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Research Article
Free radical scavenging, α-amylase, α-glucosidase, and lipase inhibitory activities of metabolites from strawberry kombucha: Molecular docking and in vitro studies
expand article infoAdriyan Pramono‡, William Ben Gunawan‡, Fahrul Nurkolis§, Darmawan Alisaputra§, Gilbert Ansell Limen|, Muhammad Subhan Alfaqih, Martha Ardiaria‡
‡ Universitas Diponegoro, Semarang, Indonesia
§ State Islamic University of Sunan Kalijaga, Yogyakarta, Indonesia
| Sam Ratulangi University, Manado, Indonesia
¶ President University, Bekasi, Indonesia

Corresponding author: Adriyan Pramono ( adriyanpramono@fk.undip.ac.id )

Academic editor: Magdalena Kondeva-Burdina
© 2024 Adriyan Pramono, William Ben Gunawan, Fahrul Nurkolis, Darmawan Alisaputra, Gilbert Ansell Limen, Muhammad Subhan Alfaqih, Martha Ardiaria.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Pramono A, Gunawan WB, Nurkolis F, Alisaputra D, Limen GA, Alfaqih MS, Ardiaria M (2024) Free radical scavenging, α-amylase, α-glucosidase, and lipase inhibitory activities of metabolites from strawberry kombucha: Molecular docking and in vitro studies. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e116794
Open Access

Abstract

Obesity, a global issue, is linked to cardiometabolic syndrome. Dietary modification is one of the recommended modes for managing cardiometabolic syndrome. Strawberries, a functional food, and kombucha, a fermented tea beverage, have gained attention for their health benefits.

This study investigated the bioactive components of strawberry kombucha drink (SKD) and their effects on antioxidant activities and improving metabolic disorder markers.

An in vitro experiment was performed to determine the effect of SKD on enzymatic parameters: lipase, α-glucosidase, and α-amylase activities. In addition, antioxidant activity using the DPPH method and quantification of the radical scavenging activity were also measured. Furthermore, untargeted metabolomic profiling of SKD and molecular docking simulation were conducted.

The findings suggest that SKD, rich in secondary metabolites, can inhibit lipase, α-glucosidase, and α-amylase activities. It demonstrated in vitro anti-obesity, anti-diabetic, and antioxidant properties, potentially reducing metabolic and inflammatory issues.

Thus, SKD could be a therapeutic beverage to alter metabolic issues associated with obesity. Nevertheless, further preclinical study is warranted to determine SKD’s potential in vivo.

Keywords

Antioxidant, fermentation, metabolite, molecular docking, strawberry kombucha

Introduction

Fermented beverages are gaining popularity due to their potential as probiotic beverages, enriched with bioactive compounds, antioxidants, and significant health benefits (Selvaraj and Gurumurthy 2023). One prominent example is kombucha, which has become a focal point in functional food research. Kombucha is a fermented beverage produced using black or green tea as a substrate, along with sucrose and a symbiotic culture of bacteria and yeast (SCOBY) (Jakubczyk et al. 2020). The microorganisms present in SCOBY consist of a mixture of acetic bacteria, lactic acid bacteria, and osmophilic yeast, which, during the fermentation process, generate new compounds from the substrate, leading to the production of various metabolites (Miranda et al. 2022).

Kombucha, made from tea, contains abundant catechins, theaflavins, flavonoids, and polyphenols (Abaci et al. 2022). The bioactive components in kombucha encompass not only tea-derived polyphenols but also various metabolite compounds produced during fermentation, including organic acids, vitamins, organic nitrogen, enzymes, minerals, and other substances (Kitwetcharoen et al. 2023). Throughout the fermentation process, the nutrient content of the beverage increases, stimulating the probiotic and prebiotic functions of kombucha. Several microorganisms found in kombucha include Komagataeibacter xylinus, Brettanomyces bruxellensis, Acetobacter pasteurianus, Acetobacter xylinum, Acetobacter aceti, Saccharomyces cerevisiae, Zygosaccharomyces bailii, Zygosaccharomyces spp., and Gluconacetobacter (Cuamatzin-García et al. 2022).

Several studies have demonstrated that kombucha exhibits significant therapeutic effects as an antioxidant, anti-inflammatory, anticancer, and antimicrobial agent. Moreover, it possesses the ability to bolster the immune system and prevent various diseases, including diabetes, hypertension, and cardiovascular diseases (Kitwetcharoen et al. 2023). As a fermented beverage, kombucha is a rich source of nutrients and phytonutrients, and thus, it holds the potential for further development (Ferruzzi et al. 2020). The exploration of kombucha’s health-beneficial effects as a beverage is ongoing. Additionally, it is noteworthy that kombucha is no longer exclusively derived from tea; it can also be produced using other basic ingredients, such as strawberries. In a systematic review study, strawberries have several functional metabolites as a functional food (Basu et al. 2014). In addition, strawberries also contain various biologically active non-nutrient compounds, mainly represented by polyphenolic phytochemicals (Giampieri et al. 2017). These strawberry phenolics have wide clinical potential for humans as an antioxidant, anti-inflammatory action, and inhibition of metabolic enzymes and receptors, alleviating oxidative stress-related conditions (Afrin et al. 2016).

Recently, kombucha is well-known as a beverage made through the fermentation process of tea and sugar with SCOBY (Villarreal-Soto et al. 2018). Recent studies have shown that because of fermentation, kombucha drink has anti-inflammatory, antioxidant, antidiabetic, cholesterol-lowering, and hepatoprotective effects (Kapp and Sumner 2019; Júnior et al. 2022). In addition, multiple studies have consistently shown that the chemical properties of fermented beverages are enhanced compared to unfermented beverages (Jafari et al. 2020; Zofia et al. 2020; Değirmencioğlu et al. 2021). Incorporating strawberries (Fragaria ananassa) into a kombucha using the SCOBY fermentation method is expected to increase its bioactive properties. This study aimed to investigate bioactive compounds of strawberry kombucha using liquid chromatography high-resolution mass spectrometry (LC-HRMS) and its effect on the modulation of the immune system related anti-oxidative (DPPH and ABTS), markers of metabolic disorders (enzymes activity: lipase, alpha-amylase, alpha-glucosidase,) through in vitro enzymatic studies and biochemical analysis, as well as molecular docking approach.

