A network pharmacology-based approach to explore potential targets of Caesalpinia pulcherima: an updated prototype in drug discovery

Caesalpinia pulcherima (CP) is a traditional herb used for the treatment of asthma, bronchitis, cancer, anti-bacterial, anti-fungal and as abortifacient. In the present study, bioactive components and potential targets in the treatment of breast cancer validated through in silico, in vitro and in vivo approach. The results for the analysis were as among 29 components, only four components were found active for further study which proved the use of CP as a multi-target herb for betterment of clinical uses. The results found by PPI states that our network has significant interactions which include the ESR-1, ESR-2, ESRRA, MET, VEGF, FGF, PI3K, PDK-1, MAPK, PLK-1, NEK-2, and GRK. Compound-target network involves 4 active compound and 150 target genes which elucidate the mechanisms of drug action in breast cancer treatment. Furthermore, on the basis of the above results the important proteins were fetched for the docking study which helps in predicting the possible interaction between components and targets. The results of the western blotting showed that CP regulates ER and EGFR expression in MCF-7 cell. In addition to this animal experimentation showed that CP significantly improved immunohistological status in MNU induced carcinoma rats. Network pharmacology approach not only helps us to confirm the study of the chosen target but also gave an idea of compound-target network as well as pathways associated to the CP for treating the complex metabolic condition as breast cancer and they importance for experimental verification.

