Constitution of a comprehensive phytochemical profile and network pharmacology based investigation to decipher molecular mechanisms of Teucrium polium L. in the treatment of type 2 diabetes mellitus

Background Type 2 diabetes mellitus (T2DM) is a metabolic disease affecting a huge population worldwide. Teucrium polium L. has been used as a folk medicine for the treatment of T2DM in Anatolia, Turkey. The antihyperglycemic effect of the plant was reported previously. However, there was no detailed study on the underlying molecular mechanisms. In this study, we generated a research plan to clarify the active constituents of the extract and uncover the molecular mechanisms using network pharmacology analysis. Methods For this purpose, we composed a dataset of 126 compounds for the phytochemical profile of the aerial parts of T. polium. Drug-likeness of the compounds was evaluated, and 52 compounds were selected for further investigation. A total of 252 T2DM related targets hit by selected compounds were subjected to DAVID database. Results The KEGG pathway analysis showed enrichment for the TNF signaling pathway, insulin resistance, the HIF-1 signaling pathway, apoptosis, the PI3K-AKT signaling pathway, the FOXO signaling pathway, the insulin signaling pathway, and type 2 diabetes mellitus which are related to T2DM . AKT1, IL6, STAT3, TP53, INS, and VEGFA were found to be key targets in protein-protein interaction. Besides these key targets, with this study the role of GSK3β, GLUT4, and PDX1 were also discussed through literature and considered as important targets in the antidiabetic effect of T. polium. Various compounds of T. polium were shown to interact with the key targets activating PI3K-AKT and insulin signaling pathways. Conclusions According to these findings, mainly phenolic compounds were identified as the active components and IRS1/PI3K/AKT signaling and insulin resistance were identified as the main pathways regulated by T. polium. This study reveals the relationship of the compounds in T. polium with the targets of T2DM in human. Our findings suggested the use of T. polium as an effective herbal drug in the treatment of T2DM and provides new insights for further research on the antidiabetic effect of T. polium.


INTRODUCTION
Diabetes mellitus is a metabolic disease characterized by high blood glucose levels. According to the reports of WHO, about 422 million people live with diabetes. Diabetes was a direct cause of about 1.6 million deaths only in 2016. Among adults, 90% of the patients have type 2 diabetes mellitus (T2DM) (Holman, Young & Gadsby, 2015). In T2DM, β-cell dysfunction and/or insulin resistance results with hyperglycemia and high glucose levels in blood. Patients with T2DM are under risk for some complications that diabetes can cause such as cardiovascular disease, renal disease, diabetic retinopathy and neuropathy (Zheng, Ley & Hu, 2018). In T2DM, multitarget treatment is used to overcome the defects caused in the organism (He et al., 2019;Vuylsteke et al., 2015).
The hypoglycemic effect of T. polium was shown by several reports (Esmaeili & Yazdanparast, 2004;Gharaibeh, Elayan & Salhab, 1988;Shahraki et al., 2007;Yazdanparast, Esmaeili & Helan, 2005). The hypoglycemic effect of T. polium was observed by Gharaibeh, Elayan & Salhab (1988) for the first time. In the research, the decoction of aerial parts was tested through three different administrations (oral, intraperitoneal, and intravenous) in normoglycemic and streptozotocin-induced hyperglycemic rats. In all administration ways of T. polium decoction, it had caused a decrease in blood glucose concentration. The decoction of T. polium had a decrease of 20.5% in blood glucose concentration by oral administration while intraperitoneal and intravenous administration of the decoction had a decrease of 26.5% and 44%, respectively. The study has suggested that the hypoglycemic effect of T. polium was a result of an increase in the peripheral utilization of glucose (Gharaibeh, Elayan & Salhab, 1988). In another study, administration of ethanol-water (7:3) extract of aerial parts of T. polium per six weeks, resulted in a decrease of 64% in blood glucose levels of streptozotocin-induced hyperglycemic rats. The result of this study also had proved that T. polium extract reduced blood glucose concentration by increasing pancreatic insulin secretion dose-dependently (Esmaeili & Yazdanparast, 2004). Although there are reports about the hypoglycemic effect of T. polium, there is still a lack of information for underlying mechanisms. In a recent study, to elucidate molecular mechanisms, effects of T. polium extract on pancreatic islets cells regeneration was investigated. It was found that the antidiabetic effect of T. polium was connected with the antioxidant defense system and Pdx1 expression in the JNK pathway (Tabatabaie & Yazdanparast, 2017).
Plants have been used for the treatment of various diseases in folk medicine. These herbal preparations consist of multiple compounds that target multiple proteins in an organism. This suits well with the multicomponent-multitarget paradigm (Zhang et al., 2019). Network pharmacology provides information to understand the underlying mechanisms of therapeutic and adverse effects of these multicomponent therapeutics (Hopkins, 2008;Keith, Borisy & Stockwell, 2005;Li & Zhang, 2013). Unlike the trend in drug research studies held in the 20th century which aims for single components affecting single targets, nowadays researchers focus on multicomponent therapeutics. Network pharmacology is a rising trend in the 21st century, mainly after 2010 (Lu et al., 2019). Network pharmacology studies revealed molecular mechanisms of several Traditional Chinese Medicine (TCM) recipes in the treatment of complex diseases already (Chen et al., 2019;Chen et al., 2018;Xiang et al., 2019).
In this study, the underlying mechanisms of T. polium in the treatment of diabetes were aimed to be elucidated. For this purpose, firstly the phytochemical content of the plant was screened through a detailed literature search. Compounds reported from T. polium were selected based on their drug-likeness properties and screened for their potential targets that play a role in the biological processes. Therapeutical targets of T2DM and targets of the compounds were merged for further investigations. Protein-protein interaction (PPI) network of common targets was constructed. Key targets were determined and the role of targets in T2DM pathways was discussed.

