US20090117562A1

US20090117562A1 - Method and kit for diagnosing Autism using gene expression profiling

Info

Publication number: US20090117562A1
Application number: US12/107,046
Authority: US
Inventors: Valerie Wailin Hu
Original assignee: Individual
Current assignee: George Washington University
Priority date: 2007-04-09
Filing date: 2008-04-21
Publication date: 2009-05-07
Also published as: US20150038369A1; US8962307B2; US20120015838A1

Abstract

This invention relates to DNA microarray technology, and more specifically to methods and kits for identifying autism and autism spectrum disorders in humans.

Description

PRIORITY

This application claims the benefit under 119(e) of U.S. 60/789,593 filed 6 Apr. 2007.

BACKGROUND

1. Field of the Invention
This invention relates to DNA microarray technology, and more specifically to methods and kits for identifying autism and autism spectrum disorders in humans.
2. Description of the Prior Art
Several publications are referenced in this application in parentheses in order to more fully describe the state of the art to which this invention pertains. Full citations for these references are found at the end of the specification. The disclosure of each of these publications is incorporated by reference herein in their entirety.
The autism spectrum encompasses a set of complex multigenic developmental disorders that severely impact the development of language, non-verbal communication, and social skills, and are associated with odd, stereotyped, repetitive behavior and restricted interests. To date, diagnosis of these neurologically based disorders relies predominantly upon behavioral observations often prompted by delayed speech or aberrant behavior, and there are no known genes that can serve as definitive biomarkers for the disorders.
Autism and related autism spectrum disorders (including Asperger's Syndrome and pervasive developmental disorder-not otherwise specified (PDD-NOS)) are considered to be among the most devastating psychiatric illnesses affecting children. The three core symptoms of autism spectrum disorders (ASD) are: 1) deficits in social interactions and understanding, 2) aberrant communication and/or language development, and 3) restricted, repetitive, and stereotyped behaviors [1]. To date, there are no definitive molecular or genetic markers that allow unequivocal diagnosis of ASD, with the exceptions of tuberous sclerosis, Rett's Syndrome, and Fragile X Syndrome [2-12]. Together, these genetically defined mutations are present in only a minority of individuals (<10%) within the broad autism spectrum. The majority of diagnoses are dependent on behavioral characteristics, according to DSM-IV guidelines, using questionnaires such as the Autism Diagnostic Interview-Revised (ADI-R) [13] or the Autism Diagnostic Observation Schedule (ADOS) [14], which are structured to evaluate children who are approximately 2 or older in mental age. Although the guidelines are relatively clear, the individual rater's (eg., parents, teachers, clinicians, therapists) perception of the evaluated behavior leaves much room for ambiguity. Moreover, with the more mildly affected individuals (eg., with Asperger's Syndrome), diagnosis is often not made until well after the child starts school and, even then, the child is often diagnosed with other more common disorders (such as attention deficit disorder or learning disability) before Asperger's Syndrome is considered, which delays appropriate intervention and effective educational programming. Thus, there is a great need to identify biomarkers that can be used consistently in a clinical setting to diagnose ASD. Furthermore, it is important to identify biological processes that are associated with specific ASD phenotypes in order to design effective drug therapies targeted to specific individuals.
Although genetic linkage analyses have identified numerous candidate genes for autism [15], there is little consistent data that would support the use of any (or a combination) of these as biomarkers for ASD. Furthermore, each candidate gene alone lends little insight into the pathophysiology of these disorders, which are believed to arise from dysregulation of multiple genes. Recently, attention has turned to transcriptional profiling approaches [16-19], which involve simultaneous, large-scale expression analysis of thousands of genes on a cDNA (or oligonucleotide) microarray slide, to unravel complex psychiatric disorders. The advantage of transcriptional profiling using microarrays is the ability to study multiple genes in the context of functional gene networks within a living cell, as opposed to forward genetic approaches. So far, application of microarrays to the study of autism has been described in just one study on post-mortem brain tissue from autistic subjects and matched tissue controls [20]. Thirty genes were identified as being differentially expressed in autistic brain samples relative to matched tissue controls on a combination of 2 separate array platforms containing 588 or 9374 cDNA probes, indicating that autism is associated with multiple disturbances in gene expression. Of this list, only a few genes related specifically to neurological functions and, of these, the glutamate receptor system was targeted for further study. In a similar vein, a recent bioinformatics analysis of autism positional candidate genes using biological databases and computational gene network prediction software demonstrates that the often disparate results from genetic studies implicating a multitude of different genes can be coalesced into interconnected but distinct pathways centered on a specific gene or genes (e.g., FOS and TP53), or on a particular biological theme, e.g., apoptosis [15]. Both of these studies suggest the involvement of multiple genes not previously associated with autism and illustrate the power of using a global approach to study this complex disorder.
The experimental strategy used in the study reported here was designed to tease out differences in gene expression among genetically identical individuals with ASD which might relate to observed differences in the degree of expression of autistic symptoms. Inasmuch as natural variations in gene expression are especially low for monozygotic twins [21, 22], such a strategy has been shown to be useful in identifying candidate genes for bipolar disorder [23] and osteoporosis [24]. Moreover, lymphoblastoid cell lines (LCL) derived from blood cells of autistic individuals were used in this study to explore the possibility that biomarkers for autism could be expressed in easily accessible peripheral cells. Indeed, it has been reported previously that LCL from individuals with bipolar disorder displayed altered gene expression in both postmortem brain tissue and lymphoblasts, although one of the genes, LIM, was altered in the opposite direction in LCL [25]. Follow-up genetic association analyses of this gene demonstrated association of a single nucleotide polymorphism with bipolar disorder [26], indicating the usefulness of LCL and DNA microarray analyses in identifying potential biomarkers of a complex neurological disease.
While studies of gene expression in brain tissue may lead to a better understanding of the mechanistic basis for ASD, it is not an appropriate target for diagnostic assays. Ideally, diagnostic assays should use easily obtained patient samples such as blood, although there is no evidence that gene expression or other markers exist in the peripheral blood of ASD patients. However, one may hypothesize that ASD might arise, in part, through dysregulation of expression of specific neuronal genes and that expression differences between affected and unaffected individuals might be present in tissues other than brain. As a test of this hypothesis, we chose to use DNA microarray analysis to examine gene expression in LCL derived from peripheral blood lymphocytes.

SUMMARY

Here we report the first study using a genome-scale approach to identify biomarkers for autism. We demonstrate by gene expression profiling on DNA microarrays that: 1) LCL derived from five monozygotic twin pairs discordant for diagnosed autism and/or language impairment show differential gene expression; 2) a number of the most differentially expressed genes are present in pathways critical to the development and function of the nervous system; 3) there appears to be a quantitative relationship between the severity of the autistic phenotype exhibited by the twins and the expression level of certain genes relative to that of the respective genes in cell lines from non-affected siblings; and 4) approximately half of the most highly differentially expressed genes map in silico to previously reported chromosomal regions containing autism susceptibility genes or quantitative trait loci.
Specifically, one embodiment of the present inventive subject matter includes a method of screening for an Autism Spectrum Disorder in a patient by analyzing differential gene expression patterns by DNA microarray analysis, comprising the steps of: obtaining a nucleic acid sample from peripheral cells of a patient; performing DNA microarray analysis on said nucleic acid samples to obtain a gene expression analysis data set; and comparing said data set to a control data set corresponding to a gene ensemble of differentially expressed genes indicative of Autism Spectrum Disorder, wherein Autism Spectrum Disorder is indicated upon observing statistically significant differential gene expression in 20 or more genes.
In preferred embodiments, the gene ensemble is a gene ensemble according to Table 1, a gene ensemble according to Table 3, a gene ensemble according to Table 4, or a gene ensemble according to Table 5.
Preferred embodiments also include gene ensembles comprising a combination of genes listed in Tables herein, but specifically Tables 1, 3, 4, and 5.
In a preferred embodiment, the peripheral cells comprise lymphoblastoid (lymphoblasts) cells, and more preferably white blood cells. In another preferred embodiment, the peripheral cells are mucosal epithelial cells from buccal swabs, particularly advantageous when considering a neonatal or pediatric patient population.
The control expression profile is preferably of nonautistic individuals, but may also be reflective of autistic individuals depending on how the method is performed.
The DNA microarray analysis preferably includes screening methods selected from the group consisting of large scale microarray analysis, qPCR analysis, Western analysis, and focused gene chip analysis. However, other equivalent techniques known to persons of skill in the art are also contemplated as included herein.
The gene ensemble of differentially expressed genes indicative of Autism Spectrum Disorder preferably include genes involved in inflammation, and more preferably neuroinflammation.
Autism spectrum disorders herein include autism, Asperger's Syndrome, and pervasive developmental disorder-not otherwise specified (PDD-NOS).
The present inventive subject matter also includes an assay for screening drugs and other agents for ability to treat autism spectrum disorder or a disease or disorder related thereto, said assay comprising the steps of: culturing an observable cellular test colony which produces a differential gene expression profile representative of an autism spectrum disorder and which has been inoculated with the drug or agent to be assayed; harvesting a cellular extract from the cellular test colony; determining the level of gene expression in the test colony; and comparing the level of gene expression in the test colony to a control level of gene expression which represents a test colony not inoculated with the drug, or to the test colony prior to inoculation with the drug or agent, wherein the ability of the drug or agent to modulate the production, stability, degradation or activity of gene expression is indicative of the drug or agent's ability to treat autism spectrum disorder.
In a preferred embodiment the Autism Spectrum Disorder is indicated upon observing statistically significant differential gene expression in 20 or more genes. In preferred assay embodiments, the differential gene expression profile representative of an autism spectrum disorder is characterized by a gene ensemble selected from gene ensembles according to Table 1, Table 3, Table 4, or Table 5. Preferred embodiments also include gene ensembles comprising a combination of genes listed in Tables herein, but specifically Tables 1, 3, 4, and 5.
Also contemplated herein are test kits to facilitate diagnosis and treatment of autism spectrum disorders, comprising: an indicator of expressed genes representative of a patient that has a differential gene expression pattern indicative of Autism Spectrum Disorder; reagents and equipment for analyzing a sample of epithelial cells and obtaining a differential gene expression pattern; and directions for use of said kit. Such kits are generally well known in the art, with the novel features herein stemming from the differential gene expression indicative of ASD. In preferred embodiments, the sample comprises white blood cells and also mucosal epithelial cells from a buccal swab, i.e. a cheek swab. It is contemplated that the kit reagents and equipment comprise cDNA microarray analysis materials.
Further preferred embodiments herein include a computer-implemented method for analyzing gene expression to screen for an autism spectrum disorder is provided wherein the method comprises the steps of: compiling data comprising a plurality of measured gene expression signals derived from cDNA microarray analysis of lymphoblastoid cells into a form suitable for computer-based analysis; and analyzing the compiled data, wherein the analyzing comprises identifying gene networks from a number of upregulated biomarker genes and downregulated biomarker genes, wherein the biomarker genes are genes that have a reported role in inflammation. In a preferred embodiment, the genes are ASS, ALOX5AP (FLAP), DAPK1, and IL6ST, as listed in bold in Table 1. It is also contemplated that the biomarker genes are genes involved in nervous system development and function. These include ALOX5AP (FLAP), CD44, CHL1, EGR2, F13A1, FLT1, IL6ST, ITGB7, and NAGLU.
Along these lines, the inventive contribution also includes a computer-readable medium on which is encoded programming code for analyzing autism spectrum disorder gene expression from a plurality of data points which comprises a gene ensemble which is filtered using a log 2 cutoff of 0.58.

