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. 2024 Feb 12;17(2):239. doi: 10.3390/ph17020239

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

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, De-an Guo 1,2,*
Editor: Elena Cichero
PMCID: PMC10892462  PMID: 38399454

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.

Keywords: UPLC/Q-TOF-MS/MS, Aurantii Fructus Immaturus, Aurantii Fructus, network pharmacology, neuroprotection

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.

Figure 1.

Figure 1

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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.

Table 1.

Compounds identified in AF and AFI by UNIFI software.

No. Compound Name Observed m/z Mass Error (mDa) Observed RT (min) Adducts AFI-CA D AF-CAD AFI-CA AF-CA
1 7-Hydroxycoumarin 167.0119 1.5 1.01 -H2O+Na ✓ ✓ ✓ ✓
2 Arginine 175.118 −1.0 1.05 +H ✓ ✓ ✓ ✓
3 Isopimpinellin 251.0299 −1.6 1.09 -H2O+Na ✓
4 Isoprenol 104.1062 −0.8 1.09 +NH4 ✓ ✓ ✓ ✓
5 Isomaltose 341.1088 −0.1 1.11 -H ✓ ✓
6 Limonin 475.1763 3.6 1.12 -H2O+Na, +H ✓ ✓ ✓ ✓
7 Farnesyl Acetate 287.196 −2.2 1.17 +Na ✓ ✓ ✓ ✓
8 Heterodendrin 262.1276 −0.9 1.18 +H ✓ ✓
9 N-Methyl Proline 130.0852 −1.0 1.23 +H ✓ ✓ ✓ ✓
10 Betonicine 160.096 −0.8 1.24 +H ✓ ✓ ✓ ✓
11 Citric Acid 215.0155 −0.7 1.35 +Na ✓ ✓ ✓ ✓
12 5-Hydroxymethyl Furaldehyde 127.0383 −0.7 1.35 +H ✓ ✓ ✓ ✓
13 5-(Hydroxymethyl)Furan-3-Carbaldehyde 287.979 −0.7 1.36 -H2O+Na ✓
14 7-Hydroxy-6-Methoxy-Coumarin 193.0481 −1.4 1.39 +H ✓ ✓ ✓
15 L-Synephrine Acetate 150.0913 0.0 1.42 -H2O+H ✓ ✓ ✓ ✓
16 Dopamine 136.0746 −1.1 1.43 -H2O+H ✓ ✓ ✓
17 L-Tyrosine 182.0808 −0.4 1.43 +H ✓ ✓ ✓
18 N-Methyltyramine 152.1065 −0.5 1.45 +H ✓ ✓ ✓
19 Tyrosol 121.064 −0.8 1.45 -H2O+H ✓ ✓ ✓ ✓
20 Dimethyl Anthranilate 166.0856 −0.7 1.55 +H ✓ ✓ ✓
21 Tyramine 120.0802 −0.6 1.56 -H2O+H ✓ ✓ ✓ ✓
22 Citronellyl Acetate 203.1389 −1.7 1.62 -H2O+Na ✓
23 Salicylic Acid 137.024 −0.4 1.69 -H ✓
24 Dehydrodieugenol 349.138 −3.1 1.73 +Na ✓ ✓ ✓ ✓
25 Vanillin 153.0531 −1.5 1.74 +H ✓ ✓ ✓
26 Epigallocatechin 324.1077 0.0 1.76 +NH4 ✓