Materials and methods

Formulation of strawberry kombucha drink (SKD)

Strawberry (Fragaria ananassa; Fragaria X ananassa ssp. ananassa; Integrated Taxonomic Information System – Report and Taxonomic Serial Number: 837344) collected from Sarangan Strawberry Garden, Raya Sarangan Street No.47, Plaosan III, Sarangan, Plaosan District, Magetan Regency, East Java 63361, Indonesia (Google Maps Coordinates = -7.6743967, 111.2308193). Plant species were identified at the Biochemistry and Biomolecular Laboratory, Brawijaya University, Malang, Indonesia. Specimens were collected for future validation. The characteristic of strawberry fruit harvested and used is the fruit derived from the strawberry plant aged 2.5 months and has a chewy-tender texture when held; the skin is dark red, and the stalk is yellowish brown. To maintain its quality, strawberry is kept in a refrigerator with a temperature of 4–8 °C before being fermented into a kombucha using SCOBY. This method adopted the well-established protocol published in other papers (Permatasari et al. 2021, 2022).

Furthermore, the SKD drink was formulated using 2,000 mL of water, 24 g of strawberry pulp, 300 g of white sugar, 166 g v/v of SCOBY starter solution, and 10 g of SCOBY gel, all contributing to a total volume of 2,500 mL. The production was initiated by boiling 2 L of water (±80 °C) followed by the addition of 300 g of table sugar. The mixture was stirred until homogenous and then added with 24 g of strawberry flesh. After that, the water was stirred until the color turns dark brownish red, turn off the stove heat, cover the pot, and let it cool. The solution was then poured into a sterile 3 L jar along with the SCOBY starter solution and SCOBY gel. A clean gauze was placed over and tied to the bottle; then the bottle was kept in anaerobic conditions at 20–25 °C for 12 days. Right after the fermentation process finished, all beverage samples were kept in a 4–8 °C refrigerator for further studies.

In vitro enzymatic parameters

Assay for lipase inhibition (%)

First, 1 mg/mL crude porcine pancreatic lipase (PPL) was solubilized in a 50 mM phosphate buffer, followed by the removal of insoluble materials through centrifugation (12,000 g). The process was then continued with the addition of buffer to the supernatants, resulting in a 10-times dilution.

The potential inhibition of lipase was determined using the method utilized by Permatasari et al. 2022. As much as 100 µL of SKD at all concentrations (50, 100, 150, 200, and 250 µg/mL along with 20 µL 10 mM p-nitrophenyl butyrate were added into the reaction buffer in a clear 96-well microplate and incubated for 10 min at 37 °C. The result was contrasted with the positive control (orlistat). The absorbance values were determined using a microplate reader at 405 nm. The unit of activity was calculated using the yield resulting from a one-minute reaction rate of 1 mol p-nitrophenol at 37 °C. When PPL activity was incubated in the test combination, the reduction percentage was used to calculate the inhibition activity of lipase inhibition. To ensure that the findings of the study are accurate, each sample was verified three times (in triplicate). The inhibitory data were obtained using the equation below:

Inhibition of lipase activity=100-(B-Bc)(A-Ac)×100%

A = Lipase inhibition activity without any inhibitor; Ac = Negative control without any inhibitor; B = Lipase inhibition activity with inhibitor; Bc = Negative control with inhibitor.

Determining the α-amylase inhibition (%)

Diluted SKD (at all concentrations) were incubated for 10 min at room temperature with 500 L of 0.02 M pH 6.9 Na3PO4 buffer, NaCl 0.006 M, and porcine pancreatic amylase 0.5 mg/mL. Then, each mixture in the assay buffer received 500 L of a 1% starch solution. After 10 minutes of incubation at 25 °C, 3,5-dinitrosalicylic acid was added to complete the process (1.0 mL). After 5 minutes in a 100 °C water bath, the test was continued and allowed to cool at 22 °C. The measurements were diluted with distilled water (10 mL) to make them readable within the permissible range (540 nm). Acarbose served as the positive control in this investigation. Enzymes and reagents are included in the reference sample but not the sample itself. This method referred to a similar methodology in Permatasari et al. 2022.

Investigation of α-glucosidase inhibition (%)

1.52 UI/mL α-glucosidase solution was created by combining 1 mg (76 UI) of the enzyme with 50 mL pH 6.9 of phosphate buffer, which was proceeded to be kept at -20 °C. Then, 0.35 mL each of the sucrose (65 mM), maltose solution (65 mM), and SKD (0.1 mL) at 50, 100, 150, 200, and 250 g/mL were added. Prior to adding the α-glucosidase solution (0.2 mL), the system was heated to 37 °C for 5 min and maintained at that temperature for 15 min. In a 100 °C water bath, the system was warmed up for 2 minutes. Acarbose was employed in this investigation as the positive control under identical conditions as SKD. The testing solution (0.2 mL), coloring reagent (3 mL), and α-glucosidase inhibitory test solution were then sequentially combined. After 5 minutes of system warming at 37 °C, the system was then evaluated at 505 nm absorbance. The amount of glucose produced during the response served as a sign of inhibitory activity.

DPPH inhibition activity assay (%)

The determination of antioxidant activity was based on the inhibition of DPPH, referring to Kaur et al. (Kaur et al. 2021). Glutathione (Sigma-Aldrich) was chosen as a positive control. The samples and the control (at all concentrations) were poured into the testing vials, followed by the addition of DPPH reagent (3 mL). The resulting DPPH extract mixture was then left undisturbed (30 min; dark cycle). The change was determined on 517 nm absorbance. The proportion of inhibition of DPPH was expressed and calculated using the following formula:

%DPPH inhibition=A0-A1A0×100%

A0 = Absorbance value of blank; A1 = Absorbance value of standard or sample.