Screening of components of CP for breast cancer. Among the 61 components, only 29 components were found to relate with breast cancer as shown in Table 1. Using TCMSP the 29 components were subjected to OB and DL criteria filtering out with only four active components of CP based on the ADME criteria. The genes of each active component were fetched from Swiss target prediction whereas genes of the disease i.e., breast cancer were retrieved from GeneCards Human database.
Construction and analysis of target PPI network. In order to enhance the visualization and understand the mechanism of the targets, it is important to study the PPI of the target genes. The target genes of the corresponding components were subjected to STRING v_11 to visualize and construct the PPI network www.nature.com/scientificreports/ for the same. The high-confidence target protein interaction data was set with a score level greater than 0.9. The interactions between the target proteins is depicted in Fig. 1, which comprises of total 124 nodes, 233 edges; each edge represents PPIs. The other parameter is average node degree which values at 3.76 and local clustering co-efficient: 0.538 corresponds to the number of targets that are connected to the network. Degree plays an important role in showing the role of proteins interaction and nodes of network. The PPI network shows the targets involved in breast cancer are ESR-1, ESR-2, ESRRA, MET, VEGF, FGF, PI3K, PDK-1, MAPK, PLK-1, NEK-2, and GRK which are the major targets in breast cancer. Along with ESR-1; EGFR, MET, and VEGFR are located centrally in the network which indicates the role of proteins in the pathogenesis of breast cancer. Basically, ESR pathway is a forthcoming starting point to discover the mechanisms of breast cancer. The main factor in ESR pathway is estrogen which is found to be an integral component involved in the development and maturation of breast. The involvement of estrogen receptors are also seen in other pathological processes including breast cancer, endometrial cancer, and osteoporosis. So, PPI network and pathway analysis of novel genes were carried out for the recognition of critical genes related to the breast cancer.
Arrangement and construction of disease-target network. To study the signaling pathway and function of the selected target genes, the data was imported to Cytoscape to construct compound-target network.   Fig. 2, the compound-target-disease interaction network was constructed which elucidate the mechanisms of drug action in the breast cancer treatment. It consists of 4 ingredients, and 150 interactive target proteins. In this network, we found that many targets were hit by multiple components. This fact inferred that the active biochemical of CP might influence these targets synergistically; it has therapeutic effects on other disease and disorders additionally to BC. The details of three topological parameters i.e. Betweenness Centrality, Closeness Centrality, and Degree are given in Table 2 which gives an important role of each target in the network structure.
GO gene enrichment analysis and KEGG pathway annotation. GO enrichment analysis was carried out to analyse the target proteins. The setting for the ClueGO was set for three criteria to analyse the target genes for GO biological (Table 3), Go molecular (Table 4), and GO cellular (Table 5) and the most important parameter as KEGG pathway (Fig. 3A). The GO term fusion was restricted to pV ≤ 0.005 that is based on the false discovery rate (Benjamini-Hochberg). The Ras-Raf-MAPK signaling pathway is a key route for the ErbB family, as is the PI3K/AKT pathway, both of which results in alteration of cell proliferation and apoptosis. As far as breast cancer is considered, over-expression of ErbB receptor may lead to Ras activation. So to analyse the statement, GO and KEGG analysis was carried to determine the signaling pathway and following pathways were found to associate: PI3K-Akt, MAPK, ErbB, Ras, Chemokine, HIF-1, FoxO, sphingolipid, AMPK, VEGF, JAK-STAT, TBF, insulin, GnRH, estrogen signaling pathway, prolactin signaling pathway, thyroid hormone signaling pathway, and relaxin signaling pathway (Fig. 3B). CP can be used for the treatment of other conditions also as hepatitis B, measles, human T-cell leukemia virus 1 infection, Kaposi sarcoma-associated herpes virus infection. As the constituents of CP was selected to examine the effects on breast cancer but via KEGG analysis ( Table 6) it was found responsible in many cancers as colorectal cancer, renal cell carcinoma, pancreatic cancer, endometrial cancer, prostate cancer, melanoma, bladder cancer, small cell lung cancer, non-small cell lung cancer, hepatocellular carcinoma, and gastric cancer. Owing to the facts and visualization CP may be used as novel drug for the treatment of various diseases and disorder.
Molecular modelling: docking of the active components. After analysing the pathways and diseases and disorder related to the genes, it is important to study structure based design of the component, as well as its ability to predict the binding-conformation of small molecule ligands to the appropriate target binding site. The  www.nature.com/scientificreports/ main reason behind selecting the proteins is that they played a major role in PPI, Compound-target network and in KEGG analysis; as well as these proteins are found to play a vital role in the mechanism of BC. Table 7 and In vitro experimental validation. The (Fig. 5).
In vivo experimental validation. In this part to reveal the molecular mechanism of EAFCP (ethyl acetate fraction of Caesalpinia pulcherima) in treating MNU (N-Methyl-N-nitrosourea) induced mammary carcinoma, the anti-tumour effect improved after treatment for 30 days. EAFCP 500 mg/kg and TAM (Tamoxifen) 2 mg/ kg tumour-bearing rats were significantly more resistant to the development tumour than control rats and observed decreased tumour development ( Fig. 6A-C). Consequently, we have examined the effect of treatment on the density of ER-α expression by immunohistochemistry ( Fig. 7A-D). After EAFCP and TAM treatment, ER-α immunoreactivity inside the nucleus was reduced significantly. The results strongly established that the treatment worsen tumour development by interfering ER.

