Genomic and transcriptomic analyses distinguish classic Rett and Rett-like syndrome
and reveals shared altered pathways
Dilek Colaka, Hesham Al-Dhalaanb, Michael Nesterb, AlBandary AlBakheetc, Banan Al-Younesc,
Zohair Al-Hassnand,1, Mohammad Al-Dosaria,1, Aziza Chedrawia,1, Muhammad Al-Owaind,
Nada AbuDheimc, Laila Al-Alwanc, Ali Al-Odaibc, Pinar Ozandc,2, Mehmet Sait Inan†, Namik Kayac,⁎
aDepartment of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
bDepartment of Neurosciences, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
cDepartment of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
dDepartment of Medical Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
a b s t r a c t a r t i c l ei n f o
Received 5 July 2010
Accepted 24 September 2010
Available online 8 October 2010
Genome-wide gene expression profiling
Functional pathway analysis
Genes related to Rett phenotype
Rett syndrome (RTT) is an X-linked neurodevelopmental disorder characterized by derangements in nervous
system especially in cognition and behavior. The present study aims to understand the molecular
underpinnings of two subtypes of RTT, classic RTT and Rett-like, and to elucidate common pathways giving
rise to common RTT phenotype using genomic and transcriptomic approaches. Mutation screening on
selected nuclear genes revealed only MECP2 mutations in a subset of classic RTT patients. MLPA assays and
mtDNA screenings were all negative. Genome-wide copy number analysis indicated a novel duplication on X
chromosome. Transcriptional profiling revealed blood gene signatures that clearly distinguish classic RTT and
RTT-like patients, as well as shared altered pathways in interleukin-4 and NF-κB signaling pathways in both
subtypes of the syndrome. To our knowledge, this is the first report on investigating common regulatory
mechanisms/signaling pathways that may be relevant to the pathobiology of the “common RTT” phenotype.
© 2010 Elsevier Inc. All rights reserved.
Rett syndrome (MIM 312750) is an X-linked neurodevelopmental
disorder; first described by Rett, a Viennese pediatrician, who
happened to notice peculiar behavior in two side by side sedating
female patients . Hagberg later on studied 35 patients and has
essentially outlined the syndrome . This is a disease almost
exclusively encountered among autistic girls. Initially the develop-
ment progresses normally for up to 18 months, then stops and
succeeds a rapid deterioration of developmental milestones. By third
year of life the neuro-degeneration progresses to severe dementia,
acquired microcephaly, intermittent hyperventilation, severe autism,
truncal ataxia and almost pathognomonic purposeful stereotypic
hand movements as hand clapping or rubbing hands. The disease may
remain stable for some years but slowly progresses with severe
neurological abnormalities as spastic paraparesis, vasomotor distur-
bances and seizures. Various skeletal abnormalities and malforma-
tions are observed. The deformities related to hands and feet are
studied in detail and revealed involvement of metacarpal and
metatarsal bones. These malformations are seen both in typical and
atypical RTT patients [3,4].
A systematic gene and related mutation screening studies linked
MECP2 as a cause of RTT . The gene encodes a methyl-CpG-binding
protein that represses transcription of some of the genes through
interacting with histone deacetylase and corepressor SIN3A. Therefore,
Genomics 97 (2011) 19–28
⁎ Corresponding author. Department of Genetics, KFSHRC, MBC, 03, Riyadh, 11211,
Kingdom of Saudi Arabia.
E-mail address: firstname.lastname@example.org (N. Kaya).
2Current address: Yildiz Technical University, Besiktas, 34349-Istanbul, Turkey.
0888-7543/$ – see front matter © 2010 Elsevier Inc. All rights reserved.
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/ygeno
two global mechanisms of gene regulation, namely DNA methylation
and histone decetylation underlie the MECP2's action. Besides its role in
have an MECP2 abnormality [7,8]. Hoffbuhr et al.  indicated that only
63% with classic or typical RTT and 33% of atypical RTT patients had the
MECP2 abnormality. Another study showed that only 20% of patients
alsobeen reported, suchasCDKL5, FOXG1, and NTNG1. Themutationsof
CDKL5 involved in early infantile epileptic encephalopathy may cause a
severe atypical form of RTT. The frame-shift mutations of CDKL5 were
encountered in two girls with RTT . The FOXG1 mutations may also
cause the atypical congenital variant of RTT . This gene codes for a
brain specific repressor of transcription and involves in the develop-
mentof telencephalon.Italsosharescommonmechanisms withMECP2
during neuronal development. A break-point in the NTNG1 gene was
alsoimplicatedinRTT.Inagirlwithcharacteristic featuresofRTT, a
translocation with t(1;7) (p13.3;q31.3) was detected. In fact not only
deletion or mutation but also duplication of MECP2 may cause mental
retardation and atypical RTT . Besides these genes, involvement of
other genes in RTT could be a possibility since there are still numerous
RTT cases with nomutation in MECP2, CDKL5, FOXG1, or NTNG1. Several
studies have been performed using different microarray-based
and other techniques to evaluate alteration in gene expression in RTT
[15–20]. Despite these efforts, the molecular basis and mechanisms
subtypes remain unclear.
In this study, we sought to understand the molecular under-
pinnings of the two subtypes of the syndrome, classic RTT and RTT-
like, and to elucidate the common pathways giving rise to common
RTT phenotype using genomics and transcriptomic approaches. We
performed genome-wide copy number variation (CNV) analysis using
high density SNP mapping assays and identified a novel duplication in
RTT-like patients. Moreover, we compared genome-wide gene ex-
pression profiles of whole blood samples from classic or typical RTT
and RTT-like patients with the age–sex matching healthy controls.
Recently, there is accumulating evidence indicating the similarities
andcommonmechanisms betweennerveand bloodvessel wiringand
function [21,22]. Such parallelism and resemblance were exploited for
such genome-wide gene expression studies for the neurologic and
neuropsychiatric diseases [23–26]. Using the same strategy, we iden-
tified blood gene signatures that clearly distinguish classic and atyp-
ical RTT patients from controls, as well as shared altered pathways in
both subtypes of the syndrome. Thus, our results serve a window to
deciphering underlying altered metabolic and signaling pathways
that may be relevant to the disease pathogenesis.
2. Materials and methods
One hundred fifty patients were referred to KFSHRC clinics for the
suspicion of having RTT disease during last 20 years, and 14 were re-
characterized as classic RTT and two were as RTT-like according to
Hagberg's criteria . Among these, five patients were re-evaluated
and followed-up in the clinics for at least few years. Two of these
patients are from a consanguineous Saudi family (Fig. 1A) whereas
the others are single cases. Two sisters showed four of the five
obligatory manifestations, six of six inclusive criteria and three of 11
supportive manifestations according to criteria listed by Hagberg
(Fig. 1B1–2 and B3–4) . The early infantile onset precluded them
from being classified as classic RTT syndrome. The detailed clinical
characteristics of these two patients are given below. The other three
patients who met all the postulated criteria were considered typical
RTT or classic RTT. Moreover, they were being screened for MECP2
mutations which were also found positive in these patients.
