Collection of ADI-R data and identification of phenotypic subgroups

Scores from a total of 123 items in ADI-R structured interviews of 85 male individuals with ASD were obtained from a previously published study by Hu and Steinberg (2009) [41]. These ADI-R score sheets were downloaded from the Autism Genetic Research Exchange (AGRE) phenotype database [42], and the individuals were selected based on previously described criteria [16]. Briefly, all individuals with ASD were males diagnosed through an ADI-R diagnostic interview. Individuals with cognitive impairment (Raven's scores <70), those with known genetic or chromosomal abnormalities (e.g., Fragile X, Rett syndrome, tuberous sclerosis, chromosome 15q11–q13 duplication), those born prematurely (<35 weeks gestation), and those with diagnosed comorbid psychiatric disorders (e.g., bipolar disorder, obsessive compulsive disorder, severe anxiety) were excluded. Impairment of spoken language was additionally confirmed based on low standard scores (<80) in the Peabody Picture Vocabulary Test.

LCLs from these individuals with ASD (n = 85) and sex-/age-matched unaffected individuals (n = 29) have been subjected to transcriptome profiling analysis [16], and the transcriptome profile data of the LCLs from these individuals have been deposited in the NCBI GEO DataSets repository [43] (GSE15402). The majority of the controls (24 out of 29) were unrelated to the cases, whereas 5 were unaffected siblings. The demographic information for all individuals examined in this study is shown in S1 Table.

Unsupervised hierarchical clustering (HCL) analysis and principal component analysis (PCA) of the ADI-R data were performed using Multiple Experiment Viewer (MeV) (http://mev.tm4.org; [44]) as previously described [41] to identify phenotypic subgroups based on clustering patterns. The use of lymphoblastoid cell lines in this study was reviewed by the GWU Office of Human Research and determined to be “Exempt” from full IRB review for human subject research because all of the cell lines and phenotypic data used in this study were deidentified with respect to the donor. Moreover, there were never any direct interactions with any of the individuals whose cells were employed in this study.

Reanalysis of transcriptome profile data

To identify differentially expressed genes in each ASD phenotypic subgroup, the transcriptome profiles of LCLs derived from individuals with ASD and sex-/age-matched unaffected individuals were downloaded from the gene expression profiling results of Hu et al. (2009) (GSE number: GSE15402) in the NCBI GEO DataSets database [16]. In addition to the GSE15402 study, we obtained transcriptome data from four other gene expression profiling studies involving peripheral blood cells or cell lines derived from peripheral blood cells from the NCBI GEO DataSets database (GSE25507, GSE6575, GSE18123, and GSE42133) [45–48]. Differentially expressed transcripts from each study were then identified, as described in previous reports [8,9]. The normalized transcriptome data were uploaded to the Multiple Experiment Viewer (MeV) program [44], and a 70% data cutoff filter was applied to remove genes for which log₂ intensity values were missing in >30% of the samples in each study. Significantly differentially expressed genes were identified using t-tests with standard Bonferroni correction (P-value < 0.05).

Collection of published proteome profile data

We searched the NCBI PubMed database to obtain a list of significantly differentially expressed proteins in ASD that have been published in the literature. A total of 14 proteomic studies in the NCBI PubMed database were used [25–38]. All of the ASD proteome studies were published from 2007–2017 and employed human tissue samples, including postmortem brain tissues, serum, plasma, neonatal blood, urine, and saliva. The significant protein hits identified and reported by all proteome studies were obtained and combined.

Cell culture

Lymphoblastoid cell lines derived from the peripheral lymphocytes of individuals with ASD (n = 36) and sex-/age-matched unaffected individuals (n = 20) who were also included in the transcriptome study GSE15402 were obtained from the Autism Genetics Resource Exchange Repository (AGRE, Los Angeles, CA, USA). The demographic information and phenotypic cluster for all individuals whose LCLs were employed for cell culture are shown in S1 Table. All of the LCLs were cultured in Hyclone RPMI-1640 (GE Healthcare, Chicago, IL, USA) supplemented with 15% fetal bovine serum (Sigma-Aldrich, St. Louis, MO, USA) and 1% penicillin-streptomycin-amphotericin B solution (Mediatech, Manassas, VA, USA) according to the protocol of the Rutgers University Cell and DNA Repository, as previously described [15]. Briefly, cell culture was performed in a humidified atmosphere of 5% CO₂ at 37°C. The cells were split 1:2 every 3−4 days and were harvested for protein isolation 3 days after splitting, when the cells were in the logarithmic growth phase. The cells were then pelleted and used for subsequent protein isolation. In addition to proteins employed for this study, we also obtained DNA and RNA from the same samples for future investigation. To allow serial isolation of DNA, RNA, and proteins from the same samples, the cells were therefore resuspended in RNAlater solution (Thermo Fisher Scientific, Waltham, MA, USA) and stored at -80°C until DNA/RNA/protein isolation. According to the manufacturer’s protocol, RNAlater solution can be used for stabilizing DNA, RNA, and proteins in cell samples.

