1. Introduction
Neuropathic pain (NP) is a chronic condition that is characterized by spontaneous and evoked pain, including cold and mechanical allodynia. NP arises from injury or malfunction of the nerves or the spinal cord, and is prevalent in clinical practice. It often occurs secondary to conditions such as trauma, stroke, infection, diabetes, multiple sclerosis, and cancer [
1,
2]. Globally, the incidence of NP accounts for 6.9% to 10% of the total population [
3]. NP leads to significant reductions in both quality of life and behavioral function, placing a heavy physical and psychological burden on patients [
4]. The pathophysiology of NP is complex and multifactorial. Despite the availability of various interventions based on different mechanisms, the results of NP treatment are still unsatisfactory [
5]. Commonly used medications for NP, such as non-steroidal anti-inflammatory drugs and opioids, often cause adverse effects, including dizziness, drowsiness, arrhythmias, and issues related to tolerance when used alone [
6]. Consequently, the development of combination therapies targeting multiple analgesic mechanisms has become a promising strategy in the treatment of NP. Indeed, clinical approaches to chronic pain often evolve from initial monotherapy to combination therapy as a more effective means of addressing the complex nature of NP [
7].
Traditional Chinese medicine (TCM) boasts an extensive history in the treatment of NP, with many herbal medicines and their active compounds demonstrating analgesic effects [
8]. These therapies represent a rich source for the screening of potential combination drugs for NP treatment. As reported, ligustrazine (LGZ) has exhibited therapeutic effects, such as alleviating migraine [
9] and spinal cord injury-induced NP [
10], in various pain animal models. Sinomenine (SIN) has demonstrated analgesic effects on inflammatory pain [
11], spared nerve injury-induced NP [
12], and diabetic peripheral NP [
13], exerting both anti-inflammatory and analgesic mechanisms. Our previous studies have shown that the combination of LGZ and SIN effectively alleviates pain in NP models, including sciatic nerve injury, trigeminal neuralgia, and spinal cord injury [
14]. Moreover, the combination of LGZ and SIN exhibits a synergistic analgesic effect, allowing for a reduction in the individual dosages of each compound. Both LGZ and SIN have been reported to have numerous potential targets and pathways, but the specific mechanisms underlying their analgesic effects in NP remain unclear.
Network pharmacology is used to analyze the relationships between drugs, targets, and diseases through network-based approaches, making it particularly well-suited for elucidating core targets and pathways in treating complex diseases via TCM, which has “multi-target” and “multi-pathway” characteristics [
15]. Through offering a systems-level perspective, network pharmacology uncovers the potential molecular mechanisms of TCM, shedding light on its holistic therapeutic effects [
16,
17]. Metabolomics, an essential component of systems biology, utilizes high-throughput and highly sensitive instruments to conduct comprehensive profiling of the endogenous components within biological samples. Through integrating multivariate statistical methods, metabolomics can reveal changes in endogenous metabolites under various physiological, pathological, or toxicological conditions [
18], enabling the identification of the key metabolic pathways within the organism [
19]. Therefore, the integration of metabolomics with network pharmacology can provide a more comprehensive understanding of the metabolic pathways and network regulatory mechanisms of the combined therapy in treating complex diseases.
In this study, we employed network pharmacology to predict the potential targets of LGZ and SIN in treating pain-related diseases, systematically analyzing both their shared and distinct targets. Subsequently, the analgesic effects of LGZ, SIN, and their combination at different time points and dosages were evaluated in a chronic constriction injury (CCI)-induced NP model in rats. Furthermore, the key metabolites in the plasma and cerebrospinal fluid (CSF) were identified and quantified using LC-MS metabolomics technology. Finally, a joint analysis of the potential targets and key metabolites was performed to determine the critical metabolic pathways through which LGZ and SIN exert their therapeutic effects in NP. This study aimed to elucidate the molecular mechanisms underlying the combined use of LGZ and SIN in treating NP. Additionally, this research aimed to offer new experimental evidence to support the clinical combined application of LGZ and SIN in treating NP.
