The eight herbs and TFDC power (0.5 g) were accurately weighed and sonicated with 50% methanol (25 ml) for 30 min at room temperature. The extracted solution was centrifuged at 1,000 × g for 10 min at 4°C. Then, the supernatant was transferred to a 50-ml volumetric flask and the precipitate was re-extracted with another 25 ml 50% methanol. After centrifugation, the extracted solution was transferred into the same volumetric flask and filled to the mark. The prepared sample solution was filtered through a 0.22-μm pore-size filter before LC-MS analysis.

The TFDC power (10 g) was accurately weighed and sonicated with water (100 ml) for 30 min at room temperature. The extracted solution was centrifuged at 1,000 × g for 10 min at 4°C. Then, the supernatant was transferred into an evaporating dish and the precipitate was re-extracted with another 100 ml water. After centrifugation, the extracted solution was transferred into the same evaporating dish and concentrated using a water bath at 55°C to 100 ml (100 mg/ml).

The compounds were identified using a Vanquish Horizon UPLC system (Thermo Fisher Scientific, Waltham, MA, USA) connected to a Q Exactive Hybrid Quadrupole-Orbitrap high-resolution mass spectrometer (Thermo Fisher Scientific). The chromatographic separation was accomplished by a Waters ACQUITY UPLC BEH Shield RP C18 column (100 mm × 2.1 mm, 1.7 µm) with a column temperature of 35°C. Gradient elution of the analytes was carried out with 0.1% formic acid in water (A) and acetonitrile (B) at a flow rate of 0.3 ml/min. A linear gradient of solvent B (v/v) was applied as follows: 0–1 min, 5% B; 1–2.4 min, 5–10% B; 2.4–13.5 min, 10–32% B; 13.5–18.5 min, 32–90% B; 18.5–19 min, 90–5% B; and 19–21 min, 5% B. The injection volume was 2 µl.

The heated electrospray ionization (H-ESI) source was operated and the MS parameters were optimized as follows: spray voltage of 3.8 and −3.2 kV in positive and negative modes, respectively; sheath gas flow, 35 a.u.; aux gas flow, 15 a.u.; aux gas heater temperature, 300°C; and capillary temperature, 350°C. A full MS scan/data-dependent MS² (full MS/dd MS²) method was used for analysis. The full mass scan range was set from 100 to 1,500 in positive and negative ion modes at resolutions of 70,000 and 17,500 for the MS² scan, respectively. The tandem mass spectrometry (MS/MS) spectra were fragmented by high-energy collision-induced dissociation (HCD) of the normalized collision energy (NCE) values at the levels of 20%, 40%, and 60%. Data acquisition and processing were accomplished with the Xcalibur software (version 4.2; Thermo Fisher Scientific).

The surgical operations for IPVS were performed as reported in the literature (Li et al., 2014). Briefly, prior to the initiation of perfusion surgical operation, about 50 ml blood was withdrawn from several rats via the abdominal aorta, and the blood incubated in a 37°C water bath was prepared to be transfused into the recipient rat. The recipient was anesthetized with chloral hydrate (400 mg/kg) by intraperitoneal injection and then restrained in a supine position under an infrared lamp to maintain the body temperature. The left jugular vein of the anesthetized rat was exposed and a 24-G BD Intima II catheter filled with heparinized saline (100 U/ml) was inserted into the vein and secured. Then, the catheter was connected by a silicone tube filled with blood to a peristaltic pump, which is placed between the donor blood reservoir and the catheter; the other end of the silicone tube was immersed in the donor blood. The abdominal cavity was carefully opened along the abdominal line to expose the jejunum and the mesenteric vein. Two ends of the jejunum segment were incised with surgical scissors, and then two silicone tubes were inserted through the small slits and secured. After that, the inlet tubing was connected to a syringe pump. Subsequently, the 24-G catheter with heparinized saline was intubated into the mesenteric vein and secured. Then, the catheter was also connected by the silicone tube to the same peristaltic pump utilized for blood supply.

