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Article

Integrated UPLC/Q-TOF-MS/MS Analysis and Network Pharmacology to Reveal the Neuroprotective Mechanisms and Potential Pharmacological Ingredients of Aurantii Fructus Immaturus and Aurantii Fructus

by
Mingyang Qiu
1,2,
Jianqing Zhang
1,
Wenlong Wei
1,
Yan Zhang
1,2,
Mengmeng Li
1,2,
Yuxin Bai
1,2,
Hanze Wang
1,2,
Qian Meng
1 and
De-an Guo
1,2,*
1
National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 200100, China
2
College of Pharmacy, Changchun University of Chinese Medicine, Changchun 130117, China
*
Author to whom correspondence should be addressed.
Pharmaceuticals 2024, 17(2), 239; https://doi.org/10.3390/ph17020239
Submission received: 19 December 2023 / Revised: 24 January 2024 / Accepted: 1 February 2024 / Published: 12 February 2024
Browse Figures
Versions Notes

Abstract

:
Aurantii Fructus (AF) and Aurantii Fructus Immaturus (AFI) have been used for thousands of years as traditional Chinese medicine (TCM) with sedative effects. Modern studies have shown that Citrus plants also have protective effects on the nervous system. However, the effective substances and mechanisms of action in Citrus TCMs still remain unclear. In order to explore the pharmacodynamic profiles of identified substances and the action mechanism of these herbs, a comprehensive approach combining ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS/MS) analysis and network pharmacology was employed. Firstly, UNIFI 2.1.1 software was used to identify the chemical characteristics of AF and AFI. Secondly, the SwissTargetPrediction database was used to predict the targets of chemical components in AF and AFI. Targets for neuroprotection were also collected from GeneCards: The Human Gene Database (GeneCards-Human Genes|Gene Database|Gene Search). The networks between targets and compounds or diseases were then constructed using Cytoscape 3.9.1. Finally, the Annotation, Visualization and Integrated Discovery Database (DAVID) (DAVID Functional Annotation Bioinformatics Microarray Analysis) was used for GO and pathway enrichment analysis. The results showed that 50 of 188 compounds in AF and AFI may have neuroprotective biological activities. These activities are associated with the regulatory effects of related components on 146 important signaling pathways, derived from the KEGG (KEGG: Kyoto Encyclopedia of Genes and Genomes), such as neurodegeneration (hsa05022), the Alzheimer’s disease pathway (hsa05010), the NF-kappa B signaling pathway (hsa04064), the hypoxia-inducible factor (HIF)-1 signaling pathway (hsa04066), apoptosis (hsa04210), the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance signaling pathway (hsa01521), and others, by targeting 108 proteins, including xanthine dehydrogenase (XDH), glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B), and glucose-6-phosphate dehydrogenase (G6PD), among others. These targets are thought to be related to inflammation, neural function and cell growth.

1. Introduction

Aurantii Fructus (AF) and Aurantii Fructus Immaturus (AFI) have been used in traditional Chinese medicine (TCM) for thousands of years [1]. AF and AFI are the fruits of Citrus aurantium L. (CA) (bitter orange) and their cultivated varieties [2]. And Citrus aurantium L. Cv. daidai (CAD) is the most commonly used cultivated variety of Citrus aurantium L. and is widely grown as a medicinal plant [3]. AF and AFI are collected at different stages of fruit growth with diverse clinical efficacy; the effect of AFI on promoting qi is obviously better than that of AF, and they are thus are recorded in the Chinese Pharmacopoeia as two distinct medicinal materials [4]. According to TCM theory, AF and AFI each have their own unique clinical applications [5]. Although AF and AFI have common effects of regulating visceral functions [6], AF is always used to alleviate chest pain and improve gastrointestinal functions, such as alleviating dyspepsia in a gentle yet efficient manner [7]. AFI, compared to AF, expresses a more rapid and robust method of action and is often employed to disperse severe abdominal distention and to eliminate phlegm [8]. We found that Citrus plants, including Citrus aurantium L., have beneficial effects on those with neurodegenerative diseases [9], suggesting AF and AFI to have potential protective effects on the nervous system. Therefore, it is reasonable to explore the protective effects of AF and AFI on nervous system. Currently, excitotoxicity and oxidative stress are recognized as two important aspects of nervous system damage [10]. Hence, we believe that it is meaningful to study the chemical components related to excitotoxicity and oxidative stress in AF and AFI. At present, chemical analysis methods, including chromatography [11], nuclear magnetic resonance (NMR) spectroscopy [12], and mass spectrometry (MS) [13], are usually used to study the chemical constituents of plant drugs. Among them, ultra-high-performance liquid chromatography (UPLC) alongside high-resolution mass spectrometry (HR-MS) can simultaneously detect a variety of chemical components in plant drugs [14]; however, to obtain accurate identification results, the UPLC-HR-MS detection results must be compared with the standard chromatogram of chemical components or the mass spectrometry database [15]. As an auxiliary mass spectrum analysis software, UNIFI supports multi-user, server-based workgroups to complete liquid chromatography (LC), LC/MS, and LC/MS/MS data collection, storage, management, mining, and sharing, which can greatly improve collaboration efficiency [16,17].
In this study, the chemical compositions of AF and AFI derived from Citrus aurantium L. and Citrus aurantium L. Cv. daidai were systematically evaluated with UNIFI software with UPLC/quadrupole time-of-flight (Q-TOF)-MS/MS. The chemical similarities and differences between AF and AFI were summarized. Furthermore, the target of compounds and the target of neuroprotection were predicted using the method of network pharmacology [18]. Finally, identifying bioactive compounds, potential targets, and signaling pathways relevant to the neuroprotection with AF and AFI was realized using an integrative network analysis [19].
The results indicated that 50 of the 188 compounds in AF and AFI may be bioactive, which may be related to their targeting of 108 targets such as XDH, GRIN2B, AKT1, PRKCG, CAPN1, CSNK2A1, G6PD, etc. One hundred and forty-six important signaling pathways were identified, including neurodegeneration (hsa05022), the Alzheimer’s disease pathway (hsa05010), the NF-kappa B signaling pathway (hsa04064), the HIF-1 signaling pathway (hsa04066), apoptosis (hsa04210), and the EGFR tyrosine kinase inhibitor resistance signaling pathway (hsa01521), etc.

2. Results and Discussion

2.1. Identification of Compounds in AF and AFI

The total ion chromatograms of AF and AFI in both positive and negative ion modes are presented in Figure 1A–F. The process of identifying compounds via UNIFI software is shown in Figure 1G. The retention times and the MS data of the characterized compounds are summarized in Table 1. A total of 188 compounds were identified by UNIFI software based on the self-built database. Among these compounds, compounds (46, 47, 92, 106, 119, 130, 154) were unambiguously identified by comparison with reference compounds.

