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Evidence-based Complementary and Alternative Medicine : eCAM logoLink to Evidence-based Complementary and Alternative Medicine : eCAM
. 2020 Jun 21;2020:4683254. doi: 10.1155/2020/4683254

Investigation on the Mechanism of Qubi Formula in Treating Psoriasis Based on Network Pharmacology

Lin Zhou 1,2, Lingyun Zhang 3, Disheng Tao 1,2,✉
PMCID: PMC7327573  PMID: 32655662

Abstract

Objective

To elucidate the pharmacological mechanisms of Qubi Formula (QBF), a traditional Chinese medicine (TCM) formula which has been demonstrated as an effective therapy for psoriasis in China.

Methods

The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, BATMAN-TCM database, and literature search were used to excavate the pharmacologically active ingredients of QBF and to predict the potential targets. Psoriasis-related targets were obtained from Therapeutic Target Database (TTD), DrugBank database (DBD), MalaCards database, and DisGeNET database. Then, we established the network concerning the interactions of potential targets of QBF with well-known psoriasis-related targets by using protein-protein interaction (PPI) data in String database. Afterwards, topological parameters (including DNMC, Degree, Closeness, and Betweenness) were calculated to excavate the core targets of Qubi Formula in treating psoriasis (main targets in the PPI network). Cytoscape was used to construct the ingredients-targets core network for Qubi Formula in treating psoriasis, and ClueGO was used to perform GO-BP and KEGG pathway enrichment analysis on these core targets.

Results

The ingredient-target-disease core network of QBF in treating psoriasis was screened to contain 175 active ingredients, which corresponded to 27 core targets. Additionally, enrichment analysis suggested that targets of QBF in treating psoriasis were mainly clustered into multiple biological processes (associated with nuclear translocation of proteins, cellular response to multiple stimuli (immunoinflammatory factors, oxidative stress, and nutrient substance), lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death) and related pathways (VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathways), indicating the underlying mechanisms of QBF on psoriasis.

Conclusion

In this work, we have successfully illuminated that Qubi Formula could relieve a wide variety of pathological factors (such as inflammatory infiltration and abnormal angiogenesis) of psoriasis in a “multicompound, multitarget, and multipathway” manner by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of QBF on psoriasis treatment.

1. Introduction

Psoriasis is a common and frequently occurring disease in dermatology, which is characterized by easy diagnosis and difficult treatment, as well as recurrent disease course [1]. An epidemiological survey shows that the overall prevalence of psoriasis is approximately 0.5% in China [2]. The pathogenesis of psoriasis is still not completely clarified. Present studies have suggested that autoimmune disorders, dysfunction in various inflammatory signal transduction pathways, abnormal expression of psoriatic susceptibility gene, and obesity might be involved in the pathogenesis of psoriasis [3–9]. The unknown pathogenesis has brought difficulties to the treatment, without curative approaches against psoriasis at present. Traditional Chinese medicine (TCM) has unique advantages in treating psoriasis, which can play therapeutic roles through multiple targets and multiple pathways, corresponding to the dysfunction of various pathways underlying the pathogenesis of psoriasis [10–13]. However, TCM also has defects. Due to the unclear component entering the blood through the compound TCM, the mechanism of action is not completely clear, which restricts the further standardization and internationalization of TCM for psoriasis.

Qubi Formula (QBF) is an experience prescription for treating psoriasis at the Department of Dermatology in our hospital. It consists of Bubali Cornu (Shuiniujiao, SNJ, 30 g), Rehmanniae Radix (Dihuang, DH, 20 g), Paeoniae Radix Rubra (Chishao, CS, 10 g), Moutan Cortex (Mudanpi, MDP, 15 g), Arnebiae Radix (Zicao, ZC, 10 g), Lonicerae Japonicae Flos (Jinyinhua, JYH, 10 g), Forsythiae Fructus (Lianqiao, LQ, 10 g), Isatidis Radix (Banlangen, BLG, 30 g), and Glycyrrhizae Radix et Rhizoma (Gancao, GC, 6 g). QBF is modified from the classical TCM formula “Xi-Jiao-Di-Huang decoction.” In this formula, Bubali Cornu is used as the sovereign drug (Jun), while Rehmanniae Radix and Isatidis Radix are utilized as the minister herbs (Chen); Paeoniae Radix Rubra, Moutan Cortex, Arnebiae Radix, Lonicerae Japonicae Flos, and Forsythiae Fructus are the assistant herbs (Zuo), whereas Glycyrrhizae Radix et Rhizoma is the messenger herbs (Shi). The mixed application of these drugs exerts the effects of clearing away heat and removing toxicity, cooling blood, and receding speckles. The effects of QBF on psoriasis have been validated by clinical practice in multiple years, especially in patients with blood-heat subtypes of psoriasis. However, the scientific basis as well as potential pharmacological mechanisms of QBF is still unclear, which needs further investigations.

Conventional researches on the mechanism of one traditional Chinese medicine mostly follow the model of “one drug-one target-one disease,” which cannot reflect the characteristics of TCM (multicompound, multitarget, and multipathway). Herein, in this study, a comprehensive approach [14] (a combination of multiple network-based computational and algorithm-based approaches) was utilized, by combining prediction of active compounds based on multiple pharmacokinetic parameters, excavation of diverse drug targets, and network analysis from a macroscopic perspective, aiming at the illumination of the underlying mechanisms of QBF on psoriasis and providing ideas for subsequent research.

