Abstract

Purpose

   The purpose of this study was to investigate the therapeutic
   mechanism(s) of Clematis chinensis Osbeck/Notopterygium incisum K.C.
   Ting ex H.T (CN).

Methods

   A network pharmacology approach integrating prediction of ingredients,
   target exploration, network construction, module partition and pathway
   analysis was used.

Results

   This approach successfully helped to identify 12 active ingredients of
   CN, interacting with 13 key targets (Akt1, STAT3, TNFsf13, TP53, EPHB2,
   IL-10, IL-6, TNF, MAPK8, IL-8, RELA, ROS1 and STAT4). Kyoto
   Encyclopedia of Genes and Genomes pathway enrichment analysis indicated
   that CN-regulated pathways were mainly classified into signal
   transduction and immune system.

Conclusion

   The present work may help to illustrate the mechanism(s) of action of
   CN, and it may provide a better understanding of antirheumatic effects.

   Keywords: targets prediction, pathways analysis, action mechanism,
   Clematis chinensis Osbeck, Notopterygium incisum K.C. Ting ex H.T.
   Chang

Introduction

   RA is a chronic autoimmune disease influenced by genetic factors,
   environmental factors and interaction.[33]1 The prevalence of RA is ~1%
   in the adult population, with a higher incidence in the elderly and
   women.[34]2 In addition to disability and joint destruction,[35]3
   patients with RA have a higher risk of dying prematurely from
   cardiovascular diseases.[36]4 Consequently, prevention and treatment of
   RA are critical in clinical therapy. Therapeutic agents for RA include
   NSAIDs, glucocorticoids, DMARDs, biologic DMARDs and, most recently,
   small molecular signal inhibitors.[37]5 However, most of current drugs,
   which play an important role in treating RA, have severe adverse
   effects, including gastrointestinal irritation, kidney injury,
   cardiovascular risk and even the so-called Cushing’s syndrome.[38]6
   Consequently, TCMs, with clinical application for thousands of years,
   have recently attracted more and more attention due to prominent
   effectiveness and less side effects.[39]7 CC and NI, as TCMs, have been
   frequently used to treat RA for their anti-inflammatory
   activity.[40]8–[41]10 Our previous study has reported that CN has
   evident anti-rheumatic effects in adjuvant-induced arthritis in
   rats.[42]11 However, the molecular mechanism(s) of CN in the treatment
   of RA remains to be elucidated. Network pharmacology is an efficient
   tool to clarify targets and mechanisms of TCMs.[43]12 The methodologies
   of network pharmacology highlight the paradigm shift from “one drug,
   one target” to “multicomponent therapeutics, biological network”.[44]13
   TCMs have the advantages of multiple components and targets, which
   correspond to the methodologies of network pharmacology. Thus, network
   pharmacology is desirable for exploring the mechanisms of TCMs. In the
   present study, we respectively collected the information of targets
   from active ingredients in CN and targets of RA from several databases
   for the first time. In order to uncover the rationality of CN, network
   construction and topological structural analysis were established,
   which offered underlying synergistic mechanisms of CN for treating RA.

Methods

Building database of ingredients

   All the chemical ingredients’ data of CC and NI were derived from
   Traditional Chinese Medicine Systems Pharmacology Database and Analysis
   Platform (TCMSP) ([45]http://lsp.nwu.edu.cn/tcmsp.php). TCMSP is a
   unique system pharmacology platform of TCMs that is capable of
   providing the relationship between drugs, targets and diseases.

Screening of active ingredients

   The active constituents from CC and NI were filtered by integrating OB
   and DL. DL helps to describe pharmacokinetic and pharmaceutical
   properties of compounds, such as solubility and chemical stability.
   Usually, the selection criterion for the “drug-like” compounds in TCMs
   is 0.18.[46]14 OB represents the relative amount of an oral drug that
   is absorbed into the blood circulation.[47]15 Since low OB is the
   primary reason responsible for the development of TCMs into therapeutic
   drugs, it is vital to conduct OB screening criterion. Based on
   literatures and suggestions in TCMSP, we selected OB≥30% and DL≥0.18 as
   a screening threshold.[48]16,[49]17 The ingredients conforming to both
   standards mentioned earlier will be preserved for further analysis.

