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. Author manuscript; available in PMC: 2019 Mar 1.
Published in final edited form as: Psychoneuroendocrinology. 2017 Dec 28;89:39–45. doi: 10.1016/j.psyneuen.2017.12.022

Biomarker discovery for disease status and symptom severity in children with autism

Ozge Oztan a,*, Lisa P Jackson a, Robin A Libove a, Raena D Sumiyoshi a, Jennifer M Phillips a, Joseph P Garner a,b, Antonio Y Hardan a, Karen J Parker a
PMCID: PMC5878709  NIHMSID: NIHMS933292  PMID: 29309996

Abstract

Autism spectrum disorder (ASD) is characterized by social impairments and repetitive behaviors, and affects 1 in 68 US children. Despite ASD's societal impact, its disease mechanisms remain poorly understood. Recent preclinical ASD biomarker discovery research has implicated the neuropeptides oxytocin (OXT) and arginine vasopressin (AVP), and their receptors, OXTR and AVPR1A, in animal models. Efforts to translate these findings to individuals with ASD have typically involved evaluating single neuropeptide measures as biomarkers of ASD and/or behavioral functioning. Given that ASD is a heterogeneous disorder, and unidimensional ASD biomarker studies have been challenging to reproduce, here we employed a multidimensional neuropeptide biomarker analysis to more powerfully interrogate disease status and symptom severity in a well characterized child cohort comprised of ASD patients and neurotypical controls. These blood-based neuropeptide measures, considered as a whole, correctly predicted disease status for 57 out of 68 (i.e., 84%) participants. Further analysis revealed that a composite measure of OXTR and AVPR1A gene expression was the key driver of group classification, and that children with ASD had lower neuropeptide receptor mRNA levels compared to controls. Lower neuropeptide receptor mRNA levels also predicted greater symptom severity for core ASD features (i.e., social impairments and stereotyped behaviors), but were unrelated to intellectual impairment, an associated feature of ASD. Findings from this research highlight the value of assessing multiple related biological measures, and their relative contributions, in the same study, and suggest that low blood neuropeptide receptor availability may be a promising biomarker of disease presence and symptom severity in ASD.

Keywords: Autism spectrum disorder, arginine vasopressin receptor 1A, oxytocin receptor, social behavior, blood biomarkers, children

1. Introduction

Autism spectrum disorder (ASD) is neurodevelopmental disorder characterized by deficits in social communication and interaction, as well as restricted, repetitive patterns of behavior, interests, or activities (American Psychiatric Association, 2013). ASD is clinically heterogeneous (e.g., cognitive capabilities range significantly) and ASD impacts an estimated 1 in 68 US children (Christensen et al., 2016), with severe health, quality of life, and financial consequences for patients, families and/or society. ASD is currently diagnosed on the basis of behavioral criteria because its underlying disease mechanisms remain poorly understood. Consequently, there are no blood-based diagnostic tools to detect, or approved medications to treat, ASD's core features. Research that identifies robust biological substrates of disease status and symptomology in ASD patients is therefore urgently needed.

Neurobiological systems that are critical for social functioning are arguably the most promising signaling pathways for ASD biomarker and therapeutic target discovery. Two such candidates are the oxytocin (OXT) and arginine vasopressin (AVP) signaling pathways. OXT and AVP are primarily synthesized in the hypothalamus and released into both the brain via distributed neural pathways and systemic circulation via the posterior pituitary (Landgraf and Neumann, 2004). OXT and AVP are nearly structurally identical nonapeptides and likely evolved due to duplication of a common ancestral gene (Donaldson and Young, 2008). OXT has a single receptor (OXTR), whereas AVP has three receptors (AVPR1A, AVPR1B and AVPR2), with AVP's prosocial effects mediated through AVPR1A (Bielsky et al., 2004; Young et al., 1999). It is well established that OXT and AVP are critical for the expression of normative social behavior (e.g., social bond formation, social motivation, social decision making, social learning and memory) (Hammock and Young, 2006; Meyer-Lindenberg et al., 2011; Parker and Lee, 2001). Targeted disruption of OXT and AVP ligand-receptor signaling through pharmacologic or genetic manipulation also produces social deficits (Bielsky et al., 2004; Ferguson et al., 2000; Takayanagi et al., 2005) and repetitive behaviors (Sala et al., 2011) in various rodent species. Studies of rodent models of human syndromes with high ASD penetrance likewise have reported social impairments and diminished hypothalamic OXT and/or AVP producing cell numbers [e.g., Fragile-X Syndrome, Prader Willi Syndrome, and cortical dysplasia and focal epilepsy syndrome as modeled using Cntnap2 knockout mice (Francis et al., 2014; Penagarikano et al., 2015)], highlighting the potential significance of dysregulated OXT and AVP signaling in ASD.

On the basis of these promising preclinical findings, clinical investigators have begun to investigate a role for the OXT and AVP signaling pathways in idiopathic ASD (Zhang et al., 2017). Several studies have shown that blood OXT and AVP concentrations each positively predict social cognition abilities in children with ASD (Carson et al., 2015; Parker et al., 2014; Zhang et al., 2016), such that individuals with the lowest neuropeptide levels exhibit the greatest social impairments. Human genetic association studies have also shown that several single nucleotide polymorphisms and haplotypes in the OXTR and AVPR1A genes increase risk for ASD (Kim et al., 2002; Wermter et al., 2010; Wu et al., 2005; Yirmiya et al., 2006), and are associated with restricted, repetitive behaviors (Francis et al., 2016; Harrison et al., 2015). Although preliminary, these findings suggest that variation in OXT and AVP biology may be associated with ASD susceptibility, but much remains unknown.

With few exceptions (Miller et al., 2013; Parker et al., 2014; Zhang et al., 2016) the majority of prior research studies have evaluated single neuropeptide measures as biomarkers of ASD and/or behavioral functioning. Given that idiopathic ASD is a heterogeneous disorder, and unidimensional ASD biomarker studies have repeatedly met with challenges in reproducibility (Walsh et al., 2011), there is a clear and important need to assimilate unidimensional neuropeptide measures into a multidimensional biomarker analysis to more powerfully interrogate disease status and symptom severity in ASD patients. The goals of the present study therefore were three-fold. First, we tested in the same study population whether four blood-based neuropeptide measures (i.e., OXT and AVP peptide concentrations; OXTR and AVPR1A gene expression) correctly classified study participants as ASD vs. control. Second, we evaluated whether these blood neuropeptide measures differed between children with ASD and control children. Finally, we tested whether the neuropeptide measures predicted symptom severity for core ASD features (i.e., social impairments and repetitive behaviors) but not associated features (i.e., intellectual impairment) in a well characterized child cohort.