2. Materials and Methods

2.1. Screening of Potential Pharmacological Active Ingredients and Targets of Qubi Formula

BATMAN-TCM [15] (http://bionet.Ncpsb.Org/batman-tcm/index.Php/Home/Index/index) is a bioinformatics analysis tool for analyzing pharmacological active ingredients of Chinese medicines. In order to obtain the information about the ingredients of QBF, “SHUI NIU JIAO, DI HUANG, CHI SHAO, MU DAN PI, ZI CAO, JIN YIN HUA, LIAN QIAO, BAN LAN GEN, and GAN CAO” were used as key words to search in the BATMAN database, giving rise to a total of 572 compounds.

The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database [16] (http://tcmspw.com/tcmsp.php) is a unique platform which can provide pharmacokinetic properties (involving oral bioavailability, drug-likeness, aqueous solubility, etc.) and potential targets for natural compounds. We searched the above 572 compounds in the TCMSP platform to obtain their pharmacokinetic parameters. After obtaining the pharmacokinetic information on the 572 compounds, the reference was screened according to oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18 (mean value for all molecules within the DrugBank database), aiming to screen potential pharmacologically active ingredients of QBF [17]. In this study, these cut-off values utilized helped to efficiently and maximally collect data from QBF using the least components, and the pharmacokinetic data reported may account for this [18]. OB [19], defined as the distribution degree of an oral dose of drug into bloodstream, is one of the most requisite premises in terms of oral drug discovery as well as clinical application. Additionally, drug-likeness, which is defined as a qualitative concept for assessment of the structural similarity of compounds with clinical therapeutics in the DrugBank database, is determined early after drug discovery [20]. In addition, through literature review, certain compounds with the OB < 30 or DL < 0.18 but with extensive pharmaceutical activities (such as oleic acid [21] and arnebinol [22]), or those with relatively higher contents (like jioglutin and alkannin) or those used for the quality identification of single herb in the Pharmacopoeia (goitrin), were also added as potential pharmacologically active ingredients of QBF.

Apart from assisting in exploring the active ingredients of TCM, TCMSP databases could also predict the potential targets of compounds based on SysDT model, HIT database, reverse molecular docking, etc. [23].

2.2. Collection of Known Psoriasis-Related Targets, Psoriasis

In order to obtain the known psoriasis-related targets, “psoriasis” was used as key word to search in Therapeutic Target Database (TTD) [24], DrugBank database [25], MalaCards database [26], and DisGeNET database [27]. After searching in the DisGeNET database, results were sorted by the disease specificity index (DSI), following by the removal of targets lower than the median of DSI obtained from all the known psoriasis-related genes. Additionally, the drugs with abnormal status in TTD and DrugBank database and their corresponding targets were also taken out. The detailed information of these known psoriasis-related was summarized in Table S1 after redundancy deletion.

2.3. Excavation of Core Targets of QBF for Treating Psoriasis and Construction of Core Network on Active Ingredients-Targets

First, targets obtained from the above two steps (potential targets of QBF and known psoriasis-related targets) were standardized in the UniProt database [28] by selecting the species “Homo sapiens,” aiming to acquire the single universal gene names. Then, both potential targets of QBF and known psoriasis-related targets were uploaded to the online Wayne diagram tool (http://bioinfogp.cnb.csic.es/tools/venny/index.html, Version 2.1.0) for mapping; that is, targets from these two sets were intersected to obtain the candidate targets of QBF for treating psoriasis.

Subsequently, the candidate targets were imported into the String database [29] to obtain protein-protein interactions (PPIs) by setting the minimum value of the combined score at 0.400 and the species as “Homo sapiens.” The topological parameters (DMNC, Degree, Closeness, and Betweenness) of each target (node) in the network were calculated using the cytoHubba plugin [30]. The median values of these four parameters of all nodes were used as a screening condition. Nodes with all the four parameter greater than the median values were considered as the main hubs that played core roles in the PPI network, that is, the core targets of QBF for psoriasis. Finally, Cytoscape was used to construct the core network of active ingredients-targets.

2.4. Enrichment Analysis of Core Targets

The ClueGO [31] plugin from Cytoscape software, integrative GO-biological process (BP), and KEGG database were applied to perform enrichment analysis on the core targets, and species was selected as “Homo” in the ClueGO interface. All the core targets were sequentially imported, followed by enrichment analysis. κ value was defaulted at 0.4 and p was set at ≤0.05, which were used as the screening conditions for plotting enrichment analysis.

3. Results

3.1. Screening of Potential Pharmacologically Active Ingredients and Their Targets of Qubi Formula

The search through the BATMAN-TCM database revealed a total of 572 compounds of QBF. Accumulative efforts have been made to clarify the therapeutic mechanisms of TCM, however, with sluggish progress on the molecular level. Due to the unavailable effective methods specifically developed for the identification of the active compounds in medicinal herbs, OB screening combined with drug-likeness assessment may be a feasible strategy. In this study, OB ≥ 30% and DL ≥ 0.18 were used as the screening conditions. Then, a total of 202 possible compounds with proper values of above two parameters were collected for potential pharmacologically active ingredients from the herbal constituents of QBF. In addition, among the compounds that were screened out, we found another 35 compounds through the literature research of PubMed. Although these 35 compounds did not meet the screening conditions of OB and DL, they were reported to use a wide range of pharmacological activities and were thus included in potential pharmacologically active ingredients. Finally, the active ingredients of SNJ, GC, CS, DH, MDP, ZC, JYH, LQ, and BLG were 6, 97, 26, 6, 12, 17, 30, 27, and 51, respectively. The proportions of the active ingredients in the sovereign drug (Jun), minister herbs (Chen), assistant herbs (Zuo), and messenger herbs (Shi) in all the 175 ingredients were 3.4%, 30.86%, 53.14%, and 55.43%, respectively. Among them, some compounds were widely present in multiple herbs of QBF, such as oleic acid, methyl linolenate, sitosterol, and paeoniflorin. The basic information of the potential pharmacologically active ingredients of Qubi Formula is shown in Table 1.

