Journal of Ethnopharmacology 197 (2017) 61–72
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Understanding the mode-of-action of Cassia auriculata via in silico and in vivo studies towards validating it as a long term therapy for type II diabetes Fazlin Mohd Fauzi a,b,n,1, Cini Mathew John a,c,1, Arunkumar Karunanidhi d, Hamse Y. Mussa b, Rajesh Ramasamy e, Aishah Adam a, Andreas Bender b a
Department of Pharmacology and Chemistry, Faculty of Pharmacy, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia Center for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, United Kingdom Department of Physiology and Pharmacology, Faculty of Medicine, University of Calgary, 3330 Hospital Dr. NW, Calgary, AB, Canada T2N 4N1 d Department of Medical Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia e Immunology Unit, Department of Pathology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia b c
art ic l e i nf o
a b s t r a c t
Article history: Received 15 February 2016 Received in revised form 13 July 2016 Accepted 21 July 2016 Available online 22 July 2016
Ethnopharmacological relevance: Cassia auriculata (CA) is used as an antidiabetic therapy in Ayurvedic and Siddha practice. This study aimed to understand the mode-of-action of CA via combined cheminformatics and in vivo biological analysis. In particular, the effect of 10 polyphenolic constituents of CA in modulating insulin and immunoprotective pathways were studied. Materials and methods: In silico target prediction was first employed to predict the probability of the polyphenols interacting with key protein targets related to insulin signalling, based on a model trained on known bioactivity data and chemical similarity considerations. Next, CA was investigated in in vivo studies where induced type 2 diabetic rats were treated with CA for 28 days and the expression levels of genes regulating insulin signalling pathway, glucose transporters of hepatic (GLUT2) and muscular (GLUT4) tissue, insulin receptor substrate (IRS), phosphorylated insulin receptor (AKT), gluconeogenesis (G6PC and PCK-1), along with inflammatory mediators genes (NF-κB, IL-6, IFN-γ and TNF-α) and peroxisome proliferators-activated receptor gamma (PPAR-γ) were determined by qPCR. Results: In silico analysis shows that several of the top 20 enriched targets predicted for the constituents of CA are involved in insulin signalling pathways e.g. PTPN1, PCK-α, AKT2, PI3K-γ. Some of the predictions were supported by scientific literature such as the prediction of PI3K for epigallocatechin gallate. Based on the in silico and in vivo findings, we hypothesized that CA may enhance glucose uptake and glucose transporter expressions via the IRS signalling pathway. This is based on AKT2 and PI3K-γ being listed in the top 20 enriched targets. In vivo analysis shows significant increase in the expression of IRS, AKT, GLUT2 and GLUT4. CA may also affect the PPAR-γ signalling pathway. This is based on the CA-treated groups showing significant activation of PPAR-γ in the liver compared to control. PPAR-γ was predicted by the in silico target prediction with high normalisation rate although it was not in the top 20 most enriched targets. CA may also be involved in the gluconeogenesis and glycogenolysis in the liver based on the downregulation of G6PC and PCK-1 genes seen in CA-treated groups. In addition, CA-treated groups also showed decreased cholesterol, triglyceride, glucose, CRP and Hb1Ac levels, and increased insulin and C-peptide levels. These findings demonstrate the insulin secretagogue and sensitizer effect of CA. Conclusion: Based on both an in silico and in vivo analysis, we propose here that CA mediates glucose/ lipid metabolism via the PI3K signalling pathway, and influence AKT thereby causing insulin secretion and insulin sensitivity in peripheral tissues. CA enhances glucose uptake and expression of glucose
Keywords: Cassia auriculata Mode of action PI3K signalling pathway In silico target prediction Immunometabolic pathway PPAR-γ Chemical compounds studied in this article: Luteolin (PubChem CID: 5280445) 7 Catechin (PubChem CID: 9064), 1,2benzenedicarboxylic acid bis(2-methylpropyl)ester (PubChem CID: 3085853/ 92160557) Epigallocatechin gallate (PubChem CID: 65064) Ellagic acid (PubChem CID: 5281855) Taxifolin (PubChem CID: 439533) Quercetin-3-O-β-D-glucopyranoside (PubChem CID: 15959354) Chrysin (PubChem CID: 5281607) Kaempferol 7-O-rhamnoside-4′-O-glucoside (PubChem CID: 5280863) Procyanidin B2 (PubChem CID: 122738)
n Corresponding author at: Department of Pharmacology and Chemistry, Faculty of Pharmacy, Universiti Teknologi MARA, 42300 Bandar Puncak Alam, Selangor Darul Ehsan, Malaysia. E-mail address:
[email protected] (F. Mohd Fauzi). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.jep.2016.07.058 0378-8741/& 2016 Elsevier Ireland Ltd. All rights reserved.
