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Introduction

Tic disorder (TD) is a childhood-onset developmental neuropsychiatric disorder (1,2). TD is characterized by motor and/or vocal tics (1,3) that typically appear between the ages of 5 and 6 years, reaching a peak in severity between the ages of 10 and 12 years, and gradually declining in severity during early adulthood (4). Most patients exhibit resolution of symptoms in adulthood (4). Of note, 50–90% of patients with TD also exhibit one or more mental or behavioral disorders of varying severity (5). The most common psychiatric comorbidities are attention deficit hyperactivity disorder, obsessive-compulsive disorder, anxiety and depression (5). Furthermore, TD is associated with an increased risk of autoimmune diseases (6), allergic diseases (7) and sleep disturbances (8). TD and these accompanying diseases reduce the quality of life of patients and their families (9,10), and impact the psychosocial function of patients (11).

The pathogenesis of TD is complex and not yet fully understood. The cortico-striato-thalamo-cortical (CSTC) circuit is a neuronal circuit that regulates motor executive, cognitive and emotional processing functions (12). Dysfunction of the CSTC circuit is related to TD (13). Previous studies using neuroimaging studies and animal model experiments support the hypothesis of dysfunction of the dopamine (14), glutamate (Glu) (15–17) and γ-aminobutyric acid (GABA) (15,18,19) system in the CSTC circuit. In a previous study, the release of striatal dopamine and the activation of D2 receptors improved tic behavior, with dysfunction of striatal dopamine as the driving factor of motor tics (14). Using an animal model, it was revealed that rats with TD, with abnormal metabolism in the ‘Glu-GABA’ loop, had higher Glu levels and lower GABA levels (15). Inhibition of Glu could eliminate tic behavior, indicating that Glu is critical for the generations of tics (16,17). Cortical GABA has a negative association with premonitory urges, and lower cortical GABA is related to more severe and frequent premonitory urges (18,19). In the CSTC circuit, the major dysregulated neurotransmitters appear to differ between the different brain regions, suggesting that each brain region may serve a different role in the pathogenesis of TD via different pathways (14,18,19). In addition to these neurotransmitters, studies have also focused on metabolites in the pathogenesis of TD (15,20–22). Previous studies have found that certain metabolites were involved in TD, such as tryptophan and amines (20,21). A case-controlled study found that tryptophan hydroxylase 2 gene polymorphisms were related to susceptibility to developing TD in the Chinese Han population (22). Our previous study using widely targeted metabolomic analysis in the thalamus in patients with TD (15) identified 34 differentially present substances between the TD group and control (CK) group (9 upregulated and 25 downregulated). Among them, neurosteroids (progesterone and corticosterone) exhibited distinct differences. However, the levels of metabolites in other brain regions in the CSTC circuit have not been thoroughly studied.

Metabolomics is a broad-spectrum technique used to detect metabolite levels (23). Previously, metabolomic methods have been widely used to analyze metabolites comprehensively (24,25). Widely targeted metabolomics integrates the advantages of non-targeted and targeted metabolite detection technologies, is a high throughput technique, and exhibits high sensitivity, precision and comprehensive coverage (24). 3,3′-iminodipropionitrile (IDPN) is a neurotoxic compound that exhibits toxic effects on various mammals, including mice and subhuman primates (26). IDPN is metabolized into cyanoacetic acid, lathyrogen-B-aminopropionitrile and β-alanine, all of which can cross the blood-brain barrier (BBB) and are thus detectable in the brain tissue (27,28). Diamond et al (29) injected IDPN intraperitoneally into rats and revealed that the dopamine system in their extrapyramidal system was disrupted, with dopamine levels continuously decreasing, leading to dopamine receptor supersensitivity and the emergence of stereotypical behavior (29). The effective dose of IDPN was 150 mg/kg/day (29), and continuous injections over 7 days induced pronounced behavioral symptoms that persisted for an extended period, typically lasting 2–3 months (30). Additionally, the IDPN-induced TD rat model was irreversible and rats induced using this model exhibited whole-body motor disturbances, such as head bobbing, rotational movement and chorea-like activity (30). Together, these previous studies show that the IDPN-induced rat model is commonly used in TD research and can effectively replicate the behavioral characteristics of TD.