Table 1.

All the potential pharmacologically active ingredients of QBF.

Herb name Molecule ID Molecule name OB (%) DL
Bubali Cornu (Shuiniujiao, SNJ) MOL000054 Arginine 47.64 0.03
MOL000065 Aspartic acid 79.74 0.02
MOL000042 Alanine 87.69 0.01
MOL001443 4-Guanidino-1-butanol 26.23 0.01
MOL006394 Guanidine 24 0
MOL000987 Cholesterol 37.87 0.68
Glycyrrhizae Radix et Rhizoma (Gancao, GC) MOL000057 DIBP 49.63 0.13
MOL000098 Quercetin 46.43 0.28
MOL000211 Mairin 55.38 0.78
MOL000239 Jaranol 50.83 0.29
MOL000354 Isorhamnetin 49.6 0.31
MOL000359 Sitosterol 36.91 0.75
MOL000392 Formononetin 69.67 0.21
MOL000417 Calycosin 47.75 0.24
MOL000422 Kaempferol 41.88 0.24
MOL000497 Licochalcone a 40.79 0.29
MOL000500 Vestitol 74.66 0.21
MOL000676 DBP 64.54 0.13
MOL001484 Inermine 75.18 0.54
MOL001792 DFV 32.76 0.18
MOL002311 Glycyrol 90.78 0.67
MOL002565 Medicarpin 49.22 0.34
MOL002844 Pinocembrin 64.72 0.18
MOL003656 Lupiwighteone 51.64 0.37
MOL003896 7-Methoxy-2-methyl isoflavone 42.56 0.2
MOL004328 Naringenin 59.29 0.21
MOL004805 (2S)-2-[4-Hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one 31.79 0.72
MOL004806 Euchrenone 30.29 0.57
MOL004808 Glyasperin B 65.22 0.44
MOL004810 Glyasperin F 75.84 0.54
MOL004811 Glyasperin C 45.56 0.4
MOL004814 Isotrifoliol 31.94 0.42
MOL004815 (E)-1-(2,4-Dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one 39.62 0.35
MOL004820 Kanzonol W 50.48 0.52
MOL004824 (2S)-6-(2,4-Dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one 60.25 0.63
MOL004827 Semilicoisoflavone B 48.78 0.55
MOL004828 Glepidotin A 44.72 0.35
MOL004829 Glepidotin B 64.46 0.34
MOL004833 Phaseolinisoflavan 32.01 0.45
MOL004835 Glypallichalcone 61.6 0.19
MOL004836 Echinatin 66.58 0.17
MOL004838 8-(6-Hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol 58.44 0.38
MOL004841 Licochalcone B 76.76 0.19
MOL004848 Licochalcone G 49.25 0.32
MOL004849 3-(2,4-Dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin 59.62 0.43
MOL004855 Licoricone 63.58 0.47
MOL004856 Gancaonin A 51.08 0.4
MOL004857 Gancaonin B 48.79 0.45
MOL004860 Licorice glycoside E 32.89 0.27
MOL004863 3-(3,4-Dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone 66.37 0.41
MOL004864 5,7-Dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone 30.49 0.41
MOL004866 2-(3,4-Dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone 44.15 0.41
MOL004879 Glycyrin 52.61 0.47
MOL004882 Licocoumarone 33.21 0.36
MOL004883 Licoisoflavone 41.61 0.42
MOL004884 Licoisoflavone B 38.93 0.55
MOL004885 Licoisoflavanone 52.47 0.54
MOL004891 Shinpterocarpin 80.3 0.73
MOL004898 (E)-3-[3,4-Dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphenyl)prop-2-en-1-one 46.27 0.31
MOL004903 Liquiritin 65.69 0.74
MOL004904 Licopyranocoumarin 80.36 0.65
MOL004905 3,22-Dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid 34.32 0.55
MOL004907 Glyzaglabrin 61.07 0.35
MOL004908 Glabridin 53.25 0.47
MOL004910 Glabranin 52.9 0.31
MOL004911 Glabrene 46.27 0.44
MOL004912 Glabrone 52.51 0.5
MOL004913 1,3-Dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone 48.14 0.43
MOL004914 1,3-Dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone 62.9 0.53
MOL004915 Eurycarpin A 43.28 0.37
MOL004917 Glycyroside 37.25 0.79
MOL004924 (−)-Medicocarpin 40.99 0.95
MOL004935 Sigmoidin-B 34.88 0.41
MOL004941 (2R)-7-Hydroxy-2-(4-hydroxyphenyl)chroman-4-one 71.12 0.18
MOL004945 (2S)-7-Hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one 36.57 0.32
MOL004948 Isoglycyrol 44.7 0.84
MOL004949 Isolicoflavonol 45.17 0.42
MOL004957 HMO 38.37 0.21
MOL004959 1-Methoxyphaseollidin 69.98 0.64
MOL004961 Quercetin der. 46.45 0.33
MOL004964 (Z)-1-(2,4-Dihydroxyphenyl)-3-phenylprop-2-en-1-one 73.18 0.12
MOL004966 3′-Hydroxy-4′-O-Methylglabridin 43.71 0.57
MOL004974 3′-Methoxyglabridin 46.16 0.57
MOL004978 2-[(3R)-8,8-Dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol 36.21 0.52
MOL004980 Inflacoumarin A 39.71 0.33
MOL004985 Icos-5-enoic acid 30.7 0.2
MOL004988 Kanzonol F 32.47 0.89
MOL004989 6-Prenylated eriodictyol 39.22 0.41
MOL004990 7,2′,4′-Trihydroxy-5-methoxy-3-arylcoumarin 83.71 0.27
MOL004991 7-Acetoxy-2-methylisoflavone 38.92 0.26
MOL004993 8-Prenylated eriodictyol 53.79 0.4
MOL004996 Gadelaidic acid 30.7 0.2
MOL005000 Gancaonin G 60.44 0.39
MOL005001 Gancaonin H 50.1 0.78
MOL005003 Licoagrocarpin 58.81 0.58
MOL005007 Glyasperin M 72.67 0.59
MOL005008 Glycyrrhiza flavonol A 41.28 0.6