27 Rutin 611.1617 1.1 1.79 +H ✓ ✓ ✓ ✓
28 Isocoumarin 147.0428 −1.2 1.93 +H ✓ ✓
29 Subaphylline 265.1542 −0.4 1.94 +H ✓ ✓
30 Tryptophan 205.097 −0.1 2.00 +H ✓ ✓ ✓ ✓
31 Geniposide 389.1408 −3.4 2.05 +H ✓
32 Palmidin A 493.1303 2.1 2.05 -H2O+H ✓ ✓
33 4-Hydroxy-3-Methoxystrychnine 195.0659 −0.4 2.06 +HCOO ✓
34 Caffetannic Acid 355.1007 −1.6 2.10 +H, +Na ✓ ✓
35 Ayapanin 177.0538 −0.8 2.12 +H ✓
36 Scolymoside 595.1667 1.0 2.20 +H ✓ ✓
37 Vicenin 595.1665 0.8 2.23 +H, -H2O+H ✓ ✓ ✓ ✓
38 5,7-Dihydroxychromone 7-rutinoside 487.1441 −0.5 2.24 +H ✓ ✓ ✓
39 Ferulic Acid 177.0537 −0.9 2.27 -H2O+H ✓ ✓ ✓ ✓
40 Hyperoside 465.1027 0.0 2.31 +H ✓ ✓
41 Chrysophanol-1-O-β-gentiobioside 623.1598 −1.9 2.35 +HCOO, -H ✓
42 Benzoic acid 105.0325 −1.0 2.39 -H2O+H ✓ ✓ ✓
43 Isorhamnetin-3-Rutinoside 625.1767 0.4 2.43 +H ✓ ✓ ✓ ✓
44 Phenethylamine 144.079 0.7 2.55 +Na ✓
45 Naringenin-4’-Glucoside-7-Rutinoside 765.2212 0.0 2.78 +Na ✓ ✓ ✓ ✓
46 (+/−)-Naringenin 273.0752 −0.6 2.78 +H ✓ ✓ ✓ ✓
47 Narirutin 581.1867 0.2 2.80 +H ✓ ✓ ✓
48 Phenylacetic acid 135.0446 −0.5 2.81 -H ✓ ✓
49 Salipurposide 435.1273 −1.3 2.82 +H ✓ ✓ ✓ ✓
50 Methyl Chlorogenate 391.0965 −3.5 2.82 +Na ✓ ✓ ✓ ✓
51 Eufin 123.0428 1.2 2.94 -H2O+Na ✓
52 Cinaroside 449.1068 −1.0 2.94 +H, -H2O+H ✓
53 Testosterone 293.1848 −2.8 2.94 -H2O+Na ✓ ✓
54 Naringenin-7-O-Glucuronide 431.0937 −3.6 2.99 -H2O+H ✓
55 2-Hydroxy-6-Methoxybenzoic Acid 151.0379 −1.1 3.10 -H2O+H, +H ✓ ✓ ✓ ✓
56 Eriodictyol-7-Glucoside 473.1056 0.2 3.27 +Na ✓ ✓ ✓ ✓
57 Coumarin 191.0345 −0.5 3.49 +HCOO ✓
58 Vitamin B 442.1463 −0.7 3.64 +H ✓
59 5,7-Dihydroxychromone 179.0328 −1.1 3.75 +H ✓ ✓ ✓
60 Butylidenephthalide 189.0897 −1.3 3.79 +H ✓ ✓ ✓
61 Helenalin 263.1256 −2.2 3.88 +H ✓ ✓ ✓ ✓
62 Emodin 8-glucoside 433.1119 −1.0 3.90 +H ✓ ✓
63 Kaempferol 287.0545 −0.5 4.15 +H ✓
64 Genioisidic Acid 379.0991 −0.9 4.23 -H2O+Na ✓
65 Eriodictuol 289.0688 −1.8 4.23 +H ✓ ✓ ✓ ✓
66 Ombuin 331.0805 −0.8 4.24 +H, -H2O+H ✓
67 Chrysophanein 417.1178 −0.2 4.24 +H ✓ ✓ ✓ ✓
68 Lonicerin 595.1643 −1.4 4.28 +H ✓ ✓ ✓ ✓