Protein isolation and purification

Proteins were extracted from LCLs using the GENEzol reagent (Geneaid Biotech Ltd., New Taipei City, Taiwan) according to the manufacturer’s protocol, which also allowed for the isolation of DNA and RNA for future study. Briefly, LCLs stored in RNAlater were centrifuged to pellet the cells. RNAlater was then removed, and the GENEzol reagent was added to the cell pellet to lyse the cells. A total of 200μL of chloroform was added to the GENEzol extract, followed by centrifugation. The aqueous phase was then removed, and 100% ethanol was added to the interphase and the organic phase. After centrifugation to pellet the DNA, the supernatant was transferred to a new tube for protein isolation. To precipitate proteins, isopropanol was added to the phenol-ethanol supernatant, followed by centrifugation for 10 minutes at 12,000 g at 4°C. The protein pellet was washed 3 times in a wash solution consisting of 0.3 M guanidine hydrochloride in 95% ethanol and then washed once in 100% ethanol. The protein pellet was subsequently air dried and resuspended in 200 μl of a lysis buffer consisting of 7 M urea, 2 M thiourea, 4% w/v CHAPS, and 100 mM dithiothreitol (DTT). The concentrations of the protein samples were measured using Bradford protein assays, with bovine serum albumin (BSA) as a standard. A 5μg sample of each protein extract was separated via 12% SDS-PAGE and stained with a colloidal Coomassie Brilliant Blue G-250 solution consisting of ammonium sulfate, 85% phosphoric acid, Coomassie Brilliant Blue G-250, and methanol to assess the integrity of the proteins prior to two-dimensional gel electrophoresis.

Two-dimensional gel electrophoresis

To screen for proteins differentially expressed in ASD with severe language impairment, two-dimensional gel electrophoresis was conducted using protein samples from LCLs derived from individuals with ASD with severe language impairment (n = 6) or sex-/age-matched unaffected individuals (n = 6). A total of 200 μg of purified protein was loaded onto Immobiline DryStrips (pH 3–10 L, 13 cm) with rehydration buffer consisting of 7 M urea, 2 M thiourea, 4% CHAPS, 100 mM DTT, 2% Biolyte, and 0.5% bromophenol blue. The strips were rehydrated at 20°C for 12 h. The proteins were subjected to isoelectric focusing with a Multiphor III Electrophoresis System (Amersham Biosciences, Little Chalfont, UK) at the following voltages: 500 V for 300 Vh (step and hold), 1000 V for 800 Vh (gradient), 8000 V for 11,300 Vh (gradient), and 8000 V for 4400 Vh (step and hold). After focusing, the immobilized pH gradient (IPG) strips were reduced in equilibration buffer consisting of 50 mM Tris-HCl (pH 8.8), 6 M urea, 30% v/v glycerol, 2% SDS w/v, and 1% bromophenol blue with 100 mM DTT for 30 minutes. The strips were then alkylated in equilibration buffer containing 150 mM iodoacetamide (IAA) for 45 minutes. The proteins were separated in the second dimension via 12.5% SDS-PAGE. Each pooled sample was run in triplicate 2D gels. The gels were stained with Coomassie Brilliant Blue G250 overnight and destained with Milli-Q water until the background color was clear. The 2D gels were scanned at a 600 μm/pixel resolution and analyzed with ImageMaster 2D Platinum version 7.0 software (Amersham Biosciences). Each sample was run in triplicate, and differentially expressed protein spots were detected as significant by ANOVA with a P-value of less than 0.05.

Tryptic in-gel digestion and protein identification by LC-MS/MS

The significantly differentially expressed protein spots were excised from the gels and destained with 50% acetonitrile (ACN) in 100 μL of 25 mM ammonium bicarbonate. After destaining, tryptic digestion was performed according to a previously described method [49]. The resulting peptide mixtures were analyzed with an UltiMate 3000 RSLCnano System (Ultimate 3000, Dionex, USA) coupled to a micrOTOF-Q II ESI-Qq-TOF mass spectrometer (Bruker Daltonics, Germany). The MS/MS spectra data from each sample were searched against the NCBI databases using the MASCOT search engine (Matrix Science, London, UK) (http://www.matrixscience.com/) [50].