2.2. RTT-like clinical characteristics
Patient 1, a girl, is from consanguineous Saudi parents with no
historyofa neurological disease(Fig.1A). Shewasfirst encounteredat
the age of 4 years for the evaluation of her seizure disorder (Fig. 1B-1
and B-2). Her younger sister also had the same condition (Fig. 1B-3
and B-4). She smiled at 4 months, sat at 18 months and could say
syllablesat 5 years of age. She had frequentvomiting,GE reflux during
first year of life and repeated chest infections at 2 years of age. At third
year of life, she started to show mental regression. On examination
her head circumference was at 2nd percentile. She showed no eye-
contact, she had hand-clapping, hand-wringing, puff blowing and
bruxism. At 12 years of age her communication and social skills were
at one month level. Her living skills were at 12 months and motor
skills were at 20 months level. She showed increased muscle tonus in
her lower extremities with brisk deep tendon reflexes and Babinski.
At 16 years of age she underwent a very detailed metabolic workup
including tandem MS in blood, urine GC/MS, biotinidase, blood amino
acids and karyotyping; all of which were normal. The MR brain
revealed mild prominence of ventricles. Her ECG was normal and her
EEG on repeated occasions showed severely abnormal wave pattern
with diffuse cortical neuronal irritability and predisposition to ep-
ileptic seizures with multifocal sensory generalized epileptic activi-
ties. All these findings indicated that according to the diagnostic
criteria of Hagberg , she had 4/5 obligatory, 6/6 inclusion and 3/11
of the supportive manifestations. She did not have gait dyspraxia,
breathing irregularities, neurogenic scoliosis, feet deformity, un-
prompted laughter, irregular breathing spells, and intensive eye com-
munication. Considering the lack of an early infantile normal
development before regression, the consensus was that she had a RTT-
Patient 2 is an 8-year-old sister of the previous patient at the time
of initial encounter (Fig. 1B-3 and B-4). She was referred for the
evaluation of her intractable complex partial seizures and secondary
generalization. Her development was greatly delayed from early
infancy on, for example she never smiled. On examination her head
circumference was at 2nd percentile while it was at 25th percentile
two years before. She showed no eye-contact, she had hand-clapping,
hand-wringing, puff blowing and bruxism. Her overall developmental
level was 1 year at the age of 8 years. She had increased tonus in her
lower limbs with brisk deep tendon reflexes and Babinski. Her devel-
opmental delay was global and profound with no social or linguistic
skills. Her EEG was markedly abnormal with diffuse background
disorganization, paroxysmal activity predominantly involving the
right hemisphere, mainly right centro-parietal and right occipital lobe
paroxysmal discharges. All these findings indicated that according to
Hagberg's criteria , she had 4/5 obligatory, 6/6 inclusion and 3/11
of the supportive manifestations. The consensus was that she had an
RTT-like syndrome, similar to her sister, due to the lack of an early
infantile normal development before regression.
2.3. Blood collection, nucleic acid isolation and PCR amplification
The collection of blood was as described before . Five milliliters
of wholeblood fromconsented patients wascollectedintoEDTAtubes
for genomicDNAisolation.The DNAwas isolatedfrom theblood using
PureGene DNA Purification Kit according to the manufacturer's
instructions (Gentra Systems Inc., Minneapolis, MN, USA). RNA was
extracted from the whole blood (2.5 ml) collected in PAXgene tubes
(QIAGEN Inc., Valencia, CA, USA). Quality and quantities of the total
RNA were determined by measuring the absorbance spectra on a UV/
Vis spectrophotometer, the NanoDrop® ND-1000 Spectrophotometer
(Nanodrop Inc., Wilmington, DE, USA), and further analyzed by RNA
6000 Nano Assay using 2100 Bioanalyzer (Agilent Technologies, Santa
Clara, CA, USA). The un-degraded, higher quality RNA was processed
further for the real-time RT-PCR and microarray experiments. DNA
D. Colak et al. / Genomics 97 (2011) 19–28
was amplified by PCR using intronic primers designed to amplify the
coding exons of the MECP2, CDKL5, NTNG1, FOXG1, and MBD2 genes. 5'
and 3' UTR of MECP2 were also included to the mutation screening
2.4. Mutation detection and analysis
Sequencing reactions after PCR amplifications were performed on
ABI 3100 Automated DNA Sequence Analyzer (Applied Biosystems,
Foster City, CA, USA) according to the manufacturer's recommenda-
tions. Briefly, purified PCR products was used for sequencing reactions
usingDNADye TerminatorCycleSequencingKit (AppliedBiosystems)
and purified with Dye-Ex protocol before it was run on ABI 3100
Automated DNA Sequence Analyzer. Collected data from the sequenc-
er was blasted to NCBI database, aligned with the publicly available
reference sequences, and analyzed using Lasergene-SeqMan version
6.1 (DNA Star Inc., WI, USA) and ChromasPro 1.31 (Technelysium Pty
Fig. 1. (A) Family pedigree revealing two affected members (dark symbols) from a consanguineous Saudi family (B) RTT-like patients (affected girls) in the family. (C) Copy number
and segregation analysis using GTC (Affymetrix). Copy number state (CNS) 2 refers to no change or wild type and 3 indicates single copy increase (duplication). Black arrows are
pointing distal breakpoints. Left black arrow (BML) indicating duplication (CNS=3) whereas right black arrow (BMR) is indicating no copy change (CNS=2). Left and right red
arrows are pointing the beginning and end of the CNV.
D. Colak et al. / Genomics 97 (2011) 19–28
2.5. mtDNA sequencing
DNA isolated from whole blood was used for PCR amplification of
mtDNA. The entire coding region of the mitochondrial genome was
amplified in 24 separate polymerase chain reactions (PCRs) using
common cycling conditions as detailed elsewhere for the patients,
certain family members, and controls. Each successfullyamplified
fragment was directly sequenced using the BigDye Terminator V3.1
Cycle Sequencing kit (Applied Biosystems), and samples were run on
the ABI Prism 3100 Sequencer (Applied Biosystems). Sequencing
results were compared to the corrected Cambridge reference se-
quence . All fragments were sequenced in both forward and
reverse directions at least twice for confirmation of any detected
2.6. Genome-wide SNP genotyping and copy number analysis
Genome-wide SNP genotyping of the patients were done using
(Affymetrix Inc., Santa Clara, CA, USA) using manufacturer's protocols,
manuals, and guidelines and by strictly following recommended
protocols. Copy number analysis was done using Genotyping Console
3.0 (Affymetrix). We performed similar analysis on ethnically-matched
100 controls. CNV frequency between cases and controls was evaluated
using Fisher's exact test. The estimated odds ratio (OR) of having a
duplication in cases compared to controls as well as its confidence
interval is calculated using the methodology designed to deal with the
estimation of OR from a 2×2 table when one of the cells is zero .
2.7. X-chromosome inactivation
The assay was performed as described . The experiments were
repeated three times independently on the mother's DNA in the
family (Fig. 1A).