BRIEF DESCRIPTION OF THE FIGURES

TABLE 1 is a chart showing significant up- and down-regulated genes from SAM analysis of microarray experiments on 3 sets of monozygotic twins discordant for autism diagnosis.

TABLE 2 is a chart showing expression of ASS, CHL1, and FLAP.

TABLE 3 is a chart showing network focus genes from Ingenuity Pathways Analysis.

TABLE 4 shows the relative expression of candidate genes in monozygotic concordant twin pairs with differential language impairment and in normal twins.

TABLE 5 shows significant genes across five sets of twins with ASD.

TABLE 6 is a global functional analysis.

TABLE 7 shows differentially expressed candidate genes from microarray experiments mapped in silico to autism susceptibility genes.

FIG. 1. Gene networks showing inter-relationship between differentially expressed genes in LCL from 3 discordant autistic twin sets using Ingenuity Pathways Analysis software. The over-expressed (red) and under-expressed (green) genes were identified as significant using SAM analysis (FDR=26.4%) of microarray data across 3 twin pairs. The log 2 expression ratio cutoff was set at ±0.58 and was based upon the mean values for each gene. Genes within this network that have a reported role in nervous system development and function include: ASS, ALOX5AP (FLAP), DAPK1, F13A1, IL6ST, NAGLU, PTGS2, and ROBO1. Gray genes are present but do not meet expression cutoff.

FIG. 2. Gene networks showing inter-relationship between differentially expressed genes in lymphoblastoid cell lines from monozygotic twins discordant in severity of autism spectrum disorder and/or language impairment. The over-expressed (red) and under-expressed (green) genes were identified as significant using SAM analysis (FDR=15.6%) of microarray data across 5 twin pairs. The log 2 expression ratio cutoff was set at ±0.58 and was based upon the mean values for each gene. Differentially expressed genes within this network that have a reported role in nervous system development and function include: ALOX5AP (FLAP), CD44, CHL1, EGR2, F13A1, FLT1, IL6ST, ITGB7, and NAGLU. Gray genes are present but do not meet expression cutoff.

SUPPLEMENTARY TABLE 2 is a table showing case description of subjects from whom LCL were derived and used in the study.

SUPPLEMENTAL TABLE 3 shows primers used for qualitative RT-PCR analyses.

SUPPLEMENTAL FIG. 1 shows a principal components analysis of microarray data from 5 sets of monozygotic twins with ASD.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

List of Abbreviations

ADIR: Autism Diagnostic Interview-Revised
ADOS: Autism Diagnostic Observation ScheduleAGRE: Autism Genetic Resource Exchange
ASD: autism spectrum disorders
ASS: argininosuccinate synthetase
CHL1: cell adhesion molecule with homology to L1CAM
DAPK1: death-associated protein kinase 1
EGR2: early growth response 2 protein (Krox-20)
FLAP: 5-lipoxygenase activating protein
5-HTT: 5-hydroxytryptamine (serotonin) transporter
ITGB7: integrin beta-7LCL: lymphoblastoid cell lines
PPVT: Peabody Picture Vocabulary Test
ROBO1: roundabout, axon guidance receptor
The following definitions are provided to facilitate an understanding of the present invention:
With reference to nucleic acids used in the invention, the term “isolated nucleic acid” is sometimes employed. This term, when applied to DNA, refers to a DNA molecule that is separated from sequences with which it is immediately contiguous (in the 5′ and 3′ directions) in the naturally occurring genome of the organism from which it was derived. An “isolated nucleic acid molecule” may also comprise a cDNA molecule or a recombinant nucleic acid molecule.
When applied to RNA, the term “isolated nucleic acid” refers primarily to an RNA molecule encoded by an isolated DNA molecule as defined above. Alternatively, the term may refer to an RNA molecule that has been sufficiently separated from other nucleic acids with which it would be associated in its natural state (i.e., in cells or tissues). An isolated nucleic acid (either DNA or RNA) may further represent a molecule produced directly by biological or synthetic means and separated from other components present during its production.
The term “oligonucleotide,” as used herein refers to sequences and probes of the present invention, and is defined as a nucleic acid molecule comprised of two or more ribo- or deoxyribonucleotides, preferably more than three. The exact size of the oligonucleotide will depend on various factors and on the particular application and use of the oligonucleotide.
With respect to single stranded nucleic acids, particularly oligonucleotides, the term “specifically hybridizing” refers to the association between two single-stranded nucleotide molecules of sufficiently complementary sequence to permit such hybridization under pre-determined conditions generally used in the art (sometimes termed “substantially complementary”). In particular, the term refers to hybridization of an oligonucleotide with a substantially complementary sequence contained within a single-stranded DNA molecule of the invention, to the substantial exclusion of hybridization of the oligonucleotide with single-stranded nucleic acids of non-complementary sequence. Appropriate conditions enabling specific hybridization of single stranded nucleic acid molecules of varying complementarity are well known in the art. For instance, one common formula for calculating the stringency conditions required to achieve hybridization between nucleic acid molecules of a specified sequence homology is set forth below (Sambrook et al., 1989):
As an illustration of the above formula, using [Na+]=[0.368] and 50% formamide, with GC content of 42% and an average probe size of 200 bases, the T_mis 57° C. The T_mof a DNA duplex decreases by 1-1.5° C. with every 1% decrease in homology. Thus, targets with greater than about 75% sequence identity would be observed using a hybridization temperature of 42° C.
The term “probe” as used herein refers to an oligonucleotide, polynucleotide or DNA molecule, whether occurring naturally as in a purified restriction enzyme digest or produced synthetically, which is capable of annealing with or specifically hybridizing to a nucleic acid with sequences complementary to the probe. The probes of the present invention refer specifically to the oligonucleotides attached to a solid support in the DNA microarray apparatus such as the glass slide. A probe may be either single-stranded or double-stranded. The exact length of the probe will depend upon many factors, including temperature, source of probe and use of the method. For example, for diagnostic applications, depending on the complexity of the target sequence, the oligonucleotide probe typically contains 15-25 or more nucleotides, although it may contain fewer nucleotides. The probes herein are selected to be complementary to different strands of a particular target nucleic acid sequence. This means that the probes must be sufficiently complementary so as to be able to “specifically hybridize” or anneal with their respective target strands under a set of pre-determined conditions. Therefore, the probe sequence need not reflect the exact complementary sequence of the target. For example, a non-complementary nucleotide fragment may be attached to the 5′ or 3′ end of the probe, with the remainder of the probe sequence being complementary to the target strand. Alternatively, non-complementary bases or longer sequences can be interspersed into the probe, provided that the probe sequence has sufficient complementarity with the sequence of the target nucleic acid to anneal therewith specifically.
The term “primer” as used herein refers to a DNA oligonucleotide, either single-stranded or double-stranded, either derived from a biological system, generated by restriction enzyme digestion, or produced synthetically which, when placed in the proper environment, is able to functionally act as an initiator of template-dependent nucleic acid synthesis. When presented with an appropriate nucleic acid template, suitable nucleoside triphosphate precursors of nucleic acids, a polymerase enzyme, suitable cofactors and conditions such as a suitable temperature and pH, the primer may be extended at its 3′ terminus by the addition of nucleotides by the action of a polymerase or similar activity to yield a primer extension product. The primer may vary in length depending on the particular conditions and requirement of the application. For example, in diagnostic applications, the oligonucleotide primer is typically 15-25 or more nucleotides in length. The primer must be of sufficient complementarity to the desired template to prime the synthesis of the desired extension product, that is, to be able anneal with the desired template strand in a manner sufficient to provide the 3′ hydroxyl moiety of the primer in appropriate juxtaposition for use in the initiation of synthesis by a polymerase or similar enzyme. It is not required that the primer sequence represent an exact complement of the desired template. For example, a non-complementary nucleotide sequence may be attached to the 5′ end of an otherwise complementary primer. Alternatively, non-complementary bases may be interspersed within the oligonucleotide primer sequence, provided that the primer sequence has sufficient complementarity with the sequence of the desired template strand to functionally provide a template-primer complex for the synthesis of the extension product.
The term “specific binding pair” as used herein includes antigen-antibody, receptor-hormone, receptor-ligand, agonist-antagonist, lectin-carbohydrate, nucleic acid (RNA or DNA) hybridizing sequences, Fc receptor or mouse IgG-protein A, avidin-biotin, streptavidin-biotin, amine-reactive agent-amine conjugated molecule and thiol-gold interactions. Various other determinant-specific binding substance combinations are contemplated for use in practicing the methods of this invention, such as will be apparent to those skilled in the art.
The term “detectably label” is used herein to refer to any substance whose detection or measurement, either directly or indirectly, by physical or chemical means, is indicative of the presence of the target bioentity in the test sample. Representative examples of useful detectable labels, include, but are not limited to the following: molecules or ions directly or indirectly detectable based on light absorbance, fluorescence, reflectance, light scatter, phosphorescence, or luminescence properties; molecules or ions detectable by their radioactive properties; molecules or ions detectable by their nuclear magnetic resonance or paramagnetic properties. Included among the group of molecules indirectly detectable based on light absorbance or fluorescence, for example, are various enzymes which cause appropriate substrates to convert, e.g., from non-light absorbing to light absorbing molecules, or from non-fluorescent to fluorescent molecules.
Polymerase chain reaction (PCR) has been described in U.S. Pat. Nos. 4,683,195, 4,800,195, and 4,965,188, the entire disclosures of which are incorporated by reference herein.
The phrase “obligate carrier” refers to an individual who is a heterozygous carrier of a gene associated with an autosomal recessive disorder.
A DNA microarray (also commonly known as gene chip, DNA chip, or biochip) is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip forming an array. Exemplary cDNA microarrays of the invention are commercially available and may be purchased from such companies as Agilent Technologies, Affymetrix Inc. (Santa Clara, Calif.), Nanogen (San Diego, Calif.) and Protogene Laboratories (Palo Alto, Calif.).
Microarrays are used to quantify mRNAs transcribed from different protein-encoding genes. RNA is extracted from a cell or tissue sample, then converted to cDNA. Fluorescent tags, (usually Cy3 and Cy5) are enzymatically incorporated into the newly synthesized cDNA or can be chemically attached to the new strands of DNA. A cDNA molecule that contains a sequence complementary to one of the single-stranded probe sequences on the array will hybridize, via base pairing, to the spot at which the complementary reporters are affixed. The spot will then fluoresce (or glow) when examined using a microarray scanner. The fluorescence intensity of each spot is then evaluated in terms of the number of copies of a particular mRNA, which ideally indicates the level of expression of a particular gene.
DNA microarrays can be used to detect RNAs that may or may not be translated into active proteins. This analysis is termed “expression analysis” or expression profiling. Since there can be tens of thousands of distinct reporters on an array, each microarray experiment can accomplish the equivalent number of genetic tests in parallel.
In a preferred embodiment of the invention, cDNA microarrays were prepared on aminoalkylsilane coated microscope slides (Sigma, St Louis, Mo.) using a pin-and-ring arrayer (Affymetrix 417, Bedford, Mass.).
Oligonucleotide microarrays (or single-channel microarrays), are also contemplated herein. There, the probes are designed to match parts of the sequence of known or predicted mRNAs. There are commercially available designs that cover complete genomes from companies such as Affymetrix, or Agilent. These microarrays give estimations of the absolute value of gene expression and therefore the comparison of two conditions requires the use of two separate microarrays.
Long Oligonucleotide Arrays are composed of 60-mers, and are produced by either ink-jet or robotic printing. Short Oligonucleotide Arrays are composed of 25-mer oligos, and are produced by photolithographic synthesis (Affymetrix). More recently, Maskless Array Synthesis from NimbleGen Systems has combined flexibility with large numbers of probes. Arrays can contain up to 390,000 spots, from a custom array design.
Statistical analysis of the expression differential can be performed using the 1-sample t-test, but also may include other statistical methods. For example, the one-class Significance Analysis of Microarrays (SAM) analysis is contemplated as within the scope of the inventive subject matter.
The following examples provide illustrative methods of practicing the instant invention, and are not intended to limit the scope of the invention in any way.