At the conclusion of the surgical operation, the proximal mesenteric vein was ligated with silk suture, and then the two catheters and pumps were switched on immediately. The perfusate was perfused at a flow rate of 0.2 ml/min and the blood was supplied at a flow rate of 0.3 ml/min. During the perfusion, the blood from the mesenteric vein was collected in heparinized centrifuge tubes within 2 h.

A certain amount of TFDC content was dissolved in 0.5% sodium carboxymethyl cellulose (NaCMC) in water to achieve a homogenous suspension at 0.15 g/ml concentration. Male Sprague–Dawley rats were given 4 ml suspensions intragastrically. After 2 h, the rats were anesthetized with chloral hydrate (400 mg/kg) by intraperitoneal injection, and blood was collected via the abdominal aorta.

The collected blood was centrifuged at 3,300 × g for 10 min, and then the supernatant was precipitated by an equal volume of acetonitrile, followed by centrifugation at 11,800 × g for 10 min at 4°C after vortexing for 2 min. Next, the supernatant was evaporated to dryness under a gentle stream of nitrogen at 50°C. Finally, the samples were reconstituted with 200 µl 50% methanol and subjected to LC/MS analysis.

Firstly, the structures of the absorbed prototype components of TFDC were generated using ChemBioDraw Ultra 19.0 and saved in SDF format. Then, based on structure similarity, the targets associated with these TFDC components were predicted with MedChem Studio (MedChem Studio, 3.0, 2012; Simulations Plus, Inc., Lancaster, CA, USA). The similarity threshold was set at 0.7.

Gout-associated targets were obtained from the following databases using “gout” and “gouty arthritis” as the keywords and limiting the species with “Homo sapiens”: DrugBank (https://go.drugbank.com/) (Wishart et al., 2018), the Online Mendelian Inheritance in Man (OMIM; https://www.omim.org) (Amberger et al., 2019), Therapeutic Targets Database (TTD; http://db.idrblab.net/ttd/) (Wang et al., 2020), and PubMed (https://pubmed.ncbi.nlm.nih.gov/). Then, the collected target gene information was standardized using UniProt (https://www.uniprot.org/) (UniProt Consortium, 2021), and the genes without the human sample, Uniprot ID, were excluded.

The intersection of the predicted targets from MedChem Studio and the known targets of gout were selected for the following bioinformatics analyses. These overlapping targets were further checked and retrieved into UniProt ID by using UniProt. These selected targets were imported into DAVID bioinformatics resources (https://david.ncifcrf.gov/) (Jiao et al., 2012) to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, where targets were only restricted to H. sapiens.

The absorbed prototype components of TFDC and the selected gout-related overlapping targets were correspondingly prepared as XLSX. These targets and the enriched KEGG pathways were also correspondingly arranged as XLSX. The two files were imported into Cytoscape 3.8.2 and merged to construct the component–target–pathway network of TFDC for treating gout.

Alkaloids are a group of basic nitrogen-containing natural products of vegetable origin (Maldoni, 1991). In the present study, the main sources of alkaloids were GM and CR. A total of 52 alkaloids were tentatively identified in the positive model, including 19 protoberberine alkaloids, 14 tetrahydroprotoberberine alkaloids, 3 aporphine alkaloids, 3 protopine alkaloids, 6 benzylisoquinoline alkaloids, and 7 other alkaloids, which are summarized in Supplementary Figure S1. Firstly, we chose berberine (peak 88), tetrahydropalmatine (peak 66), magnoflorine (peak 30), protopine (peak 55), and magnocurarine (peak 16) as the representative compounds to illustrate the characteristic fragmentation rules of protoberberine alkaloids, tetrahydroprotoberberine alkaloids, aporphine alkaloids, protopine alkaloids, and benzylisoquinoline alkaloids, respectively. Their MS² spectra and proposed fragmentation pathways in positive mode are presented in Supplementary Figures S2–S6. Then, the flowchart for the identification of the structure type of alkaloids in TFDC is summarized and presented in Figure 3. Furthermore, 45 alkaloids were identified from TFDC through a comparison of their exact molecular mass, MS/MS spectra, and chromatographic behavior with those of literature data (Cheng et al., 2010; Hong et al., 2012; Jeong et al., 2012; Ji et al., 2012; Shan et al., 2018; Tan et al., 2013; Xian et al., 2014; Wang et al., 2016; Sun et al., 2016; Zuo et al., 2018). Detailed information on these compounds is summarized in Supplementary Table S1.

