INTRODUCTION
Depression is one of the serious mental disorders worldwide. Approximately, more than 300 million people are living with depression. Nowadays, depression is ranked as the first largest global contributor to nonfatal health loss. According to the World Health Organization data, over 50 million depressed people live with disability. This disease burden affects more than 80% of low-middle-income countries (World Health Organization, 2017). The burden is originates from the declining of psychosocial function (Kessler and Bromet, 2013), suicide (Li et al., 2017), and other associated physical illnesses, especially various chronic diseases such as diabetes, hypertension, heart disease, arthritis, asthma, chronic obstructive pulmonary disorder (Bhattacharya et al., 2014), and chronic pain (Cocksedge et al., 2014). Early detection and adequate treatment should be carried out in order to reduce this disease burden (McKeever et al., 2017).
Antidepressants are an important part of the depression treatment algorithm (Gautam et al., 2017). Serotonin selective reuptake inhibitors’ (SSRIs) are widely prescribed in healthcare settings. The efficacy and relatively benign side effects are favorable advantages of SSRI application. However, the low remission rate, i.e., 30%, and long onset of SSRIs therapeutic cannot be ruled out as drug limitations (Vahid-Ansari et al., 2019). Considering these limitations, researchers have been developing alternative approaches to investigate the new potential of antidepressants. Recently, intensive researches that use plant bioactive compounds as antidepressants are growing tremendously (Matraszek-Gawron et al., 2019).
Flavonoids, natural products from various plants, was previously reported as antidepressants in animal model studies, for instance, citrus maxima leaf icariin from Epimedium brevicornum Maxim, kaempferitrin from Asteraceae, luteolin from Cirsium japonicum, and also quercetin in onion, apple, and broccoli (Sheik et al., 2014). Although the mechanism remains uncertain, it suggested that flavonoids can form an interaction with monoamine receptors, including dopaminergic receptors (Hritcu et al., 2017).
Anthocyanin is one of the plant flavonoids that previously demonstrated a preventive effect in animal models of psychological stress. Anthocyanin from blueberry showed increases in the dopamine neurotransmitter in several brain areas of those animal models (Rahman et al., 2008). Anthocyanins are also present in other plants including sweet purple potatoes (Steed and Truong, 2008). Earlier studies revealed the neuroprotective effect of anthocyanin from purple sweet potatoes both in vitro and in vivo (Rahmawati et al., 2018; Ye et al., 2010). However, the bioactivity mechanism of anthocyanin from purple sweet potatoes as antidepressant is still unclear and needs further studies.
To evaluate the mechanism of the antidepressant effect from purple sweet potatoes, we conducted in silico analysis using molecular docking between anthocyanins in purple sweet potatoes and D2 dopamine receptors (D2DRs). This study would be supporting the potential use of anthocyanin from purple sweet potatoes as a drug candidate for antidepressants. The aim of this study was to identify the physicochemical, amino acid, and anthocyanin content from purple sweet potatoes as well as to determine the function of anthocyanin as an agonist on D2DRs
MATERIAL AND METHODS
Plant material
Fresh tuber roots of purple sweet potatoes (I. batatas) with variety Antin-3 were harvested from the Legumes and Tubers Research Institute, Malang, East Java, at 4- months of planting.
Amino acids analysis
Ultraperformance liquid chromatography (UPLC) was used for amino acid analysis except for tryptophan. The UPLC was carried out according to previous procedures. In brief, the column was an AccQ.Tag Ultra C18 (2.1 × 100 mm, 1.7 μm particle, Waters Corporation). The column temperature was set at 49°C. The flow rate of the mobile phase was maintained at 0.5 ml/minutes. Eluent A was AccQ.Tag Ultra concentrate solvent A; eluent B was 10% amino acid analysis AccQ.Tag Ultra in water. The PDA (Photodiode Array Detector) detector of 260 nm wavelength was used for analysis. A volume of 1 μl was injected into the UPLC system. High-performance liquid chromatography (HPLC) was carried out for tryptophan analysis. The column was Lichrospher RP-18 (250 × 4.0 mm, 5 μm particles, Merck). The mobile phase consisted of sodium acetate (0.0085 M) and methanol (95:5), pH 4. The flow rate was adjusted to 1.5 ml/minutes in an isocratic elution. The column was maintained at ambient temperature. The PDA detector of 280 nm wavelength was used for analysis (Kurnianingsih et al., 2019; Waters Corporation, 2012).
