Gene Expression Profiling of Lymphoblasts from Autistic
and Nonaffected Sib Pairs: Altered Pathways in Neuronal
Development and Steroid Biosynthesis
Valerie W. Hu1*, AnhThu Nguyen1, Kyung Soon Kim1¤, Mara E. Steinberg1, Tewarit Sarachana1,
Michele A. Scully1, Steven J. Soldin2, Truong Luu3, Norman H. Lee3
1Department of Biochemistry and Molecular Biology, The George Washington University Medical Center, Washington, D. C., United States of America, 2Departments of
Medicine, Pharmacology, and Oncology, Georgetown University Medical Center, Washington, D. C., United States of America, 3Department of Pharmacology and
Physiology, The George Washington University Medical Center, Washington, D. C., United States of America
Despite the identification of numerous autism susceptibility genes, the pathobiology of autism remains unknown. The
present ‘‘case-control’’ study takes a global approach to understanding the molecular basis of autism spectrum disorders
based upon large-scale gene expression profiling. DNA microarray analyses were conducted on lymphoblastoid cell lines
from over 20 sib pairs in which one sibling had a diagnosis of autism and the other was not affected in order to identify
biochemical and signaling pathways which are differentially regulated in cells from autistic and nonautistic siblings.
Bioinformatics and gene ontological analyses of the data implicate genes which are involved in nervous system
development, inflammation, and cytoskeletal organization, in addition to genes which may be relevant to gastrointestinal or
other physiological symptoms often associated with autism. Moreover, the data further suggests that these processes may
be modulated by cholesterol/steroid metabolism, especially at the level of androgenic hormones. Elevation of male
hormones, in turn, has been suggested as a possible factor influencing susceptibility to autism, which affects ,4 times as
many males as females. Preliminary metabolic profiling of steroid hormones in lymphoblastoid cell lines from several pairs
of siblings reveals higher levels of testosterone in the autistic sibling, which is consistent with the increased expression of
two genes involved in the steroidogenesis pathway. Global gene expression profiling of cultured cells from ASD probands
thus serves as a window to underlying metabolic and signaling deficits that may be relevant to the pathobiology of autism.
Citation: Hu VW, Nguyen AT, Kim KS, Steinberg ME, Sarachana T, et al. (2009) Gene Expression Profiling of Lymphoblasts from Autistic and Nonaffected Sib Pairs:
Altered Pathways in Neuronal Development and Steroid Biosynthesis. PLoS ONE 4(6): e5775. doi:10.1371/journal.pone.0005775
Editor: Raphael Schiffmann, National Institutes of Health, United States of America
Received February 6, 2009; Accepted May 6, 2009; Published June 3, 2009
Copyright: ? 2009 Hu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by NIH R21 MH073393 (VWH) and, in part, by Autistic Speaks #2381 (VWH). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
¤ Current address: Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, United States of America
Autism spectrum disorders (ASD, or the ‘‘autisms’’) are a group of
pervasive neurodevelopmental disorders that are characterized by
delayed or abnormal development and use of language, poor
reciprocal social interactions, restricted interests and repetitive
behaviors . Although the etiology of ASD is not known, the core
symptomatology has focused much of autism research on the brain
[2–4]. At the same time, there is increasing evidence for the
involvement of other organ systems, especially the immune and
gastrointestinal systems, in individuals affected by ASD [5–10]. The
interactions of these systems with the nervous system are likely
mediated by biologic factors, such as hormones and cytokines that, in
turn, may be induced by environmental stressors [11–17]. Of
particular interest, Jyonouchi et al. demonstrated a relationship
between the immune and gastrointestinal symptoms in children with
ASD by showing activation of the innate immune response (rise in
TNF-a levels in response to lipopolysaccharide) in peripheral blood
mononuclear cells only in ASD children with positive gastrointestinal
symptoms . The connection between gastrointestinal symptoms
and ASD was strengthened by genetic studies which showed an
association between a single nucleotide polymorphism (SNP) in the
promoter of the MET gene (which is an oncogene involved in
cerebellar growth, immune function, and gastrointestinal repair) and
autism . The observed reduction of the MET protein in autistic
brain samples relative to matched control brain samples further
supported the involvement of MET in ASD . Recently, neural
inflammation and oxidative stress have been proposed as possible
contributing factors to the etiology of autism [20–23]. In particular,
Vargas et al.  demonstrated neuroglial activation and presence of
inflammatory cytokines in the brain of autistic patients while
Chauhan et al.  showed evidence for lipid peroxidation and
reduction of antioxidant proteins in autism. In exploring the
molecular bases of oxidative stress in ASD, James et al. studied the
metabolites involved in the methionine transmethylation and
transsulfuration pathways and found significant reduction in the
levels of methionine, S-adenosylmethionine, cysteine, and free
reduced glutathione which are reflective of oxidative stress, and
further demonstrated genetic variants in several of the genes within
these pathways in individuals with ASD . Taking an animal
PLoS ONE | www.plosone.org1 June 2009 | Volume 4 | Issue 6 | e5775
model approach to exploring possible metabolic contributions to the
pathogenesis of autistic-like behavior, MacFabe et al. investigated the
effects of propionic acid (PPA), a short chain fatty acid and an
important intermediate of cellular metabolism, on behavior,
electrographic activity, neuropathology, and oxidative stress in rats
exposed to intraventricular PPA . Interestingly, PPA is also a
fermentation by-product of a subpopulation of opportunistic enteric
bacteria (eg., clostridia, propionibacteria), which have been cited as a
putative risk factor for ASD . The results of these studies [23,26]
show that PPA not only induced behaviors characteristic of autism
(eg., repetitive dystonic behaviors, retropulsion, seizures, as well as
social avoidance), but also replicated the pathological findings
associated with ASD, such as neuroinflammation, reactive astro-
gliosis, and activated microglia, in addition to lipid and protein
oxidation and reduction of total glutathione in brain homogenates
which are collectively indicators of oxidative stress. These limited
examples demonstrate that, although the most noticeable impair-
ments in autism are those affecting higher order neurological
functions, there is increasing evidence, recently reviewed by Zecavati
and Spence , that many ‘‘neurometabolic disorders’’ are also
associated with the phenotype of ASD. Thus, the heterogeneity of
behavioral, functional, physiological, and metabolic manifestations of
ASD combined with the possible contribution of multiple organ
systems to the clinical complexity of autism suggests that a more
global, systems approach may be needed to elucidate the molecular
underpinnings of ASD in defined phenotypic subgroups of patients.
Genetic approaches to identify genes associated with
Although ASD is the most heritable of all the psychiatric
disorders based upon twin and family studies [28–31], no single
gene or consensus gene combination has been shown to be
causally linked to idiopathic autism. At present, genetic susceptibility
loci have been identified on virtually every chromosome by a
combination of whole genome scans, cytogenetics, and genetic
linkage/association analyses [32–35]. Although there is great
diversity in the genes identified as potential candidates, synapse
formation/function and axon guidance are emerging as principal
functional themes in ASD from recent studies. The respective
genes include glutamate receptors, GABA receptors, neuroligins,
neurexin 1, and SHANK3 which are involved in synapse
formation and function [36–41], and RELN, ROBO1/2, SLIT2,
ITGB1, PAK, and MET which are involved in axon guidance
[18,42–44]. Additional genes include those involved in develop-
ment, neuronal differentiation, and survival, such as WNT2,
HOXA1, and BCL2 [42,45,46]. Part of the difficulty in
conclusively identifying and confirming genes for autism is thought
to arise from the heterogeneity of phenotypes of ASD combined
with the relatively small sample sizes in the various studies. A
recently completed large-scale SNP association analysis involving
1,168 multiplex families revealed a single region on chromosome
11p12-p13 that exceeded the threshold for suggestive linkage
when data from all of the families was combined . It is
interesting that none of the previously identified candidate genes
were located in this region. On the other hand, this study and
another demonstrated for the first time that copy number variants
(some of which are de novo) are 10 times more frequent in the
autistic population than in the general population , suggesting
the contribution of epigenetic or environmental factors to ASD.