TCM-associated target prediction

   Three databases are combined to predict relevant targets of active
   ingredients in CC and NI comprehensively. GeneCards database
   ([50]http://www.genecards.org/) automatically integrates gene-centric
   data from ~125 web sources, while BATMAN-TCM
   ([51]http://bionet.ncpsb.org/batman-tcm) ranks potential drug–target
   interactions based on their similarity to the known drug–target
   interactions. STITCH database ([52]http://stitch.embl.de/) integrates
   many sources of experimental and manually curated evidence with
   text-mining information and interaction predictions. First, the active
   constituents were severally entered into GeneCards, BATMAN-TCM and
   STITCH. Then, duplications and unified names were removed from the
   targets obtained from the aforementioned three tools. Noteworthy, only
   the targets of Homo sapiens were kept for further study.

RA-associated target prediction

   Different genes associated with RA were collected from DisGeNET
   ([53]http://www.disgenet.org/web/DisGeNET). DisGeNET is a useful
   platform providing the search of the molecular underpinnings of
   diseases, the analysis of disease genes, the validation of predicted
   genes and so on.

Network construction and node screening

   The different targets from CC, NI and RA were submitted to Agilent
   Literature Search 3.1.1 (LitSearch version 2.69). Based on the human
   targets, we set “Max Engine Matches” as 10 and searched through the
   whole text. Then, the protein–protein interaction network was
   visualized by Cytoscape 3.5.1 software. Finally, we severally selected
   the top 30 targets of high-node degree as key targets for further
   analysis.

Module partition and KEGG pathway analysis

   MCODE was applied to identify the molecular network for module
   identification according to the clustering of genes in the network.
   Then, main modules obtained from CC, NI and RA were submitted to DAVID
   Bioinformatics Resources 6.8 software ([54]https://david.ncifcrf.gov/)
   to carry out GO functional enrichment analysis. “Homo Sapiens” was also
   limited to identify KEGG pathways that were significantly enriched in
   the identification module. Of note, P-value was implemented to explore
   the statistical significance of the modules. KEGG pathways with P<0.05
   (P-values were corrected using the Benjamini–Hochberg procedure) are
   significant signaling pathways.

Results

Active ingredients of CC and NI

   A total of 484 ingredients of CN were retrieved from TCMSP, including
   114 ingredients of CC and 370 ingredients of NI. In this study, 21
   active compounds from 484 compounds met both the requirements, OB≥30%
   and DL≥0.18 ([55]Table 1). It has been validated experimentally that
   some ingredients possess pharmacological activities. For example,
   β-sitosterol (OB=36.91, DL=0.75) plays a significant role in
   anti-inflammation,[56]18 anti-tumor[57]19 and
   anti-hyperlipidemia.[58]20 Moreover, nodakenin (OB=57.12, DL=0.69)
   plays an important therapeutic effect in inflammatory disorders and has
   been regarded as one of the standard ingredients of NI in Chinese
   Pharmacopoeia.[59]10,[60]21

Table 1.