Discussion
As far as traditional approach is considered "one drug, one target" theory of drug design is used, in contradictory network pharmacology which aims to explore the correlation of drugs and diseases, based on the multi-targeted therapy 17 . Novelty of this approach includes the use of systems biology, network analysis, connectivity, and redundancy. NP studies was successfully used to identify the novel targets and to determine the unknown signaling pathways interact with compounds 18,19 . The NP approach provides new insights into the systemic connection between therapeutic targets, and a disease as a whole and provides a powerful and promising tool for the clarification of disease mechanisms at a systemic level and the discovery of potential bioactive ingredients 20 .
In this context the present study generated a novel network which gives a general view of molecular mechanism of CP. Active components found in CP are ellagic acid, gallic acid, cyanidine, catechin, quercetin, rutin, β-sitosterol, myricetin, flavonoids, homo-flavonoids, pulcherrimin, lupeol, glycosides and phenols. The components were screened for its DL and OB criteria and thus, four active components i.e. quercetin, ellagic acid, cyanidine, and catechin were found suitable for further studies. BC network constructed through the plant bioactive target followed by recognition of targets associated with BC pathway. The network reveals the potential of 4 CP bioactives to modulate the BC by the interactions of 150 proteins through multiple pathways. These components inhibit cells proliferation, induces cell cycle arrest and apoptosis in different cancer cell types [21][22][23][24][25] . Through PPI interaction of the genes we found 124 nodes and 233 edges. ESR-1, ESR-2, ESRR-A, MET, FGF, VEGF, PI3K, PDK-1, MAPK, PLK-1, NEK-2, and GRK were likely to be key genes in the development of BC. GO and KEGG analysis revealed several pathways as well as other disease and disorders for the selected genes. The GO enrichment analysis showed the direct involvement of bioactive in the regulation of BC. KEGG pathway analysis proved that estrogen signaling pathway and ErbB signaling pathway may be crucial signaling pathway in the selected network which helps to support that CP may be used for BC treatment. Other than estrogen signaling pathway and ErbB, Ras, Chemokine, HIF-1, FoxO, sphingolipid, PI3K-Akt, AMPK, VEGF, JAK-STAT, TBF, insulin signaling pathway, GnRH, estrogen signaling pathway, prolactin signaling pathway, thyroid hormone signaling pathway, and relaxin signaling pathway were also found, suggesting the use of CP in multi-targets. Adding on the evidence for CP it can be used in colorectal cancer, renal cell carcinoma, pancreatic cancer, endometrial cancer, prostate cancer, melanoma, bladder cancer, small cell lung cancer, non-small cell lung cancer, hepatocellular carcinoma and gastric cancer. In addition to this, the docking study was carried for the validation of targets. It also screens the affinity between the components and targets, which can directly clarify their structure-activity relationship. On the basis of the results obtained in network pharmacology, therapeutic effect of CP was investigated by western blotting, signifying that EAFCP treatment could regulates ER signaling pathway and suggesting that breast cancer can be treated through a complex system with multi-component target disease interaction. Our earlier preclinical study on the phytochemicals from CP decrease cell proliferation and induce apoptosis, significantly improved the pathological conditions of MNU induced breast cancer rat tissue suggesting their involvement in BC treatment 16 . It is not yet known whether giving CP alone or with  www.nature.com/scientificreports/ chemotherapeutic agent will enhances the activity in treating patients with breast cancer which is future scope of the present study.

Conclusion
This study scientifically investigates the pharmacological mechanism of CP in the treatment of breast cancer through network pharmacology, docking analysis, western blotting and in vivo animal study. It is in addition worth mentioning that network pharmacology has great advantages in clearing up the mechanism of CP as TCM.

Materials and methods
The following parameters are important in order to construct a network: (1) identification and confirmation of compounds using chemical databases; (2) selection of compounds on the basis of pharmacokinetic parameter i.e. ADME (absorption, distribution, metabolism, and excretion) criteria; (3) selected compounds were further subjected to understand protein interaction and to obtain the relevant information by using publicly available database or tools; (4) genes related to target disease i.e., breast cancer were identified using human disease database and common genes of target and compounds were selected; (5) construction and analysis were carried out to understand the interaction and molecular mechanisms using visualization software; and (6) to perform docking study of the active actives.