The half maximal effective concentration (EC50), a concentration measurement of a sample that results in a 50% reduction in the starting radical concentration, expressed the radical scavenging ability of SKD and GSH.

Quantification of the radical scavenging activity of ABTS (%)

The scavenging capability of 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) was assessed with the protocol adhering to (Sancho et al. 2013). Potassium persulfate (2.4 mM) and 7 mM ABTS were mixed in a 1:1 ratio to form the stock solution. Aluminum foil was used to block the light from the combination, and it was then left to react for 14 hours at 22 °C. Afterward, 1 mL of ABST stock solution was added with 60 mL of ethanol to get an absorbance of 0.706 at 734 nm. For each test, a brand-new functioning solution was created. After allowing the samples at all concentrations to react for 7 minutes with 1 mL of the ABTS working solution, the absorbance at 734 nm was measured. As a positive control, Trolox was employed.

%ABTS radical scavenging activity=A0-A1A0×100%

A0 = Absorbance value of blank; A1 = Absorbance value of standard or sample.

The half maximal effective concentration (EC50) is the amount of sample concentration that reduces the concentration of radical levels to 50% reduction in the starting radical concentration, which expressed the radical scavenging ability of SKD and Trolox.

Untargeted metabolomic profiling of strawberry kombucha drink

Testing service at the Central Laboratory of Life Sciences, Brawijaya University, Malang, Indonesia) was utilized to analyze an untargeted metabolomic profile test on SKD using the combination of a high-performance liquid chromatography system with a high-resolution mass spectrometer (LC-HRMS) as described according to the manufacturer’s specification (Suppl. material 1). The concentration of 50 μl of SKD was reduced by 30 times using ethanol (96%) and vortexed (at 2,000 rpm for 2 min), followed by centrifugation (at 6,000 rpm for 2 min). The supernatants were accumulated and then filtrated using a 0.22 μm syringe filter prior to analysis.

Thermo Scientific Dionex Ultimate 3000 RSLC Nano High-Performance Liquid Chromatography (HPLC) and a micro-flow meter made up the LC-HRMS system. The analytical column was a Hypersil GOLD aQ with a particle size of 50 × 1 mm × 1.9 maintained at 30 °C, and the solvents A and B are 0.1% formic acid and 0.1% formic acid dissolved in water and acetonitrile, respectively. Next, they were kept apart with a 40 uL/min linear gradient for 30 minutes. HRMS utilized Thermo Scientific Q Exactive which has 70,000 resolution at its full scan capacity, a 17,500 resolution data-dependent MS2, and a 30 minute operating period in positive and negative modes. The successfully identified compounds were then analyzed in silico against the human pancreatic lipase (1LPB), α-glucosidase (2QV4), and α-amylase (3L4Y) enzymes.

Molecular docking simulation

Hardware and software

ASUS Vivobook M413ia – Ek502t with AMD Ryzen 5 4500u (2.3 GHz) processor, 8GB DDR4 memory, 512 GB SSD M.2 storage, and Windows 10 Home operating system was equipped with ChemDraw Ultra 12.0, AutoDock tools (version 4.2), and BIOVIA Discovery software. The website of Protein Data Bank (https://www.rcsb.org) and PubChem structure database (https://pubchem.ncbi.nlm.nih.gov) was also used in this study.

Preparing ligands and targets

The compounds that were identified as a constituent of the SKD metabolomic profile were used as test ligands. ChemDraw Ultra 12.0 was used to sketch the whole structure in 2D, which was then transformed to 3D and optimized using the MM2 algorithm. The selected target proteins were human pancreatic lipase (PDB ID: 1LPB), α-amylase (PBD ID: 2QV4), and α-glucosidase (PDB ID: 3L4Y). All proteins were acquired from the website of Protein Data Bank (https://www.rcsb.org). Kollman charges were applied to the receptors while the ligands were added with a Gasteiger charge.

Validation of molecular docking

Redocking was used as the molecular docking validation approach. By utilizing AutoDock tools version 4.2, the original ligand was transferred to the target pocket with specific coordinates. After the re-docking method, the ligand position’s RMSD (root-mean-square deviation) must be less than 2.0 Å.

Simulation of molecular docking

The docking parameters were developed using the findings of docking validation (Table 1). The outcome was recorded in a *dlg file for each docking’s final conformation structure. Analysis was done on the ligand-receptor interaction using Discovery Studio 2016.

Table 1.
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Identified Compounds from Untargeted Metabolomic Profiling of SKD.