Proximate analysis
The water content was determined by keeping 2 g of the sample in 105°C for 3 hours and then cooling in a desiccator. The percentage of sample weight was regarded as a measure of water content. The percentage of ash was calculated after heating 2–3 g of the sample in a furnace at 550°C until grayish ash was completely obtained. Total fat was extracted from the sample with hexane in a Soxhlet apparatus for about 3 hours. The residual fat was evaporated in a 105°C. Protein was analyzed with the Kjeldahl method as previously described (Association of Official Analytical Chemists, 2006; Khan et al., 2013; Thiex et al., 2002). The percentage of carbohydrate was calculated by the following equation: 100% − (% ash+ % water + % protein + % fat) (Rodrigues et al., 2016)
Total anthocyanin extraction
Total anthocyanin extraction was carried out at the Organic Chemistry Laboratory, Bandung Institute of Technology. Fresh tuber roots were shade dried and then shredded into small pieces. Afterward, extraction was conducted using the maceration technique in methanol-HCL 1%, pH 4, for 24 hours. After the extract was obtained, the residue was extracted with the same procedure as previously described. The collected extract was evaporated using a rotary evaporator vacuum at a temperature of 50°C–60°C (Silva et al., 2015). The extracts then were kept at 4°C for further analysis (Jiao et al., 2012).
Total anthocyanin concentration
The total anthocyanin concentration (TAC) was determined according to the pH differential method (Rafi et al., 2018). An aliquot of 0.1 g extract was diluted in pH 1 buffer and pH 4.5 buffer. After the 30-minutes equilibrium, each absorbance of the solution was measured using a spectrophotometer at 525 nm and 700 nm. The absorbance (Abs) value was then calculated as A = [(Abs 525-Abs 700) pH1] - [(Abs 525-Abs 700) pH 4.5]. The TAC was expressed as cyanidin-3-glucoside equivalent by the following equation:
Concentration (mg/g) = [(A/(ÆL)) × MW × DF × (V/m)].
Where MW (molecular weight) is the molecular weight of anthocyanin (449.2 Da); DF (dilution factor); L is the cell path length (1 cm); Æ is the molar extinction coefficient of cyanidin-3-glucoside (26,900); V is extract volume; and m is the weight of the sample (Jiang et al., 2019).
Identification of anthocyanin
The internal standard for cyanidin, cyanidin-3-glucoside, and peonidin-3-glucoside was obtained from Extrasynthese, Paris. Anthocyanins were identified according the method described by Sang et al. (2017) with minor modification. Briefly, a total of 0.5 mg anthocyanin extract from purple sweet potatoes was diluted in methanol and filtrated using filter paper HPLC grade 0.22 μm. A volume of 2 μl of the sample was injected into UPLC system, with flow rate of 0.2 ml/minutes, a mobile phase of solvent A of 0.2% formic acid, and solvent B of acetonitrile which was set in gradient decrease of solvent A, 95%; 50%; 30%; and 0%. The duration of analyses was 25 minutes. Zorbax SB-C18 2.1 × 150 mm 1.8-micron column (Agilent, USA) was used in the current study (Sang et al., 2017).
In silico study of antidepressant activity
Ligand and protein preparation
Ligands of cyanidin (CID: 128861) and cyanidin-3-glucoside (CID: 441667) were retrieved from the PubChem National Centre for Biotechnology Information database. The ligand conversion into .pdb file was conducted using PyRx 0.8 software as well as ligand energy minimization (Dallakyan, 2015). Protein D2DR (ID: 6CM4) was imported from the Protein Data Bank (http://rcsb.org). Initially, the protein was prepared for further molecular docking by removing the previous ligand bound and unwanted water molecules using the Discovery Studio Visualizer v19.1.0.18287 program (http://3dsbiovia.com/products/) (Alamri, 2019).
Molecular docking
The PyRx 0.8 software was used to dock the ligand with D2DR to predict interaction and energy binding. The interaction was visualized using the Discovery Studio Visualizer v19.1.0.18287 program (http://3dsbiovia.com/products/) (Alamri, 2019; Dallakyan, 2015).