2. Materials and Methods

2.1. Participant recruitment and eligibility criteria

This study was approved by the Stanford University School of Medicine Institutional Review Board. All participants’ parents provided informed consent prior to initiation of study procedures. Assent was also obtained from participants when the child was deemed intellectually capable of understanding the study. Forty-four children with ASD (N= 7 F, 37 M), and 24 unrelated neurotypical control children (N= 6 F, 18 M) between the ages of 6 to 12 years participated in this study. Participant demographic characteristics are presented in Table 1. Children with ASD were primarily recruited through the Autism Research Registry at Stanford University, by flyers posted in the Stanford University Autism and Developmental Disorders Clinic, or at special events (e.g., Bay Area Autism Speaks Walk). Control participants were recruited through advertisements posted online (e.g., parent listservs) or hardcopy in the surrounding community (e.g., pediatrician offices).

Table 1. Participant characteristics.

Sex Ethnicity*
Group N Female Male Caucasian Asian Other Age(years) Full-scale IQ* Blood collection time (min)*
Autism 44 7 37 12 12 20 8.54 ± 0.33 74.15 ± 3.98 14:04 PM ± 15.75
Control 24 6 18 16 3 5 8.71 ± 0.41 116.12 ± 2.57 12:32 PM ± 20.00
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Fisher's exact test was used to test whether the distribution of individuals to different groups differed by sex and ethnicity. For age, full-scale IQ, and blood collection time, differences between groups were tested using a simple one-way general linear model (* = p<0.05). The values are reported as mean ± standard error.

Children with a diagnostic history of ASD underwent a comprehensive diagnostic evaluation to determine the accuracy of their previous diagnosis based on DSM-IV-TR (American Psychiatric Association, 2000) or DSM-5 criteria (American Psychiatric Association, 2013), which was confirmed with research diagnostic methods. These diagnostic methods included the Autism Diagnostic Instrument-Revised (ADI-R) (Lord et al., 1994) and the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) (Lord et al., 2012). The ADI-R and the ADOS-2 were administered by assessors trained by a research reliable clinician, and administration was reviewed for both initial and ongoing administration and coding reliability.

All participants were: 1) pre-pubertal; 2) in good medical health; and 3) willing to provide a blood sample. Participants with ASD were included if they had a Full-Scale IQ of 50 and above. Control participants were included if they had a Full-Scale IQ in or above the average range. Cognitive functioning was determined using the Stanford Binet Scales of Intelligence, 5th Edition (Roid, 2003). Exclusion criteria for children with ASD included: 1) a genetic etiology for ASD (e.g., Fragile X Syndrome); 2) a DSM-IV-TR or DSM-5 diagnosis of any severe mental disorder (e.g., schizophrenia, schizoaffective disorder, bipolar disorder), or 3) significant illness (e.g., serious liver, renal, or cardiac pathology). Participants taking medications were included as long as their medications were stable (i.e., for at least four weeks) before the blood draw. Control children were required to: 1) be free of neurological and psychiatric disorders in the present or past on the basis of medical history and 2) have no sibling diagnosed with ASD.

2.2. Behavioral phenotyping

The core behavioral features of ASD (i.e., social impairments and restricted, repetitive behaviors) were assessed using two instruments. 1) The SRS (Constantino et al., 2003) is a norm-referenced questionnaire that measures social behavior in both clinical and non-clinical populations. The SRS Total Score is a sensitive measure (i.e., it strongly correlates with DSM criterion scores) with high reliability. 2) The Repetitive Behaviors Scale - Revised (RBS-R) (Lam and Aman, 2007) assesses a wide range of restricted and repetitive behaviors. The RBS-R includes six subscales (Stereotyped Behavior, Self-injurious Behavior, Compulsive Behavior, Ritualistic Behavior, Sameness Behavior, and Restricted Behavior), for which the psychometric validity is established (Lam and Aman, 2007).

2.3. Blood sample collection and processing procedures

Twenty mL of whole blood was drawn from the child's antecubital region by a pediatric phlebotomist at Lucile Packard Children's Hospital outpatient laboratory within two weeks of behavioral phenotyping. It has been shown that circulating levels of OXT and AVP have modest daily rhythms of secretion, and that they both rise during the period of sleep (Forsling, 2000; Trudel and Bourque, 2010). Blood samples were therefore collected during daytime hours (i.e., between 10 AM and 5 PM) to reduce circadian effects on our biological measures of interest. Whole blood was collected into chilled EDTA-treated vacutainer tubes and immediately placed on wet ice. These samples were then promptly centrifuged (1600×g at 4°C for 15 min), the plasma fraction aliquoted into polypropylene tubes, and flash-frozen on dry ice. Whole blood was also collected into PAXgene RNA tubes (Qiagen, CA) and processed per manufacturer's instructions. All samples were then stored at -80°C until quantification.

2.4. Quantification procedures

OXT and AVP are primarily synthesized in the hypothalamus and released into systemic circulation by the posterior pituitary. The gold standard by which to measure these neuropeptide concentrations in blood is therefore to use immunoassay; in our case, we used enzyme-linked immunosorbent assays (ELISA). However, OXTR and AVPR1A are expressed in body tissues (Thibonnier et al., 2001), including in blood lymphocytes (Yamaguchi et al., 2004). We therefore used the gold standard for quantifying gene expression, qPCR, to assess blood mRNA levels of these neuropeptide receptors.