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F. Mohd Fauzi et al. / Journal of Ethnopharmacology 197 (2017) 61–72
transporters in particular via the upregulation of GLUT2 and GLUT4. Thus, based on its ability to modulate immunometabolic pathways, CA appears as an attractive long term therapy for T2DM even at relatively low doses. & 2016 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Type 2 diabetes mellitus (T2DM) is characterised by: (a) insulin resistance, which is a decline in the response towards insulinstimulated glucose uptake in the adipose tissue and liver, and (b) disruption of insulin secretion due to the deterioration of pancreatic β-cell (Vijan, 2010). The risk of T2DM is high in people with a genetic predisposition to diabetes and this risk greatly increases with lifestyle factors such as lack of physical activity, overweight and obesity (Bray, 2004). Several studies have explored the link between obesity and inflammation, as well as its effect on insulin resistance and secretion (Navarro-Gonzalez et al., 2011; Chen et al., 2013). It has been shown that adipose tissues release pro-inflammatory cytokines e.g. TNF-α, IL-1B, IL-6, and such cytokines are elevated in obese patients (Stein et al., 2013). These cytokines disrupt the insulin signalling pathway by inhibiting tyrosine phosphorylation of IRS-1 (Insulin receptor substrate 1). In addition, high free fatty acid (FFA) has been shown to stimulate the release of pro-inflammatory cytokines, disrupting the insulin signalling pathway through the same mechanism described previously (Shah, 2007; Boni-Schnetzler et al., 2009). FFA has also been linked to the activation of both pro-inflammatory cytokine transcription factor NF-κB and innate immune system, leading to inflammation and insulin resistance (Sakai et al., 2003; Lee et al., 2005; Daniele et al., 2014; Esser et al., 2014). Another inflammatory mediator, PPAR-γ has been reported to reduce insulin-stimulated glucose uptake in the liver and skeletal muscles through the disruption of PI3K-AKT signalling pathway (Ortega et al., 2014). High level of inflammatory marker, C-reactive protein (CRP) has been shown to be elevated in T2DM patients (Kramer et al., 2014). These findings underline the close relationship between immune system and metabolic pathways in the pathogenesis of T2DM. In fact, proteins involved in insulin signalling pathway (IGF-1R; Insulin growth factor 1 receptor) have been linked to the pathogenesis of inflammatory diseases such as asthma (Fernández et al., 2001; Lee et al., 2013). Although T2DM on its own causes improper inflammatory response, long term use of oral antidiabetic therapy such as sulfonylureas (glibenclamide) and biguanides (metformin) have been demonstrated to further exacerbate this situation (Mello et al., 2011). Hence, this underscores the pressing need for new long term drug therapies for T2DM that not only target insulin signalling pathway, but also modulate inflammatory pathways. One such compound that targets both insulin and inflammatory pathway is Cassia auriculata (CA). The Ayurvedic name of CA is Shibi kul, and according to the Ayurvedic system, CA decreases aggravated pitta and kabha in prameha (diabetes) (Rajasekharan et al., 1982; Nanjaraj Urs et al., 2015). Many Ayurvedic formulations of CA e.g. Avaarai panchaga chooranam (Joy et al., 2012), a herbal formulation that reduces blood sugar level, were found to be beneficial (Pari et al., 2001; Babu et al., 2004) and has been widely used for treating diabetes. Several studies have reported on the beneficial effects of the long term usage of CA and the outcomes were favourable with minimal complications (Joshi et al., 2000; Latha et al., 2003). Other effects have also been reported for CA which include antibacterial and antifungal activity (Duraipandiyan and Ignacimuthu, 2007), analgesic and anti-
inflammatory effects (Nsonde Ntandou et al., 2010) and antioxidant property (Manonmani et al., 2005). Abesundara et al. (2004) proposed that CA reduces blood sugar level by improving the utilization of glucose through increased glycolysis. Additionally, Latha and Pari (2003) demonstrated that CA prevents lipid peroxidation-induced membrane damage, suggesting the antiperoxidative role of CA in T2DM. The antidiabetic potential of CA flower extract on hepatic glycolytic and gluconeogenic enzymes has also been reported in streptozotocin diabetic rats (Latha and Pari, 2003). Meanwhile, CA leaves was shown to display antidiabetic effects in mild and severe diabetic rats (Gupta et al., 2009). In our previous study, we showed the involvement of CA in modulating the immune system (John et al., 2011). Hence, in this study we attempt to elucidate the mode-of-action (MOA) of CA in modulating the immune and metabolic pathways, which as described previously is beneficial in the treatment of T2DM. The diversity of the effects reported on CA suggests that the effects may not only be from interaction with specific protein targets but may also be due to the physicochemical properties of the polyphenols. Additionally, unlike synthetic compounds, these polyphenols do not seem to have a clearly identifiable protein targets that it may interact with. Herein, CA was subjected to in silico and in vivo studies, which also serves to assess its potential for long term therapy in T2DM. In this study, electron spray ionisationmass spectrometry (ESI-MS) data of CA polyphenols obtained during our previous lab study (see Supplementary Information S1 for full detail) was used in both in silico and in vivo analyses. In silico target prediction have been extensively used in MOA analysis where potential protein target(s) modulated by a novel compound can be predicted and subsequently its MOA can be hypothesized. One of the first in silico target prediction published was the PASS (Prediction of Activity Spectra for Substances) (Poroikov et al., 2007). The current version utilises more than 260,000 biologically active compounds exhibiting over 3500 types of biological activity obtained from databases and scientific journals as the training set (Poroikov et al., 2007). When a compound is subjected to testing in PASS, it will generate two different scores, Pa and Pi (Poroikov et al., 2007). The former is the probability of the compound being active and the latter is the probability of the compound being inactive. Since then, different implementations and applications of in silico target prediction have been published in scientific literature. One such example is the work of Mohd Fauzi et al. (2013) where twenty different medicinal classes from both traditional Chinese medicine and Ayurveda were analysed in their implementation of target prediction. It was found that the phenotypes of the ‘tonifying and replenishing medicinal class’ from TCM can be connected to the targets predicted (Mohd Fauzi et al., 2013). For example, the anti-hyperglycaemic activity (Zhao et al., 2005) of this class can be connected to the prediction of sodium glucose cotransporters 1, which is responsible for the uptake of glucose. Other recent works in this area include the work of Ravindranath et al. (2015) and Cortes-Ciriano et al. (2015). Complementary to in silico target prediction, potential in vivo benefits were also evaluated by treating different doses of CA polyphenolic extract to experimental T2DM rats for 28 days. The effect of CA on insulin signalling and inflammatory pathways were analysed by measuring expression levels of genes regulating
F. Mohd Fauzi et al. / Journal of Ethnopharmacology 197 (2017) 61–72
insulin signalling pathway, glucose transporters of hepatic (GLUT2) and muscular (GLUT4) tissue, insulin receptor substrate (IRS), phosphorylated insulin receptor (AKT), gluconeogenesis (G6PC and PCK-1), along with inflammatory mediators genes (NF-κB, IL-6, IFNγ and TNF-α) and peroxisome proliferators-activated receptor gamma (PPAR-γ). The effects of CA on glucose parameters such as glucose, insulin, C-peptide and glycosylated haemoglobin (HbA1c) and inflammatory markers (CRP) were also measured in this study.