2.8. Real-time RT-PCR and multiplex ligation-dependent probe
After primer optimization, real-time RT-PCR experiments were
performed on the cDNA using Quantitech SyBG Kit (QIAGEN), employ-
ing GAPDH as endogenous control gene. We randomly selected eight
genes, NRXN1, EPRS, DOCK8, NIPBL, SLC11A2, HNRNPL, PLA2G4B, and
GABARAP, and confirmed the differential expression in our patients
compared to healthy controls (Supplementary Table 1). All reactions
deltaCTmethod.TheMLPAassays (SALSAMLPAkit P189 RETT-like
and SALSA MLPA kit P015 MECP2, MRC-Holland, Amsterdam, Holland)
were performed in patient samples for which mutations were not
analysis were done according to manufacturer's recommendations.
2.9. Affymetrix GeneChip genome-wide gene expression experiments
Briefly, the high-quality total RNA samples were converted to
cDNA and then labeled with biotin during the synthesis of cRNA. The
latter was fractionated and then hybridized to the gene chips. The
experimental procedures and quality control procedures at each step
(before hybridization as well as post-hybridization) were strictly
followed according to manufacturer's instructions. Washing, staining,
and scanning were performed using Affymetrix's Fluidics Station 450
and GCS 3000 G7, respectively, according to the manufacturer's
instructions and guidelines. The Affymetrix GeneChip/GCOS software
(Affymetrix) was used to calculate the raw expression value of each
by the values of the 3'–5' ratios for actin and glyceraldehyde-3-
phosphate dehydrogenase (GAPDH). The dChip  outlier detection
algorithmalsoused toidentifyoutlierarrays.Allsamples/chips passed
the above-mentioned quality controls.
2.10. Microarray analysis
Following previously described protocols, the transcriptional
profiles of twelve samples from RTT-like (n=2) with no MECP2
mutation (denoted as “MECP2−”), classic RTT (n=3) with MECP2
mutation (denoted as “MECP2+”) and age and sex-matched normal
controls (n=7) isolated from whole blood were probed using
Affymetrix's GeneChip® Human Genome U133 Plus 2.0 Arrays
representing over 47,000 transcripts and variants using more than
54,000 probe sets. The open source R/Bioconductor packages 
were used for processing and analysis of microarray data. The data
were normalized by the GC Robust Multi-array Average (GC-RMA)
algorithm [36,37]. One-way analysis of variance (ANOVA) was
performed to identify genes varying significantly across RTT groups
and normal controls with adjusting the probability (p) values for
multiple comparisons by false discovery rate (FDR) according to
Benjamini–Hochberg procedure . Significantly modulated genes
were defined as those with absolute fold change (FC)N1.5 and the
false discovery rate (FDR) of less than 5%. The hierarchical clustering
using Pearson's correlation with average linkage clustering was
performed by Multi Experiment Viewer (MeV4.0) [39,40]. Functional
annotation and biological term enrichment analysis were performed
using DAVID Bioinformatics Resources , Expression Analysis
Systematic Explorer (EASE)  and Ingenuity Pathways Analysis
(IPA) 6.3 (Ingenuity Systems, Mountain View, CA). A right-tailed
Fisher's exact test was used to calculate a p-value determining the
probability that the biological function (or pathway) assigned to that
data set is explained by chance alone. Statistical analyses were
performed with the MATLAB software packages (Mathworks, Natick,
MA, USA), and PARTEK Genomics Suite (Partek Inc., St. Lois, MO, USA).
3.1. Mutation analysis
We screened MECP2, NTNG1, CDKL5 and MBD2 for any putative
pathogenic changes in our patients. A subset of classic RTT patients
were found to be positive only for the MECP2 mutation and three of
these MECP2positivepatients werechosenfor furtherstudies forgene
expression profiling; however we could not identify any mutation in
RTT-like patients. Moreover, we sequenced FOXG1 to identify any
putative mutations, as FOXG1 has been reported to be involved in an
early onset variant of RTT. However, we could not find any mutation
3.2. Copy number analysis using MLPA, oligo aCGH and SNP based
mapping assay and X-inactivation experiment
We first employed commercially available MLPA assays to identify
any gross changes in MECP2, CDKL5, NTNG1, and ARX regions in all
patients. There were no detectable gross changes in these genes. Next,
high density SNP based Affymetrix Mapping Assays were utilized for
familial segregation analysis in the RTT-like family. We identified
chromosomal imbalances in the X-chromosome (duplication) com-
prising genes, VCX3B, STS, VCX, PNPLA4, GAGE1, PAGE1, PAGE4, BRWD3,
NSBP1 (HMGN5), SH3BGR, in both patients as well as the mother, and
CLCN5 (possibly AKAP4 and CCNB3) that is specific to the patients only
(Table 1, Fig. 1C). We also screened autosomal chromosomes related
pathogenic CNVs and did not find any likely candidate for further
analysis. We evaluated the region on X-chromosome comprising VCX
genes and PNPLA4 due to its involvement in developmental delay,
speech developmental and autistic behaviour, ichthyosis and mental
D. Colak et al. / Genomics 97 (2011) 19–28
retardation in other studies [43–45]. Since the mother was asymp-
tomatic, we performed an X-inactivation assay on the mother's DNA
using previously established methods . A non-random skewness
was not detected in the mother (data not shown). To exclude
the possibility of being a benign CNV, we screened 100 ethnically
matched healthy controls. Besides the evidence found in the Database
of Genomic Variants , we found two control samples that had such
a duplication for the regions comprising VCX genes and PNLP4 genes.
However, the breakpoint and segregation analysis indicated that the
distal regionof the duplicationcomprisingCLCN5 (and possibly AKAP4
and CCNB3) unique to both affected ones, not shared among the
parents and the healthy sister (Fig. 1C), and could not be found in
unrelated ethnically matched 100 nondiseased controls as well as
and hence likely to be novel. The estimated odds ratio of this
duplication in the diseased individuals relative to the nondiseased is
OR=1005 [95% CI 16.4–6.1×104] (p-valueb0.001).
3.3. Genome-wide gene expression changes associated with RTT subtypes
We analyzed whole-genome mRNA expression profiling of 12
samples from classic RTT and RTT-like patients, and age–sex-matched
healthy controls isolated from whole blood probed using Affymetrix's
GeneChip® Human GenomeU133 Plus 2.0 Arrays which includes over
47,000 transcripts and variants using more than 54,000 probe sets.
This technology is well established and reliable method to assess the
global gene expression profiling . To find differentially expressed
genes (DEG) across three subjects groups, we performed one-way
analysis of variance (ANOVA). An unsupervised principle components
analysis (PCA), which contained about 90% of the variance in the data
matrix, clearly distinguished individuals as classic RTT, RTT-like and
normal controls (Fig. 2A), hence supporting the conclusion that gene
expression profiles robustly reflected the clinical diagnosis. Compar-
ing RTT-like group with the normal controls, we found statistically
significant differential expression of 1910 genes; of which 398 sig-
nificantly down-regulated, 1512 significantly up-regulated, whose
expression varied at least 1.5-fold and were statistically significant at
a false discovery rate of b5% between patients and normal controls.