2.5. Quantification of plasma OXT and AVP concentrations

Plasma OXT and AVP concentrations were quantified using commercially available enzyme immunoassay kits (Enzo Life Sciences, Inc., NY). These kits are highly specific and exclusively recognize OXT and AVP, respectively, and not related peptides (i.e., the OXT cross-reactivity with AVP is 0.6% and the AVP cross-reactivity with OXT is <0.001%). A technician blinded to experimental conditions performed sample preparation and OXT and AVP quantification following established procedures (Carson et al., 2015; Parker et al., 2014). Briefly, plasma samples (1000 μL/participant) for each peptide were extracted per manufacturer's instructions and evaporated using compressed nitrogen. Each evaporated sample was reconstituted in 250 μL of assay buffer prior to OXT and AVP quantification to provide sufficient sample volume to run each participant's sample in duplicate wells (100 μL/well). This practice ensured that the plated samples contained high enough peptide quantities to be read above the limit of detection (15 pg/mL for OXT and 2.84 pg/mL for AVP). Samples were assayed with a tunable microplate reader (Molecular Devices, CA) for 96-well format per manufacturer's instructions. Intra- and inter-assay coefficients of variation were below 10% for both analytes.

2.6. Quantification of OXTR and AVPR1A gene expression levels

Total RNA was isolated and purified using a PAXgene blood RNA kit from blood stabilized in PAXgene RNA tubes (Qiagen, CA). RNA integrity was assessed with the Agilent 2100 Bioanalyzer (Agilent Technologies, CA), and consistently found to have RIN values (RNA integrity numbers) greater than 9.5. The first strand cDNA synthesis reaction was carried out with QuantiTect reverse transcription kit (Qiagen, CA), with a starting RNA quantity of 1 μg in a 20 μl final volume. The primer sequence information for OXTR and AVPR1A genes was obtained from published studies and was designed as follows: OXTR forward 5′- CTGAACATCCCGAGGAACTG-3′ and reverse 5′-CTCTGAGCCACTGCAAATGA-3′,(Gregory et al., 2009); AVPR1A forward 5′-CTTTTGTGATCGTGACGGCTTA-3′ and reverse 5′- TGATGGTAGGGTTTTCCGATTC-3′ (Wang et al., 2008). Two housekeeping genes, hypoxanthine phosphoribosyltransferase 1 [HPRT1; forward 5′-GGACAGGACTGAACGTCTTGC-3′ and reverse 5′-ATAGCCCCCCTTGAGCACAC-3′ (Wang et al., 2008)] and ubiquitin C [UBC; forward 5′-GCTGCTCATAAGACTCGGCC-3′ and reverse 5′- GTCACCCAAGTCCCGTCCTA-3′ (Wang et al., 2008)] were selected for normalization using geNorm. qPCR was performed on the StepOnePlus Real-Time PCR System (Life Technologies, CA) with SYBR Green (Thermo Fisher Scientific, MA). cDNA was PCR amplified in triplicate and Ct values from each sample were obtained using StepOnePlus software. The relative expression of each gene was calculated based on the ΔΔCt value, where the results were normalized to the average Ct value of HPRT1 and UBC (Lossie et al., 2012).

2.7. Statistical analyses

Data were managed using REDCap (Harris et al., 2009) and analyzed using JMP Pro 13 for Windows (SAS Institute Inc., NC). All analyses included N=44 ASD children, and (where appropriate) N=24 neurotypical control children. A logistic regression model, implemented as a Restricted Maximum Likelihood Generalized Linear Model (REML-GLIM), was used to assess whether blood neuropeptide measures (i.e., OXT and AVP peptide concentrations, expression of OXTR and AVPR1A genes) predict disease status of children with and without ASD. Age, time of blood collection, ethnicity, and sex were included as control variables (or ‘stratifiers’) in the initial model (Table S1). This model showed over-specification and quasi-complete separation, indicating collinearity between predictors, and an artefactually over-precise classification prone to false positive results (Paul, 1999). Since OXTR and AVPR1A gene expression was highly correlated, we first considered using Principle Components Analysis (PCA) to yield orthogonal components for analysis. This neatly illustrated the collinearity of the gene expression measures, which loaded onto a single factor with loadings (correlation coefficients) of 0.8058 and 0.7049 for OXTR and AVPR1A respectively. However, there were differences in the component structure when the ASD and control groups were processed separately. This precluded using a PCA to process the data from the two groups together (Howitt and Cramer, 2005; Miller et al., 2006). Given that OXT and AVP differ by only two amino acids, and their receptors likewise have a high degree of structural similarity, there is a substantial amount of documented crosstalk between these neuropeptides ligands at their receptors (Sala et al., 2011; Schorscher-Petcu et al., 2010; Song et al., 2014). Therefore, we calculated the total neuropeptide gene expression as the sum of the OXTR and AVPR1A gene expression to capture correlated expression of the two genes, and differential neuropeptide receptor gene expression as the difference between OXTR and AVPR1A gene expression to capture relative up or down regulation of these receptors. As plasma OXT and AVP concentrations were uncorrelated, we included them separately in the logistic regression model. The resulting model was robust, showing no evidence of over-specification or quasi-complete separation. Plasma AVP concentration was log-transformed in these and all other analyses to correct a skewed distribution. We confirmed the predictive power of total gene expression by running a single factor logistic regression (i.e., excluding all blocking factors and other biomarkers).

A Least Squares General Linear Model (LS-GLM), with the same control variables as those included in the logistic regression model, was used to test whether the neuropeptide measures differed between children with ASD and neurotypical controls. Each neuropeptide measure (total neuropeptide receptor gene expression, differential neuropeptide receptor gene expression, plasma AVP concentration, and plasma OXT concentration) was tested in turn, with the other three neuropeptide measures and IQ included in the model to ensure that any observed differences for a given neuropeptide measure were not better explained by group differences in the other neuropeptide measures or IQ. The assumptions of LS-GLM (homogeneity of variance, normality of error, and linearity) were tested post-hoc (Grafen and Hails, 2002).

Finally, we used an LS-GLM with the same control variables as before to test whether the neuropeptide measures predicted core behavioral phenotypes in children with ASD. We included each of the four neuropeptide measures and IQ in the model; as before, this excluded the possibility that a neuropeptide measure is significant merely due to IQ, and it allowed us to test each neuropeptide measure in the context of the others in a single model. To assess social impairments, we used the SRS Total Raw Score (instead of the sex-normalized T-score, which has lower resolution). However, given that psychometric validity for the RBS-R Total Score is not well established, we also performed the same analyses on each RBS-R subscale, but corrected our critical p-value to 0.0083, to protect against multiple comparisons and to achieve the same family-level significance as the total score. Finally, we tested whether the neuropeptide measures predicted IQ (i.e., cognitive ability) to test for core vs. associated ASD feature specificity (thus, in this model, IQ was removed as a control variable). As before, the assumptions of LS-GLM were tested post-hoc (Grafen and Hails, 2002).