2. Materials and methods 2.1. In silico target prediction In silico target prediction (see Fig. 1) generally analyses chemical patterns in protein ligands, using a particular ligand descriptor and (in this case) also a biological descriptor. This model can then be applied to new molecules in predicting the proteins they likely modulate. While many target prediction algorithms have been published before (Poroikov et al., 2007; Mohd Fauzi et al., 2013; Ravindranath et al., 2015; Cortes-Ciriano et al., 2015) this is still an actively evolving field. In this study, a tensor product was used to combine both chemical and biological similarities and the details are outlined further below. 2.1.1. Training set The in silico target prediction model used herein delivers prediction for 99 protein targets that are considered to be ‘critical nodes’ in the biological network. According to Taniguchi et al. (2006), a critical node should satisfy the following properties: (a) a link for potential crosstalk with other pathways, (b) member of related proteins i.e. isoforms where two or more of the proteins in the group have unique biological roles and (c) a protein that is highly regulated, either positively or negatively. Information on the 99 protein targets were obtained from KEGG database (Kanehisa et al., 2012) and readers are referred to Supplementary S2 for further information on the curation of the
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training set. Compounds for these 99 protein targets were collected from ChEMBL database (Gaulton et al., 2012), which is a drug discovery database consisting of small bioactivity molecules abstracted from scientific journals. The majority of the bioactivity data are from synthetic molecules. This is the case in the majority of bioactivity databases currently available where the chemical space covered by natural products is limited. Compounds collected from ChEMBL database (Gaulton et al., 2012) fulfils the following criteria: (i) compound-target association had to have an assay-totarget confidence score of 8 or 9, (ii) defined an active ligandprotein interaction as having a Ki/Kd/IC50/EC50 value of 10 nM or less and an inactive interaction as having a Ki/Kd/IC50/EC50 value above 100 nM, and (iii) each target had to have at least 10 compounds associated with it, including both active and inactive data points. Based on this, the training set consists of 55,079 compounds annotated against 99 protein targets (11,537 active pairs vs. 43,542 inactive pairs; see Supplementary Information S3 for a complete breakdown of the training set and Supplementary S4 on the full details of the protein target). 2.1.2. Chemical descriptors and similarity calculation In order to represent the compounds as vectors, Extended Connectivity Fingerprints with a diameter of four bonds (ECFP_4) and length of 1024 bits (Rogers and Hahn, 2010) was used. These fingerprints have been shown to correlate well with bioactivity in recent studies (Hert et al., 2004). The fingerprints were generated using jCompoundMapper (Hinselmann et al., 2011). The Aitchison-Aitken (AA) kernel (Aitchison and Aitken, 1976) was used to calculate chemical similarity where:
(
)
K x, x j = λ n − d . (1−λ )d
(n − d )
=e logλ
d
. e log( 1 − λ)
(1)
(2)
Fig. 1. Schema of an in silico target prediction model. Bioactivity data obtained from chemogenomics databases (top) are first compiled and analysed using a computational algorithm to determine key structural features of protein-ligand binding (bottom centre). When a novel compound is encountered, (‘novel compound’, bottom left) its probability of binding against all the protein targets in the training set will be calculated. (‘in silico target prediction’, bottom centre). A score will be generated for this compound for binding to all possible protein targets that ranges from 0 to 1. A score of 0.5 and above implies that the novel compound and protein target in question are more likely to interact than not. At the end of the analysis, a list of potential targets ranked according to likelihood of binding will be generated (bottom right). This approach provides a list of mode-of-action hypotheses for the compound under investigation, which can then be followed up experimentally.