Seven other alkaloids were also discovered in TFDC (peaks 1, 3, 5, 41, 47, 113, and 117). Among them, compounds 1 and 3 were identified as adenine and adenosine via reference standards, respectively. Based on their exact molecular mass, MS/MS spectra, and chromatographic behavior compared with those of literature data, compounds 5, 41, 47, 113, and 117 were tentatively identified as nicotinic acid (Zou et al., 2015), plantagoguanidinic acid (Wang et al., 2016), dasycarpamin (Wang et al., 2013), skimmianine (Xian et al., 2014), and γ-fagarine (Xian et al., 2014), respectively.

The basic structure of flavonoids consists of two benzene rings (A and B) linked by a heterocyclic pyrane ring (C ring). Flavonoids are further classified into several subclasses (flavones, flavonols, flavanones, flavanonol, etc.) (Zou et al., 2015). In the present study, a total of 22 flavonoids were unambiguously or tentatively identified from TFDC in the positive and negative modes, and the main source of flavonoids was SG. In order to explore the fragmentation pathways of flavonoids, seven standards—catechin (peak 40), epicatechin (peak 50), astilbin (peak 103), quercetin (peak 108), engeletin (peak 111), luteolin (peak 121), and naringenin (peak 124)—were first identified. Briefly, the MS/MS behavior of flavone aglycones was characterized by the retro Diels–Alder (RDA) fragmentation pathway and successive loss of small molecules and/or radicals such as CH₃, CO, and CO₂ (Wang et al., 2014). For flavonoid O-glycosides, the MS characteristic fragment ions were featured by producing the neutral loss of 146 and 162 Da (Luo et al., 2021b). Taking compound 103 as an example, a (M–H)⁻ ion at m/z 449.1082 (C₂₁H₂₁O₁₁) was shown, and the main fragment ions were observed at m/z 303.0501 (M–H–C₆H₁₀O₄)⁻, m/z 285.0396 (M–H–C₆H₁₀O₄–H₂O)⁻, m/z 178.9973 (M–H–C₁₃H₁₈O₆)⁻, m/z 151.0023 (M–H–C₆H₁₀O₄–C₈H₈O₃)⁻, and m/z 125.0229 (M–H–C₁₅H₁₈O₉)⁻ in the negative ion spectrum. Thus, compound 103 was definitely identified as astilbin. Its MS² spectrum and proposed fragmentation pathways in negative mode are shown in Supplementary Figure S7.

Compounds 109 and 111 (engeletin) were considered to be isomers as they displayed the same (M–H)⁻ ions at m/z 433.1133 (C₂₁H₂₁O₁₀) and m/z 433.1132 (C₂₁H₂₁O₁₀). Moreover, they had similar fragment ions at m/z 287.05 (M–H–C₆H₁₀O₄)⁻, m/z 269.04 (M–H–C₆H₁₂O₅)⁻, m/z 259.06 (M–H–C₇H₁₀O₅)⁻, m/z 178.99 (M–H–C₁₃H₁₈O₅)⁻, and m/z 152.01 (M–H–C₁₄H₁₇O₆)⁻ in the MS² spectra. By comparing their chromatographic retention times with those of literature data (Chen et al., 2014), compound 109 was tentatively identified as neoengeletin. Similarly, via a comparison of their chromatographic behavior, exact molecular masses, and MS/MS fragmentation patterns, as shown above, with those of literature data (Chen et al., 2014; Dai et al., 2015; Zou et al., 2015; Wang et al., 2016), a total of 22 flavonoids were identified, which are summarized in Supplementary Figure S8. Detailed information on these compounds is summarized in Supplementary Tables S1 and S2.