   Active ingredients of CC and NI
   Molecule ID Molecule name Structure OB (%) DL Herb
   MOL001663
   (4aS,6aR,6aS,6bR,8aR,10R,12aR,14bS)-10-hydroxy-2,2,6a,6b,9,9,12a-heptam
   ethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydropicene-4a-carbo
   xylic acid graphic file with name dddt-12-967Fig2.jpg 32.03 0.76 CC
   MOL002372
   (6Z,10E,14E,18E)-2,6,10,15,19,23-hexamethyltetracosa-2,6,10,14,18,22-he
   xaene graphic file with name dddt-12-967Fig3.jpg 33.55 0.42 CC
   MOL000358 β-Sitosterol graphic file with name dddt-12-967Fig4.jpg 36.91
   0.75 CC, NI
   MOL005594 Clematoside A′_qt graphic file with name dddt-12-967Fig5.jpg
   37.51 0.76 CC
   MOL005598 Embinin graphic file with name dddt-12-967Fig6.jpg 33.91 0.73
   CC
   MOL005603 Heptyl phthalate graphic file with name dddt-12-967Fig7.jpg
   42.26 0.31 CC
   MOL000449 Stigmasterol graphic file with name dddt-12-967Fig8.jpg 43.83
   0.76 CC
   MOL001941 Ammidin graphic file with name dddt-12-967Fig9.jpg 34.55 0.22
   NI
   MOL011962 6′-Feruloylnodakenin graphic file with name
   dddt-12-967Fig10.jpg 32.02 0.67 NI
   MOL011963 8-Geranoxy-5-methoxypsoralen graphic file with name
   dddt-12-967Fig11.jpg 40.97 0.50 NI
   MOL011968 Coumarin glycoside graphic file with name
   dddt-12-967Fig12.jpg 33.07 0.78 NI
   MOL011969 Demethylfuropinnarin graphic file with name
   dddt-12-967Fig13.jpg 41.31 0.21 NI
   MOL011971 Diversoside_qt graphic file with name dddt-12-967Fig14.jpg
   67.57 0.31 NI
   MOL011975 Notoptol graphic file with name dddt-12-967Fig15.jpg 62.97
   0.48 NI
   MOL001951 Bergapten graphic file with name dddt-12-967Fig16.jpg 41.73
   0.42 NI
   MOL001956 Cnidilin graphic file with name dddt-12-967Fig17.jpg 32.69
   0.28 NI
   MOL000359 Sitosterol graphic file with name dddt-12-967Fig18.jpg 36.91
   0.75 NI
   MOL004792 Nodakenin graphic file with name dddt-12-967Fig19.jpg 57.12
   0.69 NI
   MOL001942 Isoimperatorin graphic file with name dddt-12-967Fig20.jpg
   45.46 0.23 NI
   MOL002644 Phellopterin graphic file with name dddt-12-967Fig21.jpg
   40.19 0.28 NI
   MOL002881 Diosmetin graphic file with name dddt-12-967Fig22.jpg 31.14
   0.27 NI
   [61]Open in a new tab

   Abbreviations: CC, Clematis chinensis Osbeck; NI, Notopterygium incisum
   K.C. Ting ex H.T. Chang; OB, oral bioavailability; DL, druglikeness.

Target prediction

   TCMs give play to their pharmacological effects through multiple
   ingredients and targets. Thus, besides predicting ingredients, it is
   also necessary for the exploration of targets. However, searching for
   targets through literatures is time consuming and labor intensive. In
   the present work, predictive models including GeneCards, BATMAN-TCM and
   STITCH were used to predict 301 targets, which interacted with 12
   active ingredients. It is interesting that another nine ingredients
   were removed for having no relevant targets. In addition, the DisGeNET
   database was also used to predict 1,869 targets associated with RA.

Network construction and node screening

   Network construction was automatically performed after searching by
   Agilent Literature Search. Molecular network of CC, NI and RA
   separately consisted of 781, 267 and 746 nodes and was connected by
   2,873, 633 and 2,303 edges, respectively. The topological parameters of
   CC, NI and RA show that node-degree distribution obeys the power law
   distribution. To further investigate the synergistic mechanisms of herb
   couple, the top 30 targets of high-node degree were severally chosen.
   As shown in [62]Figure 1, the herb couple shares 13 targets (Akt1,
   STAT3, TNFsf13/APRIL, TP53, EPHB2, IL-10, IL-6, TNF, MAPK8/JNK, IL-8,
   RELA, ROS1 and STAT4) with RA, in which five targets (STAT3, Akt1,
   TP53, TNFSF13 and EPHB2) are the overlapped targets in CC and NI and
   another five targets (IL-10, IL-6, TNF, MAPK8 and IL-8) and three
   targets (RELA, ROS1 and STAT4) are separate in CC and NI.

Figure 1.

   [63]Figure 1
   [64]Open in a new tab

   Top 30 targets of CC and NI.

   Note: Green nodes are shared targets from RA and herb couple, red ones
   are shared targets from RA and CC and purple ones are shared targets
   from RA and NI.