Genomic approaches to investigation of ASD
Genomic methods involve simultaneous, large-scale expression
analysis of thousands of genes on a cDNA (or oligonucleotide)
microarray slide [49,50]. The first DNA microarray study of
autism identified ,30 genes as differentially expressed in the
cerebellum from autopsy tissue of autistic and normal subjects, and
focused on the abnormal expression of the glutamate receptor as a
potential pathogenic mechanism . Several recent applications
of global gene expression analysis to autism have evaluated gene
expression in lymphoblastoid cell lines (LCL) and in whole blood
with the goal of identifying differentially expressed genes in a
peripherally-derived tissue which may serve as diagnostic bio-
markers for ASD, or surrogate markers for dysregulated metabolic
and signaling pathways in peripheral and central tissues, which
may provide clues to the pathophysiology of the disorder
[43,44,52–54]. Our previous study on LCL from monozygotic
twins discordant in severity of ASD revealed differentially
expressed genes that function in nervous system development as
well as genes that mapped in silico to autism susceptibility regions
on chromosomes that were previously identified by numerous
genetic analyses . A recent study by Geschwind and
colleagues, which compared the gene expression profiles of LCL
from individuals with known genetic causes of autism (Fragile X
and 15q11-q13 duplication), identified 68 commonly dysregulated
genes, two of which (JAKMIP1 and GPR155) were also confirmed
as differentially expressed in LCL from male sib pairs discordant
for idiopathic autism . These studies collectively demonstrate
the power of applying a genomic approach based on global gene
expression to identify genes that may be involved in common
pathways giving rise to ASD.
In this study, we postulate that differentially expressed genes in
autistic vs. normal nonautistic siblings are at least in part
responsible for the autism phenotype, as we had earlier shown
for monozygotic twins discordant in diagnosis of autism . We
therefore analyzed gene expression profiles of LCL derived from
21 sib pairs where one of the siblings is autistic and the other is not.
To reduce the phenotypic heterogeneity among the samples, we
selected cell lines from individuals who presented with severe
language impairment as reflected by scores on the Autism
Diagnostic Interview-Revised (ADIR) questionnaire, as described
in Materials and Methods. Results from gene expression analysis
of LCL from these individuals revealed alterations in genes
involved in cholesterol metabolism and steroid hormone biosyn-
thesis, as well as genes involved in neuronal processes and
development. A steroid profile of cell extracts using HPLC-tandem
mass spectrometry methods further confirmed elevations in
testosterone levels from a subset of the autistic and their respective
Differentially expressed genes between autistic probands
and sibling controls implicate steroid biosynthetic
The log2 ratios of relative gene expression from autistic and
nonautistic siblings were analyzed by one-class SAM using 70%
data filtering, which requires that at least 70% of the samples must
have non-zero expression ratios in order for a given gene to be
included in the statistical analysis. Significant differentially
expressed genes with a log2 ratio$6,0.3 are shown in
Table 1. This expression cutoff was selected on the basis of our
ability to confirm genes exhibiting this level of differential
expression by qRT-PCR analysis. Figure 1 shows the major
multigene interaction network constructed by Pathway Studio 5
software which comprises genes that were differentially expressed
between normal and autistic siblings. Interestingly, this network
includes cellular (apoptosis, differentiation)  and disease
processes such as inflammation [12,13] and epilepsy [55,56] that
Gene Expression in Autism
PLoS ONE | www.plosone.org2June 2009 | Volume 4 | Issue 6 | e5775
are often associated with ASD . Table 2 lists the top 3 (out of
69) high level functions that were identified by Ingenuity Pathways
Analysis as being significantly overrepresented by differentially
expressed genes in this dataset. SCARB1 and SRD5A1, which are
represented in the top 2 functions (endocrine system development
and function and small molecule biochemistry), implicate
involvement of the steroid hormone biosynthetic pathway. This
is further supported by Pathway Studio 5 analysis which shows
that steroid hormones as well as neurotransmitters are an integral
part of a network of common regulators and targets of this set of
differentially expressed genes (Table 3). Among the disorders that
are identified as common targets are inflammation, epilepsy,
diabetes mellitus, digestive disorders, and hyperandrogenemia, all
of which have been associated with ASD. Of particular relevance
to these pathway-implicated disorders are the findings of elevated
inflammatory cytokines in autopsy brain tissue from autistic
patients relative to controls as well as increases in IFN-gamma and
IL-1RA in whole blood of autistic children [12,13]. Of the genes in
the relational network shown in Fig. 1, SCN5A, a cardiac Na+-
gated sodium channel also expressed in the limbic brain, may be
associated with seizures . Epilepsy and/or epileptiform EEG
abnormalities are comorbid disorders that variably affect from 5 to
46% of the autistic population , and a population-based study
further suggests that the prevalence of ASD is higher in children
who experience seizures in the first year of life . Interestingly,
kindled limbic seizures as well as autistic-like motor and social
behaviors have been induced in a rat model by intraventricular
administration of PPA, a short-chain fatty acid [23,26], and this
animal model was further demonstrated to exhibit evidence of
neuroinflammation and oxidative stress in specific brain regions
. It is noteworthy that PPA is also the metabolic end product of
enteric bacteria , and that diabetes and digestive disorders,
which are among the targets implicated by our differentially
expressed genes, have also been associated with the autistic
population [5–7,61–63]. The suggested association of hyperan-
drogenemia is especially interesting as females with congenital
adrenal hyperplasia which results in elevated levels of testosterone
were shown to exhibit higher autistic-like social behavior , and
conversely, women affected by ASD as well as their mothers
exhibited more masculine physical and behavioral traits, sugges-
tive of higher androgen levels . These findings thus support the
argument that autism may in part be considered a systemic
encephalopathic condition involving immune, digestive and
metabolic/endocrine dysfunction, which may be exacerbated by
environmental triggers in genetically sensitive subpopulations.
Gene ontological analysis using Database for Annotation,
Visualization and Integrated Discovery (DAVID) software 
further demonstrated statistically significant enrichment of differ-
entially expressed genes involved in nervous system development
as well as organization and biogenesis of the actin cytoskeleton
Confirmation of differentially expressed genes related to
steroid metabolism, inflammation, and nervous system
development by qRT-PCR analysis
Quantitative RT-PCR (qRT-PCR) was used to confirm the
differential expression of genes represented among the top
biological functions (Table 2), including those involved in
cholesterol/steroid hormone metabolism and several that are
involved in development of the nervous system and inflammation
(Figure 2). Pathway Studio 5 was then used to identify the
common regulators as well as common targets of the six confirmed
genes in order to examine their possible roles in the context of
autism. It is noteworthy that cholesterol as well as several steroid
hormones, including testosterone, progesterone, and estradiol are
among the small molecule regulators of this network of genes
(Figure 3), suggesting the possibility of feedback regulation
Table 1. Differentially expressed genes between autistic and
control siblings (FDR=13.5%).
Genbank # Gene Symbol log2(ratio)*SEM
AA932364CCDC102B 0.31 0.07
AA412053 CD9 0.350.08
R28287 unknown 0.410.09
R32996 unknown0.33 0.08
H20826 unknown 0.310.07
R12679 unknown0.29 0.07
Gene Expression in Autism
PLoS ONE | www.plosone.org3 June 2009 | Volume 4 | Issue 6 | e5775
between these metabolites and genes involved in their production.
Indeed, cholesterol and androgenic hormones (testosterone and
androstenedione) are among the common molecular targets of
these 6 genes, which are also implicated in a number of
dysfunctional processes (embryonic development, neurogenesis,
apoptosis, cytokine production) and disorders (inflammation,
digestive disorder, muscledisorder)
(Figure 4). Aside from the novel candidate genes identified in
this study, the networks in Figs. 3 and 4 also include 2 other
genes, ITGB1 and PTEN, which have been identified as candidate
ASD genes in other studies [44,66–68]. Of particular significance
is that PTEN has been demonstrated to be downregulated in a
mouse model of Purkinje cell degradation, a hallmark of autism
neuropathology . In addition, a mouse model involving limited
deletion of PTEN in the cerebral cortex and hippocampus resulted
in mice with macrocephaly, abnormal social interactions, and
increased sensitivity to sensory stimuli, characteristics that are
often associated with ASD .