No Name Formula Calculated MW RT (min) Area (Max) mzCloud Best Match
1 D-(+)-Maltose C12H22O11 364.09682 0.926 7,110,242,090.90 91.4
2 5-({[3-chloro-5-(trifluoromethyl)-2-pyridyl]methyl}thio)-4-pentyl-4H-1,2,4-triazol-3-ol C14H16 ClF3N4OS 380.07057 0.946 5,481,884,554.95 64.2
3 2-[3-methyl-2-(methylimino)-4-oxo-1,3-thiazolan-5-yl]acetic acid C7H10N2O3S 202.0445 0.904 5,110,631,469.12 70.5
4 Caffeine C8H10N4O2 194.07955 4.698 2,002,924,546.31 99.4
5 Diisobutylphthalate C16H22O4 278.15074 17.77 924,753,800.01 99.2
6 5-Hydroxymethyl-2-furaldehyde C6H6O3 126.0313 1.137 431,873,514.06 92.3
7 Pentane-1,2,3,4,5-pentol C5H12O5 174.04979 0.903 376,514,195.88 97.3
8 Bis(2-ethylhexyl) phthalate C24H38O4 390.2755 23.05 299,992,693.45 99.7
9 NP-020014 C15H26O3 276.17153 13.32 281,454,788.30 68.9
10 Isobutyraldehyde C4H8O 72.0577 1.358 157,345,987.19 72.7
11 Dibenzylamine C14H15N 197.11978 7.282 132,075,688.98 99.0
12 DL-Stachydrine C7H13NO2 143.09406 0.953 126,807,825.53 93.4
13 Monobutyl phthalate C12H14O4 222.08857 17.79 97,834,005.78 97.0
14 Theobromine C7H8N4O2 180.06409 2.037 71,557,968.75 99.0
15 D-Lactose monohydrate C12H22O11 342.11505 0.826 33,730,046.47 81.7
16 3-hydroxy-3-methylpentanedioic acid C6H10O5 184.03409 0.927 63,255,525.11 77.3
17 D-Raffinose C18H32O16 526.14952 0.836 59,525,278.77 89.6
18 (-)-Epicatechin C15H14O6 290.07784 4.541 53,638,661.97 99.1
19 Mevalonolactone C6 H10 O3 130.06259 1.488 49,822,362.80 64.3
20 (1S,4aS,7aS)-7-({[(2E)-3-phenylprop-2-enoyl]oxy}methyl)-1-{[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}-1H,4aH,5H,7aH-cyclopenta[c]pyran-4-carboxylic acid C25H28O11 542.1234 0.846 48,709,783.20 93.1
21 NP-000358 C15H14O7 306.07302 4.063 29,746,512.44 99.2
22 Tributyl phosphate C12H27O4P 266.16385 16.37 39,966,286.39 99.9
23 NP-013538 C12H16O8 288.08342 1.126 36,462,233.46 68.9
24 Methylimidazoleacetic acid C6H8N2O2 140.05799 1.061 35,722,013.31 67.2
25 4-Coumaric acid C9H8O3 164.04686 4.786 34,660,580.43 99.0
26 DEET C12H17NO 191.13039 11.61 32,410,930.57 98.5
27 2,2,6,6-Tetramethyl-1-piperidinol (TEMPO) C9H19NO 157.1462 12.23 30,810,168.71 91.9
28 Bis(3,5,5-trimethylhexyl) phthalate C26H42O4 418.30702 16.9 30,141,921.96 98.3
29 n-Pentyl isopentyl phthalate C18H26O4 323.20864 17.77 29,880,782.76 87.0
30 3,5-di-tert-Butyl-4-hydroxybenzaldehyde C15H22O2 234.16115 16.81 28,572,320.75 99.6
31 Caprolactam C6H11NO 113.08388 3.472 25,998,433.02 96.4
32 3,5-di-tert-Butyl-4-hydroxybenzoic acid C15H22O3 250.15625 14.79 19,609,117.25 95.6
33 Sulcatol C8H16O 128.11986 14.1 19,080,856.60 67.7
34 N,N-Diisopropylethylamine (DIPEA) C8H19N 129.15138 4.608 18,332,420.83 76.4
35 4-(2,3-dihydro-1,4-benzodioxin-6-yl)butanoic acid C12H14O4 244.07048 13.01 17,707,359.41 71.1
36 Tetranor-12(S)-HETE C16H26O3 248.17677 16.82 17,690,476.85 72.3
37 3-(2-Hydroxyethyl)indole C10H11NO 161.08358 8.054 17,099,586.02 91.2
38 (2R,3R)-5,7-dihydroxy-2-(3,4,5-trihydroxyphenyl)-3,4-dihydro-2H-1-benzopyran-3-yl 3,4,5-trihydroxybenzoate C22H18O11 458.08388 5.77 16,420,630.11 99.3
39 Rutin C27H30O16 610.15239 6.861 16,318,729.84 99.4
40 Choline C5H13NO 103.09958 1.04 16,193,389.87 97.0
41 α-Pyrrolidinopropiophenone C13H17NO 203.13042 16.48 14,091,680.31 88.7
42 Hesperidin C28H34O15 610.18846 7.79 13,481,790.12 96.8
43 benzyl N-(1-{[(3,4-dimethoxyphenethyl)amino]carbonyl}-2-methylpropyl)carbamate C23H30N2O5 452.17772 8.209 13,379,568.24 94.6
44 (2E)-3-(2-{[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}phenyl)prop-2-enoic acid C15H18O8 348.08101 4.757 12,724,287.81 93.3
45 Vanillin C8H8O3 152.04688 6.072 11,781,792.86 89.0
46 3,4-Dihydroxybenzaldehyde C7H6O3 138.03116 5.544 5,298,795.15 79.2
47 6-Methyl-2-pyridinemethanol C7H9NO 123.0681 1.26 10,839,550.92 80.6
48 3,4-Dihydroxyphenylpropionic acid C9H10O4 164.04688 13.31 10,660,470.60 94.5
49 N-Cyclohexyl-N-methylcyclohexanamine C13H25N 195.19823 7.233 10,459,159.69 90.4
50 7-Oxobenz[de]anthracene C17H10O 230.07582 1.272 10,388,291.21 75.3
51 Levalbuterol C13H21NO3 261.13573 17.34 10,251,911.95 93.1
52 N-Octyl-2-pyrrolidone C12H23NO 197.17739 15.25 10,113,937.91 73.5
53 Catechin gallate C22H18O10 442.08901 6.98 10,030,569.60 99.4
54 Hexamethylenetetramine C6H12N4 140.10565 26.42 9,238,125.20 92.3
55 1-Methyl-N-{[(2R,4S,5R)-5-(2-methyl-6-phenyl-4-pyrimidinyl)-1-azabicyclo[2.2.2]oct-2-yl]methyl}-4-piperidinamine C25H35N5 427.27715 2.762 9,038,413.78 90.3
56 4-Aminophenol C6H7NO 109.05258 1.251 8,177,405.86 77.9
57 3-{[(2S,3R,4S,5R,6R)-3,5-dihydroxy-6-(hydroxymethyl)-4-{[(2S,3R,4R,5R,6S)-3,4,5-trihydroxy-6-methyloxan-2-yl]oxy}oxan-2-yl]oxy}-5,7-dihydroxy-2-(4-hydroxyphenyl)-4H-chromen-4-one C27H30O15 594.15752 7.399 7,731,006.18 98.2
58 (2S)-7-{[(2S,3R,4S,5S,6R)-4,5-dihydroxy-6-(hydroxymethyl)-3-{[(2S,3R,4R,5R,6S)-3,4,5-trihydroxy-6-methyloxan-2-yl]oxy}oxan-2-yl]oxy}-2-(3,4-dihydroxyphenyl)-5-hydroxy-3,4-dihydro-2H-1-benzopyran-4-one C27H32O15 596.17308 6.736 7,698,163.80 96.6
59 L-(+)-Citrulline C6H13N3O3 394.15033 4.327 7,590,020.04 77.2
60 Sunitinib C22H27FN4O2 420.19603 6.077 7,501,984.66 84.0
61 (-)-Caryophyllene oxide C15 H24 O 220.1819 9.887 6,306,915.32 68.6
62 Nicotinamide C6 H6 N2 O 122.04772 1.247 4,680,656.48 61.4