At present, the majority of studies have primarily focused on the metabolic differences of one certain brain region at a time and there are few studies that pay attention to the metabolic characteristics of the CSTC circuit (31). The aim of the present study was to analyze the commonality and differences of metabolic abnormalities in the CSTC circuit, addressing the hypothesis that the different brain regions in the CSTC circuit may serve different roles in the pathogenesis of TD via different pathways. Widely targeted metabolomics technology was used to comprehensively detect and analyze differences in the presence of metabolites in the cortex and striatum in a rat model of TD. Combined with our previous study data, common differentially present metabolites and pathways in the CSTC circuit were analyzed. The differences in the metabolic abnormalities among the cortex, striatum and thalamus were then assessed to provide reference values for studying the pathogenesis of TD.

Materials and methods

Animals

The selection of animal age affects neurotransmitter metabolism and cellular metabolism. For example, Glu and GABA neurons undergo large and approximately proportional increases in neurotransmitter cycling and oxidative energy metabolism during the major postnatal growth spurt at postnatal day 10–30 (32). In the present study, tic modeling was performed as described previously (30). TDs are more frequently observed in males than females (33). Thus, 10 male Wistar rats (Charles River Laboratories, Inc.) weighing 45 g at 4 weeks of age were used. At the beginning of the experiment, rats were housed at room temperature and 20–30% humidity with a 12-h light/dark cycle with lights on at 6 am and ad libitum access to water and food for 7 days of adaptive rearing. Subsequently, the rats were randomly divided into the CK group (n=5) and the TD group (n=5). The CK group received intraperitoneal injections of 0.9% saline (15 ml/kg) once daily for 7 days, while the TD group received IDPN (250 mg/kg; 98%; CAS no. 111-94-4; Shanghai Aladdin Biochemical Technology Co., Ltd.) once daily for 7 days. Finally, stereotype assessment was used to verify the TD model. All IDPN-treated rats showed typical symptoms of TD, including noticeable head twitching, continuous hovering behavior and licking paws, indicating successful establishment of the model (34). Stereotypy recording was used to measure the severity of symptoms. In a quiet and dark environment, rats were placed in the open field test box and allowed to adapt for 5 min. An open field autonomous movement behavior tracking system (XR-XZ301; 100×100×50 cm; Shanghai Xinruan Information Technology Co., Ltd.) was used to record the behavior of rats. Two experimenters observed each rat for 1 h (every 5 min for 1 min each time, a total of 12 times, with the total score being recorded) after IDPN injection. Stereotyped behavior score was rated as follows. No stereotyped behavior, 0; rotation behavior, 1; excessive up and down movement of the head and neck, 2; excessive up and down movement of the head and neck plus rotational behavior, 3; lateral head swing and excessive up and down movement of the head and neck, 4.

All animal experimental procedures were approved by the Xin Hua Hospital Ethics Committee Affiliated to Shanghai Jiao Tong University School of Medicine (approval no. XHEC-F-2023-005; Shanghai, China). All attempts were made to ensure minimal animal suffering during the experiment.

Chemicals and reagents

High-performance liquid chromatography (HPLC)-grade acetonitrile (ACN) was purchased from ANPEL Laboratory Technologies (Shanghai), Inc. (cat. no. CAEQ-4-000308-4000). Methanol (MeOH) was purchased from Merck KGaA (cat. no. 1.06007.4008). Formic acid was purchased from RHAWN (cat. no. R050228-50g). The stock solutions of standards (1 mg/ml) were prepared in MeOH and other solutions. Generally, MeOH could be used to dissolve most of the standards, if MeOH could not dissolve the standards, 70% MeOH/H2O was used for strong polar standards and 50% CH2Cl2/MeOH (CH2Cl2; cat. no. D143-4; Thermo Fisher Scientific, Inc.) was used for weak polar standards. All stock solutions were stored at −20°C. The stock solutions were diluted with MeOH to obtain working solutions before use.