MOL005012 Licoagroisoflavone 57.28 0.49
MOL005013 18α-Hydroxyglycyrrhetic acid 41.16 0.71
MOL005016 Odoratin 49.95 0.3
MOL005017 Phaseol 78.77 0.58
MOL005018 Xambioona 54.85 0.87
MOL005020 Dehydroglyasperin C 53.82 0.37
Paeoniae Radix Rubra (Chishao, CS) MOL001924 Paeoniflorin 53.87 0.79
MOL000449 Stigmasterol 43.83 0.76
MOL004355 Spinasterol 42.98 0.76
MOL000358 Beta-sitosterol 36.91 0.75
MOL000359 Sitosterol 36.91 0.75
MOL002776 Baicalin 40.12 0.75
MOL006999 Stigmast-7-en-3-ol 37.42 0.75
MOL005043 Campest-5-en-3beta-ol 37.58 0.71
MOL007003 Benzoyl paeoniflorin 31.14 0.54
MOL007025 Isobenzoyl paeoniflorin 31.14 0.54
MOL001002 Ellagic acid 43.06 0.43
MOL001918 Paeoniflorgenone 87.59 0.37
MOL007016 Paeoniflorigenone 65.33 0.37
MOL006996 1-o-Beta-d-glucopyranosylpaeonisuffrone_qt 65.08 0.35
MOL007005 Albiflorin_qt 48.7 0.33
MOL006992 (2R,3 R)-4-Methoxyl-distylin 59.98 0.3
MOL006994 1-o-Beta-d-glucopyranosyl-8-o-benzoylpaeonisuffrone_qt 36.01 0.3
MOL007018 9-Ethyl-neo-paeoniaflorin A_qt 64.42 0.3
MOL006990 (1S,2S,4R)-trans-2-hydroxy-1,8-cineole-B-D-glucopyranoside 30.25 0.27
MOL000492 (+)-catechin 54.83 0.24
MOL002714 Baicalein 33.52 0.21
MOL002883 Ethyl oleate (NF) 32.4 0.19
MOL001641 Methyl linoleate 41.93 0.17
MOL000131 EIC 41.9 0.14
MOL000675 Oleic acid 33.13 0.14
MOL001746 ELD 31.2 0.14
Rehmanniae Radix (Dihuang, DH) MOL000449 Stigmasterol 43.83 0.76
MOL000359 Sitosterol 36.91 0.75
MOL000131 EIC 41.9 0.14
MOL003708 Jioglutin D 39.02 0.14
MOL003689 Aeginetic acid 48.31 0.13
MOL003706 Jioglutin B 90.71 0.13
Moutan Cortex (Mudanpi, MDP) MOL000211 Mairin 55.38 0.78
MOL000359 Sitosterol 36.91 0.75
MOL007003 Benzoyl paeoniflorin 31.14 0.54
MOL007369 4-O-methylpaeoniflorin_qt 67.24 0.43
MOL001925 Paeoniflorin_qt 68.18 0.4
MOL007382 Mudanpioside-h_qt 2 42.36 0.37
MOL007384 Paeonidanin_qt 65.31 0.35
MOL007374 5-[[5-(4-Methoxyphenyl)-2-furyl]methylene]barbituric acid 43.44 0.3
MOL000098 Quercetin 46.43 0.28
MOL000422 Kaempferol 41.88 0.24
MOL000492 (+)-Catechin 54.83 0.24
MOL000675 Oleic acid 33.13 0.14
Arnebiae Radix (Zicao, ZC) MOL000359 Sitosterol 36.91 0.75
MOL002372 (6Z,10 E,14E,18 E)-2,6,10,15,19,23-Hexamethyltetracosa-2,6,10,14,18,22-hexaene 33.55 0.42
MOL007736 Lithospermidin B 60.48 0.39
MOL007728 Lithospermidin A 75.08 0.38
MOL007714 1-Methoxyacetylshikonin 73.09 0.29
MOL007715 [(1R)-1-(5,8-Dihydroxy-1,4-dioxo-2-naphthyl)-4-methyl-pent-3-enyl] propanoate 54.64 0.29
MOL007716 Acetylshikonin 62.39 0.27
MOL007734 5-[(E)-5-(3-Furyl)-2-methyl-pent-2-enyl]-2,3-dimethoxy-p-benzoquinone 61.8 0.24
MOL007722 Isoarnebin 4 64.79 0.2
MOL007735 Des-O-methyllasiodiplodin 30.12 0.2
MOL001494 Mandenol 42 0.19
MOL002883 Ethyl oleate (NF) 32.4 0.19
MOL007719 Arnebin 7 73.85 0.18
MOL007717 Alkannin 6.09 0.35
MOL000131 EIC 41.9 0.14
MOL000675 Oleic acid 33.13 0.14
MOL007731 Arnebinol 56.66 0.14
Lonicerae Japonicae Flos (Jinyinhua, JYH) MOL003036 (3S,8 R,9R,10 R,13R,14S,17R)-17-[(E,2R,5S)-5-Ethyl-6-methylhept-3-en-2-yl]-10,13-dimethyl-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol 43.83 0.76
MOL000449 Stigmasterol 43.83 0.76
MOL000358 Beta-sitosterol 36.91 0.75
MOL003108 Caeruloside C 55.64 0.73
MOL003124 Xylostosidine 43.17 0.64
MOL002773 Beta-carotene 37.18 0.58
MOL003101 7-Epivogeloside 46.13 0.58
MOL003059 Kryptoxanthin 47.25 0.57
MOL003062 4,5′-Retro-.beta.,.beta.-carotene-3,3′-dione, 4′,5′-didehydro- 31.22 0.55
MOL002707 Phytofluene 43.18 0.5
MOL003111 Centauroside_qt 55.79 0.5
MOL003128 Dinethylsecologanoside 48.46 0.48
MOL003095 5-Hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone 51.96 0.41
MOL003014 Secologanic dibutylacetal_qt 53.65 0.29
MOL000098 Quercetin 46.43 0.28
MOL003044 Chrysoeriol 35.85 0.27
MOL000006 Luteolin 36.16 0.25
MOL002914 Eriodyctiol (flavanone) 41.35 0.24
MOL000422 Kaempferol 41.88 0.24
MOL003006 (-)-(3R,8S,9R,9aS,10aS)-9-Ethenyl-8-(beta-D-glucopyranosyloxy)-2,3,9,9a,10,10a-hexahydro-5-oxo-5H,8H-pyrano[4,3-d]oxazolo[3,2-a]pyridine-3-carboxylic acid_qt 87.47 0.23
MOL001495 Ethyl linolenate 46.1 0.2
MOL001494 Mandenol 42 0.19
MOL003117 Ioniceracetalide B_qt 61.19 0.19
MOL001398 Methyl linolenate 46.15 0.17
MOL001641 Methyl linoleate 41.93 0.17
MOL003103 Methyl octadeca-8,11-dienoate 41.93 0.17
MOL003120 Loniceracetalide A_qt 89.38 0.17
MOL002003 (-)-Caryophyllene oxide 32.67 0.13
MOL000266 Beta-cubebene 32.81 0.11
MOL002697 Junipene 44.07 0.11
Forsythiae Fructus (Lianqiao, LQ) MOL000791 Bicuculline 69.67 0.88
