The anti-depression effect and potential mechanism of the petroleum ether fraction of CDB: Integrated network pharmacology and metabolomics
State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
Department of Pharmacy, Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, China
Shaanxi Key Laboratory of Chinese Medicine Fundamentals and New Drugs Research, School of Pharmacy, Shaanxi University of Chinese Medicine, Xi'an, 712046, China
Department of Pharmacy, Shaanxi Provincial Hospital of Tuberculosis Prevention and Treatment, Xi'an, 710100, China
⁎Corresponding author. State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China. 19932015@cdutcm.edu.cnAbstract
The combination of Chaidangbo (CDB) is an antidepressant traditional Chinese medicine (TCM) prescription simplified by Xiaoyaosan (a classic antidepressant TCM prescription) through dismantling research, which has the effect of dispersing stagnated liver qi and nourishing blood in TCM theory. Although the antidepressant effect of CBD has been confirmed in animal studies, the material basis and possible molecular mechanism for antidepressant activity in CBD have not been clearly elucidated. Herein, we investigated the effects and potential mechanisms of CDB antidepressant fraction (petroleum ether fraction of CDB, PEFC) on chronic unpredictable mild stress (CUMS)-induced depression-like behavior in mice using network pharmacology and metabolomics. First, a UPLC-QE/MS was employed to identify the components of PEFC. To extract active ingredients, SwissADME screening was used to the real PEFC components that were found. Potential PEFC antidepressant targets were predicted based on a network pharmacology approach, and a pathway enrichment analysis was performed for the predicted targets. Afterward, a CUMS mouse depression model was established and LC-MS-based untargeted hippocampal metabolomics was performed to identify differential metabolites, and related metabolic pathways. Finally, the protein expressions in mouse hippocampi were determined by Western blot to validate the network pharmacology and metabolomics deduction. A total of 16 active compounds were screened in SwissADME that acted on 73 core targets of depression, including STAT3, MAPKs, and NR3C1; KEGG enrichment analysis showed that PEFC modulated signaling pathways such as PI3K-Akt signaling pathway, endocrine resistance, and MAPK to exert antidepressant effects. PEFC significantly reversed abnormalities of hippocampus metabolites in CUMS mice, mainly affecting the synthesis and metabolism of glycine, serine, and threonine, impacting catecholamine transfer and cholinergic synapses and regulating the activity of the mTOR signaling pathway. Furthermore, Western blot analysis confirmed that PEFC significantly influenced the main protein levels of the PI3K/Akt/mTOR signaling pathways in the hippocampus of mice subjected to CUMS. This study integrated metabolomics, network pharmacology and biological verification to explore the potential mechanism of PEFC in treating depression, which is related to the regulation of amino acid metabolism dysfunction and the activation of PI3K/Akt/mTOR signaling pathways in the hippocampus. The comprehensive strategy also provided a reasonable way for unveiling the pharmacodynamic mechanisms of multi-components, multi-targets, and multi-pathways in TCM with antidepressant effect.
1Introduction
Depression, characterized by behavioral despair or anhedonia, is one of the most common psychiatric disorders. It affects an estimated 3.8% of the population globally (https://www.who.int). Current treatments for depression mainly focus on the monoaminergic system, with selective serotonin reuptake inhibitors (SSRIs) being the most clinically used antidepressant [1]. Unfortunately, however, researches have shown that one-third of patients do not respond to initial treatment and frequently experience adverse effects such as nausea, tremors, and insomnia throughout long-term dosing [2]. Thus, it is essential to do additional study and produce antidepressant medications.
Traditional Chinese medicine (TCM), in various forms, has been used for thousands of years to treat depression. Xiaoyaosan is a traditional Chinese remedy that dates to the Song Dynasty in China. It is mentioned in the book Prescriptions of the Bureau of Taiping People's Welfare Pharmacy. Its benefits include nourishing blood, strengthening the spleen, and distributing sluggish liver qi to cure qi stasis [3]. The remarkable antidepression effects of Xiaoyaosan against various conditions have been demonstrated extensively [4,5]. Xiaoyaosan exerts its antidepressant effect primarily by inhibiting neuroinflammation, promoting neuroplasticity and synaptic plasticity, and regulating neuroendocrine [6] and amino acid metabolism [7]. Additionally, Xiaoyaosan has been utilized extensively in those who suffer from severe depression, and during which it was shown to possess distinct, its antidepressant effects were clearly demonstrated [4].
The combination of Chaidangbo (CDB), a streamlined formula that exerts antidepressant effects, was created by our research group (Patent No.: ZL 2014 1 0315781.3) based on the preliminary exploration of the disassembling prescription and uniform design of Xiaoyaosan [8], CDB, which disperses stagnated liver qi and nourishes blood, consists of three crude herbs, namely Radix Angelicae Sinensis (the radix of Angelica sinensis (Oliv.) Diels), Radix Bupleuri (the radix of Bupleurum chinense DC.), and Herba Menthae Haplocalycis (the herba of Mentha haplocalyx Briq.). In TCM prescriptions, Radix Bupleuri is frequently used as a therapeutic herbal medicine to alleviate depression, and it improves symptoms of depression by increasing the serum levels of NGF and BDNF in patients [9]. One study found that the low polarity fraction of Bupleuri Radix significantly alleviated depression-like behaviors in the CUMS model rats by improving metabolic profiles and gut microbiota has been reported recently [10]. Radix Angelicae Sinensis is a major component of many classical antidepressant TCM prescriptions [11]. Water extracts from Radix Angelicae Sinensis have been shown to considerably decrease the duration of immobility in mice in a behavioral despair depression model by improving the contents of monoamine neurotransmitters and neurotrophic factors in the hippocampus [12]. In addition, the active ingredient of Herba Menthae Haplocalycis has demonstrated antidepressant-like activity in CUMS-induced depressed mice [13]. More importantly, multiple investigations have established that the aqueous decoction of CDB drastically extenuates depression-like behaviors in the CUMS animal model by upregulating BDNF/TrkB/CREB pathway activity and downregulating HPA axis hyperfunction, with antidepressant effects comparable to those of Xiaoyaosan [14,15].
Pharmacological investigations that use certain extractive fractions extracted from TCM prescriptions as study objectives can efficiently produce novel TCM medications by quickly identifying the primary component groups of base material and adhering to the holistic perspective of TCM philosophy [16]. In our previous research, the antidepressant effect of petroleum ether fraction of CDB (PEFC) has been demonstrated in a mouse model of behavioral despair. However, this antidepressant effect and possible mechanisms are not well defined and must still be evaluated in other models of depression.
The development of memories, storing processes, and emotion regulation are all greatly influenced by the hippocampus, which is also intimately linked to the beginning of depression. Its large concentrations of glucocorticoid receptors and other neurotransmitters are accompanied by its dominating regulation of the HPA axis and impact on neurogenesis, and these characteristics making it more susceptible to stress [17]. Non-targeted metabolomics is a powerful emerging technology for analyzing changes in various metabolites in organisms to offer a complete description of the current metabolic state, providing new perspectives and understanding multifactorial mechanisms of diseases for assessing drug effects from an integrated and holistic perspective [18]. Therefore, for depression, hippocampal metabolomics can yield more critical complementary information in the central nervous system in depression [19]. Non-targeted metabolomics has emerged as a crucial method for figuring out TCM formulations' antidepressant effects in recent years [20]. It is beneficial because it may combine data from the outcomes of interactions between protein function, gene expression, and the cellular environment [21].
With the development of bioinformatics, network pharmacology has gradually become a hot research area. It is based on the principle of systems biology to construct biological networks and elucidate the potential mechanisms of drug therapy for complex diseases [22]. Given that multi-drugs, multi-components, and multi-targets characterize TCM formulas, network pharmacology has enabled us to effectively link Chinese medicine with its components, targets, pathways, and diseases [23]. The application of network pharmacology provides objective evidence for the intervention of herbal formulas in depression and, more importantly, provides new insight into the development of novel drugs against depression [16,24]. Moreover, an integrated approach that combines network pharmacology and metabolomics is a useful tool for explaining how formulae act as antidepressants. This can result in more comprehensive and accurate predictions of the antidepressant effects of TCM formulas [16,25].
