Rett syndrome and the urge of novel approaches to study MeCP2 functions and mechanisms of action

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Review

Rett syndrome and the urge of novel approaches to study MeCP2 functions and mechanisms of action Francesco Bedogni a,b , Riccardo L. Rossi c , Francesco Galli a , Clementina Cobolli Gigli a,b , Anna Gandaglia a,b , Charlotte Kilstrup-Nielsen b , Nicoletta Landsberger a,b,∗ a

San Raffaele Rett Research Center, Division of Neuroscience, San Raffaele Scientific Institute, Milan 20132, Italy Laboratory of Genetic and Epigenetic Control of Gene Expression, Department of Theoretical and Applied Sciences, Division of Biomedical Research, University of Insubria, Busto Arsizio 21052, Italy c Fondazione Istituto Nazionale Genetica Molecolare, Milan 20122, Italy b

a r t i c l e

i n f o

Article history: Received 17 July 2013 Received in revised form 25 October 2013 Accepted 21 January 2014 Keywords: Rett syndrome MeCP2 Structure and function Molecular genetics Perturbation in gene expression Pathway enrichment analysis

a b s t r a c t Rett syndrome (RTT) is a devastating genetic disorder that worldwide represents the most common genetic cause of severe intellectual disability in females. Most cases are caused by mutations in the Xlinked MECP2 gene. Several recent studies have demonstrated that RTT mimicking animal models do not develop an irreversible condition and phenotypic rescue is possible. However, no cure for RTT has been identified so far, and patients are only given symptomatic and supportive treatments. The development of clinical applications imposes a more comprehensive knowledge of MeCP2 functional role(s) and their relevance for RTT pathobiology. Herein, we thoroughly survey the knowledge about MeCP2 structure and functions, highlighting the necessity of identifying more functional domains and the value of molecular genetics. Given that, in our opinion, RTT ultimately is generated by perturbations in gene transcription and so far no genes/pathways have been consistently linked to a dysfunctional MeCP2, we have used higher-level bioinformatic analyses to identify commonly deregulated mechanisms in MeCP2-defective samples. In this review we present our results and discuss the possible value of the utilized approach. © 2014 Elsevier Ltd. All rights reserved.

Contents 1. 2. 3. 4. 5.

6. 7.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MeCP2: a multifaceted epigenetic reader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MeCP2: a versatile protein whose pathogenic mechanisms remain uncertain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MeCP2 and the beauty of being disorganized and post-translationally modified . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Searching for new MeCP2 target pathways: deeper insights in previous mouse studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Table 3a: intracellular signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Table 3b: cytoskeleton related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Table 3c: cell metabolism related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exploiting the pathway enrichment approach to analyze human dataset and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comments on bioinformatic approaches in Rett syndrome research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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∗ Corresponding author at: Laboratory of Genetic and Epigenetic Control of Gene Expression, Department of Theoretical and Applied Sciences, Division of Biomedical Research, University of Insubria, Busto Arsizio 21052, Italy. Tel.: +39 0331339406. E-mail address: [email protected] (N. Landsberger). http://dx.doi.org/10.1016/j.neubiorev.2014.01.011 0149-7634/© 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Bedogni, F., et al., Rett syndrome and the urge of novel approaches to study MeCP2 functions and mechanisms of action. Neurosci. Biobehav. Rev. (2014), http://dx.doi.org/10.1016/j.neubiorev.2014.01.011

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1. Introduction Rett syndrome (RTT) worldwide represents the most common genetic cause of severe intellectual disability in females (Percy and Lane, 2005). Typical RTT patients appear to develop normally throughout the first 6–18 months of life when neurological development arrests and a regression phase leading to the loss of previously acquired skills appears evident. During and after the regression phase, patients develop a host of typical symptoms including continuous stereotypic hand movements with decline of purposeful hand use, loss of language skills, autistic features, gait abnormalities, breathing defects, seizures, hypotonia, scoliosis and autonomic dysfunctions (Neul et al., 2010). Genetic analyses show that most cases are caused by mutation in the X-linked MECP2 gene (Chahrour and Zoghbi, 2007). The formal genetic proof of the involvement of the MECP2 gene in RTT is further provided by a number of diverse mouse models carrying different Mecp2 alterations, all phenotypically copying the disease (Ricceri et al., 2008). So far, hundreds of different MECP2 mutations have been associated with Rett syndrome, or, less frequently, with other forms of intellectual disabilities, such as autism, schizophrenia, mental retardation and Angelman-like syndrome; recently, duplication and triplication of the MECP2 gene have also been identified as the genetic cause of the MECP2 duplication syndrome in males (Chahrour and Zoghbi, 2007). Altogether, such molecular data suggest that MeCP2 is a key protein in the brain, and its levels and functions cannot be altered without severe consequences. Although the protein has always been considered an ubiquitously expressed factor, particularly abundant in the brain, several previous studies supported the idea that Rett syndrome was exclusively caused by the lack of MeCP2 in neurons; this concept, however, has recently been challenged by the demonstration of a role of glia and microglia in the disease (Ballas et al., 2009; Lioy et al., 2011; Derecki et al., 2012). Despite the detrimental brain effects generated by MECP2 mutations, several recent studies demonstrate in mice the reversibility of the neurological phenotypes resembling the clinical features displayed by RTT individuals, even at late stages of disease progression (Guy et al., 2007; Jugloff et al., 2008; Tropea et al., 2009). Although the recent enormous advances, our knowledge over MeCP2 activity is still limited, while the need for understanding its functional roles, its mechanisms of action and their relevance in the pathobiology of RTT is obviously growing wider and wider. Furthermore, only weak evidence so far defines the temporal steps through which the consequences of dysfunctional MeCP2 activity start to develop. In fact, mainly because of the late onset and the typical regression phase, RTT has traditionally been considered a neurodevelopmental disorder. However, considering that brains structures and the overall number of neurons appear grossly normal soon after birth in the absence of MeCP2 and its levels increase with synaptogenesis, it is highly plausible that MeCP2 functions are mainly required to maintain a fully functional mature neuron. In accordance with this hypothesis, three independent works have shown that the depletion of Mecp2 at different developmental stages (from late juvenile animals to adults) consistently causes the appearance of RTT-like phenotypes and premature death (McGraw et al., 2011; Nguyen et al., 2012; Cheval et al., 2012). Remarkably, the late ablation of Mecp2 in both hemizygous male and heterozygous female mice brings neuronal cells to pack more densely and the brain to shrink (Nguyen et al., 2012), thus resembling the reduction of neuronal soma size and microcephaly typically featured in. Two hallmarks of the RTT phenotype are a significant reduction of the neuronal soma size and the acquisition of microcephaly, mainly given by densely packed small neurons. Remarkably, the late ablation of Mecp2 in both hemizygous males and heterozygous females, brings neuronal cells to pack more densely and the brain to shrink

(Nguyen et al., 2012). Thus, the mouse models of RTT suggest that the lack of functional MeCP2 always causes severe and somehow overlapping neurological symptoms, independently of the developmental stage in which MeCP2 is lost; in other words, it appears as if the mature brain continuously relies on MeCP2 functions. In light of the need for a thorough comprehension of MeCP2 function(s) required for the development of clinical applications, in this review we will examine the current knowledge of MeCP2 structure and functions underlining the necessity of a deeper comprehension. Most of the data obtained so far suggest that the connection of MeCP2 with RTT might be related to its direct or indirect involvement in gene expression regulation; however, no genes or molecular pathways have been consistently associated with the lack of fully functional MeCP2. To shed light on this matter, in this review we present our results on whether any common features could be highlighted by a bioinformatic analysis of transcriptional data produced using RTT animal models. Moreover, by comparing the obtained data to transcriptional screenings of human RTT affected samples we analyzed whether any possible consistent analogy between the role of MeCP2 in both animal models and human samples can be uncovered, therefore suggesting possible molecular pathways affected by dysfunctional MeCP2 activity. The obtained results are discussed in light of previous experimental evidences obtained from RTT samples or cellular and animal models of autistic spectrum disorders and might provide a useful background to develop future studies.