MOL003305 Phillyrin 36.4 0.86
MOL003365 Lactucasterol 40.99 0.85
MOL000522 Arctiin 34.45 0.84
MOL003281 20(S)-Dammar-24-ene-3β,20-diol-3-acetate 40.23 0.82
MOL003315 3beta-Acetyl-20,25-epoxydammarane-24alpha-ol 33.07 0.79
MOL000211 Mairin 55.38 0.78
MOL000358 Beta-sitosterol 36.91 0.75
MOL003344 β-Amyrin acetate 42.06 0.74
MOL003348 Adhyperforin 44.03 0.61
MOL003347 Hyperforin 44.03 0.6
MOL003295 (+)-Pinoresinol monomethyl ether 53.08 0.57
MOL003306 ACon1_001697 85.12 0.57
MOL003308 (+)-Pinoresinol monomethyl ether-4-D-beta-glucoside_qt 61.2 0.57
MOL003322 Forsythinol 81.25 0.57
MOL003330 (−)-Phillygenin 95.04 0.57
MOL003290 (3R,4 R)-3,4-bis[(3,4-Dimethoxyphenyl)methyl]oxolan-2-one 52.3 0.48
MOL003283 (2R,3 R,4S)-4-(4-Hydroxy-3-methoxy-phenyl)-7-methoxy-2,3-dimethylol-tetralin-6-ol 66.51 0.39
MOL003370 Onjixanthone I 79.16 0.3
MOL000098 Quercetin 46.43 0.28
MOL000006 Luteolin 36.16 0.25
MOL000422 Kaempferol 41.88 0.24
MOL000173 Wogonin 30.68 0.23
MOL003358 Euxanthone 92.98 0.16
MOL003302 Forsythidmethylester_qt 121.84 0.12
MOL003360 Norlapachol 46.99 0.11
MOL003300 Forsythide_qt 46.6 0.1
Isatidis Radix (Banlangen, BLG) MOL001810 6-(3-Oxoindolin-2-ylidene)indolo[2,1-b]quinazolin-12-one 45.28 0.89
MOL001806 Stigmasta-5,22-diene-3beta,7beta-diol 42.56 0.83
MOL001804 Stigmasta-5,22-diene-3beta,7alpha-diol 43.04 0.82
MOL001755 24-Ethylcholest-4-en-3-one 36.08 0.76
MOL000449 Stigmasterol 43.83 0.76
MOL001771 Poriferast-5-en-3beta-ol 36.91 0.75
MOL001800 Rosasterol 35.87 0.75
MOL000358 Beta-sitosterol 36.91 0.75
MOL000359 Sitosterol 36.91 0.75
MOL002322 Isovitexin 31.29 0.72
MOL001790 Linarin 39.84 0.71
MOL000953 CLR 37.87 0.68
MOL001769 Beta-sitosterol decantate 34.57 0.57
MOL001783 2-(9-((3-Methyl-2-oxopent-3-en-1-yl)oxy)-2-oxo-1,2,8,9-tetrahydrofuro[2,3-h]quinolin-8-yl)propan-2-yl acetate 64 0.57
MOL001828 3-[(3,5-Dimethoxy-4-oxo-1-cyclohexa-2,5-dienylidene)methyl]-2,4-dihydro-1H-pyrrolo[2,1-b]quinazolin-9-one 51.84 0.56
MOL001811 Goitrin 3.23 0.01
MOL001750 Glucobrassicin 66.02 0.48
MOL001734 3-[[(2R,3 R,5R,6S)-3,5-Dihydroxy-6-(1H-indol-3-yloxy)-4-oxooxan-2-yl]methoxy]-3-oxopropanoic acid 85.87 0.47
MOL001779 Sinoacutine 49.11 0.46
MOL001803 Sinensetin 50.56 0.45
MOL001721 Isaindigodione 60.12 0.41
MOL001733 Eupatorin 30.23 0.37
MOL001749 ZINC03860434 43.59 0.35
MOL001793 (E)-2-[(3-Indole)cyanomethylene-]-3-indolinone 54.59 0.32
MOL001722 2-O-beta-D-Glucopyranosyl-2H-1,4-benzoxazin-3(4H)-one 43.62 0.31
MOL001767 Hydroxyindirubin 63.37 0.3
MOL001774 Ineketone 37.14 0.3
MOL001735 Dinatin 30.97 0.27
MOL001736 (-)-Taxifolin 60.51 0.27
MOL001798 Neohesperidin_qt 71.17 0.27
MOL001781 Indigo 38.2 0.26
MOL001782 (2Z)-2-(2-Oxoindolin-3-ylidene)indolin-3-one 48.4 0.26
MOL001814 (E)-3-(3,5-Dimethoxy-4-hydroxy-benzylidene)-2-indolinone 57.18 0.25
MOL001820 (E)-3-(3,5-Dimethoxy-4-hydroxyb-enzylidene)-2-indolinone 65.17 0.25
MOL001689 Acacetin 34.97 0.24
MOL001833 Glucobrassicin-1-sulfonate_qt 42.52 0.24
MOL001756 Quindoline 33.17 0.22
MOL001728 3-[ 2′-(5′-Hydroxymethyl)furyl]-1(2H)-isoquinolinone-7-O-beta-D-glucoside_qt 51.74 0.18
MOL001792 DFV 32.76 0.18
MOL001398 Methyl linolenate 46.15 0.17
MOL001745 Methyl vaccenate 31.9 0.17
MOL001748 Methyl (E)-octadec-8-enoate 31.9 0.17
MOL001763 3-(2-Hydroxyphenyl)quinazolin-4-one 63.58 0.16
MOL001789 Isoliquiritigenin 85.32 0.15
MOL001821 Methyl 2-ethylhexyl phthalate 65.98 0.15
MOL000432 Linolenic acid 45.01 0.15
MOL000131 EIC 41.9 0.14
MOL001746 ELD 31.2 0.14
MOL000057 DIBP 49.63 0.13
MOL000676 DBP 64.54 0.13
MOL001818 Methyl palmitelaidate 34.61 0.12
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Subsequently, we explored the potential targets of the 237 potential pharmacologically active ingredients by excavating TCMSP databases, which yielded to 939 targets (shown in Table S2). The numbers of potential targets linked by SNJ, GC, CS, DH, MDP, ZC, JYH, LQ, and BLG were 668, 234, 148, 49, 207, 90, 219, 229, and 111, respectively. The proportions of the potential targets of the sovereign drug (Jun), minister herbs (Chen), assistant herbs (Zuo), and messenger herbs (Shi) in all the 939 targets were 71.14%, 11.93%, 30.35%, and 24.92%, respectively. Although the number of targets correlated with each herb of QBF is different, significant overlaps were observed in the nine herbs, which was suggestive of the congenerous or antergic roles of the various components in QBF via the regulation of similar targets.