Widely targeted metabolomic analysis of the metabolites

All rats were placed in a small animal anesthesia machine (RWD Life Science Co., Ltd.) and euthanized using 10% isoflurane for 30 min at the end of the experiment. Death was confirmed by a lack of heartbeat, after which the brain tissue samples of striatum and cortex were quickly dissected and collected. The symptoms that could have occurred during the experimental process were weight loss, loss of appetite, weakness, infection of body organs, tumor development, death or a near-fatal event, organ system failure and failure of the circulatory system, among others, at which point the affected animal would have been humanely terminated. No animal reached the humane endpoints. The brain tissue samples of striatum and cortex were frozen with liquid nitrogen and stored at −80°C.

After the brain tissue samples of striatum and cortex were thawed and crushed, 0.05 g was mixed with 500 µl 70% MeOH/water. The sample was vortexed for 3 min at 2,500 r/min and centrifuged at 11,304 × g for 10 min at 4°C. Subsequently, 300 µl supernatant was placed in a clean centrifuge tube and stored at −20°C for 30 min. The supernatant was recentrifuged at 11,304 × g for 10 min at 4°C. After centrifugation, 200 µl supernatant was transferred to a protein precipitation plate (no. MPPT9601A; Shenzhen Biocomma Technology Co,. Ltd.) for further analysis using liquid chromatography (LC) and mass spectrometry.

The sample extracts were analyzed using an LC-electrospray ionization (ESI)-tandem mass spectrometry (MS/MS) system (UPLC, ExionLCTM AD, SCIEX; MS, QTRAP® 6,500+ System, SCIEX). According to polarity, different substances were analyzed using different analysis methods: The T3 method was selected for weak polarity and the amide method was used for strong polarity. The amide method was used after the T3 method. The analytical conditions were as follows: T3 method, HPLC column (cat. no. 186003538; Waters Corporation), Waters ACQUITY UPLC HSS T3 C18 (1.8 µm; 100×2.1 mm i.d.); solvent system, water with 0.05% formic acid (solution A) and ACN with 0.05% formic acid (solution B). The gradient was initiated using 5% solution B (0 min), increased to 95% solution B (8–9 .5 min) and finally ramped back to 5% solution B (9.6–12 min), with a flow rate of 0.35 ml/min, temperature of 40°C and injection volume of 2 µl. Amide method: HPLC column (cat. no. 186004801; Waters Corporation), ACQUITY UPLC BEH Amide (1.7 µm; 100×2.1 mm i.d.); solvent system, water with 10 mM ammonium acetate (cat. no. 10001228; Shanghai Bide Pharmaceutical Technology Co., Ltd.) and 0.3% ammonium hydroxide (500 ml; cat. no. A112080; Shanghai Aladdin Biochemical Technology Co., Ltd.) (solution C) and 90% ACN/water (V/V; solution D). The gradient was initiated with 95% solution D (0–1 .2 min), decreased to 70% solution D (8 min) and 50% solution D (9–11 min), and finally ramped back to 95% solution D (11.1–15 min). The flow rate was 0.4 ml/min, with a temperature of 40°C and an injection volume of 2 µl.

Linear ion trap and triple quadrupole scans were acquired using a triple quadrupole-linear ion trap mass spectrometer (QTRAP® 6,500+ LC-MS/MS System; SCIEX), equipped with an ESI Turbo IonSpray interface, operating in both positive and negative ion modes and controlled using Analyst version 1.6.3 software (SCIEX). The ESI source operation parameters were as follows: Ion source, ESI+/-; source temperature, 550°C; ion spray voltage, 5,500 V (positive) and −4,500 V (negative); curtain gas was set at 35 psi; flow rate, 22 l/min. Metabolites were analyzed using scheduled multiple reaction monitoring (MRM). Data were acquired using Analyst software. MultiQuant version 3.0.3 (SCIEX) was used to quantify all metabolites. Mass spectrometry parameters, including the declustering potentials (DP) and collision energies (CE) for individual MRM transitions, were determined using further DP and CE optimization. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within the corresponding period.

All targeted metabolites were detected using MetWare (Metware Biotechnology Inc.) based on the AB Sciex QTRAP® 6500+ LC-MS/MS platform.