2.2. Identification of the AFI- and AF-Associated Targets and Analysis of the “Compound–Target” Network

Using the SwissTargetPrediction databases, we obtained the 9021 target proteins of the 188 compounds in AFI and AF. The entire list of targets of each compound is provided in Supplementary Table S2. After removing redundancy, we identified 1052 AFI- or AF-associated targets (Supplementary Table S3). Compound–target networks were constructed on the basis of compounds 1 (7-Hydroxycoumarin), 6 (Limonin), 46 ((+/−)-Naringenin), 61 (Helenalin), and 63 (Kaempferol) and their corresponding targets, as shown in Figure 2. The round, yellow nodes and round, blue nodes represent the compounds and targets, respectively, and the edges represent the interactions between compounds and targets.

2.3. Identification of the Neuroprotective Targets and Analysis of the “Disease–Target” Network

By means of the available resource, namely, the GeneCards: The Human Gene Database. we obtained 151 excitotoxicity-associated targets (relevance > 1.0) and 187 antioxidant-associated targets (relevance > 1.0). And detailed information on the collected targets is provided in Supplementary Table S4 (excitotoxicity-associated targets) and Supplementary Table S5 (antioxidant-associated). Disease–target networks were constructed, as shown in Figure 3. The network consisted of two parts (A: an excitotoxicity-associated target network with 151 nodes; B: an antioxidation target network with 187 nodes). The round, blue nodes and round, yellow nodes represent the targets and diseases, respectively, and the edges represent the interactions between diseases and targets.

2.4. Recognition of the Candidate Compounds and Potential Targets and Analysis of the “Compound–Disease–Target” Network

A total of 125 overlapping protein targets were recognized, and 50 candidate compounds were obtained, as described in Supplementary Table S6. Figure 4 shows the compound–disease–target network, which was composed of one hundred and seventy-seven nodes (one hundred and twenty-five targets, fifty compounds, and two diseases) and two hundred and fifty edges. The round, yellow nodes, round, red nodes, and green nodes represent the compounds, targets, and diseases, respectively, and each node size is proportional to its degree. The edges represent the interactions between any two types of nodes. The results showed that the 50 compounds and 125 targets may be the candidate bio-active substances and the potential pharmacological targets for neuroprotection of AF and AFI. In particular, the neuroprotective candidate compounds are shown in Table 2 and Figure 5, and the potential pharmacological targets are shown in Table 3. There are significant differences in the chemical composition of AF and AFI [2], and we found that the neuroprotective effects of the compounds of AF and AFI are less different, as shown in Figure 5. Limonin in Table 2 is present in four samples, and studies have shown that it has a neuroprotective effect [20].

2.5. GO and Pathway Enrichment Analyses of Potential Targets

One of the functions of GO processes is to predict genes related to a disease [21]. GO and pathway enrichment analyses of the 108 potential targets for neuroprotection in AF and AFI were performed using the DAVID database to understand the relationships between functional units and their underlying significance in the biological system networks [22]. All of the biological processes and pathways were extracted (p ≤ 0.05). Figure 6 lists the top 30 most significantly enriched GOBP terms. Supplementary Tables S7 and S8 provide detailed information about the biological processes and signaling pathways. In total, 146 related pathways were identified, including pathways of neurodegeneration (hsa05022), the Alzheimer’s disease pathway (hsa05010), the NF-kappa B signaling pathway (hsa04064), the HIF-1 signaling pathway (hsa04066), apoptosis (hsa04210), and the EGFR tyrosine kinase inhibitor resistance signaling pathway (hsa01521). And numerous targets were involved in the memory process, gene expression, the rhythmic process, the neuron apoptotic process, and the apoptotic process.

3. Materials and Methods

3.1. Experimental Compounds Discovery

3.1.1. Chemicals and Materials

AF-CA and AFI-CA (batch number: S202108-0932, S202101-0929) were collected from Xinyu County, Jiangxi Province, China. AF-CAD and AFI-CAD (batch number: S202108-0933, S202106-0930) were collected from Jinhua County, Zhejiang Province, China. And all samples were stored at room temperature until experimentation. All collected samples have accompanying voucher specimens held in the National Engineering Research Center of TCM Standardization Technology, Shanghai Institute of Materia Medica (Shanghai Institute of Material Medical Chinese Academy of Sciences (cas.cn)) accessed on 6 February 2024), Chinese Academy of Sciences, Shanghai, China.
Seven compounds were used as reference standards (purity > 98%): namely, Hesperidins, Nobiletin, Tangeretin, Didymin, Naringin, Naringenin, and Narirutin, which were purchased from Shanghai Standard Technology Co. Ltd. (Shanghai, China) (nature-standard.com). Ultra-pure water was prepared by a Milli-Q water purification system (Millipore, Bedford, MA, USA). All other chemicals were of analytical grade and obtained commercially. All extractions used in UPLC-Q-TOF were carried out with high-performance liquid chromatography (HPLC)-grade solvents.

3.1.2. Sample Preparation

AF-CA, AFI-CA, AF-CAD, and AFI-CAD powder (100 mg) were extracted successively with 2 mL of 50% MeOH in an ultrasonic bath (40 kHz) for 30 min. After centrifuging at 15,890× g for 10 min, the supernatant was used for later analysis.

3.1.3. UPLC/Q-TOF-MS/MS Analysis

The equipment used was an ACQUITY UPLC I-Class System coupled to a Xevo G2–XS Q-TOF mass spectrometer (Waters, Milford, MA, USA). Each prepared sample was subjected to LC-MS/MS analysis with a scan event recording MS/MS spectrum in data-dependent acquisition mode. An ACQUITY UPLC® BEH C18 (1.7 µm × 2.1 × 100 mm) column was used for the separation of analytes in the extracts with a flow rate of 0.2 mL/min at 30 °C. The injection volume was 2 μL. A linear gradient program with a mobile phase system including solvent A (0.1% formic acid in water, v/v) and solvent B (0.1% formic acid in acetonitrile, v/v) was performed as follows: solvent A at 85~79% for 0.01~3 min, 79% for 3~7 min, 79~65% for 7~12 min, 65~50% for 12~16 min, 50~40% for 16~22 min, 40~20% for 22~25 min, and 20~5% for 25~29 min, with isocratic elution performed at 5% for 4 min. The MS spectra were acquired in positive and negative ion modes to provide complementary information for structural identification. The scan range was from 100 to 1200 m/z. The acquisition parameters for Q-TOF mass spectra were as follows: cone voltage at 40 V for both electron spray ionization (ESI)+ and ESI− modes. The desolvation gas was set to 800 L/h at a temperature of 300 °C, the cone gas was set to 50 L/h, and the source temperature was set to 120 °C. The mass spectrometry was operated linearly in data-dependent acquisition mode at a low energy level of 25–35 eV and a high energy level of 40–50 eV. All analyses were acquired using the LockSpray to ensure accuracy and reproducibility. Leucine-enkephalin was used as the lock mass at a concentration of 300 ng/mL and flow rate of 20 μL/min. Data were collected in continuum mode, the LockSpray interval was set at 10 s. The data acquisition rate was set to 1.5 s. All acquisition of data was controlled by Waters Masslynx v4.2 software (Waters, Manchester, UK).