3. Results

3.1. Participants

Participant demographic characteristics are presented in Table 1. Ethnicity and blood collection time unexpectedly differed between children with and without ASD. To eliminate the possibility that these confounding effects could generate false positive or false negative results (Grafen and Hails, 2002), we adopted the standard epidemiological approach to this problem, and included these variables in the statistical models as blocking factors. Less surprisingly, IQ also differed between groups. The effect of IQ in our analyses was addressed as described above.

3.2. Biomarker prediction of disease status

The logistic regression model correctly predicted disease status for 57 out of 68 (i.e., 84%) of the participants. Low levels of total neuropeptide receptor gene expression (i.e., sum of the OXTR and AVPR1A gene expression) predicted disease status (Likelihood Ratio Chi-square = 17.16; P < 0.0001; Figure 1). Low plasma OXT concentration also predicted disease status (LR Chi-sq = 4.700; P = 0.0302). However, OXT concentration was only significant in statistical models that included gene expression measures, indicating that OXT concentration serves as a moderator explaining additional variation, rather than being directly predictive. Differential neuropeptide receptor gene expression (LR Chi-sq = 3.600; P = 0.0578), and plasma AVP concentration (LR Chi-sq = 0.1023; P = 0.7491) did not significantly predict disease status. In fact, a simple logistic regression, containing only total gene expression, no stratifying (blocking) factors, and no other biomarkers, still significantly predicted disease status (LR Chi-sq = 4.265; P = 0.0389), confirming that other biomarkers and stratifiers in model serve to explain additional noise around this central biological signal.

Figure 1. Total neuropeptide receptor gene expression predicts disease status in children with and without ASD.

Figure 1

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The effect of total neuropeptide receptor gene expression (i.e., the sum of the OXTR -ACT and the AVPR1A -ACT) on predicted (line) and observed (colored circles) group is plotted, adjusted for the other terms in the model, and normalized to its original mean and standard deviation. Children with ASD plotted above, and Control children plotted beneath, the dashed line are correctly classified.

3.3. Total neuropeptide receptor gene expression differs between ASD and control children

Total neuropeptide receptor gene expression was significantly lower in children with ASD (F1,57 = 8.5263; P = 0.0050; Figure 2a). Differential neuropeptide receptor gene expression (F1,57 = 1.416; P = 0.2391; Figure 2b), plasma AVP (F1,57 = 0.3883; P = 0.5357; Figure 2c), and plasma OXT concentrations (F1,57 = 0. 6760; P = 0. 4144; Figure 2d) did not differ significantly by disease status, strengthening our previous interpretation that OXT is a moderator of gene expression.

Figure 2. Group comparisons are presented for the blood neuropeptide measures.

Figure 2

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Each of the four neuropeptide measures was evaluated in turn, controlling for the same blocking variables as well as the other three neuropeptide measures, in the model. Only total neuropeptide receptor gene expression differed significantly between the ASD and control groups. Data are plotted as LSM +/- SE (i.e., adjusted for the other variables in the model).

3.4. Total neuropeptide receptor gene expression predicts core, but not associated, features of ASD

Low levels of total neuropeptide receptor gene expression predicted greater social impairments as measured by the SRS Total (Raw) Score (F1,33 = 6.533; P = 0.0154; Figure 3a). We found no significant effect of the other neuropeptide measures on social functioning (P>0.05). Low levels of total neuropeptide receptor gene expression also predicted greater severity of stereotypies as measured by the RBS-R Stereotyped Behavior Subscale (F1,33 = 8.899; P = 0.0053; Figure 3b). None of the other neuropeptide measures significantly predicted stereotyped behavior, nor were any significant results found in the other subscales for any neuropeptide measure. Finally, neuropeptide receptor gene expression did not predict level of intellectual functioning as measured by IQ (F1,34 = 0.0190; P = 0.8913; Figure 3c), thereby demonstrating more predictive specificity for core ASD features. Finally, none of the other neuropeptide measures predicted IQ either (P>0.05).

Figure 3. Total neuropeptide receptor gene expression predicts symptom severity for core, but not associated, features of ASD.

Figure 3

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a) Social impairments, as measured by the SRS Total (Raw) Score, and b) Stereotypies, as measured by the RBS-R Stereotyped Behavior Subscale, are most severe in ASD children with the lowest levels of total neuropeptide receptor gene expression. c) Cognitive ability, as measured by the Stanford Binet IQ test, is unrelated to total neuropeptide receptor gene expression. Data are plotted adjusted for the other variables in the analysis.

4. Discussion

This study was the first to our knowledge to employ a multidimensional biomarker approach to investigate the roles of blood OXT and AVP peptides, and expression of their receptors (i.e., OXTR and AVPR1A), in predicting disease status and symptom severity in individuals with ASD. Considered collectively, these neuropeptide measures correctly classified 84% of study participants as ASD or control. Further analyses revealed that total neuropeptide receptor gene expression was the key driver of group classification, and that it was lower in children with ASD compared to control children. Moreover, total neuropeptide receptor gene expression was strongly associated with ASD symptom severity, such that lower levels of total neuropeptide receptor gene expression predicted greater social impairments and stereotyped behavior in children with ASD. Although there was a significant group difference in IQ, total neuropeptide receptor gene expression was unrelated to intellectual functioning, indicating that neuropeptide receptor gene expression is specific to core features of ASD.