Steroid profiling reveals elevated testosterone levels in
LCL extracts from autistic siblings
Based upon the qRT-PCR-confirmed differential expression of
SCARB1 and SRD5A1 which are involved in cholesterol
metabolism and steroid hormone biosynthesis, we constructed a
multilevel biomolecular network representing the possible inter-
actions and functions of the genes, gene products, and downstream
metabolites (Figure 5). From this bionetwork, we postulated that
elevations in these genes may lead to an increase in androgenic
hormone biosynthesis and conducted pilot steroid profiling
analyses on LCL extracts from 3 randomly selected sibling pairs
to test this hypothesis. Indeed, Table 5 shows that testosterone
was elevated in the extracts from all 3 autistic siblings relative to
their respective non-autistic siblings. This data is consistent with
other reports that androgenic hormones are elevated in serum
from autistic individuals relative to that from normal age- and
gender-matched controls  and provides further support for the
role of elevated male hormones as a risk factor for autism
It is becoming increasingly clear that although the neurological
symptoms of ASD are the most striking among the behavioral and
functional manifestations of affected individuals, there are many
associated peripheral physiological symptoms that have often gone
unnoticed/ignored and clinically unaddressed. These include
Figure 1. A relational gene network constructed using Pathway Studio 5 from the dataset of significant genes identified by SAM
analysis with 70% data filtering (see Table 1). Red denotes genes with increased expression in the autistic sibling while green indicates decreased
expression. Note that inflammation, epilepsy, liver disease, diabetes, and schizophrenia are among the pathological processes associated with this gene
network while apoptosis, differentiation, and regulation of action potential are among the cellular processes that are influenced by this set of genes.
Gene Expression in Autism
PLoS ONE | www.plosone.org4 June 2009 | Volume 4 | Issue 6 | e5775
gastrointestinal disorders experienced by many on the spectrum
(estimated at 50%) as well as immune disorders which have long
been described in the literature on ASD [5–10]. The large-scale
global gene expression profiling that we have undertaken on LCL
derived from peripheral blood lymphocytes of ASD probands and
their respective siblings may therefore serve as a window to the
underlying biochemical and signaling deficits that may be relevant
to understanding the broader symptomatology of autism.
Overall, our study of autistic-nonautistic sib pairs in which the
autistic sibling has been subtyped according to severity of language
impairment on the basis of cluster analysis of scores from the
ADIR diagnostic interview (Hu and Steinberg, Autism Research,
2009, in press), reveals altered expression of genes that participate
in cholesterol metabolism and androgen biosynthesis. It is
noteworthy that deficiency of 7-dehydrocholesterol reductase
(DHCR7), the terminal enzyme in cholesterol biosynthesis, is the
genetic cause of Smith-Lemli-Opitz syndrome (SLOS) which is an
autosomal recessive disorder characterized by pre- and post-natal
growth retardation, distinct facial anomalies, microcephaly, and
mental retardation [73,74]. Significantly, over 50% of individuals
with SLOS also meet the diagnostic criteria for autism [75,76].
Although the mechanism through which DHCR7 deficiency
causes any of these characteristics/phenotypes is unknown, a gene
expression study of a DHCR7 knockout mouse model reveals
altered expression of numerous genes affecting not only cholesterol
biosynthesis, but also neurodevelopment and functions such as
Wnt signaling, axon guidance, neuronal cytoskeletal assembly, and
neurodegeneration . Thus, it can be postulated that distur-
bance of cholesterol mechanism in either direction (decreased
synthesis in the case of SLOS, or increased uptake by SCARB1
and conversion to androgen by SRD5A1 in our study) can have
wide-ranging effects on neural development and function. The
predicted increase in androgen levels due to increased expression
of SRD5A1, on the other hand, is supported by our pilot study on
the metabolites within the steroid hormone biosynthetic pathway
which shows elevated testosterone in all 3 of the randomly selected
autistic siblings relative to his respective nearly age-matched
normal sibling as well as by other studies in the literature which
show elevated androgen levels in the serum of autistic individuals,
including females [14,64,71]. Our observation that at least 2 of the
genes (SCARB1 and SRD5A1) that are involved in cholesterol
import into the cell and testosterone metabolism exhibit increased
expression in the autistic siblings offers a plausible explanation for
elevated androgen levels in ASD.
The biological consequences of elevated testosterone on
neurodevelopment and function are just beginning to be
understood. While it has been known for more than 10 years
that estrogens modulate synaptic plasticity in the hippocampus of
female rats , it has only recently been shown that androgens
likewise play a role in hippocampal synaptic plasticity, but in both
males and females . Furthermore, there is increasing evidence
for the role of ‘‘neurosteroids’’ (which include DHEA and
progesterone) in neurological functions, including rapid modula-
tion of neurotransmitter receptors . In contrast to testosterone,
DHEA which has been shown to be lowered in ASD , plays a
neuroprotective role countering the effect of stress-inducing
Table 2. Biological functions identified by Ingenuity Pathway Analysis of significant differentially expressed genes (log2
ratio.60.3) identified by SAM analysis (FDR=13.5%).
Category Function Annotationp-value*Molecules
Endocrine System Development and Functionbiosynthesis of androgen/steroidogenesis 4.89E-05SCARB1, SRD5A1
proliferation of pancreatic duct cells3.69E-03 CXCR4
quantity of 4-androstene-3,17-dione1.29E-02SRD5A1
Small Molecule Biochemistry endocytosis of cholesterol1.85E-03SCARB1
breakdown of progesterone1.85E-03SRD5A1
biosynthesis of norepinephrine5.53E-03GATA3
synthesis of ganglioside GM37.37E-03CD9
uptake of taurocholic acid1.83E-02PRKCZ
Nervous System Development and Functionmorphology of neurons5.49E-04 CD9, GATA3
morphology of Purkinje cells1.85E-03ATP2B2
morphology of serotonergic neurons1.85E-03GATA3
fusion of vagus cranial nerve ganglion 1.85E-03LMO4
polarization of astrocytes1.85E-03PRKCZ
development of cerebellum1.98E-03ATP2B2, CXCR4
branching of sympathetic neuron 3.69E-03LIFR
differentiation/quantity of central nervous system cells5.43E-03 ATP2B2, LIFR
morphology of central nervous system 5.53E-03ATRN
development of Purkinje cells1.10E-02 CXCR4
migration of motor neurons1.83E-02GATA3
biogenesis of synapse2.74E-02 ATP2B2
guidance of motor axons2.74E-02 CXCR4
*Significance calculated for each function is an indicator of the likelihood of that function being associated with the dataset by random chance. The range of p-values
was calculated using the right-tailed Fisher’s Exact Test, which compares the number of user-specified genes to the total number of occurrences of these genes in the
respective functional/pathway annotations stored in the Ingenuity Pathways Knowledge Base.
Gene Expression in Autism
PLoS ONE | www.plosone.org5June 2009 | Volume 4 | Issue 6 | e5775
steroids [82,83]. Interestingly, we have observed that the plasma
levels of DHEA were lower in several of the autistic siblings
relative to their respective nonautistic siblings (unpublished data).
Clearly, it will be important to further evaluate the levels of steroid
hormones and related molecules in a broader sampling of
individuals with ASD as well as to establish a correlation between
these metabolite levels and aberrant expression of genes in this
Pathway analyses using Pathway Studio 5 also implicated
involvement of female hormones in that the estrogens were among
the small molecule regulators of the differentially expressed genes
(Fig. 3). It is further noted that SRD5A1 is involved in sex
determination . Thus, the altered expression of genes involved
in steroid hormone production and sexual dimorphism, coupled
with the differential impact of male and female steroid hormones
on brain development in male vs. female animals [78,79] may, in
part, underlie the approximately 4:1 male to female ratio in ASD.
The schematic in Figure 5 suggests that bile acid synthesis
might also be affected by some of the differentially expressed genes
in ASD, particularly SCARB1 and SRD5A1, which respectively
internalize cholesterol and participate enzymatically in bile acid
synthesis. This suggests that altered expression of genes in this
pathway may also be responsible for the digestive and hepatic
disorders associated with ASD. Indeed, in a separate case-control
study of a large number of unrelated individuals (total of 116),
hepatic cholestasis and fibrosis are strongly indicated on the basis
of the gene expression profiles of the autistic probands vs.
unrelated controls (Hu et al., Autism Research, 2009, in press).