   Abbreviations: CC, Clematis chinensis Osbeck; NI, Notopterygium incisum
   K.C. Ting ex H.T. Chang; RA, rheumatoid arthritis.

Module partition and KEGG pathway analysis

Module partition

   MCODE software was used to analyze the molecular network. A total of
   122 modules were identified from original networks with 47 modules from
   CC, 25 modules from NI and 50 modules from RA. Top 10 modules with more
   nodes were respectively selected for KEGG pathway analysis.

KEGG pathway analysis

   In order to deduce the potential pathways affected by herb couple,
   DAVID Bioinformatics Resources 6.8 software was used to perform pathway
   enrichment analysis. Since diseases arise from the dysfunctions of
   basic biological functions, we removed the KEGG pathway section of
   human diseases. We found that herb couple could totally affect 35
   signal pathways, including 16 pathways from both CC and NI, 17 pathways
   solely from CC and 2 pathways solely from NI.

   As shown in [65]Table 2A, herb couple acts on six pathways in signal
   transduction, such as PI3K–Akt signaling pathway and JAK–STAT signaling
   pathway. Immune system and endocrine system, respectively, cover three
   pathways such as NOD-like receptor signaling pathway and GnRH signaling
   pathway. Furthermore, the herb couple also regulates other pathways in
   cell process, development and nervous system. Signal pathways solely
   from CC are shown in [66]Table 2B and indicated that CC has the
   possibility of being associated with the immune system, signal
   transduction, endocrine system and cell process. Compared with the
   signal pathways in CC, NI solely regulates cytosolic DNA-sensing
   pathway and cell process, which are associated with signal transduction
   and cell process, respectively ([67]Table 2C).

Table 2.