In this study, we first identify the chemical composition of PEFC using UPLC-QE/MS, then apply network pharmacology analysis using bioinformatics tools and systems biology to predict further the genes, proteins, and signaling pathways associated with the antidepressant activity of PEFC [26]. Next, CUMS mouse model was established to assess the antidepressant effects of PEFC, and utilize an LC-MS-based hippocampal untargeted metabolomics approach was applied to validate and expand the potential antidepressant mechanism of action of PEFC. Finally, the partially predicted PEFC antidepressant pathway was validated by the Western blot method. This study evaluates the antidepressant effects of PEFC in a classical CUMS model and reveals possible antidepressant mechanisms that will lay the foundation for developing PEFC as an alternative strategy or drug for the control and treatment of depression.
2Materials and methods
2.1Reagents and materials
CDB consisted of three Chinese herbs: the radix of Bupleurum chinense DC. Radix Bupleuri (Chaihu, batch number: 20180329), the radix of Angelicae sinensis (Oliv) Diels. (Radix Angelicae Sinensis, Danggui, batch number: 20190618) and the herba of Menthae haplocalyx Briq. (Herba Menthae Haplocalycis, Bohe, batch number: 181001), all purchased from Tai Chi Pharmacy. (Chengdu, China) and authenticated by Prof. Fei Long, Department of Herbal Resources, Chengdu University of TCM. All plant names were verified on http://www.theplantlist.org. Fluoxetine hydrochloride was acquired from PATHEON FRANCE Co., Ltd (Suzhou, China). Petroleum ether was acquired from Kelong (Chengdu).
2.2The Preparation of PEFC
Radix Bupleuri, Radix Angelicae Sinensis, and Herba Menthae Haplocalycis were mixed in the ratio of 14.4: 14.4: 4.1 as mentioned previously (w/w, with a total weight of 329 g) [15]. The raw herbs were properly crushed together, soaked in 8-fold anhydrous ethanol (v/w) for 12 h, and then subjected to two 2-h extractions, with the resultant decoction concentrated at a lower temperature (45 °C) and pressure (reduced) in a rotary evaporator (Yarong, Zhengzhou, China) to get the crude alcohol extract containing the basic medication.
The alcohol extract was subsequently dispersed in pure water (330 mL) so that the final concentration of the aqueous solution of the alcohol extract was 1 g/mL. The petroleum ether fraction was then obtained by mining the alcohol extract with petroleum ether using a separatory funnel. We first dried the petroleum ether fraction by letting the solvent evaporate in a rotary evaporator before lyophilizing it using a freeze dryer (Songyuan, Beijing, China). Before being employed for analysis, the produced petroleum ether (1.86%) fractions were kept at −20 °C.
2.3PEFC ingredients identification with UPLC-QE/MS
In the subsequent inquiry, the following chromatographic conditions were used for UPLC-QE/MS analysis of the produced PEFC components: Thermo Scientific AccucoreTM C18 column (2.1 mm × 100 mm, 2.2 μm), mobile phase water + 0.1% formic acid (aqueous phase, AP), acetonitrile (organic phase, OP), gradient elution (0–5 min, 10% ∼ 40% OP; 5–30 min, 40%–55% OP; 30–38 min, 55%–60% OP; 38–50 min, 60%–90% OP; 50–55 min, 90% OP), a column temperature of 30 °C, an injection volume of 5.0 μL, and a flow rate of 0.3 mL/min. The following characteristics were chosen: spray voltage of 3000 V, 350 °C for the ion source, 35 arb for the sheath gas flow rate, 10 arb for the auxiliary gas flow rate, and 320 °C for the ion transfer tube temperature. Mass spectrometric conditions were also used. With a scan range of m/z 100 to 1500, an instantaneous resolution of 70 000, and a secondary resolution of 17 500, a complete scan/data-dependent secondary scan was employed as the scanning mode.
In order to coordinate with the possible molecular formulae, the resulting molecular ion chromatogram peaks and isotope peaks were extracted, and the measured secondary fragment spectra matched with the mzCloud online database. The collected raw data were imported into Compound Discoverer 3.0 (Thermo Scientific, USA) for peak alignment and peak extraction. In order to identify compounds analytically, ions with a matching degree score greater than 80 were chosen and further aligned with the compound information in the relevant literature and databases. Fig. S1 in the Supplementary Material displays the UPLC-QE/MS total ion flow chromatogram of PEFC. Ligustilide (the most abundant compound in PEFC) was subsequently scrutinized quantitatively with HPLC for quality control details in Supplementary Material, Fig. S2.
2.4Network pharmacology analysis
2.4.2Disease-associated target collection
The currently known potential therapeutic targets for depression were gathered from the OMIM database (http://www.omim.org), TTD database (http://db.idrblab.net/ttd/), PharmGKB database (https://www.pharmgkb.org/), NCBI database (https://www.ncbi.nlm.nih.gov/) and GeneCards database (http://www.genecards.org/) using the keywords “depression” and “antidepression”. All targets enrolled in this research were human genes/proteins.
2.4.3Network construction and topological analysis
Targets that overlapped between depression and active substances were found and added to the STRING database (https://www.string-db.org/); the species was set as “Homo sapiens”, and the interaction results were saved in TSV format (.tsv). The CytoNCA Tool in Cytoscape was utilized to do further analysis on the nodes' degree, closeness centrality, and betweenness centrality, which signify their topological significance in the network [29]. Nodes with a degree, closeness centrality, and betweenness centrality above averages were defined as core targets. The core targets were introduced into Cytoscape 3.6.0 to construct and visualize protein-protein interaction (PPI) and “component-core target” composite networks.
2.4.4GO and KEGG enrichment analyses
Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to examine biological processes, cellular components, molecular functions, and signaling pathways associated with depression and modulated by PEFC [25]. The core targets of PEFC antidepressant effects were imported into the DAVID database (https://david.ncifcrf.gov/), and the identification and species selected for GO and KEGG enrichment analysis were “OFFICIAL_GENE_SYMBOL” and “Homo sapiens”, respectively [30]. GO terms and KEGG pathways with a p-value<0.05 were considered statistically significant. The most substantial entries were chosen for visualization using the R software, and histograms for GO analyses and bubble plots for KEGG analysis were drawn.
2.5Animal experiment
2.5.1Animals
Male C57BL/6J mice aged five weeks were purchased from SPF Biotechnology Co., Ltd. in Beijing, China; qualify code: SCXK-[Jing]-2019-0010) were grown in typical cages in a room with a 12:12 light/dark cycle, consistent humidity and temperature (50–75%; 22 ± 2 °C). The Chengdu University of Traditional Chinese Medicine College of Pharmacy's Committee for Animal Care and Use of Laboratory Animals approved the experimental procedures; TCM-2019-312 was the approval number for this process.
2.5.2CUMS model protocol and treatment
The CUMS procedure is the standard paradigm in depression research because it replicates people's ongoing annoyance in reaction to uncontrollably negative social experiences [31]. The CUMS model was performed as in our previous report [3]. In brief, C57BL/6J mice in the CUMS groups were subjected to a variety of stressors following a week of adaption, including wet pad (12 h), physical restraint (3 h), food deprivation (12 h), water deprivation (12 h), swimming in ice water (4 °C, 4 min), tail clip (3 min), noise (1 h), absence of bedding (12 h), crowding (12 h), thermal stimulation (45 °C, 5 min), vinegar stimulation (3 h), overnight illumination (12 h), a 45°cage tilt (12 h), and cage shaking (10 min) for consecutive eight weeks. Every day, two distinct stressors were administered, and every seven days, the order of stressors was switched. C57BL/6J mice that were not under stress were housed in unaltered cages. The schematic of the experimental procedure is shown in Fig. 1.