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=e (n − d)logλ. e dlog(1 − λ)
−dlog
=enlogλ. e
λ (1 − λ )
(3)
(4)
where d¼ (x-xj)T(x-xj) is the number of times x and xj differ, so a distance measure between compound representations, λ is the smoothing parameter, and n is the size of the compound vector, in this case 1024 bits. The smoothing parameter in the AA kernel has a narrow range of 0.5 oλ o1.0. At λ ¼0.5, it will produce maximum smoothing and at λ¼ 1.0, no smoothing occurs, hence only values in between those extremes have been used in this work (Aitchison and Aitken, 1976; Mussa, 2013). 2.1.3. Biological descriptor and similarity calculation The similarity between two proteins was calculated from the number of substrings (of length 3) they have in common using a kernel function. Prior to the similarity calculation, the protein sequences were subjected to sequence alignment using MUSCLE, (Edgar, 2004b) which was performed using the bio3d package (Grant et al., 2006) in R. Default setting for MUSCLE sequence alignment was used and the maximum number of iterations was set at 16 (Edgar, 2004a). 2.1.4. Machine learning algorithm In this study, the Parzen-Rosenblatt Window (Lowe et al., 2012) was employed as a classification method which calculates the class conditional probability p(x|ωm) as the weighted average similarity of the unknown compound, x compared to a set of known compounds, xj, belonging to a given class ωm. The value p(x |ωm) is given by:
p( x)ωm)=
1 Nωm
∑ K ( x, xj ; λ )
(5)
In our implementation that incorporates biological similarity using the tensor product shown before the equation above then becomes:
p( x, t )ωm)=
1 Nωm
∑ K ( x, xj; λ) ⊗ K (t , t j) ( xjt j) ∈ Nωm
(6)
where (x, t) is the protein-ligand complex being analysed. Nωm and (xj, tj) are the number of protein-ligand complexes, and protein-ligand complexes in class ωm respectively. The next section outlines how chemical similarity and biological similarity were calculated. 2.1.5. Validation of the prediction model The prediction model was subjected to an internal and external validation. In the internal validation, 5-fold cross validation was performed and evaluated using sensitivity (True Positive Rate), specificity (True Negative Rate), MCC (Matthews Correlation Coefficient) and AUC (Area under the ROC Curve) for different λ values (here λ ¼0.8, 0.83, 0.86, 0.89, 0.9, 0.93, 0.96 and 0.99). From Table 1, it can be seen that the sensitivity, MCC and AUC values were the highest when λ ¼0.8 and hence this is the setting we have used for the remainder of this study. In the external validation (validation on data not related to the training set in any form) 463 compounds from DrugBank (Wishart et al., 2006) database, which interact with 58 of the protein targets in our training set were evaluated. Sensitivity was used as the key performance measure, given that only positive ligand-target interactions were known and hence using measures where information about true (and false) negative rates is required would not be possible. The result for the external validation is shown in
Table 1 Internal validation of the in silico target prediction model. Sensitivity, MCC and AUC values were the highest when λ ¼ 0.8. λ
Sensitivity
Specificity
MCC
AUC
0.8 0.83 0.86 0.89 0.9 0.93 0.96 0.99
0.6387 0.6366 0.6360 0.6354 0.6354 0.6353 0.6354 0.6353
0.8283 0.8289 0.8293 0.8296 0.8296 0.8296 0.8297 0.8301
0.4284 0.4275 0.4274 0.4274 0.4274 0.4273 0.4274 0.4279
0.8124 0.8073 0.8006 0.7957 0.7934 0.7902 0.7791 0.7639
Table 2 External and internal validation results of the test compounds against selected targets. Model
λ
TP
FN
TPþ FN
Sensitivity
Sensitivity_IV
TS1_seqid
0.8
208
255
463
0.4492
0.6387
Table 2 below. The λ was set at 0.8 as it was shown to produce the optimum result in the internal validation. In the external validation, the predictive model produced a sensitivity value of 0.4492, compared to 0.6837 in the internal validation, hence also on the external dataset close to 1 out of 2 true ligand-target interactions will be detected. 2.1.6. Analysis and normalisation of prediction results In the analysis of our result, we compiled the top 20 enriched targets for the ten polyphenols. To be included in the list, a protein target must have a score of 0.5 or higher, which signify possible activity against the protein target. Then, a normalisation rate (see below) will be calculated for each protein target. The protein targets will then be ranked according to their normalisation rate, and the top 20 targets will be kept. In target prediction exercises, enrichment is performed to normalise the result as some targets can be predicted more often than others. In this study, enrichment is calculated by comparing the target frequency (TF; number of times a protein target was predicted for a specific group of compounds) of the 10 polyphenols against a set of random compounds. The random set contains 1000 randomly selected compounds from ChEMBL. The normalisation rate (NR) for each protein target predicted is calculated as such:
NormalisationRate =
(TFtn/TFτ )actual (TFtn/TFτ )random
(7)
TFtn is hence the target frequency of a particular target, tn, in a data set and TFτ is the total number of targets predicted above the specified cut off in a data set. Hence, in the normalisation rate, the numerator calculates the ratio of those two numbers for the 10 polyphenols, while the denominator calculates the equivalent ratio for the background distribution. 2.2. Experimental animals Specific pathogen free (SPF) Sprague-Dawley (SD) rats of age 12 weeks weighing 210 g were procured from the Laboratory Animal Management Facility (LAFAM) of Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Malaysia. Animals were maintained under standard conditions of temperature and light exposure (12 h light and 12 h dark). Approval for the experimental protocol was given by the committee on animal research, UiTM [No.600FF (PT5/2)]. Care of laboratory animals was followed as per
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Guide for the Care and Use of Laboratory Animals, National Institute of Health. Animals were housed in polypropylene cages and provided with standard pellet diet (GOLD COIN, Malaysia) and tap water ad libitum. After a 14-day acclimatization period, animals were induced with diabetes as outlined below and eventually sacrificed using diethylether.
recently recorded body weights to provide the correct dose. All extract solutions were prepared freshly and the volume of CA polyphenol administered was adjusted to 1 mL 100 g 1 of body weight.
2.3. Induction of T2DM
Total RNA was extracted from the harvested organs (liver, skeletal muscles and spleen) using RNeasy mini kit (Qiagen, USA). RNA concentrations and purity was determined by using a Nanodrop spectrophotometer ND-1000 and Sensicript Reverse Transcription kit was used to obtain the cDNA (Qiagen USA). Annealing temperatures were optimized by gradient PCR. Quantification of gene was achieved using qPCR (Bio-Rad, USA). Melting curves were analysed to confirm mispriming and the possibility of a genomic DNA influence on the results was eliminated by use of primers. Specific primer pairs and β-actin gene (housekeeping gene) were designed from the gene sequence of rat (Rattus norvegicus) adapted from the National Centre for Biotechnology Information (NCBI) GenBank Database (Benson et al., 2009). The oligonucleotide sequences of primers used for qPCR are shown in Table 3. Primer pairs of INF-γ, TNF-α, IL-6 and PPAR-γ were supplied by Qiagen, USA and Next Gene Sdn. Bhd Malaysia supplied the rest of the primer sets. PCR was carried out using a Quantinova SYBR green PCR kit (Qiagen, USA) according to the manufacturer”s instructions. Each experiment was performed in triplicates and three samples were randomly selected from each group. The expression for each gene was measured according to the quantity of β-Actin expressed and the fold expression was calculated by 2 ΔΔCt. Data were analysed using the Rotor Gene software (Version 1.7, Corbett Research) and Microsoft Excel for further calculations.
