Potential Plasma Metabolic Biomarkers of Tourette Syndrome Discovery Based on Integrated Non-Targeted and Targeted Metabolomics Screening

Objective Tourette syndrome (TS) is a chronic neuropsychiatric disorder characterized by abnormal movements, phonations, and tics, but an accurate TS diagnosis remains challenging and indeed depends on its description of clinical symptoms. Our study was conducted to discover and verify some metabolite biomarkers based on nontargeted and targeted metabolomics. Methods We conducted untargeted ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) for preliminary screening of potential biomarkers on 30 TS patients and 10 healthy controls and then performed validation experiments based on targeted ultrahigh-performance liquid chromatography triple quadrupole-MS (UHPLC/MS/MS) on 35 TS patients and 14 healthy controls. Results 1775 differentially expressed metabolites were identified by partial least squares discriminant analysis (PLS-DA), fold-change analysis, T-test, and hierarchical clustering analysis (adjusted p value <0.05 and |logFC| > 1). TS plasma samples were found to be differentiated from healthy samples in our approach. Furthermore, aspartate and asparagine metabolism pathways were considered to be a significant enrichment pathway in TS progression based on metabolite pathway enrichment analysis. For the 8 metabolites involved in this pathway that we detected, we then performed validation experiments based on targeted UHPLC/MS/MS. The t-test, Mann–Whitney U test, and receiver operating characteristic (ROC) curve analysis were used to determine potential biomarkers. Ultimately, L-arginine and L-pipecolic acid were validated as significantly differentiated metabolites (p < 0.05), with an AUC of 70.0% and 80.3%, respectively. Conclusion L-pipecolic acid was defined as a potential biomarker for TS diagnosis by the combined application of nontargeted and targeted metabolomic analysis.


Introduction
Tourette syndrome (TS) is a childhood-onset chronic neuropsychiatric disorder characterized by chronic muscle movements and vocal tics, lasting more than one year [1]. Some children may have attention-defcit/hyperactivity disorder (ADHD), anxiety, obsessive-compulsive disorder (OCD), and other comorbid behavioral syndromes [2], most children with TS can be expected to develop at least one comorbid disorder throughout their life, and more than half will develop two. Compared to tics, these comorbid conditions usually cause more impairment in patients with TS [3,4]. Tics in TS typically start at 4-6 years with motor movements, such as blinking, noise twitching, and grimacing, and reach their worst severity around 10-12 years [5,6]. Tics symptoms appear age-dependently, showing a wax and wane course, and gradually ease by late teens [5,7]. 14 studies in mainstream schools and school-age youngsters in the community reported prevalence fgures for TS between the ages of 5 and 18 years varying from 0.4% to 3.8%, and 3989 (0.949%) of 420312 young people were diagnosed as having TS; therefore, it was suggested that overall TS prevalence fgure is 1% [8,9]. In addition, studies have also shown that TS is more common in males than in females, and the ratio between males and females is about 3-4 : 1 [4,8]. Although the etiology and pathogenesis of TS remain uncertain, it has been proved to be closely related to genetic factors, neurobiochemical factors, environmental factors, psychology, and other factors [10][11][12][13]. Besides, evidence has shown that the cortico-striatal-thalamo-cortex (CSTC) loop is closely related to the pathophysiology of TS [14].
To date, the accurate TS diagnosis has indeed depended on its clinical description of symptoms, and there are no laboratory tests for a positive diagnosis of TS and other tic disorders. Te diagnosis of some patients remains challenging because of untypical early symptoms. Te discovery of potential biomarkers that could help improve diagnosis is in high need. Usually, biomarkers are endogenous compounds and refect underlying disease characteristics. Metabolomics can detect, identify, and quantify small molecular endogenous metabolites to describe biomarkers or characterize disease in biological samples, and it concludes untargeted global profling and targeted quantifcation. To the best of our knowledge, no publications on the metabolomic analysis of TS patients have been reported. Terefore, the purpose of the study was to investigate the potential biomarkers in plasma of TS patients based on nontargeted and targeted metabolomic analysis.