   (A) Signal pathways of herb couple, (B) individual signal pathways of
   CC and (C) individual biological pathways of NI
   Pathway class Pathway name CC’s targets on pathway NI’s targets on
   pathway
   A
   Signal transduction FoxO signaling pathway IL-6, HRAS, SGK1, TGFB3,
   IGF1, FOXO1, IL-7R, IL-10, STAT3, PCK1, AKT1, MAPK1, NRAS, G6PC,
   CDKN1A, KRAS, CDKN2B, MAPK9, PIK3CA, MAPK8, CAT, EGF AKT1, IL-6, SMAD4,
   PIK3CA, IL-10, STAT3, CDK2
   PI3K–Akt signaling pathway FGFR1, HRAS, KITLG, NFKB1, COL2A1, BCL2L1,
   KIT, IL-7R, ATF2, AKT1, KRAS, BCL2, PIK3CA, EGF, CSF1R, IL-4, IL-3,
   IL-6, SGK1, IL-2RB, HSP90AA1, RELA, TP53, IGF1, KDR, PCK1, NRAS, MAPK1,
   CDKN1A, G6PC, IFNB1, VEGFA, PDGFRA, JAK1, PDGFRB, JAK2, EPOR IBSP,
   AKT1, IL-6, IFNA1, BCL2, RELA, TP53, KITLG, PIK3CA, COL2A1, TLR4, CDK2,
   SPP1
   NF-κB signaling pathway MAP3K7, IRAK1, TNF, TNFSF13B, LYN, BCL2, RELA,
   BCL2A1, IL-1B, NFKB1, BCL2L1, BIRC3, TRAF6, CXCL12, BLNK ICAM1, TNF,
   TNFSF11, PTGS2, RELA, BCL2, TLR4
   TNF signaling pathway CSF2, IL-6, TNF, SOCS3, RELA, NFKB1, CX3CL1,
   BIRC3, MMP3, ATF2, MAP3K7, AKT1, MAPK1, FOS, IL-1B, PIK3CA, MAPK9,
   MAPK8, FAS, MAP2K7 AKT1, ICAM1, IL-6, TNF, CCL2, PTGS2, RELA, PIK3CA
   Sphingolipid signaling pathway HRAS, TNF, RELA, TP53, NFKB1, AKT1,
   NRAS, MAPK1, KRAS, BAX, BCL2, PIK3CA, MAPK9, MAPK8, DEGS1 AKT1, TNF,
   RELA, BAX, BCL2, TP53, PIK3CA
   HIF-1 signaling pathway IL-6, ERBB2, RELA, IGF1, NFKB1, STAT3, AKT1,
   MAPK1, CDKN1A, BCL2, VEGFA, CAMK2D, PIK3CA, NOS2, EGF AKT1, IL-6, RELA,
   BCL2, PIK3CA, TLR4, NOS2, STAT3, RBX1
   Endocrine system Prolactin signaling pathway HRAS, SOCS3, RELA, NFKB1,
   STAT3, AKT1, NRAS, MAPK1, FOS, KRAS, SLC2A2, MAPK9, PIK3CA, MAPK8, JAK2
   AKT1, TNFSF11, RELA, PIK3CA, STAT3
   Insulin resistance PPARA, IL-6, TNF, SOCS3, RELA, FOXO1, NFKB1,
   PPARGC1A, CPT1A, STAT3, PTPN11, PCK1, AKT1, G6PC, CD36, SLC2A2, MAPK9,
   PIK3CA, MAPK8 AKT1, PPARA, IL-6, TNF, CD36, RELA, PIK3CA, STAT3
   Adipocytokine signaling pathway PPARA, TNF, SOCS3, RELA, NFKB1,
   PPARGC1A, CPT1A, STAT3, PCK1, PTPN11, AKT1, G6PC, ACSL1, CD36, MAPK9,
   MAPK8, JAK2 AKT1, PPARA, TNF, CD36, RELA, ADIPOQ, STAT3
   Cell process Apoptosis AKT1, IL-3, TNF, AIFM1, BCL2, RELA, NTRK1, BAX,
   TP53, PIK3CA, NFKB1, FAS, BCL2L1, BIRC3 AKT1, TNF, RELA, BAX, BCL2,
   TP53, PIK3CA
   Cytokine–cytokine receptor interaction CSF2, TNF, TGFB3, KITLG, IL-13,
   TNFSF13, KIT, CX3CL1, IL-7R, CXCL12, IL-10, IL-1B, FAS, EGF, CSF1R,
   IL-4, IL-3, IL-2RB, IL-6, FLT3, KDR, TNFSF8, ACVR2A, TNFSF13B, IFNB1,
   VEGFA, PDGFRA, PDGFRB, EPOR TNFRSF11B, IL-6, IFNA1, TNF, CCL2, TNFSF11,
   KITLG, TNFSF13, IL-10
   Development Osteoclast differentiation TNF, SOCS3, RELA, PPARG, NFKB1,
   MAP3K7, TYK2, AKT1, FOS, MAPK1, IFNB1, MAPK9, JAK1, IL-1B, PIK3CA,
   MAPK8, TRAF6, MAP2K7, BLNK, CSF1R AKT1, TYK2, TNFRSF11B, TNF, TNFSF11,
   SQSTM1, RELA, PIK3CA
   Immune system Toll-like receptor signaling pathway IRAK1, IL-6, TNF,
   RELA, NFKB1, MAP3K7, AKT1, MAPK1, FOS, IFNB1, IL-1B, PIK3CA, MAPK9,
   MAPK8, TRAF6, MAP2K7 AKT1, IL-6, IFNA1, TNF, IRF5, RELA, IRF7, PIK3CA,
   TLR4, SPP1, TLR9