3.1.4. UNIFI Data Processing Method

The chemical constituent library of AF and AFI was firstly established for component analysis [23]: The complete information on the compounds of AF and AFI was collected and obtained by searching the China National Knowledge Infrastructure (CNKI) (cnki.net, accessed on 31 January 2024), PubMed (PubMed (nih.gov) accessed on 31 January 2024), PubChem (PubChem (nih.gov) accessed on 6 February 2024), Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (tcmsp-e.com, accessed on 31 January 2024), ChemSpider (chemspider.com, accessed on 31 January 2024), and other databases. The self-built compound library, including compound name and chemical structure (saved in “mol” format), was imported into UNIFI. Among them, a total of 1190 compounds were listed (Supplement Table S1). We imported the original files on the samples solution and blank sample solution obtained by UPLC-Q-TOF-MS into the UNIFI software for sample comparison. Based on the automatic matching function of the UNIFI software, compounds can be quickly identified. The parameter settings were as follows: analysis time range, 1–36 min; quality allowable error range, ±10 ppm; quality testing range, 100 m/z to 1200 m/z; positive adducts including H+, Na+, and K+; and negative adducts containing H−, HCOO−, and Cl−. Finally, using the MassLynx workstation, the above identification results were reviewed in combination with the precise mass of excimer ions, retention time, fragment ion information, and the literature [17].

3.2. Target Prediction of the Compounds in AFI and AF and Neuroprotective Target Collection

3.2.1. Predicting Targets of Compounds in AFI and AF

According to our study (Section 3.1), all of the compounds in AFI and AF were chosen to predict the biological targets. The canonical SMILES [24] of the compounds were uploaded into the SwissTargetPrediction database (http://www.swisstargetprediction.ch/ accessed on 31 January 2024) to obtain the UniProt IDs for predicting targets [25].

3.2.2. Collecting Neuroprotective Targets

“Excitotoxicity” and “antioxidation” are considered to be the two key directions of neuroprotection [26]. The biological targets related to neuroprotection were selected from the GeneCards: The Human Gene Database [27] (https://www.genecards.org/, accessed on 6 February 2024, version 5.15.0, relevance > 1.0) using “excitotoxicity” and “antioxidation” as keywords [28].

3.3. Identification of Potential Targets for the Neuroprotection of AFI and AF

3.3.1. Screening Candidate Compounds and Potential Targets

We selected the overlapping targets of AF and AFI for neuroprotection and used the compounds corresponding to these targets as candidate compounds.

3.3.2. Gene Ontology (GO) and Pathway Enrichment of Potential Targets

The Gene Ontology (GO) biological process (BP) was analyzed to further validate whether the potential targets were indeed matched for neuroprotection [29]. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) [30] signaling pathway analyses were carried out using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/, accessed on 31 January 2024, version v2023q1). A p-value ≤ 0.05 was considered significant.

3.3.3. Constructing the Network of Compounds, Diseases, and Targets

To comprehensively understand the neuroprotection of AF and AFI, the compound–target and disease–target networks were constructed using Cytoscape 3.9.1 (Bethesda, MD, USA) [31]. In these networks, the nodes represented the compounds, diseases, targets, or signaling pathways, and the edges represented their interactions [32].

4. Conclusions

In this study, a comprehensive method combining UPLC/Q-TOF-MS/MS analysis and network pharmacology was used to reveal the differences in the chemical components of AF and AFI that applied to their neuroprotective effects. The results indicated that 50 of the 188 compounds in AF and AFI may be bioactive, which may be related to their targeting of 108 targets such as XDH, GRIN2B, AKT1, PRKCG, CAPN1, CSNK2A1, G6PD. One hundred and forty-six important signaling pathways were implicated, including neurodegeneration (hsa05022), the Alzheimer’s disease pathway (hsa05010), the NF-kappa B signaling pathway (hsa04064), the HIF-1 signaling pathway (hsa04066), apoptosis (hsa04210), and the EGFR tyrosine kinase inhibitor resistance signaling pathway (hsa01521). These findings fully reflect the multi-component, multi-target, and multi-approach characteristics of TCM in disease treatment. This study shows that AF and AFI have great potential in neuroprotection, and their neuroprotective effects deserve further study.
In some network pharmacological studies, compounds are collected indiscriminately from databases; however, this can produce false-positive results. The method we applied in this research was built on the basis of experimentally identified components and corresponding targets, which will greatly reduce the prediction range and improve the accuracy of the prediction results. However, further pharmacological experiments are needed to verify its main biological components and related targets, so as to deeply understand the neuroprotective mechanism of AF and AFI, which will be the direction of our further research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ph17020239/s1, Table S1: Chemical compounds contained in citrus traditional Chinese medicine, Table S2: The entire list of targets of each compound, Table S3 1052 AFI- or AF-associated targets, Table S4: Excitotoxicity-associated targets, Table S5: Antioxidant-associated targets, Table S6: The Candidate Compounds and Potential Targets, Table S7: Information about the biological processes, Table S8: Information about the signaling pathways.

Author Contributions

Conceptualization, M.Q. and D.-a.G.; data curation, M.Q., H.W., Y.B., Y.Z. and M.L.; funding acquisition, D.-a.G.; software, M.Q. and Q.M.; supervision, D.-a.G.; writing—original draft, M.Q. and W.W.; writing—review and editing, W.W., J.Z. and D.-a.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shanghai Sailing Program (No. 21YF1455800), National Natural Science Foundation of China (No. 82003940; No. 82003938; No. 82104385), Qi-Huang Chief Scientist Project of National Administration of Traditional Chinese Medicine (2020), Sanming Project of Medicine in Shenzhen (No. SZZYSM202106004), Key Program of National Natural Science Foundation of China (No. 82130111) and Key-Area Research and Development Program of Guangdong Province (No. 2020B1111110007).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare that they have no competing financial interests associated with this work.