Physicochemical properties and biological activity prediction
The physicochemical properties and bioactivity score of ligands cyanidin and cyanidin-3-glucoside were predicted using the Molinspiration online software (https://www.molinspiration.com). The number of hydrogen bond donors, hydrogen bond acceptors, molecular weight, and partition coefficient, and a number of violations were used to predict the drug-likeness of ligands for orally absorption and permeability (Lipinski et al., 2001). The biological activity score of ligands was classified into active for score > 0.00, moderately active for -0.05–0.00, and inactive < −0.50 (Alodeani et al., 2015).
RESULTS AND DISCUSSION
Amino acid profiles of sweet purple potatoes
Amino acids profiling showed relatively higher levels of alanine, histidine, serine, and glutamic acid. For the 15 amino acids analyzed, alanine had the highest concentration, while the lowest amino acid was isoleucine. Three amino acids were not detected in purple sweet potatoes, i.e., cystine, methionine, and tryptophan (Fig. 1). Amino acids have various physiological roles in plant tissues, including plant growth, resistance to stress, and production of secondary metabolites (Guerriero et al., 2018; Hildebrandt et al., 2015).
Our previous work suggested that different colors of tuber fleshed have an impact on amino acid profiles in purple sweet potatoes (Kurnianingsih et al., 2019). The current research revealed the differences in amino acid concentration compared to that of previous work, although both purple potatoes were planted in the same province. Environmental conditions, such as water, light, heat, and cold, play an important regulation in plant amino acid metabolism. Drought stress, heat stress, and light stress were included in abiotic stress for plants and affect the amino acid biosynthesis pathway (Galili et al., 2016).
Amino acids have important regulations for physiological function in human, for instance, protein synthesis, hormone synthesis, immune response regulation, antioxidant defenses, neurotransmitter synthesis, wound healing, and cell signaling (Hou et al., 2015; Wu, 2013). The human body can synthesize amino acids that are classified into nonessential amino acids NEAA (Nonessential Amino Acids), such as glutamate, proline, glycine, glutamine, cysteine, arginine, aspartate, alanine, asparagine, serine, and tyrosine. Other amino acids cannot be synthesized in humans; therefore, dietary requirements are needed to maintain the physiological process, i.e., histidine, leucine, tryptophan, threonine, phenylalanine, methionine, isoleucine, lysine, and valine (Choi and Coloff, 2019). Based on the amino acid content of purple sweet potatoes, we assumed a potential health benefit from purple sweet potatoes, which needs to be more explored in future studies.
Proximate analysis of purple sweet potatoes
Water was the most predominant component in purple sweet potatoes, followed by carbohydrate, protein, ash, and total fat. The percentages of total fat from purple sweet potatoes are as low as 0.02%; therefore, the energy from fat was calculated as 0.00 Kcal/100 g. Carbohydrate as another energy source was measured as 21.1%, and then total energy calculation of purple sweet potatoes was 88.38 Kcal/100 g (Fig. 1).
Our proximate analyses demonstrated lower carbohydrate, higher protein, higher water, and lower fat compared to earlier investigations using purple sweet potatoes from Malaysia (Dusuki et al., 2020). Rodrigues et al. (2016) conducted similar analyses using fresh purple sweet potatoes from Brazil. Those studies revealed a higher protein, fat, and carbohydrate compared to the current results. The variation of proximate analysis can be determined by several factors, including geographical location, soil fertility, and period of harvest (Bhandari et al., 2003).
 | Figure 1. (A) Amino acid content in purple sweet potatoes was measured using UPLC-PDA and HPLC for tryptophan. A total of 15 amino acids were identified. Three amino acids were not detected (n.d.). (B) The proximate analyses revealed the chemical and physical composition profiles of purple sweet potatoes. [Click here to view] |
Total anthocyanin
The content of total anthocyanin from our purple sweet potatoes was calculated to be 150 mg/g. This result is slightly higher than previous reported (Steed and Truong, 2008). Another study by Jiao et al., (2012). reported total anthocyanin content of 132 mg/100 g from purple sweet potato in China. The difference of solvent can affect the yield of anthocyanin extraction. The use of an acidic solvent can improve the extracted anthocyanin level (Abou-Arab et al., 2011). In addition, other factors, for instance, the color of tuber roots, varieties, climate, and agricultural characteristics, were correlated with the anthocyanin quantities (Hamouz et al., 2011). As flavonoid derivatives, anthocyanin had been attracted to be more explored. Numerous studies have well documented the health benefits of anthocyanin as antioxidant, anti-inflammatory, antiobesity, and antidiabetic and neuroprotective agents (Kim et al., 2011; Miguel, 2011; Pojer et al., 2013).
