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In addition to genetic influences on ASD etiology, envi- ronmental exposures likely play a major role. For example,
certain pesticides and industrial chemicals may influence the development of ASD, particularly in susceptible individ-
uals [24]. Further studies investigating the timing, dosage or mechanisms that induce ASD are needed [25]. Recent results
of the Childhood Autism Risks from Genetics and Environ- ment CHARGE study reported that prenatal close proxim-
ity to organophosphates produced a 60 increased ASD risk. The risk increased further for third trimester exposure [26].
Thousands of high production volume chemicals circulate through commerce [27], and hundreds of these chemicals
have recently been detected in pregnant women’s tissues [28]. Understanding the link between environmental exposures of
these chemicals and the onset of ASD is a major knowledge gap [24].
For example, a relatively new class of compounds that have begun to receive a significant amount of attention is
organophosphorous flame retardants [29]. Currently slated as a replacement for the pentabrominated diphenyl ether mix-
ture, this class of compounds is ubiquitous and exhibit ele- vated concentrations relative to other neurotoxicants, such as,
brominated diphenyl ethers in indoor environments [30–32]. Similar to the OP pesticides, triaryl-, and trialkylphosphate
flame retardants hydrolyze in the blood resulting in diaryl- and dialkylphosphates, respectively, [33,34] to conformations
conspicuously similar to OPs oxon metabolites, suggesting organophosphorous flame retardants may have OP-like neu-
rotoxicities and contribute to the onset and severity of ASD in vulnerable groups.
Whether genetically or environmentally induced or both in collaboration numerous investigations indicate biolog-
ical disturbances in ASDs [35–38], but a clear diagnostic biomarker for ASD is not available. ASDs are diagnosed
based on behavior, and despite the current existence of gold- standard behavioral tests [39, 40], biomarkers could further
improve screening and diagnosis. ASDs can be unrecog- nized in children and even in adults, and current screening
for ASDs can produce false positive or false negative results [41]. Moving beyond genetic analysis alone may provide new
inroads to diagnosis and understanding of ASD biomarkers. MS-based proteomics could provide one route for exploration.
2 MS analysis of endogenous molecules
in ASDs
2.1 Differential proteomics: qualitative and quantitative analysis of proteins in ASD
Current analyses using MS to study ASDs have focused on endogenously produced biomarkers that could aid in diagno-
sis or understanding of ASDs. Despite a variety of different proteins identified, some consistency has emerged in the liter-
ature, specifically complement proteins and apolipoproteins apos.
One study analyzed blood serum in individuals with ASD and comorbid attention-deficit hyperactivity disorder
ADHD n = 9 ASD + ADHD, ASD alone n = 7 compared to age-matched controls n = 12. Three peaks that were dif-
ferent in ASD versus controls were identified, but the amino acid sequence information of the peptides that corresponded
to these peaks was not identified [42], however, the investi- gators speculated that the peptides corresponding to these
peaks may be part of an apo protein [43]. Our group obtained the sera from these investigators, and reanalyzed them in
our lab. We confirmed increased levels of apoA1 and apoA4, in individuals with ASD [44]. We further found significant
elevations in the high-density lipoprotein associated enzyme [45] serum paraoxanasearylesterase 1 PON1 [44], which is
also involved in toxin metabolism and detoxification such as due to organophosphate exposure, and could help prevent
oxidative stress [46]. It interacts with the cholesterol-carrying proteins known as apos, which bind PON1 increasing the
stability and activity of PON1 [47]. Interestingly PON1 gene mutations have been associated with ASD [48]. Notably, one
study of 50 children with ASD found that PON1 protein ac- tivity but not gene polymorphisms was associated with ASD
[49], underscoring the need to analyze biomarkers at the pro- tein level.
Further suggesting the possibility that apos and lipid- associated molecules are indeed ASD biomarkers, eleva-
tions in apo B-100 and apo A-IV were observed in an earlier proteomic study that compared high functioning ASD with
low-functioning ASD [50]. Significantly elevated complement factor H-related protein, complement C1q and fibronectin 1
and apoB-100 was also measured in children with ASD com- pared to typically developing controls in the same study [50]. A
proteomic study of individuals with Asperger’s syndrome has also implicated apo dysregulation apoE, apoC2, and apo A1,
although this change seems to be more specific to females. Specific proteomic studies examining Asperger’s syndrome
compared to non-Asperger’s ASD could help shed light on this discrepancy [51].