2. MeCP2: a multifaceted epigenetic reader MeCP2 was originally isolated as a nuclear factor capable of binding in vitro a DNA probe containing at least one symmetrically methylated CpG-dinucleotide; in vivo, the protein accumulates in mouse cells at pericentromeric heterochromatin, which contains highly methylated satellite DNA. This heterochromatic localization is methylation-dependent. The Mecp2 (methylated CpG binding protein 2) gene was then cloned by Bird and colleagues and defined as a 486 residues long protein, containing a methyl-CpG binding domain (MBD) and a transcription repression domain (TRD; Lewis et al., 1992). After nearly two decades of structural and functional studies, more details regarding MeCP2 domains have been revealed. Due to alternative splicing between exons 1 and 2, two different MeCP2 isoforms are generated, as implied by nomenclature (Fig. 1A; Mnatzakanian et al., 2004; Kriaucionis and Bird, 2004): MeCP2e1, the longer isoform and contains 21 unique N-terminal residues highly enriched in acidic and hydrophobic residues and MeCP2e2, with 9 unique residues. Even though the two isoforms have distinct expression patterns, with MeCP2e2 being 10 times less abundant than the e1 isoform in the postnatal brain, the lack of any pathogenic mutations in the N-terminal residues distinguishing the two isoforms has led to consider them as mainly functionally equivalent (Itoh et al., 2012). Aside this difference, all the remaining amino acids are identical between the two isoforms and, in general, the primary structure is highly conserved among vertebrates. At the secondary and tertiary structural levels, MeCP2 appears as organized into five main distinct domains (Fig. 1A; Hansen et al., 2010): the N-terminal domain (NTD; residues 1–78), the MBD (residues 79–162), the intervening domain (ID; residues 163–206), the TRD (residues 207–310) and the C-terminal domain (CTD; residues 311–486). Almost half of the known disease-causing missense mutations in MECP2-related disorders affect the MBD, thus highlighting its relevance (Fig. 1B). Furthermore, a systematic study aimed at defining the structure, properties and interactions of the different domains of MeCP2 has permitted to show a structural and

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Fig. 1. MeCP2 splicing, structure and pathogenic mutations. (A) Due to alternative splicing between exon 1 and exon 2, MeCP2 has two different isoforms (MeCP2-e1 and MeCP2-e2). MeCP2-e1 is 498 amino acids long and has 21 unique N-terminal residues (vertical red stripes). MeCP2-e2 is 486 amino acids long and has 9 unique residues (horizontal red stripes). The remaining protein sequence is common to both isoforms and can be divided in 6 domains (obtained from protease digestion): the NTD (N-terminal domain, in red), the MBD (methyl-CpG binding domain, in green), the ID (intervening domain, in dark blue), the TRD (transcription repression domain, in magenta), the CTD␣ and CTD␤, (C-terminal domain, in light blue). (B) All the amino acidic point mutations with a frequency higher than 0.1 were collected from http://mecp2.chw.edu.au/. Mutations were divided into two groups: missense mutations are reported on the left; frameshift and nonsense mutations (which cause the truncation of the protein) on the right. Silent mutations were not considered. In the first group, most mutations are in the MBD (35.9%), followed by the CTD␤ (25.6%), the TRD (20.5%), the ID (12.8%) and the CTD␣ (5.1%). In this group, there are no mutations in the NTD. In the second group, the great majority of mutations occur in the TRD (45.8%), followed by the CTD␤ (29.2%), the ID (12.5%), the MBD (8.3%) and the NTD (4.2%). Interestingly, no mutations occur in the CTD␣. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

functional coupling of the MBD with different domains. These studies have demonstrated the capability of the NTD, ID and TRD to facilitate MBD dependent DNA binding and have led to consider the MBD as the structural hub of the protein (Ghosh et al., 2010). Because of that, by NMR or X-ray crystallography, the MBD structure alone or associated with methylated DNA was solved (Fig. 2A): interestingly, and unexpectedly, MeCP2 was discovered to recognize the hydration of the major groove of methylated DNA rather than cytosine methylation per se (Ho et al., 2008). Very recently it has also been demonstrated that, in the brain, MeCP2 is the major 5-hydroxymethylcytosine (5hmC)-binding protein. 5hmC represents a recently discovered epigenetic signal whose functional role still remains to be defined. Importantly, 5hmC is enriched in active genes and appears far more abundant in neurons than in any other tissue or embryonic stem cell (Mellén et al., 2012). Through in vitro studies the authors have demonstrated that the MBD portion of MeCP2 is involved in this binding and that other MBD containing proteins (with the exception of MBD3) are unable to specifically bind to 5hmC. Interestingly, the RTT-causing mutation R133C, located in the MBD, preferentially inhibits MeCP2 binding to 5hmC. Altogether, such evidence brings about considerations and tests that might contribute to a deeper comprehension of MeCP2 functions.

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Fig. 2. Structural and functional properties of MeCP2 domains. (A) Representation of the tertiary structure of the human MeCP2 MBD, obtained by modeling the protein PDB structure 1QK9 with PyMOL software [The PyMOL Molecular Graphics System, Version 1.5.0.4, Schrödinger, LLC.]. The pink portion corresponds to the ␣-MoRF 87–104; the yellow region contains the ␣-MoRF 133–150 (Ghosh et al., 2010). Missense mutations with a frequency higher than 0.1 are reported; in yellow are highlighted mutations located in the described MoRFs. For discussion on MoRFs see Section 4. (B) Scheme of the relative positions of the diverse functional and structural domains/motifs. In the figure are highlighted the two PEST domains, the NLS and the WW domain. The blue triangle highlights that the MBD binds to methylated DNA with high affinity in vivo, while the domains indicated by red triangles have high affinity for unmethylated DNA in vitro.

First of all, the identification of MeCP2 residency into transcriptionally active regions suggests, in accordance with previous publications (see below), that MeCP2 might not be exclusively considered a transcriptional repressor. It is very likely that the binding of MeCP2 to both specific sequences/epigenetic signatures of DNA and/or protein partners affects its molecular activities. This is very reasonable considering the overall disorganized structure of MeCP2 outside the MBD. Secondly, the fact that a single missense mutation in the MeCP2 MBD – R133C–, only subtly changing its structure, influences the binding properties of the methyl-binding protein makes it highly possible that post-translational modifications affect MeCP2 actions. These concepts are revisited below in the review. Eventually, together with several examples we will provide along the text, the molecular effect of the pathogenic R133C mutation suggests the importance of molecular genetics to understand the pathophysiology of the disease. Considering that patients carrying the R133C mutation are generally characterized by a milder form of RTT, with a delayed onset of regression and maintenance of some speech and motor skills (Bebbington et al., 2008), it will be interesting in the future to produce a R133C knock-in mouse line and compare its clinical manifestations, MeCP2 binding to neuronal and non-neuronal genomes and gene expression profiles to those of a model of the disease harboring a mutation affecting MeCP2 binding to methylated cytosine. The major aim would be to associate specific molecular pathways with the selective binding of MeCP2 to 5mC or 5hmC. Although MeCP2 was isolated on the basis of its preferential binding to methylated DNA, in vitro functional and structural studies of isolated domains reveal that the protein contains non specific binding sites for unmethylated DNA in the ID, TRD and CTD domains (Ghosh et al., 2010; Fig. 2B). In vivo, the capability of MeCP2 to bind unmethylated DNA is highly debated and appears to be contradicted by an elegant work in which, through neuronal ChIP-seq analyses, MeCP2 was proved to be genome-wide bound, tracking

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methylated CpG moieties (Skene et al., 2010). Considering that nonspecific DNA binding allows a DNA binding protein to bind weakly to any site and subsequently migrate along the DNA, thus reducing the effective time to search for its specific site, it is conceivable that in vivo MeCP2 binding to unmethylated DNA facilitates its subsequent association with the methylated ones (Hansen et al., 2010). From all above, it appears evident that there is a pressing need for high-resolution MeCP2 ChIP-Seq studies in different celltypes at different maturation stages: these studies would reveal whether MeCP2 might change its binding properties in function of its relative abundance and/or developmental phase. During the years, several other structured and/or functional MeCP2 motifs have been identified (Fig. 2B), including a nuclear localization signal (NLS) located within the TRD, a WW domain, linking MeCP2 to splicing (Buschdorf and Strätling, 2004) and two strong PEST sequences (73–94 and 389–426, respectively) usually correlated with rapid proteolytic degradation by the 26S ubiquitin proteasome system (Thambirajah et al., 2009). It is worthwhile to recall that PEST domains function through phosphorylation and the consequent structural destabilization. In line with this, the regions containing the PEST motifs have already been associated with phosphorylation (see Section 4 and Fig. 6); moreover, putative ubiquitination sites are present and MeCP2 modification by the small ubiquitin-related modifier has been reported (SUMO; Miyake and Nagai, 2007). Although MeCP2 expression seems to vary significantly with cell maturation, at least in neurons and muscles, we still do not know whether this developmental regulation occurs mainly at the transcriptional or post-transcriptional level. Furthermore, no studies have so far addressed whether MeCP2 abundance might be modulated by neuronal activity. To summarize we can state that: (i) the discovery of novel structural/functional regions should help providing a deeper understanding of MeCP2 functions and (ii) the analysis of distribution and possible clustering of pathogenic mutations should accelerate the identification of novel MeCP2 domains/roles relevant for the disease. Two very beautiful and recent publications elegantly prove these considerations. The first piece of work by Baker et al. (2013) found its rationale in the clinical analysis of male patients with a normal karyotype and a pathogenic MECP2 mutation; these patients offer the possibility of avoiding the confounding effect of X chromosome inactivation. According to the authors, two close mutations, R270X or G273X, located in the middle of the TRD, appear to influence differently the onset of symptoms and the severity of the syndrome, as the first truncation correlates with neonatal encephalopathy and death whereas the second one was characterized by significantly longer survival. Importantly, the generation of transgenic mice expressing either mutation in the endogenous Mecp2 locus confirmed the human clinical manifestations. The molecular characterization of the two MeCP2 derivatives showed that both R270X and G273X disrupt the TRD functions and similarly deregulate transcription. A highly conserved MeCP2 AThook domain that terminates at G273 seems to explain the obtained data; AT-hooks are generally contained in proteins that bind to AT-rich DNA. The MeCP2 derivative lacking this AT-hook domain appears impaired in its DNA binding and chromatin compaction capabilities and, in vivo, leads to the loss of ␣-thalassemia/mental retardation syndrome X-linked protein (ATRX) localization at pericentric heterochromatin (Baker et al., 2013). These findings appear of particular relevance considering that ATRX syndrome partially overlaps with RTT, but also because ATRX was already known to interact with MeCP2 (Nan et al., 2007). In a second paper by Lyst et al. (2013), considering that clusters of missense mutations have the potential to identify important functional motifs, the authors demonstrate that missense mutations causing classical RTT predominantly fall in the MBD or in the extreme C-terminal region of the TRD (residues 302–306; Lyst et al., 2013). Interestingly,