In the present study, a total of 38 terpenoids were found, including 13 iridoids, 8 monoterpene glycosides, and 17 triterpenoids and derivatives. The classification of these compounds is shown in Supplementary Figure S9. The source of iridoids was GM. To explore the fragmentation rules of iridoids, three standards, namely, loganic acid (peak 19), swertiamain (peak 24), and gentiopicrin (peak 32), were first identified. Taking compound 19 as an example, a (M–H)⁻ ion at an m/z of 375.1289 (C₁₆H₂₃O₁₀) was exhibited, and in the negative ion spectrum, the main fragment ions were observed at m/z values of 213.0762 (M–H–Glc)⁻, 169.0860 (M–H–Glc–CO₂]⁻, and 151.0755 (M–H–Glc–CO₂–H₂O)⁻. Thus, compound 19 was unambiguously identified as loganic acid. Its MS² spectrum and proposed fragmentation pathways in negative mode are shown in Supplementary Figure S10. Similarly, based on the chromatographic behavior and similar fragmentation pathways, 10 other iridoids (peaks 13, 14, 17, 27, 28, 33, 34, 38, 119, and 120) were tentatively identified as isoboonein, geniposidic acid, secologanic acid, secologanoside, 6′-O-β-d-glucosyl gentiopicroside, 4′-O-β-d-glucosyl-gentiopicroside, sweroside, olivieroside C, macrophylloside B, and macrophylloside A (Lv et al., 2012; Wang et al., 2016; Pan et al., 2016; Zhang et al., 2020), respectively.

Eight monoterpene glycosides were detected in this work, and their source was PR. Firstly, three standards—oxypaeoniflorin (peak 31), alibiflorin (peak 45), and paeoniflorin (peak 51)—were first identified by comparing with the references to illustrate the fragmentation rules. Compound 31 (oxypaeoniflorin) afforded a quasi-molecular ion at m/z 495.1507 (C₂₃H₂₇O₁₂) in the negative model, and its MS² spectra showed representative ions at m/z 465.1407 (M–H–CH₂O)⁻, m/z 137.0232 (M–H–C₁₆H₂₂O₉)⁻, and m/z 93.0332 (M–H–C₁₆H₂₂O₉–CO₂)⁻. The MS² mass spectra and the fragmentation pathways of oxypaeoniflorin are shown in Supplementary Figure S11. Consequently, via comparison of their chromatographic behavior, exact molecular masses, and MS/MS fragmentation patterns, as shown above, with those of the literature data (Li et al., 2009), the other five monoterpene glycosides (peaks 9, 83, 99, 116, and 118) were also tentatively identified as 1-O-β-d-glucopyranosyl-paeonisuffrone, galloylalbiflorin, galloylpaeoniflorin, benzoylpaeoniflorin, and isobenzoylpaeoniflorin, respectively.

Seventeen triterpenoids and derivatives, including 13 triterpenoids and 4 triterpenoid saponins, were detected in TFDC. Firstly, compound 128 was identified as limonin by comparing with the standard. Consequently, compound 122, taken as an example, afforded a quasi-molecular ion at m/z 505.3524 (C₃₀H₄₉O₆) in the positive model, and its MS² spectra showed representative ions at m/z 487.3419 (M+H–H₂O)⁺, m/z 469.3312 (M+H–2H₂O)⁺, m/z 451.3202 (M+H–3H₂O)⁺, m/z 415.2842 (M+H–C₄H₁₀O₂)⁺, m/z 397.2735 (M+H–C₄H₁₀O₂–H₂O)⁺, and m/z 353.2471 (M+H–C₄H₁₀O₂–H₂O–C₂H₄O)⁺. Thus, compound 122 was tentatively identified as 16-oxoalisol A (Yang et al., 2020). Its MS² mass spectra and the fragmentation pathways are shown in Supplementary Figure S12. Similarly, based on the similar fragmentation pathways, six triterpenoids (peaks 133, 134, 138, 139, 140, and 141) were tentatively identified as 11-anhydro-16-oxoalisol A, 11-deoxy-16-oxoalisol A, alisol C 23-acetate, 24-deacetyl-alisol O or 16,23-oxido-alisol B, 25-dehydroxy alisol A 24-acetate, and alisol B 23-acetate, respectively (Luo et al., 2021a). The remaining compounds (peaks 115, 123, 125, 126, 129, 131, 135, 136, and 137) were tentatively identified via comparison of their exact molecular masses and MS/MS spectra with those of literature data (Li et al., 2005; Fan et al., 2010; Gualdani et al., 2016; Wang et al., 2017; Luo et al., 2021b). Detailed information on these compounds is summarized in Supplementary Tables S1 and S2.