Additionally, comparison of classic RTT with the normal controls
revealed significant modulation of 2369 genes (1467 down-regulated,
902 up-regulated). Comparison of gene lists with the Venn diagram
approach revealed a significant overlap between classic RTT and RTT-
like patients compared to normal controls (Supplementary Table 2,
Fig. 2B). The significance of overlaps is calculated using hypergeo-
hierarchical clustering of commonly dysregulated genes in both RTT-
Pearson's correlation with average linkage clustering (Fig. 2C). The
hierarchical clustering revealed clear pattern of genes deregulation
defining two main transcriptome clusters, one was composed of RTT
(that is also subclustered as RTT-like or classic RTT) patients, and
another composed of normal controls (Fig. 2C).
3.4. Functional pathway and gene network analysis of dysregulated
genes in RTT subtypes
The gene ontology (GO) and functional analysis of differentially
expressed genes (up/down-regulated) in RTT-like vs. Control, classic
RTT vs. Control, and DEG common to both comparison (up- or down-
regulated concordantly in both subtypes, whose heatmap shown in
Fig. 2C) were performed using the Ingenuity knowledge base and
using DAVID Bioinformatics Resources . The biological functions
significantly dysregulated genes for RTT-like patients showed great
similarity to the classic RTT patients (Fig. 3A). Indeed, the DEG
common to both subtypes were enriched with functional categories
including cellular development, immune cell trafficking, nervous
system development and function, cell death, cellular movement,
and cellular growth and proliferation. The shared significantly altered
canonical pathways included glucocorticoid receptor signaling, IL-4
signaling and NF-κB pathways. However, oxidative phosphorylation,
mitochondrial dysfunction, p53 signaling, docosahexaenoic acid
(DHA) signaling seem to be altered uniquely in RTT-like patients
(Fig. 3B). The diseases and disorders significantly associated with the
dysregulated genes include neurological disease, genetic disorder,
skeletal and muscular disorders, inflammatory response and connec-
tive tissue disorders (all p-valuesb0.01, Supplementary Table 3). To
of RTT are interacting with genes in various pathways, the DEG
common to both subtypes were mapped to the gene networks using
the Ingenuity knowledge base. The network analysis revealed
potential critical regulatory role of IL1, IL1R1, TGf-β, Interferon-alpha
and beta, and NF-κB in pathophysiology of both classic RTT and RTT-
like syndromes (Figs. 3C and D). It appears that several altered
pathways, processes and genes described in the present study are also
implicated in various central nervous system disorders [49,50].
3.5. mtDNA sequencing
Since functional pathway and gene network analysis revealed
perturbed mitochondria related pathways; we screened complete
mtDNA genome for putative mutations in our patients. However, we
could not identify any pathogenic changes in the genome. Therefore,
we exclude the possibility of involvement of mitochondrial mutations
in our patients.
3.6. Comparison of RTT differentially expressed genes with
multi-disorder autism gene set
Recently, Wall et al.  created a phylogeny that grouped autism
together with 13 related disorders, including mental retardation,
ataxia, Rett, Fragile X, microcephaly and seizure disorder, and called
them as “autism sibling disorders”. Using OMIM and GeneCards, the
authors identified 66 genes that have been linked to autism as well as
at least one other autism sibling disorder, and called multi-disorder
List of CNVs identified at X chromosome.
CN regionGene Start positionEnd position Genomic sizeCytoband startKnown CNVCN statea
Loss/gain Family members
CLCN5, AKAP4, CCNB3
BRWD3?, NSBP1, SH3BGR
Patients1 and 2
Patients1 and 2
Mother and Patient1
aCopy number (CN) state of 2 refers to no change or wild type and 3 indicates single copy increase (duplication).
D. Colak et al. / Genomics 97 (2011) 19–28
autism gene set (MDAG) . To evaluate our data, we compared the
differentially regulated genes in classic RTT and RTT-like patients with
the MDAG gene set. The MDAG gene set showed significant number of
genes in common with our analysis results. Indeed, we found that 61%
and 33% of MDAG genes were significantly dysregulated in classic RTT
and RTT-like, respectively (Supplementary Table 4).
The present study sought to identify common regulatory mecha-
nisms and signaling pathways that may cause the “common RTT”
phenotype” in classic RTT and RTT-like patients using genomic and
transcriptomic approaches. We investigated DNA copy number altera-
tions using 500 K SNP mapping array technology and identified a novel
CNV likely to be associated with the RTT-like syndrome. The
transcriptomic analysis using whole blood samples identified disease
subtype-specific genes aswell asgenes commonlydysregulatedinboth
subtypes; hence our results provide initial evidence indicating that
whole blood gene expression may harbor valuable information for
Copy number variations (CNV) are DNA segments with gains or
losses in copy number longer than 1 kb compared to a reference
in human disease [52,53]. We performed genome-wide copy number
variation (CNV) analysis using high density SNP mapping assays, and
identified several known CNVs as well as a novel duplication. The
breakpoint and segregation analysis indicated that the distal region of
the duplication comprising CLCN5 present only in patients and not
found in parents, healthy sibling, and unrelated ethically matched 100
nondiseased controls (p-value=1.9×10−4). This gene is responsible
for chloride transport and its mutations have been associated with
DENT’ s disease, an X-linked renal tubular disorder .
The use of whole blood as a surrogate tissue to study potential
changes in brain gene expression that potentially underlie neurological
Fig. 2. (A) The three dominant PCA components that contained about 90% of the variance in the data matrix clearly distinguished individuals as RTT-like (denoted as MECP2_N or
MECP2−), classic RTT (denoted as MECP2_P or MECP2+), and normal controls. (B) Venn diagram representing the common and subtype-specific genes that resulted from disease-
induced significantly differentially regulated genes (FDRb5% and FC N1.5) from two RTT subtypes. (C) Unsupervised two-dimensional hierarchical clustering of commonly
dysregulated genes in both subtypes compared to normal controls was performed using Pearson's correlation with average linkage clustering. The hierarchical clustering revealed
clear pattern of genes deregulation defining two main transcriptome clusters, one was composed RTT patients (that is also subclustered as RTT-like (MECP2_N) or classic RTT
(MECP2_P)), and another composed of normal controls.
D. Colak et al. / Genomics 97 (2011) 19–28
disorders has been exemplified in a number of studies. Indeed, gene
expression profiles of different brain regions have been shown to have
significant similarity to whole blood . The previous studies have
found that cells derived from peripheral blood could be used to assess
neurological disease-associated gene signatures [23,26,55,56]. In our
study, the gene expression profiling clearly distinguished individuals as
conclusion that gene expression profiles robustly reflected the clinical
diagnosis, and exploited the usefulness of using whole blood, an easily
accessible tissue, in identifying etiological subtypes of RTT.
