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Abstract

Background

Autism is a neurodevelopmental disorder characterized by impairments insocial behavior, communication difficulties and the occurrence of repetitiveor stereotyped behaviors. There has been substantial evidence fordysregulation of the immune system in autism.

Methods

We evaluated differences in the number and phenotype of circulating bloodcells in young children with autism (n = 70) comparedwith age-matched controls (n = 35). Children with aconfirmed diagnosis of autism (4–6 years of age) were furthersubdivided into low (IQ<68, n = 35) or highfunctioning (IQ≥68, n = 35) groups. Age- andgender-matched typically developing children constituted the control group.Six hundred and forty four primary and secondary variables, including cellcounts and the abundance of cell surface antigens, were assessed usingmicrovolume laser scanning cytometry.

Results

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There were multiple differences in immune cell populations between the autismand control groups. The absolute number of B cells per volume of blood wasover 20% higher for children with autism and the absolute number ofNK cells was about 40% higher. Neither of these variables showedsignificant difference between the low and high functioning autism groups.While the absolute number of T cells was not different across groups, anumber of cellular activation markers, including HLA-DR and CD26 on T cells,and CD38 on B cells, were significantly higher in the autism group comparedto controls.

Conclusions

These results support previous findings that immune dysfunction may occur insome children with autism. Further evaluation of the nature of thedysfunction and how it may play a role in the etiology of autism or infacets of autism neuropathology and/or behavior are needed.

Introduction

Autism is a lifelong neurodevelopmental disorder characterized by social deficits,impaired verbal and nonverbal communication and the presence of stereotypedbehaviors or circumscribed interests . Autism, together with Asperger syndrome and pervasivedevelopmental disorder not otherwise specified, referred to as autism spectrumdisorders (ASD), form a spectrum of conditions with varying degrees of impairmentthat are classified as pervasive developmental disorders in the DSM-IV [2]. The currentestimate of prevalence is approximately 1∶110 , which is substantially higherthan earlier estimates . Numerous attempts at determining susceptibility genesthrough a number of large consortia have indicated that multiple genes, includingimmune related genes, may be associated with autism. Interestingly, none of thedefined mutations, genetic syndromes and de novo copy numbervariations account for more than 1–2% of cases of autism .

There has been substantial speculation about the etiology(ies) of ASD, but for thevast majority of cases, the cause remains unknown. It has become clear that therewill be many causes of autism that will likely have varying contributions fromgenetic and environmental factors. One persistent suggestion has been that an immunedysfunction may contribute to certain forms of autism. There have been numerousfindings of altered immune function in autism . As long as 45 years ago,Stubbs notedthat children with autism had altered responses to T cell mitogens, such asphytohemagglutinin or pokeweed antigen and these findings have been replicated insubsequent studies –. More definitive studies have since highlighted thepresence of inflammation in the brain and the activation of microglia as well asevidence for altered peripheral immune function in autism, including increasedcytokine levels in the plasma such as interleukin (IL)-1β, IL-6, and IL-8 , elevatedlevels of complement proteins , decreased cellular activity of NK cells –, increasedmonocyte activation , , and a reduced number of CD4+ T cells , , . Pliopys et al.reported that a substantial number of individuals with autism demonstrated anincreased number of HLA-DR+ T cells and this finding has been confirmed byWarren et al. . In addition, a number of studies have reported abnormalantibody responses to brain and CNS proteins , . Skewed immunoglobulin (Ig)responses, such as decreased total serum IgG levels but increased isotype IgG4, havealso been reported in autism –.

Taken together, these data are suggestive of a link between autism and immunedysfunction and that specific cellular phenotypes or activation status of immunecells may be altered in autism. Autism is also associated with a variety ofco-existing symptoms including seizures, sleep disturbances and gastrointestinalproblems [28]many of which may be influenced by altered immune function. However, the data areoften clouded by methodological concerns. The often heterogeneous populations ofsubjects analyzed, the use of siblings as controls and the disparate age rangesbetween controls and cases have led reviewers of this literature to be very cautiousin drawing conclusions. Krause et al. conclude that “Althoughvarious immune system abnormalities, involving both cellular and humoral aspects ofthe immune system, have been reported in children with autistic disorder, previousstudies are largely association based, and, it remains difficult to draw conclusionsregarding the role of immune factors in the etiopathogenesis of thisneurodevelopmental disorder.”