Changes in metabolite profiles thus may be predicted and tested
on the basis of a functional analysis of altered gene interactions
that arise from increases or decreases in gene expression within a
specific metabolic pathway. Indeed, this would be a complemen-
tary approach to that used by James et al. who used targeted
metabolite profiling of the methionine transmethylation and
transsulfuration pathways to identify potential gene defects in
ASD . Such metabolomic analyses, guided by gene expression
studies, may in turn lead to a diagnostic screen for ASD based on
metabolite profiling of serum or other easily accessible tissues (e.g.,
steroid hormone, bile acid, or redox molecule assays).
Aside from genes involved in cholesterol metabolism and steroid
hormone biosynthesis, we also confirmed the altered expression of
several other novel genes that may play a role in the
pathophysiology of autistic disorder (Fig. 2). The use of LCL cells
as a surrogate tissue to study potential changes in brain gene
expression that may mechanistically underlie autism or other
neurological disorders is not unprecedented. Gene expression
profiles of different brain regions have been shown to exhibit the
highest similarity to whole blood . Moreover, a meta-analysis
of studies performed in blood and post-mortem brain demon-
strated convergent gene expression changes , although further
studies are warranted . Because of their role in neuronal
development, migration, and morphology (Table 2), we were
particularly interested in confirming the differential expression of
CXCR4, CD9, and GATA3. Although the chemokine receptor
CXCR4 is most frequently associated with inflammatory processes
and leukocyte trafficking in the immune system, recent studies
show that it, along with the chemokine stromal cell-derived factor
1 (SDF-1), are important regulators of neuronal migration and
axonal pathfinding, particularly in the cortex and cerebellum 
where it is involved in the development and organization of
Purkinje cells, which are notably deficient in ASD . CD9 is yet
another molecule involved in cell migration, both in the immune
system as well as nervous system [89,90], with its expression in
Schwann cells regulated by axonal contact . Interestingly,
androgens have been shown to induce CD9 in human prostate
, suggesting yet another mechanism for increased expression
in autism. GATA3 is a transcription factor that is involved in both
allergic inflammation (like CXCR4) and cytokine production
[93,94]. In addition, GATA3 has also been shown to be involved
in the development of the central nervous system in mice  and
the induction of dopamine beta-hydroxylase (DBH) in primary
neural crest stem cells . With respect to the latter activity, it is
of interest to note that DBH genotype has been associated with
autism in some families [97,98] and that DBH activity has been
noted to be elevated in a subgroup of autistic patients . Like
Table 3. Common regulators and targets of differentially
expressed genes (from Table 1) identified by Pathway Studio
Small molecule Small molecule
fatty acids estradiol
glucose fatty acids
diabetes mellitusCrohn disease
neural tube malformation digestion
neuroblastoma embryonic cell viability
neural tube malformation
peripheral nerve function
Gene Expression in Autism
PLoS ONE | www.plosone.org6June 2009 | Volume 4 | Issue 6 | e5775
Table 4. Gene ontology analysis using DAVID of significant differentially expressed genes with (log2 ratio.60.3) identified by
SAM analysis (FDR=13.5%).
GO:0008092,cytoskeletal protein binding3.46E-03 SVIL, PDE4DIP, WASF2, CXCR4, SDC4,
GO:0030036,actin cytoskeleton organization and biogenesis4.18E-03 SVIL, PDE4DIP, WASF2, FGD6,
GO:0030029,actin filament-based process5.06E-03 SVIL, PDE4DIP, WASF2, FGD6,
GO:0007010,cytoskeleton organization and biogenesis8.38E-03 PRKCZ, SVIL, PDE4DIP, WASF2, FGD6,
GO:0003779,actin binding9.61E-03 SVIL, PDE4DIP, WASF2, CXCR4,
GO:0006996,organelle organization and biogenesis3.47E-02 PRKCZ, SVIL, PDE4DIP, HIST1H1A, WASF2, FGD6,
GO:0016043,cellular component organization and biogenesis4.89E-02 PRKCZ, ATP2B2, SVIL, PDE4DIP, HIST1H1A, WASF2, CXCR4, CD9,
GO:0032502,developmental process 4.01E-04 PRKCZ, SRD5A1, SVIL, HKR1, CD9, SCARB1, FGD6, LITAF,
GPR175, ATP2B2, LMO4, CXCR4, ATRN, GATA3,
GO:0000003,reproduction9.35E-03 SRD5A1, ATP2B2, HIST1H1A, CXCR4, CD9,
GO:0065007,biological regulation1.28E-02 PRKCZ, BCL7A, SVIL, HKR1, CD9, LIFR, HMBOX1, FGD6, SCN5A,
LITAF, ATP2B2, LMO4, CXCR4, ATRN, GATA3,
GO:0009653,anatomical structure morphogenesis2.83E-02ATP2B2, LMO4, CXCR4, CD9, FGD6, GATA3,
GO:0007399,nervous system development3.12E-02 ATP2B2, LMO4, CXCR4, CD9, GATA3,
GO:0048646,anatomical structure formation3.15E-02ATP2B2, CXCR4, CD9,
*Significance by Fisher’s Exact Test.
Figure 2. Confirmation of select differentially expressed genes by qRT-PCR analyses. Five representative samples were analyzed per
group for each gene, with each sample run in triplicate. The graph shows the average log2 ratios obtained for each gene for the 5 samples analyzed
by qRT-PCR, for the same 5 samples analyzed by DNA microarrays, and for all 21 samples analyzed by DNA microarrays. *p-value,0.05; **p-
value,0.004; The p-values for the qRT-PCR analysis of GATA3 was 0.069, and for the microarray analysis of SRD5A1 based on only 5 samples was
0.156. However, the p-values for GATA3 and SRD5A1 based on the microarray analysis of all 21 paired samples were ,0.00006 and ,0.002,
Gene Expression in Autism
PLoS ONE | www.plosone.org7 June 2009 | Volume 4 | Issue 6 | e5775
the 3 genes discussed above, NFKBIZ, a nuclear regulator of
NFKB activity, is also involved in inflammation and immunity. In
particular, it is induced upon stimulation of the innate immune
system  and, in turn, stimulates IL-6 production . These
characteristics of NFKBIZ are noteworthy in light of studies by
Pardo and colleagues demonstrating activation of the innate
immune system in brain tissues from autistic patients, with a
notable increase in IL-6 . Thus, the elevation of NFKBIZ in
peripheral cells derived from autistic probands may be a reflection
of a systems-wide activation of the innate immune system in
autism, providing strong support for the use of LCL as a surrogate
model to examine gene dysregulation in ASD.
In summary, gene expression profiling of LCL from autistic and
nonautistic siblings reveals alteration of genes involved in both
metabolic and signaling pathways in ASD that is consistent with the
known pathophysiology of autism which includes inflammation as
well as disturbances in axon guidance, neuronal survival, and
differentiation, biological themes also implicated in our earlier study
on monozygotic twins discordant in diagnosis and severity of autism
. The involvement of genes affecting both the immune and
nervous systems, coupled with the pleiotropic effects of dysregulated
steroid hormone biosynthesis, may further explain some of the
systemic disorders associated with autism. The genes, metabolites,
and pathways identified in this study moreover suggest novel targets
for therapeutics. Thus, gene expression profiling, which provides a
global view of functional gene networks in the context of living cells
from individuals with ASD, not only allows for the elucidation of
compromised pathways but also provides a meaningful and
complementary (with respect to genetics) approach towards
understanding the complex biology of ASD.
Lymphoblastoid cell lines (LCL) derived from lymphocytes of
autistic and normal siblings were obtained from the Autism
Genetic Resource Exchange (AGRE) and cultured in RPMI 1640
with 15% fetal bovine serum and antibiotics in a humidified
incubator under 5% CO2. Supplementary Table 1 summa-
rizes the demographic profile of subjects whose LCL were
analyzed in this study.
Selection of samples
To reduce the heterogeneity of the samples for gene expression
analyses, we used a novel clustering procedure to identify
phenotypically distinct groups of individuals on the basis of
Figure 3. Common regulators (identified by Pathway Studio 5) associated with the dataset of significant differentially expressed
qRT-PCR confirmed genes. Color coding of entities associated with gene network: Red – upregulated genes; pink – other genes which are part of
the regulatory network constructed by the pathway analysis program; green - small molecules; orange - functional class; purple - disorders.