In order to holistically and systemically obtain comprehensive understanding of the ingredient-target network in QBF, a network map was constructed by using Cytoscape, including 6273 edges and 1212 nodes (Figure 1). To be specific, the node degree indicated the number of target or edge correlated with the node according to topological analysis. A total of 142 ingredients were found in the as-established network to have the median of ≥18 degrees. Of them, quercetin, arginine, and oleic acids kaempferol and luteolin acted on 166, 96, and 54 targets, respectively, which were subsequently considered as the critical pharmacologically active ingredients of QBF.

Figure 1.

Figure 1

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Construction of the QBF compound-potential target network. The compound-potential target network was constructed by linking the candidate compounds and their potential targets of the 9 herbs, which are constituents of QBF. The nodes representing candidate compounds are shown as polychrome square and the targets are indicated by orange circle.

3.2. Excavation of the Core Targets of Qubi Formula in Treating Psoriasis

Psoriasis has been recognized as the polygenic disorder. In addition, the investigation of the interactions between genes as well as gene and environment could be used to reveal the pathogenesis of psoriasis. After the targets with abnormal status from TTD and DrugBank database and DSI < 0.535 (the median of DSI) from DisGeNET database were excluded, we collected 605 targets (Table S1) associated with psoriasis from the four accessible resources. Notably, 104 of the identified potential targets of the QBF were also the well-recognized psoriasis disease- (or therapeutic drugs) related targets (Table S3 and Figure 2(a)). And these 104 targets were defined as the candidate targets for QBF in treating psoriasis.

Figure 2.

Figure 2

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Excavation of the core target of QBF in treating psoriasis. (a) The Venn diagram showed that QBF shared 104 potential targets with known pathological course-related targets of psoriasis. (b) The PPI network of all the candidate targets of QBF in treating psoriasis. (c) The PPI of the core target of QBF in treating psoriasis.

Subsequently, to further select the core targets of QBF in treating psoriasis, the String database was used to construct the PPI network of the above targets (Figure 2(b)), followed by the calculation of the topological parameters (DMNC, Degree, Closeness, and Betweenness) of each node in the network using the cytoHubba plugin. The median values of these four parameters of all nodes were used as a screening condition. Nodes with all the four parameters greater than the median values were considered as the main hubs that played core roles in the PPI network. As a result, 27 targets (Table 2 and Table S4) were screened from the 104 candidate targets based on the values of topological parameters (Figure 2(c)), that is the core targets of QBF in treating psoriasis.