There were 5 samples of striatum and 5 samples of cortex in the CK group and TD group (total, 20 samples). Unsupervised principal component analysis (PCA) was performed using the statistics function prcomp within R (v4.3.2; www.r-project.org). The data was unit variance scaled before unsupervised PCA. The hierarchical cluster analysis (HCA) results of samples and metabolites were presented as heatmaps with dendrograms, while the Pearson correlation coefficients (PCCs) between samples were calculated using the cor function in R and presented as only heatmaps. Both HCA and PCC analysis were carried out using the R (v.3.5.1; www.r-project.org) package pheatmap (v1.20.0; http://cran.r-project.org/web/packages/pheatmap/index.html). For HCA, normalized signal intensities of metabolites (unit variance scaling) were visualized as a color spectrum. Significantly regulated metabolites between groups were determined by variable importance in projection (VIP) and absolute log2fold change (FC). VIP values were extracted from partial least squares-discriminant analysis (OPLS-DA) results, which also contain score plots and permutation plots, generated using the R (v.3.5.1; www.r-project.org) package MetaboAnalystR (v1.0.1; http://github.com/xia-lab/MetaboAnalystR). The data were log transformed (log2) and mean-centered before OPLS-DA. In order to avoid overfitting, a permutation test (200 permutations) was performed. Identified metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes compound database (http://www.kegg.jp/kegg/compound/), and annotated metabolites were then mapped to the KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html). Pathways with significantly regulated metabolites mapped to were then fed into metabolite sets enrichment analysis, and their significance was determined by hypergeometric test's P-values. P<0.05 was considered to indicate a statistically significant difference.

Results

Full-scale mass spectrometry analysis of metabolites

The specificity of full-scale mass spectrometry analysis has been previously demonstrated (15). A total of 242 metabolites were detected, including 47 nucleotides and their metabolomics, 45 amino acids and their derivatives, 23 organic acids and their derivatives, 23 small peptides, 12 hormones and hormone-related compounds, 11 acylcarnitines (CARs), 11 bile acids, 10 coenzymes and vitamins, six amines, six polyamines, five phosphate sugars, four phosphoric acids, three free fatty acids, three heterocyclic compounds, three lysophosphatidylcholines, three lysophosphatidylethanolamines, three phenolic acids, three sulfonic acids, two fatty acyls, two indoles and their derivatives, two pterdines and their derivatives, two sugars, one alcohol, one carboximidic acid, one choline, one dinucleotide, one nucleotide metabolomic, one oxidized lipid, one sugar alcohol, one sugar acid, one fatty acid and others. The similarity between the total ion currents for metabolite detection demonstrated that the mass spectrometry exhibited great signal stability in detecting the identical sample at varying times (Fig. 1). The high stability of the instruments offered a crucial assurance for the repeatability and reliability of data.

Figure 1. Overlapping total ion chromatogram for metabolite detection. The total ion chromatogram for metabolite detection had high agreement levels in retention time and peak intensity, with a high degree of curve overlap. cps, counts per second.

Metabolism in the striatum

There were 13 differentially present metabolites between the CK and TD groups in the striatum (9 upregulated and 4 downregulated; Fig. 2A). After conducting qualitative and quantitative analysis of the detected metabolites, the FC of differences was calculated. The 13 differentially present metabolites based on the FC are shown in Fig. 2B. Several metabolites were significantly upregulated, including progesterone, deoxycorticosterone, chenodeoxycholic acid, corticosterone, hyodeoxycholic acid, 11-dehydrocorticosterone, inosine monophosphate (IMP), 3β-cholic acid and H-Phe-Trp-OH. Argininosuccinic acid, N2-methylguanosine, 1,4-dihydro nicotinamide adenine dinucleotide (NADH) and γ-Glu-Met were significantly downregulated. The heatmap of differential metabolites is shown in Fig. 2C. Upregulation of hormones and hormone-related compounds, and bile acids, and downregulation of dinucleotides, and organic acid and its derivatives were observed. Correlation analysis was performed using Pearson's correlation analysis, which revealed a high degree of correlation between the significantly differentially present metabolites (Fig. 2D).

