Rett syndrome (RTT), is a rare genetic disease caused by mutations of the X-linked gene methyl-CpG-binding protein2 (MECP2), in more than 95% of patients (Amir et al., 1999; Chahrour and Zoghbi, 2007). Affected subjects, almost exclusively females, show progressive loss of acquired motor and cognitive skills and develop additional symptoms including stereotypic hand movements, drug-resistant seizures, breathing and autonomic dysfunctions (Chahrour and Zoghbi, 2007). Increased cholesterol plasma levels and changes in the activity/expression of genes and proteins involved in the cellular uptake, synthesis and feedback regulation of circulating cholesterol have recently been found in RTT individuals carrying MECP2 mutations (Grillo et al., 2013; Justice et al., 2013; Segatto et al., 2014), However, only one-third of RTT patients show elevated levels of plasma cholesterol (Justice et al., 2013).

Cholesterol homeostasis is important for central nervous system function and defects in cholesterol synthesis may cause neurologic symptoms (Waterham, 2006). Thus, altered cholesterol homeostasis may have a role in the pathogenesis of RTT and constitute a potential therapeutic target for a condition lacking specific treatments. The relationship between RTT and cholesterol is supported by recent findings showing that inactivation of Sqle, encoding squalene epoxidase, a key enzyme in the synthesis of cholesterol (Pfrieger and Ungerer, 2011), and pharmacological treatment with the cholesterol lowering drugs statins improved motor deficit and enhanced survival in Mecp2-null mice (Buchovecky et al., 2013), a widely used experimental model of RTT (Ricceri et al., 2008). Mecp2-null mice show complex changes in cholesterol levels, synthesis and metabolism in the brain and periphery, which depend on age, sex and line of mutant, among other factors (Buchovecky et al., 2013). Transient increase of total brain cholesterol levels is observed in 56 day (PND56) old male 129.Mecp2^tm1.1Bird/y mice, but not at PND70, while serum cholesterol increased at both ages in males 129.Mecp2^tm1.1Bird/y but not in 129.Mecp2^tm1.1Bird/+ heterozygous females or in another line of mutant mice, the B6.Mecp2^tm1.1Jae raised on a different background strain (Buchovecky et al., 2013). Interestingly, Cyp46a1, encoding cholesterol-24-hydroxylase, the neuronal enzyme converting cholesterol into 24S-hydroxycholesterol (24S-OHC), increased at PND28 and markedly decreased in PND56 mutant mice (Buchovecky et al., 2013). The reduced expression of Cyp46a1 was replicated in PND56 B6.Mecp2^tm1.1Jae mice suggesting that this is a robust and background independent phenomenon that may lead to altered metabolism of cholesterol in RTT. In spite 24S-OHC has emerged as a biomarker of brain cholesterol metabolism in various neurological diseases (Leoni and Caccia, 2013) and a potent positive allosteric modulator of N-methyl-D-aspartate (NMDA) receptors (Paul et al., 2013) it is not known whether alterations of this metabolite occur in RTT patients and in mouse models of the disease.

Thus, the relevance of these findings for the potential therapeutic effect of statins in RTT and the lack of replication studies of Buchovecky’s and colleagues findings prompted us to re-assess the effects of Mecp2 deletion on brain and serum cholesterol levels, and the ability of lovastatin to prolong survival and rescue motor deficits in Mecp2-null mice on a different background strain (B6), available from a commercial source. Finally, we measured brain levels of 24S-OHC and expression of Cyp46a1 in mice to assess the influence of Mecp2 on brain cholesterol metabolism.

Using the Mecp2^tm1.1Bird/y mice, one of the most used mouse models of RTT, we addressed the relevance of the recently proposed role of cholesterol and statins in the pathogenesis and therapy of RTT (Buchovecky et al., 2013). We confirmed the robustness and reliability of the model, which in our hands, closely reproduces the originally observed differences in body weight, reduced lifespan and motor deficits between Mecp2-null and WT male mice (Guy et al., 2001, 2007; Ricceri et al., 2008). However, we were not able to reproduce the raise of brain and serum cholesterol and the beneficial effect of lovastatin on motor performance and survival observed previously in Mecp2^tm1.1Bird/y mice on a different background strain (Buchovecky et al., 2013). This limits the generalizability of previous findings on the potential pathogenic role of cholesterol and the efficacy of statins in RTT.