69 natsudaidain 419.131 −2.6 4.53 +H ✓ ✓ ✓ ✓
70 Caffeic Acid 163.0376 −1.4 4.62 -H2O+H ✓ ✓ ✓ ✓
71 Oleuropein 523.1775 −3.5 4.68 -H2O+H ✓ ✓ ✓
72 Hesperetin-7-O-β-D-Glucoside 487.1202 −0.9 4.82 +Na ✓ ✓ ✓ ✓
73 Hesperidin Methyl Chalcone 625.2081 −4.6 4.85 +H ✓ ✓ ✓
74 3,4,7-Trimethoxycoumarin 237.0745 −1.3 4.94 +H ✓ ✓ ✓ ✓
75 Narirutin-isomer 581.1862 −0.3 5.02 +H, +Na ✓
76 Curculigoside 449.1429 −1.3 5.20 -H2O+H ✓ ✓ ✓ ✓
77 Homoeriodictyol 303.0846 −1.7 5.22 +H ✓ ✓ ✓ ✓
78 Chryso-Obtusin Glucoside 565.1554 −0.9 5.24 +HCOO ✓ ✓ ✓ ✓
79 Rhoifolin 579.1714 0.5 5.34 +H, +Na ✓ ✓ ✓ ✓
80 Eriocitrin 579.1701 −0.8 5.39 -H2O+H ✓ ✓ ✓ ✓
81 Meranzin Hydrate 261.1109 −1.2 5.50 -H2O+H ✓ ✓ ✓ ✓
82 Paeonioflorin 463.1566 −3.2 5.52 -H2O+H ✓ ✓ ✓ ✓
83 Gallic Acid 153.0171 −1.1 5.60 -H2O+H ✓ ✓ ✓ ✓
84 Physcion-8-O-Beta-D-Gentiobioside 609.1801 −1.3 5.79 +H ✓ ✓ ✓
85 Diosmin 609.1818 0.4 5.81 +H ✓ ✓ ✓
86 Hesperetin-7-O-Neohesperidoside 633.1781 −0.9 6.02 +Na ✓
87 Neohesperidin 633.1781 −0.9 6.02 +Na ✓
88 Torachrysone 431.1337 2.5 6.03 +Na ✓ ✓ ✓ ✓
89 Diosmetin 301.0697 −1.0 6.11 +H ✓ ✓ ✓
90 Pinoresinol Dimethyl Ether 404.2054 −1.4 6.12 +NH4 ✓ ✓ ✓ ✓
91 Rubrofusarin-6-Β-Gentiobioside 595.1663 −0.6 6.31 -H ✓ ✓
92 Hesperidins 633.18 1.0 6.65 +Na ✓
93 Obtusin 345.0963 −0.6 6.79 +H ✓ ✓ ✓ ✓
94 Coptisine 303.0894 0.4 7.08 -H2O+H ✓ ✓
95 Citromitin 449.1448 −0.6 7.12 +HCOO ✓ ✓
96 3-Tert-Butyladipic Acid 207.1001 1.0 7.66 -H2O+Na ✓
97 Nomilinic acid Glucoside 717.2709 −2.0 7.76 -H2O+Na ✓ ✓ ✓
98 Deacetylnomilin 473.2162 −0.8 7.78 +H ✓ ✓
99 5,7,4’-Trimethoxyflavone 317.0769 −1.6 7.91 -H2O+Na ✓ ✓ ✓ ✓
100 5,7-Dimethoxy Coumarin 189.0542 −0.4 7.92 -H2O+H ✓ ✓ ✓ ✓
101 Dl-3-Phenyllactic Acid 189.0535 1.3 7.93 +Na ✓ ✓ ✓ ✓
102 Seselin 227.0705 −0.8 7.94 -H ✓
103 Resveratrol 227.0707 −0.7 7.94 -H ✓
104 Salireposide 451.1231 −1.5 8.20 +HCOO ✓ ✓
105 Meranzin 261.1111 −1.0 8.23 +H ✓ ✓ ✓
106 Naringin 581.1859 −0.6 8.48 +H ✓ ✓ ✓ ✓
107 Terpinyl Acetate 241.1441 −0.5 8.80 +HCOO ✓ ✓
108 Eucommioside 385.1277 0.7 9.54 +Cl ✓
109 6-O-Benzoylphlorigidoside B 551.1747 −1.2 9.79 -H2O+H ✓