although polymorphic variants distribute broadly through the MECP2 coding sequence, they exclude these two regions, thus confirming their functional relevance. The capacity of missense mutations within the MBD to disrupt binding to methylated DNA was already well recognized (Yusufzai and Wolffe, 2000; Kudo et al., 2003); in this paper, Lyst et al. demonstrate that residues 302–306 are critical for MeCP2 binding to the NCoR/SMRT complex. Thus the combination of biochemical and genetic data suggest the importance for proper brain functions of the DNA-MeCP2-NCor/SMRT complex.

3. MeCP2: a versatile protein whose pathogenic mechanisms remain uncertain In accordance with its capability of reading a typical repressive epigenetic signature, Nan and collaborators demonstrated that MeCP2 is able to repress transcription mainly, but not exclusively, through its TRD domain (Nan et al., 1997). Given the capability of the TRD to bind corepressor complexes (Sin3A and N-CoR) containing histone deacetylase activities, the repressive activity of MeCP2 has subsequently been connected to chromatin compaction (Jones et al., 1998; Ng et al., 1999; Kokura et al., 2001). The link between MeCP2 and chromatin structure has been further reinforced by the identification of interacting partners, such as the chromatin remodeling complexes Brahma and ATRX, the corepressors c-Ski, CoREST and LANA and a H3K9 histone methyltransferase (Chahrour and Zoghbi, 2007; Guy et al., 2011). The capability of MeCP2 to work as an architectural chromatin protein was further supported by a report showing that at a high molar ratio to nucleosomes, MeCP2 mediates the formation of a novel highly compacted chromatin structure (Georgel et al., 2003). Importantly, this function of MeCP2 does not require additional factors apart from core histones, and appears independent of DNA methylation, thus raising the possibility that MeCP2 might affect the expression/structure of genomic unmethylated regions. This study is of particular relevance since it suggests, for the first time, that MeCP2 action might depend on its abundance. In accordance with that, a recent publication by Skene and colleagues (2010) demonstrates that MeCP2 in mature neurons where it is highly abundant might serve as an alternative linker histone and organize a specialized chromatin structure thus dampening overall transcriptional activity. These functions of MeCP2 would not be present in other cell types, such as glia, where the protein is 10–30 times less abundant. Accordingly, the authors have demonstrated that in mature neurons, characterized by 1.6 × 107 molecules of MeCP2 per nucleus, corresponding roughly to one molecule every second nucleosome, MeCP2 is genome-wide bound, tracks methylated DNA and affects chromatin structure. The effect on global genomic architecture is outlined by a selective increase in histone acetylation and H1 level selectively in Mecp2-null neurons, but not in glia. Consistently, neurons devoid of MeCP2, but not astrocytes, are characterized by an elevated transcriptional noise from repetitive elements and L1 retrotransposons (Muotri et al., 2010). Once again, molecular genetics seem to highlight the relevance of MeCP2 activity on chromatin structure. In fact, clinical severity of the pathogenic mutations MeCP2-270X and MeCP2273X correlates with the deficiency of the methyl-binding protein in forming higher-order structures with nucleosomal DNA in vitro and with the disruption of proper ATRX localization at pericentric heterochromatin in vivo (Baker et al., 2013). These findings strongly suggest that in mature neurons MeCP2 uses its MBD to bind methylated CpG dinucleotides with high affinity. Once bound, MeCP2 affects the nearby chromatin structure using the TRD and the included AT-hook domain, and very possibly other domains that remain to be identified.

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Fig. 4. MeCP2 is an “intrinsically disordered protein”. Graphic illustration of the MeCP2-e2 tendency to disorder related with the protein domains predicted using the IUPred-S software, which uses a parameter set suited for predicting short, probably context-dependent, disorder regions. In this application the sequential neighborhood of 25 residues is considered. As chain termini of globular proteins are often disordered in X-ray structures, this is taken into account by an end-adjustment parameter, which favors disorder prediction at the ends (Dosztányi et al., 2005). Fig. 3. MeCP2 is a multifunctional protein. MeCP2 functions reported so far have been schematically represented. Following a clockwise direction starting from the left upper cartoon, MeCP2 can function as a transcriptional repressor that recruits corepressors to silence methylated genes; it can mediate the formation of a highly compacted chromatin structure (in a methylation independent way); it may play a role in transcriptional activation mainly through its interaction with CREB1; it can be involved in mRNA alternative splicing (e.g. through its interactions with YB1) and eventually it can influence protein synthesis through modulation of the mTOR pathway.

The lack of functional MeCP2 in mature neurons will probably lead to a disorganized chromatin structure that prevents neurons to properly respond to stimuli, thus impairing synaptic plasticity. Although supported by a large body of evidence, this model leaves several unanswered questions. First: what is the main mechanism of action of MeCP2 in non-neuronal cells? Is MeCP2 a global architectural factor that turns into a gene specific transcription factor according to its abundance? And, in any case, is it possible to associate the lack of MeCP2 to the dysregulation of specific pathways? Second: how relevant is, in vivo, the capability of MeCP2 to bind to 5-hmC and what are the functional consequences of it? Third: do post-translational modifications of MeCP2 (Guy et al., 2011) affect MeCP2 functions? Fourth and most important: how many functions are associated with MeCP2 and which one(s) is/are of relevance for MECP2-related diseases? The proposed gene regulatory role has in fact been expanded beyond gene silencing and chromatin architecture to include transcriptional activation, mRNA splicing regulation and protein synthesis modulation (Fig. 3). The link between MeCP2 and splicing originated from the capacity of the methyl-binding protein to interact with YB1, a regulator of alternative splicing (Young et al., 2005). Accordingly, MeCP2 is capable of a direct binding to RNA (Jeffery and Nakienly, 2004) and, in the brain, it is part of a multiprotein complex containing Prpf3, a major component of the spliceosome, and Sdccag1, a mediator of nuclear export (Long et al., 2011). Although a RTT transgenic mouse line characterized by a truncated form of MeCP2, displays an abnormal splicing of multiple genes in the brain (Young et al., 2005), more studies are required to understand the significance of MeCP2 involvement in mRNA splicing both in mouse models and human patients. The capability to function as a transcriptional activator has been inferred from transcriptional profiling studies of RNA purified from hypothalamic and cerebella of RTT mice. The data were supported by the identification of an interaction between MeCP2 and the transcriptional activator CREB (Chahrour et al., 2008). Eventually, Ricciardi et al. (2011) showed that AKT/mTOR signaling is reduced in both Mecp2-null mice and heterozygous females and protein synthesis was significantly impaired in these mice. Even though this study did not address whether this translational defect is a direct cause of the disease or an early indirect effect, and whether it contributes to its progression, it might suggest yet another function

of MeCP2 whose importance is underlined by the well known occurrence of aberrant protein synthesis in autism spectrum disorders. Importantly, the AKT/mTOR cascade is disrupted also in a knockout mouse model of CDKL5, a kinase functionally linked to MeCP2 and involved in several forms of neurodevelopmental disorders including a severe form of atypical Rett syndrome (Wang et al., 2012). 4. MeCP2 and the beauty of being disorganized and post-translationally modified Altogether the summarized data imply that MeCP2 is a multifunctional protein, whose several activities might still have to be completely unraveled; the highly disorganized structure and posttranslational modifications (PTM) of MeCP2 possibly generate and regulate this functional versatility. Concerning the disorder, it is important to recall that to date, structural information is available only for the MBD (Fig. 2A). The lack of structural information can easily be explained by Circular Dichroism studies and theoretical predictions that have demonstrated that nearly 60% of MeCP2 is unstructured (Adams et al., 2007; Fig. 4). Thus MeCP2 is now recognized as an “intrinsically disordered protein”. The function of intrinsically disordered proteins is generally coupled to the acquisition of local secondary structure upon binding to other macromolecules. Accordingly, it has been experimentally demonstrated that the TRD especially, but also the NTD and the CTD (with the exception of the ID), can undergo coilto helix transitions that might justify the well-known capability of MeCP2 to interact with many different partners (Hite et al., 2012). In fact, it has previously been demonstrated that MeCP2 contains nine interspersed molecular recognition features (MoRFs), corresponding to short regions predicted to become structured (i.e. ␣-helix) when associated with macromolecules (Ghosh et al., 2010). By using the ANCHOR software, that predicts binding sites which will fold into any type of secondary structure ad binding and not only to ␣-helix, a total of twelve disordered binding regions can be predicted (V. Uversky, personal communication) (Fig. 5). As already stated, the MBD and TRD domains have been functionally defined. On the contrary, the other unstructured domains indicated in Fig. 2 were obtained through protease digestion. Indeed, a limited trypsin digestion of purified intact MeCP2 reproducibly generates a characteristic pattern of the six protease resistant polypeptides corresponding to the already described domains (Adams et al., 2007). The importance of these regions is again testified by the existence of pathogenic mutations in all of them (Figs. 1B and 5). Accordingly, several reports have associated these domains with specific functions and/or molecular partners (Fig. 5). For example, the N-terminal domain interacts with HP1: this interaction is required for transcriptional silencing during myogenic differentiation (Agarwal et al., 2007); the same region