The sucrose preference test (SPT) was used to determine if the CUMS model had been successfully created after four weeks of the CUMS method. Mice displaying anhedonia behavior, which is a primary CUMS symptom, were divided into 5 groups at random (n = 10 per group): groups CUMS and CUMS + FLX (fluoxetine, 13.2 mg kg−1, i.g.) [3] group, CUMS + PEFC group (0.205 g kg−1, 0.409 g kg−1, 0.818 g kg−1, i.g., corresponding to herbs 11.0 g kg−1, 22.0 g kg−1 and 44.0 g kg−1) group. The behavioral despair depression model experiment findings were used to determine the PEFC dosages for CUMS therapy (Supplementary Material, Fig. S4). PEFC or FLX was administered intragastrically, once daily, from 10:00 a.m. to 11:30 a.m. from weeks 5–8, with chronic treatment modalities and CUMS stimulation continuing throughout the treatment period. 0.5% carboxymethyl cellulose Na (CMC Na) and 0.5% TWEEN 80 solution were used to dissolve PEFC and FLX, whereas an equivalent volume of solvent was provided to the control group.
Mice were given sodium pentobarbital anesthesia 12 h after behavioral testing, and blood was drawn from their retro-orbital plexus. The blood was then centrifuged at a low speed (3000 r/min for 15 min) in order to separate the serum. After dissecting hippocampal tissues on ice, samples were quickly frozen in liquid nitrogen and kept as a backup at −80 °C.
2.5.3Behavioral tests
Testing on behavior began at the conclusion of the eighth week. To minimize interference between behavioral tests, we conducted only one behavioral test each day in the following sequence: sucrose preference test (SPT), splash test (ST), novelty-suppressed feeding test (NSFT), tail suspension test (TST) and forced swimming test (FST). To avoid evaluating the full group at once, all of the tests were carried out in parallel on all of the mice [32]. In addition, all behavioral tests were conducted in low light and noise levels with blind guidance on drug administration.
Sucrose preference test (SPT): The SPT was utilized to evaluate the level of anhedonia in mice, which is a fundamental sign of depression. To ensure that the experiment was not impacted by the eating habits of the animals, two water bottles were positioned in each cage. Prior to the experiment, every mouse underwent a 24-h water fast. On the day of the test, the home cage was left with 3 h of drinking water and a 1% sucrose solution. And sucrose preference was calculated using the following equation: sucrose preference (%) = sucrose intake/(sucrose intake + water intake) × 100% [33].
Splash test (ST): The Self-Test measures self-motivation and self-care, and its results are assumed to mirror some depressive symptoms, such apathy. Standard mouse cages without any bedding were used for the ST procedures. To encourage grooming activity, we sprayed mice's dorsal ends with a 2 mL 10% sucrose solution in their home cages. We then timed how long the mice spent grooming over the course of 5 min [34].
Novelty-suppressed feeding test (NFST): The NSFT evaluates a mouse's reaction to incentives and perceptions of its environment. The mice were placed in an open field (40 × 40 × 20 cm) with a modest amount of food placed at the center after being fasted for 36 h and allowed to acclimate to the experimental setting for an additional hour. The feeding latency was measured by timing how long it took the mice to bite into the meal; they had up to 5 min [35].
Tail suspension test (TST): The animal's behavioral desperation and lack of escape-related activity are represented by the immobility time in the TST. For a duration of 6 min, mice were hung with a tape approximately 1.5 cm from the tail tap and more than 35 cm above the floor. During this time, two observers recorded each mouse's immobility time for analysis during the last 4 min of the test [36].
Forced swimming test (FST): Another crucial test for observing animals' despairing behavior is the FST [37]. Each mouse in FST was put in a plastic bucket that was 12 cm in diameter and 25 cm deep in water (22–24 °C). The FST lasted 6 min, with the final 4 min of the test being spent immobile for two observers to analyze the data.
2.5.4Enzyme-linked immunosorbent assay
According to the manufacturer's instructions, the serum levels of Cort were measured using an ELISA kit (Lot No. E-EL-0161c, Elabscience Technology Co. Ltd., Wuhan, China). In short, a 96-well plate pre-coated with target antibody received 50 μL of frozen standards and serum samples, and then 100 μL of horseradish peroxidase-conjugated antibody was added. Following a 60-min incubation period at 37 °C, the liquid was disposed of. Following that, 350 μL of washing buffer was used to wash each well five times. Each well was then filled with 50 μL of substrate A and B, and the wells were left to incubate for 15 min in the dark at 37 °C. Following the addition of 50 μL of termination solution to each well, a microplate reader was used to measure the absorbance at 450 nm. The standard curve was used to determine the serum Cort levels.
2.5.6Western blot analysis
To assess the total protein content, 1 mM phosphatase inhibitor cocktail and 1 mM PMSF were added to each sample in a RIPA lysate buffer. Using the BCA test technique, the total protein concentration of each sample was measured, and each sample's concentration was corrected to match. Samples of protein were combined with a loading buffer of 5 × , denatured at 95 °C, separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and then electrophoretically transferred onto membranes made of polyvinylidene fluoride. These membranes were probed with rabbit anti-p-PI3 Kinase (1:1000; CST®; #17366S), rabbit anti-PI3 Kinase (1:1000; CST®; Cat. No. #4249T), rabbit anti-p-Akt (1:1000; CST®; #4060S), rabbit anti-Akt (1:1000; CST®; #4691S), rabbit anti-p-mTOR (1:1000; CST®; #2983S), rabbit anti-mTOR (1:1000; CST®; #5536S), rabbit anti-GR (1:1000; CST®; #12041S) and rabbit anti-β actin (1:1000; Servicebio®; #GB113225) antibodies overnight at 4 °C, anti-rabbit IgG HRP-conjugated antibody (1:3000; Servicebio®; #GB23303) was added the next day at indoor temperature for 80 min. A Chemi-Doc XRS + image analysis system (BioRad, USA) was used to measure total protein.
2.6Statistical analysis
LC-MS raw data were processed using the Proteowizard software (v3.0.8789). A mass-to-nucleus ratio (m/z) data mapping was performed, and retention time and peak area (intensity) information were obtained. The data were exported to Microsoft Excel for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) using the online tool Biodeep cloud platform (http://www.biodeep.cn/home). The online tool was also used to help identify metabolite differences and construct metabolic pathways. The means ± standard deviations were used to represent all data, including those from the biochemical analysis and behavioral testing. Using SPSS 21 (IBM, Armonk, NY, USA), the one-way analysis of variance was utilized for all analyses (for pairwise comparisons between groups, SNK was utilized). Differences were considered statistically significant at P < 0.05.