110 Obacunone 455.205 −1.4 9.84 +H ✓ ✓
111 Xanthotoxol 201.0183 −1.0 10.37 -H ✓ ✓ ✓ ✓
112 Kaempferol-3-Arabofuranoside 441.0769 −2.3 10.56 +Na ✓ ✓ ✓
113 Novobiocin 639.1925 0.0 10.66 -H2O+Na ✓ ✓
114 Luteolin 285.04 −0.4 10.80 -H ✓
115 Eucommin A 573.1938 −0.4 10.93 +Na ✓ ✓ ✓ ✓
116 Citrusin B 573.1932 −1.1 10.94 -H2O+Na ✓ ✓
117 (+)-Threo-Guaiacylglycerol 219.0644 1.7 11.23 -H2O+Na
118 Genipingentiobioside 585.1607 1.5 11.30 +Cl ✓
119 Didymin 595.2036 1.4 11.35 +H, +Na ✓ ✓ ✓ ✓
120 Isosakuranetin 287.0915 0.1 11.36 +H ✓ ✓ ✓ ✓
121 Pectolinarin 623.197 −0.1 11.40 +H ✓ ✓ ✓ ✓
122 Emodin Anthrone 257.0797 −1.1 11.54 +H ✓ ✓ ✓
123 Lignans 415.1381 −0.6 11.79 +H ✓
124 3,3’,4’,5,6,7,8-heptamethoxyflavone 433.1491 −0.2 11.80 +H ✓ ✓ ✓ ✓
125 Physcion 283.0598 −1.4 12.41 -H ✓ ✓
126 Apigenin 269.045 −0.5 12.81 -H ✓
127 Genistein 269.0445 −1.1 12.84 -H ✓ ✓
128 IsoMeranzin 243.1011 −0.5 13.17 -H2O+H, +H, +Na ✓ ✓ ✓ ✓
129 5,2’,6’-Trihydroxy-7,8-Dimethoxyflavone 329.0651 −1.6 13.22 -H ✓ ✓
130 Tangeretin 373.1281 −0.1 14.19 +H, +Na ✓ ✓ ✓ ✓
131 Chrysoobtusin 357.0971 −0.9 14.27 -H ✓
132 Gardenin B 359.111 −1.6 14.29 +H ✓
133 P-Cymene 135.1158 −1.1 14.31 +H ✓
134 Coniferin 297.1477 −0.8 14.32 -H2O+H ✓ ✓ ✓
135 Isolimonic Acid 489.2118 −0.1 14.36 -H2O+H ✓ ✓
136 Vitamin E 491.2274 −4.9 14.39 +H ✓
137 Marmin 355.151 −0.6 14.58 +Na ✓ ✓ ✓ ✓
138 7-Hydroxyl-3,5,6,3′,4′-Pentamethoxyflavone 389.1222 −0.9 14.60 +H ✓ ✓ ✓ ✓
139 Majudin 217.0487 −0.8 14.64 +H ✓ ✓ ✓
140 7-Methoxy-5-Prenyloxycoumarin 283.0932 −0.9 14.69 +Na ✓
141 5,2’,5’-Trihydroxy-6,7,8-Trimethoxyflavone 359.0775 0.2 14.75 -H ✓
142 Gardenin A 419.132 −1.7 14.87 +H ✓ ✓ ✓ ✓
143 Columbianadin 329.1352 −3.1 15.02 +H ✓
144 Cucurbic Acid 211.1332 −0.7 15.04 -H ✓
145 Isosinensetin 373.1262 −2.0 15.25 +H ✓
146 Sinensetin 373.1267 −1.5 15.26 +H, +Na ✓ ✓ ✓
147 Obacunoic Acid 473.2157 −1.3 15.28 +H ✓ ✓ ✓ ✓
148 3,5,6-Trihydroxy-7,4’-Dimethoxyflavone 313.07 −0.7 15.48 -H2O+H ✓ ✓
149 Javanicin 313.0693 1.1 15.48 +Na ✓
150 4’,5,7,8-Tetramethoxyflavone 343.1172 −0.5 15.55 +H, +Na ✓ ✓ ✓ ✓