Gene Expression in Autism
PLoS ONE | www.plosone.org8 June 2009 | Volume 4 | Issue 6 | e5775
severity associated with 123 items on the Autism Diagnostic
Interview-Revised scoresheets. This procedure, described in
another manuscript (submitted for publication), resulted in
separation of the autistic individuals into 4 phenotypic subgroups,
as illustrated in Fig. 6. For this study, we selected autistic male
individuals from the distinct subgroup associated with severe
language impairment, each of whom had a male sibling who was
not affected by autism who served as a control in a paired
statistical analysis of gene expression data derived from LCL of the
respective siblings. To further reduce the heterogeneity within the
samples and eliminate confounding factors due to co-existing
conditions or known genetic abnormalities, LCL from females,
individuals with specific genetic and chromosomal abnormalities
(e.g., Fragile X, chromosome 15q11-q13 duplication) and with
diagnosed co-morbid disorders (e.g., bipolar disorder, obsessive
compulsive disorder), and those born prematurely (,35 weeks of
gestation) were excluded from this study.
DNA Microarray Analysis and Data Sharing
RNA was isolated from LCL 3 days after the last passage using
TRIzol Reagent (Invitrogen) according to the manufacturer’s
protocol. Fluorescently labeled sample cDNA was obtained by
incorporation of amino-allyl dUTP during first-strand synthesis,
followed by coupling to the ester of Cyanine (Cy)-3 as previously
described . Stratagene Universal human reference RNA was
used as a common reference RNA sample for all hybridizations, in
which the reference cDNA was labeled with Cy-5 dye. For two-
color DNA microarray analyses, sample and reference cDNA were
co-hybridized onto a custom printed microarray containing
39,936 human PCR amplicon probes derived from cDNA clones
purchased from Research Genetics (Invitrogen ). After hybridiza-
tion and washing according to published procedures , the
microarrays were scanned for fluorescence signals using a Genepix
4000B laser scanner. Normalized gene expression levels were
derived from the resulting image files using TIGR SpotFinder,
MIDAS, and MeV analysis programs which are all part of the
TM4 Microarray Analysis Software Package available at www.
tm4.org. Within MeV, the Significance Analysis of Microarray
(SAM) module  was employed to obtain statistically
significant differentially expressed genes using a one-class SAM
analysis of the log2 ratios of relative expression data from the
autistic and nonautistic sib pairs. Details of this analysis as well as
Figure 4. Common targets (identified by Pathway Studio 5) associated with the dataset of significant differentially expressed qRT-
PCR confirmed genes. Colored entities are defined in legend to Fig. 3. Yellow entities describe cellular processes.
Gene Expression in Autism
PLoS ONE | www.plosone.org9 June 2009 | Volume 4 | Issue 6 | e5775
the raw and normalized intensity data for all samples have been
deposited into GEO (Accession # GSE15451).
Quantitative PCR Analysis
Select genes were confirmed by real time RT-PCR on an ABI
Prism 7300 Sequence Detection System using Invitrogen’s
Platinum SYBR Green qPCR SuperMix-UDG with ROX. These
included genes involved in cholesterol and steroid hormone
metabolism as well as genes implicated in nervous system
development that were enriched within the top biological functions
identified by Ingenuity Pathway Analysis (Table 2). Total RNA
(same preparations used in microarray analyses) was reverse
transcribed into cDNA using the iScript cDNA Synthesis Kit (Bio-
Rad, Hercules, CA). Briefly, 1 mg of total RNA was added to a
20 ml reaction mix containing reaction buffer, magnesium
chloride, dNTPs, an optimized blend of random primers and
oligo(dT), an RNase inhibitor and a MMLV RNase H+ reverse
transcriptase. The reaction was incubated at 25uC for 5 minutes
followed by 42uC for 30 minutes and ending with 85uC for
5 minutes. The cDNA reactions were then diluted to a volume of
50 ml with water and used as a template for quantitative PCR.
PCR primers for genes identified by microarray analysis as
differentially expressed were selected for specificity by the National
Center for Biotechnology Information Basic Local Alignment
Search Tool (NCBI BLAST) of the human genome, and amplicon
specificity was verified by first-derivative melting curve analysis
with the use of software provided by PerkinElmer (Emeryville, CA)
and Applied Biosystems. Sequences of primers used for the real-
time RT-PCR are given in the Supplemental Table S2.
Quantitative reverse transcriptase-PCR analyses (qRT-PCR) were
performed on a representative set of 5 pairs of case-controls from the
Figure 5. A bionetwork that shows the relationships and interactions between SCARB1 and SRD5A1 at the gene, protein, and
metabolite levels. Briefly, SCARB1 is responsible for the uptake of cholesterol into cells while SRD5A1 converts testosterone to 5-a-
dihydrotestosterone (DHT), a more potent form of the male hormone. We propose that increases in the expression of these genes may lead to an
overall increase in the production of androgens. It is also of interest that bile acid synthesis is linked to this same pathway, thereby suggesting that
altered expression of these genes in ASD may lead to disturbances of bile acid synthesis in some tissues as well.
Table 5. Concentration of testosterone in LCL extracts from 3
pairs of autistic-nonautistic siblings as determined by HPLC-
HI036618 autistic 241 1.14
HI276910 autistic251 1.22
HI277213 normal 206
*Below level of detection.
Gene Expression in Autism
PLoS ONE | www.plosone.org10June 2009 | Volume 4 | Issue 6 | e5775
sib pair analyses, with quantification and normalization of relative
gene expression using universal 18S rRNA primers, with samples
normalized to their 18S rRNA standard curves. The qPCR reactions
were done in triplicate. A one-sample t-test was used to determine
significance of differential expression across the 5 paired samples.
Pathway and Functional Analyses
The datasets of differentially expressed genes between autistic
probands and unaffected siblings were analyzed using Ingenuity
Pathway Analysis and Pathway Studio 5 to identify molecular and
cellular processes, high level functions, disorders, and smallmolecules
associated with the gene regulatorynetworks.DAVID Bioinformatics
Resources (http://david.abcc.ncifcrf.gov) was also used for additional
functional annotation . In both types of analyses, statistically
significant functions were determined using the Fisher Exact test.
Metabolic profiling of steroid hormones in LCL
Metabolites were extracted from LCL using acetonitrile and
analyzed by isotope dilution liquid chromatography-photospray
ionization tandem mass spectrometry, a highly sensitive method
which has been developed for the simultaneous determination of 11
steroids . Briefly, 300 ml of acetonitrile containing the
deuterated internal standards is added to the cell pellet containing
26108cells, vortexed, and incubated for 30 min at RT. Two
hundred ml of water is then added along with internal standards and
the mixture is centrifuged to precipitate the proteins. After protein
removal, 350 ml of supernatant is diluted with 1.4 ml of water and
1.5 ml of the resulting solution is injected into the LC-APPI-MS/MS
(Applied Biosystems API-5000 triple quadrupole mass spectrometer
equipped with an atmospheric pressure photoionization source).
Found at: doi:10.1371/journal.pone.0005775.s001 (0.02 MB
Demographic profile of subjects
Found at: doi:10.1371/journal.pone.0005775.s002 (0.03 MB
Primer sequences used for qRT-PCR
We thank Dr. Ian Toma for his assistance with the preparation of Figure 5.
We also gratefully acknowledge the resources provided by the Autism
Genetic Resource Exchange (AGRE) Consortium* and the participating
AGRE families. We especially thank Dr.Vlad Kustanovich of AGRE for
providing us with additional information about subjects (such as age at time
of blood collection) which were not easily retrievable in the database.
*The AGRE Consortium: Dan Geschwind, M.D., Ph.D., UCLA, Los
Angeles, CA; Maja Bucan, Ph.D., University of Pennsylvania, Philadel-
phia, PA; W.Ted Brown, M.D., Ph.D., F.A.C.M.G., N.Y.S. Institute for
Basic Research in Developmental Disabilities, Long Island, NY; Rita M.
Cantor, Ph.D., UCLA School of Medicine, Los Angeles, CA; John N.
Constantino, M.D., Washington University School of Medicine, St. Louis,
MO; T.Conrad Gilliam, Ph.D., University of Chicago, Chicago, IL;
Martha Herbert, M.D., Ph.D., Harvard Medical School, Boston, MA;
Clara Lajonchere, Ph.D, Cure Autism Now, Los Angeles, CA; David H.