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Fig. 5. MeCP2 functional properties and binding partners. Schematic representation containing: (i) MeCP2 predicted disordered binding regions (yellow bars) obtained through the ANCHOR software (personal communication by Dr. Vladimir Uversky) and (ii) groups of clustering missense mutations with a frequency higher than 0.1. Below are listed the candidate protein interactors of MeCP2, with respect to the involved MeCP2 regions. Dashed boxes indicate still unmapped interactions. See Refs. (Carro et al., 2004, Forlani et al., 2010, Fuks et al., 2003, Harikrishnan et al., 2005, Kimura and Shiota, 2003, Lunyak et al., 2002, and Matsumura et al., 2010). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

modulates the MBD affinity for DNA (Ghosh et al., 2010). Concerning the TRD, its binding to corepressor complexes (Sin3A and NCoR) containing histone deacetylase activities was demonstrated initially, thereby linking the transcriptional repressive activity of MeCP2 to chromatin compaction (Jones et al., 1998; Kokura et al., 2001). Since then, the disorganized TRD has been shown to be an important recruitment platform for several other factors, including c-Ski, Brahma, DNA methyltransferase I, histone H3K8 methyltransferase, PU1, RNA and factor associated with RNA splicing (Fig. 5 and the included references).

As already mentioned, the intervening domain harbors a strong methylation-independent binding activity but also facilitates MBDdependent binding (Ghosh et al., 2010). Furthermore, this domain is likely required for the interaction of many of the proteins associated with the TRD. Eventually, the C-terminal portion appears functionally related to DNA and chromatin binding and appears involved in nucleosomal array compaction and oligomerization (Ghosh et al., 2010). MeCP2 structure and function might be modulated through post-translational modifications (PTMs) as well. Indeed, several

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Fig. 6. MeCP2 post-translational modifications. On the mouse MeCP2e2 sequence are reported post-translational modifications divided in: phosphorylations that occur in active neurons ( ), phosphorylations in resting neurons (), not yet characterized phosphorylations (䊉), ubiquitination () and acetylation (). The reported posttranslational modifications have been mainly deduced from Ebert et al. (2013), Gonzales et al. (2012), Tao et al. (2009), Zhou et al. (2006). The two PEST domains closely localized to identified phosphorylation sites are highlighted in yellow.

Table 1 Brief overview of the general and behavioral features displayed by the RTT mimicking mouse models so far most thoroughly characterized: 1. Guy et al. (2001); 2. Chen et al. (2001); 3. Moretti et al. (2006); 4. Goffin et al. (2011); 5. Jentarra et al. (2010); 6. Lawson-Yuen et al. (2007); 7./8. Baker et al. (2013); 9. Tao et al. (2009); 10. Cohen et al. (2011), Hutchinson et al. (2012); 11. Li et al. (2011); 12. Ebert et al. (2013). Name

Description

Phenotype

Importance

Constitutive knock-out 1. Mecp2-Bird

Null allele

2. Mecp2-Jaenisch

C-terminal peptide present

Neurologic symptoms from 3 to 8 weeks including ataxic gait, hindlimb clasping, hypoactivity, tremor, breathing problems, piloerection, seizures. Death at 6–10 weeks As Mecp2-Bird model

These mouse models have been instrumental to establish that the loss of Mecp2 is sufficient to cause a RTT-like phenotype. These models remain those mainly used to study RTT pathophysiology

Human pathogenic mutations 3. Mecp2T308X 4. Mecp2T158A 5. Mecp2A140V Ubiquitous expression of Mecp2 with RTT 6. Mecp2R168X causing missense or nonsense mutations. 7. Mecp2R273X 8. Mecp2R270X Phospho-defective 9. Mecp2S80A 10. Mecp2S421A 11. Mecp2S421,424A 12. Mecp2T306A

Mecp2 derivatives carrying substitutions of specific serines with the non-phosphorylatable alanine

Milder and/or partial phenotypes with prolonged life-span

Useful for understanding the pathophysiology of RTT

Similar to Mecp2 Bird/Jaenisch Locomotor activity and behavior are similar to Mecp2 Bird/Jaenisch Abnormal responses to novelty Increased spatial memory and locomotor activity Lower seizures threshold

Useful for understanding in vivo the role of specific phosphorylation events

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recent reports have testified that differential phosphorylation of MeCP2 in response to neuronal activity is a key mechanism by which the methyl-binding protein modulates gene expression. In particular, in rodent brain the two major phosphorylation sites under resting conditions are serine 80 (S80) and S399, whereas S86, S274, T308, S421 and S424 show specific activity-dependent phosphorylation (Zhou et al., 2006; Tao et al., 2009; Ebert et al., 2013). A number of additional residues are found phosphorylated in brain; these data, together with recent evidence that PTMs other than phosphorylation occur on MeCP2, further support the idea that a complex pattern of PTMs transforms the protein into a regulatory platform whose activities respond to various signaling pathways (Fig. 6). The relevance of these PTMs as fine tuners of MeCP2 functions appears from the analyses of the first phospho-defective mice. A mouse model that cannot be phosphorylated on MeCP2-S80 is characterized by weight gain and decreased locomotor activity, two symptoms already observed in other models of RTT (Tao et al., 2009). The disruption of MeCP2 S421 phosphorylation in vivo leads to defects in dendritic and synaptic development and in abnormal behavioral responses to novel experience (Cohen et al., 2011). Although it was suggested that S421 phosphorylation leads to the specific detachment of MeCP2 from selected genes (Chen et al., 2003), studies of knock-in mice harboring the phosphodefective S421A mutation, did not show any detectable effect on gene transcription, thus challenging this hypothesis (Cohen et al., 2011). On the contrary, phosphorylation of T308 has been functionally linked to the capability of MeCP2 of interacting with NCoR; this specific PTM, in fact, blocks the interaction with the corepressor and suppresses the ability of MeCP2 to repress transcription (Ebert et al., 2013). A comparison between prevalent phenotypic features displayed by the most thoroughly analyzed animals models of RTT (null or carrying human pathogenic mutations) and phosphordefective Mecp2 knock-in mice is depicted in Table 1. Altogether, these studies not only highlight the urgent need of a deeper knowledge of MeCP2 post-translational modifications but they also imply that MECP2-related disorders are at least in part caused by defects in experience-dependent brain functionality throughout life.

5. Searching for new MeCP2 target pathways: deeper insights in previous mouse studies Ever since Rett syndrome was associated with mutations in MECP2, much effort was aimed at the discovery of specific genes to target with therapeutic purposes. Several different attempts were thus made in the field of transcriptional regulation in many heterogeneous systems, including human samples (post mortem brain tissues or skin explants) and mouse models (knock-out or mutant Mecp2 mice). Due to the high variability, such studies produced fairly non-reproducible results and so far only few validated target genes and no molecular pathways have yet been identified. Many reasons could explain such variability, from the high heterogeneity of the samples analyzed (from cerebral areas of animal models to reprogrammed neurons obtained from patient’s skin biopsies or human tissues), to the different timing of the analysis, or the statistical approach that could yield different results according to the stringency of the used method. The evidence reviewed here does however suggest that such variability may intrinsically reside in the role of MeCP2 itself that can play as either a transcriptional repressor or activator, possibly balancing between the two according to either its interactors and/or the affinity for such interactors or, even, the timing of their interaction. All these reasons might therefore disguise the identification of MeCP2 activity on any specific targets. Moreover, the aforementioned abundance of MeCP2 in mature neurons and its