Anthocyanin identification
As shown in Figure 2, three anthocyanins were tentatively identified as cyanidin-3-glucoside, peonidin-3-glucoside, and cyanidin from the total anthocyanin extract. The retention time and concentration of cyanidin, cyanidin-3-glucose, and peonidin are shown in Figure 2. Earlier research identified similar aglycone, i.e., cyanidin and peonidin from various cultivars of sweet purple potatoes using UPLC-PDA, which were identified as cyanidin 3-caffeoyl-p-hydroxybenzoyl sophoroside-5-glucoside, cyanidin 3-caffeoyl-vanilloyl sophoroside-5-glucoside, and peonidin 3-caffeoyl-vanilloyl sophoroside-5-glucoside (He et al., 2016). Other studies have demonstrated two major acetylated anthocyanins in purple sweet potatoes; the proposed compound is peonidin 3-O-(6-O-(E)-caffeoyl-(2-O-(6-O-p-hydroxyben-zoyl)-b-D-glucopyranosyl)-b-D-glucopyranoside)-5-O-(b-D-glucopyranoside) and peonidin 3-O-(6-O-(E)-caffeoyl-(2-O-(6-O-(E)-feruloyl)-b-D-glucopyranosyl)-b-D-glucopyranoside)-5-O-(b-D-glucopyranoside) (Zhang et al., 2018). The current study used three anthocyanin standards; therefore, the broad anthocyanin characteristics in purple sweet potatoes have not been fully identified. Anthocyanin content in plant tissues can be influenced by nutrition during plant growth, for instance, application of magnesium, potassium, calcium, and nitrogen as nutrients resulting in a positive effect for anthocyanin in various fruits (Jezek et al., 2018). Anthocyanin is a secondary metabolite that is responsible for providing spectrum color blue, purple, and red in various plant tissues (Khoo et al., 2017). The color spectrum was influenced by the ratio of cyanidin and peonidin. Previous studies have well documented blue color predominance in sweet potatoes which has peonidin/cyanidin ratio < 1, the so-called cyanidin type, whereas the peonidin type has the ratio of peonidin/cyanidin > 1; that is, dominantly red in color is dominant (Jezek et al., 2018).
In silico molecular docking
Four conventional hydrogen bonds, one electrostatic bond, six hydrophobic bonds, and six van der walls bonds facilitated the interaction between cyanidin with D2DR. The binding sites of hydrogen bonds were located at Ser193, Cys118, Tyr416, and Ser197 amino residues. The binding affinity of that interaction was −9.6 kcal/mol (Fig. 4A). Compared to cyanidin, cyanidin-3-glucoside demonstrated a lesser binding site towards D2DR. According to Figure 5A, amino residues of Glu95, Pro405, Tyr416, and Trp413 were bond as conventional hydrogen bond. The cyanidin-3-glucoside and D2DR complexes resulted in the binding affinity of −8.9 kcal/mol.
 | Figure 2. Anthocyanin profiles of purple sweet potatoes-extracted acidic methanol solvent determined by UPLC analysis. The level of absorbance was measured at 520nm wavelengths. Three anthocyanins were identified as cyanidin, cyanidin-3-glucoside, and peonidin-3-glucoside. [Click here to view] |
 | Figure 3. A) The three-dimensional interaction between D2DR (grey) and dopamine (blue) is showed in panel A1-3. The interaction between D2DR_dopamine complex with cyanidin (red) or cyanidin-3-glucoside (red) is showed in panel A5-7 and A9-11 respectively. The two-dimensional structure between those interaction is showed in panel A4;8;12. 3B) The details of those interaction are showed as binding site, distance, category and type of interaction. The interaction analyzed by Discovery Studio Visualizer v19.1.0.18287 program. [Click here to view] |
A molecular docking using D2DR agonist ligand, i.e., dopamine (Figures. 3, 4, and 5), showed that cyanidin has a more similar binding site besides cyanidin-3-glucoside, that is,which is at amino residues of Asp114, Tyr416, Thr119, Ser197, Cys118, Phe389, and Phe390. Kalani et al. (2004) correlated our findings with an earlier study, which predicted the binding site of agonist human D2DR. The essential amino acid residues were Asp114 in transmembrane 3) domain and Ser197 in TM5 domain. The most hydrophobic pocket components for dopamine binding as agonist were Cys118, Phe189, Trp386, and Phe390.