The functional pathway and network analysis revealed that primary
immunodeficiency signaling, interleukin-4 (IL-4) and NF-κB signaling
pathways were significantly altered in both RTT-like and classic RTT
patients (Fig. 3). NF-κB's involvement in the nervous system was
comprehensively detailed in a previous report . As a pathway and a
complex system with various members, NF-κB plays critical role in
nervous system development and function, particularly in synaptic
transmission, plasticity, cognition and behavior . Indeed, recent
recessive mental retardation . In this study, the functional analysis
subtypes of RTT syndrome were significantly associated with immune,
inflammatory response and nervous system development and function
(p-valueb0.001), that is consistent with the previous studies of
neurological disorders [23,55,59]. Indeed, many genes that were once
thought to encode proteins relevant only to the immune system-
including cytokines, chemokines, major histocompatibility complex
(MHC) are now known to have major functions in the central nervous
system (CNS) at all stages of development[60,61]. Numerous cytokines
with clearly defined functions in the immune system, including IL-1β
regulation of cognitive function . Most recently, the immune
malfunctionis suggestedasa potentially contributory or evencausative
factor in the etiology of neurodevelopmental, cognitive, and psychiatric
Bioinformatics and gene ontological analyses revealed that the
differentially expressed genes shared by both subtypes involved in
and FDFT1). The initial step in neuronal connections is outgrowth of
axons which require calcineurine/NFAT signaling . The Ca2+/
calcineurine-NFAT mediated signaling pathways regulate diverse bio-
logical functions by either stimulating or inhibiting them . Calci-
Fig. 3. (A) Functional and (B) canonical pathway analysis of DEG (up- or down-regulated) in classic RTT and RTT-like patients. X-axis indicates the significance (−log p-value) of the
functional/pathway association that is dependent on the number of genes in a class as well as biologic relevance. Dark bars represent RTT-like, and light bars represent classic RTT.
The threshold line represents a p-value of 0.05. (C) Gene network analysis of DEG commonly dysregulated in both RTT subtypes. Top two scoring gene interaction networks (with
highest relevance scores) were shown. Nodes represent genes, with their shape representing the functional class of the gene product, and edges indicate biological relationship
between the nodes (see legend). Green (red) indicates down- (up-) regulated, in RTT compared to controls. The color intensity is correlated with fold change. DEG, differentially
D. Colak et al. / Genomics 97 (2011) 19–28
is an absolute requirement for normal neurotransmission. Neuregulins
initiate an increase in cytosolic Ca2+ which in turn activates calcineurin
and in downstream the NFATC3 and NFATC4. Hence, the deficient NFAT
complex activity is primarily responsible for the abnormal neuronal
connections and disruption of synaptic proliferation .
The calcium channel voltage dependent alpha2/subunit 2 (CAC-
NA2D2) encodes the alpha-2/delta subunit of a protein in the voltage-
should be highly relevant to the phenotype. Indeed, the mouse mutant
ducky that represent a model for absence epilepsy characterized by
spike-wave seizures and cerebellar ataxia have mutations in Cacna2d2,
which results in abnormalities in their Purkinje cell dendritic trees .
Ermin (ERM, also known as ERMN) is a protein homologous to Ezrin,
Radixin and Moesin. It is expressed in fetal and adult brains and
particularly in oligodendrocytes. During embryogenesis, its expression
parallels the period when oligodendrocytes are actively myelinating.
ERMN is an inducer of various cellular processes among which is the
reorganization of the cytoskeleton arrangement therefore causing
multiple changes in cell morphology in neural and other cells . It
may be hypothesized that in this manner it participates in the
neuropathology observed in the brain of RTT patients.
Another gene that we found significantly altered in both subtypes
of RTT is Farnesyldiphosphate farnesyl transferase (FDFT1) that
encodes the first specific enzyme in cholesterol biosynthesis.
Cholesterol is required for the development of a normal brain since
it modifies the hedgehog signaling proteins in development . It is
noteworthy that deficiency of 7-α-dhyro-cholesterol reductase, the
terminal enzyme in cholesterol biosynthesis, is the genetic cause of
Smith–Lemli–Opitz (SLO) syndrome, which is an autosomal recessive
malformation and mental retardation syndrome . The study of
Sikoraet al. indicatedthatmostchildrenwithSLOsyndrome have
some variant of autism and suggested a link between cholesterol
metabolism and autism. Also maternal apo E genotype influences the
efficiency of cholesterol transport to the fetus thus modulating
embryonic development and malformations . These observations
may alsobe relevantto theusual mildmalformationsobserved among
The network analysis indicated potentially important role of genes
involved in nervous system development and function, neuronal
morphogenesis (PRDX4, IL1R1, TYMP, FPR1 and B3GNT5). Peroxire-
doxin 4 (PRDX4) encodes for an antioxidant enzyme. Antioxidants
govern intracellular reduction-oxidation (redox) status, which plays a
critical role in activation of NF-κB transcription factor . As detailed
previously, NF-κB plays critical role in nervous system development
and function, synaptic transmission, cognition and behavior . The
protein encoded by Interleukin 1 ReceptorType 1 (IL1R1) is a cytokine
receptor that belongs to the interleukin 1 receptor family. It is an
important mediator involved in many cytokine induced immune and
inflammatory responses. IL1R1 is a hypothalamic receptor which
activates pathways that suppress bone formation . The overrep-
resentationofthisgeneactivityin bothtypesof RTTmightbelinkedto
the well-established metacarpal and metatarsal malformations in the
The dysregulated genes shared by both subtypes (Supplementary
Table 2) appears to play a major role in defining the RTT phenotype,
regardless of MECP2 mutation findings. The involvement of genes
related to immune and nervous systems, coupled with dysregulation of
neuronal functions appear to be of significance to the pathobiology of
RTT. Our gene network analysis revealed potential critical regulatory
role of IL1, IL1R1, TGf-β, Interferon-α and β, and NF-κB in pathobiology
of both typical or classic RTT and RTT-like syndromes; some of which
Tables 3 and 4) [49–51,59]. To validate our results, we compared the
dysregulated genes in our RTT patients with the multi-disorder autism
gene (MDAG) set whichincludes genes that have been linked toautism
as well as at least one other “autism sibling disorder” compiled from 13
autism related disorders  and found significant number of genes in
common (Supplementary Table 4). Moreover, we confirmed the high
expression of eight randomly selected genes by using realtime RT-PCR
from the blood of RTT patients, adding to the validity of the present
be potentially useful as indicators of genes or metabolic/signaling
pathways that may contribute to the autistic phenotype.
In conclusion, to our knowledge, this is the first study reporting a
possible involvement of a novel CNV inclusive of CLCN5 (and possibly
AKAP4 and CCNB3) in RTT-like syndrome; however, the link between
this CNV and its pathogenic affect on RTT-like phenotype needs to be
further studied. Moreover, this study reports the first time, the
common regulatory mechanisms and signaling pathways that may
cause the “common RTT” phenotype in RTT-like and classic RTT
patients that may be relevant to the pathobiology of the RTT common
clinical phenotype. Furthermore, our results provide evidence that
whole blood gene expression is likely to be useful for identifying
etiological subsets of RTT and exploring its pathophysiology.