Table 2.

Topological feature values of all the core targets for QBF against psoriasis.

Node name DMNC Degree Closeness Betweenness
APOE 1.19 47.00 74.33 41.76
BCL2L1 1.19 49.00 75.33 46.09
CCL2 1.15 73.00 87.50 105.15
CXCL8 1.12 75.00 88.50 144.89
EGF 1.18 63.00 82.33 83.27
HMOX1 1.19 56.00 79.00 64.03
ICAM1 1.24 64.00 82.83 48.67
IFNG 1.21 62.00 81.83 59.30
IGF1 1.23 62.00 81.83 48.60
IL10 1.13 74.00 88.00 128.25
IL2 1.17 62.00 81.83 117.83
IL4 1.20 65.00 83.33 67.65
JAK2 1.24 48.00 74.67 47.23
JUN 1.15 74.00 88.00 111.79
LEP 1.17 59.00 80.50 68.01
MAPK1 1.14 67.00 84.33 135.31
MAPK14 1.23 56.00 78.83 41.89
MAPK3 1.12 73.00 87.33 164.81
MAPK8 1.14 71.00 86.50 114.31
MMP9 1.13 73.00 87.50 225.47
NOS3 1.21 55.00 78.33 50.16
PTEN 1.17 49.00 75.33 46.86
PTGS2 1.14 73.00 87.50 224.73
RELA 1.18 54.00 77.83 41.41
SPP1 1.25 51.00 76.50 39.41
STAT3 1.16 72.00 86.83 132.54
TLR4 1.12 72.00 87.00 135.66
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3.3. Construction of Active Ingredients-Targets Core Network for Qubi Formula in Treating Psoriasis

In order to further understand the “multicompound and multitarget” mechanism of Qubi Formula in treating psoriasis, we searched for the potential ingredients of Qubi Formula which could affect the 27 core targets based on the relationship between the ingredients and their targets, followed by construction of the core network on active ingredients-targets (Figure 3(a)) using Cytoscape software and the statistical analysis of the degree of each node in the network. As shown in Figure 3(b), the degrees of active ingredients ranged from 1 to 18 in the core network, with the median of 2, indicating that more than half of the compounds acted on more than one target, while the degree of the target ranged from 1 to 160, with the median of 2. Among all the active ingredients, the top 4 were quercetin, luteolin, kaempferol, and wogonin in terms of Degree. Previous preclinical studies have confirmed that all of them could delay the progression of psoriasis and relieve symptoms in multiple animal models [32–34]. Among all the targets, the top three were PTGS2, MAPK14, and NOS3 in terms of Degree, and all of them have been demonstrated to play important roles in the pathogenesis of psoriasis, such as inflammatory infiltration, abnormal differentiation of keratinocytes, and oxidative stress injury [35, 36].

Figure 3.

Figure 3

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Construction of active ingredients-targets core network for QBF in treating psoriasis (a), and the statistical analysis of the degree of each ingredient (b) and target (c) in the network. All nodes were sorted and calculated according to the degree of freedom, and the node size in the network was associated with degree.

3.4. Enrichment Analysis of the Core Targets of Qubi Formula in Treating Psoriasis

In order to further understand the mechanism of “multitarget and multipathway” of Qubi Formula in treating psoriasis, ClueGO plugin was used to perform enrichment analysis of GO-PB and KEGG on core targets and to excavate the biological processes and signaling pathways regulated by Qubi Formula in treating psoriasis. These 27 core targets were involved in several biological process, mainly including nuclear translocation of proteins, cellular response to multiple stimuli (immunoinflammatory factors, oxidative stress, and nutrient substance), lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death (Figure 4(a)). Moreover, according to the pvalues of enriched pathways and their correlation with psoriasis, we were most interested in the following five representative signal pathways including VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathway (Figure 4(b) and Table 3).

Figure 4.

Figure 4

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Enrichment analysis of candidate targets for QBF against psoriasis. The enrichment analysis is generated by ClueGO and the most vital term in the group is labeled. Functionally related groups partially overlap. Representative enriched biological process or pathway (P < 0.05) interactions among core QBF targets. The larger circle indicated the greater degree of enrichment, and the closer color suggested the more similar function in biological network. (a) Core QBF targets enriched in the representative biological process. (b) Core QBF targets enriched in the representative signaling pathway.

Table 3.

Representative enriched KEGG pathway of the core targets of Qubi Formula in treating psoriasis.

Pathway Gene count P value Pathway ID Associated genes
Th17 cell differentiation 11 3.87E − 12 ko04659 IFNG, IL2, IL4, JAK2, JUN, MAPK1, MAPK14, MAPK3, MAPK8, RELA, STAT3
Th1 and Th2 cell differentiation 10 4.32E − 11 ko04658 IFNG, IL2, IL4, JAK2, JUN, MAPK1, MAPK14, MAPK3, MAPK8, RELA
Toll-like receptor signaling pathway 9 7.14E − 11 ko04620 JUN, MAPK1, MAPK14, MAPK3, MAPK8, RELA, CXCL8, SPP1, TLR4
JAK-STAT signaling pathway 9 1.20E − 09 ko04630 IL2, IL4, JAK2, STAT3, BCL2L1, EGF, IL10, LEP, IFNG
VEGF signaling pathway 5 1.14E − 06 ko04370 MAPK1, MAPK14, MAPK3, NOS3, PTGS2
NF-kappa B signaling pathway 6 2.24E − 06 ko04064 PTGS2, BCL2L1, RELA, CXCL8, TLR4, ICAM1
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4. Discussion

Qubi Formula is an experience prescription for psoriasis at the Department of Dermatology in our hospital. It is especially suitable for patients with blood-heat subtype of psoriasis, with radiated skin lesions throughout the whole body, redness, obvious scales, itching and burning, red tongue, and yellow fur. In this formula, SNJ, DH, JYH, and BLG can clear heat, cool blood, and remove toxic materials. MDP and CS are responsible for cooling blood and removing blood stasis. GC is in charge of detoxifying and reconciling medicine. The therapeutic effects of Qubi Formula in the clinical treatment of psoriasis are significant. However, the active ingredients and potential targets of Qubi Formula are unclear, which hinders the further development and application of the prescription.