T2DM was induced by administering Nicotinamide (NA) þ Streptozotocin (STZ) intraperitoneally. Animals received NA (120 mg kg 1 bw in saline) 15 min before the administration of STZ (45 mg kg 1, i.p.). STZ was freshly dissolved in 0.05 M citrate buffer, pH 4.5 immediately before use (Ramirez-Espinosa et al., 2011; Kumar et al., 2012; Islam and Loots du, 2009). Blood glucose was measured after 72 h and the animals were allowed to develop and stabilise diabetes for one week. Rats with glucose level above 9 mmol L 1 were selected for the study. 2.4. Preparation of CA and treatment of animals C. auriculata flowers were collected freshly from Kuala Lumpur, Malaysia; identified and authenticated at Forest Research Institute of Malaysia (FRIM). Samples were stored in a herbarium as Voucher No. 18-1. The extraction of polyphenol compounds in CA was followed according to the method described earlier (John et al., 2011). SD rats were randomly divided into six groups as follows: nondiabetic control (N), T2DM control (D) receiving a daily dose of CMC (0.5%) vehicle only, and metformin at 100 mg kg 1 as therapeutic control. CA polyphenol dosage groups were divided into 25, 50 and 100 mg kg 1 (based on acute toxicity studies performed prior to the current study, data not shown) by oral gavages from days 0-28. Individual dosages were based on the most
2.5. Measurement of gene expression
2.6. Measurement of glucose level Table 3 Oligonucleotide sequences of primers used for qPCR.
Fasting serum glucose was measured weekly in blood samples collected from the orbital eye sinus using commercially available reagent kits (Instrumentation Laboratory, USA) and measured using a ILab Chemistry Analyzer 300 PLUS (Instrumentation Laboratory, USA).
Genes
Nucleotide sequence of primers (5′–3′)
Accession number/ID
TNF-α
GCCGATTTGCCACTTCATAC GGACTCCGTGATGTCTAAGTAC
NM_012675.3
INF-γ
TCAGCAACAACATAAGTGTCATCG TTCCGCTTCCTTAGGCTAGATTC
NM_138880
IL-6
TCAACT CCATCTGCCCTTCAG AAGGCAGTGGCTAACAAC
NM_012589
NF-κB
CGATCTGTTTCCCCTCATCT TGCTTCTCTCCCCAGGAATA
NM_199267.2
Actba
ATGGTGGGTATGGGTCAG CAATGCCGTGTTCAATGG
NM_031144.2
GLUT-2
TCTGTGCTGCTTGTGGAG ACTGACGAAGAGGAAGATGG
NM_012879.2
GLUT-4
CCCACAAGGCACCCTCACTA TGCCACCC ACAGAGAAGATG
NM_012751.1
2.8. Measurement of HbA1c
AKT-1
TAGGCATCCCTTCCTTACAG GCCCGAAGTCCGTTATCT
NM_033230
IRS
AGCTATGCTGACATGCGGACA CGGCCCCTTGAGGTGTAA
NM_012969
Glycosylated haemoglobin (HbA1c) was measured in fresh blood collected post treatment using ELISA kit (CUSABIO BIOTECH CO Ltd., Japan).
G6PC
ATGATGGCTGAAGACTAC ACTTGAAGACGAGGTTGAG
NM_176077.3
PCK-1
AACGTTGGCTGGCTCTC GAACCTGGCGTTGAATGC
NM_198780.3
PPAR-γ
GCCTGCGGAAGCCCTTTGGT AAGCCTGGGCGGTCTCCACT
NM_001145366
a
Housekeeping gene.
2.7. Measurement of insulin level Plasma insulin level was measured by Mercodia Rat Insulin ELISA kit (Sweden) pre and post treatment. The optical densities of the samples were read at 450 nm using a 96-well plate reader. The concentration of insulin level was obtained by computerized data reduction of the absorbance for the calibrators, except for calibrator 0, versus the concentration using cubic spline regression.
2.9. Measurement of C-peptide Plasma C-peptide level was measured using Mercodia Rat C-peptide ELISA kit (Sweden). The optical densities of the samples were read at 450 nm using a 96-well plate reader. The concentration of C-peptide was obtained by using cubic spline regression similar to the method of Insulin ELISA kit as mentioned above.
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Table 4 The top 20 enriched targets predicted for the 10 polyphenols of CA. It can be seen that several of the targets listed are involved in insulin signalling pathway e.g. PTPN1, AKT2, PI3K-γ and PKC-α. (NR: Normalisation rate). Abbreviation Protein name
NR
VEGFR-2 PTPN1 NTRK1 HSD11B1 PDGFRA MAOB FGFR1 PKC-α PI3K-γ
4.412 4.286 4.206 4.206 4.186 4.186 4.167 4.091 4.091
EGFR PKC-β CYP19A1 GRM5 ROCK2 SRC AKT2 PKC-δ P2RX7 PDGFR-β KIT
Vascular endothelial growth factor receptor 2 Tyrosine-protein phosphatase non-receptor type 1 High affinity nerve growth factor receptor Corticosteroid 11-beta-dehydrogenase isozyme 1 Platelet-derived growth factor receptor alpha Amine oxidase [flavin-containing] B Fibroblast growth factor receptor 1 Protein kinase C alpha type Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform Epidermal growth factor receptor Protein kinase C beta type Aromatase Metabotropic glutamate receptor 5 Rho-associated protein kinase 2 Proto-oncogene tyrosine-protein kinase Src RAC-beta serine/threonine-protein kinase Protein kinase C delta type P2X purinoceptor 7 Platelet-derived growth factor receptor beta Mast/stem cell growth factor receptor Kit
4.072 4.072 4.072 4.036 4.000 3.982 3.982 3.982 3.982 3.965 3.965
2.10. Measurement of C-reactive protein C-reactive protein levels were measured in blood plasma using Randox full range CRP (frCRP) commercial kit (All Eight (M) Sdn.