Findings from the present research highlight the value of assessing related biological measures, and their relative contributions, in the same study. We found no evidence of group differences in blood OXT or AVP concentrations, consistent with some previous reports (Carson et al., 2015; Miller et al., 2013; Parker et al., 2014), and neither peptide was found to predict symptom severity. Inclusion of these peptide measures did improve the statistical model's accuracy to correctly classify participants as ASD or control, suggesting that blood OXT and AVP concentrations may serve as moderators explaining additional variation in, rather than being directly predictive of, disease status. In contrast, total OXTR and AVPR1A gene expression levels robustly predicted disease status, differed between ASD and control children, and predicted symptom severity in children with ASD. This effect was specific to overall receptor levels, as we found no evidence in any of the analyses for differential neuropeptide receptor gene expression effects when relative expression was contrasted within participants. Although genetic association studies have previously linked structural variation in the OXTR (Harrison et al., 2015) and AVPR1A (Francis et al., 2016) genes to social impairments and repetitive behaviors in ASD, to our knowledge, this is the first study to implicate the expression of these genes in ASD diagnosis and phenotype.

Biomarker discovery research in ASD has been largely restricted to peripheral tissue, such as blood, due to its ease in collection. Blood-based biomarkers can be valuable in the development of diagnostic tools to detect disease, or useful as companion diagnostics to identify patients most likely to benefit from specific pharmacotherapies. Exactly how representative of disease biology peripheral measures are for brain disorders such as ASD, however, remains largely unexplored. Nevertheless, one small study on OXTR regulation in ASD patients provides potential insight. In this study, individuals with ASD had significantly increased OXTR gene methylation levels in postmortem temporal cortical tissue compared to controls, and hypermethylation of the OXTR gene correlated with reduced OXTR mRNA levels (Gregory et al., 2009). Increased OXTR gene methylation levels were also observed in peripheral blood samples in an independent cohort of individuals with ASD compared to controls. Thus, like other genes (Kaminsky et al., 2012), OXTR methylation patterns, and hence OXTR gene expression, may be relatively conserved across tissue types, and OXTR methylation/expression in blood may be a useful surrogate for OXTR methylation/expression in the brain. If the same holds true for AVPR1A, a global decrease in total neuropeptide receptor gene expression may alter brain protein levels in a manner that functionally contributes to the observed relationship in the present study between peripheral gene expression and core features of ASD.

Intranasally administered OXT and AVP are currently being evaluated as potential pharmacotherapies to improve social abilities in individuals with ASD. Evidence from published OXT treatment trials in ASD patients have been equivocal, with several studies reporting efficacy (Watanabe et al., 2015; Yatawara et al., 2016), whereas others have found no improvement in the trial's primary outcome measure (Guastella et al., 2015; Munesue et al., 2016). Many OXT treatment trials have documented significant variability in responses to OXT, highlighting the need to identify specific factors that contribute to treatment efficacy. Because ASD is a heterogeneous disorder, one possible explanation for these ambiguous OXT treatment trial outcomes is that individual differences in OXTR and/or AVPR1A availability may influence the ability of a patient to respond, or not respond, to neuropeptide treatment. This observation has been recently reported by our group whereby inclusion of pretreatment neuropeptide measures in the statistical model was critical to accurately assessing OXT treatment efficacy (Parker et al., 2017). Hence, a careful evaluation of pre-treatment OXTR and AVPR1A gene expression levels to predict treatment efficacy in neuropeptide treatment trials is needed and may lead to use of neuropeptide gene expression as a tool to identify patients most likely to benefit from neuropeptide pharmacotherapy.

There are several limitations of the present study that merit consideration. First, our sample was male-biased and not statistically powered to detect sex differences or sex-by-group interactions in our analyses. Second, we collected only one blood sample per participant (due to the invasive nature of venipuncture, particularly in children), which limited our ability to assess within-individual consistency of our biological measures. Third, some of our study participants were not medication-free. Although their medications were stable (i.e. for at least four weeks) before blood collection, it is possible that our findings were influenced by their medication status. Fourth, down-regulation of OXTR and AVPR1A gene expression would almost certainly impact peripheral processes, as OXTR and AVPR1A are expressed in a variety of tissues throughout the body (Thibonnier et al., 2001). Because our study was designed to identify biomarkers of core ASD symptoms, the functional significance of this peripheral receptor down- regulation remains to be explored, as does its disease specificity (i.e., discerning ASD from other neurodevelopmental disorders or neuropsychiatric disorders, i.e., anxiety). Additionally, our study participants were comprised of children already diagnosed with ASD. Future studies should test whether infants at familial risk for developing ASD demonstrate lower total blood neuropeptide receptor gene expression prior to the onset of behavioral symptoms, or coincident with it. Finally, our behavioral phenotyping measures relied on parent report. Although we used gold-standard instruments, these measures were nevertheless subjective in nature.

In conclusion, this study was the first to assimilate unidimensional neuropeptide measures into a multidimensional biomarker analysis to more powerfully interrogate disease status and symptom severity in individuals with ASD. Total neuropeptide receptor gene expression was identified as the key driver of group classification, and low neuropeptide receptor gene expression predicted greater social impairments and stereotyped behaviors in children with ASD. Findings from the present study suggest that low blood neuropeptide receptor availability may be a promising biomarker of disease presence and symptom severity in individuals with ASD.

Supplementary Material

1
2

Highlights.

  • Blood neuropeptide measures accurately predict autism status in 84% of participants

  • Neuropeptide receptor gene expression is the key driver of group classification

  • Neuropeptide receptor gene expression is lower in children with autism

  • Low neuropeptide receptor gene expression predicts greater social impairments

  • Low neuropeptide receptor gene expression predicts greater stereotyped behaviors

Acknowledgments

We are grateful to the study participants and their families for their participation. This research was supported by the National Institutes of Health (HD083629, KJP; MH100387, AYH and KJP), The Mosbacher Family Fund for Autism Research (KJP), The Child Health Research Institute (OO, AYH, KJP), and The Yani Calmidis Memorial Fund for Autism Research (KJP).

Footnotes

Conflict of Interest: The authors declare no conflicts of interest.

Contributions: OO and KJP designed the study. OO, AYH, and KJP secured funding to underwrite scientific data collection. AYH oversaw patient recruitment and enrollment. OO, LPJ, RAL, RDS, and JMP contributed to clinical, behavioral, and/or biological data acquisition. OO quantified neuropeptide receptor gene expression. RDS quantified neuropeptide concentrations. OO, KJP, and JPG conducted statistical analyses and interpreted the study data. OO and KJP wrote the first draft of the manuscript. All authors read, significantly edited, and approved the final manuscript.