Literature based search for phytochemical content of T. polium
Previous phytochemical studies on T. polium were reviewed and compounds that were isolated or determined listed. Literature search was performed using 'Scopus' and 'Web of Science-Clarivate' databases with the keyword 'Teucrium polium' upto June 2020. After the review process, it was found that 126 compounds were reported from T. polium by several reports. Due to their structure, compounds were listed under 3 groups (phenolics, terpenoids and amino acid derivatives). All the compounds were converted to Canonical SMILES format using PubChem (https://pubchem.ncbi.nlm.nih.gov/) or CS Chemdraw Ultra.

Evaluation of drug likeness of the compounds
The absorption and permeation abilities of the compounds in the extract play a critical role in the biological activity observed. In this study, Lipinski's rule of five was used to filter compounds which possess good absorption and permeation so that could be a new drug candidate. According to this criteria compounds which have; (i) molecular weight (MW) greater than 500, (ii) the calculated logP value above 5, (iii) more than 5 hydrogen bond donors (HBD) and (iv) more than 10 hydrogen bond acceptors (HBA) were filtered (Lipinski et al., 2001;Turner & Agatonovic-Kustrin, 2007). All 126 compounds were subjected to SWISSADME to obtain data for pharmacokinetics and drug-likeness (Daina, Michielin & Zoete, 2017). 52 compounds meet the Lipinski's rule of 5 and further used to construct 'Compound-Target' network. The detailed results for all the dataset of 126 compounds can be found in Table S1.

Construction of 'Compound-Target' network
In an herbal extract, each compound has an interaction with specific targets. The biological effects of the extract are a result of these interactions. Targets of the selected 52 compounds were searched through TCSMP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform), and SYMMAP databases (Ru et al., 2014;Wu et al., 2018). Compounds were also subjected to SwissTargetPrediction for target fishing (Daina, Michielin & Zoete, 2019). During these screening, targets were limited for Homo sapiens. To avoid confusion, targets obtained were screened for UniprotKB ID for their unique identifiers (http://www.uniprot.org/) (UniProt Consortium, 2018). Duplicate targets for the same compounds were removed and 'Compound-Target' network was obtained including 704 targets (Table S2). Cytoscape 3.8.0 was used for the visualization of the network (Shannon et al., 2003).

Collection of T2DM targets and network construction
T2DM related genes were collected from DisGeNET (https://www.disgenet.org/). 'Type 2 diabetes mellitus' was used as a keyword. DisGeNET is a platform with collections of genes associated with diseases (Bauer-Mehren et al., 2010). 1513 genes related to T2DM were obtained. The data obtained were transferred to Cytoscape 3.8.0.

'Compound-Target-Disease' network and the 'Protein-Protein Interaction' network construction
For further investigation, the intersection of 'Compound-Target' network and T2DM related genes were set as 'Compound-Target-Disease' network. This network consisted of 252 genes (Table S3). For an illustration of the roles of selected genes in biological systems, the STRING database (http://string-db.org/, version 11) was used. STRING is a database that helps understanding associations between expressed proteins in a cellular function (Szklarczyk et al., 2018). Protein-protein interaction (PPI) map of 252 genes were generated. The confidence score was set as high (> 0.7). The key targets were defined using topological analysis. Topological network parameters cover some properties such as; degree distributions, stress centrality, betweenness centrality, closeness centrality (Doncheva et al., 2012). In this study, degree distributions were selected to identify key targets.
annotations (Huang, Sherman & Lempicki, 2008;Huang, Sherman & Lempicki, 2009). The 252 genes which were common for compounds and disease were uploaded to DAVID (https://david.ncifcrf.gov/). The results were listed based on their p values. Top 20 results with lower p value were selected. The results of gene ontology (GO) function and (Kyoto Encyclopedia of Genes and Genomes) KEGG pathway analysis were evaluated and discussed (Ashburner et al., 2000;Gene Ontology Consortium, 2019;Mi et al., 2019).