EXAMPLES

Differential Gene Expression in Lymphoblastoid Cell Lines from Monozygotic Twins Discordant for Classic Autism

To determine whether LCL derived from individuals with autism exhibit patterns of gene expression that may be relevant to autism spectrum disorders (ASD), gene expression profiling was performed on LCL derived from 3 sets of male monozygotic twins, one of each pair who met standard diagnostic criteria for autism based on the ADI-R. In each case, the other twin, while not clinically autistic, exhibited autistic traits and was classified either as “broad spectrum” or “not quite autistic” according to guidelines described by the Autism Genetic Resource Exchange (AGRE) repository (http://www.agre.org). Two of the three twin pairs had an unaffected sibling and these were also used for comparison with their respective twin siblings. All of these assays employed an experimental design in which RNA from twin siblings were cohybridized on two-color spotted microarrays containing 39,936 human cDNA elements. Each microarray experiment involved dye-reversal replicates, and was performed in duplicate or, in one case, triplicate for the different sets of twins. The mean log 2ratios of each gene were used for SAM analyses of the biological replicates.
Principal components analysis (PCA) of the combined microarray data with respect to samples from the 3 discordant twin sets showed that genotype is responsible for the major portion of the variation in differential gene expression, reflecting the expected transcriptome heterogeneity among unrelated individuals (Supplementary FIG. 1). The microarray data from the 3 sets of discordant twins was analyzed using SAM in order to identify genes that were significantly different from log 2=0 across the biological replicates (n=3). Twelve hundred genes were identified as significant with a median FDR of 26%. Twenty-five genes were found to be up-regulated at least 1.5-fold in the more severely affected twin relative to the other twin (log 2(ratio)≧0.58) and 19 genes were down-regulated by at least 1.5-fold (Table 1). Of these, nine of the 26 known genes (representing seven unique genes) correspond to genes involved in neurological development, function, or disease. Because of this surprising finding, we used quantitative RT-PCR (qPCR) to confirm the differential expression of these specific genes. As shown in Table 1, qPCR confirmed the relative expression levels of all but one of these, including argininosuccinate synthetase (ASS), close homologue of L1 (CHL1), cell death-associated kinase (DAPK1), 5-lipoxygenase-activating protein (ALOX5AP or FLAP), interleukin-6 signal transducer (IL6ST), and Roundabout homolog 1 precursor (ROBO-1).
Moreover, when the expression profile of cells from the autistic twin was directly compared against that of his respective normal sibling in a dye-reversal microarray experiment, neurologically relevant genes represented 3 of the top 5 most differentially expressed genes (Table 2). Interestingly, the mean log 2(ratio) of each of these, ASS, CHL1, and FLAP, are higher for the autistic twin than for the more mildly affected twin when each is compared against their respective normal sibling, suggesting a quantitative relationship between differential gene expression (relative to normal individuals) and severity of autistic symptoms. These quantitative differences have also been confirmed by qPCR analyses.
Network analysis shows overlap of pathways involving differentially expressed, neurologically relevant genes
Pathway analysis using Ingenuity Pathways Analysis Software of the significant genes from the SAM analysis further revealed that 25 out of 58 network focus genes with log 2(ratio) greater than ±0.58 (±1.5-fold) in at least one discordant twin set are involved in neurological function or disease (Table 3). This expression cutoff was selected because of reports in the literature that 1.47-fold increases or decreases in gene expression are generally reproducible when Lowess normalization is used (Yang et al, 2002), and our own ability to confirm expression changes of at least 1.5-fold by qPCR. Of particular note is the gene network that is derived from pathway analysis of the mean expression values (with log 2 ratio ≧±0.58) across 3 sets of discordant twins which shows that the majority of significantly differentially expressed genes are part of an extended network centered on TNF (FIG. 1). The neurological functions of 7 of these network focus genes are described in Table 3. The collective data from the above-mentioned microarray and qPCR studies suggested a short list of novel candidate autism susceptibility genes for further evaluation.
Differential Expression of Autism Candidate Genes in “Concordant” Autistic Twins
Expression analysis of autism candidate genes in LCL from two sets of twins in which both individuals were diagnosed as autistic surprisingly showed differential expression of several of the candidate genes (Table 4). However, in each case, the “concordant” autistic twin siblings were found to be discordant with respect to severity of language impairment based on each twin's scores on the Peabody Picture Vocabulary Test (PPVT) (see Supplementary Table X for profile of subjects studied). Thus, when microarray data from the more language-impaired twin (lower percentile PPVT score) was compared relative to the less impaired twin, the differential gene expression profile was consistent with the results obtained from the “discordant” twin sets. This result underscores the importance of considering autistic phenotype and/or severity as a means of reducing heterogeneity of gene expression in the search for biomarkers of autism. Interestingly, as shown in Table 4, expression analysis of the candidate genes in cells from monozygotic nonautistic twins demonstrated that two of the genes, CHL1 and ROBO1, were differentially expressed. However, it is worth noting that this set of “normal” twins has two autistic siblings. It is therefore possible that the differential expression of these two neurologically relevant genes is not coincidental, but does not, by itself, meet the threshold for association with an autistic phenotype. Alternatively, this result might suggest that these genes are not involved in autism. Clearly, this observation on only one set of normal twins warrants further investigation preferably with twins with no autism in their family background, but it is difficult to obtain normal monozygotic twins with no autistic siblings from the AGRE repository which focuses on collecting samples from pedigrees with familial autism.
The serotonin transporter (5-HTT) gene is also differentially expressed in lymphoblastoid cells from monozygotic twins discordant in severity of autism and/or language impairment
To evaluate whether differential expression of the serotonin transporter (5-HTT), which is strongly implicated in autism, can be detected in LCL from the autistic and nonautistic twins, qPCR analyses were performed, as 5-HTT is not represented on the microarray platform. Results indicated that, while there is no difference in 5-HTT expression between the nonautistic twins, there is a significant decrease in expression in the more severely affected twin in all of the autistic twin pairs studied, as shown in Table 4. Reduced expression of 5-HTT in blood-derived cells may explain hyperserotonemia in a subset of autistic individuals [27]. It should also be noted that a polymorphism in the promoter region of 5-HTT which results in reduced transcription of 5-HTT is a factor in anxiety related traits [28, 29], common in autism. The present finding suggests that LCL, or their precursor blood lymphocytes, may be useful as reporter cells to evaluate neurologically relevant gene expression differences between autistic and normal individuals.
Network and global functional analyses of the pooled microarray data on monozygotic twins with autism highlight genes involved in nervous system development and function
Because of the observed relationship between severity of symptoms and differential expression of candidate genes across the 5 autistic twin pairs studied, SAM was applied to microarray data from all 5 sets of twins to identify genes that were significantly up- or down-regulated across all twin pairs, each pair of which exhibited differential severity with respect to language ability (Table 5). Once again, pathway analysis of the differentially expressed significant genes revealed an extended network centered on TNF, connecting a number of neurologically relevant genes (FIG. 2). Global functional analysis of the significant differentially expressed genes from 5 pairs of twins further shows that genes related to “nervous system development and function” are among the most statistically significant, enriched genes across the 5 sets of twins (Table 6).
In Silico Mapping of Differentially Expressed Genes to Chromosomal Regions Containing Autism Candidate Genes or Quantitative Trait Loci (QTLs)
Although most of the differentially expressed genes identified in this study are novel candidate genes with respect to autism, Table 7 shows that 6 out of 8 of the novel candidate genes listed in Table 4 [and approximately half of the differentially expressed genes listed in Tables 3 and 5 (Supplementary Table 1)] map to chromosomal regions containing previously reported autism candidate genes or recently identified QTLs for spoken language and nonverbal communication. This observation is interesting in that the overlay of expression data onto genetic data allows us to focus on expressed, neurologically relevant genes that may relate to the functional phenotype. Taken together with the network and global functional analyses described above, these results present a compelling argument for further investigation of blood-derived cells as surrogates to identify biomarkers for autism.