3Results
3.1Compound identification of PEFC and active component screening
Peak area normalization was utilized to ascertain the respective component's percentage. The exact chemical makeup of the discovered PEFC is displayed in Table 1. Seventy-two compounds were found in all, making up 93.47% of the area of all the peaks in PEFC that were discovered. Among these compounds, Ligustilide (C42), 3-n-butylphthalide (C38), Levistilide A (C59) were originated from Angelica sinensis with 62.640%, 10.013%, 9.377% in PEFC, respectively; Carvone (C23) from Herba Menthae and Saikosaponin A (C25) from Radix Bupleuri with 0.803% and 0.593% in PEFC, respectively. The five compounds mentioned above make up the main component, accounting for nearly 80% of all peak areas. Next, we extracted compounds with relative content greater than 0.1% as the main components of PEFC. Sixteen active compounds were screened in the SwissADME database according to specific parameters, including GI absorption, BBB penetration, and drug-likeness (Table 2.).No. tR/min Component name Relative content (%) Mw ppm PubChem CID Molecular formula Mode MS/MS C1 0.757 dl-Arginine 0.1900 174.2 −0.71 232 C6H14N4O2 Pos(+) 175.12[M+H]+ C2 0.791 Choline 0.0750 104.17 1.6 305 C5H14NO+ Pos(+) 104.11[M]+ C3 0.82 D-(+)-Maltose 0.0410 342.3 −0.99 439186 C12H22O11 Neg(−) 341.11[M − H]-; 313.11[M-H-CO]- C4 0.827 α,α-Trehalose 0.3960 342.12 −0.23 3231 C12H22O11 Neg(−) 341.11[M − H]-; 313.11[M-H-CO]- C5 0.828 α-Lactose 0.0250 359.14 1.91 242081360 C12H22O11 Pos(+) 360.15[M+H]+; 381.18[M + Na–H]+ C6 0.834 D-(+)-Arabitol 0.0190 152.15 −3.9 94154 C5H12O5 Neg(−) 151.06[M − H]-; 134.86[M − H2O]- C7 0.855 4-Guanidinobutyric acid 0.0130 145.16 0.13 500 C5H11N3O2 Neg(−) 144.99[M − H]-; 127.41[M − H2O]- C8 4.181 Vanillin 0.0470 152.15 0.03 1183 C8H8O3 Neg(−) 151.04[M − H]-; 136.02[M − NH2]- C9 4.383 Ferulic acid 0.1420 194.18 −0.01 445858 C10H10O4 Pos(+) 195.07[M+H]+; 177.05[M + H–H2O]+; 154.99 [M + H–Na]+ C10 5.555 Geranic acid 0.0340 168.12 0.53 5275520 C10H16O2 Pos(+) 169.98[M+H]+; 196.02[M + CO]+ C11 6.078 Quercetin 0.0170 302.23 0.72 5280343 C15H10O7 Pos(+) 303.05[M+H]+; 327.01[M+2H+2Na]+ C12 6.139 10-Hydroxy-2-decenoic acid 0.0290 186.25 1.35 5312738 C10H18O3 Neg(−) 187.86[M+H]-; 168.89[M − H2O]- C13 6.478 Ethyl caffeate 0.0100 208.21 −0.82 5317238 C11H12O4 Neg(−) 207.07[M − H]-; 189.90[M-NA + H]- C14 6.948 Saikosaponin C 0.0440 972.53 0.86 131801344 C48H78O18 Neg(−) 971.52[M − H]-; 649.37[M-Glc-Fuc+3H]- C15 7.081 Diosmetin 0.0230 300.26 0.32 5281612 C16H12O6 Neg(−) 299.06[M − H]-; C16 7.24 3-Methoxy-5,7,3′,4′-tetrahydroxy-flavone 0.0300 316.06 0.28 ND C16H12O7 Pos(+) 317.07[M+H]+; 348.27[M+2O]+ C17 7.435 (15Z)-9,12,13-Trihydroxy-15-octadecenoic acid 0.0390 330.5 0.06 24066906 C18H34O5 Neg(−) 329.23[M − H]-; 329.23[M-H + CO]- C18 7.557 Methoxsalen 0.0300 216.19 0.54 4114 C12H8O4 Pos(+) 217.05[M+H]+; 279.16[M+2Na + H2O]+ C19 7.982 Oleanonic acid 0.0250 454.7 −1.01 12313704 C30H46O3 Pos(+) 455.35[M+H]+; 484.31[M+2H + CO]+ C20 8.07 6-Pentyl-2H-pyran-2-one 0.0480 166.22 −0.1 33960 C10H14O2 Pos(+) 167.11[M+H]+; 182.99[M + NH2]+ C21 8.333 Ethyl ferulate 0.0040 222.24 0.32 736681 C12H14O4 Pos(+) 221.08[M − H]-; 195.81[M − CO]- C22 8.592 Isophorone 0.0620 138.21 0.49 6544 C9H14O Pos(+) 139.11[M+H]+ C23 8.761 Carvone 0.8030 150.22 0.67 7439 C10H14O Pos(+) 151.11[M+H]+; 169.98[M + H + H2O]+ C24 9.08 Scoparone 0.3850 206.19 −0.23 8417 C11H10O4 Pos(+) 207.07[M+H]+ C25 9.121 Saikosaponin A 0.5930 781 0.01 167928 C42H68O13 Pos(+) 781.41[M+H]+; 763.46[M + H + H2O]+; 455.35[M-Glc-Fuc]+ C26 9.14 4-hydroxy-3-(3-methylbut-2-enyl)benzoic acid 0.0500 206.24 −1.13 443852 C12H14O3 Pos(+) 207.07[M+H]+; 274.27[M + C5H6]+ C27 10.585 β-Elemonic acid 0.0330 454.34 −0.97 348290102 C30H46O3 Pos(+) 455.35[M+H]+; 437.34[M + H–H2O]+; 459.72[M+Na]+ C28 10.917 Skullcapflavone II 0.0710 374.3 0.13 124211 C19H18O8 Pos(+) 375.11[M+H]+; C29 11.007 Wogonin 0.0190 284.26 0.02 5281703 C16H12O5 Pos(+) 285.08[M+H]+; 331.04[M+3H + CO2]+ C30 11.012 Methyl cinnamate 0.1990 162.18 −0.26 637520 C10H10O2 Pos(+) 163.08[M+H]+; 131.05[M + CO–H2O]+ C31 11.344 Jasmone 0.0120 164.24 0.42 1549018 C11H16O Pos(+) 165.13[M+H]+; 182.99[M + H + H2O]+; 189.09[M + H + Na]+ C32 11.871 Kurarinone 0.0080 438.5 0.42 11982640 C26H30O6 Pos(+) 439.36[M+H]+; 421.35[M + H–H2O]+ C33 12.073 9S,13R-12-Oxophytodienoic acid 0.0200 292.4 −0.14 14037063 C18H28O3 Pos(+) 393.21[M+H]+; 316.28[M + H + Na]+ C34 12.766 Senkyunolide A 0.8640 192.25 0.71 3085257 C12H16O2 Pos(+) 193.12 [M+H]+ C35 13.082 α-Pinene-2-oxide 0.2690 152.12 −0.39 387130156 C10H16O Pos(+) 154.99 [M+2H]+; 169.98[M + H2O]+ C36 15.187 2-Aminooctadec-4-yne-1,3-diol 0.0120 297.5 0.12 2802958 C18H35NO2 Pos(+) 298.27[M+H]+; 316.28[M + H + H2O]+; 327.01[M+2H + CO]+ C37 15.447 2-Amino-1,3,4-octadecanetriol 0.0400 317.5 −0.46 248575 C18H39NO3 Pos(+) 318.30[M+H]+; 389.12[M + C4H10N]+ C38 15.562 3-n-Butylphathlide 10.0130 190.24 0.2 61361 C12H14O2 Pos(+) 191.11[M+H]+; 173.10[M + H–H2O]+ C39 16.448 4-methyl-6-phenyl-5,6-dihydro-2H-pyran-2-one 0.3720 188.22 0.15 2817525 C12H12O2 Pos(+) 189.09[M+H]+ C40 16.673 α-Linolenic acid 0.0190 278.22 −0.6 184021992 C18H30O2 Pos(+) 279.23[M+H]+; 280.24[M+2H]+ C41 16.753 Sedanolide 0.0560 194.27 0.52 5018391 C12H18O2 Pos(+) 195.14[M+H]+; 415.21[2 M + Na]+ C42 17.399 Ligustilide 62.6400 190.24 0.2 5319022 C12H14O2 Pos(+) 191.11[M+H]+; 173.10[M + H–H2O]+ C43 17.502 Isoeugenol acetate 0.0610 206.24 1.63 876160 C12H14O3 Neg(−) 205.55[M − H]-; 197.81[M − H2O]-; C44 17.967 (±)9-HpODE 0.0410 312.4 0.85 6439847 C18H32O4 Neg(−) 311.22[M − H]-; 245.89[M − C5H7]- C45 18.071 3-Butylidenephthalide 0.5020 188.22 −0.71 642376 C12H12O2 Pos(+) 191.11 [M+3H]+; 207.10[M + H2O]+ C46 18.558 Bis(4-ethylbenzylidene)sorbitol 0.0300 414.2 −0.46 387149854 C24H30O6 Pos(+) 415.21[M+H]+; 455.35[M + Na + H2O]+ C47 18.612 Dehydrotrametenolic acid 0.0190 454.7 −0.88 15391340 C30H46O3 Pos(+) 455.35[M+H]+; 519.20[M + C5H6]+ C48 19.519 Gardenin B 0.2370 358.11 −0.28 96539 C19H18O7 Pos(+) 359.11[M+H]+ C49 20.955 9-(acetyloxy)-8,8-dimethyl-2-oxo-9,10-dihydro-2H,8H-pyrano [2,3-f]chromen-10-yl 2-methyl-2-butenoate 0.5420 386.4 −0.51 5316002 C21H22O7 Pos(+) 287.09[M-C5H8O2+H]; 404.17[M + H2O]+ C50 21.447 Methyl palmitate 