The type of Mecp2 mutation, dose of lovastatin, route and schedule of drug administration, mouse age, housing, behavioral tests and the lack of cholesterol in the diet used in our study are identical to those used by Buchovecky et al. Thus, other factors may account for the discrepancies between the present and the previous study. The main difference is the background strain of the Mecp2^tm1.1Bird/y mice, B6 in our study and 129 S6/SvEvTac (129) in Buchovecky et al. We observed a reduction of serum cholesterol in PND28 and PND56 Mecp2-null mice and no effect on brain cholesterol, regardless the age. The same allele deletion on the 129 background determined the opposite effect on serum and a transient increase of brain cholesterol (Buchovecky et al., 2013). It is known that genetic background is a source of variability that influences the expression of phenotype and drug response (Cervo et al., 2005; Crawley et al., 1997; Sittig et al., 2016). In mouse models of RTT, genetic background influences the effects of Mecp2 deletion on body weight, cognitive, anxiety and social phenotypes resulting, in several cases, in directionally opposite effects of the same allele (Guy et al., 2001; Katz et al., 2012; Samaco et al., 2013). Mecp2^tm1.1Bird/y male mice on a 129 background are obese, likely because of a systemic metabolic disorder (Kyle et al., 2016), while they are underweight in a B6 background (Guy et al., 2001; present study). However, other phenotypes, such as motor deficits, are reproduced in different mouse models of RTT regardless of the genetic background (Lombardi et al., 2015; Samaco et al., 2013). In agreement with our findings, brain cholesterol and the expression of Cyp46a1 was not affected in Mecp2^tm1.1Bird/y mice on the B6 background, aged 7 to 56 days and no changes of brain levels of the cholesterol precursor desmosterol were observed at 7 and 14 days (Lopez et al., 2017). In addition, no increase in serum and brain cholesterol was detected in another line of Mecp2-null mice, the Mecp2^tm1.1Jae on a B6 background (Buchovecky et al., 2013). These findings indicate that Mecp2 deletion is not sufficient by itself to affect brain cholesterol homeostasis but may require the interaction with modifier genes, as already suggested for the influence of genetic background on the effect of Mecp2 on body weight (Guy et al., 2001).

The efficacy of lovastatin in improving motor performance in male 129.Mecp2-null mice (Buchovecky et al., 2013) cannot be confirmed in mice carrying the same allele on a B6 background. Interestingly, lovastatin had no effects on serum cholesterol in WT mice regardless of the background strain (Buchovecky et al., 2013). These findings are consistent with the scarce effect of statins in reducing circulating cholesterol in mice (Pecoraro et al., 2014) and indicate that neither the background strain nor the mutation of Mecp2 alone are sufficient to explain the effects of lovastatin on serum cholesterol observed in 129.Mecp2-null mice. This suggests that indirect mechanisms are likely involved in the ability of lovastatin to improve motor performance and survival in Mecp2-null mice.

It could be argued that lovastatin may be effective only in mice showing altered cholesterol homeostasis, such as 129.Mecp2-null mice. The empirical finding that statins are more effective in lowering serum cholesterol when this was increased through a cholesterol-enriched diet or because of the deletion of the Apo-E carrier (Pecoraro et al., 2014) apparently support this view. However, the fact that fluvastatin is effective in improving motor deficits in 129.Mecp2^tm1.1Bird/+ females, in which serum cholesterol levels are not changed, argues against this interpretation. In addition, it is unlikely that the concentration of lovastatin reaching the brain after the administration of 1.5 mg/kg is sufficient to suppress cholesterol synthesis. Even in mice given 3 mg/kg, brain levels of lovastatin were below 25 nM (Chen et al., 2007), far less than the IC₅₀ and Ki for the inhibition of 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA) reductase estimated in vitro (77 and 150 nM, respectively) (Bischoff et al., 1997; Bischoff and Heller, 1998). Taken together, these findings suggest that direct inhibition of HMG-CoA reductase is unlikely the mechanism by which lovastatin lowers cholesterol synthesis and improves motor deficits and survival in 129.Mecp2-null mice.