Clinical Participants.
From March 2020 to December 2020, TS patients and healthy controls were recruited from the Nanjing Hospital of Chinese Medicine, Nanjing Hospital of Chinese Medicine Afliated to the Nanjing University of Chinese Medicine. TS patients were diagnosed by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5 ® ) [15]. Healthy controls without TS and other known infections were included. Te study protocol conformed to the ethical guidelines of the current Declaration of Helsinki and received approval from the Ethics Committee of the Nanjing Hospital of CM. Written consent was obtained from all participants and their parents/guardians. Finally, a total of 89 participants were recruited: 30 TS samples and 10 healthy controls for untargeted UHPLC-Q-TOF/MS metabolomic analysis were included. Te average age of TS patients was 7.9 years, and 21 males (70.0%) and 9 females (30.0%) were included. Te mean age of healthy controls was 9.2 years, and 6 males (60.0%) and 4 females (40%) were included (sex p � 0.559, age p � 0.211). Another 35 TS samples and 14 healthy controls for targeted UHPLC/MS/ MS analysis were included. Te average age of TS patients was 8.086 years, and 24 males (69.57%) and 11 females (31.43%) were included. Te mean age of healthy controls was 9.5 years, and 9 males (64.29%) and 5 females (35.71%) were included(sex p � 0.773, age p � 0.064). Tere was no statistical signifcance in both age and gender in TS and healthy controls. Te clinical characteristics of TS patients and healthy controls are summarized in Table 1.

Sample Preparation.
Blood samples were collected from each participant; then, the samples were centrifuged for 15 min (3000 × rcf, 4°C) within 1 hour of collection. Each aliquot (1 mL) of the plasma samples was stored at −80°C until UHPLC-Q-TOF/MS and UHPLC/MS/MS processing. For the untargeted analysis, the plasma samples were thawed at 4°C. 400 μL of methanol/acetonitrile (1 : 1, v/v) was added to 100 μL of plasma. After 60 sec of the vortex, the mixture was stored at −20°C for 1 hour to remove protein and then centrifuged at 14,000 × rcf for 20 min at 4°C. Supernatants were subjected to UHPLC-Q-TOF/MS. Quality control (QC) samples: 10 μL of each plasma sample was mixed and treated in the same way as plasma samples. Te QC samples were inserted in every 8 samples to monitor the system stability of UHPLC-Q-TOF/MS. For the targeted analysis, each plasma sample was thawed at 4°C for 30 min and 200 μL of aliquots was mixed with 200 μL of methanol. Ten, tubes were vortexed for 60 sec and then centrifuged for 15 min (13,000 × rcf, 4°C). Preparation of the standard solution: each 1 mg of the analytical standard was dissolved in methanol (1 mg/ml) and then diluted in methanol (10 μg/ml). Te supernatant was transferred to an autosampler vial and subjected to UHPLC/MS/MS analysis.

UHPLC-Q-TOF/MS Processing and Data Analysis.
UHPLC-Q-TOF/MS analysis was performed on an Agilent 1290 Infnity LC system (Agilent Technologies, Santa Clara, California, USA) equipped with an AB SCIEX Triple TOF 5600 system (AB SCIEX, Framingham, MA, USA) in both positive and negative modes. Chromatographic separation was performed on ACQUITY HSS T3 1.8 μm (2.1 × 100 mm) columns. Te column temperature was kept at 25°C, and the fow rate was 0.3 mL/min. Te UHPLC system consists of Electrospray ionization (ESI) source conditions were set as follows: ion source gas 1 (gas 1) was 60 psi; ion source gas 2 (gas 2) was 60 psi; curtain gas (CUR) was 30 psi; the source temperature was set to 600°C; ion spray voltage foating (ISVF) was 5000 V (+) and −5000 V (−). Informationdependent acquisition (IDA) is a product ion scan mode based on artifcial intelligence, which is used to detect and identify MS/MS spectra. Parameters were set as follows: the declustering potential (DP) was set to 60 V (+) and −60 V (−);collision energy was 35 ± 15 eV; exclude isotopes within 4 Da, candidate ions to monitor per cycle: 6.
Raw data were generated by using the ProteoWizard msConvert tool and processed by using XCMS online software (https://xcmsonline.scripps.edu/landing_page. php?pgcontent=mainPage), including nonlinear alignment, automatic integration, and peak extraction. After being normalized and integrated, MetaboAnalystR (3.0.3) [16] was employed for statistical analysis (including PLS-DA analysis, hierarchical cluster analysis, fold-change analysis, and t-test) and bioinformatics (pathway enrichment analysis) (https://www.metaboanalyst.ca). It performs in-house mapping of common compound names to a wide variety of database identifers, including KEGG, HMDB, ChEBI, METLIN, and PubChem. Signifcance was analyzed using adjusted p value <0.05 and |logFC| > 1. For pathway enrichment analysis, two enrichment algorithms integrated mummichog and GSEA were used, and p < 0.05 was considered statistically signifcant.