3. Discussion
NP is a secondary condition associated with various clinical disorders which significantly impacts the quality of life of patients. However, the precise pathogenesis of NP remains poorly understood and involves complex interactions between multiple signaling pathways. Currently, several widely accepted mechanisms are believed to contribute to the development of NP, including inflammation, peripheral sensitization, central sensitization, dysfunction of the descending inhibitory system, and changes in ion channels. Clinical metabolomics studies have recently reported significant alterations in the plasma metabolite profiles of patients with NP, particularly in the levels of amino acid analogs such as histidine, asparagine, glutamine, tyrosine, phenylalanine, proline, and choline [
20]. Furthermore, phenylalanine and tyrosine levels in the CSF of patients with localized pain syndromes have been shown to be markedly elevated [
21]. Some important metabolites, such as tyrosine, purine, asparagine, histidine, serine, and glutamate, have been shown to be involved in the onset and development of neuropathic pain [
22]. Therefore, exploring pathological mechanisms from the perspective of metabolomics, identifying clinical biomarkers, and further developing therapeutic drugs are highly necessary.
Multi-drug combination therapies, encompassing both combined pharmaceutical agents and multi-target TCM, can partially address clinical treatment needs. Chuanxiong Rhizoma, derived from the rhizome of
Ligusticum chuanxiong Hort., has been widely used in traditional medicine since the Han dynasty (~1800 years ago), though it is typically employed as an adjunctive or supporting medicine according to TCM theory. Its formulations, such as ligustrazine injection and salvia miltiorrhiza ligustrazine injection, are primarily used in China for the treatment of occlusive cerebrovascular diseases [
23,
24]. Sinomenii Caulis, sourced from the stems of
Sinomenium acutum (Thunb.) Rehd. et Wils., is used clinically for the treatment of rheumatism, rheumatoid arthritis, and related pain symptoms [
25,
26]. Chuanxiong Rhizoma and Sinomenii Caulis, widely used in clinical practice as TCM, have long been recognized for their substantial efficacy in treating various pain-related conditions. Based on these findings, we selected their active components, LGZ and SIN, for combined application, to explore their potential therapeutic effects on NP. These natural products, or TCM, frequently exhibit multi-target properties, complicating the precise identification of their therapeutic effects [
27]. However, for drugs with unclear mechanisms of action, identifying their key therapeutic targets is essential.
In previous studies, we investigated the analgesic effects of the combined use of LGZ and SIN in models of inflammatory pain, sciatic nerve injury, and spinal cord injury NP [
14]. Given that the CCI model simulated both neuropathic and inflammatory pain characteristics, we examined the analgesic effects of LGZ and SIN, both in combination and individually, in the CCI model rats to comprehensively assess the benefits of their combined use. In previous research, LGZ and SIN have been administered via intraperitoneal injection [
12], even though both have established oral administration protocols [
28,
29]. Therefore, in this study, we evaluated the analgesic effects of the oral administration of LGZ and SIN in combination. Additionally, the experimental design evaluated the analgesic effects of different time points (0, 0.5, 2, 4, and 6 h), dosages (LGZ 25 mg⋅kg
−1⋅d
−1 + SIN 25 mg⋅kg
−1⋅d
−1; LGZ 50 mg⋅kg
−1⋅d
−1 + SIN 50 mg⋅kg
−1⋅d
−1; and LGZ 100 mg⋅kg
−1⋅d
−1 + SIN 100 mg⋅kg
−1⋅d
−1) and days (1, 2, and 3 days) for both combined and single-drug treatments to comprehensively characterize the analgesic properties of the LGZ-SIN combination. Results from the MWT test, cold allodynia test, and incapacitance test demonstrated that both the combined and individual treatments of LGZ and SIN effectively alleviated mechanical allodynia, cold pain sensitivity, and spontaneous pain in CCI-induced NP. Furthermore, the combination of LGZ and SIN exhibited significant greater analgesic effects than single-drug treatments, reinforcing the rationale for their combined use. We also found a dose dependence of the combination of LGZ and SIN in the MWT test. Moreover, the results suggested that the combined use of LGZ and SIN also had beneficial effects on plasma inflammation, sciatic nerve inflammation and repair in CCI rats. Clinical studies have shown that IL-6 levels are elevated in the plasma of NP patients, which is consistent with the trend observed in our CCI rat model, where IL-6 levels were detected in both the sciatic nerve and plasma. However, we did not observe a significant change in IL-6 levels, which may be attributed to the relatively short treatment duration, which may not have allowed sufficient time for the drug’s regulatory effects to fully manifest [
30].