Differences in the rotarod protocol between studies deserve some comments. We administered two rotarod sessions to mice on PND49-50 and PND56-57 while in the previous study mice underwent a single session. We were able to show deficits in rotarod performance in our Mecp2-null mice comparable to those seen previously in the same line of mutant mice on a B6 background (Mellios et al., 2014). Our mice learn the task as testified by the increased performance (latency to fall) from trial 1 to 8 while the performance of untreated 129.Mecp2-null mice used in Buchovecky’s study was very poor and did not improve across trials. It is unlikely that our protocol prevented the effect of lovastatin in the rotaroad since Mecp2-null mice of the same mutant line, genetic background, age, sex, and similar basal performance in the rotarod improved in response to clenbuterol (Mellios et al., 2014). In addition, using another motor test, the hanging-wire, we confirmed that lovastatin failed to rescue motor deficits of mutant mice. Thus, it is unlikely that our protocol masked potential effects of lovastatin on motor performance. However, we cannot exclude that previous learning of the task precluded the assessment of any effects lovastatin could have on motor learning in PND56-57 mice.

We found no changes in the expression of brain Cyp46a1 in PND28 and PND56 Mecp2-null mice. This fully agrees with recent data showing that Cyp46a1 expression was not affected in B6.Mecp2-null mice aged 14, 43 or 56 days (Lopez et al., 2017), but is in sharp contrast with previous findings (Buchovecky et al., 2013). However, we found a moderate, but highly significant reduction of brain 24S-OHC in males and confirmed this effect in females Mecp2^+/- mice. Further studies are needed to clarify whether the reduction of 24S-OHC may be attributable to reduced activity of cholesterol-24-hydroxylase or other mechanisms. Since 24S-OHC is produced to a large extent by brain neurons (Björkhem et al., 1998), low levels suggests reduced metabolism of neuronal cholesterol. The fact that 24S-OHC was reduced in fully symptomatic mutant mice, but not in younger, pre-symptomatic mice suggests that reduced cholesterol metabolism is mainly associated to the symptomatic stage of the disease. Interestingly, 24S-OHC was recently found to control neuron excitability through allosteric modulation of the NMDA receptor (Paul et al., 2013). Excitatory/inhibitory imbalance is found in the brain of Mecp2 mutant mice. Selective impairment in excitatory synaptic transmission as well as elevation of neuronal activity occurs in different brain regions of Mecp2-null mice (Calfa et al., 2015; Dani et al., 2005; Nelson et al., 2006; Shepherd and Katz, 2011). Altered composition of NMDA receptor subunits in cortical circuits contribute to the regression of visual cortical function in Mecp2-null mice (Durand et al., 2012) and the NMDA receptor antagonist ketamine has been shown to reverse functional deficits of forebrain circuits’ activity induced by Mecp2 deletion (Kron et al., 2012). These findings support the involvement of NMDA receptors in the excitatory/inhibitory imbalance found in the brain of Mecp2 mutant mice and suggest that changes in brain levels of 24S-OHC, by modulating NMDA receptor activity may contribute to Mecp2-induced alterations of brain circuits’ excitability.

The importance of replication studies has recently been highlighted by the failure to confirm the potential benefit of bone marrow transplantation in a mouse model of RTT (Derecki et al., 2012; Wang et al., 2015). Assessing the influence of background strain on the phenotypic expression of Mecp2 mutation may ultimately contribute to our understanding of the causes of the disease, the mechanism involved in drug action and the development of treatments for patients with Rett syndrome.

The present results show that lovastatin had no effect on motor performance and survival when the deletion of the Mecp2 allele is expressed on the particular background strain we have studied and do not negate the role of cholesterol and the potential therapeutic effect of statins in RTT. The present findings suggest that the response to lovastatin is under the control of inherent genetic or other biological mechanisms specific to the effects of lovastatin on brain function. Thus, the efficacy of lovastatin in RTT may be limited to those patients that reproduce alteration of cholesterol homeostasis through mechanisms similar to those described in mice responding to statins (Buchovecky et al., 2013). Further studies are needed to establish the mechanisms involved in the reduction of brain levels of 24S-OHC, its role in RTT and the feasibility of using plasma levels of this metabolite as a biomarker of brain cholesterol metabolism, which may help to sort out patients that may best respond to treatment aimed at re-balancing cholesterol homeostasis.