global chromatin binding has led to consider transcriptional screenings a mere waste of efforts, thus postponing the recognition of any specific target (Skene et al., 2010; Cohen and Greenberg, 2010). Furthermore, it is widely accepted that MeCP2, being broadly and abundantly expressed, only subtly affects the transcription of its target genes, thus hampering their recognition. Because of that and aware of the fact that more and more often new and powerful biostatistic tools and database screening techniques become available for research, we reasoned that a comparison between transcriptional data deposited so far could highlight similarities between different studies, providing the samples were homogeneous and the analysis was not aimed at the recognition of specific genes consistently deregulated in all the considered data sets. Thus, we figured that a different and possibly powerful way to identify common deregulated mechanisms in Mecp2-null models might consist in evaluating whether a functional analysis at the level of biological functions and canonical pathways using transcriptional data could bring to the identification of new molecular mechanisms deregulated in such models. To do that, we surveyed the literature for transcriptional data of mouse Mecp2-null model and human RTT mutations and we selected those with a reasonable data accessibility to conduct further analysis. We were able to find four works in mouse models (Table 2) with same kind of wild type controls and MecP2-null mutants, performed on standard cell types or tissues, thus assuring a sufficiently comparable experimental design. Moreover, works were also selected according a few simple requirements: supplemental data must have a completely annotated probeset; data must be easily downloaded in a raw format in order to be processed, merged and analyzed from scratch. We then downloaded the datasets from the Gene Expression Omnibus repository and extracted the probes shared by the different platforms used in the four works to produce a common dataset to be analyzed for canonical pathways enrichment: we determined the common top differentially expressed genes in Mecp2-null samples compared to wild type with a global ratio metric and used them as input data for a Fisher exact test (as implemented by IPA, Ingenuity Inc.) to find the enriched pathways in those genes. Of interest, given that the samples analyzed were fairly heterogeneous (as generated from cerebral cortex, hippocampus, midbrain, locus ceruleus and cerebellum), by considering only those (differentially regulated) genes common to all the samples analyzed, our approach allows a comparison between molecular pathways that are not specific to any peculiar cerebral area; conversely, such approach could contemplate a wider pathway analysis by considering dataset originated from homogeneous tissues. We found more than sixty significantly enriched canonical pathways with a cutoff of 0.05 p-value, shown in Table 3: half of them (37 pathways) can be easily divided into three functional categories herein discussed: intracellular signaling, cytoskeleton related and cell metabolism related. 5.1. Table 3a: intracellular signaling Most of the pathways described in Table 3A directly target expression through the activation of nuclear transcriptional mechanisms, therefore resulting of pivotal importance for mediating the response of the cell to external stimuli. Several different pathways are significantly enriched in our analysis and therefore likely to be regulated; interestingly the EIF2 signaling path reached the top statistical score compared to the others of this category. EIF signaling is of particular interest in Rett syndrome and autism spectrum disorder (ASD) as it mediates insulin activity: in fact, growth factor triggered responses, and IGF-1 (insulin-like growth factor-1) treatment has been proposed as a promising pharmaceutical approach for RTT patients. First glimpses of a direct role of MeCP2 in insulin action were produced through the analysis of the ability of MeCP2

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Table 2 Mouse data sets selected for IPA analysis. GEO ID

Reference

Platform

Tissues

# Upregulated genes in KO

# Downregulated genes in KO

GSE 8720

Not available

Affymetrix GPL1261

138

107

GSE 42987 GSE 32870 GSE 11596

Baker et al. (2013) Grosser et al. (2012) Urdinguio et al. (2008)

Affymetrix GPL6246 Agilent GPL10333 CNIO GPL6903

Cortex, locus coeruleus, cerebellum (Purkinje cells) Hippocampus Hippocampus (CA1) Cortex, midbrain, cerebellum

433 279 255

131 348 216

Table 3 The table shows the canonical pathways significantly enriched by differentially expressed genes in Mecp2-null mouse mutants that can be grouped in intracellular signaling (A), related to cytoskeleton (B) or related to cell metabolism (C). The pathways are ranked starting from the most significant (higher −log[p-values]). Differentially expressed molecules common to all the mouse datasets that are responsible for the enrichment of each pathway are shown. Significance p-value cutoff was set to 0.05 (corresponding to 1.33 in the negative log scale used in the table). −log(p-value)

Molecules

A: Intracellular signaling EIF2 Signaling

3.59E00

14-3-3-Mediated Signaling

3.32E00

RhoGDI Signaling

3.24E00

Signaling by Rho Family GTPases

2.41E00

PPAR␣/RXR␣ Activation

2.35E00

Tec Kinase Signaling RhoA Signaling Rac Signaling Cdc42 Signaling Toll-like Receptor Signaling G␣12/13 Signaling mTOR Signaling

2.28E00 1.82E00 1.69E00 1.55E00 1.46E00 1.4E00 1.34E00

RPL24, RPL22, RPL37A, RPS18, RPL17, RPL14, EIF3G, RPS27, RPL39, RPL39, RPS26, UBA52, EIF2B1, RPS15A, RPL18, RPL38, RPL13A FOS, TRAF2, YWHAG, PLCE1, TUBA8, TUBG1, MAPK8, TUBA4A, PLCL2, TUBA3E, TUBB2B, PRKCA ITGB1, ACTR2, ARHGDIG, ARPC5, ARHGEF17, CDC42, CDH11, GNG10, GNG10, ARHGAP5, CDH2, RHOA, ARHGEF3, ACTC1, ACTA1, PRKCA ITGB1, ACTR2, SEPT3, ARPC5, MAPK8, ARHGEF17, MYLK, CDC42, CDH11, GNG10, FOS, CDH2, RHOA, ARHGEF3, ACTC1, ACTA1 MAP2K6, PRKAB2, MAPK8, BMPR2, NR2C2, PLCL2, ACVR2B, PRKAG1, PLCE1, NFKBIA, RXRA, ACVR2A, PRKCA ITGB1, BLK, VAV2, FOS, YES1, RHOA, MAPK8, ACTC1, FGR, ACTA1, PRKCA, GNG10 ARHGAP5, KTN1, ACTR2, SEPT3, RHOA, ARPC5, MYLK, ACTC1, ACTA1 ITGB1, ACTR2, IQGAP2, RHOA, ARPC5, MAPK8, BRK1, CDC42 ITGB1, VAV2, ACTR2, FOS, IQGAP2, ARPC5, HLA-B, MAPK8, MYLK, CDC42 MAP2K6, ECSIT, FOS, NFKBIA, MAPK8 VAV2, CDH2, NFKBIA, F2R, RHOA, MAPK8, CDC42, CDH11 EIF3G, RPS27, PRKAB2, RPS26, RHOA, RPS18, RPS15A, RPS6KA2, PRKAG1, PGF, PRKCA

B: Cytoskeleton related Remodeling of Epithelial Adherens Junctions

6.55E00

Epithelial Adherens Junction Signaling

6.48E00

Clathrin-mediated Endocytosis Signaling

3.86E00

Gap Junction Signaling

2.73E00

Caveolar-mediated Endocytosis Signaling Axonal Guidance Signaling

2.57E00 2.38E00

Reelin Signaling in Neurons Actin Cytoskeleton Signaling

2.35E00 2.32E00

Regulation of Actin-based Motility by Rho Integrin Signaling

2.25E00 1.86E00

Ephrin B Signaling

1.48E00

C: Cell metabolism related Mitochondrial Dysfunction

3.78E00

Superpathway of Cholesterol Biosynthesis Superpathway of Citrulline Metabolism Mevalonate Pathway I Superpathway of Geranylgeranyldiphosphate Biosynthesis I (via Mevalonate) Ketogenesis LXR/RXR Activation Melatonin Signaling Citrulline-Nitric Oxide Cycle Arginine Biosynthesis IV Urea Cycle Glycogen Biosynthesis II (from UDP-D-Glucose) Citrulline Biosynthesis Glutamine Biosynthesis I

ACTR2, RAB5A, ACTN2, ARPC5, TUBG1, TUBA4A, TUBB2B, TUBA8, VCL, TUBA3E, ACTC1, ACTA1, DNM2 VAV2, ACTR2, ACTN2, TUBG1, ARPC5, TUBA4A, BMPR2, ACVR2B, CDC42, TUBB2B TUBB2B, CDH2, YES1, TUBA8, RHOA, VCL, TUBA3E, ACTC1, ACVR2A, ACTA1 ITGB1, ACTR2, APOE, AP2B1, APOB, RAB5A, F2R, SH3GL3, ARPC5, CDC42, PGF, ALB, LDLR, STAM, ACTC1, ACTA1, DNM2 TUBG1, TUBA4A, PLCL2, PRKG2, PRKAG1, TUBB2B, PLCE1, SP3, TUBA8, TUBA3E, ACTC1, ACTA1, PRKCA ITGB1, ALB, RAB5A, HLA-B, ACTC1, ACTA1, DNM2, PRKCA EFNB3, PRKCA, ITGB1, ACTR2, ADAMTS1, C9orf3, TUBG1, TUBA4A, EFNA3, PLCL2, GNG10, RHOA, WNT1 PLXNA3, UNC5A, ARPC5, CDC42, PRKAG1, TUBB2B, PGF, EFNB2, PLCE1, MYSM1, TUBA8, TUBA3E ITGB1, BLK, PAFAH1B2, APOE, YES1, MAPK8, ARHGEF3, FGR ITGB1, VAV2, ACTR2, F2R, ACTN2, ARPC5, BRK1, MYLK, CDC42, GSN, IQGAP2, RHOA, VCL, ACTC1, ACTA1 ACTR2, RHOA, ARPC5, MYLK, CDC42, GSN, ACTC1, ACTA1 ITGB1, ACTR2, ACTN2, ARPC5, MAPK8, MYLK, CDC42, ARHGAP5, RHOA, VCL, TSPAN6, ACTC1, ACTA1 VAV2, EFNB2, RHOA, EFNB3, CDC42, GNG10