Following the evaluation of LGZ, SIN, and their combination, we conducted network pharmacology and metabolomics studies to explore their potential mechanisms in treating NP. The network pharmacology approach elucidated the analgesic mechanism of LGZ and SIN by examining their individual contributions. First, both LGZ and SIN demonstrated multi-target properties. Second, pathway analysis confirmed that both LGZ and SIN could regulate multiple signaling pathways to exert their synergistic effects. Based on network pharmacology results, modulation of the tyrosine metabolism and phenylalanine metabolism pathways may be the key mechanisms through which the combined use of LGZ and SIN exerted its analgesic effects. In addition, the combination of LGZ and SIN regulate arginine and proline metabolism, as well as histidine metabolism.
As NP affects both the peripheral and central systems, this study analyzed plasma and CSF samples to investigate the metabolic regulatory effects of LGZ and SIN, both in combination and individually, on CCI rats. The results of metabolic pathway analysis showed that the combined treatment of LGZ and SIN regulated more metabolic pathways in both CSF and plasma samples compared to either LGZ or SIN used alone, exhibiting a synergistic effect. Finally, joint pathway analysis revealed that tyrosine metabolism and phenylalanine metabolism were the key pathways enriched in both CSF and plasma samples. These pathways were considered the most critical. Among these, LGZ had a greater impact on tyrosine metabolism in CSF, while SIN exhibited a stronger effect on the tyrosine metabolism in plasma. The arginine and proline metabolism pathways contained the most targets and differential metabolites enriched by the combined treatment of LGZ and SIN in plasma samples. Therefore, the combined treatment of LGZ and SIN may alleviate pain in CCI model rats by co-regulating tyrosine metabolism and phenylalanine metabolism in both the CSF and plasma, as well as by modulating the arginine and proline metabolism in the plasma. Moreover, the number of differential metabolites in the metabolic pathways regulated by the combination of LGZ and SIN was much higher than that of LGZ and SIN alone, and interestingly, some of the differential metabolites were not present in LGZ or SIN alone, which were new differential metabolites generated by the combination. The enhanced effect of combining the two also suggests that we may be able to achieve the same effect of LGZ and SIN alone at a lower dose when they are combined. Thus, the combination of LGZ and SIN may produce a synergistic effect.
Tyrosine is an essential amino acid, and phenylalanine serves as its precursor. Both tyrosine and phenylalanine serve as precursors for monoamine neurotransmitters, including dopamine, norepinephrine, and epinephrine. The descending monoaminergic pathways, particularly those involving norepinephrine and serotonin transmission, play a crucial role in the endogenous pain modulation system, a mechanism that is well documented in NP [
31]. Studies have shown that CCI leads to a reduction in the neurotransmitters crucial for descending pain regulation pathways, such as serotonin and norepinephrine [
32]. Serotonin and dopamine potentiate noradrenergic effects to inhibit neuropathic pain. Moreover, antidepressants that inhibit the reuptake of norepinephrine and serotonin have been shown to be effective in chronic neuropathic pain [
5]. The metabolomics results showed that serotonin and its precursor, tryptophan, increased in the plasma of the model group, but were restored after treatment. The LGZ+SIN group exhibited a more pronounced recovery compared to the individual treatments. Arginine, a non-essential amino acid, serves as a precursor for nitric oxide, proline, and glutamate. Studies have demonstrated that arginine could increase pain sensitivity in animal models [
33]. Small-scale patient studies have suggested that L-arginine might have an analgesic effect on chronic pain [
34]. The central glutamatergic system plays a critical role in the onset and persistence of persistent pain, including both neuropathic and inflammatory pain [
35]. Following nerve injury, the downregulation of GABA and opioid receptors in the spinal cord leads to increased glutamate release, which may contribute to the development of neuropathic pain [
36]. Studies have shown that CCI-induced NP reduces the GABA levels and neuronal activity in the dorsal horn [
37]. Furthermore, the glutamatergic system could exacerbate chronic neuropathic pain by activating N-methyl-D-aspartate receptors (NMDARs) [
38]. Studies have demonstrated that NMDARs play a crucial role in modulating both peripheral and central sensitization in NP [
39]. The metabolomics analysis revealed a decrease in glutamine levels in the model group, which was subsequently restored following treatment, potentially contributing to this effect. In addition, reduced arginine levels may lead to neurotransmitter depletion, contributing to inflammatory pain [
40]. Meanwhile, histidine plays a crucial role in the inflammatory process by regulating the synthesis of histamine neurotransmitters [
41]. Therefore, arginine and histidine metabolism may be closely linked to the anti-inflammatory effects of the LGZ and SIN combination. In our experiment, the improvement of inflammatory factors in the sciatic nerve and plasma of CCI rats after treatment might be related to this.