Ledbetter, Ph.D., Emory University, Atlanta, GA; Christa Lese-Martin,
Ph.D., Emory University, Atlanta, GA; Janet Miller, J.D., Ph.D., Cure
Autism Now, Los Angeles, CA; Stanley F. Nelson, M.D., UCLA School of
Medicine, Los Angeles, CA; Gerard D. Schellenberg, Ph.D., University of
Washington, Seattle, WA; Carol A. Samango-Sprouse, Ed.D., George
Washington University, Washington, D.C.; Sarah Spence, M.D., Ph.D.,
UCLA, Los Angeles, CA; Matthew State, M.D., Ph.D., Yale University ,
New Haven, CT. Rudolph E. Tanzi, Ph.D., Massachusetts General
Hospital, Boston, MA.
Conceived and designed the experiments: VWH. Performed the
experiments: AN KSK MES MAS SJS. Analyzed the data: VWH.
Contributed reagents/materials/analysis tools: VWH. Wrote the paper:
VWH. Analyzed the microarray data: VWH. Prepared functional
genomics schematic (Fig. 5): TS. Performed steroid analyses: SJS.
Responsible for printing and quality control of the DNA microarray
slides: TL. Contributed to the discussion and editing of the manuscript:
NHL. Performed the microarray experiments: KSK MAS. Performed
Figure 6. Separation of 1351 autistic probands (each represented by a point) into phenotypic groups on the basis of principal
components analysis(PCA)of123scoreditemsontheAutismDiagnosticInterview-Revised(ADIR)questionnaires foreachindividual
which was obtained from the AGRE phenotypic database. PCA divided the autistic individuals into 2 main groups. Hierarchical clustering of the
ADIR data (data not shown)revealed that individuals in the smaller groupwere characterizedbyhigherseverity scores onspoken languageitems onthe
ADIR. These individuals are represented by the red points in the PCA. Hierarchical clustering also suggested 3 other phenotypic groups that were
characterized by lower severity scores across all items (individuals coded blue), higher frequency of savant skills (individuals coded yellow), and
intermediate severity across all items (individuals coded green). To restrict sample heterogeneity, this study used LCL only from individuals with severe
language impairment (coded red) as identified by these 2 cluster analyses. Detailed methods used for the identification of distinct ASD behavioral
phenotypes based on cluster analyses of ADIR scores are described by Hu and Steinberg (Autism Research (2009) in press).
Gene Expression in Autism
PLoS ONE | www.plosone.org11 June 2009 | Volume 4 | Issue 6 | e5775
qRT-PCR analyses: AN. Assisted in sample and manuscript preparation:
MES. Constructed the biochemical network shown in Fig. 5: TS.
Responsible for the steroid hormone analyses: SJS. Responsible for
printing and quality control of the DNA microarray slides: TL.
Contributed to the discussion and editing of the manuscript: NHL.
1. Volkmar FR, Klin A, Siegel B, Szatmari P, Lord C, et al. (1994) Field trial for
autistic disorder in DSM-IV. Am J Psychiatry 151(9): 1361–7.
2. Bauman ML, Kemper TL (2005) Neuroanatomic observations of the brain in
autism: A review and future directions. Int J Dev Neurosci 23(2–3): 183–7.
3. Pickett J, London E (2005) The neuropathology of autism: A review.
J Neuropathol Exp Neurol 64(11): 925–35.
4. Palmen SJ, van Engeland H, Hof PR, Schmitz C (2004) Neuropathological
findings in autism. Brain 127(Pt 12): 2572–83.
5. Levy SE, Souders MC, Ittenbach RF, Giarelli E, Mulberg AE, et al. (2007)
Relationship of dietary intake to gastrointestinal symptoms in children with
autistic spectrum disorders. Biol Psychiatry 61(4): 492–7.
6. Herbert MR, Russo JP, Yang S, Roohi J, Blaxill M, et al. (2006) Autism and
environmental genomics. Neurotoxicology 27(5): 671–84.
7. Valicenti-McDermott M, McVicar K, Rapin I, Wershil BK, Cohen H, et al.
(2006) Frequency of gastrointestinal symptoms in children with autistic
spectrum disorders and association with family history of autoimmune disease.
J Dev Behav Pediatr 27(2 Suppl): S128–36.
8. Horvath K, Papadimitriou JC, Rabsztyn A, Drachenberg C, Tildon JT (1999)
Gastrointestinal abnormalities in children with autistic disorder. J Pediatr
9. Jyonouchi H, Geng L, Ruby A, Zimmerman-Bier B (2005) Dysregulated innate
immune responses in young children with autism spectrum disorders: Their
relationship to gastrointestinal symptoms and dietary intervention. Neuropsy-
chobiology 51(2): 77–85.
10. Cohly HH, Panja A (2005) Immunological findings in autism. Int Rev
Neurobiol 71: 317–41.
11. Jyonouchi H, Sun S, Le H (2001) Proinflammatory and regulatory cytokine
production associated with innate and adaptive immune responses in children
with autism spectrum disorders and developmental regression. J Neuroimmunol
12. Vargas DL, Nascimbene C, Krishnan C, Zimmerman AW, Pardo CA (2005)
Neuroglial activation and neuroinflammation in the brain of patients with
autism. Ann Neurol 57(1): 67–81.
13. Croonenberghs J, Bosmans E, Deboutte D, Kenis G, Maes M (2002) Activation
of the inflammatory response system in autism. Neuropsychobiology 45(1): 1–6.
14. Ingudomnukul E, Baron-Cohen S, Wheelwright S, Knickmeyer R (2007)
Elevated rates of testosterone-related disorders in women with autism spectrum
conditions. Horm Behav 51(5): 597–604.
15. Corbett BA, Mendoza S, Abdullah M, Wegelin JA, Levine S (2006) Cortisol
circadian rhythms and response to stress in children with autism. Psychoneur-
oendocrinology 31(1): 59–68.
16. Knickmeyer RC, Baron-Cohen S (2006) Fetal testosterone and sex differences
in typical social development and in autism. J Child Neurol 21(10): 825–45.
17. Baron-Cohen S, Knickmeyer RC, Belmonte MK (2005) Sex differences in the
brain: Implications for explaining autism. Science 310(5749): 819–23.
18. Campbell DB, Sutcliffe JS, Ebert PJ, Militerni R, Bravaccio C, et al. (2006) A
genetic variant that disrupts MET transcription is associated with autism. Proc
Natl Acad Sci U S A 103(45): 16834–9.
19. Campbell DB, D’Oronzio R, Garbett K, Ebert PJ, Mirnics K, et al. (2007)
Disruption of cerebral cortex MET signaling in autism spectrum disorder.
Annals of Neurology 62(3): 243–250.
20. Pardo CA, Vargas DL, Zimmerman AW (2005) Immunity, neuroglia and
neuroinflammation in autism. Int Rev Psychiatry 17(6): 485–95.
21. Chauhan A, Chauhan V (2006) Oxidative stress in autism. Pathophysiology
22. James SJ, Melnyk S, Jernigan S, Cleves MA, Halsted CH, et al. (2006)
Metabolic endophenotype and related genotypes are associated with oxidative
stress in children with autism. Am J Med Genet B Neuropsychiatr Genet
23. MacFabe DF, Cain DP, Rodriguez-Capote K, Franklin AE, Hoffman JE, et al.
(2007) Neurobiological effects of intraventricular propionic acid in rats: Possible
role of short chain fatty acids on the pathogenesis and characteristics of autism
spectrum disorders. Behav Brain Res 176(1): 149–69.
24. Chauhan A, Chauhan V, Brown WT, Cohen I (2004) Oxidative stress in
autism: Increased lipid peroxidation and reduced serum levels of ceruloplasmin
and transferrin–the antioxidant proteins. Life Sci 75(21): 2539–49.
25. Finegold SM, Molitoris D, Song Y, Liu C, Vaisanen M-, et al. (2002)
Gastrointestinal microflora studies in late-onset autism. Clinical Infectious
Diseases 35(SUPPL. 1): S6–S16.
26. Shultz SR, MacFabe DF, Ossenkopp K-, Scratch S, Whelan J, et al. (2008)
Intracerebroventricular injection of propionic acid, an enteric bacterial
metabolic end-product, impairs social behavior in the rat: Implications for an
animal model of autism. Neuropharmacology 54(6): 901–911.