3.51E00 3.05E00 3.05E00 2.61E00

NDUFV1, COX6B1, UCP2, COX6A1, NDUFS7, MAPK8, NDUFB8, NDUFA13, NDUFA13, MAOB, PARK7, NDUFB7, NDUFS6, COX5B, COX6C, PINK1 HADHB, DHCR7, IDI1, HSD17B7, HMGCR, HMGCS1 PRODH, GLS, ASS1, ASL HADHB, IDI1, HMGCR, HMGCS1 HADHB, IDI1, HMGCR, HMGCS1

2.32E00 2.17E00 2.09E00 1.99E00 1.83E00 1.83E00 1.83E00 1.57E00 1.48E00

HADHB, HMGCL, HMGCS1 APOE, TTR, ALB, APOB, LDLR, C3, IRF3, HMGCR, RXRA, AGT MAP2K6, PLCE1, RORA, PLCL2, RORC, PRKAG1, PRKCA ASS1, ASL ASS1, ASL ASS1, ASL UGP2, GBE1 PRODH, GLS GLUL

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to bind the promoter of IGFBP3 (insulin-like growth factor binding protein 3); the lack of repression in Mecp2-null mice or in mutant RTT patients leads to overexpression of IGFBP3 and to levels that are more typical of immature/under-developed brains, paving the way to the hypothesis that higher levels of IGFBP3 could therefore delay full development of the brain (Itoh et al., 2007). Through the years other approaches reinforced the importance of insulin in MeCP2 defective systems and the first attempt to use IGF-1 as a pharmacological approach dates back to 2009, when extension of life span, amelioration of locomotor functions, breathing behavior and heart functions were seen after the administration of IGF-1 in Mecp2-null mice (Tropea et al., 2009). While the likeliness of IGF-1 treatments as a therapeutic intervention for both RTT and ASD is still under examination (Pini et al., 2012; Bozdagi et al., 2013), a recent paper showed that the ability of IGF-1 to produce beneficial effects is strictly related to dosage and timing of the administration (Pitcher et al., 2013). Second in terms of statistical significance is the 14-3-3 mediated signaling, in which 14-3-3 proteins affect several different pathways, including cell cycle, PI3K–Akt signaling and Hippo pathway, possibly by overlapping with Akt activity on targets such as BAD, TSC, and YAP. Although no direct connections with RTT have been identified so far, a possible involvement of 14-3-3 signaling in ASD and general developmental delays exists in humans and in animal models (Capra et al., 2012; Cheah et al., 2012). In fact, 14-3-3z-null animals display cognitive and behavioral abnormalities, possibly caused by the lack of a partner of Disc1. More interestingly a link between Akt and mTOR signaling (significantly perturbed in our analysis, Table 3A) and Rett syndrome has already been proposed, therefore reinforcing the therapeutic effectiveness of protein synthesis targeting (Ricciardi et al., 2011). Typical of this kind of biostatistical approach is the fact that most of the pathways identified somehow overlap due to the fact that the same molecules can act within different pathways. Accordingly, some molecules stand out for their over-representation; this is the case for Mapk8 and RhoA, genes that can be considered “hubs” for different converging pathways (Meng and Xia, 2011). Mapk8 (alias JNK) is pivotal in its standing as a target where many different signaling paths involved in proliferation, differentiation, inflammation, apoptosis and cell cycle converge (http://www.genome.jp/kegg); it modulates gene expression both directly through the activation of the JunD/c-Jun (AP1) transcriptional complex (Mapk signaling pathway, ErbB signaling or neurotrophin intracellular cascade) and, indirectly, inhibiting Hippo signaling. Moreover, its indirect interaction with Akt is well documented, since both of them widely act as signaling transducers. Of interest, the involvement of Mapk8 with plasticity phenomena is well documented, as well as its link with ASD and general cognitive defects (de Anda et al., 2012; Ziats and Rennert, 2011; Pavlowsky et al., 2010a,b), while to the best of our knowledge no evidences of its involvement in RTT has so far been produced. RhoA (Ras homology family member A, alias ARH12, ARHA or Rho12) is an important mediator in inducing cytoskeleton changes thanks to its direct action on Rock proteins, and is therefore involved in mediating cellular morphology alterations triggered by axon guidance molecules, focal adhesion molecules, adherens and tight junctions (http://www.genome.jp/kegg; Lessey et al., 2012). Eventually, RhoA is indirectly involved in Mapk signaling acting as an inhibitor of Akt and in the modulation of mTor pathway through its activity on Tsc2 (Lesma et al., 2013). 5.2. Table 3b: cytoskeleton related Many different pathways related to the plasticity of the cytoskeleton are significantly modulated in Mecp2-null tissues, as listed in Table 3B. Acta1 and Actc1 are genes encoding various forms

of alpha actin expressed at different levels in the brain and in other tissues (such as cardiac and muscle tissues) where they contribute at various levels in the mechanisms through which actin fibers mediate cell motility, migration or, in general, plasticity. So far, such category of phenomena has been only partly associated with Rett syndrome and MeCP2 mechanism of actions. Degano et al. (2009) in fact describe axonal defects in MeCP2 null olfactory bulbs tracts, while Belichenko et al. (2009, 1997) depict general morphological defects in various brain areas of Mecp2-null mice at the dendritic levels. Interestingly, Smrt et al. (2007) show early defects in axonal growth in newly born neurons of the adult dentate gyrus, opening the intriguing question whether adult neurogenesis might be somehow impaired in Mecp2-null mice or whether the integration of such newly born neurons might result defective. Moreover, Abuhatzira et al. (2009) find altered levels of TUBA1B and TUBA3 in Rett and Angelman syndromes brain tissues. Interestingly, a recent report demonstrates that administration to a symptomatic mouse model of Rett syndrome of cytotoxic necrotizing factor 1 (CNF1), a bacterial toxin able to reshape actin cytoskeleton, enhancing neurotransmission in synaptic plasticity, markedly improves behavioral phenotype and astrocytic deficits (De Filippis et al., 2012). Our analysis shows that many of the molecules represented in 3B can act downstream different signaling pathways that are triggered by, for example, cell–cell interaction (Remodeling of Epithelial Adherens Junctions, Epithelial Adherens Junction Signaling, Gap Junction Signaling, Integrin Signaling), or ligand–receptor interaction in the case of axon guidance related messages (Axonal Guidance Signaling, Ephrin B Signaling). On this matter, our observations could somehow converge with those by Borg et al. (2005), Archer et al. (2006) and Nectoux et al. (2007) describing a possible indirect connection between RTT and the expression of the axon guidance molecule Netrin G1 (NTNG1), therefore linking dendritic spine defects, one of the most evident and thorough studied feature of Rett syndrome, with a structural molecular base. Moreover, the interest in cell–cell interactions in RTT is not limited to their role in neurons, structurally one of the most plastic cell types of the brain, but also to the function in MeCP2 deficient astrocytes or microglia. In fact, it is well-known that defects in astrocyte signaling can lead to a general impairment of neuronal development and maintenance possibly through gap junctions (Maezawa et al., 2009; Maezawa and Jin, 2010); accordingly, conditioned medium derived from Mecp2-null microglia is able to disrupt normal neuronal activity through the toxic release of abnormally high levels of glutamate, again through gap junctions (Maezawa and Jin, 2010). A possible involvement of cell–cell interaction in RTT and ASD has also been proposed by Betancur et al. (2009). The role of the mentioned pathways, having in common the final target of inducing structural changes that might prelude axonal growth, synaptic reinforcement and general plasticity events, is of sure interest in the field given the long debated, and eventually widely accepted, role of BDNF in RTT (for a review see Li and Pozzo-Miller, 2013). BDNF levels have been thoroughly evaluated in RTT models, as the down-regulation of it is thought to mediate at least part of the neurological defects displayed in Rett, while the modulation of its levels is thought to ameliorate such animal models. Given the ability of BDNF, and neurotrophins in general, to mediate brain plasticity and, in particular, synaptic plasticity, it is not surprising that our data show perturbations in those molecular paths that lead to architectural changes; of interest, the path connecting Trks (tropomyosin-related kinase, able to bind neurotrophins) leading to cytoskeletal modification is subjected to the control of Akt, thus highlighting the extent by which the pathways mentioned in Table 3A overlap with those listed in Table 3B. However, a large number of intracellular mechanisms involving Ras-Raf or IP3 effectors, all ultimately able to induce transcription through CREB (http://www.genome.jp/kegg) are triggered by TrkB,