In the present study, a total of 23 organic acids were detected: 3 aliphatic organic acids, 11 phenolic acid derivatives, and 9 quinic acid and derivatives. The main sources of organic acids were CR and PR, and the classification of these compounds is shown in Supplementary Figure S13. Firstly, by comparing their exact molecular masses and MS/MS spectra with those of literature data, three aliphatic organic acids (peaks 6, 8, and 92) were tentatively identified as malic acid (Zou et al., 2015), citric acid (Zou et al., 2015), and azelaic acid (Rea Martinez et al., 2020), respectively. For the phenolic acid derivatives, the primary MS/MS behavior was the neutral losses of glucoside, H₂O, and CO₂. By comparing with the standards, compounds 12 and 101 were unequivocally identified as gallic acid and salicylic acid, respectively. Compounds 7, 10, and 11 showed the same molecular formula and similar (M-H)⁻ ions at m/z values of 493.1197 (C₁₉H₂₅O₁₅), 493.1201 (C₁₉H₂₅O₁₅), and 493.1198 (C₁₉H₂₅O₁₅), respectively. Moreover, they exhibited similar fragment ions at m/z 331.06 (M–H–Glc)⁻, m/z 313.05 (M–H–Glc–H₂O)⁻, m/z 169.01 (M–H–C₁₂H₂₀O₁₀)⁻, and m/z 125.02 (M–H–C₁₂H₂₀O₁₀–CO₂)⁻. Thus, by comparing their chromatographic retention times, exact molecular masses, and MS/MS spectra with those of literature data (Li et al., 2009) and the Orbitrap Traditional Chinese Medicine Library (OTCML), compounds 7, 10, and 11 were tentatively identified as 1′-O-galloylsucrose, 6′-O-galloylsucrose, and 6-O-galloylsucrose, respectively. Taking compound 7 as an example, its MS² spectrum and proposed fragmentation pathways in negative mode are shown in Supplementary Figure S14. Similarly, based on the chromatographic behavior and the similar fragmentation pathways, five other phenolic acid derivatives (peaks 15, 35, 64, 78, 84, and 96) were tentatively identified as protocatechuic acid, 4-hydroxybenzoic acid, 5-O-caffeoylshikimic acid, p-hydroxy-cinnamic acid, acteoside, and isoacteoside, respectively (Li et al., 2009; Ccana-Ccapatinta et al., 2020).

The source of the nine quinic acids and derivatives was PC. Firstly, two standards, cryptochlorogenic acid (peak 37) and chlorogenic acid (peak 44), were first identified by comparing with the references to illustrate the fragmentation rules. Consequently, compound 37 (cryptochlorogenic acid), taken as an example, gave a quasi-molecular ion at m/z 353.0872 (C₁₆H₁₇O₉) in the negative model, and its MS² spectra showed representative ions at m/z 191.0553 (M–H–caffeoyl)⁻, m/z 179.0340 (M–H–C₇H₁₀O₅)⁻, m/z 173.0445 (M–H–C₉H₈O₄)⁻, m/z 135.0440 (M–H–C₇H₁₀O₅–CO₂)⁻, and m/z 93.0332 (M–H–2H₂O–CO₂)⁻. Thus, compound 37 was unequivocally identified as cryptochlorogenic acid. Its MS² spectrum and proposed fragmentation pathways in negative mode are shown in Supplementary Figure S15. Similarly, the remaining compounds (peaks 4, 20, 36, 46, 56, 59, and 61) were tentatively identified via comparison of their chromatographic retention times, exact molecular masses, and MS/MS spectra with those of literature data (Sun et al., 2016; Ccana-Ccapatinta et al., 2020). Detailed information on these compounds is summarized in Supplementary Tables S1 and S2.