Chemical databases. The chemical components of CP were identified through literature and the Traditional
Chinese Medicine Ingredient Database 26 (TCMID, https ://www.megab ionet .org/tcmid /); the TCM Database@ Taiwan 27 (https ://tcm.cmu.edu.tw/), most comprehensive databases on global scale. The chemical components were subjected to database called Traditional Chinese Medicine Systems Pharmacology 28 (TCMSP, https ://lsp. nwu.edu.cn/tcmsp .php) to screen for breast cancer. TCMSP helps to promote integration of both traditional as well as modern medicines in order to accelerate the drug discovery which builds the framework for system pharmacology and covers the ADME information. Chemical structures, synonyms, molecular weight, canonical SMILES and physicochemical properties were collected with the help of ChEMBL 29 (https ://www.ebi.ac.uk) and Pubchem 30 (https ://pubch em.ncbi.nlm.nih.gov).
Evaluation of pharmacokinetics parameters. The selected components were further screened for oral bioavailability and drug-likeness pharmacokinetics parameters which include ADME. The ADME characteristics of the drug indicate the ratio of the oral drug to oral dosage of the blood circulatory system. The parameters to access the components druggability is analysed according to the set parameters as oral bioavailability (OB ≥ 30) value and drug-likeness (DL ≥ 0.18) indices recommended by TCMSP 28 . Among all the selected components, only the components which fit in the criteria were selected for construction of network.  Table 7. Docking score of tyrosine kinase (interacting amino acid). Bold signifies that among each class of tyrosine kinase the maximum score of the active component. www.nature.com/scientificreports/

Identification of target genes and construction of target PPI (protein-protein interaction)
network. PPI is important aspect to study the involvement of proteins in various biochemical processes in order to understand the cellular organization, bioprocess and functions. This can be done by using the virtual screening database called STRING 11.0 (https ://strin g-db.org/) 31 . The genes of the selected components were uploaded to STRING to get the information about PPIs. The setting for generating the PPI network was in accordance with 'Homo sapiens' and the confidence in the interaction among the target protein was set to the highest confidence data > 0.9. The network nodes represent proteins whereas the edge represents associated protein-protein.

Identification of disease target genes.
In order to construct the compound-target network, it is important to identify the genes related to disease. The information related to breast cancer associated target genes was collected from GeneCards 32 (https ://www.genec ards.org), human gene database which provides information related to all annotated and predicted human genes.

Construction of compound-target network.
Once the protein-protein interaction was carried, the next step is to understand the molecular mechanism which is achieved by constructing the compound-target network using Cytoscape 33 visualization software v_3.7.1. The compound-target network helps to understand and analyse the mechanism of the components with target as well as the pathway involved. another Cytoscape plug-in which gives a network-based visualization to diminish redundancy of results from pathway enrichment analysis 34 . GO is carried out to analyse the gene cluster in the network to improvise the data prediction. GO provides a hierarchically organized set of thousands of standardized terms for biological processes, molecular functions and cellular components, with curated and predicted gene annotations based on these terms for multiple species. Biological process GO annotation is frequently used resource for pathway enrichment analysis. The study objective is to identify the biological process in order to layout the meaningful functional information. KEGG is used to study gene functions and the metabolic pathway of the inputted network of genes and molecules. It also helps us to find out the contributing pathway of the target associated with the disease.   Immunohistochemical analysis. IHC analysis was performed on formalin-fixed, paraffin-embedded tissue sections using standard histologic procedures. The primary antibodies for ER-α was incubated at a dilution of 1:50 for one and half hour at room temperature or 16 h (overnight) at 4 °C. The antigen retrieval was performed with thermic treatment by microwave using 3 cycles of 5 min for ER-α with citrate buffer solution. The tissues were counterstained with hematoxylin. The immunoexpression of ER-α was evaluated as per the Allred score for ER nuclear positivity, the proportion score (PS) (0-5) and the % positive tumor cells are respectively, 0 (0%), 1 (< 1%), 2 (1-10%), 3 (11-33%), 4 (34-66%), and 5 (67-100%). The intensity of staining (IS) for the nuclear positivity of the cells graded as 0, 1, 2, and 3 was as none, mild, moderate, and strong, respectively. So the total scores for ER is given as TS = PS + IS. TS 0 and 2 are negative scores, and 3, 4, 5, 6, 7, and 8 are positive scores 37,38 . www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.