0.0180 270.5 0.23 8181 C17H34O2 Pos(+) 271.26[M+H]+; 287.09[M + H2O–H]+ C51 21.683 10-(acetyloxy)-8,8-dimethyl-2-oxo-2H,8H,9H,10H-pyrano[2,3-h]chromen-9-yl (2Z)-2-methylbut-2-enoate 0.0430 403.16 0.21 9821539 C21H22O7 Pos(+) 404.17[M+H]+; 432.2[M + H + CO]+ C52 23.155 Senkyunolide H 0.1830 224.25 −0.83 13965088 C12H16O4 Pos(+) 207.10[M+H]+; 230.14[M + H + Na]+; 189.09[M-H2O]+ C53 23.559 2-Methoxy-9H-xanthen-9-one 0.0130 226.23 −0.22 71034 C14H10O3 Pos(+) 227.07[M+H]+; 245.08[M + H + H2O]+ C54 24.956 9-(Acetyloxy)-8,8-dimethyl-2-oxo-9,10-dihydro-2H,8H-pyrano [2,3-f]chromen-10-yl 2-methyl-2-butenoate 0.1130 386.4 1.56 5316002 C21H22O7 Pos(+) 404.17[M + NH4]+ C55 25.276 12-Oxo phytodienoic acid 0.0260 292.4 0.24 188366 C18H28O3 Pos(+) 293.21[M+H]+; 309.21[M + NH3+]+; 315.19[M+Na]+ C56 27.087 3-Methoxycinnamic acid 0.0200 178.18 −1.39 637668 C10H10O3 Pos(+) 179.19[M+H]+ C57 31.094 9-Oxo-10,12-octadecadienoic acid 0.6390 294.41 −0.25 5283011 C18H30O3 Neg(−) 293.21[M − H]-; 245.89[M-4H–CO2]- C58 32.205 (9cis)-Retinal 0.0440 284.21 −0.47 6436082 C20H28O Pos(+) 285.22[M+H]+; C59 35.279 Levistilide A 9.3770 380.5 −0.65 70698035 C24H28O4 Pos(+) 381.21 [M+H]+; 398.23[M + H2O]+; 403.19[M+Na]+ C60 42.038 Methyl alpha-eleostearate 0.0160 292.24 −0.03 21718552 C19H32O2 Pos(+) 293.25[M+H]+; 338.34[M+2H + CO2]+ C61 44.365 1-Linoleoyl glycerol 0.0790 354.5 −0.23 117319 C21H38O4 Neg(−) 279.16[M-C3H7O2]-; 261.29[M–C3H7O2–H2O]- C62 44.674 16-Hydroxyhexadecanoic acid 0.7740 272.42 0.47 10466 C16H32O3 Neg(−) 271.23[M − H]-; 243.90[M–CO–H]- C63 44.701 α-Eleostearic acid 0.5530 278.22 0.08 7850014 C18H30O2 Pos(+) 279.23[M+H]+; 338.34[M+3Na]+ C64 45.003 Oleanolic acid 0.5600 456.7 0.71 10494 C30H48O3 Neg(−) 455.35[M − H]-; 271.23[M-C15H28+Na]- C65 45.182 γ-Linolenic acid ethyl ester 0.0150 306.26 −0.34 445551954 C20H34O2 Pos(+) 307.26[M+H]+; 325.22[M + H + H2O]+ C66 47.037 Oleamide 0.5550 281.5 −0.65 5283387 C18H35NO Pos(+) 282.28[M+H]+; 327.23[M+2Na]+ C67 48.02 Dibutyl sebacate 0.0100 314.5 0.11 7986 C18H34O4 Pos(+) 315.25[M+H]+; 339.16[M+2H + Na]+ C68 48.293 Lupenone 0.0190 424.7 −0.36 92158 C30H48O Pos(+) 425.38[M+H]+; 482.46[M + H + C4H8]+ C69 48.551 Isoreserpin 0.0770 608.7 −0.58 5052 C33H40N2O9 Pos(+) 609.27[M+H]+; 634.45[M+3H + Na]+ C70 50.618 Palmitic acid 0.0170 256.24 0.28 985 C16H32O2 Neg(−) 255.23[M − H]-; 208.07[M-2H–2Na]- C71 51.095 Elaidic acid 0.0270 282.5 0.31 637517 C18H34O2 Pos(+) 283.26[M+H]+; 326.34[M + CO2]+ C72 51.787 Stearamide 1.0140 283.5 −0.85 31292 C18H37NO Pos(+) 284.29[M+H]+; 312.33[M + CO + H]+ C73 52.314 1-Stearoylglycerol 0.0190 358.31 −0.71 22035687 C21H42O4 Pos(+) 437.19[M + H + C6H6]+ No. Component Relative content (%) Formula C42 Ligustilide 62.64 C12H14O2 C38 3-n-Butylphathlide 10.013 C12H14O2 C59 Levistilide A 9.377 C24H28O4 C34 Senkyunolide A 0.864 C12H16O2 C23 Carvone 0.803 C10H14O C25 Saikosaponin A 0.593 C42H68O13 C64 Oleanolic acid 0.56 C30H48O3 C49 9-(acetyloxy)-8,8-dimethyl-2-oxo-9,10-dihydro-2H,8H-pyrano [2,3-f]chromen-10-yl 2-methyl-2-butenoate 0.542 C21H22O7 C45 3-Butylidenephthalide 0.502 C12H12O2 C24 Scoparone 0.385 C11H10O4 C39 4-methyl-6-phenyl-5,6-dihydro-2H-pyran-2-one 0.372 C12H12O2 C35 α-Pinene-2-oxide 0.269 C10H16O C48 Gardenin B 0.237 C19H18O7 C30 Methyl cinnamate 0.199 C10H10O2 C1 dl-Arginine 0.19 C6H14N4O2 C9 Ferulic acid 0.142 C10H10O4
3.2Target and network analysis of PEFC for depression
The SMILES of the 16 abovementioned active compounds were plotted in the SwissTargetPrediction database to each compound's potential targets, establishing 372 compound-related targets after removing duplicates (Supplementary Material, Table S1). Additionally, five databases (PharmGKB, GeneCards, OMIM, NCBI, and TTD) produced 2314 targets for depression after screening (Supplementary Material, Table S2). Analyses showed that the 372 compound-related targets matched the 2314 disease-related targets, yielding 152 overlapping targets (Fig. 2A; Supplementary Material, Table S3). Subsequently, the 152 overlapping targets were imported into STRING, and the TSV format files of the interaction results were analyzed using the CytoNCA tool in Cytoscape. 73 nodes with a degree, closeness centrality, and betweenness centrality above the median were screened as the core targets of PEFC antidepressants. The compound-target network of PEFC for depression was developed and presented using Cytoscape with the addition of 73 overlapped genes and 16 active compounds. 89 nodes and 147 edges made up this network (Fig. 2B). The core antidepressant targets of PEFC were introduced into STRING and visualized using Cytoscape PPI network maps for further analysis. In the PPI network diagram, darker the color and larger the area of the node, greater the “degree” and the higher relevance of its involvement in the antidepressant effect of PEFC. Genes such as STAT3, MAPK1/3, PIK3CA, ESR1, MAPK8/14, NR3C1, and JAK2 could be potential research targets for PEFC in treating depression (Fig. 2C).
3.3GO and KEGG enrichment analyses of the antidepressant effect of PEFC
GO enrichment analyses were performed on the 73 overlapping targets to predict the gene/protein functions and signaling pathways of PEFC involved in the treatment of depression further (Supplementary Material, Table S4). The GO biological process (BP), cellular component (CC), and molecular function (MF) top 20 items are ranked by p-value, as seen in Fig. 3A, which showed that these potential targets are mainly located in cellular components like receptor complexes, axons, glutamatergic synapses, and neuronal cell body; regulating molecular functions like protein binding, enzyme binding, ATP binding, and protein kinase binding; and affecting biological processes such as protein phosphorylation, kinase activity, cell migration, and MAPK/protein kinase B signaling cascade. Per KEGG analysis, the 73 overlapping genes in which PEFC exerts antidepressant effects were significantly enriched in multiple signaling pathways (P < 0.05, Supplementary Material, Table S5). The top 30 pathways were selected for bubble map visualization (Fig. 3B), where the PI3K-Akt pathway, endocrine resistance, pathways of neurodegeneration, and the MAPK signaling pathway exhibited a high degree of enrichment.