27. Zecavati N, Spence SJ (2009) Neurometabolic disorders and dysfunction in
autism spectrum disorders. Curr Neurol Neurosci Rep 9(2): 129.
28. Folstein S, Rutter M (1977) Infantile autism: A genetic study of 21 twin pairs.
J Child Psychol Psychiatry 18(4): 297–321.
29. Bailey A, Le Couteur A, Gottesman I, Bolton P, Simonoff E, et al. (1995)
Autism as a strongly genetic disorder: Evidence from a british twin study.
Psychol Med 25(1): 63–77.
30. Hallmayer J, Glasson EJ, Bower C, Petterson B, Croen L, et al. (2002) On the
twin risk in autism. Am J Hum Genet 71(4): 941–6.
31. Bolton P, Macdonald H, Pickles A, Rios P, Goode S, et al. (1994) A case-
control family history study of autism. J Child Psychol Psychiatry 35(5):
32. Polleux F, Lauder JM (2004) Toward a developmental neurobiology of autism.
Ment Retard Dev Disabil Res Rev 10(4): 303–17.
33. Yonan AL, Palmer AA, Smith KC, Feldman I, Lee HK, et al. (2003)
Bioinformatic analysis of autism positional candidate genes using biological
databases and computational gene network prediction. Genes Brain Behav 2(5):
34. Santangelo SL, Tsatsanis K (2005) What is known about autism: Genes, brain,
and behavior. Am J Pharmacogenomics 5(2): 71–92.
35. Gupta AR, State MW (2007) Recent advances in the genetics of autism. Biol
Psychiatry 61(4): 429–37.
36. Jamain S, Betancur C, Quach H, Philippe A, Fellous M, et al. (2002) Linkage
and association of the glutamate receptor 6 gene with autism. Mol Psychiatry
37. Ma DQ, Whitehead PL, Menold MM, Martin ER, Ashley-Koch AE, et al.
(2005) Identification of significant association and gene-gene interaction of
GABA receptor subunit genes in autism. Am J Hum Genet 77(3): 377–88.
38. Jamain S, Quach H, Betancur C, Rastam M, Colineaux C, et al. (2003)
Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4
are associated with autism. Nat Genet 34(1): 27–9.
39. Talebizadeh Z, Lam DY, Theodoro MF, Bittel DC, Lushington GH, et al.
(2006) Novel splice isoforms for NLGN3 and NLGN4 with possible
implications in autism. J Med Genet 43(5): e21.
40. Feng J, Schroer R, Yan J, Song W, Yang C, et al. (2006) High frequency of
neurexin 1beta signal peptide structural variants in patients with autism.
Neurosci Lett 409(1): 10–3.
41. Durand CM, Betancur C, Boeckers TM, Bockmann J, Chaste P, et al. (2007)
Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are
associated with autism spectrum disorders. Nat Genet 39(1): 25–7.
42. Fatemi SH, Stary JM, Halt AR, Realmuto GR (2001) Dysregulation of reelin
and bcl-2 proteins in autistic cerebellum. J Autism Dev Disord 31(6): 529–35.
43. Hu VW, Frank BC, Heine S, Lee NH, Quackenbush J (2006) 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: 118.
44. Baron CA, Liu SY, Hicks C, Gregg JP (2006) Utilization of lymphoblastoid cell
lines as a system for the molecular modeling of autism. J Autism Dev Disord
45. Wassink TH, Piven J, Vieland VJ, Huang J, Swiderski RE, et al. (2001)
Evidence supporting WNT2 as an autism susceptibility gene. Am J Med Genet
46. Ingram JL, Stodgell CJ, Hyman SL, Figlewicz DA, Weitkamp LR, et al. (2000)
Discovery of allelic variants of HOXA1 and HOXB1: Genetic susceptibility to
autism spectrum disorders. Teratology 62(6): 393–405.
47. Szatmari P, Paterson AD, Zwaigenbaum L, Roberts W, Brian J, et al. (2007)
Mapping autism risk loci using genetic linkage and chromosomal rearrange-
ments. Nat Genet 39(3): 319–28.
48. Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, et al. (2007) Strong
association of de novo copy number mutations with autism. Science 316(5823):
49. Simon R (2003) Diagnostic and prognostic prediction using gene expression
profiles in high-dimensional microarray data. Br J Cancer 89(9): 1599–604.
50. Lee NH, Saeed AI (2007) Microarrays: An overview. Methods Mol Biol 353:
51. Purcell AE, Jeon OH, Zimmerman AW, Blue ME, Pevsner J (2001)
Postmortem brain abnormalities of the glutamate neurotransmitter system in
autism. Neurology 57(9): 1618–28.
52. Walker SJ, Segal J, Aschner M (2006) Cultured lymphocytes from autistic
children and non-autistic siblings up-regulate heat shock protein RNA in
response to thimerosal challenge. Neurotoxicology 27(5): 685–92.
53. Nishimura Y, Martin CL, Vazquez-Lopez A, Spence SJ, Alvarez-Retuerto AI,
et al. (2007) Genome-wide expression profiling of lymphoblastoid cell lines
distinguishes different forms of autism and reveals shared pathways. Hum Mol
Genet 16(14): 1682–98.
54. Gregg JP, Lit L, Baron CA, Hertz-Picciotto I, Walker W, et al. (2008) Gene
expression changes in children with autism. Genomics 91(1): 22–29.
55. Spence SJ, Schneider MT (2009) The role of epilepsy and epileptiform EEGs in
autism spectrum disorders. Pediatr Res; In press.
Gene Expression in Autism
PLoS ONE | www.plosone.org12 June 2009 | Volume 4 | Issue 6 | e5775
56. Saemundsen E, Ludvigsson P, Hilmarsdottir I, Rafnsson V (2007) Autism Download full-text
spectrum disorders in children with seizures in the first year of life - A
population-based study. Epilepsia 48(9): 1724–1730.
57. Lathe R (2006) Autism, brain, and enviornment. London: Jessica Kingsley
Publishers. 288 p.
58. Noebels JL (2002) Sodium channel gene expression and epilepsy. Novartis
Foundation Symposium 241: 109–123.
59. MacFabe DF, Rodrı ´guez-Capote K, Hoffman JE, Franklin AE, Mohammad-
Asef Y, et al. (2008) A novel rodent model of autism: Intraventricular infusions
of propionic acid increase locomotor activity and induce neuroinflammation
and oxidative stress in discrete regions of adult rat brain. American Journal of
Biochemistry and Biotechnology 4(2): 146–166.
60. Jan G, Belzacq A-, Haouzi D, Rouault A, Mee ´tivier D, et al. (2002)
Propionibacteria induce apoptosis of colorectal carcinoma cells via short-chain
fatty acids acting on mitochondria. Cell Death Differ 9(2): 179–188.
61. Freeman SJ, Roberts W, Daneman D (2005) Type 1 diabetes and autism: Is
there a link? Diabetes Care 28(4): 925–926.
62. Iafusco D, Vanelli M, Songini M, Chiari G, Cardella F, et al. (2006) Type 1
diabetes and autism association seems to be linked to the incidence of diabetes
. Diabetes Care 29(8): 1985–1986.
63. Horvath K, Perman JA (2002) Autism and gastrointestinal symptoms. Curr
Gastroenterol Rep 4(3): 251–8.
64. Knickmeyer R, Baron-Cohen S, Fane BA, Wheelwright S, Mathews GA, et al.
(2006) Androgens and autistic traits: A study of individuals with congenital
adrenal hyperplasia. Horm Behav 50(1): 148–53.
65. G. D Jr, Sherman BT, Hosack DA, Yang J, Gao W, et al. (2003) DAVID:
Database for annotation, visualization, and integrated discovery. Genome Biol
66. Buxbaum JD, Cai G, Chaste P, Nygren G, Goldsmith J, et al. (2007) Mutation
screening of the PTEN gene in patients with autism spectrum disorders and
macrocephaly. American Journal of Medical Genetics, Part B: Neuropsychi-
atric Genetics 144(4): 484–491.
67. Butler MG, Dazouki MJ, Zhou X-, Talebizadeh Z, Brown M, et al. (2005)
Subset of individuals with autism spectrum disorders and extreme macroceph-
aly associated with germline PTEN tumour suppressor gene mutations. Journal
of Medical Genetics 42(4): 318–321.