1. 1. Amir RE
   2. Van den Veyver IB
   3. Wan M
   4. Tran CQ
   5. Francke U
   6. Zoghbi HY

   (1999) Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2

   Nature Genetics 23:185–188.

   https://doi.org/10.1038/13810

   • PubMed
   • Google Scholar
2. 1. Bischoff H
   2. Angerbauer R
   3. Bender J
   4. Bischoff E
   5. Faggiotto A
   6. Petzinna D
   7. Pfitzner J
   8. Porter MC
   9. Schmidt D
   10. Thomas G

   (1997) Cerivastatin: pharmacology of a novel synthetic and highly active HMG-CoA reductase inhibitor

   Atherosclerosis 135:119–130.

   https://doi.org/10.1016/S0021-9150(97)00188-3

   • PubMed
   • Google Scholar
3. 1. Bischoff H
   2. Heller AH

   (1998) Preclinical and clinical pharmacology of cerivastatin

   The American Journal of Cardiology 82:18J–25.

   https://doi.org/10.1016/S0002-9149(98)00433-0

   • PubMed
   • Google Scholar
4. 1. Björkhem I
   2. Lütjohann D
   3. Diczfalusy U
   4. Ståhle L
   5. Ahlborg G
   6. Wahren J

   (1998)

   Cholesterol homeostasis in human brain: turnover of 24s-hydroxycholesterol and evidence for a cerebral origin of most of this oxysterol in the circulation

   Journal of Lipid Research 39:1594–1600.

   • Google Scholar
5. 1. Buchovecky CM
   2. Turley SD
   3. Brown HM
   4. Kyle SM
   5. McDonald JG
   6. Liu B
   7. Pieper AA
   8. Huang W
   9. Katz DM
   10. Russell DW
   11. Shendure J
   12. Justice MJ

   (2013) A suppressor screen in Mecp2 mutant mice implicates cholesterol metabolism in Rett syndrome

   Nature Genetics 45:1013–1020.

   https://doi.org/10.1038/ng.2714

   • PubMed
   • Google Scholar
6. 1. Calfa G
   2. Li W
   3. Rutherford JM
   4. Pozzo-Miller L

   (2015) Excitation/inhibition imbalance and impaired synaptic inhibition in hippocampal area CA3 of Mecp2 knockout mice

   Hippocampus 25:159–168.

   https://doi.org/10.1002/hipo.22360

   • PubMed
   • Google Scholar
7. 1. Cervo L
   2. Canetta A
   3. Calcagno E
   4. Burbassi S
   5. Sacchetti G
   6. Caccia S
   7. Fracasso C
   8. Albani D
   9. Forloni G
   10. Invernizzi RW

   (2005) Genotype-dependent activity of tryptophan hydroxylase-2 determines the response to citalopram in a mouse model of depression

   Journal of Neuroscience 25:8165–8172.

   https://doi.org/10.1523/JNEUROSCI.1816-05.2005

   • PubMed
   • Google Scholar
8. 1. Chahrour M
   2. Zoghbi HY

   (2007) The story of Rett syndrome: from clinic to neurobiology

   Neuron 56:422–437.

   https://doi.org/10.1016/j.neuron.2007.10.001

   • PubMed
   • Google Scholar
9. 1. Chen C
   2. Lin J
   3. Smolarek T
   4. Tremaine L

   (2007) P-glycoprotein has differential effects on the disposition of statin acid and lactone forms in mdr1a/b knockout and wild-type mice

   Drug Metabolism and Disposition 35:1725–1729.

   https://doi.org/10.1124/dmd.107.015677

   • PubMed
   • Google Scholar
10. 1. Crawley JN
    2. Belknap JK
    3. Collins A
    4. Crabbe JC
    5. Frankel W
    6. Henderson N
    7. Hitzemann RJ
    8. Maxson SC
    9. Miner LL
    10. Silva AJ
    11. Wehner JM
    12. Wynshaw-Boris A
    13. Paylor R

    (1997) Behavioral phenotypes of inbred mouse strains: implications and recommendations for molecular studies

    Psychopharmacology 132:107–124.

    https://doi.org/10.1007/s002130050327

    • PubMed
    • Google Scholar
11. 1. Dani VS
    2. Chang Q
    3. Maffei A
    4. Turrigiano GG
    5. Jaenisch R
    6. Nelson SB