Construction of 'Compound-Target-Pathway' network
'Compound-Target-Disease' network and selected KEGG pathways were intersected to give 'Compound-Target-Pathway' network. Cytoscape was used for visualization.

Screening of chemical compounds in T. polium and selection for the potential active compounds
Through a detailed literature search, 126 compounds were listed in the aerial parts of T. polium (Table S4). Mainly phenolic compounds (flavonoids, phenylethanoid glycosides, phenolic acids), terpenoids (secoiridoids, iridoids, sesquiterpenoids, diterpenoids, triterpenoids) and amino acid derivatives (cyanogenic glycosides) were identified. Druglikeness of these compounds were scanned through Lipinski's rule of 5 (the parameters of the compounds were given in Table S1). 80 compounds that met the selected criteria were searched for their potential targets using SYMMAP, TCMSP and Swiss Target Prediction databases. The databases provided information for 52 compounds ( Table 1).

Analysis of compound-target interactions and determination of the common targets of T. polium and T2DM related genes
There is a total of 704 genes related to 52 compounds. The 'Compound-Target' network consists of 756 nodes and 4023 edges. Quercetin, apigenin and luteolin were found to be in relation with more targets than the other compounds (edge numbers were 254, 184 and 158 respectively). For further investigation, the common targets for T. polium and T2DM were determined. Firstly, 1513 T2DM related genes were imported from DisGeNet. All the targets were converted to Uniprot IDs to avoid confusion. The merge process of compound-target network and disease-target network resulted in 252 common targets (Fig. 1). These targets were selected for further investigation to understand the mechanisms of T. polium in the treatment of T2DM.

Construction of PPI networks and determination of the key targets
To understand the metabolic processes, PPIs play a key role. It comprises a network including direct and indirect interactions between proteins which give researchers new insights in understanding biological phenomena (Ijaz, Ansari & Iqbal, 2018;Szklarczyk et al., 2018). To clarify the key targets in the 'Compound-Target-Disease' network, the target genes were subjected to STRING 11.0 using a confidence score of > 0.7 (high) to achieve PPI network. The PPI network had 252 nodes and 1912 edges. According to topological analysis, degree distributions were evaluated. Degree shows the interaction numbers of the targets within the network. The nodes with a higher degree are referred to as a hub. The Kutluay and Diker (2020), PeerJ, DOI 10.7717/peerj.10111 5/25  hub plays a key role in the biological process as it is related with more targets. Two-fold of the mean of the degree was selected as a threshold for the determination of key targets. 37 targets with a higher degree than 30.3 was thought to have a critical role for the mechanism of action (AKT1, INS, VEGFA, IL6, TP53, STAT3, MAPK1, APP, TNF, MAPK8, CXCL8,  EGFR, PIK3CA, PIK3R1, SRC, MMP9, IL10, PTGS2, IL1B, CCL2, RELA, HRAS, GAPDH,  PTEN, IL2, IL4, MTOR, TLR4, CASP3, JAK2, ICAM1, ESR1 and might have a role in the antidiabetic effects of T. polium (Fig. 2). PPI network for key targets had 37 nodes and 425 edges. Interleukin-6 (IL6), signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase 1 (MAPK1), insulin (INS), and vascular endothelial growth factor A (VEGFA) were the proteins with a higher number of interactions (Fig. 3).

Gene enrichment analysis using DAVID database for GO and KEGG
252 common targets were subjected to DAVID database for gene enrichments. GO and KEGG gene enrichment results were put in order according to their p values. GO enrichment results were given in three parts; molecular, biological, and cellular. Top 20 results for each analysis were plotted in a graph produced by Graphpad Prism 6 (Figs. 4 and 5).
The results for GO analysis were evaluated through related terms option of DAVID database. According to biological process results; response to drug (GO:0042493), negative regulation of apoptotic process (GO:0043066) and positive regulation of transcription from RNA polymerase II promoter (GO:0045944) showed higher target numbers in count (Fig. 4). Negative regulation of apoptotic process, inflammatory response (GO:0006954), positive regulation of cell proliferation (GO:0008284), glucose homeostasis (GO:0042593) and glucose transport (GO:0015758) were found to be related with at least one of the KEGG pathways such as PI3K-Akt signaling pathway (hsa04151), TNF signaling pathway (hsa04668), insulin resistance (hsa04931), insulin signaling pathway (hsa04910), FoxO signaling pathway (hsa04068), adipocytokine signaling pathway (hsa04920), AMPK signaling pathway (hsa04152) and type 2 diabetes mellitus (hsa04930). Molecular function results with higher target numbers were protein binding (GO:0005515), protein homodimerization activity (GO:0042803), and identical protein binding (GO:0042802) (Fig. 4). Kinase activity (GO:0016301) and insulin receptor substrate binding (GO:0043560) were found to be related to at least one of the KEGG pathways such as insulin resistance, insulin signaling pathway, FoxO signaling pathway, PI3K-Akt signaling pathway, and type 2 diabetes mellitus. KEGG enrichment results supported these findings. Results of 252 common targets were listed as (related with T2DM); TNF signaling pathway, insulin resistance, apoptosis, HIF-1 signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway, insulin signaling pathway and type 2 diabetes mellitus (Fig. 5) suggested that, compounds reported from T. polium might also leads new insights for the treatment of cancer (Table 2).

