FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch

  1. Zachary Daniel Burkett  Is a corresponding author
  2. Nancy F Day
  3. Todd Haswell Kimball
  4. Caitlin M Aamodt
  5. Jonathan B Heston
  6. Austin T Hilliard
  7. Xinshu Xiao
  8. Stephanie A White
  1. University of California, Los Angeles, United States
  2. Stanford University, United States

Abstract

Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans.

Data availability

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Zachary Daniel Burkett

    Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, United States
    For correspondence
    zburkett@ucla.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5153-485X
  2. Nancy F Day

    Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Todd Haswell Kimball

    Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Caitlin M Aamodt

    Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Jonathan B Heston

    Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7479-1122
  6. Austin T Hilliard

    Department of Biology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Xinshu Xiao

    Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Stephanie A White

    Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (RO1MH07012)

  • Stephanie A White

National Institutes of Health (5T32HD007228)

  • Zachary Daniel Burkett

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

Reviewing Editor

  1. Liqun Luo, Howard Hughes Medical Institute, Stanford University, United States

Ethics

Animal experimentation: All animal use was in accordance with NIH guidelines for experiments involving vertebrate animals and approved by the University of California, Los Angeles Chancellor's Institutional Animal Care and Use Committee (IACUC) under protocol (#2001-54). All surgical procedures were performed under isoflurane anesthetic.

Version history

  1. Received: July 24, 2017
  2. Accepted: January 22, 2018
  3. Accepted Manuscript published: January 23, 2018 (version 1)
  4. Version of Record published: February 26, 2018 (version 2)
  5. Version of Record updated: March 9, 2018 (version 3)

Copyright

© 2018, Burkett 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

  • 3,510
    views
  • 427
    downloads
  • 16
    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 Daniel Burkett
  2. Nancy F Day
  3. Todd Haswell Kimball
  4. Caitlin M Aamodt
  5. Jonathan B Heston
  6. Austin T Hilliard
  7. Xinshu Xiao
  8. Stephanie A White
(2018)
FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch
eLife 7:e30649.
https://doi.org/10.7554/eLife.30649

Share this article

https://doi.org/10.7554/eLife.30649

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Weichen Song, Yongyong Shi, Guan Ning Lin
    Tools and Resources

    We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

    1. Computational and Systems Biology
    Qianmu Yuan, Chong Tian, Yuedong Yang
    Tools and Resources

    Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.