    (2005) Reduced cortical activity due to a shift in the balance between excitation and inhibition in a mouse model of Rett syndrome

    PNAS 102:12560–12565.

    https://doi.org/10.1073/pnas.0506071102

    • PubMed
    • Google Scholar
12. 1. Derecki NC
    2. Cronk JC
    3. Lu Z
    4. Xu E
    5. Abbott SB
    6. Guyenet PG
    7. Kipnis J

    (2012) Wild-type microglia arrest pathology in a mouse model of Rett syndrome

    Nature 484:105–109.

    https://doi.org/10.1038/nature10907

    • PubMed
    • Google Scholar
13. 1. Durand S
    2. Patrizi A
    3. Quast KB
    4. Hachigian L
    5. Pavlyuk R
    6. Saxena A
    7. Carninci P
    8. Hensch TK
    9. Fagiolini M

    (2012) NMDA receptor regulation prevents regression of visual cortical function in the absence of Mecp2

    Neuron 76:1078–1090.

    https://doi.org/10.1016/j.neuron.2012.12.004

    • PubMed
    • Google Scholar
14. 1. Grillo E
    2. Lo Rizzo C
    3. Bianciardi L
    4. Bizzarri V
    5. Baldassarri M
    6. Spiga O
    7. Furini S
    8. De Felice C
    9. Signorini C
    10. Leoncini S
    11. Pecorelli A
    12. Ciccoli L
    13. Mencarelli MA
    14. Hayek J
    15. Meloni I
    16. Ariani F
    17. Mari F
    18. Renieri A

    (2013) Revealing the complexity of a monogenic disease: rett syndrome exome sequencing

    PLoS One 8:e56599.

    https://doi.org/10.1371/journal.pone.0056599

    • PubMed
    • Google Scholar
15. 1. Guy J
    2. Gan J
    3. Selfridge J
    4. Cobb S
    5. Bird A

    (2007) Reversal of neurological defects in a mouse model of Rett syndrome

    Science 315:1143–1147.

    https://doi.org/10.1126/science.1138389

    • PubMed
    • Google Scholar
16. 1. Guy J
    2. Hendrich B
    3. Holmes M
    4. Martin JE
    5. Bird A

    (2001) A mouse Mecp2-null mutation causes neurological symptoms that mimic Rett syndrome

    Nature Genetics 27:322–326.

    https://doi.org/10.1038/85899

    • PubMed
    • Google Scholar
17. 1. Justice MJ
    2. Buchovecky CM
    3. Kyle SM
    4. Djukic A

    (2013) A role for metabolism in Rett syndrome pathogenesis: New clinical findings and potential treatment targets

    Rare Diseases 1:e27265.

    https://doi.org/10.4161/rdis.27265

    • PubMed
    • Google Scholar
18. 1. Kaplan EL
    2. Meier P

    (1958) Nonparametric estimation from incomplete observations

    Journal of the American Statistical Association 53:457–481.

    https://doi.org/10.1080/01621459.1958.10501452

    • Google Scholar
19. 1. Katz DM
    2. Berger-Sweeney JE
    3. Eubanks JH
    4. Justice MJ
    5. Neul JL
    6. Pozzo-Miller L
    7. Blue ME
    8. Christian D
    9. Crawley JN
    10. Giustetto M
    11. Guy J
    12. Howell CJ
    13. Kron M
    14. Nelson SB
    15. Samaco RC
    16. Schaevitz LR
    17. St Hillaire-Clarke C
    18. Young JL
    19. Zoghbi HY
    20. Mamounas LA

    (2012) Preclinical research in Rett syndrome: setting the foundation for translational success

    Disease Models & Mechanisms 5:733–745.

    https://doi.org/10.1242/dmm.011007

    • PubMed
    • Google Scholar
20. 1. Kilkenny C
    2. Browne WJ
    3. Cuthill IC
    4. Emerson M
    5. Altman DG

    (2010) Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research

    PLoS Biology 8:e1000412.

    https://doi.org/10.1371/journal.pbio.1000412

    • PubMed
    • Google Scholar
21. 1. Kita T
    2. Brown MS
    3. Goldstein JL

    (1980) Feedback regulation of 3-hydroxy-3-methylglutaryl coenzyme A reductase in livers of mice treated with mevinolin, a competitive inhibitor of the reductase