To the best of our knowledge, this is the first report on the therapeutic effects and potential mechanisms of combining LGZ and SIN for the treatment of neuropathic pain induced by CCI using metabolomics and network pharmacology approaches. The combination of two drugs, LGZ and SIN, also offers a new combination of clinical treatment options for neuropathic pain. On one hand, special attention should be given to their specific targets and related metabolic signaling pathways to uncover the molecular-level regulatory mechanisms. On the other hand, extending the treatment duration and integrating in-depth studies at the cytokine level could provide a more comprehensive assessment of their anti-inflammatory, analgesic, and other potential effects. This multi-layered research approach will contribute to a more thorough understanding of the pharmacological mechanisms of LGZ and SIN combination therapy, thereby providing a stronger scientific foundation for its clinical application.
4. Materials and Methods
4.1. Chemicals and Materials
The Easyflow independent ventilation cage was purchased from Tecniplast, Italy. The Von Frey filaments were obtained from Ugo Basile Biological Apparatus Company. The Incapacitance Meter (BIO-SWB-TOUCH-R) was purchased from Bioseb, French. The pain testing frame was made in our laboratory.
AB Sciex HPLC-MS/MS system (Framingham, MA, USA) comprised an ExionLC-20AC high-performance liquid chromatography (HPLC) system, Ion DriveTM Turbo V ion source, Sciex 6500+ triple quadrupole mass spectrometer, Analyst 1.7 data acquisition software, and MultiQuant 3.0.3 data processing software. The Targin VX-Ⅲ multi-tube vortexer was purchased from Beijing Targin Technology Co., Ltd. (Beijing, China). The Forma 88,000 Series −86 °C ultra-low temperature freezer was obtained from Thermo Scientific (Waltham, MA, USA). The Rotanta 460R high-speed refrigerated centrifuge was acquired from Hettich (Kirchlengern, Germany). The MC-8 integrated cryogenic centrifuge concentrator was obtained from Beijing JM Technology Co., Ltd. (Beijing, China). The Synergy2 multifunctional microplate reader was purchased from Bio Tek (Winooski, VT, USA). The desktop anesthetic machine was supplied by Harvard Apparatus (Cambridge, MA, USA). The ThermoStar body temperature maintenance device was purchased from RWD Life Science Co., Ltd, (Shenzhen, China). The optical microscope (Olympus BX50) was purchased from Olympus Optical Co. (Tokyo, Japan).
Ligustrazine (ligustrazine hydrochloride, lot number: DT201803-19) and sinomenine (sinomenine hydrochloride, lot number: DT201806-22), both with a purity of ≥98%, were provided by Shanxi Datian Biotechnology Ltd. (Xi’an, China). Pregabalin (lot number: 295422) was provided by Beijing J&K Scientific Ltd. (Beijing, China). The isoflurane (lot number: 217180801) was purchased from RWD Life Science Co., Ltd.
IL-6, IL-1β, and TNF-α ELISA kits were purchased from Raybiotech (Peachtree Corners, GA, USA). The tissue lysis buffer (EL-lysis) was obtained from Raybiotech. The BCA protein assay kit was purchased from Thermo Fisher (Waltham, MA, USA) and used to calibrate the content of inflammatory factors. The ProcartaPlexTM Rat Cytokine and Chemokine Panel was purchased from Thermo Fisher. The 4-0 chromic gut sutures were obtained from Shandong Boda Medical Products Co., Ltd. (Heze, China).
Mass spectrometry library kits and reference standards for glucose metabolism, amino acids, bile acids, and others were purchased from Sigma for the establishment of a widely targeted metabolomics analysis platform in our laboratory. Internal standards, including d-3 norepinephrine, d-4 dopamine, d-5 serotonin, and MSK-A2, were obtained from Cambridge Isotope Laboratories. Reference standards for metabolic pathways, including tyrosine, sodium borate, benzoyl chloride, and d-5 benzoyl chloride were purchased from Sigma. All reference and internal standards had a purity greater than 99%. LC/MS-grade acetonitrile, methanol, formic acid, and ammonium formate were obtained from Beijing Dikma Technologies Inc. (Beijing, China).