3.4Treatment with PEFC reversed CUMS-induced depression-like behaviors
After four weeks of CUMS stimuli, all CUMS mice had a substantial drop in their sucrose preference rate (P < 0.05; Fig. 4A), suggesting that the model mice had a malfunctioning brain reward mechanism, a central symptom of depression. According to these findings, the CUMS model was effectively replicated by week four, when medication delivery to the treatment groups started in addition to ongoing CUMS stimulation.
Treatment with PEFC (22.0 g kg−1, 44.0 g kg−1, P < 0.05) and FLX (P < 0.05) for 4 weeks markedly reversed the CUMS-induced reduction in the percentage of sucrose preference (Fig. 4B). Further behavioral testing was done to ascertain the antidepressant impact of PEFC, including the ST, NFST, TST, and FST. In the ST, combing time reduced considerably in the CUMS group animals compared to control animals (P < 0.05), reflecting the poorer self-care and apathetic behavior exhibited in depression. Mice given PEFC (11.0 g kg−1, 22.0 g kg−1, 44.0 g kg−1, P < 0.05) or FLX (P < 0.05) for 4 weeks had substantially increased combing time compared to mice in the CUMS group (Fig. 4C). The NSFT revealed that mice in the CUMS group had a significantly longer feeding latency time than mice in the control groups (P < 0.05). This suggests that the CUMS process caused anxiety, which in turn led to a decreased desire for food in an unfamiliar setting. Treatment with PEFC (11.0 g kg−1, 22.0 g kg−1, 44.0 g kg−1, P < 0.05) or FLX (P < 0.05) for four weeks significantly decreased the model animals' eating latency (Fig. 4D).
Desperate behaviors in mice in the TST are measured by recording their immobility for a specific period. In the TST and FST, CUMS group remained immobile for significantly longer than those in the control group. (P < 0.05). In the TST, either one of PEFC (11.0 g kg−1, 22.0 g kg−1, 44.0 g kg−1, P < 0.05) or FLX (P < 0.05) pointedly reduced the immobility time of the model animals (Fig. 4E). However, in the FST, only high doses of PEFC (44.0 g kg−1, P < 0.05) glaringly downregulated FST immobility time (Fig. 4F). The lack of efficacy of FLX in FST is possibly associated with the reported adverse effects of the continuous administration of fluoxetine that cause cognitive and motor impairment manifestations [2]. These findings imply that PEFC can considerably reduce the depressive-like behaviors brought on by CUMS.
3.5PEFC attenuated CUMS-induced Cort-GR dysfunction in mice
Hyperactivation of the HPA axis is the principal neuroendocrine change in depression and is primarily reflected by a significant increase in serum Cort. Serum Cort levels were substantially elevated in CUMS mice, and GR levels were significantly reduced in the mouse hippocampus compared with the control (P < 0.01). However, the administration of PEFC resulted in a drastic decline in serum Cort levels in model mice in a dose-dependent manner (11.0 g kg−1, P < 0.05; 22.0 g kg−1, P < 0.01; 44.0 g kg−1, P < 0.01) (Fig. 5A) with a significant increase in hippocampal GR levels (22.0 g kg−1, P < 0.05; 44.0 g kg−1, P < 0.01); FLX had a similar modulatory effect (P < 0.01) (Fig. 5B and C).
3.6PEFC improves the metabolic profiles of CUMS mice
To understand the metabolic role of PEFC in the CUMS model and to more intuitively predict its potential targets for antidepressant action, this research examined and analyzed differential metabolites in mouse hippocampal sample. Unsupervised PCA was used to scrutinize the data quality of metabolomics analysis, with the PCA scores showing complete separation of the CUMS model group from the control group (Fig. 6A), indicating that CUMS caused a disruption in the metabolic profiles of the mouse hippocampus. There were also significant changes in the hippocampal metabolic profiles of mice after FLX or PEFC treatment (Fig. 6B). Supervised analysis techniques, orthogonal partial least squares discriminant analysis (OPLS-DA), were utilized to maximize differences and screen for differential metabolites that led to class separation. All groups in the OPLS-DA model satisfied the 95% Hotelling T-squared distribution and showed primary partition (R2X = 0.748; R2Y = 0.993; Q2 = 0.654) (Fig. 6C). The model demonstrated strong prediction, according to a 200-permutation cross-validation test used to evaluate statistical significance and predictive accuracy (Fig. 6D).
Metabolites meeting P < 0.05, FDR <0.05, and VIP >1.0 were retained as metabolites with massive differences on the online tool Biodeep Cloud Platform. The changes in metabolites induced by CUMS and FLX or PEFC treatments are shown in Table 3 and Fig. 7. According to these findings, the levels of a total of 44 metabolites were drastically changed in the CUMS group compared with the control group: four metabolites (including Vanillylmandelic acid, 3,4-Dihydroxyphenylpropanoate, and 2-Ketobutyric acid) were elevated, and 40 metabolites (including l-Proline, l-Asparagine, and l-Glutamine) were reduced. Remarkably, PEFC markedly restored the levels of metabolites including, l-Proline, l-Asparagine and l-Glutamine in CUMS mice. Furthermore, the metabolic pathways analysis was conducted to understand the underlying molecular functions of these hippocampal metabolite biomarkers. The potential biomarkers were found to be primarily involved in 61 disturbed metabolic pathways. Fig. 8 displays the metabolic pathway analysis of these metabolites that experienced considerable alteration. These results suggest that amino acid biosynthesis, lysine degradation, glycine, serine and threonine metabolism, catecholamine inhibition, as well as the mTOR signaling pathway contribute to antidepressant effects of PEFC in CUMS mice.No. Metabolites p.value -log10(p) FDR vip Trends in CUMS vs. control Trends in treatment vs. CUMS M1 Cycloheximide 0.0048 2.3144 0.0490 1.345 down up M2 Benzaldehyde 0.0067 2.1718 0.0490 1.756 down up M3 l-Proline 0.0067 2.1718 0.0490 1.876 down up M4 Phenylethylamine 0.0083 2.0785 0.0490 1.344 down up M5 l-Allothreonine 0.0084 2.074 0.0490 1.546 down up M6 l-Asparagine 0.0091 2.0385 0.0490 1.192 down up M7 Pipecolic acid 0.0091 2.0385 0.0490 1.657 down up M8 Spermidine 0.0091 2.0385 0.0490 1.818 down up M9 12-Hydroxydodecanoic acid 0.0097 2.012 0.0490 1.177 down up M10 8-Amino-7-oxononanoate 0.0101 1.9942 0.0490 1.766 down up M11 Azelaic acid 0.0101 1.9942 0.0490 1.761 down up M12 3,4-Dihydroxyphenylpropanoate 0.0106 1.9721 0.0490 1.365 up down M13 Guanidinosuccinic acid 0.0106 1.9721 0.0490 1.365 down up M14 Phenylacetic acid 0.0106 1.9721 0.0490 1.187 down up M15 6-Hydroxynicotinate 0.0121 1.9147 0.0490 1.715 down up M16 Pyridoxamine 0.0121 1.9147 0.0490 1.582 down up M17 Uracil 0.0121 1.9147 0.0490 1.684 down up M18 Oxoadipic acid 0.0125 1.9014 0.0490 1.068 down up M19 Polygodial 0.0132 1.8794 0.0490 1.36 down up M20 d-Ribose 0.0136 1.8661 0.0490 1.732 down up M21 Vanillylmandelic acid 0.0149 1.8265 0.0490 1.242 up down M22 5-Guanidino-3-methyl-2-oxopentanoate 0.0173 1.7605 0.0490 1.591 down up M23 Phenyl acetate 0.0173 1.7605 0.0490 1.461 down up M24 Erucic acid 0.0175 1.7561 0.0490 1.706 down up M25 2,3-Butanediol 0.0178 1.7474 0.0490 1.604 down up M26 Phosphorylcholine 0.0190 1.721 0.0490 1.261 down up M27 l-Methionine 0.0195 1.7079 0.0490 1.692 down up M28 Ascorbate 0.0197 1.7035 0.0490 1.575 down up M29 gamma-Aminobutyric acid 0.0197 1.7035 0.0490 1.21 down up M30 Piperidine 0.0227 1.6423 0.0490 1.282 down up M31 3,4-Dihydroxymandelic acid 0.0230 1.6379 0.0490 1.005 down up M32 Aminoadipic acid 0.0257 1.5899 0.0490 1.221 down up M33 Cytosine 0.0259 1.5855 0.0490 1.297 down up M34 Stearic acid 0.0259 1.5855 0.0490 1.068 up down M35 Inosine 0.0273 1.5637 0.0490 1.323 down up M36 l-Glutamine 0.0289 1.5376 0.0490 1.039 down up M37 21-Deoxycortisol 0.0307 1.5115 0.0490 1.209 down up M38 Niacinamide 0.0308 1.5115 0.0490 1.243 down up M39 l-Homoserine 0.0317 1.4985 0.0490 1.403 down up M40 1,2,3-Trihydroxybenzene 0.0321 1.4942 0.0490 1.227 down up M41 Taurine 0.034 1.4681 0.049495 1.37 down up M42 2-Ketobutyric acid 0.0391 1.4076 0.0536 1.337 up down M43 l-Rhamnofuranose 0.0454 1.3429 0.0405 1.11 down up M44 Maltol 0.0454 1.3429 0.0405 1.246 down up
3.7Integrated analysis of network pharmacology and metabolomics
Based on network pharmacology and metabolomics, an interaction network was established to provide an integrated view of the processes by which PEFC combats depression. MetScape provides a bioinformatics framework for the visualization and interpretation of metabolomic and expression profiling data in the context of metabolism. It allows users to build and analyze networks of genes and metabolites [38]. The 44 differential metabolites resulting from metabolomics analysis were imported into Metscape, and “compound-gene” was selected as the network type and then the network was constructed to obtain 158 target genes associated with the differential metabolites. 158 target genes obtained by differential metabolites using the Metscope tool intersect with the 73 differential genes obtained in network pharmacology, 17 of them overlapped in the Venn diagram analysis (Fig. 9A; Supplementary Material, Table S6). The String database combined with Cytoscape was adopted for protein-protein interaction (PPI) network analysis of the intersecting targets, as shown in Fig. 9B. Targets, including MTOR, MYC, and PIK3CA, exert essential impacts on the antidepressant effects of PEFC. Fig. 9C and D show the top 10 key terms in GO functional enrichment and the top 10 critical KEGG pathways, respectively. KEGG analysis suggested that the ErbB signaling pathway and PI3K/Akt signaling pathway exhibited a relatively high number of target connections.