    Journal of Clinical Investigation 66:1094–1100.

    https://doi.org/10.1172/JCI109938

    • PubMed
    • Google Scholar
22. 1. Kron M
    2. Howell CJ
    3. Adams IT
    4. Ransbottom M
    5. Christian D
    6. Ogier M
    7. Katz DM

    (2012) Brain activity mapping in Mecp2 mutant mice reveals functional deficits in forebrain circuits, including key nodes in the default mode network, that are reversed with Ketamine treatment

    Journal of Neuroscience 32:13860–13872.

    https://doi.org/10.1523/JNEUROSCI.2159-12.2012

    • PubMed
    • Google Scholar
23. 1. Kyle SM
    2. Saha PK
    3. Brown HM
    4. Chan LC
    5. Justice MJ

    (2016) MeCP2 co-ordinates liver lipid metabolism with the NCoR1/HDAC3 corepressor complex

    Human Molecular Genetics 25:ddw156.

    https://doi.org/10.1093/hmg/ddw156

    • PubMed
    • Google Scholar
24. 1. Leoni V
    2. Caccia C

    (2013) 24S-hydroxycholesterol in plasma: a marker of cholesterol turnover in neurodegenerative diseases

    Biochimie 95:595–612.

    https://doi.org/10.1016/j.biochi.2012.09.025

    • PubMed
    • Google Scholar
25. 1. Lombardi LM
    2. Baker SA
    3. Zoghbi HY

    (2015) MECP2 disorders: from the clinic to mice and back

    Journal of Clinical Investigation 125:2914–2923.

    https://doi.org/10.1172/JCI78167

    • Google Scholar
26. 1. Lopez AM
    2. Chuang JC
    3. Posey KS
    4. Turley SD

    (2017) Suppression of brain cholesterol synthesis in male Mecp2-deficient mice is age dependent and not accompanied by a concurrent change in the rate of fatty acid synthesis

    Brain Research 1654:77–84.

    https://doi.org/10.1016/j.brainres.2016.10.021

    • PubMed
    • Google Scholar
27. 1. Mellios N
    2. Woodson J
    3. Garcia RI
    4. Crawford B
    5. Sharma J
    6. Sheridan SD
    7. Haggarty SJ
    8. Sur M

    (2014) β2-Adrenergic receptor agonist ameliorates phenotypes and corrects microRNA-mediated IGF1 deficits in a mouse model of Rett syndrome

    PNAS 111:9947–9952.

    https://doi.org/10.1073/pnas.1309426111

    • PubMed
    • Google Scholar
28. 1. Nelson ED
    2. Kavalali ET
    3. Monteggia LM

    (2006) MeCP2-dependent transcriptional repression regulates excitatory neurotransmission

    Current Biology 16:710–716.

    https://doi.org/10.1016/j.cub.2006.02.062

    • PubMed
    • Google Scholar
29. 1. Paul SM
    2. Doherty JJ
    3. Robichaud AJ
    4. Belfort GM
    5. Chow BY
    6. Hammond RS
    7. Crawford DC
    8. Linsenbardt AJ
    9. Shu HJ
    10. Izumi Y
    11. Mennerick SJ
    12. Zorumski CF

    (2013) The major brain cholesterol metabolite 24(S)-hydroxycholesterol is a potent allosteric modulator of N-methyl-D-aspartate receptors

    Journal of Neuroscience 33:17290–17300.

    https://doi.org/10.1523/JNEUROSCI.2619-13.2013

    • PubMed
    • Google Scholar
30. 1. Pecoraro V
    2. Moja L
    3. Dall'Olmo L
    4. Cappellini G
    5. Garattini S

    (2014) Most appropriate animal models to study the efficacy of statins: a systematic review

    European Journal of Clinical Investigation 44:848–871.

    https://doi.org/10.1111/eci.12304

    • PubMed
    • Google Scholar
31. 1. Pfrieger FW
    2. Ungerer N

    (2011) Cholesterol metabolism in neurons and astrocytes

    Progress in Lipid Research 50:357–371.

    https://doi.org/10.1016/j.plipres.2011.06.002

    • PubMed
    • Google Scholar
32. 1. Ricceri L
    2. De Filippis B
    3. Laviola G

    (2008) Mouse models of Rett syndrome: from behavioural phenotyping to preclinical evaluation of new therapeutic approaches