The current study was designed to search for cellular markers of autism. Participantswere selected from a very narrow age range (4 to 6 years) of children, to coincidewith peak symptom presentation and to ensure a stable diagnosis. In addition,participants were only enrolled in the diagnostic group if they had a confirmeddiagnosis of strictly defined autistic disorder (N = 70).Similarly, an age and gender matched control group of typically developing children(N = 35) was comprehensively evaluated to avoid inclusion ofindividuals with an autism spectrum or other neurodevelopmental disorder. The aim ofthe investigation was to evaluate changes in the frequency of distinct cellularphenotypes in autism with the goal of identifying immune-specific differences thatcould be further investigated for a potential role as biomarkers. A number ofparameters of immune system status including cell number, cell ratios and cellsurface antigen intensities were assessed using microvolume laser scanningcytometry.

Methods

Participants

The experimental subjects were recruited from the UC Davis M.I.N.D. InstituteClinic and community support groups such as Families for Early Autism Treatment(FEAT), Regional Centers, referrals from clinicians, and area school districts.Parents of children who met the diagnostic criteria were provided with aninformation sheet containing a description of the study and contact information.Typically developing subjects were recruited from area school districts andcommunity centers. Informed written consent was obtained from parents prior toany assessments or procedures. The study was explained in simple language to thechildren and verbal assent was obtained from higher functioning children whowere capable of understanding the study process. All participants were assigneda numerical code to maintain anonymity of the children and their test results.The subject number served as the primary identifier on research data forms.Following informed consent, interested subjects completed the diagnostic andpsychological measures. All facets of the study were approved by the Universityof California Davis Institutional Review Board (IRB). For the duration of thestudy, there were no adverse events.

The inclusion criteria for the three groups consisted of the following. Thechildren in the autism diagnostic group required a diagnosis of AutisticDisorder based on the DSM-IV criteria [2]. Children with pervasivedevelopmental disorder-not otherwise specified (PDD-NOS) or Asperger Syndromewere excluded from the study. The diagnosis of Autistic Disorder wascorroborated by: 1) the Autism Diagnostic Observation Schedule-Generic (ADOS-G;, 2)Autism Diagnostic Interview-Research (ADI-R; , , and 3) clinical judgment byone of the authors (BAC). The ADOS-G was used to assess children with autism toconfirm diagnosis for inclusion in the study and is comprised of four differentmodules that are administered based on the language ability of the child. TheADOS provides an algorithm with cut-offs for autism and autism spectrumdisorders .The Autism Diagnostic Interview-Revised (ADI-R; was administered to theparents of children with suspected autism. The ADI-R generates a diagnosticalgorithm based on the DSM-IV [2] criteria for Autistic Disorder. The autism diagnosticgroup was further divided based on the level of intellectual functioning asfollows: High functioning autism (HFA) having an IQ≥68 and low-functioningautism (LFA) having an IQ<68. The typically developing children (TYP) hadintellectual functioning within the average to above average range with aminimum IQ≥68.

Typically developing control children were assessed using the SocialCommunication Questionnaire (SCQ; , a valuable screeningtool that was completed by parents to ensure the absence of symptoms of autism.Children who had scores above the cutoff were excluded from the typicallydeveloping group, and were referred for further diagnostic evaluation(N = 1). In addition, all children were assessed using theStanford-Binet Intelligence Scale-Fourth Edition [SB4; 35], a standardizedmeasure of cognitive functioning for children between 2 and 18 years, which isdivided into several parts, including Verbal Reasoning, Abstract/VisualReasoning, Quantitative Reasoning, and Short-term Memory. The SB4 was used toobtain a measure of overall IQ for inclusion into the study as well as for theassignment to high (≥68) and low (<68) functioning autism groups.Similarly, all children underwent assessment with the Vineland Adaptive BehaviorScales-Interview Edition (VABS; [36], a structured parentinterview format designed to assess a child's ability to perform dailyactivities required for personal and social sufficiency; this was administeredto obtain a measure of adaptive functioning across all participants. As afurther assessment, the AGRE Medical History Forms were given to the parents ofall subjects to provide a comprehensive medical history. The interview requiresthe parent to provide demographic, medical and family history information.

The exclusion criteria for all subjects consisted of the presence of Fragile X orother serious neurological (e.g., seizures), psychiatric (e.g., bipolardisorder) or known medical conditions including autoimmune disease andinflammatory bowel diseases/celiac disease. All subjects were screened viaparental interview for current and past physical illness. Children with knownendocrine, cardiovascular, pulmonary, liver or kidney disease were excluded fromenrollment in the study.

A total of 136 children between 4-years, 0-months and 6-years, 11-months wereenrolled in this investigation. Twenty-one children were excluded due to failureto meet inclusion criteria or noncompliance with the majority of the protocol.The final sample consisted of 105 children who were selected to balance age andgender across experimental groups. Demographic information for the 105 subjectsis presented in Table 1including gender, age, IQ and ethnicity across the groups.

Table 1

Participation in the study required two visits. Child assessments and parentalinterviews were conducted at the UC Davis M.I.N.D. Institute Research Clinic onthe first visit; testing lasted for approximately 3 1/2 contact hours. Theparents were sent letters providing the results of their child'sperformance. The second visit consisted of a blood draw completed by a pediatricphlebotomist and research staff via standardized procedures (see below).

Sample Collection Procedures

For each child, approximately 5 ml of blood was drawn by one of two clinicalphlebotomists into Vacutainer tubes containing EDTA (BD Biosciences, San Jose,CA). Immediately following collection, the tube was gently inverted 8 to 10times to mix the anticoagulant with the blood. The tube was then wrapped inparafilm and bubble wrap and placed in a biohazard bag between coolant packs ina Styrofoam transport container. The blood draws were taken within one week ofthe diagnostic and psychological assessments for each of the children. All blooddraws were conducted in the early morning hours between 8:00 am and 10:00 amfollowing an overnight fast (no consumption of food or drink other than waterafter midnight). Topical anesthetics were not employed to prevent contaminationof the sample. If the participant was ill (presented with a cold, fever or othercommon illness), the blood draw was not taken until the child's healthstatus was stable/recovered for 48 hours. The samples were sent via Courier toPPD Biomarker Discovery Sciences (Menlo Park, CA, USA, formerly known asSurroMed, Inc.) arriving in the lab within six hours of the blood draw.Cytometric analyses of the blood samples were carried out immediately on receiptof the samples in the lab. PPD personnel were blind to the diagnosis until afterall samples were assayed and a report of preliminary findings was presented.

Analytic Methods

The protocol for immune phenotyping included 64 three-color cellular assaysperformed by microvolume laser scanning cytometry on the SurroScan™ system–. The assays are well-suitedfor evaluating cellular immune markers. Monoclonal antigen-specific antibodieswere purchased from various commercial vendors and developed into PPD assays.Three different fluorophores, Cy5, Cy5.5 , and the tandem dyeCy7-APC ,were coupled to individual monoclonal antibodies specific for different cellularantigens in each assay. Each fluorophore was measured in a separate detectionchannel. Aliquots of whole blood were added to 96-well micro-titer platescontaining the appropriate antibody-dye combinations for each assay, incubatedin the dark at room temperature for 20 minutes, diluted with an appropriatebuffer and loaded into Flex32™ capillary arrays (PPD) and analyzed withSurroScan™. Images were converted to a list-mode data format with in-housesoftware [44].Fluorescence intensities were compensated for spectral overlap of the dyes sovalues are proportional to cell surface antigen density. Standard beads were runwith every sample and were used to monitor systematic instrument errors.

Prior to this study, PPD developed and established quality and baseline measureswith twenty blood bank samples for the 64 different three-reagent cellularassays used in this study. Standard template gates were established using theseresults plus additional staining controls for all individual reagent and dyecombinations. Template gates were established using FlowJo™ cytometryanalysis software (Tree Star, Inc., Ashland, OR) customized for PPD to enableupload of gates to an Oracle database. Gating information was stored in thedatabase and applied to the scan data for each assay using SurroGate™database-driven cytometry analysis software in order to generate the resultingcell count and antigen intensity data.

The assay panel allows the enumeration of major cell populations: granulocytes,eosinophils, monocytes, CD4 and CD8 T cells, B cells and NK cells. In addition,the assays allow for finer phenotyping of cell subtypes based on the expressionof specific cell surface markers of activation, adhesion molecules, receptors,etc. The assays monitor cell counts of more than 200 different cell populations,plus the relative levels of the different cell surface antigens on specificpopulations. Template gates were used to enumerate the cell populations ofinterest in all of the assays. Invalid assays and those that do not support thetemplate gates were flagged. An analyst visually reviewed all assay resultsprior to data upload. In this study, 105 subject samples were analyzed with 64assays for a total 6720 assays. Among the assays, only 0.67% were invaliddue to technical difficulties and have been excluded from the analysis. Anadditional 4.8% required non-standard gates due to slightinter-individual differences. These results are used in the statisticalanalysis. Cell counts were generally not affected but cell surface expressionresults may have a larger but not statistically significant variation due to theinclusion of these data.

Statistical Analyses

Statistical analyses were conducted to assess differences in cell populations, 1)between the combined autistic group (HFA+LFA) and the control group, 2)between each of the autism subgroups and 3) among the three groups. With regardto two-group comparison statistics, we applied to all data a univariate meancomparison test that was either parametric or non-parametric depending on thenormality of the data. If the data were approximately normally distributed, thenparametric statistics were used (t-test); if not, the nonparametric rank test(Wilcoxon or Kruskal-Wallis test) was applied. All tests of hypotheses weretwo-sided. Goodness-of-fit statistics (Shapiro-Wilk) and tests of skewness andkurtosis were performed to assess normality. Three-group comparisons wereperformed by ANOVA. The data set for this study is broad, i.e., there are manymore variables than subjects. Consequently, many multivariate statistics such asmultivariate analysis of variance, which require more subjects than variables,could not be conducted. This study was underpowered for the number of variablesbeing studied and some interesting results would be overlooked if the univariatestatistics were ignored. Although the groups were carefully controlled andmatched for sex and ethnicity, the study is underpowered to find differencesbased on sex and ethnicity. Unadjusted P values are presented since this studyis preliminary and is the first to begin to explore cellular markers ondifferent blood cells. Moreover, the use of correction for multiple comparisonsin this area is debated . Our hypothesis tests included 644 variables fromcell counts and cell surface marker intensities. Multiple measures of the samecell population (e.g. CD4 T cells) were combined into a single average for theanalysis. Differences at the univariate p-value<0.05 or lower, warrantfurther consideration.

Results

There were multiple significant differences observed in immune cell numbers and thesurface expression of markers on immune cells in children with autism compared withage and gender-matched typically developing controls. For example, at the p<0.05level there were 151 variables where either cell count or the intensity of cellsurface markers were different between children with autism and controls (Table 2).

Table 2

In general, more differences were observed between children with autism and typicallydeveloping controls than between the low functioning (LFA) and high functioning(HFA) autism groups based on IQ, although 33 variables were different between HFAand LFA at the p<0.05 level. A summary of the significant measures for each ofthe comparisons is shown in Table2. For each statistical level (p-value) the number of false-positivevariables expected to appear by chance (assuming all are independent) is given inthe first column. The significant differences between autism and controls includedboth differences in cell counts and, separately, differences in the intensity ofcell surface marker expression. Tables 3 and ​and4represent4represent variables that were significantly different for cell counts and cellsurface expression markers intensities, respectively.

Table 3

Table 4

Analysis of immune cell counts

Cell count data were available for the major immune cell populations i.e.,neutrophils, lymphocytes, eosinophils, monocytes and platelets. We found thatthe absolute numbers (cells per microliter) of B cells and NK cells in childrenwith autism were significantly higher than counts from typically developingcontrols. Although there were higher mean absolute numbers of total white bloodcells (WBC), neutrophils, T cells, the CD4 and CD8 T cell subpopulations,monocytes, eosinophils and platelets in children with autism, these differencesin cell counts did not reach statistical significance (Table 5).

Table 5

Analysis of B cells

Absolute numbers of B cells were 20 to 25% higher in the autism groupscompared with the typically developing controls. The B cell value represents anaverage based on nine separate B cell assays that use CD20 as the B cellidentifier. No differences were seen within the autism group when comparing HFAwith LFA. B cell counts were significantly higher for both LFA(p = 0.009) and HFA (p = 0.011) afteradjustment for multiple comparison compared with typically developing controls(Figure 1). In addition,there were significant differences in activated B cell subsets, includingstatistically significant increases in B cells that expressed the activationmarker CD38 in autism compared with controls (394.7±119.9 vs.479.7±210.2, p = 0.0081, Table 6). The difference in CD38 positive(CD38p) B cell number in autism tracked with the increase in total B cellpopulation. CD38 negative (CD38n) cell numbers were also higher but to a lesserextent (164.5±99.3 vs. 193.4±84.6,p = 0.013). There were also increases in mature B cellnumbers as denoted by the absence of CD5 staining (CD5n) on B cells in autismsubjects compared with typically developing controls (Table 6, p = 0.0001).Notably, although the number of B cells was increased in autism, the number ofimmature B cells, as denoted by positive CD5 staining, was not different betweenautism and controls (278.3±142.1 vs. 329.4±171.8,p = 0.14). These data suggest that B cells are increased inautism and it is preferentially the activated and mature phenotype which differsfrom controls.

The absolute count values are based on 9 separate B cell assays that useCD20 as the B cell identifier. P-values = * HFAvs. N = 0.011, ** LFA vs.N = 0.009, and A vs. N 0.003. HFA vs.LFA = not significant (0.7).

Table 6

Analysis of NK cells

Absolute numbers for NK cells were approximately 40% higher in childrenwith autism compared with controls (Table 5). The measure of NK cells was basedon an average of two separate NK cell assays that use the markers CD56 and CD3,so that NK cells are identified as CD56pCD3n. The difference in NK cell numberswas significant for both HFA vs. controls (p = 0.037) andLFA vs controls (p = 0.023), but no differences wereobserved between HFA and LFA (Figure 2).

The average is based on 2 separate NK cell assays that use the CD56p andCD3n as the NK cell identifier. P-values = *HFAvs. N = 0.037, *LFA vs.N = 0.023, and A vs.N = 0.011. HFA vs. LFA = notsignificant (0.8).

Analysis of cell surface marker intensities

As indicated in Table 4,there were many significant differences in the intensity of cell surface markersexpressed on immune cells. At the p<0.05 level, there were 108 differentvariables denoting intensities that were different between children with autismand controls (Table 4). Tohelp organize the data that are based on cell surface marker intensities, wereviewed the top 20 intensity variables with differences greater than 15%between the autism and control groups and had adjusted p-values<0.05. Thesevariables are listed in Table7. It is important to note that a number of these variables representcell surface markers that are only present on rare cells for which the observedcell counts were very low.

Table 7

Of interest, HLA-DR, a marker of cellular activation, was higher on CD8 T cellsand CD4 T cells in the autism group compared with typically developing controls.The T cell marker CD26/dipeptidyl peptidase IV, which is associated with aneffector cell phenotype and is markedly elevated in human CNS disorders such asmultiple sclerosis, was increased on CD8 T cells in autism compared withcontrols. Another noteworthy finding was that CD95 expression was increased onCD14 expressing monocytes compared with controls. The marker CD95 is oftenexpressed on activated cells as a means of making those cells more susceptibleto apoptosis in order to limit the inflammatory response. Increased CD95 onmonocytes from children with autism may represent an activated subset ofmonocytes that have upregulated the surface expression of this apoptosismarker.

Discussion

The current study was designed to search for cellular markers of autism. Given thegenetic heterogeneity of autism and the near certainty that autism spectrum disorders havemany etiologies and trajectories, it is noteworthy that the current study hasidentified several indications of immune differences in children with autism. Ingeneral, we found that the frequencies and phenotypes of whole blood immune cellsubpopulations under non-stimulated conditions were different in children withautism compared with well matched, typically developing controls. Based on 644measurements relating cell counts of immune subsets and the abundance of cellularmarkers, as determined by the intensity of antibody staining directed to thesemarkers, nearly a quarter (151) of these measurements were different betweenchildren with autism compared with controls at the univariate p<0.05 statisticallevel. There was additional evidence of differences between higher functioningautism participants and lower functioning autism participants with 33 of themeasured variables being different between the autism groups at the p<0.05 level.Notably, the data highlighted significantly higher absolute numbers of B cells andNK cells in children with autism compared with controls. In addition, increasedmarkers of cellular activation, such as CD38 on B cells, and HLA-DR and CD26 on Tcell subsets, were observed on cells from autism participants compared withcontrols.

Previous reports have demonstrated differences in lymphocyte populations in autism,including increased numbers of NK cells and reduced numbers of T cells, , , , or alteredactivation status of T cell subsets , . In the current study,participants were selected in a very narrow age range (4-to-6 years), to coincidewith peak symptom presentation and to ensure a stable diagnosis; the male to femalefrequency was the same in each group (29 males to 6 females). Participants in thediagnostic group had strictly defined autistic disorder and were compared to acomprehensively evaluated control group of typically developing children, none ofwhom were siblings of the autism cases. It is difficult to make direct comparisonsbetween our study and the majority of previous studies due to differences inanalytical technique, the age range of the subjects, the diagnostic criteria used,the lack of confirmation of the absence of ASD or other neurodevelopmental disordersin the controls and the use of siblings as controls. In addition, in previousstudies there may also have been unintentional selection bias due to recruitmentthrough specialist clinics that may have skewed selection of only children withregression or children with overt gastrointestinal symptoms. However, takentogether, the current study and existing literature would strongly support thehypothesis that cellular immune abnormalities exist in a substantial subset ofchildren with autism.

Findings from our current study describe increases in NK cells from children withautism compared to typically developing controls. These data are in line with aprevious report of greater frequencies of NK cells and increased gene expression ofNK cell-related cellular receptors and effector molecules in children with autism. Anumber of studies have however, shown decreased responsiveness of NK cells toin vitro stimulation –. The increase in NK cellnumbers seen in this study may therefore reflect a compensatory mechanism toincrease cell numbers to make up for possible deficits in NK cell function. However,reduced activation after stimulation could also occur if the NK cells were alreadymaximally stimulated in vivo, a phenomenon frequently observed inautoimmune diseases. Furthermore, NK cells have been shown to play a critical rolein the initiation of autoimmune-like responses in diabetes and celiac disease .The increased presence of auto-antibodies to brain and CNS proteins is a commonfinding in autism and may reflect an ongoing inflammatory and or autoimmune processin children with autism that could be initiated by abnormal NK cell activation . In this case,the expansion of NK cell numbers may result from heightened immune/autoimmuneresponses most likely mediated through the increased production of homeostatic andgrowth factors such as cytokines.

The frequency of mature (CD5n) and activated (CD38p) B cells were also increased inthis study and could also contribute to increased production of auto-antibodies.Cytokines such as IL-6 participate in the activation and differentiation of B cellsand their production of antibodies; previous studies have shown that there areincreased plasma IL-6 levels in children with autism which could modulate B cellactivity . Inaddition, T cells help B cells to produce antibodies typically through theproduction of cytokines. Recent studies show that in vitrostimulation of T cells leads to increased production of IL-13 and IL-5 that promoteactivation and antibody production from B cells , . Moreover, in this study we findthat T cells express a profile of cell surface markers such as HLA-DR and CD26 thatare indicative of activation and are in line with previous reports of altered T cellactivation in children with autism , . Taken together, our results suggests that there is anactivation of immune responses in children with autism that leads to increasedfrequency of NK cells, and activated B cells and T cells. It is tempting to furthersuggest that these cell types may interact in such a way as to break immunologicaltolerance to self proteins and to elicit auto-immune responses leading to theproduction of auto-antibodies. The balance between regulatory T cells andT_H17 cells are important in the initiation of autoimmune diseases. Inour previous studies, we did not find a difference in the frequency of circulatingFoxP3⁺ or CD25⁺⁺ regulatory T cells underresting conditions , ; however, regulatory cell function in children withautism may be altered as we have shown decreases in TGFβ1 levels and IL-10production ,. So far,in children with autism aged between 2 and 5 years of age we have found nodifferences in IL-17 plasma levels , T_H17 cell frequency at baseline levels orfollowing stimulation, and IL-17 production following stimulation . Taken togetherthese data do not suggest that there are differences in T_H17 in childrenwith autism of this age range but they can not rule out earlier alterations inT_H17 cell function that may be linked to causation of autism. Furtherassessments of T_H17 and regulatory T cells and their interactions inautism needs further investigation.

We, and others, have performed preliminary analyses that suggest that certainimmunological parameters are associated with specific behavioral symptoms in autism.Impairments in social behaviors, for example, are associated with decreased levelsof TGFβ1 , increased IL-1β and IL-13 , increased macrophageinhibitory factor , decreased platelet-endothelial adhesion molecule , total IgG, increasedIgG4 isotype , altered T cell responses , chemokine levels and activatedmonocyte responses . In addition, parallel proteomic analysis of sera samplesfrom the same participants in this study showed increased immune profiles withmarked differences in complement components in children with autism compared withtypical developing controls . The causal link, however, between these immune factorsand behavioral output remains unexplored. Moreover, no one marker is diagnostic ofautism and the results reported here imply that panels or “signature”profiles containing multiple markers may associate more specifically with distinctsymptomatology. Further work to determine the potential use of immune based panelsas the basis for defining autism phenotypes is called for. These studies shouldinclude “at-risk” groups where identification of autism beforebehavioral symptoms are manifest would be a major advance for the care andmanagement of such individuals. However, a major challenge in the identification ofreliable early biomarkers is to minimize the effects of confounding factors such asmedication, which may be more prevalent in the autism group. In this study, werecruited drug-naïve participants, and carefully screened and selectedwell-matched controls. Future studies will need to consider collecting even morein-depth and extensive demographic information about all aspects of lifestyle inorder to minimize the effects of as yet unidentified potential confounders.

Currently there is insufficient evidence to refute or confirm the presence ofspecific immune dysfunction in autism and it is still unclear whether immunealterations are reflective of specific immune dysfunction, or a bystander effect ofupstream regulatory mechanisms, or result from tissue pathology associated with thedisorder. This study does not seek to confirm or address specific immunologicalissues in children with autism but rather to investigate whether immune parametersmay be useful as biological markers in autism. To start to address the complexity ofthis symptomatically defined disorder, we need to conduct studies aimed atidentifying clear and consistent differences between individuals with autism andcontrols. One potential limitation of the current study is that it wascross-sectional and that we only looked at the immune parameters at one time point.Immune responses are exceedingly dynamic and it is often hard to achieve acomprehensive assessment of an immune response by looking at just one point in time.Also, physiological inflammation may be transient and could be missed ifparticipants are sampled at one time point only. Larger immunological studies needto be carried out, where several parameters can be measured and correlated for thesame individual with follow up tests throughout the progression of the disorder.

A major problem of studies of autism to date has been the inability of one study toreplicate the biological markers found in another study. This applies to almost anyaspect or facet measured in subjects with autism. As autism is a complex andheterogeneous disorder that may have multiple causes, pathologies and trajectories,the assumption that autism is a single disorder, and that a given finding shouldextend across all subjects with autism, may not be tenable. In fact, one of the manydifficulties in understanding autism may be that many different abnormalities mayconverge to produce similar behavioral symptoms in autism subjects. In order tobegin to untangle the interaction between complex genetic and/or environmentalfactors in autism, it is essential to identify a reliable biomarker(s) that willhelp provide invaluable insight to elucidate mechanisms of action that underlie thecauses of autism. Identifying these biomarkers could help identify differentsubgroups within the autism population. If possible, the discovery of truebiomarkers will almost certainly yield more successful genetic and functionalstudies and ultimately will help in the design of efficacious treatments forautism.

A lack of objective, biomedical analytical tools is a serious limitation to thediagnosis of autism. There are several reasons to adopt multiplex immunoassays,including those presented in this study, as a technology to determine possiblebiomarkers in autism. “Signature” profiles of biomarkers that provideinformation regarding possible subclasses within autism and that correlate withbehavioral states of autism would be extremely informative. An important aspect forfuture clinical application of such technology is that the potential biologicalsignatures are suited to analyzing serial samples in longitudinal studies. Thesearch for the identification of autism markers in the laboratory is an importantresearch endeavor, yet the translation of such findings into the clinic is the realchallenge and requires the investigation of much larger sample cohorts, ideallycollected in different clinical centers. Further studies to examine the clinicalpower and utility of putative markers for autism, including those related to theimmune response, are needed to help determine the usefulness in assisting earlydiagnosis of autism.

Acknowledgments

We would like to thank the families and the participants that were part of thisstudy. We thank Jun Deng for her technical assistance.

Footnotes

Competing Interests: HS and AK are employees of a commercial company, PPD Biomarker DiscoverySciences. Their involvement in this company does not alter the authors'adherence to all the PLoS ONE policies on sharing data and materials, asdetailed online in the guide for authors http://www.plosone.org/static/policies.action#sharing.

Funding: Download tema crows zero nokia e63. Funding was from gift monies from private donors (individuals/families) to theMIND Institute. The donors all stated gifts were unrestricted donations toperform research. The funders had no role in the study design, the datacollection and analysis, the decision to publish, or the prepartion of themanuscript.

References

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