Supplementary materials related to this article can be found online
Authors wish to thank the patients and family members for their
participation in this study and extend our appreciation to KFSHRC for
financial support and KFSHRC research advisory council for their kind
approval of this project. We also wish to extend our special thanks to
Mohamed M Shoukri for help and discussions. Authors also would like
to acknowledge efforts of late Dr. Mehmet S. Inan for this study and
would like to dedicate this work to his memory.
 A. Rett, On a unusual brain atrophy syndrome in hyperammonemia in childhood,
Wien. Med. Wochenschr. 116 (1966) 723–726.
 B. Hagberg, J. Aicardi, K. Dias, O. Ramos, A progressive syndrome of autism,
dementia, ataxia, and loss of purposeful hand use in girls: Rett's syndrome: report
of 35 cases, Ann. Neurol. 14 (1983) 471–479.
 H. Leonard, M. Thomson, E. Glasson, S. Fyfe, S. Leonard, C. Ellaway, J.
Christodoulou, C. Bower, Metacarpophalangeal pattern profile and bone age in
Rett syndrome: further radiological clues to the diagnosis, Am. J. Med. Genet. 83
 H. Leonard, M. Thomson, C. Bower, S. Fyfe, J. Constantinou, Skeletal abnormalities
in Rett syndrome: increasing evidence for dysmorphogenetic defects, Am. J. Med.
Genet. 58 (1995) 282–285.
 R.E. Amir, I.B. Van den Veyver, M. Wan, C.Q. Tran, U. Francke, H.Y. Zoghbi, Rett
syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-
binding protein 2, Nat. Genet. 23 (1999) 185–188.
 G.J. Pelka, C.M. Watson, J. Christodoulou, P.P. Tam, Distinct expression profiles of
Mecp2 transcripts with different lengths of 3'UTR in the brain and visceral organs
during mouse development, Genomics 85 (2005) 441–452.
 R.E. Amir, P. Fang, Z. Yu, D.G. Glaze, A.K. Percy, H.Y. Zoghbi, B.B. Roa, I.B. Van den
Veyver, Mutations in exon 1 of MECP2 are a rare cause of Rett syndrome, J. Med.
Genet. 42 (2005) e15.
 I.B. Van den Veyver, H.Y. Zoghbi, Genetic basis of Rett syndrome, Ment. Retard.
Dev. Disabil. Res. Rev. 8 (2002) 82–86.
 K. Hoffbuhr, J.M. Devaney, B. LaFleur, N. Sirianni, C. Scacheri, J. Giron, J. Schuette, J.
Innis, M. Marino, M. Philippart, V. Narayanan, R. Umansky, D. Kronn, E.P. Hoffman,
S. Naidu, MeCP2 mutations in children with and without the phenotype of Rett
syndrome, Neurology 56 (2001) 1486–1495.
 A.M. Raizis, M. Saleem, R. MacKay, P.M. George, Spectrum of MECP2 mutations in
New Zealand Rett syndrome patients, NZ Med. J. 122 (2009) 21–28.
 E. Scala, F. Ariani, F. Mari, R. Caselli, C. Pescucci, I. Longo, I. Meloni, D. Giachino, M.
Bruttini, G. Hayek, M. Zappella, A. Renieri, CDKL5/STK9 is mutated in Rett
syndrome variant with infantile spasms, J. Med. Genet. 42 (2005) 103–107.
 F. Ariani, G. Hayek, D. Rondinella, R. Artuso, M.A. Mencarelli, A. Spanhol-Rosseto,
M. Pollazzon, S. Buoni, O. Spiga, S. Ricciardi, I. Meloni, I. Longo, F. Mari, V. Broccoli,
M. Zappella, A. Renieri, FOXG1 is responsible for the congenital variant of Rett
syndrome, Am. J. Hum. Genet. 83 (2008) 89–93.
D. Colak et al. / Genomics 97 (2011) 19–28
 I. Borg, K. Freude, S. Kubart, K. Hoffmann, C. Menzel, F. Laccone, H. Firth, M.A.
Ferguson-Smith, N. Tommerup, H.H. Ropers, D. Sargan, V.M. Kalscheuer,
Disruption of Netrin G1 by a balanced chromosome translocation in a girl with
Rett syndrome, Eur. J. Hum. Genet. 13 (2005) 921–927.
 S. Akbarian, Y. Jiang, G. Laforet, The molecular pathology of Rett syndrome:
synopsis and update, Neuromolecular Med. 8 (2006) 485–494.
 I.J. Delgado, D.S. Kim, K.N. Thatcher, J.M. LaSalle, I.B. Van den Veyver, Expression
profiling of clonal lymphocyte cell cultures from Rett syndrome patients, BMC
Med. Genet. 7 (2006) 61.
 J. Traynor, P. Agarwal, L. Lazzeroni, U. Francke, Gene expression patterns vary in
clonal cell cultures from Rett syndrome females with eight different MECP2
mutations, BMC Med. Genet. 3 (2002) 12.
 M. Tudor, S. Akbarian, R.Z. Chen, R. Jaenisch, Transcriptional profiling of a mouse
model for Rett syndrome reveals subtle transcriptional changes in the brain, Proc.
Natl Acad. Sci. USA 99 (2002) 15536–15541.
 C. Jordan, H.H. Li, H.C. Kwan, U. Francke, Cerebellar gene expression profiles of
mouse models for Rett syndrome reveal novel MeCP2 targets, BMC Med. Genet.
8 (2007) 36.
 C. Colantuoni, O.H. Jeon, K. Hyder, A. Chenchik, A.H. Khimani, V. Narayanan, E.P.
Hoffman, W.E. Kaufmann, S. Naidu, J. Pevsner, Gene expression profiling in
postmortem Rett Syndrome brain: differential gene expression and patient
classification, Neurobiol. Dis. 8 (2001) 847–865.
 S. Ben-Shachar, M. Chahrour, C. Thaller, C.A. Shaw, H.Y. Zoghbi, Mouse models of
MeCP2 disorders share gene expression changes in the cerebellum and
hypothalamus, Hum. Mol. Genet. 18 (2009) 2431–2442.
 P. Carmeliet, M. Tessier-Lavigne, Common mechanisms of nerve and blood vessel
wiring, Nature 436 (2005) 193–200.
 A. Eichmann, T. Makinen, K. Alitalo, Neural guidance molecules regulate vascular
remodeling and vessel navigation, Genes Dev. 19 (2005) 1013.
 S.M. Kurian, H. Le-Niculescu, S.D. Patel, D. Bertram, J. Davis, C. Dike, N. Yehyawi, P.
Geyer, M.T. Tsuang, N.J. Schork, D.R. Salomon, A.B. Niculescu, Identification of blood
biomarkers for psychosis using convergent functional genomics, Mol. Psychiatry.
Advance online publication, 24 November (2009), doi:10.1038/mp.2009.117.
 C.G. Saris, S. Horvath, P.W. van Vught, M.A. van Es, H.M. Blauw, T.F. Fuller, P.
Langfelder, J. DeYoung, J.H. Wokke, J.H. Veldink, L.H. van den Berg, R.A. Ophoff,
Weighted gene co-expression network analysis of the peripheral blood from
amyotrophic lateral sclerosis patients, BMC Genomics 10 (2009) 405.