151 Elemicin 231.1007 1.6 15.75 +Na ✓ ✓
152 Balanophonin 401.1233 −0.9 15.81 +HCOO ✓
153 Isolimonicacid 16->17-Lactone 471.2008 −0.6 15.92 -H2O+H, +H ✓ ✓ ✓ ✓
154 Nobiletin 403.1412 2.5 16.40 +H ✓ ✓ ✓ ✓
155 Thaliglucinone 388.1136 −2.0 16.42 +Na ✓ ✓ ✓ ✓
156 Eupatoretin 373.0931 0.2 16.60 -H ✓
157 Cassiaside 403.1016 −1.8 16.65 -H ✓
158 Nomilinicacid 515.2277 0.2 16.96 -H2O+H ✓ ✓ ✓ ✓
159 Nomilin 515.2261 −1.5 16.98 +H ✓ ✓
160 Palmitic Acid 274.2735 −0.5 17.29 +NH4 ✓ ✓ ✓ ✓
161 Caffeic Acid Dimethyl Ether 191.0693 −0.9 17.39 -H2O+H ✓
162 3,5,6-Trihydroxy-7,3’,4’-Trimethoxyflavone 343.0804 −0.8 17.70 -H2O+H ✓ ✓ ✓ ✓
163 Vomifoliol 247.1317 1.2 18.86 +Na ✓
164 2,4,4-Trimethyl-3-(3-Oxobutyl) Cyclohex-2-Enone 209.152 −1.6 18.89 +H ✓ ✓ ✓ ✓
165 Tauremisin 265.1423 −1.1 18.90 +H, -H2O+H ✓ ✓ ✓
166 Dodec-2-Enal 200.1996 −1.3 18.94 +NH4 ✓ ✓ ✓ ✓
167 Phytosphingosine 318.2987 −1.6 20.36 +H ✓ ✓ ✓ ✓
168 L-Leucine 130.0867 −0.6 20.57 -H ✓ ✓ ✓ ✓
169 Aurapten 297.1522 2.6 20.80 -H ✓ ✓ ✓ ✓
170 Thalcimine 619.2839 3.7 21.01 -H2O+H ✓
171 Dodecanoic Acid 297.1523 0.4 21.04 +HCOO ✓ ✓ ✓ ✓
172 Magnograndiolide 265.147 2.5 21.24 -H ✓ ✓
173 Isotetrandrine 640.3442 6.1 21.29 +NH4 ✓ ✓
174 Palmitoleic Acid 277.215 1.2 21.38 +Na ✓ ✓ ✓
175 Zoomaric Acid 277.2152 1.4 21.42 +Na ✓
176 Methyl Palmitate 315.2523 −1.8 21.45 +HCOO ✓ ✓ ✓
177 Civetone 295.2277 −0.1 23.51 +HCOO ✓ ✓ ✓ ✓
178 1-Palmitoyl-Sn-Glycero-3-Phosphocholine 496.3394 −0.3 24.01 +H ✓ ✓ ✓
179 Ochrolifuanine A 483.2731 −3.5 24.62 +HCOO ✓ ✓
180 Phthalic acid 149.0222 −1.1 25.28 -H2O+H ✓ ✓ ✓ ✓
181 Diisobutyl phthalate 279.1582 −0.9 25.28 +H ✓ ✓ ✓
182 Monopalmitin 353.2665 0.2 26.50 +Na ✓ ✓ ✓ ✓
183 Aplotaxene 277.2166 −0.7 27.71 +HCOO ✓ ✓ ✓
184 Magnoflorine 377.1413 1.3 27.72 +Cl ✓
185 Β-Sitosterol 397.3823 −0.6 28.02 -H2O+H ✓
186 (3R)-3-Methylpentanal 123.078 0.0 28.81 +Na ✓ ✓
187 Linoleic 263.2364 −0.6 29.19 -H2O+H, +NH4 ✓ ✓ ✓ ✓
188 β-Ecdysterone 481.313 −3.0 29.64 +H ✓ ✓
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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.