References

  1. Wu, J.; Huang, G.; Li, Y.; Li, X. Flavonoids from Aurantii Fructus Immaturus and Aurantii Fructus: Promising phytomedicines for the treatment of liver diseases. Chin. Med. 2020, 15, 89. [Google Scholar] [CrossRef] [PubMed]
  2. Bai, Y.; Zheng, Y.; Pang, W.; Peng, W.; Wu, H.; Yao, H.; Li, P.; Deng, W.; Cheng, J.; Su, W. Identification and Comparison of Constituents of Aurantii Fructus and Aurantii Fructus Immaturus by UFLC-DAD-Triple TOF-MS/MS. Molecules 2018, 23, 803. [Google Scholar] [CrossRef]
  3. Fang, C.; He, J.; Xiao, Q.; Chen, B.; Zhang, W. Development of the Volatile Fingerprint of Qu Aurantii Fructus by HS-GC-IMS. Molecules 2022, 27, 4537. [Google Scholar] [CrossRef] [PubMed]
  4. Mu, Q.; Zhang, Y.; Cui, Y.; Chai, X.; Liu, J.; Li, Y.; Yu, H.; Wang, Y. Study on Closely Related Citrus CMMs based on Chemometrics and Prediction of Components-Targets-Diseases Network by Ingenuity Pathway Analysis. Evid. Based Complement. Alternat Med. 2022, 2022, 1106353. [Google Scholar] [CrossRef] [PubMed]
  5. Liu, S.; Zhu, J.J.; Li, J.C. The interpretation of human body in traditional Chinese medicine and its influence on the characteristics of TCM theory. Anat. Rec. 2021, 304, 2559–2565. [Google Scholar] [CrossRef] [PubMed]
  6. Gao, T.; Jiang, M.; Deng, B.; Zhang, Z.; Fu, Q.; Fu, C. Aurantii Fructus: A systematic review of ethnopharmacology, phytochemistry and pharmacology. Phytochem. Rev. 2021, 20, 909–944. [Google Scholar] [CrossRef]
  7. Zhu, J.; Tong, H.; Ye, X.; Zhang, J.; Huang, Y.; Yang, M.; Zhong, L.; Gong, Q. The Effects of Low-Dose and High-Dose Decoctions of Fructus aurantii in a Rat Model of Functional Dyspepsia. Med. Sci. Monit. 2020, 26, e919815. [Google Scholar] [CrossRef] [PubMed]
  8. Luo, H.; Wu, H.; Yu, X.; Zhang, X.; Lu, Y.; Fan, J.; Tang, L.; Wang, Z. A review of the phytochemistry and pharmacological activities of Magnoliae officinalis cortex. J. Ethnopharmacol. 2019, 236, 412–442. [Google Scholar] [CrossRef]
  9. Qiu, M.; Wei, W.; Zhang, J.; Wang, H.; Bai, Y.; Guo, D.A. A Scientometric Study to a Critical Review on Promising Anticancer and Neuroprotective Compounds: Citrus Flavonoids. Antioxidants 2023, 12, 669. [Google Scholar] [CrossRef]
  10. Rahman, M.M.; Islam, M.R.; Emran, T.B. Clinically important natural products for Alzheimer’s disease. Int. J. Surg. 2022, 104, 106807. [Google Scholar] [CrossRef]
  11. Ji, S.; Wang, S.; Xu, H.; Su, Z.; Tang, D.; Qiao, X.; Ye, M. The application of on-line two-dimensional liquid chromatography (2DLC) in the chemical analysis of herbal medicines. J. Pharm. Biomed. Anal. 2018, 160, 301–313. [Google Scholar] [CrossRef]
  12. Kumar, D. Nuclear Magnetic Resonance (NMR) Spectroscopy For Metabolic Profiling of Medicinal Plants and Their Products. Crit. Rev. Anal. Chem. 2016, 46, 400–412. [Google Scholar] [CrossRef] [PubMed]
  13. Stavrianidi, A. A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines. J. Chromatogr. A 2020, 1609, 460501. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, J.; Leung, D. Applications of ultra-performance liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry on analysis of 138 pesticides in fruit- and vegetable-based infant foods. J. Agric. Food Chem. 2009, 57, 2162–2173. [Google Scholar] [CrossRef] [PubMed]
  15. Rivera-Mondragon, A.; Tuenter, E.; Ortiz, O.; Sakavitsi, M.E.; Nikou, T.; Halabalaki, M.; Caballero-George, C.; Apers, S.; Pieters, L.; Foubert, K. UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae). Phytochemistry 2020, 177, 112438. [Google Scholar] [CrossRef] [PubMed]
  16. Deng, L.; Shi, A.M.; Liu, H.Z.; Meruva, N.; Liu, L.; Hu, H.; Yang, Y.; Huang, C.; Li, P.; Wang, Q. Identification of chemical ingredients of peanut stems and leaves extracts using UPLC-QTOF-MS coupled with novel informatics UNIFI platform. J. Mass. Spectrom. 2016, 51, 1157–1167. [Google Scholar] [CrossRef] [PubMed]
  17. Xu, L.; Liu, Y.; Wu, H.; Wu, H.; Liu, X.; Zhou, A. Rapid identification of chemical profile in Gandou decoction by UPLC-Q-TOF-MSE coupled with novel informatics UNIFI platform. J. Pharm. Anal. 2020, 10, 35–48. [Google Scholar] [CrossRef]
  18. Wu, J.; Zhang, F.; Li, Z.; Jin, W.; Shi, Y. Integration strategy of network pharmacology in Traditional Chinese Medicine: A narrative review. J. Tradit. Chin. Med. 2022, 42, 479–486. [Google Scholar] [CrossRef]
  19. Yi, P.; Zhang, Z.; Huang, S.; Huang, J.; Peng, W.; Yang, J. Integrated meta-analysis, network pharmacology, and molecular docking to investigate the efficacy and potential pharmacological mechanism of Kai-Xin-San on Alzheimer’s disease. Pharm. Biol. 2020, 58, 932–943. [Google Scholar] [CrossRef]
  20. Gao, X.; He, D.; Liu, Y.; Cui, M.; Li, Z.; Li, J.; He, Y.; Wang, H.; Ye, B.; Fu, S.; et al. Oral administration of Limonin (LM) exerts neuroprotective effects by inhibiting neuron autophagy and microglial activation in 6-OHDA-injected rats. Int. Immunopharmacol. 2023, 123, 110739. [Google Scholar] [CrossRef]
  21. Sarker, B.; Khare, N.; Devignes, M.D.; Aridhi, S. Improving automatic GO annotation with semantic similarity. BMC Bioinform. 2022, 23, 433. [Google Scholar] [CrossRef] [PubMed]