Discussion

These studies show, for the first time, that candidate genes for autism may be expressed in peripheral cell lines derived from individuals with ASD. This observation is a critical step towards development of a diagnostic screen for autism based on biomarker detection in an easily accessible tissue (i.e., blood)
In this study, DNA microarrays containing ˜40K human cDNA probes were utilized to examine differences in gene expression profiles in LCL derived from 5 pairs of monozygotic twins with ASD. Three sets of twins were discordant with respect to clinical diagnosis of autism, and 2 sets (both diagnosed as autistic) differed with respect to severity of language impairment. The most remarkable finding of this study is that global functional analysis of the significant differentially expressed genes in LCL from these 5 sets of twins identifies “Nervous system development and function” as a top “high level function” that is significantly enriched within the gene datasets (Table 6). Moreover, in silico mapping of our gene expression data demonstrates that many of the differentially expressed genes are located in or close to chromosomal regions previously identified as autism susceptibility loci by genetic analyses (Table 7 and Supplementary Table 3). Quantitative RTPCR analysis has further confirmed the differential expression of a subset of our novel candidate genes in the majority of twin sets studied. Several of these candidate genes and their associated gene networks may provide insight into potential mechanisms involved in the autistic phenotype(s). One of the striking results of the pathway analyses is that a relatively large number of the differentially expressed, neurologically relevant genes are linked in networks that are centered on genes involved in inflammation (see FIGS. 1 and 2). These include the proteins ASS, ALOX5AP (FLAP), DAPK1, F13A1, IL6ST, NAGLU, and ROBO1. The protein ASS regulates the rate-limiting step involved in nitric oxide (NO) production through regeneration of arginine from citrulline, a byproduct of the nitric oxide synthetase (NOS) reaction [30]. Since NO is a major signaling molecule in the brain that has been implicated in several psychiatric disorders, including autism [31], the increased expression of ASS may be of potential relevance to the autistic phenotype. ASS has also been shown to be induced in a rat model of brain inflammation [32], which would be consistent with the hypothesis that neural inflammation may play a role in autism [33]. DAPK1, a cell death-associated serine/threonine kinase which is involved in suppression of integrin activity and disruption of matrix survival signals [34], is also induced by inflammation [35]. Furthermore, the fact that IL6ST (gp130) is increased in LCL from the more severely affected twin, may complement previous observations that IL-6 is the most elevated inflammatory cytokine in the middle frontal gyrus and anterior cingulate gyrus of brain autopsy tissue from autistic individuals [33]. While upregulation of ASS, DAPK1, and IL6ST may be responses to inflammation, ALOX5AP (FLAP) mediates inflammation through activation of 5-lipoxygenase which is involved in leukotriene production [36]. Interestingly, 5-lipoxygenase has been implicated in aging and neurodegenerative diseases [37], as well as other psychiatric disorders [38], including anxiety and depression, which are frequently co-morbid conditions of autism. Collectively, the involvement of these specific genes that are associated with neurological function and disease and their presence in pathways regulated by inflammatory mediators lend further support to the neural inflammation model for autism.
In addition to the possible role of genes involved in inflammation, a review of the gene list in Table 3 suggests several additional recurring themes among the differentially expressed genes with neurological functions: neuronal survival, neurite extension/guidance, and myelination. In this regard, altered expression of EGR2 may be particularly significant. EGR2 (Krox-20) is a transcription factor involved in the development of the brain and peripheral nervous system, routing of axons, and myelination [39]. Some of these functions may be related to EGR2-mediated regulation of ROBO1, which is involved in neuronal differentiation and axon guidance [40, 41], and integrin beta-7 (ITGB7) which has been implicated in chronic demyelinating disease [42]. The expression levels of all three of these genes are relatively reduced with increased severity of autism or language impairment. The involvement of cell migration and survival in the pathophysiology of autism is also implicated by the higher expression level of CHL1, a novel neural cell adhesion molecule that is involved in neurite migration, outgrowth, connectivity, and survival, which is associated with the more autistic phenotype. Deficiency in CHL1 has been shown to be associated with mental and motor impairments as well as with alterations in exploratory and emotional behavior in mice [43, 44], characteristics that are often associated with autism. However, the effect of CHL1 overexpression has yet to be determined. While the function of such neurologically relevant genes in lymphoblastoid cell lines is unknown, if expression of these genes can be shown to be consistently altered in LCL, these cells, and by inference their precursor blood lymphocytes, can potentially be used as reporter cells for diagnosis of ASD.
The observed relationship between differential gene expression and severity of ASD between monozygotic twins suggests a role for epigenetic factors in ASD. Indeed, epigenetic differences between monozygotic twins have been examined as possible causes for discordancy in schizophrenia as well as bipolar disorder [46-48]. A recent report on normal monozygotic twins indicates that epigenetic differences arise over time, increasing with age and with separation from each other after birth [45]. Possible epigenetic mechanisms leading to differences in gene expression include differential methylation, differences in histone acetylation, and micro RNA, although there is no available evidence linking any of these to autism at this time. On the other hand, a mutation in a methyl-CpG binding protein, X-linked MeCP2, has been identified as being involved in 80% of all cases of Rett Syndrome, a developmental disorder which overlaps ASD, thus implicating the importance of methylation-dependent gene expression in at least this related disorder. One could therefore postulate that differential methylation or differential histone acetylation might give rise to differential expression in LCL from monozygotic twins with ASD and test for global changes in methylation or histone acetylation as done by Fraga et al [45]. If present, these differences could, in turn, be the result of stochastic processes, environmental factors, or the immortalization procedure used to generate cell lines from primary lymphocytes. The latter possibility could be further tested by evaluation of the methylation/acetylation patterns of DNA/histones in primary lymphocytes from monozygotic twins discordant in severity of autism or language impairment within autism which, while interesting, is outside of the scope of this study. Regardless of origin, the gene expression differences between monozygotic twins who present with differential severity along the autism spectrum or within a specific domain (eg., language) are potentially useful, not only as biomarkers for ASD, but also as indicators of genes or metabolic/signaling pathways that may contribute to the autistic phenotype. While our short list of candidate genes focuses on genes that are similarly differentially expressed between twin sets, the set of differentially expressed, neurologically relevant genes that are unique to a given twin set may also be important to the determination of a specific autistic phenotype. Inasmuch as our microarray analyses directly compared genetically matched individuals who differ only in degree of expression of autistic symptoms, it is likely that other genes, not identified in our study, also play a role in the etiology and pathophysiology of autism. This experimental design possibly explains why the candidate genes identified here are different from those reported by an earlier genomic study [20] which compared autopsy brain tissues from autistic and normal (nonautistic) controls (i.e., case-control studies). On the other hand, it is interesting that many of our novel genes map to genetically identified autism susceptibility loci or QTLs (Table 7 and Supplementary Table 1).
Aside from identifying novel candidate genes for autism, our study also demonstrates the need for phenotype definition or subgrouping according to severity along a specific behavioral domain for biological studies of autism. Specifically, the results show that the differential gene expression profiles of concordantly autistic twins with differential severity of language impairment mirror some of the differences in gene expression which are observed in the twins with discordant diagnosis of autism, who also exhibit differential language deficits. Thus, for case-control studies in which individuals from the general population are compared against unrelated controls, subgrouping the autistic individuals by phenotype or stratifying them according to severity of symptoms may provide more clarity in analyzing biological data. Towards this goal, we have used several different clustering methods to divide over 1300 autistic individuals into endophenotypic subgroups (eg., language, nonverbal communication, and savant skills) based on item scores on the ADIR questionnaire (manuscript in preparation). Based on these methods, the twin siblings analyzed in this study each fall into different clusters in all but one case involving a discordant twin set. These “endophenotypic” differences may therefore account for some of the differences in gene expression profiles between the siblings as well as among the different sets of twins.