Prediction of biological functions and networks

The biological functions and networks associated with the differentially expressed proteins were predicted using Ingenuity Pathway Analysis (IPA) version 6.0 (QIAGEN Bioinformatics, https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/). IPA is a manually curated web-based functional analysis and knowledge discovery tool for omic data. IPA Knowledgebase collects from the literature all information about diseases, canonical pathways, biological functions, interactions, and regulators associated with genes and proteins in any context. Thus, a single gene/protein may have been reported to be involved in several different categories based on previously published research. To determine the association between a list of genes/proteins and each disease/process/pathway, IPA overlaps the list of genes/proteins provided by the user with the list of genes/proteins associated with a category in the database. The P-value is then calculated using the Fisher Exact test as a measure of the likelihood that the association between a set of focus genes in the experiment and a given disease/process/pathway is due to random chance. A P-value < 0.05 is considered to be a statistically significant, nonrandom association. The GeneCards database was also employed to predict functions and phenotypes associated with proteins (www.genecards.org) [51]. Moreover, to determine whether the differentially expressed proteins had previously been associated with ASD or reported in ASD studies as “ASD candidate” genes/proteins, the list of differentially expressed proteins was searched against AutismKB Knowledgebase (http://autismkb.cbi.pku.edu.cn/) [52].

Western blot analysis

Western blot analysis was performed to validate selected differentially expressed proteins in the ASD phenotypic subgroup with severe language impairment identified by 2D gel electrophoresis and subsequent mass spectrometry analysis. A total of 15 μg of each LCL protein sample was separated via 10% SDS-PAGE and transferred to a 0.2 μM Immun-Blot PVDF membrane (Bio-Rad Laboratories, Hercules, CA, USA) in a Mini-PROTEAN Tetra system. The membranes were then blocked with 5% nonfat dry milk (Vivantis Technologies Sdn. Bhd., Malaysia) in TBST for 1 h at room temperature. After blocking, the membranes were incubated overnight with a 1:8,000 dilution of rabbit monoclonal anti-human IDH2 antibody [EPR7576] (ab129180, Abcam) or a 1:12,000 dilution of rabbit polyclonal anti-human DBI antibody (ab196485, Abcam), followed by incubation in a 1:12,000 dilution of preadsorbed donkey anti-rabbit IgG H&L (HRP) (ab7083, Abcam). As a protein loading control, the membranes were stripped with 0.2 M NaOH for 5 minutes and reprobed overnight with a 1:8,000 dilution of a rabbit polyclonal anti-GAPDH antibody (sc25778, Santa Cruz Biotechnology, Dallas, Texas, USA). All of the membranes were visualized with Amersham ECL Select Western Blotting Detection Reagent with ECL Hyperfilm. The band images were obtained with the Syngene Gbox EF Gel Documentation System using the GeneTools program. The significance of differences between the groups was calculated using two-tailed paired t-test analysis. Pearson correlation analysis of the levels of selected proteins and ADI-R scores was conducted using IBM SPSS Statistics version 22.

Transcriptome profiling analysis of LCLs

To identify differentially expressed transcripts in these 85 individuals with ASD and in each of the four ASD phenotypic subgroups identified by the cluster analysis, the transcriptome profiles of LCLs derived from these 85 individuals with ASD and 29 sex-/age-matched unaffected individuals were obtained from the NCBI GEO DataSets database (GSE15402) and reanalyzed. To ensure that the expression fold-change determinations for a particular gene/transcript were based on a majority of the samples tested, a 70% cutoff filter was applied. As many as 14,838 out of 41,472 probes were abundantly expressed in at least 70% of samples, which reflected substantial abundance of transcripts across LCL samples for the subsequent significance analysis of differentially expressed transcripts and also supported the feasibility of using LCLs for gene expression profiling analysis. Significantly differentially expressed transcripts in ASD and in each ASD phenotypic subgroup were identified via t-test analysis with standard Bonferroni correction (P-value < 0.05). The numbers of significantly differentially expressed transcripts and the corresponding proteins in the all-ASD group and in each subgroup are shown in Table 1. When all of the individuals with ASD were combined into one group, as many as 815 transcripts encoding 384 proteins were found to be dysregulated in ASD. After the ASD cases were further subgrouped via the ADI-R cluster analysis, we found that the G3 (red) subgroup exhibited the highest number of differentially expressed transcripts, including 3,024 transcripts corresponding to 1,726 proteins. The lists of differentially expressed transcripts and corresponding proteins in the all-ASD group and in each subgroup are shown in S2 Table. An overlap analysis of these differentially expressed transcripts revealed 549 transcripts that were altered in more than one subgroup as well as transcripts that were disrupted in only a specific subgroup (Fig 3). We next used IPA to analyze the biological functions of these differentially expressed transcripts. We found that the significantly differentially expressed transcripts in every subgroup were significantly associated with the “developmental disorder” and “neurological disease” categories (P-value < 0.05). Interestingly, we found that the differentially expressed transcripts in G3 (red) were significantly associated with “autism or intellectual disability” (n = 59 genes, P-value = 0.008), “mental retardation” (n = 56 genes, P-value = 0.006), and “congenital malformation of the brain” (n = 28 genes, P-value = 0.016). The results of this gene ontology analysis are shown in S3 Table. Compared to the other subgroups, the G3 (red) subgroup exhibited the greatest number of differentially expressed transcripts, suggesting that this subgroup might have consisted of individuals with the most severe form of ASD. Moreover, while all of the lists of differentially expressed transcripts from all phenotypic subgroups were significantly associated with ASD-related neurological functions or comorbid disorders, it is notable that the list of differentially expressed transcripts in the G3 (red) subgroup was the only list that was significantly associated with “autism” by IPA. We therefore focused on the G3 (red) subgroup in the subsequent proteome profiling analysis.