   RIG-I-like receptor signaling pathway MAP3K7, TNF, IFNB1, RELA, MAPK9,
   NFKB1, MAPK8, TRAF6 IFNA1, TNF, ATG5, RELA, IRF7
   JAK–STAT signaling pathway IL-4, CSF2, IL-3, IL-6, IL-2RB, SOCS3,
   IL-13, BCL2L1, IL-24, IL-7R, IL-10, STAT3, PTPN11, TYK2, AKT1, STAT4,
   IFNB1, JAK1, PIK3CA, EPOR, JAK2 AKT1, TYK2, IL-6, STAT4, IFNA1, PIK3CA,
   IL-10, STAT3
   Nervous system Neurotrophin signaling pathway IRAK1, HRAS, RELA, TP53,
   NFKB1, PTPN11, NTRK3, AKT1, NRAS, MAPK1, BDNF, KRAS, BCL2, BAX, NTRK1,
   CAMK2D, SH2B3, MAPK9, PIK3CA, MAPK8, ABL1, TRAF6, MAP2K7 AKT1, RELA,
   BAX, BCL2, TP53, PIK3CA
   B
   Immune system NOD-like receptor signaling pathway MAP3K7, MAPK1, IL-6,
   TNF, HSP90AA1, RELA, MAPK9, IL-1B, NFKB1, MAPK8, TRAF6, BIRC3
   B-cell receptor signaling pathway AKT1, MAPK1, NRAS, FOS, HRAS, KRAS,
   LYN, RELA, PIK3CA, NFKB1, BLNK
   Chemokine signaling pathway ITK, HRAS, LYN, RELA, NFKB1, CX3CL1,
   CXCL12, STAT3, AKT1, NRAS, MAPK1, KRAS, PTK2B, PIK3CA, JAK2
   Intestinal immune network for IgA production IL-4, IL-6, TNFSF13B,
   TNFSF13, CXCL12, IL-10
   Fc epsilon RI signaling pathway IL-4, CSF2, IL-3, HRAS, TNF, LYN,
   IL-13, AKT1, NRAS, MAPK1, KRAS, MAPK9, PIK3CA, MAPK8, MAP2K7
   Hematopoietic cell lineage IL-4, CSF2, IL-3, IL-6, TNF, FLT3, KITLG,
   ANPEP, KIT, IL-7R, CD36, MS4A1, IL-1B, EPOR, CSF1R
   T-cell receptor signaling pathway IL-4, PTPRC, ITK, CSF2, HRAS, TNF,
   RELA, CBL, NFKB1, IL-10, MAP3K7, AKT1, NRAS, MAPK1, FOS, KRAS, PAK4,
   PIK3CA, MAP2K7
   Endocrine system Progesterone-mediated oocyte maturation AKT1, MAPK1,
   HSP90AA1, KRAS, PIK3CA, MAPK9, IGF1, MAPK8
   GnRH signaling pathway MAPK1, NRAS, HRAS, KRAS, PTK2B, CAMK2D, MAPK9,
   MAPK8, MAP2K7
   Estrogen signaling pathway AKT1, MAPK1, NRAS, FOS, HRAS, HSP90AA1,
   KRAS, SP1, FKBP5, PIK3CA, ATF2
   Signal transduction Rap1 signaling pathway FGFR1, HRAS, KITLG, IGF1,
   CDH1, KIT, KDR, CTNNB1, AKT1, NRAS, MAPK1, KRAS, VEGFA, PDGFRA, PIK3CA,
   PDGFRB, EGF, CSF1R
   MAPK signaling pathway FGFR1, HRAS, TNF, TGFB3, NFKB1, ATF2, MAP3K7,
   AKT1, FOS, BDNF, KRAS, IL-1B, FAS, EGF, TRAF6, MAP2K7, RELA, NF1, TP53,
   DUSP5, NRAS, MAPK1, NTRK1, MAPK8IP2, PDGFRA, MAPK9, PDGFRB, MAPK8
   VEGF signaling pathway AKT1, MAPK1, NRAS, HRAS, KRAS, VEGFA, PIK3CA,
   KDR
   ErbB signaling pathway HRAS, ERBB2, CBL, AKT1, NRAS, MAPK1, CDKN1A,
   KRAS, PAK4, CAMK2D, MAPK9, PIK3CA, MAPK8, ABL1, EGF, MAP2K7, ABL2
   Ras signaling pathway FGFR1, HRAS, KITLG, NFKB1, KIT, BCL2L1, AKT1,
   KRAS, PAK4, PIK3CA, EGF, CSF1R, RELA, NF1, IGF1, KDR, PTPN11, NRAS,
   MAPK1, RASSF1, VEGFA, PDGFRA, PDGFRB, MAPK9, MAPK8, ABL1, ABL2
   Cell process Focal adhesion HRAS, ERBB2, IGF1, COL2A1, BIRC3, KDR,
   CTNNB1, AKT1, MAPK1, BCL2, PAK4, VEGFA, PDGFRA, MAPK9, PIK3CA, PDGFRB,
   MAPK8, EGF
   Signaling pathways regulating pluripotency of stem cells FGFR1, HRAS,
   TBX3, IGF1, STAT3, CTNNB1, AKT1, ACVR2A, INHBA, WNT1, NRAS, MAPK1,
   KRAS, PIK3CA, JAK1, JAK2, APC
   C
   Signal transduction Cytosolic DNA-sensing pathway IL-6, IFNA1, RELA,
   IRF7, IL-33
   Cell process Cell cycle HDAC1, TP53, SMAD4, CHEK1, CUL1, CDK2, RBX1
   [68]Open in a new tab