Conclusions

In summary, this data indicates that LCL from genetically identical autistic individuals who differ in severity of autistic symptoms and/or language impairment exhibit differential expression of genes relevant to neurological development, structure, and function. Many of these genes map to regions previously identified by genetic analyses as harboring autism susceptibility genes or QTLs, demonstrating the power of combined genomic-genetic analyses to prioritize autism candidate genes for further study. In addition, a quantitative relationship is seen between severity of symptoms and expression of several autism candidate genes when twins with classic autism or with milder autistic traits are compared against their respective normal siblings. The finding that gene expression differences were also observed in cells from twins who were both diagnosed as autistic, but who differed in severity in language deficits strongly suggests that autistic phenotype as well as severity of symptoms must be considered in gene expression studies on autistic individuals in order to reduce biological heterogeneity due to these factors. Collectively, these studies provide proof-of-principle that LCL (or peripheral blood cells) may exhibit biomarkers relevant to autism, and further suggest their potential usefulness as reporter cells in developing a diagnostic screen for autism. While it is unlikely that microarray studies on LCL will identify the etiology(ies) of autism, this global approach to gene expression is expected to highlight molecular or pathway defects related to the pathophysiology of the condition which can be targeted for drug therapies. Moreover, as opposed to fixed autopsy tissues in which RNA may have degraded, a live cell model can be used to examine the functional consequences of the genomic alteration(s) and the efficacy of different pharmacological agents in ameliorating the impaired function.
Materials and Methods
Cell Lines and Culture Conditions
Lymphoblastoid cell lines (LCL) derived from lymphocytes of 5 pairs of monozygotic twins with ASD were obtained from the Autism Genetic Resource Exchange (AGRE; Los Angeles, Calif.) and cultured in DMEM with 15% fetal bovine serum and 1% penicillin-streptomycin. Cell lines from normal siblings of 2 sets of twins were also obtained for comparison of gene expression profile with that of their respective autistic siblings. In addition, cell lines from a set of non-autistic monozygotic twins were also studied. To minimize differences in gene expression due to culture and sample workup conditions, all samples that underwent direct comparison of gene expression profile were cultured and harvested at the same time using the same medium preparation and RNA isolation reagents.
Description of Individual Donors of Cell Lines
Supplementary Table 2 provides a case description of all of the subjects included in this study. In brief, all of the twin pairs and normal siblings, with the exception of 1 set of twins, were Caucasian males between the ages of 6 and 16 at the time that blood was drawn. The remaining set of twins (age 12) was of mixed race (black, Hispanic) but had the same mother as one of the Caucasian pairs of autistic twins. For 3 sets of twins (designated “discordant” twins), one twin of each pair met standard diagnostic criteria for autism based on the Autism Diagnostic Interview-Revised (ADIR) [13]. In each case, his co-twin, while not clinically autistic, exhibited autistic traits and could be considered to be on the autism spectrum. These co-twins were described either as “Broad spectrum” or “Not quite autistic (NQA)” by the AGRE repository according to criteria established on the basis of ADI-R scores. Gene expression in cell lines from two of these twin pairs were also directly compared against the gene expression profile in cell lines from their respective “normal” sibling. Two of the 5 sets of twins with ASD (designated “concordant” twins) were examples in which both co-twins were diagnosed with autism, but who were discordant in severity of language impairment, as indicated by their respective percentile scores on the Peabody Picture Vocabulary Test (PPVT). The Autism Diagnostic Observation Schedule (ADOS) [14] was used to diagnose one of these sets of twins. None of the individuals whose cells were used presented with any co-morbid condition or mental retardation. All of the phenotypic data were obtained through the AGRE databases (www.agre.org).
DNA Microarray Analyses
RNA was isolated from the LCL using TRIzol Reagent (Invitrogen, CA) according to the manufacturer's protocol. The RNA was further purified using Centricon-X columns and tested for purity on RNA 6000 NanoChips using the Agilent 2100 Bioanalyzer. Labeled cDNA was obtained by incorporation of 5-(3-aminoallyl)-2′deoxyuridine-5′-triphosphate (Ambion, TX) during first-strand synthesis, followed by coupling to the ester of cyanine (Cy)-3 or Cy-5 (Molecular Probes, OR) as appropriate according to Standard Operating Protocol (SOP) M004 on The Institute for Genomic Research (TIGR) website (http://www.tigr.org). For two-color microarray analyses, the Cy5- and Cy3labeled cDNA from each pair of twins (or twin and normal sib) were co-hybridized using TIGR SOP M005 to spotted microarrays (TIGR 40K Human Set) containing 39,936 human cDNA probes which were obtained from Research Genetics. Dye reversal (flip-dye) replicates were included in all analyses, and at least 2 sets of replicates were carried out for each pair of monozygotic twins. Gene expression levels were derived from the scanned hybridized arrays using TIGR SpotFinder, MIDAS, and MeV analysis programs which are all part of the TM4 Microarray Analysis Software Package available at the above-cited website. These programs have all been previously described in detail [49]. Data analyses included normalization using local LOWESS followed by standard deviation regularization across individual subarrays, and flip-dye consistency checking for dye reversal replicates as implemented in MIDAS [50, 51]. The SAM (Significance analysis of microarrays) module within MeV was used to determine statistical significance of differential expression and false discovery rates (FDR) for genes from biological replicates.
Quantitative RT-PCR
Total RNA was reverse transcribed into cDNA using the iScript cDNA Synthesis Kit (Bio-Rad, Hercules, Calif.). Briefly, 2 μg of RNA were added to a 40 μl 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 25° C. for 5 minutes followed by 42° C. for 30 minutes and ending with 85° C. for 5 minutes. The cDNA reactions were then diluted to a volume of 100 μl with water. Real-time PCR was carried out on a 7900HT Sequence Detection System from Applied Biosystems using the iTaq SYBR Green Supermix with ROX (Bio-Rad, Hercules, Calif.). Gene-specific primers at a final concentration of 200 nM and 1 μl of cDNA templates were combined into 20 μl reaction mixes and run through 40 cycles of PCR. Quantitation was performed using the Universal 18S rRNA primers (Ambion, Austin, Tex.) with samples normalized to their 18S rRNA standard curves. Forward and reverse primers are described in Supplementary Table 3.
Network Prediction Analyses
Lists of differentially expressed genes identified as “significant” by the 1-sample t-test on microarray data across different sets of twins were analyzed using Ingenuity Pathways Analysis (Ingenuity Systems, Inc.), a web-delivered application that enables biologists to discover, visualize and explore therapeutically relevant networks significant to their specific experimental results (e.g., gene expression array data sets). Specifically, a data set containing gene identifiers (in this case, GenBank Accessions) and their corresponding expression values were uploaded as an Excel spreadsheet using the template provided in the application. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. The gene list was filtered prior to analysis with Ingenuity by using a log 2 cutoff of 0.58. These genes were then used as the starting point for generating biological networks. The networks are displayed graphically as nodes (genes/gene products) and edges (the biological relationships between the nodes). Human, mouse, and rat orthologs of a gene are stored as separate objects in the knowledge base, but are represented as a single node in the network. The intensity of the node color indicates the degree of up-(red) or down-(green) regulation. When networks from different samples are merged, yellow node color denotes overlapping differentially expressed genes from two or more samples. Nodes are displayed using various shapes that represent the functional class of the gene product. (See Ingenuity's website [http://www.ingenuity.com] for shape legend).
Global Functional Analyses
Biological functions were assigned to the overall analysis (across data from 5 monozygotic twin pairs) by using the findings that have been extracted from the scientific literature and stored in the Ingenuity Pathways Knowledge Base. The biological functions assigned to the analysis are ranked according to the significance of that biological function to the analysis. A Fisher's exact test is used to calculate a p-value determining the probability that the biological function assigned to the analysis is explained by chance alone.
In Silico Mapping of Differentially Expressed Genes
The physical locations of each of the significant differentially expressed genes with log 2ratio≧±0.58 were obtained using TIGR's Resourcerer Gene Annotation Software. These locations were then compared manually to those of autism candidate genes (ACG) or quantitative trait loci (QTLs) identified on the basis of genetic linkage and association studies.
Systems for Analysis of Arrays
In an embodiment, the present invention provides systems for carrying out array analysis. Thus, in an embodiment, the present invention comprises a computer-readable medium on which is encoded programming code for analyzing gene expression from a plurality of data points comprising a gene list which is filtered using a log 2 cutoff of 0.58.
Also in an embodiment, the present invention may comprise a computer-readable medium on which is encoded programming code for analyzing gene expression comprising code for: (a) determining cross-correlation between at least two genes within a group, wherein cross-correlation indicates a gene involved with neuronal development and function.
Embodiments of computer-readable media include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor with computer-readable instructions. Other examples of suitable media include, but are not limited to, a floppy disk, CD-ROM, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. The instructions may comprise code from any computer-programming language, including, for example, C, VISUAL C#®, VISUAL BASIC®, VISUAL FOXPRO®, Java, and JavaScript.
As described above, the system may comprise an imaging unit as well as a means for the user to interact with the system as the analysis proceeds. Thus, in an embodiment, the present invention further comprises a unit for collecting and/or compiling data from said plurality of measured signals and transmitting said data to said computer, and a unit for transmitting the results of said analysis to a user.