Figure 2. Differentially present metabolites in the striatum. (A) Volcano plot of metabolites. Each point represents a metabolite. Red represents upregulated differentially present metabolites, green represents downregulated differentially present metabolites and grey represents metabolites not differentially present. The abscissa represents the log2FC of metabolites, the ordinate represents the-log10(P-value). The size of each point corresponds to the VIP value (from small to large, VIP of 0.4, 0.8, 1.2 and 1.6). (B) The 13 metabolites based on the FC. The abscissa is the log2FC of the differentially present metabolites and the ordinate shows the differential metabolites. Red represents upregulated differentially present metabolites and green represents downregulated differentially present metabolites. (C) Differentially present metabolite heatmap. The cluster line at the top of the figure is the sample cluster line and the cluster line on the left is the metabolite cluster line. Different colors depict different values obtained following standardization. Red represents high expression levels and green represents low expression levels. 1–5, numbers of rats in the CK and TD groups. (D) Differentially present metabolite chord diagram. The size of each point represents the log2FC of the differentially present metabolites, the color of each point indicates the metabolite classification as per the key and the color of the lines represents the correlation coefficient value of the differentially present metabolites in the corresponding position. IMP, inosine monophosphate; NADH, 1,4-dihydro nicotinamide adenine dinucleotide; FC, fold change; CK, control; S, striatum; TD, tic disorder; VIP, variable importance in projection.

KEGG pathway enrichment analysis was performed using the differentially present metabolites (Fig. 3). Among the enriched pathways, the ‘steroid hormone biosynthesis’ pathway contained the most significantly differentially present metabolites and exhibited a high enrichment degree. Additionally, the ‘aldosterone synthesis and secretion’ pathway was prominently enriched.

Figure 3. Kyoto Encyclopedia of Genes and Genomes enrichment map of the differentially present metabolites in the striatum. The abscissa represents the corresponding Rich factor of each pathway and the ordinate shows the pathway name. The size of the points represents the number of enriched differentially present metabolites as per the key, the color of each point represents the P-value and an increase in the redness represents an increase in the significance of enrichment.

Metabolism in the cortex

There were 21 differentially present metabolites between the CK and TD groups in the cortex (12 upregulated and 9 downregulated; Fig. 4A). After conducting qualitative and quantitative analysis of the detected metabolites, the FC of differences was calculated. The top 20 differentially present metabolites based on the FC are shown in Fig. 4B. The upregulated metabolites included progesterone, chenodeoxycholic acid, deoxycorticosterone, octadecanedioate (C18), 11-dehydrocorticosterone, corticosterone, disodium D-fructose-6-phosphate, hyodeoxycholic acid, 3β-cholic acid, 5-hydroxymethylcytidine, IMP and glycodeoxycholic acid. Adenine, L-leucine-L-alanine, isobutyryl-L-CAR, isoleucyl-isoleucine, NADH, kynurenine, γ-Glu-Met and 3-phenyllactic acid were significantly downregulated. The heatmap of differential metabolites is shown in Fig. 4C. Upregulated of bile acids, and hormones and hormone-related compounds was observed. Correlation analysis was performed using Pearson's correlation analysis, which revealed a high degree of correlation between the significantly differentially present metabolites (Fig. 4D).

Figure 4. Differentially present metabolites in the cortex. (A) Volcano plot of metabolites. Each point represents a metabolite. Red represents upregulated differentially present metabolites, green represents downregulated differentially present metabolites and grey represents metabolites not differentially present. The abscissa represents the log2FC of metabolites, the ordinate represents the-log10(P-value). The size of each point corresponds to the VIP value (from small to large, VIP of 0.4, 0.8, 1.2 and 1.6). (B) Top 20 metabolites based on the FC. The abscissa is the log2FC of the differentially present metabolites and the ordinate shows the differential metabolites. Red represents upregulated differentially present metabolites and green represents downregulated differentially present metabolites. (C) Differentially present metabolite heatmap. The cluster line at the top of the figure is the sample cluster line and the cluster line on the left is the metabolite cluster line. Different colors depict different values obtained following standardization. Red represents high expression levels and green represents low expression levels. 1–5, numbers of rats in the CK and TD groups. (D) Differentially present metabolite chord diagram. The size of each point represents the log2FC of the differentially present metabolites, the color of each point indicates the metabolite classification as per the key and the color of the lines represents the correlation coefficient value of the differentially present metabolites in the corresponding position. C, cortex; IMP, inosine monophosphate; NADH, 1,4-dihydro nicotinamide adenine dinucleotide; FC, fold change; FFA, free fatty acid; CAR, acylcarnitine; CK, control; TD, tic disorder; VIP, variable importance in projection.