4.2. Animals and Treatment
Adult male Sprague Dawley rats (180–200 g, 6–7 weeks) were obtained from Beijing HFK Bioscience Co., Ltd. (Beijing, China). A total of 3 rats were housed per cage in an SPF-grade lab at a constantly maintained temperature (22 ± 2 °C) with a 12 h light/dark cycle and free access to food and water.
Following the successful establishment of the model, 42 rats were randomly assigned into 7 groups, with 6 animals per group. The groups were as follows: the model group (model, 10 mL⋅kg−1⋅d−1 saline); LGZ+SIN low-dose group (LGZ 25 mg⋅kg−1⋅d−1 + SIN 25 mg⋅kg−1⋅d−1); LGZ+SIN medium-dose group (LGZ 50 mg⋅kg−1⋅d−1 + SIN 50 mg⋅kg−1⋅d−1); LGZ+SIN high-dose group (LGZ 100 mg⋅kg−1⋅d−1 + SIN 100 mg⋅kg−1⋅d−1); Ligustrazine group (LGZ, 100 mg⋅kg−1⋅d−1); Sinomenine group (SIN, 100 mg⋅kg−1⋅d−1); and Pregabalin-positive control group (Pgb, 30 mg⋅kg−1⋅d−1). In addition, 6 healthy rats were set as the sham operation group (sham, 20 mL⋅kg−1⋅d−1 saline). All animals were orally administered their respective treatments twice daily (morning and evening) for a period of three consecutive days.
4.3. CCI Model Establishment
The CCI model in rats was established following the method described by Bennett [
42]. After anesthetizing the rats with isoflurane, they were placed on a heating pad to maintain a body temperature of approximately 37 °C. The skin below the left femur was incised and the left sciatic nerve was exposed following blunt dissection of the surrounding tissue. The nerve was then ligated with 4–0 chromic gut sutures tied in four knots, each approximately 1 mm apart. The degree of ligation was adjusted to induce slight twitching of the calf muscles without compromising the blood supply to the nerve epineurium. In the sham group, the sciatic nerve was exposed but left unligated. The MWT test was conducted both prior to surgery and on day 7 post-surgery to evaluate the success of the model.
4.4. Pharmacodynamic Research
The body weight of the rats was recorded daily. Behavioral tests were conducted on days 1, 2, and 3 following drug administration. The behavioral tests included the MWT test, cold allodynia test, and incapacitance test. The MWT and cold allodynia tests were conducted at 0, 0.5, 2, 4, and 6 h after drug administration each day. The incapacitance test was conducted 4 h after drug administration each day. After the final behavioral test, samples of the affected sciatic nerve were collected for hematoxylin and eosin (H&E) staining and enzyme-linked immunosorbent assay (ELISA) analysis. Plasma and CSF samples were also collected for subsequent metabolomic analysis.
The MWT test was assessed using Von Frey filaments [
43]. The rats were placed in a plastic chamber (20 cm × 20 cm × 15 cm) with a transparent acrylic lid, and they were allowed to acclimate for 30 min. Von Frey filaments, ranging from 4 g to 26 g, were used during the test. The “up-and-down” method was employed to determine the MWT value of each rat [
44].
The cold allodynia test was performed by spraying 0.1 mL of acetone on the affected hind paw of the rat. The responses of the rats, including paw withdrawal and licking behavior, were observed, and these were then scored based on the degree of reaction: 0 points for no response, 1 point for mild reaction or rapid withdrawal of the hind paw, 2 points for repeated paw shaking, and 3 points for sustained or repeated lifting and licking of the hind paw [
45].
The incapacitance test was conducted by placing the rats in a transparent box with an inclined platform, on which the rats stood on their hind feet. The left and right hind feet were positioned on separate sensor panels. Care was taken to ensure that the rats maintained an exploratory posture without leaning against the sides of the box. A capacimeter was used to measure the weight (in grams) on each panel over a 3 s period. Each rat underwent three measurements, with a minimum of 1 min between readings. The average of the readings for each hind foot was used to calculate the weight distribution. The bipedal balance bearing value was recorded as the percentage of total body weight supported by each hind foot. In normal rats, the weight distribution is nearly symmetrical (50:50%), whereas pain resulting from injury leads to a reduction in the load-bearing capacity of the injured hind foot. The incapacitance test result was calculated using the following formula: result = weight on the affected hind foot/(weight on the left hind foot + weight on the right hind foot) × 100% [
46].