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63. Bacchelli E, Blasi F, Biondolillo M, Lamb J A, Bonora E, Barnby G, Parr J, Beyer K S, Klauck S M, Poustka A et al: Screening of nine candidate genes for autism on chromosome 2q reveals rare nonsynonymous variants in the cAMP-GEFII gene. Mol Psychiatry 2003, 8 (11):916-924.
64. Philippe A, Martinez M, Guilloud-Bataille M, Gillberg C, Rastam M, Sponheim E, Coleman M, Zappella M, Aschauer H, Van Maldergem L et al: Genome-wide scan for autism susceptibility genes. Paris Autism Research International Sibpair Study. Hum Mol Genet 1999, 8 (5):805-812.
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66. Wassink T H, Piven J, Vieland V J, Huang J, Swiderski R E, Pietila J, Braun T, Beck G, Folstein S E, Haines J L et al: Evidence supporting WNT2 as an autism susceptibility gene. Am J Med Genet 2001, 105 (5):406-413.
67. Jamain S, Quach H, Betancur C, Rastam M, Colineaux C, Gillberg I C, Soderstrom H, Giros B, Leboyer M, Gillberg C et al: Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat Genet 2003, 34 (1):27-29.
68. Lamb J A, Barnby G, Bonora E, Sykes N, Bacchelli E, Blasi F, Maestrini E, Broxholme J, Tzenova J, Weeks D et al: Analysis of IMGSAC autism susceptibility loci: evidence for sex limited and parent of origin specific effects. J Med Genet 2005, 42 (2):132-137.

These references are incorporated herein in their entirety, particularly as they relate to teaching the level of ordinary skill in this art and for any disclosure necessary for the commoner understanding of the subject matter of the invention as defined by the claims.
It will be clear to a person of ordinary skill in the art that the above embodiments may be altered or that insubstantial changes may be made without departing from the scope of the invention. Accordingly, the scope of the invention is determined by the scope of the following claims and their equitable Equivalents.

TABLE 1

Significant up- and down-regulated genes from SAM analysis of microarray experiments
on 3 sets of monozygotic twins discordant for autism diagnosis with log2 (ratio) ≧±0.58

Genbank#	Gene name or description	Mean log2 (ratio)*	Obs. d score^Δ	qPCR^¥

	Upregulated (log2 (ratio) ≧0.58)
R45254	Unknown protein	1.19	2.95
AA448599	F13A1, clotting factor XIIIa precursor	1.08	2.26
AA676466	ASS, argininosuccinate synthetase (aa 1-412)	0.92	5.92
AA992985	Unknown protein	0.88	2.38
AA676405	ASS, argininosuccinate synthetase (aa 1-412)	0.85	4.56	1.47
W07099	NAGLU, N-acetylglucosaminidase, alpha	0.85	2.24	−0.28
AA044267	P2X5a	0.80	2.36
T49652	FLAP, ALOX5AP	0.79	1.88	1.01
H57830	histone H1(0) (aa 1-194)	0.78	4.53
R00276	CD38 alt	0.76	1.45
AA488070	Unknown protein	0.75	1.63
W69399	histone H1(0) (aa 1-194)	0.74	7.15
H09567	PAG1	0.74	1.97
AA609189	Unknown protein	0.73	1.87
H02307	FLAP, ALOX5AP	0.70	1.79
N50114	PAG1	0.69	2.30
AI091671	Unknown protein	0.67	2.14
N70181	PLEKHG1	0.66	2.85
H24011	Homeodomain-like protein	0.64	1.66
AA412520	Unknown protein	0.63	1.97
AA521362	CR2 receptor	0.62	1.80
AI371096	DAPK1, death-associated protein kinase 1	0.62	1.58	0.65
T61343	IL6ST, IL6 signal tranducer, gp130	0.59	1.91	0.58
N29918	ZBTB10	0.59	1.55
T90067	EIF2C2	0.59	3.24
	Downregulated (log2 (ratio) ≦−0.58)
T67053	IGLC2	−2.39	−5.93
W73790	IGLL1	−2.00	−8.34
H18423	Unknown protein	−1.98	−8.42
AA448157	CYP1B1	−0.95	−2.38
AA644099	Unknown protein	−0.89	−1.73
AA933744	ECAT11	−0.84	−1.97
AI018127	Unknown protein	−0.83	−2.26
AA451886	CYP1B1	−0.77	−2.90
AA682565	Unknown protein from neuroblastoma	−0.72	−3.06
AI223429	Unknown protein	−0.69	−2.37
AA450353	ELMOD1	−0.69	−1.87
AA873578	IGHG1	−0.67	−2.29
R33402	SAMSN1	−0.67	−1.92
AA173755	ROBO1, roundabout 1	−0.66	−3.00	−0.93
AA022886	retinal degeneration B beta	−0.64	−2.70
AA063573	SAMSN1	−0.64	−1.74
H99699	mitochondrial aconitase	−0.63	−2.79
AI290663	CYBASC3	−0.60	−3.20
AA449333	Rab22b	−0.58	−1.71

*Mean log₂(ratio) of gene expression in lymphoblastoid cell lines from children exhibiting classic autism to cell lines from less affected monozygotic twin sibs.
^ΔObserved d score from SAM analysis for which median FDR was 26.4%.
^≠Mean log₂(ratio) of qPCR data from 3 sets of monozygotic twins. Each gene was analyzed in triplicate and the mean log₂(ratio) for each respective gene was averaged among the 3 sets. Only one gene, NAGLU, was not confirmed by qPCR, possibly because of suboptimal choice of primers for qPCR.
Genes in boldface type have been shown to be relevant to neurological development, structure, or function (See Table 3).

TABLE 2

Expression of ASS, CHL1, and FLAP in 2 sets of discordant monozygotic
twins relative to expression levels in their respective normal siblings

Gene name	Genbank #	A1	M1	A2	M2

ASS	AA676405	0.65 (0.81)*	−0.13 (0.27)	1.77 (3.04)	0.18 (0.97)
CHL1	H15267	1.60 (1.64)	0.67 (1.39)	1.15 (0.78)	0.95 (0.60)
FLAP	T49652	0.71 (1.10)	0.13 (0.77)	1.40 (1.45)	−0.09 (−0.39)

Values are mean log₂(ratio) measures of gene expression between a twin and his respective normal sibling, obtained by DNA microarray analyses with dye reversal replicates
*Values in parentheses are mean log₂(ratio) measures obtained by triplicate qPCR analyses.
A = autistic twin as diagnosed by ADI-R scoresheet
M = more mildly affected twin who did not meet ADI-R criteria for autism

TABLE 3

Network focus genes from Ingenuity Pathways Analysis with
log₂(ratio) >±0.58

GenBank #	Gene	Neurological function or disease

Upregulated

T49652	ALOX5AP	neuronal signaling; possibly
		neurodegenerative diseases
AA991590	APOC1
AA147170	ALS4	ataxia-ocular apraxia
AA676466	ASS	involved in nitric oxide production
H21041	ATF3	extension of neurites
AA702350	AUTS2	Asperger's syndrome
AI341427	BCAT1
AA430367	CBS
R00276	CD38
AA283949	CDC14A
N67039	CDK6
H15267	CHL1	extension of neurites; organization of mossy
		fibers
AA521362	CR2
AA884403	CTF1	myelination, differentiation of neurons
AI371096	DAPK1	apoptosis of hippocampal neurons
W00789	DST	coalignment of neurofilaments, projection
		of axons; dysmyelination
AA448599	F13A1	stroke
AA149640	FLT1	VEGF-induced release of nitric oxide
AA070902	GGA2
AI375302	HMGB1	extension of neurites
AI539460	IL7
AA406546	IL6ST	myelination, development of motor neurons,
		retraction of dendrites
H09062	MLSTD1
W07099	NAGLU	neurogenesis; vacuolation of neurons
AA598611	NR4A2	neurogenesis; metabolism of dopamine
AA707195	NTRK2	survival of Purkinje cells; apoptosis
		of neurons
AA044267	P2RX5
H09567	PAG	possible role in chronic neuroinflammation
AA972337	PAWR
AA489629	PBEF1
AI016039	PLXNB2
R80217	PTGS2	activation of astrocytes; spatial memory
		in mice; apoptosis of neurons
AA495950	RRM2B
R27457	SLC38A2
AI091460	SOS1
N63153	SPRED1
AI040821	TERE1
AA970358	TSLP

Downregulated

AA779727	ADAM19	development of septum
R01732	AMPD3
AA478589	APOE	quantity/morphology of neurons; neurite
		extension; learning in mice
AA984646	C7orf2
AA448157	CYP1B1
AA446027	EGR2	myelination; development of motor
		neurons; routing of axons
AA149096	HCK
AA620511	HSPA8
W73790	IGLL1
AI380522	ITGB7	chronic demyelinating disease
AA679503	KIF1B	morphology and size of brain; neuron
		survival
AA029283	LARGE
T83159	LSP1
AI351740	LTB	neurological disorder in rats
AA022886	PITPNC1
AI126054	PTK2
AA173755	ROBO1	axon guidance
AA457700	SCD	neural regeneration
AA504211	TNFSF11
N68465	UAP1

Dataset included genes derived from SAM test across three twin sets that met expression cutoff in at least one of the twin sets.
Genes in Boldfaced type are ones of neurological relevance.