 | Figure 4. A) The three-dimensional interaction between D2DR (grey) and cyanidin (red) is showed in panel A1-3. The interaction between D2DR_cyanidin complex with dopamine (blue) is showed in panel A5-7. The two-dimensional structure between those interaction is showed in panel A4 and A8. 4B) The details of those interaction are showed as binding site, distance, category and type of interaction. The interaction analyzed by Discovery Studio Visualizer v19.1.0.18287 program. [Click here to view] |
Additionally, we carried out docking interaction between cyanidin– D2DR complex with ligand dopamine. Cyanidin was not inhibiting the dopamine binding to its receptor at amino residue Asp114. The amino acid of aspartate has a carboxyl group which formed a tight salt bridge with all types of human dopamine receptors with 2.6 Å distance (Kalani et al., 2004). Intriguingly, in silico interaction between cyanidin-3-glucoside– D2DR complex with dopamine results in hydrogen bond at Ser409 of D2 receptor and hydrophobic bond with cyanidin-3-glucoside. In accordance with our analyses, we predicted that cyanidin might have a stronger potential to substitute dopamine at D2 receptor.
D2DR distribution in central nervous systems is associated with emotional regulation at the dopaminergic pathway, that is, striatum, including nucleus accumbens and ventral tegmental area (Bonci and Hopf, 2005). Considering that depression is correlated with dopaminergic dysregulation, therefore, D2DRs are interesting to develop as a potential therapeutic target for depression (Belujon and Gace, 2017; Bonci and Hopf, 2005). Hence, to ensure the possibility of both anthocyanins as a drug candidate, we analyzed their physicochemical properties and biological activities (Table 1). According to Lipinski’s rule of five (Lipinski et al., 2001), cyanidin was predicted to have better absorption and permeability than cyanidin-3-glucoside due to cyanidin-3-glucoside which has eight hydrogen bond donors (OH and NH groups) and 11 hydrogen bond acceptors (N and O). For biological activity prediction, based on the score, both cyanidin and cyanidin-3-glucoside were predicted as enzyme and kinases inhibitors. Cyanidin was predicted as moderately active protease inhibitors in contrast to cyanidin-3-glucoside, which were predicted to be inactive as protease inhibitors.
 | Figure 5. A). The three-dimensional interaction between D2DR (grey) and cyanidin-3-glucoside (red)) is showed in panel A1-3. The interaction between D2DR_cyanidin-3-glucoside complex with dopamine (blue) is showed in panel A5-7. The two-dimensional structure between those interaction is showed in panel A4 and A8. 5B) The details of those interaction are showed as binding site, distance, category and type of interaction. The interaction analyzed by Discovery Studio Visualizer v19.1.0.18287 program. [Click here to view] |
 | Table 1. The physicochemical properties and biological activity prediction of anthocyanins analyzed by mMolinspiration online software (http://molinspiration.com). [Click here to view] |
CONCLUSION
This study indicated that cyanidin is a major anthocyanin from purple sweet potatoes (I. batatas) and has being more potential as an antidepressant through D2DR interaction. Future studies are necessary to confirm this antidepressant function in preclinical approaches.
ACKNOWLEDGMENTS
The authors acknowledge the Ministry of Research and Technology/National Research and Innovation Agency of the Republic of Indonesia for financially supporting this research. They gratefully acknowledge James Robert Ketudat Cairns, Ciptati, Anna Safitri, and SMONAGENES members for supporting this research.
AUTHORS’ CONTRIBUTIONS
NK, RR, TAN, MA, and FF contributed to designing the research; NK and FF drafting manuscript; and NK, RR, TAN, MA, and FF revising it critically for intellectual content.
CONFLICT OF INTEREST
There are no conflicts of interest.
FUNDING
This work was financially supported by Ministry of Research and Technology/National Agency for Research and Innovation, Republic of Indonesia (Grant No: 127/SP2H/LT/DRPM/2020).
ETHICAL APPROVALS
This study does not involve experiments on animals or human subjects.
PUBLISHER’S NOTE
This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.
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