   Abbreviations: CC, Clematis chinensis Osbeck; NI, Notopterygium incisum
   K.C. Ting ex H.T. Chang.

Discussion

   RA is a chronic autoimmune disease that is implicated in inflammation,
   angiogenesis, bone destruction and the immune regulation. Therapeutic
   agents for RA, including NSAIDs, glucocorticoids and DMARDs, are
   limited due to their side effects.[69]22 TCMs are common drugs for the
   treatment of RA, with clinical effectiveness and less adverse effects.
   Our prior study has noted that herb couple of CC and NI was
   experimentally validated possessing anti-rheumatic effects.[70]11
   However, TCM, as a multi-component synergistic system agent, is
   comprehensive and abstruse. Therefore, their research method is
   different from chemical drugs. In the present study, to better
   recognize the drug combination of CN, we proposed a network
   pharmacology approach integrating prediction of ingredients and pathway
   analysis strategy of targets for CN. The method was applied to explore
   the potential regulation of inflammatory response, immune system and
   angiogenesis of CN and provide a new sight for the treatment of RA.
   Nonetheless, our method still has some limitations and needs to further
   improve. The approach just predicts and analyzes the potential
   synergetic mechanism of CC and NI from the perspective of biological
   network. Clinical and experimental trials are required to be further
   validated.

Targets analysis of herb couple

Targets related to RA

   Synovial inflammation is a basic pathological change in RA and results
   in swelling and pain in the joints of RA patients. Thus,
   anti-inflammation is critical in the treatment of RA. Results of key
   targets’ analysis found that RA and CN shared a total of 13 targets.
   Among these targets, IL-6, IL-8 and IL-10 all belonged to the IL
   family. In RA, IL-6 can be released by monocytes, macrophages and
   endothelial cells and influences T-cell development, which indirectly
   promotes the production of Th1, Th2 and Th17 cells with proinflammatory
   properties. IL-6 can also increase the level of VEGF in synovial
   fibroblasts, aggravating joint inflammation and damage.[71]23 In
   addition to inflammation, IL-6 increases osteoclast recruitment by
   acting on hematopoietic stem cells, leading to joint damage in
   RA.[72]24,[73]25 Therefore, blockade of IL-6 action is effective to
   reduce both inflammation and joint destruction in RA.[74]26 The
   anti-inflammatory response is essential to control the degree and
   duration of the inflammatory response in RA. In macrophages, the
   anti-inflammatory response relies on IL-10/JAK/STAT3 signaling pathway.
   IL-10 signaling cascade starts upon IL-10 binding to IL-10R and
   activates STAT3 via the JAK1 kinase.[75]27 STAT3 stimulates the
   transcription of specific genes and in turn represses proinflammatory
   cytokines such as IL-1, IL-6, IL-12 and TNF-α. Moreover, MAPK8, also
   known as JNK, is activated in RA synovium and mediates joint
   destruction in adjuvant arthritis of rats.[76]28 MAPK8 signalosome
   represents a target to prevent joint destruction.[77]29 TNFSF13
   sustains B-cell activation and thus enhances autoimmune diseases. It
   also regulates synovial inflammation in RA.[78]30 Therefore, TNFSF13
   could also be a therapeutic strategy aimed at downregulating synovial
   inflammation. Furthermore, other targets are also involved in the
   process of RA, such as STAT4,[79]31 EphB2,[80]32 TP53,[81]33 Akt1[82]34
   and RELA.[83]35 Upregulation or downregulation of abovementioned
   targets contributes to treating RA. On the other hand, ROS1, highly
   expressed in a variety of tumor cell lines, is used as a drug target to
   suppress tumors clinically.[84]36 Based on our predictions, we
   speculate that ROS1 may have some relevance with RA, and the result
   would be validated in our future study.