Consistent with the possible presence of dysregulated complement proteins in ASD, a different MS analysis of blood
plasma used surface-enhanced laser desorptionionization TOF MS to examine peptides in plasma of children with ASD
compared to typically developing controls. Increases in three peptides were measured, corresponding to C3 complement
protein fragments [52,53]. Additional proteomic studies need to be conducted to confirm the existence of complement pro-
tein and apo disturbances in ASDs. These studies may help to identify proteomic biomarker signatures, and possible ASD
subtypes associated with specific biomarkers.
Although most proteomic investigations focus on analyz- ing blood serum or blood plasma, saliva may provide another
biomaterial for analysis. Saliva contains about 2290 proteins, compared to approximately 2698 proteins found in blood
plasma, making it a potential biomarker source [54]. Sali- vary protein hypophosphorylation of histatin, statherin, and
proline-rich phosphopeptide has been observed in ASD, one
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Prot. Clin. Appl. 2014, 00, 1–10
3 of the few published proteomic studies of salivary biomarkers
in ASD [55]. Our group has recently used 2D differential in-gel elec-
trophoresis 2D-DIGE to investigate the differences between the salivary proteomes of children with ASD and matched
controls. DIGE is primarily used in protein expression pro- filing experiments of at least two samples or conditions
allowing the determination of the relative abundance of pro- teins [56]. In 2D-DIGE, proteins are labeled with fluores-
cent cyanine dyes Cy2, 3, and 5 prior to 2D-PAGE [57], run on 2D-PAGE and then scanned for quantitative analy-
sis of the two proteomes investigated [58, 59]. In our 2D- DIGE experiments, we observed that many proteins were
differentially expressed between the ASD and control con- ditions mostly observed as either green or red colored gel
spots; the yellow colored gel spots contained proteins with unchanged levels Fig. 1. Nano-LC-MSMS and MALDI-
MSMS analysis of the proteins contained in the red or green-colored gel spots identified many of the differen-
tially expressed proteins. One such protein upregulated in ASD was S100-A9 also called migration inhibitory factor-
related protein 14 MRP-14 or calgranulin-B Fig. 1. This protein has not been previously reported to be associated
with ASD.
In considering proteomic biomarker analysis in ASDs, specific attributes of the individuals studied, including age
and gender also need to be taken into consideration. Two studies have emphasized the possibility of gender-specific
differences in ASD biomarkers, finding that males with Asperger’s syndrome tend to have altered levels of cytokines
and other inflammatory molecules, whereas biomarkers in fe- males with Asperger’s seem to be growth factors, hormones
and factors associated with lipid transport, and metabolism [51,60]. Further investigation into gender specific biomarkers
in ASDs is warranted, particularly since there is a gender bias in ASDs, favoring diagnosis in males. Indeed, neuroimaging
Figure 1. Difference gel electrophoresis DIGE. A Analytical DIGE gel, where protein samples were labeled with fluorescent cyanine dyes Cy2, 3, and 5 prior to 2D PAGE. The differentially expressed proteins in ASD cy3 or green and matched controls cy5 or red are either
green or red; the yellow ones are unchanged between ASD and controls. B Preparative DIGE gel, from which the differentially expressed proteins were picked and digested by trypsin and analyzed by nano-LC-MSMS or MALDI-MS. The spots that were specific to ASD left or
control right samples are indicated. C MALDI-MSMS spectrum, whose analysis led to the identification of a peptide with the sequence LGHPDTLNQGEFK which is part of protein S100-A9, also named migration inhibitory factor-related protein 14 MRP-14 or calgranulin-B.
This protein was found to be upregulated in ASD in the DIGE experiments The y- and b-ion series, the amino acid sequence of the peptide top of the spectrum and the name of the protein left are indicated. Reprinted with permission from [56].
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evidence has suggested that the neuroanatomy of ASD may differ in males versus females [61].
With regard to age differences, one study has reported that 12 proteins vary with age in individuals with ASD, including
those involved with inflammation, growth, and hormonal sig- naling. Examples include higher levels of adiponectin in ASD
with increased age and a decrease with age in typically de- veloping individuals. Other potential markers that increase
with age in ASD include C-reactive protein, haptoglobin, TRAIL-R3, matrix metalloproteinase, thyroglobulin, and can-
cer antigen 19–9. Many of these molecules are suggestive of an inflammatory condition. Age-related changes in biomark-
ers and the possible impact of these molecules on behavioral phenotypes would be a valuable focus of future studies [62].
2.2 Differential proteomics: analysis of PTMs of proteins in ASD