The sciatic nerve tissue was fixed in 4% paraformaldehyde and subsequently embedded in paraffin to prepare 5 μm thick sections. The sections were stained with hematoxylin for 5 min, followed by eosin for 3 min. Changes in the sciatic nerve were observed under an optical microscope.
The concentrations of IL-1β, IL-6, and TNF-α in the sciatic nerve were measured by ELISA. The experimental procedure was strictly followed according to the instructions provided with the kits. The assay of 22 cytokines and chemokines in plasma was performed and analyzed independently by Laizee Biotech (Shanghai, China) via a Luminex200 instrument and ProcartaPlex Analyst 1.0 software.
4.5. Network Pharmacology Analysis
First, the potential targets of LGZ and SIN were identified using the SWISS Target Prediction database (
http://swisstargetprediction.ch/, accessed on 11 April 2024). These targets were then verified and refined using the UniProt database to obtain accurate target names for each compound. Subsequently, pain-related target information was retrieved from the Genecards database (
https://www.genecards.org/, accessed on 11 April 2024) and the OMIM database (
https://omim.org/, accessed on 15 April 2024). After removing duplicates, the remaining targets were considered pain-related targets for further analysis. The intersection of LGZ and SIN alkaloid targets with those associated with pain was identified using the Bioinformatics platform (
http://www.bioinformatics.com.cn, accessed on 15 April 2024), yielding common genes across the different compounds. This gene set was then analyzed based on the uniqueness of the drug–target interactions.
Next, the intersecting target genes were entered into the STRING database to construct a protein–protein interaction (PPI) network. The network was visualized using Cytoscape 3.8.2, and the CytoHubba plugin was used to identify the core targets for further differential analysis.
Finally, the core target genes underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using the Metascape database. A significance threshold of p < 0.01 was established for all analyses. The GO analysis covered three subcategories: biological process (BP), molecular function (MF), and cellular component (CC). Furthermore, based on the relationships between protein targets and signaling pathways, a compound–target–pathway association network was built.
4.6. Plasma and CSF Metabolomics Analysis
4.6.1. Plasma and CSF Sample Preparation
For sample preparation, 50 μL of the test sample was mixed with 450 μL of ice-cold extraction solvent containing internal standards (methanol: acetonitrile: water = 2:2:1). The mixture was vortexed for 3 min and then placed at −20 °C for 2 h. The samples were then centrifuged at 20,000× g for 15 min at 4 °C. The supernatant was carefully transferred to a 1.5 mL Eppendorf tube and subjected to vacuum concentration at 35 °C and 1500 rpm for 2 h. The residue was reconstituted with 100 μL of extraction solvent that was devoid of internal standards. The sample was centrifuged again at 20,000× g for 15 min at 4 °C, and 80 μL of the supernatant was collected for analysis. Additionally, 10 μL of each sample was pipetted to pool a quality control (QC) sample.
4.6.2. Widely Targeted Metabolomics Analysis
The metabolites were identified using an in-house reference database. An ACQUITY UPLC BEH Amide column (2.1 × 50 mm, 1.7 μm, Waters, Milford, MA, USA) and a pre-column (2.1 mm × 5 mm, 1.7 μm, Waters, USA) were used for sample separation. The mobile phase consisted of Solvent A (95% water: 5% acetonitrile, with 5 mM of ammonium formate and 0.01% formic acid) and Solvent B (95% acetonitrile: 5% water, containing 5 mM of ammonium formate and 0.01% formic acid). The gradient elution program was as follows: 0–2 min, 95–95% B; 2–4 min, 95–90% B; 4–6 min, 90–90% B; 6–9 min, 90–85% B; 9–12 min, 85–85% B; 12–15 min, 85–75% B; 15–16 min, 75–75% B; 16–18 min, 75–50% B; 18–20 min, 50–50% B; 20–22 min, 50–25% B; 22–24 min, 25–25% B; 24–25 min, 25–95% B; and 25–30 min, 95–95% B. Flow rate: 0.3 mL/min; column temperature: 35 °C; temperature: 4 °C; and injection volume: 5 μL.