3.8Validation of the PEFC antidepressant mechanism through the PI3K/Akt/mTOR signaling pathway
Metabolomics and KEGG analysis indicated that regulation of PI3K/Akt signaling pathway and mTOR are critical for PEFC to exert antidepressant effects. As shown in Fig. 9E, the panel displays the PI3K/Akt signaling pathway from the KEGG result. The red rectangle nodes stand for the most prominent genes associated with PEFC pharmacological actions. Western blot analysis (Fig. 10A) showed that p-PI3K/PI3K (P < 0.05), p-Akt/Akt (P < 0.01) and, p-mTOR/mTOR protein expression levels in the CUMS mouse model declined dramatically compared to the control group, and the phosphorylation levels of PI3K, AKT and mTOR were significantly higher after PEFC (44.0 g kg−1) treatment (Fig. 10B–D). These results demonstrated that the antidepressant effects of PEFC were associated with the activation of the PI3K/AKT/mTOR signaling cascade.
4Discussion
Because of the complex pathogenesis of depression and the unclear changes in molecular and cellular signaling pathways, the current classical antidepressant treatments rarely have satisfactory efficacy, can cause severe side effects, and brought a heavy burden to the medical system. As a result, TCM has received much attention in the prevention and treatment of depression due to its multi-component, multi-target, multi-pathway, and low toxicity advantages [39]. Among them, the antidepressant effect of Xiaoyaosan has been demonstrated extensively [4,5]. CDB, as a streamlined formulation of Xiaoyaosan, has been verified to exert antidepressant effects comparable to those of Xiaoyaosan in different animal models [14,15]. However, the material basis of its antidepressant effect and potential mechanisms have not been fully identified, which limited the development as a new antidepressant herbal formula. Here, we obtained PEFC and characterized it by using UPLC-QE/MS. According to the findings, 73 components in all were detected from PEFC, 16 active compounds of which were further obtained via SwissADME screening. Among, Ligustilide, 3-n-butylphthalide, Levistilide A (from Angelicae sinensis (Oliv) Diels.); Carvone (from Menthae haplocalyx Briq.); and Saikosaponin A (from Bupleurum scorzonerifolium Willd.) were the most prevalent active compounds in PEFC, accounting for more than 80% of all detected peaks. Reportedly, Ligustilide could effectively attenuate CUMS-induced depression-like behavior in rats, and the possible mechanism was associated with higher levels of progesterone and allopregnanolone in the prefrontal cortex and hippocampus [40]. 3-n-butylphthalide has been found to exhibit antidepressant effects by activating the BDNF/ERK/mTOR cascade in the cortex of CUMS rat model, thereby enhancing synaptic protein synthesis [41]. Carvone, one of the significant components of coriander volatile oil, allegedly showed antidepressant properties in a rat model of Alzheimer's disease with β-amyloid (1–42) [42]. Reports also verified that Saikosaponin A significantly improves depression-like behaviors in multiple depressed animal models, and its antidepressant mechanisms include the activation of the CREB/BDNF pathway in the hippocampal DG region to inhibit hippocampal neuronal apoptosis [43], the upregulation of proline-rich transmembrane protein 2 (PRRT2) expression to increase hippocampal dopamine content [44], and the inhibition of the HPA axis hyperactivation to reduce hippocampal neuroinflammation [45]. These findings suggested that these compounds are the material basis for the antidepressant effect of PEFC and explain its multi-targeted action characteristics.
Depression is directly linked to stress in both its onset and progression. The CUMS technique is one of the most widely used mouse chronic stress models in recent research because it replicates the ongoing annoyance that people experience in reaction to uncontrollably negative social experiences [3]. CUMS resulted in mice showing a series of typical depression-like features such as reducing sucrose preference, prolonging TST/FST immobility, extending feeding latency, and shortening grooming time in mice. According to our findings, CUMS-induced depression-like traits in mice were significantly alleviated after four weeks of treatment with PEFC. This suggests that PEFC possesses antidepressant qualities comparable to those of FLX. The over-activation of the HPA axis is a widely recognized marker for the diagnosis and therapy of depression [46]. An imbalance in the feedback regulation of the HPA axis results in elevated levels of cortisol peripherally and centrally. The glucocorticoid receptor (GR) is a crucial component of the negative feedback regulation of the HPA axis specifically for Cort [47]. In this study, CUMS mice exhibited a rise in serum Cort level and a significant reduction in GR expression in the hippocampus, showing functional abnormalities of CORT-GR. This status was reversed by PEFC treatment, which reflected that the antidepressant effects of PEFC are partially consistent with the predicted role of the NR3C1 gene and the action on the endocrine resistance mechanism predicted in the network pharmacology analysis.
Network pharmacology analysis revealing that STAT3, MAPK1/3, PIK3CA, ESR1, JAK2, NR3C1 and other subjects as the potential antidepressant targets of PEFC. GO and KEGG enrichment analysis further found PEFC might be exert its antidepressant effects through regulating the PI3K-Akt signaling pathway, endocrine resistance, the MAPK signaling pathway, the neurotrophy signaling pathway and the JAK-STAT3 signaling pathway. Evidence suggests that STAT3 was a key coordinator of cytokine activation in cellular immune responses [48,49], stress can trigger the phosphorylation of the JAK2/STAT3 signaling pathway, leading to abnormal microglia activation, which further upregulated the level of cytokines, exacerbating neuroinflammation in depression [50]. Additionally, MAPK1/3 genes encode ERK2 and ERK1 on the MAPK signaling pathway, which play essential roles in neuronal and glial cell proliferation/differentiation and synaptic plasticity [51]. The neuroendocrine pathway in which the hormones are involved is also a major underlying mechanism of depression: ESR1 encode the estrogen receptor 1, and estrogen is implicated in the neuroprotective activity and regulation of neuronal activity is crucial to understanding the etiology of mood disorders [52]. These results predict that PEFC might be able to influence multiple genes/proteins in the development of depression, whereby reflecting the multi-target, multi-pathway characteristics of PEFC as an antidepressant.