Selection of Metabolites for Targeted Metabolomics.
For the selection of biomarkers, the afected metabolic pathway containing abundant afected metabolites was the principal criterion. Te preliminary identifcation of these metabolites was conducted by matching with selfconstructed databases (the secondary spectral library of standard samples established in the same experimental system, about 2500 kinds). In addition, the similarity values for the accuracy of compound identifcation and the number of diferentially expressed metabolites detected in each test sample were also important reference factors [17]. Ten, selected metabolites were identifed by standards and tandem mass spectrometry.
In UHPLC/MS/MS targeted analyses, Student's t-test and the Mann-Whitney U test were used for comparisons between healthy and TS patients. All statistical analyses were analyzed by using GraphPad Prism8 (GraphPad Software corporation, California, USA). Statistically, signifcance was defned by p < 0.05 (two-tailed), and the area under the curve (AUC) was calculated to further analyze diferential metabolites.
Tere were 29 compounds matched in aspartate and asparagine metabolism pathways and 42 compounds matched in ascorbate (vitamin C) and aldarate metabolism pathways. However, compared to healthy controls, the diference in TS among these compounds had a more consistent trend in aspartate and asparagine metabolism pathways than in ascorbate (vitamin C) and aldarate metabolism pathways. Tus, we selected 29 compounds matched in aspartate and asparagine metabolism pathways for further study. Following matched the 29 compounds involved in the aspartate and asparagine metabolism pathway with the selfconstructed database which has MS secondary spectrum data, we got 8 highly feasible compounds: L-Glutamate, Ornithine, D-Proline, L-Arginine, L-Pipecolic acid, L-Carnitine, D-Pipecolinic acid, and N-(omega)-Hydroxyarginine. Tese 8 metabolites were selected for subsequent targeted validation experiments. Te basic characteristics of 8 metabolites are summarized in Table 2.

Validated TS Biomarkers Using Targeted Metabolomic
Analysis. Te specifc concentration of 8 metabolites was determined by UHPLC/MS/MS (Supplementary Figure S2). Selected MRM transitions and optimized conditions for MS are summarized in Table 3. Every metabolite linear standard curve was generated from mixed standard solutions (Supplementary Figure S3). Te R 2 values of 8 metabolites stand curve linearity were all greater than 0.99, and these results indicate their accurate concentration calculation based on these curves (Supplementary Table S1). Te concentrations of L-glutamate, D-ornithine monohydrochloride, Larginine, L-carnitine, and D-homoproline increased in TS plasma than in healthy plasma, and the concentrations of Dproline, L-pipecolic acid, and L-ornithine monohydrochloride decreased in TS plasma. Two (L-arginine and Lpipecolic acid) of 8 verifed metabolites had signifcant diferences (p < 0.05) (Figure 4).

L-Pipecolic Acid Could Be Used as a New Biomarker for TS Diagnosis.
To evaluate the diagnostic value of 8 metabolites for TS, the ROC curve analysis of these metabolites was performed, and we calculated their area under the curve (AUC) and Youden indexes. As shown in Figure 5