  22. Huang, D.W.; Sherman, B.T.; Tan, Q.; Kir, J.; Liu, D.; Bryant, D.; Guo, Y.; Stephens, R.; Baseler, M.W.; Lane, H.C.; et al. DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007, 35, W169–W175. [Google Scholar] [CrossRef] [PubMed]
  23. Chang, H.; Lv, S.; Yuan, T.; Wu, H.; Wang, L.; Sang, R.; Zhang, C.; Chen, W. Identification and Analysis of Chemical Constituents and Rat Serum Metabolites in Gushuling Using UPLC-Q-TOF/MS Coupled with Novel Informatics UNIFI Platform. Evid. Based Complement. Alternat Med. 2021, 2021, 2894306. [Google Scholar] [CrossRef] [PubMed]
  24. O’Boyle, N.M. Towards a Universal SMILES representation—A standard method to generate canonical SMILES based on the InChI. J. Cheminform 2012, 4, 22. [Google Scholar] [CrossRef] [PubMed]
  25. Gfeller, D.; Grosdidier, A.; Wirth, M.; Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: A web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014, 42, W32–W38. [Google Scholar] [CrossRef] [PubMed]
  26. Zhu, T.; Wang, L.; Wang, L.P.; Wan, Q. Therapeutic targets of neuroprotection and neurorestoration in ischemic stroke: Applications for natural compounds from medicinal herbs. Biomed. Pharmacother. 2022, 148, 112719. [Google Scholar] [CrossRef] [PubMed]
  27. Stelzer, G.; Dalah, I.; Stein, T.I.; Satanower, Y.; Rosen, N.; Nativ, N.; Oz-Levi, D.; Olender, T.; Belinky, F.; Bahir, I.; et al. In-silico human genomics with GeneCards. Hum. Genomics 2011, 5, 709–717. [Google Scholar] [CrossRef] [PubMed]
  28. Fan, X.; Wang, H.; Zhang, L.; Tang, J.; Qu, Y.; Mu, D. Neuroprotection of hypoxic/ischemic preconditioning in neonatal brain with hypoxic-ischemic injury. Rev. Neurosci. 2020, 32, 23–34. [Google Scholar] [CrossRef]
  29. Chagoyen, M.; Pazos, F. Quantifying the biological significance of gene ontology biological processes—Implications for the analysis of systems-wide data. Bioinformatics 2010, 26, 378–384. [Google Scholar] [CrossRef]
  30. Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
  31. Doncheva, N.T.; Morris, J.H.; Gorodkin, J.; Jensen, L.J. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J. Proteome Res. 2019, 18, 623–632. [Google Scholar] [CrossRef]
  32. Kohl, M.; Wiese, S.; Warscheid, B. Cytoscape: Software for visualization and analysis of biological networks. Methods Mol. Biol. 2011, 696, 291–303. [Google Scholar] [CrossRef]
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Figure 1. Identification of compounds in AF and AFI. (A) AF-CA; (B) AFI-CA; (C) AF-CAD; (D) AFI-CAD; (E) total ion chromatography of samples in positive ion mode; (F) total ion chromatography of samples in negative ion mode; (G) the identification process of compounds in UNIFI software: No. 142 compound identified as Gardenin A.
Figure 1. Identification of compounds in AF and AFI. (A) AF-CA; (B) AFI-CA; (C) AF-CAD; (D) AFI-CAD; (E) total ion chromatography of samples in positive ion mode; (F) total ion chromatography of samples in negative ion mode; (G) the identification process of compounds in UNIFI software: No. 142 compound identified as Gardenin A.
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Figure 2. Compound–target networks for AFI and AF. (A) Compound 1 (7-Hydroxycoumarin) compound–target network; (B) Compound 6 (Limonin) compound–target network; (C) Compound 46 ((+/−)-Naringenin) compound–target network; (D) Compound 61 (Helenalin) compound–target network; (E) Compound 63 (Kaempferol) compound–target network.
Figure 2. Compound–target networks for AFI and AF. (A) Compound 1 (7-Hydroxycoumarin) compound–target network; (B) Compound 6 (Limonin) compound–target network; (C) Compound 46 ((+/−)-Naringenin) compound–target network; (D) Compound 61 (Helenalin) compound–target network; (E) Compound 63 (Kaempferol) compound–target network.
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Figure 3. Disease–target networks for neuroprotection. (A) Excitotoxicity-associated target network; (B) antioxidation target network.
Figure 3. Disease–target networks for neuroprotection. (A) Excitotoxicity-associated target network; (B) antioxidation target network.
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Figure 4. Compound–disease–target network. The yellow, red, and green nodes represent the compounds (the numbers represent the serial numbers of the compounds in Table 1), targets and diseases, respectively, and a node’s size is proportional to its degree. The edges represent the interactions between any two nodes.
Figure 4. Compound–disease–target network. The yellow, red, and green nodes represent the compounds (the numbers represent the serial numbers of the compounds in Table 1), targets and diseases, respectively, and a node’s size is proportional to its degree. The edges represent the interactions between any two nodes.
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Figure 5. A Venn diagram of neuroprotective candidate compounds among AF-CA, AFI-CA, AF-CAD, and AFI-CAD.
Figure 5. A Venn diagram of neuroprotective candidate compounds among AF-CA, AFI-CA, AF-CAD, and AFI-CAD.
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Figure 6. The top 30 enriched gene ontology terms for the biological processes of potential targets.
Figure 6. The top 30 enriched gene ontology terms for the biological processes of potential targets.