Statistical analysis

In the early phase of the study, in vitro data regarding antioxidants (DPPH and ABTS) were analyzed using an unpaired T-test CI 95% with the Windows version of GraphPad Prism 9.4.1 software (San Diego, California USA, www.graphpad.com). EC50 datasets were each acquired from nonlinear regression models. GraphPad Prism 9.4.1 was used to present the graphic visualizations.

Results and discussion

Metabolite profile of strawberry kombucha drink (SKD)

A total of 45 compounds were identified in SKD (Table 2). The majority of these compounds exhibited several health benefits, ranging from antioxidant, neuroprotective, and hepatoprotective to hypolipidemic and protection against cardiometabolic risk factors. The identification produced a spectrum that may be compared to those in the database by combining electrospray ionization and Fourier processing (Fig. 1A). An electrospray positive weak peak with 1.80 × 106 counts is transformed into an appropriate spectrum (m/z 50–750 Da) (Fig. 1B). Table 1 lists the detected chemicals in detail based on the findings of non-targeted metabolomic profiling using LC-HRMS.

Figure 1. 

Total Ion Chromatogram LC-MS of Strawberry Kombucha Drink. Total ion chromatogram (ESI +) and the LC-MS metabolite profiles of SKD (A). Positive ion mass spectra (FTMS-ESI (+)) of the m/z range 50–750 of SKD (B). S#: Number of scans; RT: Retention time; AV: Averaged number of scans; SB: Subtracted (followed by subtraction information); NL: Neutral loss; T: Scan type; F: Scan filter.

Table 2.
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Validation of molecular docking simulation.

No Drug Target PDB ID Docking Site (x;y;z) Docking Area (x.y.z) RMSD (Å) ΔG (kcal/mol) Number in Cluster (/100) Judgement (<2Å)
1 Human Pancreatic Lipase 1LPB 4.448, 27.955, 49.675 40×40×40 1.89 -6.70 25 Valid
2 Human Pancreatic α-Amylase 2QV4 12.942, 47.170, 26.200 42×40×40 1.77 -9.60 22 Valid
3 Human Pancreatic α-Glucosidase 3L4Y -1.542, -19.201, -21.043 42×40×40 1.53 -5.23 33 Valid

In silico study of α-amylase, α-glucosidase, and lipase inhibitory activities

As shown in Table 2, identified compounds of SKD were validated by computational in silico or molecular docking assays on the enzymes lipase, a-glucosidase, and a-amylase. After the validation process was done, the molecular docking tests against lipase, α-amylase, and α-glucosidase enzymes were performed on 47 compounds (due to the availability of the databases), acarbose (as the control for α-amylase and α-glucosidase), and orlistat (as a control for lipase). This examination found that 1-Methyl-N-{[(2R,4S,5R)-5-(2-methyl-6-phenyl-4-pyrimidinyl)-1-azabicyclo[2.2.2]oct-2-yl]methyl}-4-piperidinamine had the best result based on the ligand test in each receptor when compared with other compounds and controls. This comparison is based on the value of ∆G (kcal/mol), and the results of molecular docking tests were listed in Table 3.

Table 3.
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Molecular Docking Parameter of Identified Compounds of SKD.