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References

  1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: Diagnostic Criteria for Autistic Disorder. 5th 2013. [Google Scholar]
  2. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: Diagnostic Criteria for Autistic Disorder. 4th. DSM Library; 2000. [Google Scholar]
  3. Bielsky IF, Hu SB, Szegda KL, Westphal H, Young LJ. Profound impairment in social recognition and reduction in anxiety-like behavior in vasopressin Via receptor knockout mice. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2004;29:483–493. doi: 10.1038/sj.npp.1300360. https://doi.org/10.1038/sj.npp.1300360. [DOI] [PubMed] [Google Scholar]
  4. Carson DS, Garner JP, Hyde SA, Libove RA, Berquist SW, Hornbeak KB, Jackson LP, Sumiyoshi RD, Howerton CL, Hannah SL, Partap S, Phillips JM, Hardan AY, Parker KJ. Arginine Vasopressin Is a Blood-Based Biomarker of Social Functioning in Children with Autism. PloS One. 2015;10:e0132224. doi: 10.1371/journal.pone.0132224. https://doi.org/10.1371/journal.pone.0132224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Christensen DL, Baio J, Van Naarden Braun K, Bilder D, Charles J, Constantino JN, Daniels J, Durkin MS, Fitzgerald RT, Kurzius-Spencer M, Lee LC, Pettygrove S, Robinson C, Schulz E, Wells C, Wingate MS, Zahorodny W, Yeargin-Allsopp M Centers for Disease Control and Prevention (CDC) Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. Morb Mortal Wkly Rep Surveill Summ Wash DC 2002. 2016;65:1–23. doi: 10.15585/mmwr.ss6503a1. https://doi.org/10.15585/mmwr.ss6503a1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Constantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL, Metzger LM, Shoushtari CS, Splinter R, Reich W. Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. J Autism Dev Disord. 2003;33:427–433. doi: 10.1023/a:1025014929212. [DOI] [PubMed] [Google Scholar]
  7. Donaldson ZR, Young LJ. Oxytocin, vasopressin, and the neurogenetics of sociality. Science. 2008;322:900–904. doi: 10.1126/science.1158668. https://doi.org/10.1126/science.1158668. [DOI] [PubMed] [Google Scholar]
  8. Ferguson JN, Young LJ, Hearn EF, Matzuk MM, Insel TR, Winslow JT. Social amnesia in mice lacking the oxytocin gene. Nat Genet. 2000;25:284–288. doi: 10.1038/77040. https://doi.org/10.1038/77040. [DOI] [PubMed] [Google Scholar]
  9. Forsling ML. Diurnal rhythms in neurohypophysial function. Exp Physiol 85 Spec No. 2000:179S–186S. doi: 10.1111/j.1469-445x.2000.tb00022.x. [DOI] [PubMed] [Google Scholar]
  10. Francis SM, Kim SJ, Kistner-Griffin E, Guter S, Cook EH, Jacob S. ASD and Genetic Associations with Receptors for Oxytocin and Vasopressin-AVPR1A, AVPR1B, and OXTR. Front Neurosci. 2016;10:516. doi: 10.3389/fnins.2016.00516. https://doi.org/10.3389/fnins.2016.00516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Francis SM, Sagar A, Levin-Decanini T, Liu W, Carter CS, Jacob S. Oxytocin and vasopressin systems in genetic syndromes and neurodevelopmental disorders. Brain Res. 2014;1580:199–218. doi: 10.1016/j.brainres.2014.01.021. https://doi.org/10.1016Zj.brainres.2014.01.02. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Grafen A, Hails R. Modern statistics for the life sciences. Oxford: Oxford University Press Google Scholar; 2002. [Google Scholar]
  13. Gregory SG, Connelly JJ, Towers AJ, Johnson J, Biscocho D, Markunas CA, Lintas C, Abramson RK, Wright HH, Ellis P, Langford CF, Worley G, Delong GR, Murphy SK, Cuccaro ML, Persico A, Pericak-Vance MA. Genomic and epigenetic evidence for oxytocin receptor deficiency in autism. BMC Med. 2009;7:62. doi: 10.1186/1741-7015-7-62. https://doi.org/10.1186/1741-7015-7-62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Guastella AJ, Gray KM, Rinehart NJ, Alvares GA, Tonge BJ, Hickie IB, Keating CM, Cacciotti-Saija C, Einfeld SL. The effects of a course of intranasal oxytocin on social behaviors in youth diagnosed with autism spectrum disorders: a randomized controlled trial. J Child Psychol Psychiatry. 2015;56:444–452. doi: 10.1111/jcpp.12305. https://doi.org/10.1111/jcpp.12305. [DOI] [PubMed] [Google Scholar]
  15. Hammock EAD, Young LJ. Oxytocin, vasopressin and pair bonding: implications for autism. Philos Trans R Soc Lond B Biol Sci. 2006;361:2187–2198. doi: 10.1098/rstb.2006.1939. https://doi.org/10.1098/rstb.2006.1939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381. doi: 10.1016/j.jbi.2008.08.010. https://doi.org/10.1016/jjbi.2008.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Harrison AJ, Gamsiz ED, Berkowitz IC, Nagpal S, Jerskey BA. Genetic variation in the oxytocin receptor gene is associated with a social phenotype in autism spectrum disorders. Am J Med Genet Part B Neuropsychiatr Genet Off Publ Int Soc Psychiatr Genet. 2015;168:720–729. doi: 10.1002/ajmg.b.32377. https://doi.org/10.1002/ajmg.b.32377. [DOI] [PubMed] [Google Scholar]
  18. Howitt D, Cramer D. Introduction to statistics in psychology. Pearson Education 2005 [Google Scholar]
  19. Kaminsky Z, Tochigi M, Jia P, Pal M, Mill J, Kwan A, Ioshikhes I, Vincent JB, Kennedy JL, Strauss J, Pai S, Wang S-C, Petronis A. A multi-tissue analysis identifies HLA complex group 9 gene methylation differences in bipolar disorder. Mol Psychiatry. 2012;17:728–740. doi: 10.1038/mp.2011.64. https://doi.org/10.1038/mp.2011.64. [DOI] [PubMed] [Google Scholar]