Bhd, Malaysia). Liquid assayed specific protein control level 1 and 2 were used as control. Upon preparation of blood samples, 500 mL of plasma was transferred to each Selectra tube and the results were obtained using a Vita lab Selectra machine. 2.11. Statistical analysis Data are expressed as mean 7 SEM and statistical analysis was performed using One way analysis of variance (ANOVA) followed by Duncan”s post hoc test. Differences were considered to be significant when p o0.05. All statistical analysis was performed using the Statistical Package for Social Science (SPSS; v20).
3. Results 3.1. In silico target prediction The top 20 enriched targets for the polyphenols can be seen in Table 4. From Table 4, it can be seen that several of the top 20 enriched targets predicted for the polyphenols are involved in key signalling pathway e.g. PTPN1, AKT2, PIK3CG and PKC-α. Some of the predictions have been found to be supported by scientific literature. One such case is PI3K where Waltner-Law et al. (2002) studied the activity of Epigallocatechin gallate (EGCG) on PI3K and MAPK on H4IIE cells. It was found that EGCG increases PI3K and MAPK activity, similar to insulin but at a lower rate
Fig. 2. Effect of CA supplementation on glucose homeostatis parameters (a) fasting blood glucose levels (b) HbA1c (c) Insulin and (d) C-peptide of normal and T2DM rats. T2DM rats were treated with CA (25, 50 and 100 mg kg 1 bw) or MET 100 mg kg 1 for 28 days and sacrificed. Fasting blood glucose test was measured weekly. Insulin and C-peptide levels were measured pre and post-experiments. Values are expressed as mean 7 SEM. a,b,c represents the significance between each groups (n¼ 5). Statistical test used was one way ANOVA followed by post hoc intergroup comparison by Duncan”s test (po 0.05).
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Table 5 Effect of CA supplementation on lipid parameters cholesterol and triglyceride levels. Groups (T2DM)
Cholesterol mmol L 1
Control (N) Control (D) CA 10 mg CA 25 mg CA 50 mg CA 100 mg MET
5.6 8.9 6.4 5.7 5.3 5.4 6.9
7 7 7 7 7 7 7
1.8 1.8** 0.6* 1.3* 2.2* 0.7* 1.2
Triglycerides mmol L 1 1.7 2.8 1.8 1.6 1.3 1.6 2.3
7 7 7 7 7 7 7
1.2 0.4** 0.7* 0.2* 0.5* 0.2* 0.4
T2DM rats were treated with CA (25, 50 and 100 mg kg 1bw) or MET 100 mg kg 1 for 28 days and sacrificed. Cholesterol and triglycerides were measured post-experiments. Values are expressed as mean 7 SEM.
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increase in glucose transporters [hepatic GLUT-4 (Fig. 3a) and muscular GLUT-2 (Fig. 3b)] compared to T2DM control. The insulin receptor gene (IRS) and protein kinase (AKT) were upregulated in all CA treatment groups (po 0.05) (Fig. 3c and d). The expression of genes involved in the gluconeogenesis (PCK and G6PC) were significantly downregulated (p o0.05) and the different treatment groups did not show any significant differences among groups (Fig. 3e and f). No clear dose response pattern has been established for CA treatment in the given dose range (i.e. full response could also be achieved at the lowest dose), likely hinting at the fact that even lower doses than 25 mg kg 1 can lead to efficacious treatments. 3.4. Effect of CA on inflammatory parameters
*
Represents the significance and ** as the highest significance between each groups (n¼ 5). Statistical test used was one way ANOVA followed by post hoc intergroup comparison by Duncan”s test (p o 0.05).
(Waltner-Law et al., 2002). It should be noted that MAPK was predicted with reasonably high normalisation rate although it was not one of the top 20 enriched targets (data not shown). Another study by Chen et al. (2002) also supported our prediction. Five compounds from Broussonetia papyrifera were isolated and tested against PTPN1 enzyme. One of these compounds, Quercetin, was found to inhibit PTPN1, which is one of the top 10 enriched targets, with an IC50 value of 23.3 μM. A study by Kuo et al. (2015) also supported our prediction of PKC-α where Ellagic acid was shown to modulate the PKC-α/ERK/PPAR-γ/NF-κB signalling pathways in the endothelial cells. In addition to the literature support of the predictions generated, in vivo analysis are also complementary to the in silico findings in particular for AKT2 and PI3K in vivo (see later). 3.2. Effect of CA on glucose homeostasis related parameters Weekly fasting serum glucose levels are shown in Fig. 2a. It can be seen that the T2DM control rats had a high level of glucose level across the treatment period while CA treatment normalised glucose levels in week 1 across the dose range. After four weeks of treatment, T2DM rats that received CA at any dose, or MET, had significantly reduced blood glucose concentrations compared to T2DM control group (p o0.05) and on the same level as the nondiabetic control. Next, the haemoglobin test was performed to measure the amount of glucose carried by the red blood cells (results shown in Fig. 2b). The percentage of HbA1c was significantly increased in the T2DM rats, while upon CA treatment haemoglobin was normalised (p o0.05) in a similar manner across the dose range and in agreement with the MET treated group. Plasma insulin level, which correlates with C-peptide, also shows improvement in the levels in all treatment groups (Fig. 2c and d). It can be noted that CA treatment was found to be effective at all three tested concentrations (25, 50 and 100 mg kg 1) in all parameters measured, on the same level as MET. The effects of CA on serum lipids levels (cholesterol and triglycerides) are shown in Table 5. A marked increment in the frequency of cholesterol and triglycerides were observed in T2DM rats, whereas CA treatment significantly reduced the lipid levels in all doses, dosage CA 50 mg has given consistent lowest reduction in both parameters. It can be highlighted that the effect is found to be better as compared to the MET dosage group. 3.3. Effect of CA on insulin signalling pathway genes The effect of CA on genes involved in insulin signalling pathway are shown in Fig. 3. The CA treated T2DM rats showed a significant
The effect of CA on inflammatory mediator genes is shown in Fig. 4. CA treated T2DM model caused a significant reduction in the inflammatory genes IL-6 (Fig. 4a), IFN-γ (Fig. 4e), TNF-α (Fig. 4d) and NF-κB (Fig. 4c) in spleen and plasma CRP (Fig. 4f) compared to T2DM control rats. The activation of PPAR-γ (Fig. 4b) in liver was significant (po 0.05) in all CA treated groups.