 P.F. Sullivan, C. Fan, C.M. Perou, Evaluating the comparability of gene expression in
blood and brain, Am. J. Med. Genet. B Neuropsychiatr. Genet. 141B (2006)
 Y. Tang, D.L. Gilbert, T.A. Glauser, A.D. Hershey, F.R. Sharp, Blood gene expression
profiling of neurologic diseases: a pilot microarray study, Arch. Neurol. 62 (2005)
 B. Hagberg, Clinical manifestations and stages of Rett syndrome, Ment. Retard.
Dev. Disabil. Res. Rev. 8 (2002) 61–65.
 N. Kaya, M. Al-Owain, A. Albakheet, D. Colak, A. Al-Odaib, F. Imtiaz, S. Coskun, M.
Al-Sayed, Z. Al-Hassnan, H. Al-Zaidan, B. Meyer, P. Ozand, Array comparative
genomic hybridization (aCGH) reveals the largest novel deletion in PCCA found in
a Saudi family with propionic acidemia, Eur. J. Med. Genet. 51 (2008) 558–565.
 M.J. Rieder, S.L. Taylor, V.O. Tobe, D.A. Nickerson, Automating the identification of
DNA variations using quality-based fluorescence re-sequencing: analysis of the
human mitochondrial genome, Nucleic Acids Res. 26 (1998) 967–973.
 M.C. Brandon, M.T. Lott, K.C. Nguyen, S. Spolim, S.B. Navathe, P. Baldi, D.C. Wallace,
MITOMAP: a human mitochondrial genome database—2004 update, Nucleic Acids
Res. 33 (2005) D611–613.
 S.D. Walter, R.J. Cook, A comparison of several point estimators of the odds ratio in
a single 2×2 contingency table, Biometrics 47 (1991) 795–811.
 R.C. Allen, H.Y. Zoghbi, A.B. Moseley, H.M. Rosenblatt, J.W. Belmont, Methylation
of HpaII and HhaI sites near the polymorphic CAG repeat in the human androgen-
receptor gene correlates with X chromosome inactivation, Am. J. Hum. Genet. 51
 K.J. Livak, T.D. Schmittgen, Analysis of relative gene expression data using real-
time quantitative PCR and the 2(-Delta Delta C(T)) Method, Methods 25 (2001)
402–408 (San Diego, Calif).
 C. Li, W.H. Wong, Model-based analysis of oligonucleotide arrays: expression index
computation and outlier detection, Proc. Natl Acad. Sci. USA 98 (2001) 31–36.
 R.C. Gentleman, V.J. Carey, D.M. Bates, B. Bolstad, M. Dettling, S. Dudoit, B. Ellis, L.
Gautier, Y. Ge, J. Gentry, K. Hornik, T. Hothorn, W. Huber, S. Iacus, R. Irizarry, F.
Yang, J. Zhang, Bioconductor: open software development for computational
biology and bioinformatics, Genome Biol. 5 (2004) R80.
 Z. Wu, R.A. Irizarry, Preprocessing of oligonucleotide array data, Nat. Biotechnol.
22 (2004) 656–658 (author reply 658).
 Z. Wu, R.A. Irizarry, Stochastic models inspired by hybridization theory for short
oligonucleotide arrays, J. Comput. Biol. 12 (2005) 882–893.
 Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: a practical and
powerful approach to multiple testing, J. R. Stat. Soc. Ser. B 57 (1995) 289–300.
 A.I. Saeed, N.K. Bhagabati, J.C. Braisted, W. Liang, V. Sharov, E.A. Howe, J. Li, M.
Thiagarajan, J.A. White, J. Quackenbush, TM4 microarray software suite, Meth.
Enzymol. 411 (2006) 134–193.
 A.I. Saeed, V. Sharov, J. White, J. Li, W. Liang, N. Bhagabati, J. Braisted, M. Klapa, T.
Currier, M. Thiagarajan, A. Sturn, M. Snuffin, A. Rezantsev, D. Popov, A. Ryltsov, E.
Kostukovich, I. Borisovsky, Z. Liu, A. Vinsavich, V. Trush, J. Quackenbush, TM4: a
free, open-source system for microarray data management and analysis,
Biotechniques 34 (2003) 374–378.
 G. Dennis Jr., B.T. Sherman, D.A. Hosack, J. Yang, W. Gao, H.C. Lane, R.A. Lempicki,
DAVID: database for annotation, visualization, and integrated discovery, Genome
Biol. 4 (2003) P3.
 D.A. Hosack, G. Dennis Jr., B.T. Sherman, H.C. Lane, R.A. Lempicki, Identifying
biological themes within lists of genes with EASE, Genome Biol. 4 (2003) R70.
 S. Chocholska, E. Rossier, G. Barbi, H. Kehrer-Sawatzki, Molecular cytogenetic
analysis of a familial interstitial deletion Xp22.2–22.3 with a highly variable
phenotype in female carriers, Am. J. Med. Genet. A 140 (2006) 604–610.
 M. Fukami, S. Kirsch, S. Schiller, A. Richter, V. Benes, B. Franco, K. Muroya, E. Rao, S.
Merker, B. Niesler, A. Ballabio, W. Ansorge, T. Ogata, G.A. Rappold, A member of a
gene family on Xp22.3, VCX-A, is deleted in patients with X-linked nonspecific
mental retardation, Am. J. Hum. Genet. 67 (2000) 563–573.
 N. Hosomi, N. Oiso, K. Fukai, K. Hanada, H. Fujita, M. Ishii, Deletion of distal
promoter of VCXA in a patient with X-linked ichthyosis associated with
borderline mental retardation, J. Dermatol. Sci. 45 (2007) 31–36.
 J.Zhang,L. Feuk, G.E. Duggan, R. Khaja, S.W. Scherer, Development of bioinformatics
human genome, Cytogenet. Genome Res. 115 (2006) 205–214.
 N.H. Lee, A.I. Saeed, Microarrays: an overview, Meth. Mol. Biol. 353 (2007)
265–300 (Clifton, N.J.).
 N.B. Ivanova, J.T. Dimos, C. Schaniel, J.A. Hackney, K.A. Moore, I.R. Lemischka, A
stem cell molecular signature, Science 298 (2002) 601–604 (New York, N.Y.).
 V. Tseveleki, R. Rubio, S.S. Vamvakas, J. White, E. Taoufik, E. Petit, J. Quackenbush,
L. Probert, Comparative gene expression analysis in mouse models for multiple
sclerosis, Alzheimer's disease and stroke for identifying commonly regulated and
disease-specific gene changes, Genomics 2 (2010) 82–91.
 I. Tesseur, K. Zou, L. Esposito, F. Bard, E. Berber, J.V. Can, A.H. Lin, L. Crews, P.
Tremblay, P. Mathews, L. Mucke, E. Masliah, T. Wyss-Coray, Deficiency in neuronal
TGF-beta signaling promotes neurodegeneration and Alzheimer's pathology, J.
Clin. Investig. 116 (2006) 3060–3069.