Six other compounds (peaks 2, 22, 26, 72, 87, and 94) were also detected in the negative mode in TFDC. Firstly, compound 94 was identified as cyasterone via a reference standard. Consequently, by comparing their exact molecular masses and MS/MS spectra with those of literature data and OTCML, the remaining compounds (peaks 2, 22, 26, 72, and 87) were tentatively identified as sucrose, protocatechualdehyde, syringin, (+/−)8-(4-hydroxy-3-methoxyphenyl)-6,7-bis(hydroxymethyl)-3-methoxy-5,6,7,8-tetrahydro-2-naphthalenyl-β-d-glucopyranoside (Xian et al., 2014), and syringaresinol (Sim et al., 2013), respectively.

Although 64 absorbed prototype components were detected, five compounds (peak 21, 23, 54, 95, and 126) were excluded, for which structural information of these components was uncertain. Therefore, the remaining 59 compounds were selected to predict the targets of TFDC with MedChem Studio; their detailed structural information is provided in Supplementary Table S3. As a result, a total of 753 putative targets were collected (Supplementary Table S4).

Based on available data from various gout disease databases, 222 gout-related targets were collected and are presented in Supplementary Table S5. By mapping the compound targets with these gout-related targets, 50 common targets were identified. Among the 59 components used for target prediction, 35 were selected for their predicted targets overlapping the targets of gout. Therefore, 35 components of TFDC and 50 gout-related targets were used for further analysis.

For the GO terms and KEGG pathway analysis, 50 common targets described above, which were entered into the DAVID database and with p-values less than 0.05, were enriched. The top 10 terms for biological process (BP), cellular components (CC), and molecular function (MF) are shown as bar plot in Figure 5A. The enriched BP ontologies focused on various metabolic processes, including doxorubicin metabolic process, daunorubicin metabolic process, steroid metabolic process, xenobiotic metabolic process, and progesterone metabolic process. Besides, it is worth noting that the GO terms “leukotriene metabolic process” and “leukotriene biosynthetic process” were also significant entries in BP, which played an important role in leukotriene B₄ (LTB₄) metabolism, and recent research has indicated that the amount of LTB₄ in plasma was closely associated with gout (Luo et al., 2019). The majority of the protein responses were located in a variety of cell components such as the extracellular exosome, organelle membrane, endoplasmic reticulum membrane, cytosol, apical plasma membrane, cell surface, intercellular canaliculus, nuclear envelope lumen, protein complex, and extracellular space. Moreover, the enriched MF ontologies were dominated by aldo-keto reductase (NADP) activity, heme binding, iron ion binding, alditol:NADP⁺ 1-oxidoreductase activity, oxidoreductase activity, electron carrier activity, oxygen binding, steroid hormone receptor activity, oxidoreductase activity, and indanol dehydrogenase activity.

To further explore the significant signaling pathways of the major target genes, KEGG pathway enrichment analysis was performed and resulted in 20 pathways with significant enrichment (p < 0.05). The pathways are presented in Figure 5B, which could be categorized into four major functional groups: metabolism (such as steroid hormone biosynthesis, steroid hormone biosynthesis, metabolism of xenobiotics by cytochrome P450, arachidonic acid metabolism, and pentose and glucuronate interconversions), organismal systems (such as ovarian steroidogenesis and bile secretion), human diseases (such as chemical carcinogenesis and leishmaniasis), and membrane transport (such as ABC transporters).