4. Discussion Based on the results obtained from in silico and in vivo studies, possible MOAs of the ways CA exerts its antidiabetic and immunoprotective effects have been hypothesized (the summary can be seen in Fig. 5). Firstly, CA appears to enhance glucose uptake and glucose transporters expression via the IRS signalling pathway. In the insulin signalling pathway, when insulin binds to IR (insulin receptor) in the muscle cells, this triggers the recruitment and phosphorylation of IRS. This inturn activates PI3K, which subsequently activates AKT, a protein that regulates glucose transport. Besides regulating insulin sensitivity, IRS genes also regulate the release of pro-inflammatory cytokines. These cytokines are sensitive to insulin, where it has been shown that a decrease in insulin sensitivity results in increased levels of pro-inflammatory cytokines (Daniele et al., 2014). Complications related to T2DM such as microvascular complications have been attributed to be inflammatory-related (Graves and Kayal, 2008; Popko et al., 2010; Navarro-Gonzalez et al., 2011; Chen et al., 2013). In the in vivo analysis, IRS gene showed a significant increase in all CA-treated groups compared to control (see Fig. 3c). Additionally, AKT2 and PI3K-γ were listed in the top 20 enriched targets. The expression of AKT, GLUT-2 and GLUT-4 genes were found to be significantly higher in CA-treated groups compared to control (see Fig. 3a–d). Hence, overall both in silico and in vivo studies support the hypothesis that CA enhances glucose uptake and glucose transporter expression via the IRS signalling pathway. Secondly, CA also appears to have an effect on the PPAR-γ signalling pathway. PPAR-γ is involved in several key activities such as (a) inflammatory signalling pathways mediated by NF-κB, which down regulates pro-inflammatory cytokines (IL-6, TNF-α, IFN-γ), (Sylow et al., 2014) (b) regulation of FFA level and (c) regulation of GLUT-4 in lipid metabolism. The release of pro-inflammatory cytokines blocks major anabolic downstream cascades of insulin signalling pathway, resulting in the disruption of insulin homeostasis thereby inducing gluconeogenesis. PPAR-γ agonists have been shown to initiate FFA uptake and storage in subcutaneous adipose tissue. Reduction of FFA has been associated with an increase in insulin sensitivity, which is inversely correlated with proinflammatory cytokines level (Verges, 2004). In a study done by Fang et al. (2008), kaempferol was tested on 3T3-L1 cells and no
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Fig. 3. Effect of CA supplementation on the mRNA levels of Insulin signalling pathway genes, determined by qPCR. The mRNA expression for each sample was measured from the liver except GLUT4, which is from the skeletal muscle according to the quantity of β-actin, while the number of fold expression was calculated by 2 ΔΔCt. (a) GLUT4 (b) GLUT2 (c) IRS (d) AKT (e) PCK1 and (f) G6PC of normal and T2DM rats. T2DM rats were treated with CA doses (25, 50 and 100 mg kg 1 bw) or MET 100 mg kg 1 for 28 days and sacrificed. Values are expressed as mean 7 SEM. a,b,c represents the significant difference between each groups (n ¼3). Statistical test used was one way ANOVA followed by post hoc intergroup comparison by Duncan”s test (p o 0.05).
differentiation was observed in the 3T3-L1 preadipocytes. However, when rosiglitazone was added, kaempferol was found to inhibit 3T3-L1 differentiation in a dose-dependent manner (Fang et al., 2008). Further investigation through competitive ligandbinding assay confirmed that kaempferol competes with rosiglitazone for the same binding pocket of PPAR-γ and it was hence concluded that kaempferol acts as partial agonists of PPAR-γ (Fang et al., 2008). In the in silico analysis, PPAR-γ was not one of the top 20 enriched targets, although it was predicted with reasonably high normalisation rate (data not shown). In addition, all CAtreated groups showed significant activation of PPAR-γ in the liver compared to control (see Fig. 4b). Based on both in silico and
in vivo analyses it can be concluded that CA interacts with PPAR-γ, possibly at least via some of its constituents in an agonistic manner, which leads to increased insulin sensitivity and regulates NFκB signalling which subsequently reduced the release of pro-inflammatory cytokines. Thirdly, CA appears to be involved in the regulation of gluconeogenesis and glycogenolysis in the liver. Both gluconeogenesis and glycogenolysis results in the formation of G6PC which has to be hydrolysed by the enzyme glucose 6 phosphatase before being released as glucose into the circulation (Kehlenbrink et al., 2009). The in vivo analysis showed that CA-treated groups significantly downregulate the expression of G6PC and PCK-1 genes, which
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Fig. 4. Effect of CA supplementation on the mRNA levels of inflammatory genes, determined by qPCR. The mRNA expression for each sample was measured from spleen except PPAR-γ which is from the liver, according to the quantity of β-actin. While the number of fold expression was calculated by 2-ΔΔCt. CRP which was measured from the blood plasma collected on pre and post treatment from T2DM rats and normal rats (a) IL-6 (b) PPAR-γ (c) NF-κB (d) TNF-α (e) IFN- γ and (f) plasma CRP. T2DM rats were treated with CA doses (25, 50 and 100 mg kg 1 bw) or MET 100 mg kg 1 for 28 days and sacrificed. Values are expressed as mean 7 SEM. a,b,c represents the significant differences between each groups (n¼ 3). Statistical test used was one way ANOVA followed by post hoc intergroup comparison by Duncan”s test (po 0.05).