68. Varga EA, Pastore M, Prior T, Herman GE, McBride KL (2009) The
prevalence of PTEN mutations in a clinical pediatric cohort with autism
spectrum disorders, developmental delay, and macrocephaly. Genetics in
Medicine 11(2): 111–117.
69. Ford GD, Ford BD, Steele Jr EC, Gates A, Hood D, et al. (2008) Analysis of
transcriptional profiles and functional clustering of global cerebellar gene
expression in PCD3J mice. Biochem Biophys Res Commun 377(2): 556–561.
70. Kwon C-, Luikart BW, Powell CM, Zhou J, Matheny SA, et al. (2006) Pten
regulates neuronal arborization and social interaction in mice. Neuron 50(3):
71. Geier DA, Geier MR (2007) A prospective assessment of androgen levels in
patients with autistic spectrum disorders: Biochemical underpinnings and
suggested therapies. Neuroendocrinology Letters 28(5): 565–573.
72. Knickmeyer R, Baron-Cohen S, Raggatt P, Taylor K (2005) Foetal
testosterone, social relationships, and restricted interests in children. J Child
Psychol Psychiatry 46(2): 198–210.
73. Tierney E, Nwokoro NA, Kelley RI (2000) Behavioral phenotype of RSH/
Smith-lemli-opitz syndrome. Ment Retard Dev Disabil Res Rev 6(2): 131–4.
74. Kelley RI (2000) Inborn errors of cholesterol biosynthesis. Adv Pediatr 47:
75. Sikora DM, Pettit-Kekel K, Penfield J, Merkens LS, Steiner RD (2006) The
near universal presence of autism spectrum disorders in children with smith-
lemli-opitz syndrome. Am J Med Genet A 140(14): 1511–8.
76. Tierney E, Bukelis I, Thompson RE, Ahmed K, Aneja A, et al. (2006)
Abnormalities of cholesterol metabolism in autism spectrum disorders.
Am J Med Genet B Neuropsychiatr Genet 141(6): 666–8.
77. Waage-Baudet H, C DW Jr, Dehart DB, Hiller S, Sulik KK (2005)
Immunohistochemical and microarray analyses of a mouse model for the
smith-lemli-opitz syndrome. Dev Neurosci 27(6): 378–96.
78. Zamani MR, Desmond NL, Levy WB (2000) Estradiol modulates long-term
synaptic depression in female rat hippocampus. J Neurophysiol 84(4): 1800–8.
79. MacLusky NJ, Hajszan T, Prange-Kiel J, Leranth C (2006) Androgen
modulation of hippocampal synaptic plasticity. Neuroscience 138(3): 957–65.
80. Compagnone NA, Mellon SH (2000) Neurosteroids: Biosynthesis and function
of these novel neuromodulators. Front Neuroendocrinol 21(1): 1–56.
81. Strous RD, Golubchik P, Maayan R, Mozes T, Tuati-Werner D, et al. (2005)
Lowered DHEA-S plasma levels in adult individuals with autistic disorder. Eur
Neuropsychopharmacol 15(3): 305–9.
82. Kalimi M, Shafagoj Y, Loria R, Padgett D, Regelson W (1994) Anti-
glucocorticoid effects of dehydroepiandrosterone (DHEA). Molecular and
Cellular Biochemistry 131(2): 99–104. 10 June 2008.
83. Kimonides VG, Spillantini MG, Sofroniew MV, Fawcett JW, Herbert J (1999)
Dehydroepiandrosterone antagonizes the neurotoxic effects of corticosterone
and translocation of stress-activated protein kinase 3 in hippocampal primary
cultures. Neuroscience 89(2): 429–436.
84. Ellis JA, Wong ZYH, Stebbing M, Harrap SB (2001) Sex, genes and blood
pressure. Clinical and Experimental Pharmacology and Physiology 28(12):
85. Sullivan PF, Fan C, Perou CM (2006) Evaluating the comparability of gene
expression in blood and brain. American Journal of Medical Genetics -
Neuropsychiatric Genetics 141 B(3): 261–268.
86. Middleton FA, Pato CN, Gentile KL, McGann L, Brown AM, et al. (2005)
Gene expression analysis of peripheral blood leukocytes from discordant sib-
pairs with schizophrenia and bipolar disorder reveals points of convergence
between genetic and functional genomic approaches. American Journal of
Medical Genetics - Neuropsychiatric Genetics 136 B(1): 12–25.
87. Matigian NA, McCurdy RD, Fe ´ron F, Perry C, Smith H, et al. (2008)
Fibroblast and lymphoblast gene expression profiles in schizophrenia: Are non-
neural cells informative? PLoS ONE 3(6).
88. Stumm R, Hollt V (2007) CXC chemokine receptor 4 regulates neuronal
migration and axonal pathfinding in the developing nervous system:
Implications for neuronal regeneration in the adult brain. Journal of Molecular
Endocrinology 38(3–4): 377–382.
89. Kagawa T, Mekada E, Shishido Y, Ikenaka K (1997) Immune system-related
CD9 is expressed in mouse central nervous system myelin at a very late stage of
myelination. Journal of Neuroscience Research 50(2): 312–320.
90. Anton ES, Hadjiargyrou M, Patterson PH, Matthew WD (1995) CD9 plays a
role in schwann cell migration in vitro. Journal of Neuroscience 15(1 II):
91. Banerjee SA, Patterson PH (1995) Schwann cell CD9 expression is regulated by
axons. Molecular and Cellular Neurosciences 6(5): 462–473.
92. Chuan Y, Pang S-, Bergh A, Norstedt G, Pousette A (2005) Androgens induce
CD-9 in human prostate tissue. International Journal of Andrology 28(5):
93. Zhang D-, Yang L, Cohn L, Parkyn L, Homer R, et al. (1999) Inhibition of
allergic inflammation in a murine model of asthma by expression of a
dominant-negative mutant of GATA-3. Immunity 11(4): 473–482.
94. Zhu J, Yamane H, Cote-Sierra J, Guo L, Paul WE (2006) GATA-3 promotes
Th2 responses through three different mechanisms: Induction of Th2 cytokine
production, selective growth of Th2 cells and inhibition of Th1 cell-specific
factors. Cell Research 16(1): 3–10.
95. Nardelli J, Thiesson D, Fujiwara Y, Tsai F-, Orkin SH (1999) Expression and
genetic interaction of transcription factors GATA-2 and GATA-3 during
development of the mouse central nervous system. Developmental Biology
96. Hong SJ, Choi HJ, Hong S, Huh Y, Chae H, et al. (2008) Transcription factor
GATA-3 regulates the transcriptional activity of dopamine b-hydroxylase by
interacting with Sp1 and AP4. Neurochemical Research 33(9): 1821–1831.
97. Robinson PD, Schutz CK, Macciardi F, White BN, Holden JJA (2001)
Genetically determined low maternal serum dopamine b-hydroxylase levels
and the etiology of autism spectrum disorders. American Journal of Medical
Genetics 100(1): 30–36.
98. Jones MB, Palmour RM, Zwaigenbaum L, Szatmari P (2004) Modifier effects
in autism at the MAO-A and DBH loci. American Journal of Medical Genetics
- Neuropsychiatric Genetics 126 B(1): 58–65.
99. Garnier C, Barthelemy C, Leddet I (1986) Dopamine-beta-hydroxylase (DBH)
and homovanicillic acid (HVA) in autistic children. Journal of Autism and
Developmental Disorders 16(1): 23–29.
100. Muta T (2006) IkB-f: An inducible regulator of nuclear factor-kB. Vitamins
and Hormones 74: 301–316.
101. Motoyama M, Yamazaki S, Eto-Kimura A, Takeshige K, Muta T (2005)
Positive and negative regulation of nuclear factor-kB-mediated transcription by
IkB-f, an inducible nuclear protein. Journal of Biological Chemistry 280(9):
102. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays
applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98(9):
103. Guo T, Taylor RL, Singh RJ, Soldin SJ (2006) Simultaneous determination of
12 steroids by isotope dilution liquid chromatography-photospray ionization
tandem mass spectrometry. Clin Chim Acta 372(1–2): 76–82.
Gene Expression in Autism
PLoS ONE | www.plosone.org13 June 2009 | Volume 4 | Issue 6 | e5775