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Table 1. Compounds identified in AF and AFI by UNIFI software.
Table 1. Compounds identified in AF and AFI by UNIFI software.
No.Compound NameObserved m/zMass Error (mDa)Observed RT (min)AdductsAFI-CA DAF-CADAFI-CAAF-CA
17-Hydroxycoumarin167.01191.51.01-H2O+Na✓✓✓✓
2Arginine175.118−1.01.05+H✓✓✓✓
3Isopimpinellin251.0299−1.61.09-H2O+Na✓
4Isoprenol104.1062−0.81.09+NH4✓✓✓✓
5Isomaltose341.1088−0.11.11-H ✓ ✓
6Limonin475.17633.61.12-H2O+Na, +H✓✓✓✓
7Farnesyl Acetate287.196−2.21.17+Na✓✓✓✓
8Heterodendrin262.1276−0.91.18+H✓ ✓
9N-Methyl Proline130.0852−1.01.23+H✓✓✓✓
10Betonicine160.096−0.81.24+H✓✓✓✓
11Citric Acid215.0155−0.71.35+Na✓✓✓✓
125-Hydroxymethyl Furaldehyde127.0383−0.71.35+H✓✓✓✓
135-(Hydroxymethyl)Furan-3-Carbaldehyde287.979−0.71.36-H2O+Na ✓
147-Hydroxy-6-Methoxy-Coumarin193.0481−1.41.39+H✓✓✓
15L-Synephrine Acetate150.09130.01.42-H2O+H✓✓✓✓
16Dopamine136.0746−1.11.43-H2O+H✓✓✓
17L-Tyrosine182.0808−0.41.43+H✓ ✓✓
18N-Methyltyramine152.1065−0.51.45+H✓✓✓
19Tyrosol121.064−0.81.45-H2O+H✓✓✓✓
20Dimethyl Anthranilate166.0856−0.71.55+H✓ ✓✓
21Tyramine120.0802−0.61.56-H2O+H✓✓✓✓
22Citronellyl Acetate203.1389−1.71.62-H2O+Na ✓
23Salicylic Acid137.024−0.41.69-H ✓
24Dehydrodieugenol349.138−3.11.73+Na✓✓✓✓
25Vanillin153.0531−1.51.74+H✓ ✓✓
26Epigallocatechin324.10770.01.76+NH4 ✓
27Rutin611.16171.11.79+H✓✓✓✓
28Isocoumarin147.0428−1.21.93+H ✓✓
29Subaphylline265.1542−0.41.94+H ✓✓
30Tryptophan205.097−0.12.00+H✓✓✓✓
31Geniposide389.1408−3.42.05+H ✓
32Palmidin A493.13032.12.05-H2O+H✓✓
334-Hydroxy-3-Methoxystrychnine195.0659−0.42.06+HCOO ✓
34Caffetannic Acid355.1007−1.62.10+H, +Na✓✓
35Ayapanin177.0538−0.82.12+H ✓
36Scolymoside595.16671.02.20+H✓ ✓
37Vicenin595.16650.82.23+H, -H2O+H✓✓✓✓
385,7-Dihydroxychromone 7-rutinoside487.1441−0.52.24+H✓✓✓
39Ferulic Acid177.0537−0.92.27-H2O+H✓✓✓✓
40Hyperoside465.10270.02.31+H✓ ✓
41Chrysophanol-1-O-β-gentiobioside623.1598−1.92.35+HCOO, -H ✓
42Benzoic acid105.0325−1.02.39-H2O+H✓✓✓
43Isorhamnetin-3-Rutinoside625.17670.42.43+H✓✓✓✓
44Phenethylamine144.0790.72.55+Na ✓
45Naringenin-4’-Glucoside-7-Rutinoside765.22120.02.78+Na✓✓✓✓
46(+/−)-Naringenin273.0752−0.62.78+H✓✓✓✓
47Narirutin581.18670.22.80+H✓ ✓✓
48Phenylacetic acid135.0446−0.52.81-H✓ ✓
49Salipurposide435.1273−1.32.82+H✓✓✓✓
50Methyl Chlorogenate391.0965−3.52.82+Na✓✓✓✓
51Eufin123.04281.22.94-H2O+Na ✓
52Cinaroside449.1068−1.02.94+H, -H2O+H✓
53Testosterone293.1848−2.82.94-H2O+Na ✓✓
54Naringenin-7-O-Glucuronide431.0937−3.62.99-H2O+H ✓
552-Hydroxy-6-Methoxybenzoic Acid151.0379−1.13.10-H2O+H, +H✓✓✓✓
56Eriodictyol-7-Glucoside473.10560.23.27+Na✓✓✓✓
57Coumarin191.0345−0.53.49+HCOO✓
58Vitamin B442.1463−0.73.64+H✓
595,7-Dihydroxychromone179.0328−1.13.75+H✓ ✓✓
60Butylidenephthalide189.0897−1.33.79+H✓✓✓
61Helenalin263.1256−2.23.88+H✓✓✓✓
62Emodin 8-glucoside433.1119−1.03.90+H✓✓
63Kaempferol287.0545−0.54.15+H✓
64Genioisidic Acid379.0991−0.94.23-H2O+Na ✓
65Eriodictuol289.0688−1.84.23+H✓✓✓✓
66Ombuin331.0805−0.84.24+H, -H2O+H✓
67Chrysophanein417.1178−0.24.24+H✓✓✓✓
68Lonicerin595.1643−1.44.28+H✓✓✓✓
69natsudaidain419.131−2.64.53+H✓✓✓✓
70Caffeic Acid163.0376−1.44.62-H2O+H✓✓✓✓
71Oleuropein523.1775−3.54.68-H2O+H ✓✓✓
72Hesperetin-7-O-β-D-Glucoside487.1202−0.94.82+Na✓✓✓✓
73Hesperidin Methyl Chalcone625.2081−4.64.85+H ✓✓✓
743,4,7-Trimethoxycoumarin237.0745−1.34.94+H✓✓✓✓
75Narirutin-isomer581.1862−0.35.02+H, +Na ✓
76Curculigoside449.1429−1.35.20-H2O+H✓✓✓✓
77Homoeriodictyol303.0846−1.75.22+H✓✓✓✓
78Chryso-Obtusin Glucoside565.1554−0.95.24+HCOO✓✓✓✓
79Rhoifolin579.17140.55.34+H, +Na✓✓✓✓
80Eriocitrin579.1701−0.85.39-H2O+H✓✓✓✓
81Meranzin Hydrate261.1109−1.25.50-H2O+H✓✓✓✓
82Paeonioflorin463.1566−3.25.52-H2O+H✓✓✓✓
83Gallic Acid153.0171−1.15.60-H2O+H✓✓✓✓
84Physcion-8-O-Beta-D-Gentiobioside609.1801−1.35.79+H✓ ✓✓
85Diosmin609.18180.45.81+H✓✓✓
86Hesperetin-7-O-Neohesperidoside633.1781−0.96.02+Na ✓
87Neohesperidin633.1781−0.96.02+Na✓
88Torachrysone431.13372.56.03+Na✓✓✓✓
89Diosmetin301.0697−1.06.11+H✓✓✓
90Pinoresinol Dimethyl Ether404.2054−1.46.12+NH4✓✓✓✓
91Rubrofusarin-6-Β-Gentiobioside595.1663−0.66.31-H ✓✓
92Hesperidins633.181.06.65+Na ✓
93Obtusin345.0963−0.66.79+H✓✓✓✓
94Coptisine303.08940.47.08-H2O+H✓ ✓
95Citromitin449.1448−0.67.12+HCOO✓✓
963-Tert-Butyladipic Acid207.10011.07.66-H2O+Na ✓
97Nomilinic acid Glucoside717.2709−2.07.76-H2O+Na✓✓ ✓
98Deacetylnomilin473.2162−0.87.78+H✓✓
995,7,4’-Trimethoxyflavone317.0769−1.67.91-H2O+Na✓✓✓✓
1005,7-Dimethoxy Coumarin189.0542−0.47.92-H2O+H✓✓✓✓
101Dl-3-Phenyllactic Acid189.05351.37.93+Na✓✓✓✓
102Seselin227.0705−0.87.94-H ✓
103Resveratrol227.0707−0.77.94-H✓
104Salireposide451.1231−1.58.20+HCOO ✓ ✓
105Meranzin261.1111−1.08.23+H✓✓✓
106Naringin581.1859−0.68.48+H✓✓✓✓
107Terpinyl Acetate241.1441−0.58.80+HCOO ✓ ✓
108Eucommioside385.12770.79.54+Cl ✓
1096-O-Benzoylphlorigidoside B551.1747−1.29.79-H2O+H✓
110Obacunone455.205−1.49.84+H✓ ✓
111Xanthotoxol201.0183−1.010.37-H✓✓✓✓
112Kaempferol-3-Arabofuranoside441.0769−2.310.56+Na✓✓✓
113Novobiocin639.19250.010.66-H2O+Na ✓✓
114Luteolin285.04−0.410.80-H✓
115Eucommin A573.1938−0.410.93+Na✓✓✓✓
116Citrusin B573.1932−1.110.94-H2O+Na✓ ✓
117(+)-Threo-Guaiacylglycerol219.06441.711.23-H2O+Na
118Genipingentiobioside585.16071.511.30+Cl✓
119Didymin595.20361.411.35+H, +Na✓✓✓✓
120Isosakuranetin287.09150.111.36+H✓✓✓✓
121Pectolinarin623.197−0.111.40+H✓✓✓✓
122Emodin Anthrone257.0797−1.111.54+H✓✓✓
