1. Evolutionary Biology
  2. Neuroscience
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Evidence for evolutionary divergence of activity-dependent gene expression in developing neurons

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Cite this article as: eLife 2016;5:e20337 doi: 10.7554/eLife.20337

Abstract

Evolutionary differences in gene regulation between humans and lower mammalian experimental systems are incompletely understood, a potential translational obstacle that is challenging to surmount in neurons, where primary tissue availability is poor. Rodent-based studies show that activity-dependent transcriptional programs mediate myriad functions in neuronal development, but the extent of their conservation in human neurons is unknown. We compared activity-dependent transcriptional responses in developing human stem cell-derived cortical neurons with those induced in developing primary- or stem cell-derived mouse cortical neurons. While activity-dependent gene-responsiveness showed little dependence on developmental stage or origin (primary tissue vs. stem cell), notable species-dependent differences were observed. Moreover, differential species-specific gene ortholog regulation was recapitulated in aneuploid mouse neurons carrying human chromosome-21, implicating promoter/enhancer sequence divergence as a factor, including human-specific activity-responsive AP-1 sites. These findings support the use of human neuronal systems for probing transcriptional responses to physiological stimuli or indeed pharmaceutical agents.

Article and author information

Author details

  1. Jing Qiu

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Jamie McQueen

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Bilada Bilican

    MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Owen Dando

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Dario Magnani

    MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Karolina Punovuori

    MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0297-1225
  7. Bhuvaneish T Selvaraj

    MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Matthew Livesey

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Ghazal Haghi

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  10. Samuel Heron

    School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  11. Karen Burr

    MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  12. Rickie Patani

    MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  13. Rinku Rajan

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  14. Olivia Sheppard

    Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  15. Peter C Kind

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  16. T Ian Simpson

    School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0495-7187
  17. Victor LJ Tybulewicz

    Division of Immune Cell Biology, MRC National Institute for Medical Research, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2439-0798
  18. David JA Wyllie

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4957-6049
  19. Elizabeth MC Fisher

    Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  20. Sally Lowell

    MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4018-9480
  21. Siddharthan Chandran

    MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, United Kingdom
    For correspondence
    siddharthan.chandran@ed.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
  22. Giles E Hardingham

    School of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom
    For correspondence
    Giles.Hardingham@ed.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7629-5314

Funding

Medical Research Council

  • Giles E Hardingham

Wellcome

  • Giles E Hardingham

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

Ethics

Animal experimentation: Animals used in this study were treated in accordance with UK Animal Scientific Procedures Act (1986) and the work subject to local ethical review approval by the University of Edinburgh Ethical Review Committee. The relevant project licence is 7009008, and the use of genetically modified organisms approved by local committee reference SBMS 13_007.

Reviewing Editor

  1. Anne West, Duke University School of Medicine, United States

Publication history

  1. Received: August 12, 2016
  2. Accepted: September 30, 2016
  3. Accepted Manuscript published: October 1, 2016 (version 1)
  4. Accepted Manuscript updated: October 12, 2016 (version 2)
  5. Version of Record published: November 2, 2016 (version 3)
  6. Version of Record updated: November 8, 2016 (version 4)

Copyright

© 2016, Qiu 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.

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Further reading

    1. Chromosomes and Gene Expression
    2. Evolutionary Biology
    Mathias Scharmann et al.
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    Differences between males and females are usually more subtle in dioecious plants than animals, but strong sexual dimorphism has evolved convergently in the South African Cape plant genus Leucadendron. Such sexual dimorphism in leaf size is expected largely to be due to differential gene expression between the sexes. We compared patterns of gene expression in leaves among 10 Leucadendron species across the genus. Surprisingly, we found no positive association between sexual dimorphism in morphology and the number or the percentage of sex-biased genes (SBGs). Sex bias in most SBGs evolved recently and was species specific. We compared rates of evolutionary change in expression for genes that were sex biased in one species but unbiased in others and found that SBGs evolved faster in expression than unbiased genes. This greater rate of expression evolution of SBGs, also documented in animals, might suggest the possible role of sexual selection in the evolution of gene expression. However, our comparative analysis clearly indicates that the more rapid rate of expression evolution of SBGs predated the origin of bias, and shifts towards bias were depleted in signatures of adaptation. Our results are thus more consistent with the view that sex bias is simply freer to evolve in genes less subject to constraints in expression level.

    1. Evolutionary Biology
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    Jan Clemens et al.
    Research Article Updated

    How neural networks evolved to generate the diversity of species-specific communication signals is unknown. For receivers of the signals, one hypothesis is that novel recognition phenotypes arise from parameter variation in computationally flexible feature detection networks. We test this hypothesis in crickets, where males generate and females recognize the mating songs with a species-specific pulse pattern, by investigating whether the song recognition network in the cricket brain has the computational flexibility to recognize different temporal features. Using electrophysiological recordings from the network that recognizes crucial properties of the pulse pattern on the short timescale in the cricket Gryllus bimaculatus, we built a computational model that reproduces the neuronal and behavioral tuning of that species. An analysis of the model’s parameter space reveals that the network can provide all recognition phenotypes for pulse duration and pause known in crickets and even other insects. Phenotypic diversity in the model is consistent with known preference types in crickets and other insects, and arises from computations that likely evolved to increase energy efficiency and robustness of pattern recognition. The model’s parameter to phenotype mapping is degenerate – different network parameters can create similar changes in the phenotype – which likely supports evolutionary plasticity. Our study suggests that computationally flexible networks underlie the diverse pattern recognition phenotypes, and we reveal network properties that constrain and support behavioral diversity.