Abstract

Extant protein-coding sequences span a huge range of ages, from those that emerged only recently, to those present in the last universal common ancestor. Because evolution has had less time to act on young sequences, there might be 'phylostratigraphy' trends in any properties that evolve slowly with age. A long-term reduction in hydrophobicity and hydrophobic clustering was found in previous, taxonomically restricted studies. Here we perform integrated phylostratigraphy across 435 fully sequenced species, using sensitive HMM methods to detect protein domain homology. We find that the reduction in hydrophobic clustering is universal across lineages. However, only young animal domains have a tendency to have higher structural disorder. Among ancient domains, trends in amino acid composition reflect the order of recruitment into the genetic code, suggesting that the composition of the contemporary descendants of ancient sequences reflects amino acid availability during the earliest stages of life, when these sequences first emerged.

Data availability

All scripts used in this work can be accessed at: https://github.com/MaselLab/ProteinEvolution. Our raw data, and homology files used in our analyses, are available at https://doi.org/10.6084/m9.figshare.12037281.

The following previously published data sets were used
    1. NCBI
    (2020) NCBI
    NCBI, https://www.ncbi.nlm.nih.gov/.
    1. Ensembl
    (2020) Ensembl
    Ensembl, https://uswest.ensembl.org/index.html.

Article and author information

Author details

  1. Jennifer E James

    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
    For correspondence
    jejames@arizona.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0518-6783
  2. Sara M Willis

    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Paul G Nelson

    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Catherine Weibel

    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Luke J Kosinski

    Department of Molecular Cell Biology, University of Arizona, Tucson, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Joanna Masel

    Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, United States
    For correspondence
    masel@arizona.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7398-2127

Funding

John Templeton Foundation (60814)

  • Joanna Masel

National Institutes of Health (GM-104040)

  • Joanna Masel

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

Copyright

© 2021, James 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

  • 1,914
    views
  • 208
    downloads
  • 24
    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. Jennifer E James
  2. Sara M Willis
  3. Paul G Nelson
  4. Catherine Weibel
  5. Luke J Kosinski
  6. Joanna Masel
(2021)
Universal and taxon-specific trends in protein sequences as a function of age
eLife 10:e57347.
https://doi.org/10.7554/eLife.57347

Share this article

https://doi.org/10.7554/eLife.57347

Further reading

    1. Evolutionary Biology
    Matthew Osmond, Graham Coop
    Research Article Updated

    Spatial patterns in genetic diversity are shaped by individuals dispersing from their parents and larger-scale population movements. It has long been appreciated that these patterns of movement shape the underlying genealogies along the genome leading to geographic patterns of isolation-by-distance in contemporary population genetic data. However, extracting the enormous amount of information contained in genealogies along recombining sequences has, until recently, not been computationally feasible. Here, we capitalize on important recent advances in genome-wide gene-genealogy reconstruction and develop methods to use thousands of trees to estimate per-generation dispersal rates and to locate the genetic ancestors of a sample back through time. We take a likelihood approach in continuous space using a simple approximate model (branching Brownian motion) as our prior distribution of spatial genealogies. After testing our method with simulations we apply it to Arabidopsis thaliana. We estimate a dispersal rate of roughly 60 km2/generation, slightly higher across latitude than across longitude, potentially reflecting a northward post-glacial expansion. Locating ancestors allows us to visualize major geographic movements, alternative geographic histories, and admixture. Our method highlights the huge amount of information about past dispersal events and population movements contained in genome-wide genealogies.

    1. Evolutionary Biology
    Dario Galanti, Jun Hee Jung ... Oliver Bossdorf
    Research Article

    Understanding the genomic basis of natural variation in plant pest resistance is an important goal in plant science, but it usually requires large and labor-intensive phenotyping experiments. Here, we explored the possibility that non-target reads from plant DNA sequencing can serve as phenotyping proxies for addressing such questions. We used data from a whole-genome and -epigenome sequencing study of 207 natural lines of field pennycress (Thlaspi arvense) that were grown in a common environment and spontaneously colonized by aphids, mildew, and other microbes. We found that the numbers of non-target reads assigned to the pest species differed between populations, had significant SNP-based heritability, and were associated with climate of origin and baseline glucosinolate contents. Specifically, pennycress lines from cold and thermally fluctuating habitats, presumably less favorable to aphids, showed higher aphid DNA load, i.e., decreased aphid resistance. Genome-wide association analyses identified genetic variants at known defense genes but also novel genomic regions associated with variation in aphid and mildew DNA load. Moreover, we found several differentially methylated regions associated with pathogen loads, in particular differential methylation at transposons and hypomethylation in the promoter of a gene involved in stomatal closure, likely induced by pathogens. Our study provides first insights into the defense mechanisms of Thlaspi arvense, a rising crop and model species, and demonstrates that non-target whole-genome sequencing reads, usually discarded, can be leveraged to estimate intensities of plant biotic interactions. With rapidly increasing numbers of large sequencing datasets worldwide, this approach should have broad application in fundamental and applied research.