DISCUSSION
The potent hypoglycemic effect of T. polium extract has been reported by several reports (Esmaeili & Yazdanparast, 2004;Gharaibeh, Elayan & Salhab, 1988 Yazdanparast, 2017;Yazdanparast, Esmaeili & Helan, 2005). According to the study performed by Tabatabaie and Yazdanparast, T. polium extract lowered fasting blood glucose levels closely to the control group. In addition to the hypoglycemic effect, T. polium treated rats had lower triglyceride and cholesterol levels when compared with diabetic rats (Tabatabaie & Yazdanparast, 2017). These experimental data show the potential of T. polium as a promising herb in the treatment of T2DM. According to the findings of this study, we are considering that T. polium show its antidiabetic effect via enhancing β-cell number and function and exhibiting insulinlike effect through PI3K-AKT pathway. Previous reports performed on the extract and compounds of T. polium support our findings of the network pharmacology assisted analysis. According to the KEGG enrichment results in our study, 18 key targets take part in PI3K-AKT pathway and 11 key targets take part in insulin resistance pathway (Table 2). For this view, it is essential to understand the roles of these pathways in T2DM.
In a previous study about molecular mechanisms of the effects of T. polium extract on pancreatic β-cells regeneration, it was showed that c-jun N-terminal kinase (JNK) pathway provoked with oxidative stress leads to the inactivation of pancreas/duodenum homeobox protein 1 (PDX1) via FoxO1. In the same study, T. polium extract increased FoxO1 phosphorylation and, promoted the expression of PDX1 (Tabatabaie & Yazdanparast, 2017). Tyrosol (TP21), a polyphenol reported from T. polium, also was shown to inhibit ER-stress induced β-cell apoptosis by JNK phosphorylation (Lee et al., 2016). JNK pathway induces insulin receptor substrate 1 (IRS1) inhibition by causing serine phosphorylation which is an important step in downstream of insulin receptor signaling. This impairs the insulin signaling pathway by inhibiting IRS1-IRS2/PI3K-AKT pathway (Kaneto et al., 2005). IRS1-IRS2/PI3K-AKT pathway inactivates FoxO1 which supports pancreatic β-cells proliferation by enhancing PDX1 expression (Kitamura et al., 2002).
VEGFA is a growth factor that has an important role in angiogenesis, vasculogenesis and endothelial cell growth. Apigenin (TP5), luteolin (TP13), quercetin (TP14) and prunasin (TP33) showed interaction with the target VEGFA (Table S5). Hyperglycemic situations result in overexpression of VEGFA which is a critical factor in diabetic complications such as diabetic retinopathy (Caldwelll et al., 2003).

CONCLUSIONS
In the last decade, network pharmacology driven omics methods play an important role to understand the role of the herbal prescriptions used as a folk medicine in various diseases. In this study, molecular mechanisms of T. polium in the treatment of T2DM were evaluated with the help of bioinformatics. Though there were studies on T. polium extract's antihyperglycemic effect via in vitro and in vivo assays, the underlying molecular mechanisms has not totally determined yet. We constructed a comprehensive dataset of compounds reported in T. polium. 126 compounds previously isolated or determined from the aerial parts of the plant were listed through a detailed literature search. In this view, this study serves the most detailed data on the content of the phytochemical profile of T. polium so far.
In the present network pharmacological analysis, insulin resistance and PI3K-AKT signaling pathway were shown to take place in the center of the mechanism of action of T. polium. T. polium is an insulin-sensitizing plant. Even though insulin resistance has an important role in the pathophysiology of T2DM, insulin resistance does not result in T2DM in all cases. It turns to T2DM with a loss in β-cell mass in pancreatic islets. The results of the present network pharmacology studies taken together with the previously reported data, suggested that T. polium could be a promising herb for the treatment of T2DM through ameliorating insulin resistance and enhancing β-cell mass.

ADDITIONAL INFORMATION AND DECLARATIONS Funding
The authors received no funding for this work.