Electrospray ionization (ESI) was used as the ionization source. The curtain gas (N2) was set to 40 psi, the collision gas (N2) to 9 psi, and the spray voltage was set at +5500 V and −4500 V for positive and negative ion modes, respectively. The nebulizer temperature was set to 550 °C, with ion source gas (Ion Source Gas1, N2) and auxiliary gas (Ion Source Gas2, N2) both maintained at 55 psi. Scanning was performed in both positive and negative ion modes. Optimized ion pairs and mass spectrometry parameters were applied for each metabolite.
4.6.3. Targeted Metabolomics Analysis
The method for measuring the tyrosine pathway was adapted from previously published protocols [
47], with the necessary modifications outlined below.
Sample preparation: a 50 μL aliquot of the sample was mixed with 150 μL of acetonitrile (1:3, v/v). The mixture was vortexed at 8000 rpm for 5 min, followed by centrifugation at 20,000× g for 10 min. Subsequently, 10 μL of the supernatant was transferred and added to 10 μL of 100 mM sodium borate and 10 μL of 1% benzoyl chloride. The mixture was vortexed for 5 min, incubated at 25 °C for 5 min, and then centrifuged at 20,000× g for 10 min. The resulting supernatant (24 μL) was mixed with 6 μL of an internal standard solution (a mixture of tyrosine pathway standards and d-5 benzoyl chloride for derivatization). The mixture was then vortexed and prepared for injection.
A PFP C18 column (2.1 × 50 mm, 1.8 μm, Waters, Milford, MA, USA) was used to separate the samples. Water with 0.1% formic acid and 5 mM of ammonium formate served as Mobile Phase A and acetonitrile served as Mobile Phase B. The gradient programs were as follows: 0–1 min, 20–20% B; 1–2 min, 20–50% B; 2–6 min, 50–70% B; 6–6.5 min, 70–95% B; 8–8.1 min, 95–20% B; and 8.1–10 min, 20–20% B. Flow rate: 0.3 mL/min; column temperature: 35 °C; sample temperature: 4 °C; and injection volume: 2 μL.
Electrospray ionization (ESI) was used as the ionization source. The curtain gas (N
2) was set to 35 psi, collision gas (N
2) to 9 psi, and the spray voltage was set at 5500. The nebulizer temperature was set to 550 °C, with ion source gas (Ion Source Gas1, N
2) and auxiliary gas (Ion Source Gas2, N
2) both at 55 psi. The analysis was performed in multiple reaction monitoring (MRM) mode with positive ion scanning. The specific ion pair parameters used for the analysis are provided in
Supplementary Table S1.
4.6.4. Metabolomics Analysis
To ensure QC for the metabolomics analysis, a QC sample was injected after every ten experimental sample during the chromatography run. All LC-MS data were meticulous preprocessing using MultiQuant 3.0.3 software, including key steps such as peak detection, peak identification, peak area calculation, and calibration.
PCA was initially performed to identify the major variability patterns within the dataset. OPLS-DA was then applied to identify metabolites that might differentiate between groups. The quality of the OPLS-DA model was evaluated using the parameters R
2Y and Q
2. Additionally, permutation testing was conducted to assess the risk of false positives in the OPLS-DA model. Potential biomarkers with significant statistical and biological relevance were selected based on the following criteria: VIP > 1,
t-test (
p) < 0.05, and fold change (FC) > 1.2 or <0.83. Finally, metabolic pathways associated with the differentially expressed metabolites were determined using a significance threshold of
p < 0.05. Metabolomics data were analyzed using the Metware Metabolomics Cloud Platform (
https://cloud.metware.cn/, accessed on 20 December 2024) and MetaboAnalyst 6.0 (
https://www.metaboanalyst.ca/, accessed on 27 December 2024).
4.7. Joint Pathway Analysis
A joint pathway analysis was performed using the “Joint-Pathway Analysis” module in MetaboAnalyst 6.0, in order to correlate the key targets predicted by network pharmacology with the differential metabolites identified in the metabolomics analysis. Pathways exhibiting the highest enrichment of both targets and metabolites were considered key pathways.
4.8. Statistical Analysis
Statistical analyses were performed using SPSS 20.0 and GraphPad Prism 8.0. All data are presented as the mean ± standard error of the mean (SEM). The significance analysis of differences between two groups was assessed using a t-test, while multiple comparison was conducted using one-way or two-way analysis of variance (ANOVA). A p value < 0.05 indicated statistical significance, and p < 0.01 indicated highly statistical significance.