After receiving PEFC therapy, the levels of 44 metabolites in the CUMS group showed significant changes in the LC-MS-based non-targeted hippocampus metabolomic study. Amino acids are well known to be involved in the pathophysiological mechanisms of depression [53]. Glutamate (Glu) is synthesized from glutamine (Gln) by glutaminase (GS) in neurons and then released into the synaptic gap to exert neuroexcitatory effects [54]. Glu is also metabolized to γ-aminobutyric acid (GABA) by glutamic acid decarboxylase (GAD). The Gln-Glu-GABA cycle is critical to excitatory and inhibitory neurotransmission as part of glial-neuronal communication and has significant implications for depressive processes [55]. Studies using the CUMS model have shown that Gln is reduced in both the plasma and brain of depressed mice, a possible indicator for the objective evaluation of depression [56]. High levels of CORT suppress Gln levels, resulting in poor neural viability and inadequate excitatory neurotransmission; enhancing Gln provides promising clues for the treatment of depression [57]. In another research, in a systematic analysis of the metabolomic changes in animal models of depression employing vote counting statistics (VCS), γ-aminobutyric acid, although acting as an inhibitory neurotransmitter, was more downregulated in the hippocampal region (VCS = −10, p = 0.038) [58]. In the present investigation, glutamine and γ-aminobutyric acid levels were decreased in the hippocampus. l-proline is a structural homolog of GABA, and its accumulation in GABAergic neurons can competitively inhibit glutamate decarboxylase, leading to reduced GABA production and impairing synaptic plasticity [59]. Asparagine has a sedative effect under conditions of biological central stress and is associated with neurological defects during embryonic development [60]. Reduced levels of serum asparagine or proline have been reported in Wistar Kyoto (WKY) rats (a model rat for depression) and in patients with depressed mood and can be reversed by antidepressant treatment in both scenarios [61,62], consistent with our findings. Glycine is a non-essential amino acid that can be converted to serine under certain conditions and plays a crucial role in metabolic regulation and neurological function [63]. Glycine- or serine-conjugated glutamate acts as a co-antagonist to help activate N-methyl-d-aspartate receptors (NMDARs), and the abnormal activity of NMDARs has been demonstrated to reduce neuroplasticity, leading to emotional and cognitive impairment [64,65]. A recent study on serum metabolomic analysis in depression patients revealed that the dysregulation of glycine and serine metabolism can lead to depressive and anxiety symptoms [66]. The main biological functions of l-methionine include methylation (transmethylation) of various molecules and synthesis of cysteine, and eventually serine, regulating the production of endogenous polysulfides, which may exert neuroprotective effects through glutathione production [67,68]. Previous reports found that plasma levels of the related sulfide Thiocystine in depressed patients regressed after treatment with Xiaoyaosan, suggesting that Xiaoyaosan may exert antidepressant effects by regulating cysteine and methionine metabolism [69]. In our current investigation, consistent with the mechanism of action of most documented antidepressants, PEFC upregulated the levels of glutamine, γ-aminobutyric acid, asparagine, and proline in the hippocampus of CUMS mice and regulated the metabolism of glycine, serine, and threonine.
In the ensuing integrated analysis of network pharmacology and metabolomics, targets such as PIK3CA, MTOR, MYC, and the PI3K/Akt/mTOR signaling pathways were highlighted. PIK3CA encodes the p110 catalytic subunit of PI3K, as an important intracellular kinase is involved in the PI3K/Akt signaling pathway, one of the most active signaling pathway for intercellular communication, which regulated cell differentiation, metabolism, several growth factors, and cytoskeletal reorganization, leading to neuronal survival and apoptosis [8,70]. The maturation of stem cells and brain progenitor cells at the synaptic level is intimately associated with PI3K activity during development [71]. Additionally, reduced brain volume accompanying behavioral deficits in Akt gene-deficient mice, suggested a key role for Akt in depression [72]. mTOR integrates a range of extracellular signals crucial in protein synthesis and neuroplasticity, making it a key effector protein downstream of PI3K/Akt [73]. The activation of mTOR pathway accelerates the cell cycle interphase of neural precursor cells, thus rapidly tracking the proliferation and differentiation of neurons, and mTOR further increases the expression of long-temporal enhancement (LTP)-related proteins, which play a positive role in neuroplasticity [74]. Furthermore, the inhibition of mTOR has been achieved in clinical [75,76] and animal studies. Cumulative reports indicated that the activation of mTOR is the basis of antidepressant effects of a variety of natural products [77]. More importantly, the activation of mTOR signaling is required for the rapid antidepressant effects of NNMDA antagonists (e.g. ketamine) and could be a potential target for the development of fast-acting antidepressants [78]. In the present study, Western blot of mouse hippocampal samples was used to validate the prediction of the combined network pharmacology and metabolomics analysis. Consistent with previous studies [79,80], CUMS inhibits PI3K/Akt/mTOR signalling in the hippocampus and PEFC reverses this impairment, which implies that PI3K/Akt/mTOR signaling was a target of PEFC antidepressants.
5Conclusion
In conclusion, the current work used a thorough approach integrating metabolomics to examine potential PEFC processes in the treatment of depression, network pharmacology and molecular biology experiments. Our results indicate that PEFC attenuates CUMS-induced depression-like behavior mainly associated with activation of PI3K/Akt/mTOR pathways, which is at least partially responsible for the improvement of depression by PEFC. This study confirmed the antidepressant effectiveness of PEFC and elucidated more specific mechanisms, which may provide solid evidence for the mechanistic exploration and preclinical application of PEFC.
Funding statement
This work was supported by the Applied Basic Research Project of 10.13039/501100004829Science and Technology Department of Sichuan Province (20YYJC0640), the Open Research Fund of 10.13039/501100008402Chengdu University of Traditional Chinese Medicine Key Laboratory of Systematic Research of Distinctive Chinese Medicine Resources in Southwest China (2020XSGG002), and the Xinglin Scholar Research Promotion Project of 10.13039/501100008402Chengdu University of Traditional Chinese Medicine (CDTD2018014, QNXZ2018022), Chengdu Medical Research Project (2019039).
Data availability statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Ethical approval
All processes involving mice were authorized by the Chengdu University of Traditional Chinese Medicine College of Pharmacy's Committee for Animal Care and Use of Laboratory Animals approved the experimental procedures; TCM-2019-312 was the approval number for this process. Animal studies were performed in compliance with the ARRIVE guidelines. All animals received humane care according to the National Institutes of Health (USA) guidelines.
Additional information
The following supplementary materials can be seen in the submitted manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Abbreviations
- Akt
- protein kinase B
- BDNF
- brain derived neurotrophic factor
- BP
- biological process
- CC
- cellular component
- CDB
- Chaidangbo
- Cort
- corticosterone
- CREB
- cAMP-response element binding protein
- CUMS
- chronic unpredictable mild stress
- ERK
- extracellular regulated protein kinases
- FLX
- fluoxetine hydrochloride
- FST
- forced swimming test
- GABA
- γ-aminobutyric acid
- Gln
- glutamine
- Glu
- Glutamate
- GO
- Gene Ontology
- HPLC
- high-performance liquid chromatography
- JAK
- Janus kinase
- JNK
- c-Jun N-terminal kinase
- KEGG
- Kyoto Encyclopedia of Genes and Genomes
- MAPK
- mitogen-activated protein kinase
- MF
- molecular function
- mTOR
- mammalian target of rapamycin
- NFST
- novelty suppressed feeding test
- NMDARs
- N-methyl-d-aspartate receptors
- OPLS-DA
- orthogonal partial least squares discriminant analysis
- PCA
- principal component analysis
- PEFC
- petroleum ether fraction of CDB
- PI3K
- phosphatidylinositol 3-kinase
- PPI
- protein-protein interaction
- SPT
- sucrose preference test
- ST
- splash test
- STAT3
- Signal Transducer and Activator of Transcription 3
- TCM
- Traditional Chinese medicine
- TST
- tail suspension test
Appendix ASupplementary data
The following are the Supplementary data to this article: