Manipulations of MeCP2 in glutamatergic neurons highlight their contributions to Rett and other neurological disorders

Abstract

Many postnatal onset neurological disorders such as autism spectrum disorders (ASDs) and intellectual disability are thought to arise largely from disruption of excitatory/inhibitory homeostasis. Although mouse models of Rett syndrome (RTT), a postnatal neurological disorder caused by loss-of-function mutations in MECP2, display impaired excitatory neurotransmission, the RTT phenotype can be largely reproduced in mice simply by removing MeCP2 from inhibitory GABAergic neurons. To determine what role excitatory signaling impairment might play in RTT pathogenesis, we generated conditional mouse models with Mecp2 either removed from or expressed solely in glutamatergic neurons. MeCP2 deficiency in glutamatergic neurons leads to early lethality, obesity, tremor, altered anxiety-like behaviors, and impaired acoustic startle response, which is distinct from the phenotype of mice lacking MeCP2 only in inhibitory neurons. These findings reveal a role for excitatory signaling impairment in specific neurobehavioral abnormalities shared by RTT and other postnatal neurological disorders.

Article and author information

Author details

  1. Xiangling Meng

    Department of Neuroscience, BCM, Houston, United States
    Competing interests
    No competing interests declared.
  2. Wei Wang

    Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    Competing interests
    No competing interests declared.
  3. Hui Lu

    Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    Competing interests
    No competing interests declared.
  4. Ling-jie He

    Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    Competing interests
    No competing interests declared.
  5. Wu Chen

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    No competing interests declared.
  6. Eugene Chao

    Department of Neuroscience, BCM, Houston, United States
    Competing interests
    No competing interests declared.
  7. Marta L Fiorotto

    Children's Nutrition Research Center, Department of Pediatrics, BCM, Houston, United States
    Competing interests
    No competing interests declared.
  8. Bin Tang

    Jan and Dan Duncan Neurological Research Institute, BCM, Houston, United States
    Competing interests
    No competing interests declared.
  9. Jose A Herrera

    Jan and Dan Duncan Neurological Research Institute, BCM, Houston, United States
    Competing interests
    No competing interests declared.
  10. Michelle L Seymour

    Huffington Center on Aging, BCM, Houston, United States
    Competing interests
    No competing interests declared.
  11. Jeffrey L Neul

    Department of Neurosciences, UCSD, San Diego, United States
    Competing interests
    No competing interests declared.
  12. Frederick A Pereira

    Huffington Center on Aging, BCM, Houston, United States
    Competing interests
    No competing interests declared.
  13. Jianrong Tang

    Department of Pediatrics, Baylor College of Medicine, Houston, United States
    Competing interests
    No competing interests declared.
  14. Mingshan Xue

    Department of Neuroscience, BCM, Houston, United States
    Competing interests
    No competing interests declared.
  15. Huda Y Zoghbi

    Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
    For correspondence
    hzoghbi@bcm.edu
    Competing interests
    Huda Y Zoghbi, Senior Editor, eLife.

Ethics

Animal experimentation: Mice were housed in an AAALAS-certified animal facility. All procedures to maintain and use these mice were approved by the Institutional Animal Care and Use committee for Baylor College of Medicine (Animal protocol number AN-1013 ).

Copyright

© 2016, Meng 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

  • 3,327
    views
  • 983
    downloads
  • 80
    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. Xiangling Meng
  2. Wei Wang
  3. Hui Lu
  4. Ling-jie He
  5. Wu Chen
  6. Eugene Chao
  7. Marta L Fiorotto
  8. Bin Tang
  9. Jose A Herrera
  10. Michelle L Seymour
  11. Jeffrey L Neul
  12. Frederick A Pereira
  13. Jianrong Tang
  14. Mingshan Xue
  15. Huda Y Zoghbi
(2016)
Manipulations of MeCP2 in glutamatergic neurons highlight their contributions to Rett and other neurological disorders
eLife 5:e14199.
https://doi.org/10.7554/eLife.14199

Share this article

https://doi.org/10.7554/eLife.14199

Further reading

    1. Neuroscience
    Ana Fló, Lucas Benjamin ... Ghislaine Dehaene-Lambertz
    Research Article

    Interest in statistical learning in developmental studies stems from the observation that 8-month-olds were able to extract words from a monotone speech stream solely using the transition probabilities (TP) between syllables (Saffran et al., 1996). A simple mechanism was thus part of the human infant’s toolbox for discovering regularities in language. Since this seminal study, observations on statistical learning capabilities have multiplied across domains and species, challenging the hypothesis of a dedicated mechanism for language acquisition. Here, we leverage the two dimensions conveyed by speech –speaker identity and phonemes– to examine (1) whether neonates can compute TPs on one dimension despite irrelevant variation on the other and (2) whether the linguistic dimension enjoys an advantage over the voice dimension. In two experiments, we exposed neonates to artificial speech streams constructed by concatenating syllables while recording EEG. The sequence had a statistical structure based either on the phonetic content, while the voices varied randomly (Experiment 1) or on voices with random phonetic content (Experiment 2). After familiarisation, neonates heard isolated duplets adhering, or not, to the structure they were familiarised with. In both experiments, we observed neural entrainment at the frequency of the regularity and distinct Event-Related Potentials (ERP) to correct and incorrect duplets, highlighting the universality of statistical learning mechanisms and suggesting it operates on virtually any dimension the input is factorised. However, only linguistic duplets elicited a specific ERP component, potentially an N400 precursor, suggesting a lexical stage triggered by phonetic regularities already at birth. These results show that, from birth, multiple input regularities can be processed in parallel and feed different higher-order networks.

    1. Neuroscience
    François Kroll, Joshua Donnelly ... Jason Rihel
    Research Article

    By exposing genes associated with disease, genomic studies provide hundreds of starting points that should lead to druggable processes. However, our ability to systematically translate these genomic findings into biological pathways remains limited. Here, we combine rapid loss-of-function mutagenesis of Alzheimer’s risk genes and behavioural pharmacology in zebrafish to predict disrupted processes and candidate therapeutics. FramebyFrame, our expanded package for the analysis of larval behaviours, revealed that decreased night-time sleep was common to F0 knockouts of all four late-onset Alzheimer’s risk genes tested. We developed an online tool, ZOLTAR, which compares any behavioural fingerprint to a library of fingerprints from larvae treated with 3677 compounds. ZOLTAR successfully predicted that sorl1 mutants have disrupted serotonin signalling and identified betamethasone as a drug which normalises the excessive day-time sleep of presenilin-2 knockout larvae with minimal side effects. Predictive behavioural pharmacology offers a general framework to rapidly link disease-associated genes to druggable pathways.