Lovastatin fails to improve motor performance and survival in methyl-CpG-binding protein2-null mice

Abstract

Previous studies provided evidence for the alteration of brain cholesterol homeostasis in 129.Mecp2-null mice, an experimental model of Rett syndrome. The efficacy of statins in improving motor symptoms and prolonging survival of mutant mice suggested a potential role of statins in the therapy of Rett syndrome. In the present study, we show that Mecp2 deletion had no effect on brain and serum cholesterol levels and lovastatin (1.5 mg/kg, twice weekly as in the previous study) had no effects on motor deficits and survival when Mecp2 deletion was expressed on a background strain (C57BL/6J; B6) differing from that used in the earlier study. These findings indicate that the effects of statins may be background specific and raise important issues to consider when contemplating clinical trials. The reduction of the brain cholesterol metabolite 24S-hydroxycholesterol found in B6.Mecp2-null mice suggests the occurrence of changes in brain cholesterol metabolism and the potential utility of using plasma levels of 24S-OHC as a biomarker of brain cholesterol homeostasis in RTT.

Article and author information

Author details

  1. Claudia Villani

    Laboratory of Neurochemistry and Behaviour, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6334-9013
  2. Giuseppina Sacchetti

    Laboratory of Neurochemistry and Behaviour, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
    Competing interests
    The authors declare that no competing interests exist.
  3. Renzo Bagnati

    Analytical Instrumentation Unit, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
    Competing interests
    The authors declare that no competing interests exist.
  4. Alice Passoni

    Analytical Instrumentation Unit, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
    Competing interests
    The authors declare that no competing interests exist.
  5. Federica Fusco

    Genetics of Neurodegenerative Diseases Unit, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
    Competing interests
    The authors declare that no competing interests exist.
  6. Mirjana Carli

    Laboratory of Neurochemistry and Behaviour, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
    Competing interests
    The authors declare that no competing interests exist.
  7. Roberto William Invernizzi

    Laboratory of Neurochemistry and Behaviour, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
    For correspondence
    rinvernizzi@marionegri.it
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6017-9781

Funding

Istituto di Ricerche Farmacologiche Mario Negri (Intramural funding)

  • Roberto William Invernizzi

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

Ethics

Animal experimentation: The Istituto di Ricerche Farmacologiche "Mario Negri" adheres to the principle set out in the following law, regulations, and policies governing the care and use of laboratory animals: Italian Governing Law (D.lgs.26/2014; Authorisation n. 19/2008-A issued March 6, 2008 by Ministry of Health); Mario Negri Institutional Regulations and Policies providing internal authorization for persons conducting animal experiments (Quality Management System Certificate - UNI EN ISO 9001:2008 - Reg. N{degree sign} 6121); the NIH Guide for the Care and Use of Laboratory Animals (2011 edition) and EU directives and guidelines (EEC Council Directive 2010/63/UE). The statement of Compliance (Assurance) with the Public Health Service (PHS) Policy on Human Care and Use of Laboratory Animals has been recently reviewed (9/9/2014) and will expire on September 30, 2019 (Animal Welfare Assurance #A5023-01). The protocol was approved by the Italian Ministry of Health (Permit Number 946/2015-PR).

Copyright

© 2016, Villani 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

  • 1,081
    views
  • 186
    downloads
  • 14
    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. Claudia Villani
  2. Giuseppina Sacchetti
  3. Renzo Bagnati
  4. Alice Passoni
  5. Federica Fusco
  6. Mirjana Carli
  7. Roberto William Invernizzi
(2016)
Lovastatin fails to improve motor performance and survival in methyl-CpG-binding protein2-null mice
eLife 5:e22409.
https://doi.org/10.7554/eLife.22409

Share this article

https://doi.org/10.7554/eLife.22409

Further reading

    1. Neuroscience
    Proloy Das, Mingjian He, Patrick L Purdon
    Tools and Resources

    Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters – the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations – all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.