Subsequently, a network was constructed by linking the validated components, target genes, and significant pathways to achieve a comprehensive understanding of the mechanism of action, as shown in Figure 6. The network consisted of 105 nodes and 373 edges, with the innermost and purple diamond representing the 35 validated compounds of TFDC, the middle and red circle indicating the 50 gout-related targets, and the outermost “V” shape in yellow indicating the 20 enriched KEGG pathways. It is worth noting that the arachidonic acid metabolism pathway was highly enriched in the KEGG pathway analysis, which played an important role in gout treatment (Graham et al., 2013; Napagoda et al., 2018; Deng et al., 2020).

Gout, defined as a type of inflammatory arthritis caused by the deposition of monosodium urate (MSU) crystals in the synovial fluid and other tissues (Dalbeth et al., 2019), has four clinical phases: asymptomatic hyperuricemia, acute gouty arthritis, intercritical gout, and chronic tophaceous gout (Bohata et al., 2021). According to the network analysis, the anti-gout mechanism of TFDC may be associated with the regulation of the arachidonic acid pathway. As shown in Supplementary Table S8, the TFDC putative targets involved in the arachidonic acid metabolism pathway included phospholipase A2 (PLA2G1B, also called PLA2), prostaglandin G/H synthase 1 (PTGS1, also called COX 1), PTGS2, polyunsaturated fatty acid 5-lipoxygenase (ALOX5, also called 5-LOX), aldo-keto reductase family 1 member C3 (AKR1C3), and cytochrome P450 2B6 (CYP2B6). Figure 7 shows the arachidonic acid metabolism pathway affected by the major putative targets of TFDC; the details are discussed below.

Arachidonic acid generally is incorporated into the plasma membrane, and the release of arachidonic acid from membrane phospholipids is through the activation of various phospholipase enzymes, mainly PLA2 (Munaron 2011). Therefore, the antagonists of PLA2 can block PLA2 to release arachidonic acid. Based on our predicted results, four compounds (92, 94, 101, and 125) were considered as PLA2 antagonists, so they may partially contribute to the anti-gout effects of TFDC.

Prostaglandin G/H synthase (PTGS, also called COX), including two isomerases, namely, PTGS1 and PTGS2, and ALOX5 are two rate-limiting enzymes involved in the metabolism of arachidonic acid. Metabolism by PTGS leads to the formation of prostaglandins (PGs), whereas metabolism by ALOX5 leads to the generation of leukotrienes (LTs). These two types of metabolites are actively involved in the development of inflammatory diseases. The PGs are connected with vascular permeability, vasodilatation, fever, and pain amplification (Laupattarakasem et al., 2003), and the LTs are key mediators in inflammatory and allergic processes (Patil et al., 2019). Therefore, we can suppress the production of LTs and PGs by inhibiting PTGS1, PTGS2, and ALOX5, leading to anti-inflammatory and analgesic effects in gout. Many of the identified active compounds from TFDC provide strong support for the inhibitory effect of TFDC on arachidonic acid metabolism. It has been shown that compounds such as geniposidic acid (peak 14) (Ramirez-Cisneros et al., 2015), loganic acid (peak 19) (Ramirez-Cisneros et al., 2015), protocatechualdehyde (peak 22) (Chang et al., 2011), phellodendrine (peak 25) (Li et al., 2016), magnoflorine (peak 30) (Li et al., 2019), oblongine (peak 39) (Li et al., 2019), menisperine (peak 42) (Li et al., 2019), cyasterone (peak 94) (Cao et al., 2017), salicylic acid (peak 101) (Paterson and Lawrence, 2001), astilbin (peak 103) (Wang et al., 2018), engeletin (peak 111) (Wu et al., 2016), and apigenin (peak 130) (Lajter et al., 2015) have suppressive effects on PTGS, while compounds such as salicylic acid (peak 101) (Lapenna et al., 2009) and apigenin (peak 130) (Lajter et al., 2015) were reported to downregulate ALOX5. Therefore, TFDC may play an anti-gout role through regulating the arachidonic acid metabolism pathway by inhibiting PTGS and ALOX5.