  20. Kim SJ, Young LJ, Gonen D, Veenstra-VanderWeele J, Courchesne R, Courchesne E, Lord C, Leventhal BL, Cook EH, Insel TR. Transmission disequilibrium testing of arginine vasopressin receptor 1A (AVPR1A) polymorphisms in autism. Mol Psychiatry. 2002;7:503–507. doi: 10.1038/sj.mp.4001125. https://doi.org/10.1038/sj.mp.4001125. [DOI] [PubMed] [Google Scholar]
  21. Lam KSL, Aman MG. The Repetitive Behavior Scale-Revised: independent validation in individuals with autism spectrum disorders. J Autism Dev Disord. 2007;37:855–866. doi: 10.1007/s10803-006-0213-z. https://doi.org/10.1007/s10803-006-0213-z. [DOI] [PubMed] [Google Scholar]
  22. Landgraf R, Neumann ID. Vasopressin and oxytocin release within the brain: a dynamic concept of multiple and variable modes of neuropeptide communication. Front Neuroendocrinol. 2004;25:150–176. doi: 10.1016/j.yfrne.2004.05.001. https://doi.org/10.1016/j.yfrne.2004.05.001. [DOI] [PubMed] [Google Scholar]
  23. Lord C, Rutter M, DiLavore P, Risi S, Gotham K, Bishop S. Autism diagnostic observation schedule-2nd edition (ADOS-2) Los Angel CA West Psychol Corp 2012 [Google Scholar]
  24. Lord C, Rutter M, Le Couteur A. Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24:659–685. doi: 10.1007/BF02172145. [DOI] [PubMed] [Google Scholar]
  25. Lossie AC, Lo CL, Baumgarner KM, Cramer MJ, Garner JP, Justice MJ. ENU mutagenesis reveals that Notchless homolog 1 (Drosophila) affects Cdkn1a and several members of the Wnt pathway during murine pre-implantation development. BMC Genet. 2012;13:106. doi: 10.1186/1471-2156-13-106. https://doi.org/10.1186/1471-2156-13-106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Meyer-Lindenberg A, Domes G, Kirsch P, Heinrichs M. Oxytocin and vasopressin in the human brain: social neuropeptides for translational medicine. Nat Rev Neurosci. 2011;12:524–538. doi: 10.1038/nrn3044. https://doi.org/10.1038/nrn3044. [DOI] [PubMed] [Google Scholar]
  27. Miller KA, Garner JP, Mench JA. Is fearfulness a trait that can be measured with behavioural tests? A validation of four fear tests for Japanese quail Anim Behav. 2006;71:1323–1334. [Google Scholar]
  28. Miller M, Bales KL, Taylor SL, Yoon J, Hostetler CM, Carter CS, Solomon M. Oxytocin and vasopressin in children and adolescents with autism spectrum disorders: sex differences and associations with symptoms. Autism Res Off J Int Soc Autism Res. 2013;6:91–102. doi: 10.1002/aur.1270. https://doi.org/10.1002/aur.1270. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Munesue T, Nakamura H, Kikuchi M, Miura Y, Takeuchi N, Anme T, Nanba E, Adachi K, Tsubouchi K, Sai Y, Miyamoto KI, Horike SI, Yokoyama S, Nakatani H, Niida Y, Kosaka H, Minabe Y, Higashida H. Oxytocin for Male Subjects with Autism Spectrum Disorder and Comorbid Intellectual Disabilities: A Randomized Pilot Study. Front Psychiatry. 2016;7:2. doi: 10.3389/fpsyt.2016.00002. https://doi.org/10.3389/fpsyt.2016.00002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Parker KJ, Garner JP, Libove RA, Hyde SA, Hornbeak KB, Carson DS, Liao CP, Phillips JM, Hallmayer JF, Hardan AY. Plasma oxytocin concentrations and OXTR polymorphisms predict social impairments in children with and without autism spectrum disorder. Proc Natl Acad Sci U S A. 2014;111:12258–12263. doi: 10.1073/pnas.1402236111. https://doi.org/10.1073/pnas.1402236111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Parker KJ, Lee TM. Central vasopressin administration regulates the onset of facultative paternal behavior in microtus pennsylvanicus (meadow voles) Horm Behav. 2001;39:285–294. doi: 10.1006/hbeh.2001.1655. https://doi.org/10.1006/hbeh.2001.1655. [DOI] [PubMed] [Google Scholar]
  32. Parker KJ, Oztan O, Libove RA, Sumiyoshi RD, Jackson LP, Karhson DS, Summers JE, Hinman KE, Motonaga KS, Phillips JM. Intranasal oxytocin treatment for social deficits and biomarkers of response in children with autism. Proc Natl Acad Sci. 2017;201705521 doi: 10.1073/pnas.1705521114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Paul DA. Logistic regression using the SAS system: theory and application. SAS Inst Corp USA 1999 [Google Scholar]
  34. Peñagarikano O, Lázaro MT, Lu XH, Gordon A, Dong H, Lam HA, Peles E, Maidment NT, Murphy NP, Yang XW, Golshani P, Geschwind DH. Exogenous and evoked oxytocin restores social behavior in the Cntnap2 mouse model of autism. Sci Transl Med. 2015;7:271ra8. doi: 10.1126/scitranslmed.3010257. https://doi.org/10.1126/scitranslmed.3010257. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Roid GH. Stanford-Binet intelligence scales. Riverside Publishing; Itasca, IL: 2003. [Google Scholar]
  36. Sala M, Braida D, Lentini D, Busnelli M, Bulgheroni E, Capurro V, Finardi A, Donzelli A, Pattini L, Rubino T, Parolaro D, Nishimori K, Parenti M, Chini B. Pharmacologic rescue of impaired cognitive flexibility, social deficits, increased aggression, and seizure susceptibility in oxytocin receptor null mice: a neurobehavioral model of autism. Biol Psychiatry. 2011;69:875–882. doi: 10.1016/j.biopsych.2010.12.022. https://doi.org/10.1016Zj.biopsych.2010.12.022. [DOI] [PubMed] [Google Scholar]