123Lignans415.1381−0.611.79+H ✓
1243,3’,4’,5,6,7,8-heptamethoxyflavone433.1491−0.211.80+H✓✓✓✓
125Physcion283.0598−1.412.41-H✓ ✓
126Apigenin269.045−0.512.81-H✓
127Genistein269.0445−1.112.84-H✓✓
128IsoMeranzin243.1011−0.513.17-H2O+H, +H, +Na✓✓✓✓
1295,2’,6’-Trihydroxy-7,8-Dimethoxyflavone329.0651−1.613.22-H✓ ✓
130Tangeretin373.1281−0.114.19+H, +Na✓✓✓✓
131Chrysoobtusin357.0971−0.914.27-H ✓
132Gardenin B359.111−1.614.29+H✓
133P-Cymene135.1158−1.114.31+H ✓
134Coniferin297.1477−0.814.32-H2O+H✓ ✓✓
135Isolimonic Acid489.2118−0.114.36-H2O+H ✓✓
136Vitamin E491.2274−4.914.39+H ✓
137Marmin355.151−0.614.58+Na✓✓✓✓
1387-Hydroxyl-3,5,6,3′,4′-Pentamethoxyflavone389.1222−0.914.60+H✓✓✓✓
139Majudin217.0487−0.814.64+H✓✓ ✓
1407-Methoxy-5-Prenyloxycoumarin283.0932−0.914.69+Na ✓
1415,2’,5’-Trihydroxy-6,7,8-Trimethoxyflavone359.07750.214.75-H✓
142Gardenin A419.132−1.714.87+H✓✓✓✓
143Columbianadin329.1352−3.115.02+H ✓
144Cucurbic Acid211.1332−0.715.04-H ✓
145Isosinensetin373.1262−2.015.25+H✓
146Sinensetin373.1267−1.515.26+H, +Na ✓✓✓
147Obacunoic Acid473.2157−1.315.28+H✓✓✓✓
1483,5,6-Trihydroxy-7,4’-Dimethoxyflavone313.07−0.715.48-H2O+H ✓✓
149Javanicin313.06931.115.48+Na✓
1504’,5,7,8-Tetramethoxyflavone343.1172−0.515.55+H, +Na✓✓✓✓
151Elemicin231.10071.615.75+Na✓✓
152Balanophonin401.1233−0.915.81+HCOO ✓
153Isolimonicacid 16->17-Lactone471.2008−0.615.92-H2O+H, +H✓✓✓✓
154Nobiletin403.14122.516.40+H✓✓✓✓
155Thaliglucinone388.1136−2.016.42+Na✓✓✓✓
156Eupatoretin373.09310.216.60-H✓
157Cassiaside403.1016−1.816.65-H✓
158Nomilinicacid515.22770.216.96-H2O+H✓✓✓✓
159Nomilin515.2261−1.516.98+H✓ ✓
160Palmitic Acid274.2735−0.517.29+NH4✓✓✓✓
161Caffeic Acid Dimethyl Ether191.0693−0.917.39-H2O+H ✓
1623,5,6-Trihydroxy-7,3’,4’-Trimethoxyflavone343.0804−0.817.70-H2O+H✓✓✓✓
163Vomifoliol247.13171.218.86+Na ✓
1642,4,4-Trimethyl-3-(3-Oxobutyl) Cyclohex-2-Enone209.152−1.618.89+H✓✓✓✓
165Tauremisin265.1423−1.118.90+H, -H2O+H✓ ✓✓
166Dodec-2-Enal200.1996−1.318.94+NH4✓✓✓✓
167Phytosphingosine318.2987−1.620.36+H✓✓✓✓
168L-Leucine130.0867−0.620.57-H✓✓✓✓
169Aurapten297.15222.620.80-H✓✓✓✓
170Thalcimine619.28393.721.01-H2O+H ✓
171Dodecanoic Acid297.15230.421.04+HCOO✓✓✓✓
172Magnograndiolide265.1472.521.24-H✓✓
173Isotetrandrine640.34426.121.29+NH4✓ ✓
174Palmitoleic Acid277.2151.221.38+Na✓✓✓
175Zoomaric Acid277.21521.421.42+Na ✓
176Methyl Palmitate315.2523−1.821.45+HCOO✓✓✓
177Civetone295.2277−0.123.51+HCOO✓✓✓✓
1781-Palmitoyl-Sn-Glycero-3-Phosphocholine496.3394−0.324.01+H✓ ✓✓
179Ochrolifuanine A483.2731−3.524.62+HCOO✓✓
180Phthalic acid149.0222−1.125.28-H2O+H✓✓✓✓
181Diisobutyl phthalate279.1582−0.925.28+H✓ ✓✓
182Monopalmitin353.26650.226.50+Na✓✓✓✓
183Aplotaxene277.2166−0.727.71+HCOO✓✓✓
184Magnoflorine377.14131.327.72+Cl✓
185Β-Sitosterol397.3823−0.628.02-H2O+H ✓
186(3R)-3-Methylpentanal123.0780.028.81+Na✓ ✓
187Linoleic263.2364−0.629.19-H2O+H, +NH4✓✓✓✓
188β-Ecdysterone481.313−3.029.64+H ✓✓
Table 2. Neuroprotective candidate compounds in AF and AFI.
Table 2. Neuroprotective candidate compounds in AF and AFI.
No.Compound NameAFI-CADAF-CADAFI-CAAF-CANo.Compound NameAFI-CADAF-CADAFI-CAAF-CA
17-Hydroxycoumarin✓✓✓✓60Butylidenephthalide✓✓✓
2Arginine✓✓✓✓63Kaempferol✓
3Isopimpinellin✓ 65Eriodictuol✓✓✓✓
5Isomaltose ✓ ✓67Chrysophanein✓✓✓✓
6Limonin✓✓✓✓743,4,7-Trimethoxycoumarin✓✓✓✓
7Farnesyl Acetate✓✓✓✓82Paeonioflorin✓✓✓✓
8Heterodendrin✓ ✓ 88Torachrysone✓✓✓✓
9N-Methyl Proline✓✓✓✓98Deacetylnomilin✓✓
11Citric Acid✓✓✓✓1005,7-Dimethoxy Coumarin✓✓✓✓
147-Hydroxy-6-Methoxy-Coumarin✓✓✓ 101Dl-3-Phenyllactic Acid✓✓✓✓
15L-Synephrine Acetate✓✓✓✓102Seselin ✓
16Dopamine✓✓✓ 123Lignans ✓
20Dimethyl Anthranilate✓ ✓✓125Physcion✓ ✓
22Citronellyl Acetate ✓127Genistein✓✓
23Salicylic Acid ✓ 130Tangeretin✓✓✓✓
24Dehydrodieugenol✓✓✓✓1407-Methoxy-5-Prenyloxycoumarin ✓
29Subaphylline ✓✓152Balanophonin ✓
32Palmidin A✓✓ 155Thaliglucinone✓✓✓✓
34Caffetannic Acid✓✓ 160Palmitic Acid✓✓✓✓
35Ayapanin ✓161Caffeic Acid Dimethyl Ether ✓
44Phenethylamine ✓ 165Tauremisin✓ ✓✓
45Naringenin-4’-Glucoside-7-Rutinoside✓✓✓✓166Dodec-2-Enal✓✓✓✓
46(+/−)-Naringenin✓✓✓✓168L-Leucine✓✓✓✓
53Testosterone ✓✓169Aurapten✓✓✓✓
552-Hydroxy-6-Methoxybenzoic Acid✓✓✓✓176Methyl Palmitate✓✓✓
Table 3. The potential neuroprotective pharmacological targets of AF and AFI.
Table 3. The potential neuroprotective pharmacological targets of AF and AFI.
ExcitotoxicAntioxidant
XDHGRIN2BAPPIL1BCSNK2A1G6PD
AKT1PRKCGPRKCACAPN1NFKB1FABP1
DAOGRM2MAPK10SLC8A1STAT3NR1I3
GSRADORA2ATP53SLC1A1CASP3NR1I2
PARP1GAPDHPPARGGRIK1MAPK14PPARA
SNCAHSPA8PLA2G2AGRIA2VCPIL6
ACHESLC1A2GLULBIRC3BCL2ICAM1
NOS2CHRNA7MAPTBIRC2CTSBVCAM1
NOS1PTGS2PIK3CGGRIN2ANR1H4HMOX1
JAK2SRCCDK5 PTGS1ODC1
VEGFAGRIN1TGFB1 ALBCREBBP
FGF2THGRK2 NQO1PGD
DRD2HTTXIAP EP300SOAT1
FOLH1GRM5TGM2 NOX4HDAC3
OPRM1CYP19A1NTRK3 MPOPLA2G6
MAPK1GRIA4HDAC9 CSNK2A2PON1
TNFDAPK1PLAT NFE2L2CXCR3
BCL2L1RPS6KA5SLC1A3 ABCC1SIRT3
KCNJ5MAPK8NTRK2 CXCL8NR0B2
CNR1GRIA1PSEN1 GSTA1
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Qiu, M.; Zhang, J.; Wei, W.; Zhang, Y.; Li, M.; Bai, Y.; Wang, H.; Meng, Q.; Guo, D.-a. Integrated UPLC/Q-TOF-MS/MS Analysis and Network Pharmacology to Reveal the Neuroprotective Mechanisms and Potential Pharmacological Ingredients of Aurantii Fructus Immaturus and Aurantii Fructus. Pharmaceuticals 2024, 17, 239. https://doi.org/10.3390/ph17020239

