Lovastatin fails to improve motor performance and survival in methyl-CpG-binding protein2-null mice
- Istituto di Ricerche Farmacologiche Mario Negri, Italy
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
Roberto William Invernizzi
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.
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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
Further reading
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Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.