TABLE 4

Relative expression of candidate genes in monozygotic “concordant” twin pairs with
differential language impairment (PPVT percentile scores) and in normal twins

Candidate Gene	Genbank #	PPVT - 30/42*	PPVT - 0.1/1	Discordant twins^¥	Normal twins

ASS	AA676405	0.03	(−0.26)^#	−0.01	(−0.69)	0.85	(1.37)	0.24	(0.54)
CHL1	H15267	1.83	(1.48)	1.99	(1.29)	0.61	(0.46)	1.40	(1.45)
IL6ST	T61343	0.88	(1.33)	0.28	(−0.26)	0.58	(0.49)	0.37	(0.23)
IL6ST	AA406546	1.02	(0.85)	0.34	(−0.14)	0.58	(0.61)	0.43	(0.47)
DAPK1	AI371096	−0.56	(−0.92)	−0.49	(−1.05)	0.70	(0.57)	−0.13	(−0.18)
FLAP	T49652	1.18	(1.20)	0.28	(−0.25)	0.71	(0.58)	−0.19	(−0.34)
ITGB7	AI380522	−1.12	(−1.13)	−0.20	(−0.92)	−0.60	(−0.76)	0.15	(0.04)
EGR2	AA446027	−2.02	(−3.10)	−1.26	(−2.16)	−0.43	(−0.79)	−0.23	(−0.37)
ROBO1	AA173755	−0.13	(0.25)	0.41	(−0.18)	−0.66	(−1.10)	−0.45	(−0.80)
5-HTT^¶	BC069484	NP^¶	(−2.39)	NP	(−0.42)	NP	(−0.96)	NP	(−0.02)

*Values are mean log2 ratios of gene expression from DNA microarray data from 2 sets of monozygotic autistic twins who both met criteria for autism by either ADOS or ADI-R diagnostic tests, but have differential language impairment as indicated by their respective PPVT percentile scores. Data from 2 separate dye-reversal microarray experiments were averaged for each twin set. For each pair of twins, microarray data from the twin with the lower PPVT score was used as the numerator in calculating the gene expression ratio. PPVT - 30/42 refers to the twin pair whose PPVT percentile scores are 30 and 42, while PPVT - 0.1/1 refers to the twin pair whose percentile scores are 0.1 and 1. Interestingly, the two sets of twins share the same mother. The PPVT - 30/42 set are Caucasian males, as are the 3 sets of discordant twins, while the more severely language-impaired twins (PPVT - 0.1/1) are black, Latino males.
^#Values in parentheses are mean log₂(ratio) expression measures obtained by triplicate qPCR analyses.
^¥Mean expression value across 3 sets of twins discordant for diagnosis of classic autism.
^¶Not present (NP) on microarray

TABLE 5

Significant genes with mean log₂(ratio) ≧0.58 across 5 sets
of twins with ASD

		Mean log₂	Obs. d
Genbank#	Gene name or description	(ratio)*	score^Δ

	Upregulated (log2 (ratio) ≧0.58)
AA448599	F13A1, clotting factor XIIIa	1.50	3.15
	precursor
H15267	CHL1, neural cell adhesion molecule	1.10	2.82
AA521362	CR2 receptor	1.07	2.21
R00276	CD38 alt	0.83	2.10
W07099	NAGLU, N-acetylglucosaminidase,	0.77	3.77
	alpha
T49652	FLAP, ALOX5AP	0.77	2.85
AA044267	P2X5a	0.76	3.22
R40400	CHL1, neural cell adhesion molecule	0.75	2.25
H09567	PAG1	0.71	2.64
AI400399	CYP7B1	0.70	2.01
AA149640	FLT1	0.67	3.61
H17800	Unknown protein	0.67	3.06
H02307	FLAP, ALOX5AP	0.67	2.71
AA917693	Unknown protein	0.66	2.64
AI017382	ATXN7L1	0.66	1.95
AI091671	Unknown protein	0.65	2.74
N50114	PAG1	0.65	2.82
H95977	Nmd protein, PLA1A	0.65	1.97
AA040389	Unknown protein	0.64	2.58
H24011	Homeodomain-like protein	0.64	2.71
AI275120	Unknown protein	0.63	2.59
AA708955	SCHIP1, schwannomin interacting	0.62	2.07
	protein 1
AA406546	IL6ST, IL6 signal transducer, gp130	0.62	3.19
R79082	PTPRK	0.59	2.24
AI241341	CHL1, neural cell adhesion molecule	0.59	2.41
T61343	IL6ST, IL6 signal transducer, gp130	0.59	3.07
	Downregulated (log2 (ratio) ≦−0.58)
AA446027	EGR2, Krox-20 homolog	−0.90	−2.41
AA630734	seryl-tRNA synthetase	−0.86	−2.15
R47893	CCL3L1	−0.80	−2.14
AA682565	Unknown protein from	−0.76	−3.03
	neuroblastoma
R78530	COTL1	−0.73	−2.55
AA933744	ECAT11	−0.73	−2.52
N58443	GPR55	−0.68	−3.04
H99699	mitochondrial aconitase	−0.64	−4.86
H03494	CD44	−0.63	−2.42
AA450353	ELMOD1	−0.63	−2.86
AA458965	IL32, natural killer cell protein,	−0.63	−2.87
	transcript 4
R33402	SAMSN1	−0.62	−3.29
AA111969	CD83 antigen	−0.60	−2.55
AI380522	ITGB7, integrin beta-7 subunit	−0.60	−2.51
AA682637	CHST2	−0.59	−2.85

*Mean log₂(ratio) of gene expression across 5 sets of twins with ASD. SAM analysis revealed 1281 significant genes with a median FDR of 15.6%.
Genes in boldface type have been shown to be relevant to neurological development, structure, or function.

TABLE 6

Global Functional Analysis: Enrichment of high level functions represented in datasets
of differentially expressed genes across 5 sets of monozygotic twins

Twin Sets

	361/360	809/810	2369/2368	2596/2596	2597/2598
High Level Function	Significance*	Significance	Significance	Significance	Significance

Nervous system development	0.008-3.85 × 10⁻²	0.12-2.55 × 10⁻²	0.81-4.79 × 10⁻²	0.12-4.39 × 10⁻²	0.02-4.53 × 10⁻²
and function
Tissue morphology	0.008-4.27 × 10⁻²	NA	0.81-4.79 × 10⁻²	0.08-4.38 × 10⁻²	0.51-4.03 × 10⁻²
Cell death	0.01-4.27 × 10⁻²	3.74 × 10⁻²	0.09-4.79 × 10⁻²	0.18-4.65 × 10⁻²	0.09-4.53 × 10⁻²
Cellular development	0.01-4.27 × 10⁻²	NA	0.81-4.79 × 10⁻²	0.12-4.39 × 10⁻²	0.03-3.54 × 10⁻²
Immune and lymphatic system	0.03-3.85 × 10⁻²	NA	0.81-4.79 × 10⁻²	0.33-4.39 × 10⁻²	0.19-4.03 × 10⁻²
development and function

Global functional analysis of differential gene expression across 5 sets of monozygotic autistic twins (identified by blood sample numbers (eg., 361/360) who are discordant with respect to severity of autism or language impairment was performed using Ingenuity's Pathways Analysis Software.
*Significance calculated for each function is an indicator of the likelihood that the high level function is associated with the dataset by random chance. The p-value, which is calculated using the right-tailed Fisher's Exact Test, compares the number of user-specified genes of interest (in this cases, differentially expressed genes with a log₂(ratio) cutoff ≧±0.58) that panicipate in a given function or pathway to the total number of occurrences of these genes in all functional/pathway annotations stored in the lagenuity Pathways Knowledge Base. It is noteworthy that genes related to nervo us system development and function rank first among the top 5 out of 74 high level functions identified in lymphoblastoid cell lines on the basis of differentially expressed genes across 5 sets of twins with autism spectrum disorders. The range of significance values for each high level function relates to the different significance values for specific subfunctions within the category.
NA: no significance value for this function.

TABLE 7

Differentially expressed candidate genes from microarray experiments mapped in
silica to autism susceptibility genes and QTL.

			Reported closely mapped
Candidate gene	Genbank #	Physical Location	autism candidate genes or QTL*	Ref

ASS	AA676405	chr9 (130, 349, 862-130, 406 214)	dopamine beta-hydroxylase (9q34)	53
CHL1	H15267	chr3 (423, 533-426, 095)	KIAA0121 (3p25.2)	54
IL6R-beta, go130	T61343	chr5 (55, 267, 950-55, 272, 766)	Spoken language QTL chr5: 40(0-67)	52
IL69T	AA406546	chr5 (55, 271, 809-55, 272, 305)	Spoken language QTL chr5: 40(0-67)	52
DAPK1	AI371096	chr9 (87, 552, 642-57, 553, 009)
FLAP, ALOX5AP	T49652	chr13 (30, 207, 643-30, 236, 962)	AUTS3 (13q14.22), HTR2A.2	57, 58
			(serotonin recep. 2A (13q14.21)
ITGB7	AI380522	chr12 (51, 871, 361-51, 887, 333)	arginine vasopressin receptor 1A (12q14-15)	60
EGR2	AA446027	chr10 (64, 241, 755-64, 246, 081)	Spoken language QTL chr10: 107(72-126): HTR-7	52
ROBO1	AA173755	chr3 (76, 729, 062-76, 729, 496)

SUPPLEMENTARY TABLE 2

Case description of subjects from whom LCL were derived and used in
this study.

