogenic E. coli from regions with no history of MCR-1 acquisition, suggesting that pre-existing lpxC polymorphisms potentiated the evolution of high-level colistin resistance by MCR-1 acquisition. More broadly, these findings highlight the importance of standing genetic variation and plasmid/chromosomal interactions in the evolutionary dynamics of antibiotic resistance.