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thus highlighting how intricate the intracellular pathways possibly leading to cytoskeletal modifications are. 5.3. Table 3c: cell metabolism related A possible link between cellular metabolic impairments and RTT has already been proposed and is becoming a field of interest for the search of new therapeutic molecular targets. Indeed, in 2007 a paper by Viola et al. showed that in Mecp2-null mice the defects in brain growth, osmoregulation and neurotransmission could be associated with abnormal levels of different metabolic markers representing different cell types, including astrocytes and neurons (Viola et al., 2007). The data listed in Table 3C are representative of a less dramatic deregulation of metabolism related pathways compared to those so far described (Table 3A and B); however, the statistical significance and the relative novelty for the RTT field makes those pathways of particular interest. Among the enrichment of “Mitochondrial dysfunction” and the very well described involvement of mitocondria in Rett syndrome (Armstrong, 1992; Grosser et al., 2012), the Superpathway of Cholesterol Biosynthesis is the second most significantly regulated in statistical terms, sharing almost all the genes representative of the path with three more molecular networks related to Mevalonate, Geranylgeranyldiphosphate Biosynthesis and Ketogenesis. A possible link between Rett syndrome and sterol biosynthesis has been fairly long debated: the first evidence or hypothesis dates back to Lekman et al. (1991), while a more solid link between cholesterol and ASD has recently been proposed (Lee and Tierney, 2011; Woods et al., 2012). The role of cholesterol biosynthesis is of interest in Rett syndrome as it is involved in synaptic plasticity, neurite outgrowth, microtubular stability and synaptogenesis (Tsutsui, 2012; Haraguchi et al., 2012). Cholesterol importance in adulthood and senescence is also underlined by its involvement in some neurological disorders, including Alzheimer’s and Huntington diseases (Gamba et al., 2012; Valenza and Cattaneo, 2011). Furthermore, a direct link between cholesterol biosynthesis and BDNF has also been demonstrated, since HMGCR (3-hydroxy-3-methylglutarylCoA reductase) levels are regulated by BDNF (Suzuki et al., 2007), while BDNF can play a crucial role in synaptic plasticity by recruiting its receptor TrkB in the cholesterol-rich lipid rafts of the membrane (Suzuki et al., 2003). A molecular connection between structural plasticity, intracellular mechanisms and cholesterol has been suggested in a work by Wu et al. (2008) in which the authors demonstrate that Simvastatin (an hypolipidemic drug) upregulates VEGF and BDNF, activates PI3K/Akt and thereafter modulates neurogenesis. To conclude, the relevance of cholesterol metabolism in Rett syndrome, and indirectly of our bioinformatics approach seems to be highlighted by a very recent publication in which the severity of Rett syndrome in mice was successfully treated with cholesterol drugs, such as statins. The rationale of this research was given by the identification as a modifier gene of RTT of the gene Sqle, encoding squalene epoxidase and functioning as a rate limiting enzyme of cholesterol synthesis. Interestingly enough, the presence of high cholesterol inside and outside of the brain of the RTT mouse model, together with high levels of triglycerides in the liver, a sign of metabolic syndrome were found (Buchovecky et al., 2013). Importantly, the laboratory of dr. Jeffrey Neul has recently published that mice with a Mecp2-null allele develop a metabolic syndrome (Pitcher et al., 2013). 6. Exploiting the pathway enrichment approach to analyze human dataset and conclusions As already stated, the analysis described above has been produced exploiting data produced from Mecp2-null male mice.

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Although most of the experimental data deduced from mice exploited these models, it is important to recall that only 10% of mutations in RTT patients consists of deletions sufficiently large to approximate a null condition. Furthermore, although male mice have mainly been used so far, it is clear that they lack the genetic mosaicism characteristic of the vast majority of RTT patients, deriving from X chromosome inactivation. Thus, in an effort to verify whether the bio-analytical approach proposed so far could also unmask hidden aspects of transcriptional perturbations affecting human tissues, we applied the pathway enrichment analysis to the data deposited to Array Express (E-MEXP-1956) by Nectoux et al. (2010). This work describes the microarray transcriptional screening of fibroblasts obtained from three RTT patients carrying either non-sense (p.R255X; p.R279X) or frameshift (c.1156del41) mutations. Similarly to what we did with the mouse datasets, we determined the differentially expressed genes between RTT patients and healthy donors and used the obtained gene lists as input set for a pathway enrichment analysis by Fisher exact test. Since we wanted to compare the results obtained with this human dataset to the results from mouse datasets, we applied the very same filtering and querying options to the analysis (with the exception of the reference to a different species) and the same reference set. The pathway enrichment analysis on this dataset reveals that many deregulated pathways are related to metabolism (Table 4) and intracellular signaling, while pathways connected with cytoskeleton modifications do not appear to be as deeply affected as seen in the mouse screening already described. Nonetheless, we are aware that this could be due to the different nature of the samples analyzed (mouse brain tissues versus human fibroblast) and, accordingly, to the tissue specific levels of MeCP2 and distribution between 5mC and 5hmC. Of extreme interest, however, is the fact that, to a certain extent, an interesting overlap exists with the analysis described in Table 3A–C and this is highlighted by the enrichment of mainly three different pathways: “Reelin Signaling in Neurons”, “Axonal Guidance Signaling” and “LXR/RXR Activation” (Table 4). These three pathways are the only canonical ones that are enriched to a significant degree (pvalue < 0.05 or, in negative log scale used in the tables, higher than 1.33) in all datasets of both mouse and human analyses. Reelin is an extracellular matrix soluble ligand able to interact with Integrins, thus triggering actin rearrangement and mediating both neuronal migration (during development), cytoskeletal modification and cell adhesion (Frotscher, 2010). A significant link highlighted by both murine and human analyses between MeCP2 impairments and defective cytoskeleton structural modifications is further supported by our results showing that Axon Guidance Signaling is significantly perturbed in human fibroblasts and mouse brains (see Tables 4 and 3B). Eventually, the human dataset displays a large number of pathways associated with cellular metabolism as shown in Table 4; interestingly, LXR/RXR (liver-X-receptors/retinoic acid receptors) activation is not only deregulated in mice carrying MeCP2 mutations (Table 3C), but is also clearly connected to metabolism. In fact, the activated LXR/RXR complex transcriptionally influences the expression of several down-stream genes many of which are related to the modulation of cholesterol and lipids biosynthesis and activity (Song et al., 1994; Willy et al., 1995).

7. Comments on bioinformatic approaches in Rett syndrome research Higher level bioinformatics analyses such as pathway enrichment are based on the assumption that genes involved in biological processes and functions are also likely to be correlated in terms

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Table 4 The table shows all the canonical pathways significantly enriched by differentially expressed genes in the human dataset of RTT patients compared to healthy donors. The pathways are ranked starting from the most significant (higher −log[p-values]) and the differentially expressed molecules responsible for the enrichment of each pathway are shown. Significance p-value cutoff was set to 0.05 (corresponding to 1.33 in the negative log scale used in the table). Canonical pathways

−log(p-value)

Molecules

Hepatic Fibrosis/Hepatic Stellate Cell Activation

2.83E00

Fatty Acid ␣-oxidation Reelin Signaling in Neurons Ovarian Cancer Signaling

2.83E00 2.59E00 2.37E00

Axonal Guidance Signaling

2.18E00

Histamine Degradation Granulocyte Adhesion and Diapedesis

1.94E00 1.94E00

Wnt/␤-Catenin Signaling

1.9E00

Eicosanoid Signaling Endothelin-1 Signaling

1.84E00 1.77E00

Role of Macrophages, Fibroblasts and Endothelial Cells in Rheumatoid Arthritis

1.75E00

ICAM1, LEP, IL1RL1, IGFBP5, VEGFA, TLR4, CCL2, CCL2, EDN1, TGFB2, EDNRA, STAT1, IL1RAP, TNFRSF11B ALDH1A1, ALDH1A3, ALDH3A2, PTGS2 ITGB2, ARHGEF4, PIK3R1, ITGA2, HCK, ITGA6, VLDLR, RELN, ITGA4 VEGFA, FZD8, PMS2, EDN1, PIK3R1, FGF9, PTGS1, WNT16, WNT4, EDNRA, PTGS2, WNT2 ADAMTS8, PIK3R1, WNT16, ABLIM1, NTN1, WNT2, EPHB6, VEGFA, EFNB2, NTNG1, SEMA3D, GLIS1, ADAM19, PLCB1, WNT4, SEMA3B, ITGA4, PAPPA, PLXNC1, ITGA2, GNG2, L1CAM, SLIT2, FZD8, ADAMTS6, SEMA3C ALDH1A1, ALDH1A3, ALDH3A2 ITGB2, ICAM1, SDC1, CCL2, CLDN1, IL1RL1, ITGA2, ITGA6, IL1RAP, ITGA4, TNFRSF11B SOX4, SFRP4, SFRP2, WNT16, WNT2, FZD8, TGFB2, WNT4, NR5A2, SOX9, SFRP1, ACVR1C, ACVR2A PLA2G4A, PTGFR, PTGER3, PTGS1, PTGER2, PTGS2 PLA2G4A, EDN1, PIK3R1, PTGS1, ADCY3, CASP1, PLCB1, EDNRA, PTGER2, PTGS2, PLD1, GUCY1B3 SFRP4, SOCS1, ICAM1, SFRP2, IL1RL1, PIK3R1, WNT16, WNT2, VEGFA,