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Fig 3. Overlap analysis of differentially expressed transcripts in the ASD phenotypic subgroups, as determined by the cluster analysis of ADI-R scores.

The Venn diagram shows differentially expressed transcripts across the identified ASD subtypes, with significance determined using t-tests with standard Bonferroni correction (P-value < 0.05).

https://doi.org/10.1371/journal.pone.0214198.g003

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Table 1. Numbers of differentially expressed transcripts and corresponding protein products in different comparisons.

https://doi.org/10.1371/journal.pone.0214198.t001

Proteome profiling analysis of ASD

To determine whether the differentially expressed transcripts in LCLs reflected dysregulation at the proteome level, we conducted a hypergeometric distribution analysis between the list of differentially expressed transcripts in the all-ASD group and the list of differentially expressed proteins identified by previously published proteomic analyses of ASD. For this analysis, we employed the list of 815 differentially expressed transcripts in the all-ASD group that corresponded to 384 proteins (S2 Table). Notably, the number of predicted protein products was lower than the number of transcripts because multiple transcripts encode the same protein. Moreover, several transcripts in the original transcriptome dataset encode hypothetical proteins, potential noncoding RNAs, or unidentified probes. For the list of proteins, we collected data on significantly differentially expressed proteins from 14 proteomic studies in the NCBI PubMed database [25–38]. The details of all of the selected studies as well as the numbers of differentially expressed proteins identified in each study are shown in Table 2. A total of 145 proteins were identified as differentially expressed in at least one ASD proteomic study. Ontology analysis of these proteins using IPA revealed that these proteins were associated with the “neurological disease” (n = 86 proteins, P-value = 7.46E-23–3.41E-08), “psychological disorder” (n = 66 proteins, P-value = 7.46E-23–6.01E-08), and “embryonic development” (n = 9 proteins, P-value = 4.62E-08–4.62E-08) categories. Moreover, these proteins were associated with known ASD-related functions, including “inflammatory disease” (n = 71 proteins, P-value = 9.06E-26–1.55E-07), “lipid metabolism” (n = 31 proteins, P-value = 1.86E-22–1.47E-07), “free radical scavenging” (n = 26 proteins, P-value = 8.58E-20–2.36E-09), and “digestive system development and function” (n = 18 proteins, P-value = 3.27E-15–6.15E-12) [53–56]. A biological network of these proteins is shown in Fig 4.

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Fig 4. Biological network of differentially expressed proteins identified by previous ASD proteome studies.

The list of 145 differentially expressed proteins from previous ASD proteome studies was analyzed, and a protein-protein interaction network was created using IPA. One of the networks is shown in this figure. Several proteins in the network were significantly associated with developmental disease (highlighted in purple), neurological disease (highlighted in light blue), and both developmental disease and neurological disease (highlighted in blue).

https://doi.org/10.1371/journal.pone.0214198.g004

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Table 2. Previously published ASD proteome profiling studies with data used in this study and the number of significant proteins identified by each study.

https://doi.org/10.1371/journal.pone.0214198.t002

A hypergeometric distribution analysis between the list of 145 differentially expressed proteins identified in previous ASD proteomic analyses and the 384 proteins encoded by the differentially expressed transcripts in the all-ASD group revealed a significant association (P-value < 0.05) between the transcriptome and proteome data, with 4 proteins in common (Fig 5). Moreover, the list of 3,024 differentially expressed transcripts in the G3 (red) subgroup that corresponded to 1,726 proteins (S2 Table) was also used for comparison. A total of 8 proteins were found in common.

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Fig 5. Overlap analysis between the list of differentially expressed proteins from previous ASD proteomic studies and the list of proteins encoded by differentially expressed transcripts from ASD transcriptome studies.

P-values were calculated via hypergeometric distribution analysis.

https://doi.org/10.1371/journal.pone.0214198.g005

In addition to the LCL transcriptome dataset (GSE15402), we compared the list of 145 differentially expressed proteins from previous ASD proteomic analyses to the lists of predicted proteins identified by four other ASD transcriptome studies (GSE6575, GSE18123, GSE25507, and GSE42133) involving peripheral blood cells or whole blood (Fig 5). Consistent with the LCL transcriptome dataset, there was a significant association between differentially expressed proteins in ASD from previous proteome studies and the lists of predicted proteins from all four ASD transcriptome studies. Among the proteins encoded by the transcripts that were differentially expressed in ASD LCLs or blood, several have been reported to be dysregulated in ASD serum/plasma or blood based on proteome studies (Table 3). These findings suggest that the dysregulation of at least some transcripts in LCLs or peripheral blood cells might reflect molecular changes at the proteome level.