The results of KEGG pathway enrichment analysis are shown in Fig. 5. Notably, ‘steroid hormone biosynthesis’, ‘aldosterone synthesis and secretion’ and ‘tryptophan metabolism’ contained more significantly differentially present metabolites, and thus, had a higher degree of enrichment.

Figure 5. Kyoto Encyclopedia of Genes and Genomes enrichment map of the differentially present metabolites in the cortex. The abscissa represents the corresponding Rich factor of each pathway and the ordinate shows the pathway name. The size of the points represents the number of enriched differentially present metabolites as per the key, the color of each point represents the P-value and an increase in the redness represents an increase in the significance of enrichment.

Metabolism across the CSTC circuit

In our previous study, the differentially present metabolites and metabolic pathways in the thalamus were identified (15). In the present study, by analyzing the data, the common significantly differentially present metabolites and metabolic pathways in the striatum, cortex and thalamus were identified using a similar approach.

Compared with the CK group, progesterone, corticosterone, deoxycorticosterone, 11-dehydrocorticosterone, chenodeoxycholic acid and hyodeoxycholic acid were commonly upregulated in the striatum, cortex and thalamus in the TD group (Fig. 6). Among these metabolites, progesterone had the highest FC (Fig. 2B and Fig. 4B). Hormones and hormone-related compounds, bile acids, nucleotides and their metabolomics, small peptides, and organic acid and its derivatives exhibited significant changes.

Figure 6. Differentially present metabolites in the three brain regions. The abscissa represents the differentially present metabolites, the ordinate represents the sample group on the left and the brain region on the right, and the cluster line at the top is the metabolite cluster line. Red represents upregulated differentially present metabolites and blue represents downregulated differentially present metabolites. The data for the thalamus were from our previous study (15). LPE, lysophosphatidylethanolamine; CAR, acylcarnitine; FFA, free fatty acid; LPC, lysophosphatidylcholine; CK, control; TD, tic disorder; IMP, inosine monophosphate; NADH, 1,4-dihydro nicotinamide adenine dinucleotide.

In the CSTC circuit, ‘steroid hormone biosynthesis’ and ‘aldosterone synthesis and secretion’ were common differentially expressed metabolic pathways (Fig. 3 and Fig. 5).

Differences in the CSTC circuit

In the CK group, there were 43 differentially present metabolites between the cortex and striatum, 43 differentially present metabolites between the cortex and thalamus, and 44 differentially present metabolites between the thalamus and striatum (Fig. S1). The specific differentially present metabolites among the different areas of the brain are shown in Fig. 7A.

Figure 7. Differentially present metabolites in the cortico-striato-thalamo-cortical circuit. (A) Specific differentially present metabolites among the CK groups. (B) Specific differentially present metabolites among the TD groups. S, striatum; C, cortex; T, thalamus; CK, control; TD, tic disorder; NADH, 1,4-dihydro nicotinamide adenine dinucleotide.

In the TD group, there were 33 differentially present metabolites between the cortex and striatum, 24 differentially present metabolites between the cortex and thalamus, and 24 differentially present metabolites between the thalamus and striatum (Fig. S2). The specific differentially present metabolites among the different areas of the brain are shown in Fig. 7B.

The difference in enriched pathways was the enrichment of the ‘tryptophan metabolism’ pathway, which was only observed in the cortex (Fig. 3 and Fig. 5).