indicates that gluconeogenesis and glycogenolysis is being controlled. Glucose formed via the gluconeogenic process has been implicated in innate immune system functions (Popko et al., 2010). Thus regulating gluconeogenesis by downregulating G6PC and PCK-1 controls the release of glucose into the circulation and thereby contributes to modulating the immune system in a beneficial manner. Some of the in silico predictions of CA polyphenols that were not validated in the in vivo phase of this study are supported by scientific literature. This is the case of PTPN1 and PKC-α, where quercetin was found to inhibit PTPN1 with an IC50 value of 23.3 μM (Chen et al., 2002) and ellagic acid was shown to modulate the
PKC-α/ERK/PPAR-γ/NF-κB signalling pathways in the endothelial cells (Kuo et al., 2011). Hypercholesterolemia and hypertriglyceridemia are the common lipid metabolic abnormalities in T2DM resulting in insulin resistance, and reduces the capacity of glucose utilization stimulated by insulin (Chen et al., 2013). CA supplementation significantly decreased the cholesterol and triglyceride levels in diabetic rats (Table 5), thereby improving lipid metabolism and utilization, CA at a dose of 50 mg kg 1 bw have improved the lipid level in the current study. CA treatment showed a significant reduction in CRP levels (Fig. 4f), which is an inflammatory marker linked with lipid metabolism (Kramer et al., 2014). This might
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Fig. 5. Schematic representation of the underlying cellular MOA of C. auriculata in T2DM. of C assia auriculata in T2DM. Insulin binding causes activation of the insulin receptor (IR), which phosphorylates different substrate adaptors such as the insulin receptor substrate (IRS). Upon phosphorylation, IRS displays binding sites for many signalling partners. Among these signalling pathways, PI3K has the main role in insulin signalling pathway, activating the AKT cascades. AKT activation inhibits the activation of gluconeogenic enzymes, PCK1 and G6PC. The activated AKT eventually leads to translocation of muscular GLUT4 to plasma membrane and glucose uptake mediated by hepatic GLUT2 and improves glucose/lipid metabolism. The activated IRS via peroxisome proliferators-activated receptor γ (PPAR-γ) agonist activity inhibits inflammatory signalling pathways mediated by NF-κB and thus down regulates inflammatory cytokines (IL-6, IFN-γ, TNF-α).
correspond to a lower incidence of diabetic pathologies such as cardiovascular disease, chronic subclinical inflammation and a reduced risk of metabolic and atherosclerotic disease upon CA treatment. Baseline biochemical markers (glucose, HbA1c, insulin, C-peptide) were compared to the diabetic group which was found to be significantly improved in CA-treated groups (Fig. 2a–d). The capacity of CA to increase insulin and C-peptide level, and reduce glucose and HbA1C levels, demonstrates the insulin secretagogue and sensitizer effect of CA. These findings suggest the possible role of CA in improving the immunometabolic system. While these results successfully suggest the possible MOA for CA, some shortcomings of the current method exist. Firstly, as mentioned previously, most bioactivity databases such as ChEMBL has limited information on the chemical space covered by natural products in comparison to synthetic compounds. Additionally, given the diverse type of effects reported for CA, this suggests that the polyphenols do not have a clearly identifiable protein targets that it may interact with, unlike synthetic compounds. Hence, the predictions generated by the in silico target prediction is not comprehensive as the predictions are dependent on the training set and the chemical space that it covers. The applicability domain of the in silico target prediction to natural product-based chemistry need to be quantified in a more detailed manner, where this will be analysed in future works. In addition, predictions generated by in silico methods need to complemented with in vivo and/ or in vitro studies. Secondly, in this study we measured the total expression level of several target genes e.g. IRS-2, AKT. However, the post-translational modifications in the phosphorylation at specific residues e.g. SER473 for AKT induced by CA was not performed here, which is a limitation of this study. Future work will involve analysing such effect(s) by CA.
5. Conclusion Our current study provides several possible MOAs of CA in the pathogenesis of T2DM by in silico and in vivo analysis. Based on both analyses, CA appears to mediate glucose/lipid metabolism by PI3K signalling pathways influencing AKT and improving insulin secretion, energy metabolism and insulin sensitivity in peripheral tissues. CA also enhanced glucose uptake and expression of glucose transporter proteins, in particular through the upregulation and translocation of muscular GLUT4 and hepatic GLUT2 in T2DM rat model. By mediating NF-κB signalling, CA may effectively inhibit the release of proinflammatory cytokines. Thus, we conclude that CA appears to be an attractive therapeutic avenue for T2DM that has a beneficial effect on the immunometabolic pathways involved in T2DM.
Conflict of interest None to declare.
Acknowledgements The authors are grateful to Dr. Sreenivasa Rao Sagineedu from International Medical University for providing us with ESI-MS measurements and helpful discussions regarding our ESI-MS experiments. We acknowledge the financial support from Universiti Putra Malaysia (UPM), Universiti Teknologi MARA (UiTM) and Ministry of Higher Education, Malaysia through the Fundamental Research Grant Schemes (FRGS) 04-01-12-1131FR and FRGS/1/ 2014/SKK02/UITM/03/1, and the UiTM LESTARI Grant, 600-RMI/ DANA 5/3/LESTARI (22/2015).
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Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.jep.2016.07.058.
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