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Table 3. Hypergeometric distribution analysis between the list of differentially expressed proteins from previous ASD proteomic studies and the list of proteins encoded by differentially expressed transcripts from ASD transcriptomic studies using LCLs, peripheral blood cells, or whole blood.

https://doi.org/10.1371/journal.pone.0214198.t003

Proteome profiling analysis of LCLs from patients with ASD with severe language impairment.

Because the differentially expressed transcripts in the G3 (red) ASD subgroup (i.e., ASD with severe language impairment) were significantly associated with “autism” based on IPA analysis, we selected this subgroup for further investigation of proteome profiles. Two-dimensional gel electrophoresis analysis of LCLs derived from six individuals with ASD in the G3 (red) subgroup and six sex-/age-matched unaffected controls was performed. A total of 527 protein spots were detected via 2D-gel electrophoresis analysis. Among these protein spots, as many as 82 spots were significantly differentially expressed (ANOVA; P-value < 0.05), as shown in S4 Table. A total of 18 protein spots that exhibited a greater than twofold up- or downregulation in the ASD with severe language impairment group when compared to controls were selected for further protein identification through LC-MS/MS analysis (Fig 6 and S1 Fig). To identify the proteins, the MS/MS spectra data corresponding to peptide m/z values from the digestion of each protein spot by trypsin were analyzed using the MASCOT Peptide Mass Fingerprint program in the MASCOT Server (http://www.matrixscience.com/). For each sample, the MASCOT score was calculated to determine the probability that the protein prediction was a random event (Table 4).

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Fig 6. Differentially expressed protein spots in ASD with severe language impairment compared with age-matched controls.

Representative images show differentially expressed protein spots identified through 2D gel electrophoresis in the ASD with severe language deficit group compared with sex-/age-matched controls. The red circles show detectable proteins in the gel, and the green circles show significantly differentially expressed protein spots based on statistical analysis via ANOVA (P-value < 0.05).

https://doi.org/10.1371/journal.pone.0214198.g006

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Table 4. List of the top differentially expressed proteins in ASD with severe language impairment.

https://doi.org/10.1371/journal.pone.0214198.t004

To predict the biological functions associated with these 18 proteins, ontology analysis was conducted using several tools. Predicted functions and phenotypes associated with these proteins were determined using the GeneCards database. Interestingly, as many as 13 of the 18 differentially expressed proteins are involved in neurological functions/disorders (Table 5). In addition, 5 of the 18 proteins (i.e., PGAM1, DLD, IDH2, ENO1, and COX5A) are involved in mitochondrial function and energy production, processes that have been implicated in ASD. Furthermore, we searched for these 18 proteins in the AutismKB ASD database [52]. Of the 18 proteins, as many as 14 have been associated with ASD according to the AutismKB database (Table 5). IPA revealed that these proteins are associated with endocrine system disorders, neurological disease, lipid metabolism, inflammatory disease, developmental disorders, and psychological disorders (Table 6). Detailed IPA results are shown in S5 Table. Biological network analysis showed that these proteins interact with one another (Fig 7). Interestingly, several proteins were also found to be encoded by differentially expressed transcripts in ASD transcriptome studies (GSE15402, GSE6575, GSE18123, GSE25507 and GSE42133) (S6 Table).

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Fig 7. Biological network of the top 18 differentially expressed proteins in the ASD subgroup with severe language impairment identified via 2D-PAGE, followed by LC-MS/MS analysis.

We employed the list of predicted differential protein spots from the 2D gel coupled with nano-LC-MS/MS to assemble a protein-protein interaction network using Ingenuity Pathway Analysis (IPA) software.

https://doi.org/10.1371/journal.pone.0214198.g007

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Table 5. Biological functions and phenotypes associated with the top differentially expressed proteins in ASD with severe language impairment.

https://doi.org/10.1371/journal.pone.0214198.t005

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Table 6. Top diseases associated with differentially expressed proteins in ASD with severe language impairment.

https://doi.org/10.1371/journal.pone.0214198.t006

Overlap analysis of proteome-transcriptome profiles.

We next determined whether the differentially expressed proteins identified via our proteome profiling analysis have been identified in previous proteome studies and/or are related to the differentially expressed transcripts identified in the transcriptome analysis. We therefore compared the list of 18 differentially expressed proteins identified via 2D-PAGE followed by LC-MS/MS analysis with the list of proteins from previous proteome studies and the list of differentially expressed transcripts in patients with ASD with severe language impairment (Fig 8 and Table 7). Among the 18 top differentially expressed proteins, isocitrate dehydrogenase (IDH2) was also differentially expressed at the transcript level, and two proteins (ENO1, AHSG) have been identified as significantly affected proteins in previous proteome studies.