No. Substance Number in Cluster (/100) 1G (kcal/mol) Ki
1LPB 2QV4 3L4Y 1LPB 2QV4 3L4Y 1LPB 2QV4 3L4Y
1 Orlistat 5 –2.42 5.44 mM
2 Acarbose 13 13 –4.22 –1.01 38.46 µM 3.76 mM
3 D-(+)-Maltose 90 23 24 -3,44 -3,22 -2,43 246.63 uM 210.11 uM 725.66 uM
4 5-({[3-chloro-5-(trifluoromethyl)-2-pyridyl]methyl}thio)-4-pentyl-4H-1,2,4-triazol-3-ol 67 81 9 -7,94 -6,66 -5,18 492.27 nM 4.04 uM 97.00 uM
5 2-[3-methyl-2-(methylimino)-4-oxo-1,3-thiazolan-5-yl]acetic acid 50 40 94 -4,45 -3,3 -3,71 287.10 uM 2.65 mM 1.51 mM
6 Caffeine 100 100 100 -5,07 -4,17 -4,25 185.64 uM 883.60 uM 731.38 uM
7 Diisobutylphthalate 38 99 88 -5,89 -4,37 -4,09 7.59 uM 378.61 uM 366.15 uM
8 Dibenzylamine 98 79 69 -6,11 -7,09 -7,48 28.29 uM 3.44 uM 1.94 uM
9 DL-Stachydrine 92 62 73 -4,25 -3,37 -4,08 687.26 uM 2.55 mM 916.46 uM
10 Monobutyl phthalate 40 43 49 -4,95 -3,14 -2,76 86.18 uM 2.92 mM 3.46 mM
11 Theobromine 90 100 93 -4,92 -4,33 -3,99 246.05 uM 667.24 uM 1.16 mM
12 D-Lactose monohydrate 80 35 42 -3,11 -2,9 -1,65 466.97 uM 716.25 uM 9.15 mM
13 3-hydroxy-3-methylpentanedioic acid 77 84 47 -1,79 -1,17 -0,63 20.80 mM 25.49 mM 65.57 mM
14 D-Raffinose 95 17 18 1,91 -2,5 -0,29 21.32 mM 1.11 mM 51.82 mM
15 (-)-Epicatechin 100 100 59 -8,68 -6,58 -5,72 350.40 nM 6.17 uM 25.33 uM
16 (1S,4aS,7aS)-7-({[(2E)-3-phenylprop-2-enoyl]oxy}methyl)-1-{[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}-1H,4aH,5H,7aH-cyclopenta[c]pyran-4-carboxylic acid 20 16 28 -0,75 -4,41 -3,62 64.12 uM 80.72 uM 74.13 uM
17 Tributyl phosphate 96 45 40 -4,6 -2,78 -2,71 122.83 uM 3.27 mM 3.93 mM
18 Methylimidazoleacetic acid 77 46 82 -3,44 -2,17 -2,56 2.23 mM 23.10 mM 6.16 mM
19 4-Coumaric acid 78 100 64 -4,5 -4,06 -3,09 497.84 uM 905.62 uM 5.08 mM
20 2,2,6,6-Tetramethyl-1-piperidinol (TEMPO) 100 75 50 -5,57 -4,76 -5,48 81.88 uM 325.17 uM 89.09 uM
21 Bis(3,5,5-trimethylhexyl) phthalate 41 12 13 -4,98 -4,68 -4,04 1.60 uM 95.96 uM 151.73 uM
22 3,5-di-tert-Butyl-4-hydroxybenzaldehyde 48 96 80 -6,5 -5,54 -5,32 15.49 uM 73.08 uM 98.98 uM
23 3,5-di-tert-Butyl-4-hydroxybenzoic acid 72 59 54 -6,11 -4,56 -3,68 26.28 uM 289.18 uM 1.56 mM
24 Sulcatol 49 100 35 -4,61 -3,94 -4,49 278.89 uM 1.04 mM 256.04 uM
25 N,N-Diisopropylethylamine (DIPEA) 100 100 100 -3,74 -4,24 -6,00 1.55 mM 680.93 uM 23.19 uM
26 4-(2,3-dihydro-1,4-benzodioxin-6-yl)butanoic acid 62 88 32 -5,4 -4,18 -3,38 89.41 uM 605.65 uM 2.40 mM
27 Tetranor-12(S)-HETE 40 11 20 -4,56 -2,97 -3,01 55.34 uM 628.46 uM 296.13 uM
28 3-(2-Hydroxyethyl)indole 82 75 56 -5,95 -4,96 -5,26 39.70 uM 212.49 uM 84.40 uM
29 (2R,3R)-5,7-dihydroxy-2-(3,4,5-trihydroxyphenyl)-3,4-dihydro-2H-1-benzopyran-3-yl 3,4,5-trihydroxybenzoate 52 40 25 -5,48 -6,75 -6,7 1.09 uM 2.27 uM 222.88 nM
30 Rutin 11 20 16 27,59 -4,87 -5,94 – 43.93 uM 2.77 uM
31 Choline 86 100 56 -3,41 -3,91 -5,43 2.00 mM 1.15 mM 72.44 uM
32 α-Pyrrolidinopropiophenone 53 66 91 -6,59 -6,61 -7,3 13.40 uM 12.51 uM 2.22 uM
33 Hesperidin 49 23 8 37,35 -6,93 -3,95 – 189.65 nM 144.03 uM
34 benzyl N-(1-{[(3,4-dimethoxyphenethyl)amino]carbonyl}-2-methylpropyl)carbamate 31 25 22 -5,74 -4,86 -5,61 8.47 uM 66.17 uM 9.42 uM
35 (2E)-3-(2-{[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}phenyl)prop-2-enoic acid 27 58 23 -3,66 -4,06 -1,86 1.00 mM 268.73 uM 30.25 mM
36 Vanillin 52 61 58 -4,72 -3,85 -5,1 304.63 uM 1.30 mM 124.12 uM
37 3,4-Dihydroxybenzaldehyde 100 89 60 -5,73 -4,7 -5,21 59.00 uM 319.73 uM 115.44 uM
38 3,4-Dihydroxyphenylpropionic acid 74 81 49 -4,93 -3,9 -2,9 78.86 uM 634.27 uM 4.99 mM
39 Levalbuterol 55 43 45 -5,06 -6,41 -7,89 63.50 uM 6.38 uM 298.18 nM
40 Catechin gallate 75 43 35 -5,32 -6,98 -7,58 6.83 uM 1.10 uM 382.96 nM
41 1-Methyl-N-{[(2R,4S,5R)-5-(2-methyl-6-phenyl-4-pyrimidinyl)-1-azabicyclo[2.2.2]oct-2-yl]methyl}-4-piperidinamine 94 32 60 -8,73 -10,74 -13,6 186.36 nM 2.45 nM 61.95 pM
42 4-Aminophenol 100 67 78 -4,09 -3,36 -4,36 984.59 uM 3.32 mM 534.15 uM
43 3-{[(2S,3R,4S,5R,6R)-3,5-dihydroxy-6-(hydroxymethyl)-4-{[(2S,3R,4R,5R,6S)-3,4,5-trihydroxy-6-methyloxan-2-yl]oxy}oxan-2-yl]oxy}-5,7-dihydroxy-2-(4-hydroxyphenyl)-4H-chromen-4-one 28 20 17 43,81 -4,91 -5,3 – 55.56 uM 12.10 uM
44 (2S)-7-{[(2S,3R,4S,5S,6R)-4,5-dihydroxy-6-(hydroxymethyl)-3-{[(2S,3R,4R,5R,6S)-3,4,5-trihydroxy-6-methyloxan-2-yl]oxy}oxan-2-yl]oxy}-2-(3,4-dihydroxyphenyl)-5-hydroxy-3,4-dihydro-2H-1-benzopyran-4-one 17 19 18 33,13 -5,61 -5,04 – 7.58 uM 41.59 uM
45 L-(+)-Citrulline 53 56 47 -2,87 -4,11 -4,72 1.35 mM 99.10 uM 94.18 uM
46 (-)-Caryophyllene oxide 100 68 74 -6,99 -6,09 -5,49 7.51 uM 34.23 uM 86.43 uM
47 Nicotinamide 99 99 55 -4,56 -4,27 -4,86 435.81 uM 682.74 uM 262.66 uM

Lipase inhibition activity by SKD

The inhibition of lipase by SKD and orlistat are detailed in Fig. 2. Statistical analysis revealed that orlistat showed a significantly higher inhibition against lipase than SKD at the dose of 50 μg/mL (Fig. 2A). Interestingly, the lipase inhibition activity of SKD at 100, 150, 200, and 250 μg/mL were equal to orlistat. However, further validation through the analysis of EC50 values of SKD and orlistat concluded that the lipase inhibition of orlistat is without a doubt stronger than SKD (Fig. 2B).