 D.P. Wall, F.J. Esteban, T.F. Deluca, M. Huyck, T. Monaghan, N.Velez de Mendizabal,
J. Goni, I.S. Kohane, Comparative analysis of neurological disorders focuses
genome-wide search for autism genes, Genomics 93 (2009) 120–129.
 E.Gonzalez, H. Kulkarni, H. Bolivar, A. Mangano, R. Sanchez, G. Catano, R.J.Nibbs, B.I.
Freedman, M.P. Quinones, M.J. Bamshad, K.K. Murthy, B.H. Rovin, W. Bradley, R.A.
S.K.Ahuja,The influence of CCL3L1 gene-containingsegmentalduplications onHIV-
1/AIDS susceptibility, Science 307 (2005) 1434–1440 (New York, N.Y.).
 J. Sebat, B. Lakshmi, D. Malhotra, J. Troge, C. Lese-Martin, T. Walsh, B. Yamrom, S.
Yoon, A.Krasnitz, J. Kendall,A. Leotta, D. Pai, R. Zhang, Y.H. Lee, J. Hicks, S.J. Spence,
A.T. Lee, K. Puura, T. Lehtimaki, D. Ledbetter, P.K. Gregersen, J. Bregman, J.S.
Sutcliffe, V. Jobanputra, W. Chung, D. Warburton, M.C. King, D. Skuse, D.H.
Geschwind, T.C. Gilliam, K. Ye, M. Wigler, Strong association of de novo copy
number mutations with autism, Science 316 (2007) 445–449 (New York, N.Y.).
 F. Claverie-Martin, H. Gonzalez-Acosta, C. Flores, M. Anton-Gamero, V. Garcia-
Nieto, De novo insertion of an Alu sequence in the coding region of the CLCN5
gene results in Dent's disease, Hum. Genet. 113 (2003) 480–485.
 V.W. Hu, B.C. Frank, S. Heine, N.H. Lee, J. Quackenbush, Gene expression profiling
of lymphoblastoid cell lines from monozygotic twins discordant in severity of
autism reveals differential regulation of neurologically relevant genes, BMC
Genomics 7 (2006) 118.
 A. Lonneborg, Biomarkers for Alzheimer disease in cerebrospinal fluid, urine, and
blood, Mol. Diagn. Ther. 12 (2008) 307–320.
 S. Memet, NF-kappaB functions in the nervous system: from development to
disease, Biochem. Pharmacol. 72 (2006) 1180–1195.
 O. Philippe, M. Rio, A. Carioux, J.M. Plaza, P. Guigue, F. Molinari, N. Boddaert, C. Bole-
Feysot,P.Nitschke,A.Smahi,A.Munnich,L.Colleaux,Combinationof linkage mapping
and microarray-expression analysis identifies NF-kappaB signaling defect as a cause of
autosomal-recessive mental retardation, Am. J. Hum. Genet. 85 (2009) 903–908.
 V.W. Hu, A. Nguyen, K.S. Kim, M.E. Steinberg, T. Sarachana, M.A. Scully, S.J. Soldin,
T. Luu, N.H. Lee, Gene expression profiling of lymphoblasts from autistic and
nonaffected sib pairs: altered pathways in neuronal development and steroid
biosynthesis, PLoS ONE 4 (2009) e5775.
 L.M. Boulanger, Immune proteins in brain development and synaptic plasticity,
Neuron 64 (2009) 93–109.
 L.M. Boulanger, C.J. Shatz, Immune signalling in neural development, synaptic
plasticity and disease, Nat. Rev. Neurosci. 5 (2004) 521–531.
 N.C. Derecki, A.N. Cardani, C.H. Yang, K.M. Quinnies, A. Crihfield, K.R. Lynch, J.
Kipnis, Regulation of learning and memory by meningeal immunity: a key role for
IL-4, J. Exp. Med. 207 (2010) 1067–1080.
brain diseases of immune malfunction? Mol. Psychiatry 15 (2010) 355–363.
 I.A. Graef, F. Wang, F. Charron, L. Chen, J. Neilson, M. Tessier-Lavigne, G.R. Crabtree,
Neurotrophins and netrins require calcineurin/NFAT signaling to stimulate
outgrowth of embryonic axons, Cell 113 (2003) 657–670.
 S.H. Im, A. Rao, Activation and deactivation of gene expression by Ca2+/
calcineurin-NFAT-mediated signaling, Mol. Cells 18 (2004) 1–9.
 S.C. Kao, H. Wu, J. Xie, C.P. Chang, J.A. Ranish, I.A. Graef, G.R. Crabtree, Calcineurin/
NFAT signaling is required for neuregulin-regulated Schwann cell differentiation,
Science 323 (2009) 651–654 (New York, N.Y.).
 M.V. Johnston, O.H. Jeon, J. Pevsner, M.E. Blue, S. Naidu, Neurobiology of Rett
syndrome: a genetic disorder of synapse development, Brain Dev. 23 (Suppl 1)
 J. Brodbeck, A. Davies, J.M. Courtney, A. Meir, N. Balaguero, C. Canti, F.J. Moss, K.M.
Page, W.S. Pratt, S.P. Hunt, J. Barclay, M. Rees, A.C. Dolphin, The ducky mutation in
Cacna2d2 results in altered Purkinje cell morphology and is associated with the
D. Colak et al. / Genomics 97 (2011) 19–28
expression of a truncated alpha 2 delta-2 protein with abnormal function, J. Biol. Download full-text
Chem. 277 (2002) 7684–7693.
 D. Brockschnieder, H. Sabanay, D. Riethmacher, E. Peles, Ermin, a myelinating
oligodendrocyte-specific protein that regulates cell morphology, J. Neurosci. 26
 J.A. Porter, K.E. Young, P.A. Beachy, Cholesterol modification of hedgehog signaling
proteins in animal development, Science 274 (1996) 255–259 (New York, N.Y.).
 D.M. Sikora, K. Pettit-Kekel, J. Penfield, L.S. Merkens, R.D. Steiner, The near
universal presence of autism spectrum disorders in children with Smith–Lemli–
Opitz syndrome, Am. J. Med. Genet. A 140 (2006) 1511–1518.
 M. Witsch-Baumgartner, M. Gruber, H.G. Kraft, M. Rossi, P. Clayton, M. Giros, D.
Haas, R.I. Kelley, M. Krajewska-Walasek, G. Utermann, Maternal apo E genotype is
a modifier of the Smith–Lemli–Opitz syndrome, J. Med. Genet. 41 (2004)
 D.Y. Jin, H.Z. Chae, S.G. Rhee, K.T. Jeang, Regulatory role for a novel human
thioredoxin peroxidase in NF-kappaB activation, J. Biol. Chem. 272 (1997)
 A. Bajayo, I. Goshen, S. Feldman, V. Csernus, K. Iverfeldt, E. Shohami, R. Yirmiya, I.
Bab, Central IL-1 receptor signaling regulates bone growth and mass, Proc. Natl
Acad. Sci. USA 102 (2005) 12956–12961.
D. Colak et al. / Genomics 97 (2011) 19–28