Figure 2.

Figure 2

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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.

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.

Figure 3.

Figure 3

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Disease–target networks for neuroprotection. (A) Excitotoxicity-associated target network; (B) antioxidation target network.

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].

Figure 4.

Figure 4

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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.

Table 2.

Neuroprotective candidate compounds in AF and AFI.

No. Compound Name AFI-CAD AF-CAD AFI-CA AF-CA No. Compound Name AFI-CAD AF-CAD AFI-CA AF-CA
1 7-Hydroxycoumarin ✓ ✓ ✓ ✓ 60 Butylidenephthalide ✓ ✓ ✓
2 Arginine ✓ ✓ ✓ ✓ 63 Kaempferol ✓
3 Isopimpinellin ✓ 65 Eriodictuol ✓ ✓ ✓ ✓
5 Isomaltose ✓ ✓ 67 Chrysophanein ✓ ✓ ✓ ✓
6 Limonin ✓ ✓ ✓ ✓ 74 3,4,7-Trimethoxycoumarin ✓ ✓ ✓ ✓
7 Farnesyl Acetate ✓ ✓ ✓ ✓ 82 Paeonioflorin ✓ ✓ ✓ ✓
8 Heterodendrin ✓ ✓ 88 Torachrysone ✓ ✓ ✓ ✓
9 N-Methyl Proline ✓ ✓ ✓ ✓ 98 Deacetylnomilin ✓ ✓
11 Citric Acid ✓ ✓ ✓ ✓ 100 5,7-Dimethoxy Coumarin ✓ ✓ ✓ ✓
14 7-Hydroxy-6-Methoxy-Coumarin ✓ ✓ ✓ 101 Dl-3-Phenyllactic Acid ✓ ✓ ✓ ✓
15 L-Synephrine Acetate ✓ ✓ ✓ ✓ 102 Seselin ✓
16 Dopamine ✓ ✓ ✓ 123 Lignans ✓
20 Dimethyl Anthranilate ✓ ✓ ✓ 125 Physcion ✓ ✓
22 Citronellyl Acetate ✓ 127 Genistein ✓ ✓
23 Salicylic Acid ✓ 130 Tangeretin ✓ ✓ ✓ ✓
24 Dehydrodieugenol ✓ ✓ ✓ ✓ 140 7-Methoxy-5-Prenyloxycoumarin ✓
29 Subaphylline ✓ ✓ 152 Balanophonin ✓
32 Palmidin A ✓ ✓ 155 Thaliglucinone ✓ ✓ ✓ ✓
34 Caffetannic Acid ✓ ✓ 160 Palmitic Acid ✓ ✓ ✓ ✓
35 Ayapanin ✓ 161 Caffeic Acid Dimethyl Ether ✓
44 Phenethylamine ✓ 165 Tauremisin ✓ ✓ ✓
45 Naringenin-4’-Glucoside-7-Rutinoside ✓ ✓ ✓ ✓ 166 Dodec-2-Enal ✓ ✓ ✓ ✓
46 (+/−)-Naringenin ✓ ✓ ✓ ✓ 168 L-Leucine ✓ ✓ ✓ ✓
53 Testosterone ✓ ✓ 169 Aurapten ✓ ✓ ✓ ✓
55 2-Hydroxy-6-Methoxybenzoic Acid ✓ ✓ ✓ ✓ 176 Methyl Palmitate ✓ ✓ ✓
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Figure 5.

Figure 5

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A Venn diagram of neuroprotective candidate compounds among AF-CA, AFI-CA, AF-CAD, and AFI-CAD.

Table 3.

The potential neuroprotective pharmacological targets of AF and AFI.

Excitotoxic Antioxidant
XDH GRIN2B APP IL1B CSNK2A1 G6PD
AKT1 PRKCG PRKCA CAPN1 NFKB1 FABP1
DAO GRM2 MAPK10 SLC8A1 STAT3 NR1I3
GSR ADORA2A TP53 SLC1A1 CASP3 NR1I2
PARP1 GAPDH PPARG GRIK1 MAPK14 PPARA
SNCA HSPA8 PLA2G2A GRIA2 VCP IL6
ACHE SLC1A2 GLUL BIRC3 BCL2 ICAM1
NOS2 CHRNA7 MAPT BIRC2 CTSB VCAM1
NOS1 PTGS2 PIK3CG GRIN2A NR1H4 HMOX1
JAK2 SRC CDK5 PTGS1 ODC1
VEGFA GRIN1 TGFB1 ALB CREBBP
FGF2 TH GRK2 NQO1 PGD
DRD2 HTT XIAP EP300 SOAT1
FOLH1 GRM5 TGM2 NOX4 HDAC3
OPRM1 CYP19A1 NTRK3 MPO PLA2G6
MAPK1 GRIA4 HDAC9 CSNK2A2 PON1
TNF DAPK1 PLAT NFE2L2 CXCR3
BCL2L1 RPS6KA5 SLC1A3 ABCC1 SIRT3
KCNJ5 MAPK8 NTRK2 CXCL8 NR0B2
CNR1 GRIA1 PSEN1 GSTA1
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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.

Figure 6.

Figure 6

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The top 30 enriched gene ontology terms for the biological processes of potential targets.

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.

pharmaceuticals-17-00239-s001.zip (645.3KB, zip)

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.

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.

Funding Statement

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).

Footnotes

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

pharmaceuticals-17-00239-s001.zip (645.3KB, zip)

Data Availability Statement

Data is contained within the article and Supplementary Materials.

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