Figure 2. 

Lipase Inhibition Activity Test of SKD and Orlistat. The inhibition of lipase was presented in % activity (A) and EC50 value (B).

α-Amylase inhibition activity of SKD

Fig. 3 showed the α-amylase inhibition activity by SKD and acarbose. An α-amylase inhibition activity that was equal to acarbose was observed in SKD at 50 and 250 μg/mL, which are the lowest and highest doses (Fig. 3A). However, at 100, 150, and 200 μg/mL, the α-amylase inhibition property of SKD differs significantly from orlistat. The EC50 value of SKD was also lower than orlistat, suggesting greater α-amylase inhibition than the positive control (Fig. 3B).

Figure 3. 

α-Amylase Inhibition Activity Test of SKD and Acarbose. The inhibition of α-amylase was presented in % activity (A) and EC50 value (B).

α-Glucosidase inhibition activity by SKD

The antidiabetic property of SKD was compared to acarbose, which was shown in Fig. 4. The potential of SKD in inhibiting α-glucosidase was identical to acarbose at 50, 150, and 250 μg/mL while on the other doses, they differ notably from acarbose (Fig. 4A). The EC50 value of SKD and acarbose were 15.03 μg/mg and 10.62 μg/mg, respectively (Fig. 4B).

Figure 4. 

α-Glucosidase Inhibition Activity Test of SKD and Acarbose. The inhibition of α-glucosidase was presented in % activity (A) and EC50 value (B).

Free radical scavenging activity of SKD

The free radical scavenging activity of SKD was observed through the inhibition of DPPH and ABTS, which were compared against glutathione (Figs 5, 6). The statistical approach found that SKD showed a significantly lower DPPH inhibition than glutathione at the dose of 50 and 100 μg/mL (Fig. 5A). Interestingly, the free radical scavenging activity of SKD at 150, 200, and 250 μg/mL was equal to the control. On the other side, the ABTS inhibition activity of all doses of SKD still fell short of the control, significantly (Fig. 6A). The determination of EC50 values also revealed that DPPH inhibition by glutathione is notably stronger than SKD (Fig. 5B). The EC50 values of SKD and glutathione regarding the inhibition of ABTS were 18.52 μg/mg and 19.61 μg/mg, respectively (Fig. 6B).

Figure 5. 

DPPH Inhibition Activity Test of SKD and Glutathione. The inhibition of DPPH was presented in % activity (A) and EC50 value (B).

Figure 6. 

ABTS Inhibition Activity Test of SKD and Trolox. The inhibition of ABTS was presented in % activity (A) and EC50 value (B).

Discussion

In this study we demonstrated that SKD had 62 potential secondary metabolites that may be beneficial for metabolic health. In addition, we also showed inhibition activity of these metabolites on the activity of α-amylase, α-glucosidase, and lipase at least from in silico modelling. Furthermore, SKD had the ability to inhibit of α-amylase, α-glucosidase, and lipase in vitro. Interestingly, we also showed the free radical scavenging activity of SKD in vitro.

Clinical evidence showed that strawberries promoted health and prevented diseases due to several nutritive and non-nutritive bioactive compounds (Afrin et al. 2016; Miller et al. 2019). This work incorporated strawberries into a kombucha probiotic drink and evaluated its activity in silico and in vitro. The initial work successfully identified 62 secondary metabolites in the strawberry kombucha drink (SKD) (Table 1). These results also highlighted that SKD contained far more metabolites from the strawberry in the absence of fermentation, with a comparison of 62 to 12–20 metabolites (Zhang et al. 2011; Antunes et al. 2019). This is in line with other studies that the fermentation process – especially using kombucha or SCOBY – can increase bioactive compounds in a food product (Leonard et al. 2021; Permatasari et al. 2022). Furthermore, we confirmed the potential biological significance of these metabolites to the biological properties of SKD using molecular docking (Table 3). Finally, in vitro examination also revealed the potential antiobesity, antidiabetic, and antioxidant activity of SKD.

Based on molecular docking simulation, the docking protocol was valid as shown by the RMSD <2.0 Å (Table 2). From the identified bioactive compounds of SKD, 1-Methyl-N-{[(2R,4S,5R)-5-(2-methyl-6-phenyl-4-pyrimidinyl)-1-azabicyclo[2.2.2]oct-2-yl]methyl}-4- was the best ligand that can interact with all target proteins, namely human pancreatic lipase, a-glucosidase, and a-amylase. 1-Methyl-N-{[(2R,4S,5R)-5-(2-methyl-6-phenyl-4-pyrimidinyl)-1-azabicyclo[2.2.2]oct-2-yl]methyl}-4-piperidinamine – as the best metabolite – demonstrated lowest binding affinity against lipase (∆G = -8.73 kcal/mol), a-amylase (∆G = -10.74 kcal/mol), and a-glucosidase (∆G = -13.60 kcal/mol) compared to orlistat (1LPB, ∆G = -8.73 kcal/mol) and acarbose (2QV4, ∆G = -4.22 kcal/mol; 3L4Y; ∆G = -1.01 kcal/mol) (Table 3). Following the best metabolite, other compounds such as dibenzylamine, (-)-epicatechin, α-pyrrolidinopropiophenone, levalbuterol, 5-({[3-chloro-5-(trifluoromethyl)-2-pyridyl]methyl}thio)-4-pentyl-4H-1,2,4-triazol-3-ol, catechin gallate, (2R,3R)-5,7-dihydroxy-2-(3,4,5-trihydroxyphenyl)-3,4-dihydro-2H-1-benzopyran-3-yl 3,4,5-trihydroxybenzoate, and (-)-caryophyllene oxide also showed an overall lowest binding energy to target proteins. From these metabolites, (-)-epicatechin and catechin gallate have been revealed to improve obesity and its comorbidities (Wu et al. 2018; Cremonini et al. 2020). Possible therapeutic actions of caryophyllene have also been reviewed (Hashiesh et al. 2020). Overall, 39 compounds were identified as potential antiobesity, followed by 27 and 44 compounds based on a-amylase and a-glucosidase inhibitions, respectively. These results concluded that SKD consisted of various bioactive substances that contribute to the antiobesity, antidiabetic, and antioxidant activities of SKD.