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Fig 8. Overlap analysis of the top differentially expressed proteins identified via 2D-PAGE and LC-MS/MS analysis, differentially expressed transcripts, and significant proteins from previous proteomic studies.

This Venn diagram shows the number of overlapping transcripts/proteins among the three types of analyses. The list of significant proteins from previous proteomic studies was obtained from the studies listed in Table 2.

https://doi.org/10.1371/journal.pone.0214198.g008

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Table 7. Overlap analysis of the top differentially expressed proteins identified via 2D-PAGE and LC-MS/MS analysis, differentially expressed transcripts, and significantly affected proteins from previous proteomic studies.

https://doi.org/10.1371/journal.pone.0214198.t007

Western blot analysis.

To validate the dysregulation of the proteins identified via 2D gel electrophoresis, DBI and IDH2 proteins were selected for the Western blot analysis (Fig 9). Changes in DBI have been reported in ASD urine samples [35], and loss of function of this gene/protein causes disruption of social interaction [57], a key behavioral domain that is negatively impacted in ASD. IDH2 is disrupted at the transcript level and is involved in energy production in association with ASD [16]. The results of the Western blot analysis showed that the DBI protein level was significantly decreased in individuals with ASD with severe language impairment compared to sex-/age-matched controls (P-value < 0.05) but was not significantly changed in the other ASD subgroups (S2 Fig). The expression level of IDH2 in individuals with ASD with severe language impairment tended to be lower than that in the age-matched controls but was not changed in the other subgroups (S2 Fig). Although the reduction of IDH2 in ASD was not statistically significant, it was notable that IDH2 expression in four individuals with ASD with severe language impairment was lower than the average IDH2 level in the control group.

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Fig 9. DBI protein levels are decreased in ASD with severe language impairment.

Western blot analysis of LCLs from individuals with ASD with severe language impairment and sex-/age-matched controls was conducted using GAPDH as the endogenous control. P-values were determined with the two-tailed t-test. The error bars represent the S.E.M. The protein expression of DBI was significantly reduced in LCLs from individuals with ASD with severe language impairment (P-value < 0.05).

https://doi.org/10.1371/journal.pone.0214198.g009

To determine whether a correlation exists between the protein levels of DBI and IDH2 and ASD-related clinical phenotypes, we conducted a correlation analysis between the protein levels of DBI and IDH2 and 123 ADI-R scores of individuals with ASD from all four subgroups (Table 8). Although significant differences in the ADI-R items were not observed after multiple testing correction using the Benjamini-Hochberg procedure (FDR = 0.05), it is interesting to note that the DBI protein levels were correlated with certain ADI-R items (nominal P-value < 0.05), including direct gaze, visuospatial ability, memory skill, musical ability, drawing skill, reading ability, and computational ability. Moreover, the IDH2 protein level were correlated with the overall level of language, reciprocal conversation, neologisms/idiosyncratic language, verbal rituals, head shaking, initiation of appropriate activities, undue general sensitivity to noise, other complex mannerisms or stereotyped body movements, gait, aggression to non-caregivers or nonfamily members, visuospatial ability, memory skill, reading ability, and computational ability.

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Table 8. Correlation analysis between the levels of DBI or IDH2 proteins and ADI-R scores.

https://doi.org/10.1371/journal.pone.0214198.t008

Proteomic analysis reveals novel differentially expressed proteins in a subtype of ASD

PCA and HCL analyses of ADI-R scores showed that the 85 ASD cases could be divided into four subgroups/clusters based on their clinical phenotypes (Fig 2). Reanalysis of the transcriptome profile data of LCLs derived from ASD cases revealed specific transcriptome profiles that were disrupted in each subgroup (Fig 3 and S2 Table). Notably, the protein products of certain differentially expressed transcripts identified in LCLs or in blood have been reported to be disrupted in previous proteome analyses of ASD (Fig 5), which prompted us to examine the proteomic profile of LCLs for which transcriptomic data had been already obtained [16]. We focused on the G3 (red) subgroup because it was the only subgroup with differentially expressed transcripts that were significantly associated with the term “autism” by the IPA. A proteome profiling analysis of LCLs from this subgroup of ASD patients and sex-/age-matched controls using 2D-PAGE showed that as many as 82 protein spots were differentially expressed in this subgroup (S4 Table). Based on at least a 2-fold change in spot intensity (Fig 6), a total of 18 spots were selected for protein identification via LC-MS/MS analysis. Table 4 shows the list of proteins identified by the LC-MS/MS analyses with high confidence levels, as indicated by the high MASCOT scores. Interestingly, 14 of the 18 identified proteins have been associated with neurological functions/diseases (Table 4, Table 5, Fig 7, and S5 Table). A Western blot analysis of the LCLs further showed that the DBI and IDH2 proteins were specific for the severely language-impaired group, suggesting their potential as protein biomarkers for this subgroup (Fig 9). Interestingly, the expression levels of both proteins showed significant inverse correlations with all six of the savant skill items (visuospatial ability, memory skill, musical ability, drawing skill, reading ability, and computational ability) (Table 8), suggesting their possible involvement in critical neural pathways related to the development of these skills. In addition, the level of IDH2 protein is directly correlated with a number of language items on the ADI-R scoresheet (including overall level of language), complex mannerisms or stereotyped body movements, unusual gait, and auditory sensitivity, which are frequently associated with ASD. Although the association of these genes with these specific ADI-R items is intriguing, further investigations are needed to determine the molecular and/or physiological mechanisms through which deficits in these proteins may increase the manifestation of savant skills and other stereotyped behaviors characteristic of ASD.