Discussion
Tourette syndrome is a complex neurological disorder characterized by repetitive, sudden, involuntary motor, and phonic tics. Most children with TS can be estimated to develop other associated comorbid conditions. However, there are problems with the actual diagnosis of TS. Untypical early symptoms and complicated symptoms of TS patients still pose a challenge to its diagnosis. Tere is a high need for discovering some biomarkers that could help improve diagnosis. Metabolomics provides us a unique perspective to understand the regulation of metabolic networks in the biological system. Furthermore, it is also emerging as a new tool to research the central nervous system, and there are already some research studies on metabolomic signatures in schizophrenia, depression, and bipolar disease [18][19][20][21]. To the best of our knowledge, this was the frst study to explore potential plasma metabolic biomarkers of TS through nontargeted combined with targeted metabolic profling.
In untargeted metabolomics analysis, there was a clear distinction between TS plasma and healthy plasma, and this also refected our strict inclusion criteria of TS patients based on the existing clinical diagnosis. Furthermore, aspartate and asparagine metabolism pathways were found to be signifcantly afected by TS, and 8 diferential metabolites in these pathways were identifed by matching with selfconstructed databases (D-ornithine, D-proline, Dhomoproline, L-glutamate, L-arginine, L-ornithine, Dpipecolinic acid, and L-carnitine). To further validate the potential biomarkers in TS patients, UHPLC/MS/MStargeted quantitative analysis was performed. At this stage, we collected TS patients as possible as we could. However, the sample size of TS patients in the study is limited because of the low incidence and informed consent of parents of children. Te fnal results showed that only 2 (L-arginine and L-pipecolic acid) of the 8 metabolites had statistical differences between TS patients and healthy controls (p < 0.05). Coincidentally, L-pipecolic acid also performed better in the area under the curve analysis than other 7 metabolites (AUC � 80.3%). We also tried to perform stratifed k-fold validation (k � 5) for ROC analyses. Honestly, stratifed sampling AUC results were so unstable to determine which metabolites were better biomarkers for TS (Supplementary  Table S2). We recognized some limitations in this work, among which is the small size of the cohorts, which might be the core reason why cross-validation was not stable. However, the results indicated that the AUC value of Dpipecolinic acid was always more than 0.7 in 5 batches of stratifed samplings. So we thought it could serve as a biomarker with more robustness.
Te etiology and pathogenesis of TS are still uncertain, but the CSTC circuit appears to be closely related to the pathophysiology of TS, and some amino acids, including GABA and glutamate, act as neurotransmitters and neuromodulators, play a key role in CSTC circuitry, and Evidence-Based Complementary and Alternative Medicine 5  Evidence-Based Complementary and Alternative Medicine participate in habitual behavior formation and pathophysiology of tics [22][23][24]. As the main regulatory element in the CSTC circuit, the role of the striatum in TS was also reported in large studies [25][26][27]. Studies found that glutamate increased in TS, while in our study, glutamate showed no statistical diferences between TS patients and healthy controls, which may be due to our limited size of samples.
Besides, aspartate and asparagine metabolism pathways, Larginine and L-pipecolic acid, found in the study also indicate that amino acids participate in the pathogenesis of TS. As precursors and intermediates of neurotransmitters, amino acids play an important role in neurotrophic development and information transmission. It is difcult to determine the amino acid level of brain tissue and  . Te x-axis is GSEA enrichment-log10(p), and the y-axis is mummichog enrichment-log10(p). Te matched metabolites from the diferent analyses in the aspartate and asparagine metabolism pathways (c) and ascorbate (vitamin C) and alternate metabolism pathways (d).
Evidence-Based Complementary and Alternative Medicine cerebrospinal fuid in TS patients, while free amino acids in human body fuid could be stable in the TS active period. In addition, amino acids could pass through the blood-brain barrier, so the level of amino acids in blood can also indirectly refect the situation of amino acids in the brain.