Discussion

The present study utilized UPLC-MS/MS technology to measure the levels of metabolites in the CK and TD groups. Subsequently, PCA, OPLS-DA, VIP, FC, cluster analysis, K_means, KEGG and DA score analyses were performed to determine the differences in the metabolite levels between the groups and the different regions, and to explore the related metabolic pathways. In our previous study, compared with the CK group, the steroid hormone biosynthesis pathway, primary bile acid biosynthesis pathway, and aldosterone synthesis and secretion pathway contained more significantly differentially present metabolites in the thalamus in the TD group (15). In the CSTC circuit, the common differentially present metabolites included progesterone, corticosterone, deoxycorticosterone, 11-dehydrocorticosterone, chenodeoxycholic acid and hyodeoxycholic acid. Among these metabolites, progesterone had the highest FC. The common differentially present metabolite classes included bile acids, and hormones and hormone-related compounds. The common differentially expressed metabolic pathways included ‘steroid hormone biosynthesis’ and ‘aldosterone synthesis and secretion’.

Progesterone is a neuroactive steroid that is synthesized in astrocytes (35), and can modulate the interaction between Glu and dopamine systems in the striatum (36), and the plasticity of neurons and astrocytes (37). TD is associated with neuronal hyperactivity (38) and perturbations in astrocytic-neuronal coupling systems (39). Striatal dopamine system dysfunction is also associated with TD (14). Consequently, progesterone may be involved in the pathogenesis of TD via the altered neuronal activity, the astrocytic-neuronal coupling systems and the dopamine system. Corticosterone can modulate the GABAergic pathway (40). Dysfunction of cortical GABA is related to the premonitory urges in TD (18,19). Progesterone, deoxycorticosterone and 11-dehydrocorticosterone are related to corticosterone synthesis. Progesterone is converted to deoxycorticosterone catalyzed by 21-hydroxylase (41), deoxycorticosterone is converted to corticosterone catalyzed by 11β-hydroxylase (42), and 11β-hydroxysteroid dehydrogenase catalyzes the interconversion of active corticosterone and inert 11-dehydrocorticosterone (43). Progesterone, corticosterone, deoxycorticosterone and 11-dehydrocorticosterone are all steroid hormones. In the TD group, these steroid hormone metabolites were significantly upregulated and ‘steroid hormone biosynthesis’ was a significantly enriched metabolic pathway. Therefore, the ‘steroid hormone biosynthesis’ pathway may be implicated in the pathogenesis of TD via the GABAergic pathway. Stress is related to the expression and severity of tics in TD (44), and chronic stress is a triggering factor of TD (45). Corticosterone is the primary stress response hormone in rats (46), while cortisol is the primary stress response hormone in humans (47) and the hypothalamic-pituitary-adrenal (HPA) axis is the central stress response system in humans (48). A clinical trial showed that patients with TD had higher plasma cortisol levels during stress response (48). A previous study reported that in children at high risk for developing tics, children who developed tics had a higher hair cortisol concentration 5 months before the onset of tics than children without tics (49). Additionally, within 2–5 months before tic onset, for each 1 pg/mg higher hair cortisol concentration, the relative probability of the onset of tics increased by 30% (49). Therefore, the steroid hormone biosynthesis pathway and the HPA axis may be critical in the pathogenesis of TD via the induction of chronic stress.

The ‘aldosterone synthesis and secretion’ pathway was identified as a significant metabolic pathway in the TD group. Corticosterone can also be converted to aldosterone, which is catalyzed by aldosterone synthase (50). Increased corticosterone levels may affect the aldosterone synthesis and secretion pathway. Aldosterone is a corticosteroid involved in salt and ionic homeostasis that serves an essential role in maintaining water and salt balance and regulating vasoconstriction (51). Aldosterone can be detected in physiological fluids such as blood and saliva (51,52). A study reported that aldosterone levels in the saliva were increased under conditions of psychological stress in children aged 8–11 years following soccer matches when playing against unknown competitors (52). In TD, the aldosterone synthesis and secretion pathway may be associated with the increased corticosterone levels due to the high physical stress of tics and co-morbidities. Thus, the aldosterone synthesis and secretion pathway may serve important roles in the pathogenesis of TD.

Chenodeoxycholic acid and hyodeoxycholic acid are bile acids that can pass through the BBB (53) and can be detected in the brain (54). In the TD group, chenodeoxycholic acid and hyodeoxycholic acid were significantly upregulated. A persistent increase in glucocorticoid levels increases the absorption of bile acid by increasing the expression of apical sodium-dependent bile acid transporter in the ileum, to raise the plasma levels and reduce fecal loss of bile acids (55), which may lead to an increase in the intracerebral level of bile acids. Corticosterone, deoxycorticosterone and 11-dehydrocorticosterone are all glucocorticoids, which were upregulated in the TD group. The increased levels of these glucocorticoids may be the reason for the increased chenodeoxycholic acid and hyodeoxycholic acid levels.