IDH2 was also found to be dysregulated at the transcript level, and the proteins AHSG and ENO1 have been reported in previous proteome studies of ASD [31,35]. These findings suggest that certain differentially expressed proteins in LCLs from the ASD subgroup with severe language impairment are reproducible in different ASD cohorts, thus emphasizing the importance of reducing the heterogeneity of ASD by clinical phenotype clustering or other methods in future proteomic research on ASD.

Comparison of ASD subgroups obtained by cluster analyses of ADI-R scores

Recent transcriptome studies have demonstrated that subgrouping individuals with ASD based on ADI-R scores helps reduce the heterogeneity of ASD populations, thus permitting the identification of differentially expressed genes and molecular mechanisms specific to ASD subpopulations. Subgroup-specific genes might be useful in the development of ASD subtype biomarkers or personalized treatments for specific groups of individuals with ASD [41]. In this study, we used the same subgrouping strategy, by performing cluster analysis of the ADI-R scores of 85 individuals with ASD whose LCLs were previously subjected to a transcriptome profiling analysis using cDNA microarrays [16]. Similar to a previous study by Hu et al. (2009), cluster analyses of the ADI-R scores showed that the individuals with ASD clustered into distinct subpopulations. Our study revealed at least four distinct subgroups, designated G1-4, among the original 85 cases used for the transcriptomic analyses (Fig 2). The HCL analysis revealed that each subgroup exhibited distinct patterns of ADI-R scores, with the G3 (red) subgroup consisting of individuals who represented a subset of the original ASD subgroup identified as “severely language-impaired”[16]. A closer examination of the HCL data in Fig 2A reveals that the G2 (green) subgroup represents individuals with severe language impairment who also have notable savant skills (notably high ADI-R scores in the 12 columns at the extreme right of the graph for the G2 cluster), and they are separated from the language-impaired G3 group without savant skills. The remaining two subgroups, G1 (blue) and G4 (yellow), show some discrepancies with the blue and yellow subgroups from the earlier transcriptomic study. The source of the minor discrepancies can be explained in part by the number of cases included in the cluster analyses. For the original transcriptomic study [16], cluster analyses were performed on ADI-R scores from 1,954 individuals with ASD which resulted in the current G2 and G3 subgroups being represented within one severely language-impaired subgroup [41]. Three other subgroups were identified, with two of them similar in phenotype to the G1 and G4 subgroups identified in this study. The remaining subgroup (called “green” or “intermediate” in the original paper) was not included in the expression profiling analyses and thus is not represented here. The present study shows that it is possible to further divide the ASD population into more clinically defined subgroups by applying hierarchical cluster analyses of ADI-R scores for smaller groups of individuals with ASD (85 cases vs. 1,954 cases) or by selecting smaller clusters from the dendrogram resulting from HCL analyses of larger samples.

Genes and pathways implicated by functional and network prediction analyses

DBI has not been identified in any other blood-based proteomic study of ASD, but a recent study indicated that DBI protein was differentially expressed among as many as 231 proteins in urine from individuals with ASD [35]. DBI is also known as acyl-CoA-binding protein (ACBP) or endozepine (EP). DBI can displace diazepam from the benzodiazepine (BZD) recognition site located in the GABA type A receptor and act as a neuropeptide to modulate its action [58]. DBI is highly expressed in several regions of the brain, including the cerebellar cortex, area postrema, and ependyma of the third ventricle [59]. A recent study in mice reported that genetic loss of DBI leads to reduced social interest in both males and females, with a greater effect in males than in females [57]. Moreover, male and female mice lacking DBI exhibit increased repetitive grooming [57]. These altered behaviors are key phenotypes of ASD, and the sex differences in the effects of DBI loss on social behavior may be related to the sex bias of ASD, which deserves further investigation. Moreover, in addition to LCLs, the reduction of DBI protein levels should be further confirmed in peripheral blood or brain samples from individuals with ASD with severe language impairment in a larger cohort.