Aspartate and asparagine are two nonessential amino acids with similar structures. Aspartate exists in two enantiomeric forms, L-aspartic acid and D-aspartic acid. As excitatory neurotransmitters in the central nervous system, aspartate takes part in the long-range information exchange   Evidence-Based Complementary and Alternative Medicine via activation of glutamate receptor channels [28], while high levels of aspartate could reduce synaptic plasticity, impair cognitive function, and early spatial memory [29,30]. N-methyl-D-aspartic acid (NMDA) is a high-energy form of aspartic acid and one of the well-known agonists for a class of glutamate receptors. Aspartate can selectively activate extrasynaptic NR1-NR2B NMDA receptors. Aspartate participates in immature neurons through activating NR1-NR2B receptors, results in the substantial Ca 2+ infux, then activates cAMP-dependent gene transcription, and inhibits cAMP-responseelement-binding protein (CREB) function, reducing the expression of brain-derived neurotrophic factor (BDNF) and inducing excitotoxic neuronal death in mature neurons [31]. Excessive activation of NMDA receptors is associated with memory, learning impairment, and excitotoxic cell death [32][33][34][35]. It is interesting to note that previous studies showed that the number of NMDA receptors in the hippocampus decreases with aging [34], while symptom intensity and frequency of TS also decrease with age, and whether this feature of TS is related to NMDA receptors still needs to be further studied. In addition, Nacetylaspartate (NAA), a noninvasive marker for neuronal health, which is synthesized from aspartate and acetyl coenzyme A in neurons, refects the extent of neuronal impairment and dysfunction. Several studies have found that the decreased concentration of NAA is involved inneuropsychiatric disorders listed in DSM-IV 1R, the acknowledged compendium of clinical psychiatric diseases [36,37]. Tere are a few kinds of research between NAA and TS. One study found lower levels of NAA in the left putamen and frontal cortex in TS plasma and suggested the compromised neuronal integrity and insufcient density of neuronal and nonneuronal cells [38]. Taken together, these research studies suggested that aspartate and asparagine metabolism pathways have a close relationship with the pathogenesis of TS. In this study, the levels of L-arginine were upregulated in TS patients. L-arginine is a semiessential amino acid involved in the synthesis of L-ornithine, L-glutamate, and polyamines and a precursor for nitric oxide (NO) synthesis [39]. Te L-arginine/NO pathway is involved in physiological processes, such as vasodilation, memory, neuroprotection, and immune defense in cardiovascular, immune, and nervous systems [40,41]. Te efects of L-arginine in the nervous system are often attributed to NO. In the nervous system, NO acts as a neurotransmitter and plays an important role in synaptic plastic, neural development, regeneration, transcriptional activity, learning and memory, and neuroprotection [42][43][44]. Small quantities of NO are neuroprotective, and an excessive amount of NO becomes noxious, which could cause cell damage and is involved in various disorders, such as major depressive disorder (MDD), autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), Alzheimer's disease (AD), attention-  defcit hyperactivity disorder (ADHD), and other neurodegenerative disorders [40,[45][46][47][48][49]. Te role of L-arginine and NO in TS has not been explored. Our results for the frst time showed that the concentration of L-arginine was higher in TS patients, and whether the L-arginine/NO pathway is involved in the development of TS needs to be further studied.
Pipecolic acid (PA) is a metabolite of lysine and has two diferent enantiomers, including L-pipecolic acid (L-PA) and D-pipecolic acid (D-homoproline; D-PA). L-PA is a signifcant marker for the diagnosis of peroxisomal disorders. D-PA is believed to originate mainly from the catabolism of dietary lysine by intestinal bacteria and found to increase in patients with liver diseases [50,51]. PA is known to be a GABA receptor agonist, which could inhibit neuronal GABA uptake and/or enhance its release, while D-PA is found to be more efective in restraining the actions of the central GABA system than L-PA acid [52,53]. GABA is a major inhibitory neurotransmitter involved in the CSTC pathway, and its dysfunction has a close relationship with TS. In this study, D-pipecolic acid increased in TS and displayed moderate efciency (AUC � 80.3) as a potential biomarker. We speculated that D-PA may participate in the pathophysiology of TS through the central GABA system.
However, there are two limitations in the study. First, 89 TS patients and healthy controls were recruited; however, the size of samples was limited. Second, the global generalizability of the fndings may be afected because all participants recruited in this study were from China. Furthermore, a large number of participants are needed to validate the potential utility of these plasma metabolic biomarkers for the diagnosis of TS.

Conclusion
In summary, we employed UHPLC/Q-TOF/MS nontargeted analysis and UHPLC/MS/MS-targeted quantitative analysis to identify the plasma metabolic profle in TS. Aspartate and asparagine metabolism pathways were found to be signifcantly afected by TS. L-pipecolic acid may be used as a potential diagnostic biomarker for TS. Furthermore, our study also confrmed that the imbalance of amino acid neurotransmitters is closely associated with the pathophysiology of TS, while the role of amino acids in TS still deserves further exploration.

Data Availability
Te data that support the fndings of this study are available from the corresponding author upon reasonable request.

Additional Points
Code Availability. Raw metabolomic data were processed using ProteoWizard and XCMS software. Metabolomic analyses were performed using R package MetaboAnalystR