TNF-α, a cytokine, can induce fever and stimulate synovial cells to produce collagenase and prostaglandin E₂ (PGE₂) and thus is considered to contribute to joint damage in inflammatory conditions such as rheumatoid arthritis (Warren, 1990). Recently, a study has shown that arachidonic acid can stimulate TNF-α production in Kupffer cells (Cubero and Nieto 2012). Moreover, TNF-α further exerts secondary inflammatory effects by stimulating IL-6 synthesis in several cell types. Upregulation of IL-6 production has been observed in patients with gout disease (Mitsuyama et al., 2008). Several of the identified active compounds from TFDC have shown potent inhibition on TNF-α and IL-6, such as magnoflorine (peak 30) (Guo et al., 2018), astilbin (peak 103) (Wang et al., 2018), and engeletin (peak 111) (Jiang et al., 2018). Besides, the pharmacological activity of a single herb of TFDC on gout has also been reported. The water extract of SG can reduce the serum levels of TNF-α and IL-6 in hyperuricemia and gouty mice and showed significant effects in ameliorating murine hyperuricemia and gout induced by potassium oxonate and monosodium urate (Liang et al., 2019). Taken together, the anti-gout function of TFDC may be partially attributed to the downregulation of the expressions of TNF-α and IL-6.

Reactive oxygen species (ROS) is an umbrella term including oxygen radicals and certain non-radicals that either are oxidizing agents or are easily converted into radicals or both (Biswas 2016). Recent studies have demonstrated that reducing the production of ROS via eucalyptol or ROS scavengers significantly alleviated pain and inflammation in a mouse with gout, demonstrating the key role of ROS in mediating gout pain and inflammation (Yin et al., 2020a; Yin et al., 2020b). Besides, it has been shown that arachidonic acid can promote the production of ROS (Shin and Kim 2009). Several components have been shown to play antioxidative roles by reducing the levels of ROS , such as astilbin (peak 103) (Wang et al., 2018) and engeletin (peak 111) (Zhao et al., 2020). Therefore, TDFC may partially exert its anti-gout effect by decreasing ROS formation.

Nitric oxide (NO), a small gas molecule, is synthesized by three isoforms of NO synthase (NOS): endothelial NOS, neuronal NOS, and inducible NOS (iNOS) (Michel and Feron 1997). Among them, the overexpression of iNOS is linked to a variety of pathological conditions, including gouty arthritis (Chen et al., 2004), and inhibition of iNOS attenuates MSU-induced inflammation in mice (Ju et al., 2011). In addition, an earlier study has shown that, in pathological conditions, arachidonic acid can stimulate the expression of NO (Mariotto et al., 2007). Several components have been shown to effectively inhibit NOS, such as magnoflorine (peak 30) (Wang et al., 2019), menisperine (peak 42) (Zhao et al., 2020), cyasterone (peak 94) (Cao et al., 2017), astilbin (peak 103) (Zhao et al., 2020), engeletin (peak 111) (Ko et al., 2004), and apigenin (peak 130) (Ko et al., 2004). Therefore, TFDC may play an anti-gout role by inhibiting NOS expression.

In summary, by acting on multiple target genes/proteins, or genes/proteins associated with multiple pathways, the multiple constituents contained in TFDC exert their therapeutic effects on gout. However, there are some unavoidable limitations to this study. Firstly, in some situations, it is the metabolites rather than the parent compounds that possess therapeutic effects. Secondly, the effects and targets of chemicals are likely tissue- and dose-specific, which may affect the accuracy of our predicted results. Thirdly, it is indeed difficult to confirm the predicted targets with inhibitory or activating effects in vivo. Fourthly, the constituents in TFDC were treated equally, without considering their contents. Finally, and importantly, the theoretical predictions may be influenced by potential biases in the highly studied biological processes (Yu et al., 2019).