Pathway analysis of herb couple

Pathways related to immune response

   As shown in [85]Table 2, many signal pathways are classified into
   immune system and regulate the balance of the immune system. Innate
   immune is the first line of defense against foreign pathogens and is
   the basis and initiator of adaptive immunity. B and T cells are well
   known to be related to adaptive immune response.[86]37 [87]Table 2B
   shows that CC is associated with B-cell receptor signaling pathway and
   T-cell receptor signaling pathway, indicating that CC may have a play
   in the adaptive immune response. In addition, we also found that CN
   could regulate some proinflammatory molecule-involved pathways such as
   chemokine signaling pathway and Fc epsilon RI signaling pathway. In
   addition, cytosolic DNA-sensing pathway, MAPK signaling pathway and
   apoptosis are highly associated with the function of immune response,
   although not classified into the immune system.[88]38,[89]39
   Considering the effects of immune pathways on disease progress and
   joint destruction, modulation of these pathways may have important
   implications for treating RA.

Pathways related to inflammation

   Another large category of signal pathways is signal transduction,
   including a number of well-known signal pathways that are related to
   inflammation, such as JAK–STAT signaling pathway, NF-κB signaling
   pathway and TNF signaling pathway. For example, canonical NF-κB
   signaling pathway is critical for the regulation of the inflammation
   response. Although less extensively studied, non-canonical pathway
   plays an indirect role in synovial inflammation via the high expression
   of its activators such as CD40L, CD40, BAFF/BAFF-R and RANKL in RA
   synovium.[90]40 Since both canonical and non-canonical NF-κB signaling
   pathways participate in inflammatory response and the pathogenesis of
   RA, inhibitors of these two pathways can play a role in the treatment
   of RA. TNF, as the upstream target of the NF-κB pathway, has already
   been regarded as a therapeutic target in RA.[91]41 Moreover, the
   JAK–STAT pathway is an important pathway for the transduction of
   cytokines associated with RA and is regarded as a target in
   inflammatory and autoimmune diseases.[92]42 Tofacitinib, a JAK
   inhibitor, proves effective in the treatment of RA through reducing the
   expression of metalloproteinase and interferon-regulated gene in RA
   synovium.[93]43

Pathways related to angiogenesis

   Angiogenesis is a complex process involving the growth of new blood
   vessels and plays an important role in the growth, metastasis and
   prognosis of tumor. It is accompanied by the entire process of RA and
   can foster the infiltration of inflammatory cells into the joints,
   leading to synovial hyperplasia and progressive bone destruction.[94]44
   Ras signaling pathway and VEGF signaling pathway regulated solely by CC
   are related to angiogenesis. VEGF signaling pathway plays an important
   role in promoting the proliferation of vascular endothelial cells and
   the formation of new blood vessels,[95]45,[96]46 while Ras signaling
   pathway is related to tumor angiogenesis and vascular
   permeability.[97]47 Certainly, inhibiting VEGF signaling is a feasible
   antiangiogenic and anti-inflammatory therapeutic strategy in RA.[98]48

Conclusion

   TCMs usually exert a multicomponent and multi-pathway synergetic
   efficacy in the treatment of various diseases. Therefore, the research
   approach applied to TCMs should correspond to the mechanisms of
   synergy. In this study, we applied a network pharmacology approach to
   identify the RA-related targets and signal pathways of CN, making it
   possible to connect genomic space to pharmacological space. In summary,
   we predicted the action mechanism(s) of herb couple for treating RA
   through the analysis of key targets and KEGG pathways.

Acknowledgments