The other differentially expressed proteins in LCLs from individuals with ASD with severe language impairment that were reproducible were AHSG, ENO1, and CALM1. AHSG, or fetuin-A, is found in the peripheral blood and is produced by the liver [60]. The protein is involved in several processes, including brain development, endocytosis, and the formation of bone tissue [61]. A proteomic study using liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS) analysis revealed the upregulation of AHSG in the serum of low-functioning ASD individuals compared with controls [26]. Moreover, a genomic linkage scan performed in a study of SNPs in individuals with ASD also identified AHSG as a potential candidate gene for a chromosome 3q26.31–q27.3 region mutation [62]. ENO1 and CALM1 were reported to be decreased in another previous proteome study using isobaric tags for relative and absolute quantitation (iTRAQ)-based mass spectrometry analysis of plasma from patients with ASD and age-matched unaffected individuals [31]. ENO1 (alpha-enolase) is a multifunctional enzyme that plays a role in glycolysis and functions as an activator of plasminogen on the cell surface of leukocytes and neurons. CALM1 (calmodulin 1) is a calcium-binding protein involved in the regulation of a number of ion channels and enzymes through calcium binding and is important for neurological functions. Like DBI, these proteins deserve further study.

In addition to these differentially expressed proteins identified in previous proteome studies, other proteins have also been linked to ASD. One example is TUBB, which is the major constituent of microtubules and is highly expressed in the developing brain. Mutations in the gene encoding this protein lead to cognitive impairment with motor and language delay, ataxia, and severely delayed psychomotor development, all of which are associated with ASD [63,64]. Notably, a reduction in TUBB protein levels was also observed in the ASD G3 subgroup, whose members exhibited severe language impairment.

In addition to neurological functions, several differentially expressed proteins are involved in mitochondrial functions and energy production, which have been reported to be negatively impacted in ASD [see reviews in [65,66]]. For example, IDH2 (isocitrate dehydrogenase) is found in the mitochondria and plays a role in intermediary metabolism and energy production by catalyzing the oxidative decarboxylation of isocitrate to 2-oxoglutarate. In the present study, we found that IDH2 transcript and protein levels were dysregulated in ASD with severe language impairment. The expression of mutated IDH2 has been associated with white matter abnormalities throughout the central nervous system (CNS) and with muscular dystrophy in transgenic adult mice [67]. Both of these conditions have been associated with ASD [68–70]. Western blot analysis of protein from the ASD with severe language impairment group showed that IDH2 protein levels tended to be lower in the ASD subgroup, although this difference was not statistically significant (Fig 9). The potential role of this protein should be further examined in a larger cohort.

Other proteins related to mitochondrial functions identified by this study included PGAM1, DLD, ENO1, and COX5A. PGAM1 is an enzyme of the glycolytic pathway that catalyzes the conversion of 3-phosphoglycerate to 2-phosphoglycerate. DLD functions as the E3 subunit of three mitochondrial multienzyme complexes: pyruvate dehydrogenase (PDH), alpha-ketoglutarate dehydrogenase (α-KGDH) and branched chain 2-oxoacid dehydrogenase (BCKDH). COX5A is the nuclear-encoded Va subunit of the human mitochondrial respiratory chain enzyme. Abnormalities in these important mitochondrial proteins might be responsible for susceptibility to ASD and therefore require further investigation.

Limitations and future directions

Our study shows that ASD subgrouping performed to reduce clinical heterogeneity can identify proteins with the potential for use as molecular marker candidates in future studies. All LCLs used in this study were obtained from the AGRE. The control LCLs were derived from male unaffected individuals who do not have ASD. These LCLs have been used for gene expression profiling analysis using high-throughput microarrays in a previous study [16], allowing us to obtain transcriptome data for comparison with the proteome data in this study and others. A limitation of this study with regard to proteomic data is that we combined the lists of differentially expressed proteins from different proteomics studies on ASD for comparison with differentially expressed genes from a handful of transcriptomics analyses. However, a risk of bias and lack of consistency may occur because of the different analytical techniques, types of tissues, and other confounding factors among the various studies. In future work, a comprehensive meta-analysis and/or a systematic review of published proteome profiles might be conducted to generate a list of differentially expressed proteins in each type of tissue. Another limitation may be the unknown ethnicity of the majority of the individuals whose LCLs were used in this study. Although we do not have information about the specific ethnicity of the individuals in this study, most individuals were Caucasian and described as “Not Hispanic or Latino” according to the AGRE. Whether ancestry has a notable impact on proteome and/or transcriptome profiles has not been clarified; therefore, the findings from this study should be confirmed in a larger cohort with age-, sex-, and ethnicity-matched controls in the future.