AMA Style

Qiu M, Zhang J, Wei W, Zhang Y, Li M, Bai Y, Wang H, Meng Q, Guo D-a. Integrated UPLC/Q-TOF-MS/MS Analysis and Network Pharmacology to Reveal the Neuroprotective Mechanisms and Potential Pharmacological Ingredients of Aurantii Fructus Immaturus and Aurantii Fructus. Pharmaceuticals. 2024; 17(2):239. https://doi.org/10.3390/ph17020239

Chicago/Turabian Style

Qiu, Mingyang, Jianqing Zhang, Wenlong Wei, Yan Zhang, Mengmeng Li, Yuxin Bai, Hanze Wang, Qian Meng, and De-an Guo. 2024. "Integrated UPLC/Q-TOF-MS/MS Analysis and Network Pharmacology to Reveal the Neuroprotective Mechanisms and Potential Pharmacological Ingredients of Aurantii Fructus Immaturus and Aurantii Fructus" Pharmaceuticals 17, no. 2: 239. https://doi.org/10.3390/ph17020239

APA Style

Qiu, M., Zhang, J., Wei, W., Zhang, Y., Li, M., Bai, Y., Wang, H., Meng, Q., & Guo, D. -a. (2024). Integrated UPLC/Q-TOF-MS/MS Analysis and Network Pharmacology to Reveal the Neuroprotective Mechanisms and Potential Pharmacological Ingredients of Aurantii Fructus Immaturus and Aurantii Fructus. Pharmaceuticals, 17(2), 239. https://doi.org/10.3390/ph17020239

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