  37. Schorscher-Petcu A, Sotocinal S, Ciura S, Dupré A, Ritchie J, Sorge RE, Crawley JN, Hu SB, Nishimori K, Young LJ. Oxytocin-induced analgesia and scratching are mediated by the vasopressin-1A receptor in the mouse. J Neurosci. 2010;30:8274–8284. doi: 10.1523/JNEUROSCI.1594-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Song Z, McCann KE, McNeill JK, Larkin TE, Huhman KL, Albers HE. Oxytocin induces social communication by activating arginine-vasopressin V1a receptors and not oxytocin receptors. Psychoneuroendocrinology. 2014;50:14–19. doi: 10.1016/j.psyneuen.2014.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Takayanagi Y, Yoshida M, Bielsky IF, Ross HE, Kawamata M, Onaka T, Yanagisawa T, Kimura T, Matzuk MM, Young LJ, Nishimori K. Pervasive social deficits, but normal parturition, in oxytocin receptor-deficient mice. Proc Natl Acad Sci U S A. 2005;102:16096–16101. doi: 10.1073/pnas.0505312102. https://doi.org/10.1073/pnas.0505312102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Thibonnier M, Coles P, Thibonnier A, Shoham M. The basic and clinical pharmacology of nonpeptide vasopressin receptor antagonists. Annu Rev Pharmacol Toxicol. 2001;41:175–202. doi: 10.1146/annurev.pharmtox.41.1.175. [DOI] [PubMed] [Google Scholar]
  41. Trudel E, Bourque CW. Central clock excites vasopressin neurons by waking osmosensory afferents during late sleep. Nat Neurosci. 2010;13:467–474. doi: 10.1038/nn.2503. https://doi.org/10.1038/nn.2503. [DOI] [PubMed] [Google Scholar]
  42. Walsh P, Elsabbagh M, Bolton P, Singh I. In search of biomarkers for autism: scientific, social and ethical challenges. Nat Rev Neurosci. 2011;12:603–612. doi: 10.1038/nrn3113. https://doi.org/10.1038/nrn3113. [DOI] [PubMed] [Google Scholar]
  43. Wang SS, Kamphuis W, Huitinga I, Zhou JN, Swaab DF. Gene expression analysis in the human hypothalamus in depression by laser microdissection and real-time PCR: the presence of multiple receptor imbalances. Mol Psychiatry. 2008;13:786–799. doi: 10.1038/mp.2008.38. , 741. https://doi.org/10.1038/mp.2008.38. [DOI] [PubMed] [Google Scholar]
  44. Watanabe T, Kuroda M, Kuwabara H, Aoki Y, Iwashiro N, Tatsunobu N, Takao H, Nippashi Y, Kawakubo Y, Kunimatsu A, Kasai K, Yamasue H. Clinical and neural effects of six-week administration of oxytocin on core symptoms of autism. Brain J Neurol. 2015;138:3400–3412. doi: 10.1093/brain/awv249. https://doi.org/10.1093/brain/awv249. [DOI] [PubMed] [Google Scholar]
  45. Wermter AK, Kamp-Becker I, Hesse P, Schulte-Körne G, Strauch K, Remschmidt H. Evidence for the involvement of genetic variation in the oxytocin receptor gene (OXTR) in the etiology of autistic disorders on high-functioning level. Am J Med Genet Part B Neuropsychiatr Genet Off Publ Int Soc Psychiatr Genet. 2010;153B:629–639. doi: 10.1002/ajmg.b.31032. https://doi.org/10.1002/ajmg.b.31032. [DOI] [PubMed] [Google Scholar]
  46. Wu S, Jia M, Ruan Y, Liu J, Guo Y, Shuang M, Gong X, Zhang Y, Yang X, Zhang D. Positive association of the oxytocin receptor gene (OXTR) with autism in the Chinese Han population. Biol Psychiatry. 2005;58:74–77. doi: 10.1016/j.biopsych.2005.03.013. https://doi.org/10.1016Zj.biopsych.2005.03.013. [DOI] [PubMed] [Google Scholar]
  47. Yamaguchi Y, Yamada K, Suzuki T, Wu YP, Kita K, Takahashi S, Ichinose M, Suzuki N. Induction of uPA release in human peripheral blood lymphocytes by [deamino- Cysl,D-Arg8]-vasopressin (dDAVP) Am J Physiol Endocrinol Metab. 2004;287:E970–976. doi: 10.1152/ajpendo.00027.2003. https://doi.org/10.1152/ajpendo.00027.2003. [DOI] [PubMed] [Google Scholar]
  48. Yatawara CJ, Einfeld SL, Hickie IB, Davenport TA, Guastella AJ. The effect of oxytocin nasal spray on social interaction deficits observed in young children with autism: a randomized clinical crossover trial. Mol Psychiatry. 2016;21:1225–1231. doi: 10.1038/mp.2015.162. https://doi.org/10.1038/mp.2015.162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Yirmiya N, Rosenberg C, Levi S, Salomon S, Shulman C, Nemanov L, Dina C, Ebstein RP. Association between the arginine vasopressin 1a receptor (AVPR1a) gene and autism in a family-based study: mediation by socialization skills. Mol Psychiatry. 2006;11:488–494. doi: 10.1038/sj.mp.4001812. https://doi.org/10.1038/sj.mp.4001812. [DOI] [PubMed] [Google Scholar]
  50. Young LJ, Nilsen R, Waymire KG, MacGregor GR, Insel TR. Increased affiliative response to vasopressin in mice expressing the V1a receptor from a monogamous vole. Nature. 1999;400:766–768. doi: 10.1038/23475. https://doi.org/10.1038/23475. [DOI] [PubMed] [Google Scholar]
  51. Zhang HF, Dai YC, Wu J, Jia MX, Zhang JS, Shou XJ, Han SP, Zhang R, Han JS. Plasma Oxytocin and Arginine-Vasopressin Levels in Children with Autism Spectrum Disorder in China: Associations with Symptoms. Neurosci Bull. 2016;32:423–432. doi: 10.1007/s12264-016-0046-5. https://doi.org/10.1007/s 12264-016-0046-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Zhang R, Zhang HF, Han JS, Han SP. Genes Related to Oxytocin and Arginine- Vasopressin Pathways: Associations with Autism Spectrum Disorders. Neurosci Bull. 2017;33:238–246. doi: 10.1007/s12264-017-0120-7. https://doi.org/10.1007/s12264-017-0120-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

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