The capability of SKD in inhibiting lipase was examined in this study. On a weight basis, lipase inhibition activity of SKD at doses of 100, 150, 200, and 250 μg/mL was similar to orlistat, a control that inhibits lipase in obesity (Son and Kim 2020). SKD had an EC50 value of lipase inhibition of 39.70 μg/mg (Fig. 2B) while strawberry extract had a reported EC50 of around 5 μg/mL (McDougall et al. 2009). Lipase inhibition will improve lipid metabolism in obese individuals by reducing the accumulation of fatty acids, maintaining the HDL-to-LDL ratio, and preventing adipocyte growth (Liu et al. 2020). On the other hand, strawberry supplementation also demonstrated health benefit implications in obese adults (Basu et al. 2016). Sustaining a metabolically healthy condition in obesity may also decrease the risk of diabetes, NAFLD, and metabolic syndrome (Godoy-Matos et al. 2020).

The α-amylase and α-glucosidase inhibition activities of SKD were observed. SKD at doses of 50 and 250 μg/mL showed similar α-amylase inhibition to acarbose while inhibition of α-glucosidase of SKD did not differ significantly from acarbose at doses of 50, 150, and 250 μg/mL. Acarbose is an α-amylase and α-glucosidase inhibitor with the potential as a calorie restriction mimetic and weight-loss agent (Smith et al. 2021). SKD showed an EC50 value of α-amylase inhibition of 5.39 μg/mg while strawberry extract had a reported EC50 of 96.82–398.46 μg/mL (Huneif et al. 2022). The same trend was observed in the inhibition of α-glucosidase, where SKD exhibited an EC50 value of α-glucosidase inhibition of 15.03 μg/mg while strawberry extract had a reported EC50 of 117.54–429.39 μg/mL (Huneif et al. 2022). The health-promoting effects of strawberries may be contributed to the phenolics and antioxidant compounds contained in strawberries (Giampieri et al. 2012, 2017). Dietary strawberry was also shown to decrease the risk factors of obesity-related disorders (Zunino et al. 2012). These findings also support the fact that kombucha showed anti-diabetic potential based on the inhibition of α-amylase and α-glucosidase (Permatasari et al. 2021, 2022).

The antioxidant activity of plant-based fermented drinks was widely studied since fermentation will result in a high-quality beverage with enhanced antioxidant, total phenolic, and bioactive compounds (Hur et al. 2014; Yang et al. 2018). In this study, SKD showed DPPH inhibition similar to glutathione at 150, 200, and 250 μg/mL doses. Interestingly, at all doses, SKD had a similar ABTS inhibition to Trolox. The EC50 value of DPPH inhibition of SKD was also greater than strawberry crude extract (17.28 μg/mg to 59.55–349.35 μg/mL (Huneif et al. 2022). A diverse range of aspects, such as total phenolic, organic acids, vitamins, and microbial hydrolysis in fermentation can influence the antioxidant activity of kombucha drink (Massoud et al. 2022). Subsequently, the identified phenolic compounds (catechin and epicatechin) and flavonoids (such as rutin) have been proven to improve metabolic function and inflammation while also acting as an antioxidant (Simos et al. 2012; Muvhulawa et al. 2022). On the other hand, dietary strawberry supplementation has induced beneficial effects on lipid profile, antioxidant, and inflammatory markers in obese adults (Zunino et al. 2012; Basu et al. 2016). Inflammation and oxidative stress themselves have been linked with the pathophysiology of obesity and metabolic syndrome (Ruiz-Ojeda et al. 2018). Therefore, SKD may become a nutraceutical with antiobesity and antidiabetic activities along with antioxidant and anti-inflammatory properties.

Conclusion

Strawberry or Fragaria ananassa can be processed or innovated into a functional probiotic drink (SKD) with several secondary metabolites that inhibit the activity of lipase, α-glucosidase, and α-amylase as proved by in silico study. SKD also exhibited potential antiobesity, antidiabetic, and antioxidant properties which may play a role in attenuating metabolic and inflammatory disorders in vitro, further reinforcing the potential health benefits of SKD. However, our study has not studied the potential of SKD using cell lines, hence the limitation of our study. These findings suggest that SKD can be a promising therapeutic functional food in preventing metabolic disorders and obesity.

Author contributions

Concept and design: AP, WBG, FN. Analysis and interpretation: AP, WBG, FN, DA. Data collection: FN. Writing the article: AP, WBG, FN, GAL, MSA. Critical revision of the article: AP, MA. Final approval of the article: all authors. Statistical analysis: WBG, FN. Obtained funding: AP. Overall responsibility: AP.

Acknowledgements

We want to thank the faculty and technical staff for their administrative support. AP would like to thank RPI fellowship no. 225-18/UN7.D2/PP/IV/2023 from LPPM Universitas Diponegoro.

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Supplementary material

Supplementary material 1 

The manufacturer specification

Adriyan Pramono, William Ben Gunawan, Fahrul Nurkolis, Darmawan Alisaputra, Gilbert Ansell Limen, Muhammad Subhan Alfaqih, Martha Ardiaria

Data type: docx

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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