Oxidative Ethanol Degradation III Tryptophan Degradation X (Mammalian, via Tryptamine) Putrescine Degradation III Ethanol Degradation IV LXR/RXR Activation Dopamine Degradation tRNA Splicing Colorectal Cancer Metastasis Signaling

1.75E00 1.67E00 1.67E00 1.59E00 1.59E00 1.52E00 1.48E00 1.47E00

Role of JAK2 in Hormone-like Cytokine Signaling LPS/IL-1 Mediated Inhibition of RXR Function

1.44E00 1.39E00

Growth Hormone Signaling Protein Citrullination Thio-molybdenum Cofactor Biosynthesis Tumoricidal Function of Hepatic Natural Killer Cells

1.39E00 1.39E00 1.39E00 1.33E00

of expression, localization, or allele occurrences. Nevertheless, this correlation is never a one-to-one correlation: redundancy (more genes for a single effect) and pleiotropicity (a single gene producing more than one effect) are in fact widespread in nature’s mechanisms and they are indeed very common in molecular signal transduction. For this reason, we believe that when studying a knocked out or a mutated tissue it is important to analyze the effects of that perturbation also at a higher level since functional differences, which are not observable at the gene level, might emerge when considering the overall picture of the collective action of genes inside the pathways framework. When such differences are consistently observed as common to datasets or when they are conserved through species they are likely to be the important core of a shared regulatory mechanism and worth of further attention even if they are not enforced by the same genes. This is particularly true when dealing with gene products with a pleiotropic action or with regulators such as MeCP2 showing a broad range of binding with little promoter specificity. Looking at the effects of MeCP2 deficiency from a higher-level perspective might be useful to pinpoint actual downstream targets. Thus, due to molecular redundancy it is not surprising to find different genes enforcing the same function; such a situation is indeed observed for the three pathways enriched in both mouse and human analyses. Far from inferring any strikingly novel field in which RTT research should aim its efforts, with our analysis we would like to suggest that a transcriptional map of deregulated pathways (for all the above reasons to be preferred to genes) can actually evoke

FZD8, TLR4, CCL2, PLCB1, WNT4, SFRP1, IL1RAP, TNFRSF11B, CAMK2B ALDH1A1, ALDH1A3, ALDH3A2 ALDH1A1, ALDH1A3, ALDH3A2 ALDH1A1, ALDH1A3, ALDH3A2 ALDH1A1, ALDH1A3, ALDH3A2 TLR4, CCL2, IL1RL1, MYLIP, PTGS2, IL1RAP, TNFRSF11B, APOD ALDH1A1, ALDH1A3, ALDH3A2 PDE3A, PDE4B, PDE1A, PDE1C PTGER3, DIRAS3, PIK3R1, ADCY3, GNG2, WNT16, WNT2, VEGFA, FZD8, TLR4, TGFB2, WNT4, PTGS2, PTGER2, STAT1 SOCS1, GHR, STAT1, STAT5B HS3ST3B1, TLR4, ALDH1A1, ALDH1A3, IL1RL1, ALDH3A2, MAP3K1, NR5A2, ABCC3, IL1RAP, ALDH6A1, TNFRSF11B SOCS1, IGF2, GHR, PIK3R1, STAT1, STAT5B PADI2 MOCOS SERPINB9, ICAM1, SRGN

molecular mechanisms by which the lack of MeCP2 exerts its detrimental effects on the brain or, extending the analysis, in all tissues, given that all the cellular types express MeCP2 at different levels. The fact that we found a very strong statistical significance in pathways related to cytoskeleton modification, by far the most thoroughly studied features of RTT, appears to strengthen our conclusions and the power of the method used in our analysis. Thus, our analyses demonstrate that a proper biostatistical approach has a chance of unmasking molecular aspects of a fairly complicated pathology such as Rett syndrome, where the transcriptional perturbations are only subtle, thus hampering the capability to highlight specific effects. Given the aforementioned considerations about molecular redundancy and considering that this redundancy in a specific canonical pathway is often enforced by more than one single signal transduction chain triggering a downstream signal and, ultimately, a phenotype, the study of functional outcomes and pathway involvement should be able to overcome the obstacle of specific effects that are not detectable at the single gene level. Considering the high variability of the different phenotypes displayed by RTT patients harboring diverse MeCP2 mutations and gene combinations, in the future it might be interesting to probe our approach to experimental data obtained from patients’ postmortem samples and iPS cells. Moreover, it will be of interest to address whether the list of deregulated pathways will be significantly influenced by the mutations affecting the patients under analysis. An inventory of the molecular pathways deregulated by specific MECP2 mutations could represent an attempt to create a

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genotype–phenotype grid that could, ideally, help classifying clinical outcomes according to the genre of mutations. To conclude, MeCP2 is a multifunctional protein responsible of a large spectrum of diverse neurological diseases in children. Experiments on mice have demonstrated that MeCP2-related features are probably caused by a dysfunction in neurons and non-neuronal cells, rather than neural degeneration. These findings suggest the possibility to reverse some or most symptoms, providing that we understand the consequences of a dysfunctional MeCP2 inside and outside the central nervous system. Although we find crucial that the RTT research community insists in better defining all MeCP2 functions and the mechanisms of their regulation through post-translational modifications, we believe that defects in transcriptional regulation, directly or indirectly caused by a dysfunctional MeCP2, ultimately cause the cellular and circuital defects observed in RTT. Thus, although the global binding of MeCP2 to mature neuronal chromatin might suggest the futility of looking for direct binding-target of the protein, we would like to propose that the identification of pathways commonly altered in MECP2related disorders that might be therapeutically targeted remains an important challenge for the field and that novel bioinformatic approaches might give us the opportunity to exploit previous and future studies for a faster reach of this aim. Acknowledgments We would like to dedicate this article to proRETTricerca, an Italian association of parents that during the last years has supported our studies and to all their girls that daily motivate our work. We apologize to all colleagues whose work could not be cited here. We thank Dr. Christopher Woodcock, Dr. Rajarshi Ghosh and Dr. Vladimir Uversky for having shared with us novel information on predicted disordered binding regions of MeCP2. This work was supported by Fondazione Cariplo (Grant 2010-0724 to NL), FP7-PEOPLE-ITN-2008 (CKN), Jerome Lejeune Foundation (NL), Fondazione Telethon (Grant GGP10032 to NL), Ministero della Salute (Ricerca finalizzata 2008–Bando Malattie Rare to NL) and IRSF (CKN). References Abuhatzira, L., Shemer, R., Razin, A., 2009. MeCP2 involvement in the regulation of neuronal alpha-tubulin production. Hum. Mol. Genet. 18, 1415–1423. Adams, V.H., McBryant, S.J., Wade, P.A., Woodcock, C.L., Hansen, J.C., 2007. Intrinsic disorder and autonomous domain function in the multifunctional nuclear protein, MeCP2. J. Biol. Chem. 282, 15057–15064. Agarwal, N., Hardt, T., Brero, A., Nowak, D., Rothbauer, U., Becker, A., Leonhardt, H., Cardoso, M.C., 2007. MeCP2 interacts with HP1 and modulates its heterochromatin association during myogenic differentiation. Nucleic Acids Res. 35, 5402–5408. Archer, H.L., Evans, J.C., Millar, D.S., Thompson, P.W., Kerr, A.M., Leonard, H., Christodoulou, J., Ravine, D., Lazarou, L., Grove, L., Verity, C., Whatley, S.D., Pilz, D.T., Sampson, J.R., Clarke, A.J., 2006. NTNG1 mutations are a rare cause of Rett syndrome. Am. J. Med. Genet. A 140, 691–694. Armstrong, D.D., 1992. The neuropathology of the Rett syndrome. Brain Dev. 14, S89–S98. Baker, S.A., Chen, L., Wilkins, A.D., Yu, P., Lichtarge, O., Zoghbi, H.Y., 2013. An AT-hook domain in MeCP2 determines the clinical course of Rett syndrome and related disorders. Cell 152, 984–986. Ballas, N., Lioy, D.T., Grunseich, C., Mandel, G., 2009. Non-cell autonomous influence of MeCP2-deficient glia on neuronal dendritic morphology. Nat. Neurosci. 12, 311–317. Bebbington, A., Anderson, A., Ravine, D., Fyfe, S., Pineda, M., de Klerk, N., Ben-Zeev, B., Yatawara, N., Percy, A., Kaufmann, W.E., Leonard, H., 2008. Investigating genotype–phenotype relationships in Rett syndrome using an international data set. Neurology 70, 868–875. Belichenko, P.V., Hagberg, B., Dahlström, A., 1997. Morphological study of neocortical areas in Rett syndrome. Acta Neuropathol. 93, 50–61. Belichenko, P.V., Wright, E.E., Belichenko, N.P., Masliah, E., Li, H.H., Mobley, W.C., Francke, U., 2009. Widespread changes in dendritic and axonal morphology in Mecp2-mutant mouse models of Rett syndrome: evidence for disruption of neuronal networks. J. Comp. Neurol. 514, 240–258.

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