Finally, a notable result was the abnormality of the ‘tryptophan metabolism’ pathway, which was only observed in the cortex of the TD group. A key pathway of tryptophan metabolism is the kynurenine pathway (56). The kynurenine pathway produces a series of metabolites that impact human inflammation, immune response and the central nervous system (57). In the present study, kynurenine levels in the cortex were lower in the TD group compared with the CK group. Kynurenine is an intermediate during the synthesis of NAD+ from tryptophan, which is neuroactive and may affect Glu N-Methyl-D-aspartate (NMDA) receptor signaling and glutamatergic neurotransmission (58). The abnormality of the tryptophan metabolism pathway can influence the Glu NMDA receptor by affecting the levels of kynurenine, inducing abnormal glutamatergic neurotransmission (58). A previous study used α-[11C]methyl-L-tryptophan (AMT) positron emission tomography to assess tryptophan metabolism in the brain, and the results revealed that AMT uptake was decreased in the bilateral dorsolateral prefrontal cortex and increased in the thalamus, demonstrating the abnormality in tryptophan metabolism in the cortex and subcortex (21). Therefore, the tryptophan metabolism pathway may be involved in the pathogenesis of TD in the cortex by affecting Glu NMDA receptor signaling and glutamatergic neurotransmission.

The present study considered the commonalities and differences of metabolic abnormalities in the different regions of the brain in the CSTC circuit, revealing the general regularity in the CSTC circuit and unique cortical changes. In the CSTC circuit, the upregulation of progesterone, corticosterone, deoxycorticosterone and 11-dehydrocorticosterone was a common regularity. ‘Steroid hormone biosynthesis’ and ‘aldosterone synthesis and secretion’ were commonly dysregulated pathways in the CSTC circuit. These common changes may be involved in the pathogenesis of TD by altering neuronal activity, neurotransmitter activity and chronic stress. In the cortex, the tryptophan metabolism pathway may be involved in the pathogenesis of TD by affecting the transmission of neurotransmitter activity.

The present study has some limitations. First, the analysis of metabolites focused on the CSTC circuit only and did not examine the distinction between intracellular and extracellular metabolites in the local brain parenchyma. In the future, experiments will be performed at the cellular level, focusing on the distinction between intracellular and extracellular metabolites in local brain parenchyma. Secondly, metabolite levels in the CSTC circuit were investigated using metabolomics. However, the enzymes, receptors and transporters in the metabolic circuit and the morphologic changes in the circuitry were not further investigated. A more comprehensive study is required to understand the potential network-level mechanism in TD. Finally, there is bidirectional communication between the gut flora and the central nervous system. Metabolites such as bile acids and 5-hydroxytryptophan can pass through the BBB, serving a role in regulating brain function, neurodevelopment and aging (59–61). In the future, experiments should focus on the gut flora and the gut-brain axis to gain an improved understanding of the pathogenesis of TD.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

The present study was supported by grants from the National Natural Science Foundation of China (grant nos. 82205192 and 82004418), Zhejiang Medical Health Science and Technology Program (grant no. 2022RC280) and Shaoxing University College-Level Research Projects (grant nos. 20205038 and 2020LG1007).

Availability of data and materials

The data generated in the present study may be found in the European Molecular Biology Laboratory-European Bioinformatics Institute MetaboLights database under accession number MTBLS11764 or at the following URL: https://www.ebi.ac.uk/metabolights/MTBLS11764.

Authors' contributions

JH and JY designed the study and wrote the manuscript. JH, JY, GY and GX performed the experiments. JS, ZZ, YW and XZ analyzed the data. JH and JY confirmed the authenticity of all the raw data. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by the Xin Hua Hospital Ethics Committee Affiliated to Shanghai Jiao Tong University School of Medicine (approval no. XHEC-F-2023-005; Shanghai, China). All applicable international, national and institutional guidelines for the care and use of animals were followed.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References