    Behavioural Pharmacology 19:501–517.

    https://doi.org/10.1097/FBP.0b013e32830c3645

    • PubMed
    • Google Scholar
33. 1. Samaco RC
    2. McGraw CM
    3. Ward CS
    4. Sun Y
    5. Neul JL
    6. Zoghbi HY

    (2013) Female Mecp2(+/-) mice display robust behavioral deficits on two different genetic backgrounds providing a framework for pre-clinical studies

    Human Molecular Genetics 22:96–109.

    https://doi.org/10.1093/hmg/dds406

    • PubMed
    • Google Scholar
34. 1. Segatto M
    2. Trapani L
    3. Di Tunno I
    4. Sticozzi C
    5. Valacchi G
    6. Hayek J
    7. Pallottini V

    (2014) Cholesterol metabolism is altered in Rett syndrome: a study on plasma and primary cultured fibroblasts derived from patients

    PLoS One 9:e104834.

    https://doi.org/10.1371/journal.pone.0104834

    • PubMed
    • Google Scholar
35. 1. Shepherd GM
    2. Katz DM

    (2011) Synaptic microcircuit dysfunction in genetic models of neurodevelopmental disorders: focus on Mecp2 and Met

    Current Opinion in Neurobiology 21:827–833.

    https://doi.org/10.1016/j.conb.2011.06.006

    • PubMed
    • Google Scholar
36. 1. Sittig LJ
    2. Carbonetto P
    3. Engel KA
    4. Krauss KS
    5. Barrios-Camacho CM
    6. Palmer AA

    (2016) Genetic background limits generalizability of genotype-phenotype relationships

    Neuron 91:1253–1259.

    https://doi.org/10.1016/j.neuron.2016.08.013

    • PubMed
    • Google Scholar
37. 1. Stearns NA
    2. Schaevitz LR
    3. Bowling H
    4. Nag N
    5. Berger UV
    6. Berger-Sweeney J

    (2007) Behavioral and anatomical abnormalities in Mecp2 mutant mice: a model for Rett syndrome

    Neuroscience 146:907–921.

    https://doi.org/10.1016/j.neuroscience.2007.02.009

    • PubMed
    • Google Scholar
38. 1. Wang J
    2. Wegener JE
    3. Huang TW
    4. Sripathy S
    5. De Jesus-Cortes H
    6. Xu P
    7. Tran S
    8. Knobbe W
    9. Leko V
    10. Britt J
    11. Starwalt R
    12. McDaniel L
    13. Ward CS
    14. Parra D
    15. Newcomb B
    16. Lao U
    17. Nourigat C
    18. Flowers DA
    19. Cullen S
    20. Jorstad NL
    21. Yang Y
    22. Glaskova L
    23. Vingeau S
    24. Vigneau S
    25. Kozlitina J
    26. Yetman MJ
    27. Jankowsky JL
    28. Reichardt SD
    29. Reichardt HM
    30. Gärtner J
    31. Bartolomei MS
    32. Fang M
    33. Loeb K
    34. Keene CD
    35. Bernstein I
    36. Goodell M
    37. Brat DJ
    38. Huppke P
    39. Neul JL
    40. Bedalov A
    41. Pieper AA

    (2015) Wild-type microglia do not reverse pathology in mouse models of Rett syndrome

    Nature 521:E1–E4.

    https://doi.org/10.1038/nature14444

    • PubMed
    • Google Scholar
39. 1. Waterham HR

    (2006) Defects of cholesterol biosynthesis

    FEBS Letters 580:5442–5449.

    https://doi.org/10.1016/j.febslet.2006.07.027

    • PubMed
    • Google Scholar

Author details

1. Claudia Villani

   Laboratory of Neurochemistry and Behaviour, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy

   Contribution

   CV, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "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

   Contribution

   GS, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   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

   Contribution

   RB, Acquisition of data, Drafting or revising the article

   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

   Contribution

   AP, Acquisition of data, Drafting or revising the article

   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

   Contribution

   FF, Acquisition of data, Analysis and interpretation of data

   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

   Contribution

   MC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   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

   Contribution

   RWI, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   rinvernizzi@marionegri.it

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-6017-9781

• 1,096

  views

• 188

  downloads

• 16

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.