Network pharmacology is a new strategy for drug design and development based on the rapid development of systematical biology and multidirectional pharmacology. This concept was first proposed by Hopkins AL in 2007, which was switched from previous “disease-single target-single drug” model of new drug development to the “disease-multitarget-multidrug” model. This idea coincides with the “holistic view” of TCM. Therefore, the application of the network pharmacology method can provide certain research ideas for discovering the mechanism of Qubi Formula in treating psoriasis.

In this study, a total of 175 potential active ingredients of Qubi Formula in treating psoriasis were screened through a series of network pharmacological methods, which corresponded to 27 core targets. At present, the widely acknowledged histopathological features of psoriasis include four major aspects: the inflammatory infiltration in dermis and epidermis, the abnormal biological behaviours (differentiation, hyperproliferation, and apoptosis) of keratinocytes, metabolic disturbance in skin tissue, and the tortuously increased dermal blood vessels and capillaries [37–41]. Firstly, among the 27 core targets, most of them (PTGS2, ILs, JAK2, STAT3, RELA, CCL2, CXCL8, EGF, IFNG, and TLR4) have been shown to be involved in abnormal inflammatory infiltration, which could regulate the differentiation and chemotaxis of lymphocytes, cytokines produced, and immunological inflammatory reaction in dermis and epidermis [42–48]. Secondly, RELA, MAPKs, JUN, and BCL2L1 are associated with the aberrant biological behaviours of keratinocytes in psoriasis [49–51]. Thirdly, APOE, HMOX1, LEP, IGF1, and SPP1 have been demonstrated to take part in metabolic disturbance (including lipids, peroxides, and carbohydrates) in skin tissue [52–54]. Finally, ICAM1, MM9, and NOS3 are closely associated with endothelial cell proliferation, migration, and adhesion, which are related to the tortuously increased dermal blood vessels and capillaries [55, 56].

Enrichment analysis of GO-BP and KEGG on the core targets further suggests that Qubi Formula could intervene in psoriasis through multiple biological processes by acting on several signaling pathways, involving VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathways. As shown in the Figure 4(a), these five signaling pathways cross-talk effects in the network. VEGF signal pathway has been demonstrated to not only induce pathological angiogenesis in psoriatic lesions by regulating the proliferation and differentiation of endothelial cells, but also aggravate the inflammatory response via increasing vascular permeability to promote inflammatory cell infiltration [57, 58]. Moreover, as it is well known that psoriasis is an inflammatory disease mediated by T lymphocytes, abnormal differentiation of T lymphocytes (especially Th1 and Th17 cells) and excessive secretion of proinflammatory factors (such as ILs) are closely related to the progression of the disease [59]. In this study, we have also demonstrated that there are diverse critical signaling pathways related to T lymphocytes differentiation and proinflammatory factors production (TLRs, JAK-STAT, and NF-κB) regulated by QBF on psoriasis therapy [60–62].

Our team has previously confirmed the safety and effectiveness of Qubi Formula in treating psoriasis through clinical observations. Based on the results of this study, we speculate that the regulatory role of Qubi Formula on psoriasis is not unilateral, but is directly or indirectly involved in the comprehensive treatment of the four major pathological factors of psoriasis through multiple signaling pathways associated with immunoinflammatory response, metabolism, and abnormal angiogenesis. Despite the valuable discoveries, there are still certain limitations. In the present study concerning the network pharmacological analysis on Qubi Formula, only the interactions between the components of QBF and the psoriasis-related targets were considered, but the interactions between the ingredients, the dosage of each ingredient, and the effects of the different processing methods of medicinal materials were neglected. Therefore, the obtained results must be verified by further experiments.

5. Conclusion

The present study illustrates the systemic “multicompound and multitarget” efficacy of QBF against psoriasis. Moreover, this study also provided a theoretical basis to determine the synergistic effects of TCM in treating diseases and the role of systematic network pharmacology in elucidating the potential mechanisms of action of TCMs. However, as this study was based on data mining and data analysis, further validation studies should be undertaken.

Acknowledgments

The authors thank the members of their laboratory and their collaborators for their research work. This article was funded by the Construction Project of Key Clinical Special Disease of Jiangsu Province Academy of Traditional Chinese Medicine.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding the publication of this paper.

Supplementary Materials

Supplementary Materials

Table S1. Known psoriasis-related targets. Table S2. All the potential targets of QBF. Table S3. QBF shared 104 potential targets with known psoriasis-related targets. Table S4. Topological feature values of all the candidate targets for QBF against psoriasis.

Click here for additional data file. (200.2KB, zip)

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Associated Data

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

Supplementary Materials

Supplementary Materials

Table S1. Known psoriasis-related targets. Table S2. All the potential targets of QBF. Table S3. QBF shared 104 potential targets with known psoriasis-related targets. Table S4. Topological feature values of all the candidate targets for QBF against psoriasis.

Click here for additional data file. (200.2KB, zip)

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

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