						PPVT
Individual ID	Blood ID	Ethnicity	Zygosity	Age*	Status	(% ile)	Raven

AU002704	HI0361	Caucasian	MZ	8	Autism		108
AU002703	HI0360	Caucasian	MZ	8	Br. Spec.	79 (8)	105
AU057904	HI0809	Caucasian	MZ	6	Autism	35 (0.1)	83
AU057905	HI0810	Caucasian	MZ	6	Br. Spec.	117 (87)	110
AU057903	HI0813	Caucasian		10	nonautistic
AU0885303	HI2369	Caucasian	MZ	16	Autism	No data	No data
AU0885302	HI2368	Caucasian	MZ	16	NQA	″	″
AU0885304	HI2357	Caucasian		19	nonautistic	″	″
AU0616301	HI2595	Caucasian	MZ	15	Autism^¥	92 (30)	104
AU0616302	HI2596	Caucasian	MZ	15	Autism^¥	97 (42)	94
AU0616303	HI2597	Mixed,	MZ	12	Autism	40 (<0.1)	80
		Hispanic
AU0616304	HI2598	Mixed,	MZ	12	Autism	66 (1)	107
		Hispanic
AU1165305	HI2745	Caucasian	MZ	9	nonautistic
AU1165306	HI2744	Caucasian	MZ	9	nonautistic

*Age at time of inclusion in study
^¥Diagnosed with ADOS rather than ADIR

SUPPLEMENTARY TABLE 3

Primers used ror quantitative RT-PCR analyses

	GenBank #	Forward Primer (5′ to 3′)	Reverse Primer (5′ to 3′)

Gene Name
ASS	AA676405	GAAGTGCGCAAAATCAAACA	CTGCACTTTCCCTTCCACTC

CHL1	H15627	TTTAGATGCACCCGTGTTTG	AGCACACCAACATTTCTCATT

DAPK1	AI371096	CGCTACCTCTCTGTCCCTTG	AGGATTCCCTTCTCCCCTTT

EGR2	AA446027	CCCATCACAGGTTTTTGACC	TCTTTTTGCTGTCCCCACTT

FLAP	T49652	GACGATCTCCACCACCATCT	AGAATGCTCTCAAGAGCTGAA

IL6ST	T61343	TTAAAAGGTGGCAGCTCAGG	TCATCACACGACCCATCAAC

IL6ST	AA406546	GCTGGGCTCATGTAGTTATGG	CATCAGAGTGGCTTAGGGACA

ITGB7	AI380522	CATCACGACCACCATCAATC	CTCCAGTTCCCACTGTCCTC

NAGLU	W07099	TCCAACAGCACCAGTTTGAC	AGCCGGGGTAATATTTGAGG

NAGLU	W07099	ACACTCCGGAGCAGTAGCC	AAAGGACCCAGTGCCAGATT

ROBO1	AA173755	CTGACCCCAGTGGAAAACA	CCCTTAGTACTGCACGCCTTT

5-HTT	BC069484	CCTCCAGCCACTTATTTCCA	ACCTCCATCCACATCCTCAC

Control Primers
MLC1	AA196486	TGAAGAGCTGAATGCCAAGA	CCTTGTCAAAGACACGCAGA

TCRb	AA909476	CATGAGCATCAGCCTTCTGT	GAAAGGCCTGTCCACTCTCC

Werner HIP	AA189052	GGCTATGGCAAAGGCTAGAA	GAGTCAGCACCTCCTCTGCT

Claims

1. A method for screening of an Autism Spectrum Disorder in a subject comprising the steps of:

i) obtaining a sample of peripheral cells from the subject

ii) determining a gene expression profile of one or more ASD biomarker genes in the sample; and

iii) comparing the gene expression profile of the one or more ASD indicative genes in the sample to a positive control comprising an average gene expression level of the ASD indicative genes in a plurality of reference samples that are derived from subjects displaying symptoms of Autistic Spectrum Disorder to determine differential gene expression between the sample and the positive control, wherein presence of the Autistic Spectrum Disorder is indicated if there is no statistically significant differential gene expression between the gene expression profile of one or more ASD biomarker genes in the sample and the positive control, wherein the ASD biomarker genes comprise one or more genes of Table 1, Table 3, Table 4, Table 5, or a combination thereof.

2. The method of claim 1, wherein the differential gene expression distinguishes among phenotypic variants of the Autism Spectrum Disorder.

3. The method of claim 1, wherein the phenotypic variants of the Autism Spectrum Disorder comprises, verbal and non-verbal language abilities, social skills, repetitive behavior, restricted interest, savant skills, or a combination thereof.

4. The method of claim 1, wherein the peripheral cells-comprise white blood cells.

5. The method of claim 4, wherein the lymphoblasts the white blood cells comprise lymphoblasts.

6. The method of claim 1, wherein the peripheral cells are mucosal epithelial cells.

7. The method of claim 1, wherein the gene expression profile is obtained by using DNA microarray analysis comprising, oligonucleotide microarray, c-DNA microarray, and focused gene chip analysis, or a combination thereof.

8. The method of claim 1, wherein the one or more genes of Table 1 comprise ASS, ALOX5AP (FLAP), DAPK1, F13A1, IL6ST, NAGLU, and ROBO1.

9. The method of claim 8, wherein the one or more genes of Table 1 comprise genes involved in neuroinflammation comprising ASS, ALOX5AP (FLAP), DAPK1, and IL6ST.

10. The method of claim 1, wherein the Autism Spectrum Disorder comprises autism, Asperger's Syndrome, and pervasive developmental disorder-not otherwise specified (PDD-NOS).

11. An assay for screening an agent for its ability to treat or prevent one or more symptoms of Autism Spectrum Disorder comprising the steps of:

i) isolating a nucleic acid control sample from a peripheral cell of an autistic individual that produces a differential gene expression profile representative of an autism spectrum disorder and determining the level of gene expression in the control sample;

ii) subjecting the peripheral cell to the agent;

iii) isolating a nucleic acid test sample from the peripheral cell after subjection of the peripheral cell to the agent of step (ii) and determining the level of gene expression in the test sample;

iv) comparing production, stability, degradation and/or activation of gene expression between the control sample and the test sample to find differential gene expression profile between the test sample and the control sample;

wherein a differential gene expression profile between the test sample as compared with the control sample is indicative of the ability of the agent to prevent or treat one or more symptoms of Autism Spectrum Disorder.

12. The assay of claim 11, wherein Autism Spectrum Disorder is indicated upon observing statistically significant differential gene expression in an ensemble of genes comprising a gene ensemble according to Table 1, a gene ensemble according to Table 3, a gene ensemble according to Table 4, and a gene ensemble according to Table 5, or a combination thereof.

13. The assay of claim 11, wherein the Autism Spectrum Disorder is selected from the group consisting of autism, Asperger's Syndrome, and pervasive developmental disorder-not otherwise specified (PDD-NOS).

14. A test kit to facilitate diagnosis and treatment of Autism Spectrum Disorders; comprising one or more nucleic acid samples derived from an autistic individual as a positive control; reagents and equipment for isolating and analyzing a nucleic acid sample of peripheral cells of a subject in order to compare gene expression profile of the subject with the positive control; and directions for use of the kit, wherein the one or more nucleic acid sample comprise an ensemble of genes comprising a gene ensemble according to Table 1, a gene ensemble according to Table 3, a gene ensemble according to Table 4, and a gene ensemble according to Table 5, or a combination thereof.

15. The kit of claim 14, wherein Autism Spectrum Disorders comprise autism, Asperger's Syndrome, and pervasive developmental disorder-not otherwise specified (PDD-NOS).

16. The kit of claim 14, wherein the sample of peripheral cells comprises white blood cells or their lymphoblast derivatives.

17. The kit of claim 14, wherein the sample of peripheral cells comprises buccal mucosal epithelial cells.

18. The kit of claim 14, wherein the reagents and equipment comprise DNA microarray analysis materials including oligonucleotide microarray, c-DNA microarray, and focused gene chip, or a combination thereof.

19. A computer-implemented method for analyzing gene expression to screen for an Autism Spectrum Disorder comprising the steps of:

i) compiling data comprising a plurality of measured gene expression signals derived from cDNA microarray analysis of peripheral cells into a form suitable for computer-based analysis: and

ii) analyzing the compiled data, wherein the analyzing comprises identifying gene networks from a number of upregulated biomarker genes and downregulated biomarker genes, wherein the biomarker genes are genes that have been identified as associating with presence or severity of autism or autism spectrum disorder, said genes comprising genes listed in Table 1, genes listed in Table 3, genes listed in Table 4, and genes listed in Table 5, or a combination thereof.

20. The computer-implemented method of claim 19, wherein the biomarker genes comprise genes involved in nervous system development and/or function.

21. A computer-readable medium encoding a programming code for analyzing Autism Spectrum Disorder gene expression from a plurality of data points which comprises a gene ensemble which is filtered using a log 2(ratio) as a criterion for differential expression, said gene ensemble comprising a gene ensemble according to Table 1, a gene ensemble according to Table 3, a gene ensemble according to Table 4, and a gene ensemble according to Table 5, or a combination thereof.