The prevalence of postmenopausal women worldwide is gotten higher over several decades1. This increase is closely related to a direct decline in quality of life2,3. Women in the postmenopause phase will experience various disease complaints caused by estrogen deficiency conditions, one of which is a neurodegenerative disorder4.

Neuroinflammation is one of the leading causes of neurodegenerative disorders arising from estrogen deficiency5,6. Neuroinflammation occurs due to an increase in activated microglia in the M1 polarization situation so that it can increase the expression of proinflammatory signaling factors such as major histocompatibility complex II (MHC II) and other inflammatory cytokines such as interleukin-1 (IL-1), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and nitric oxide (NO) in the brain7,8. Increased inflammatory cytokines will reduce synapsis and plasticity functions of neuron cells and induce cell death9.

Celecoxib, ibuprofen, minocycline, and aspirin are some medicines used to treat neuroinflammation10,11. However, those medicines appear with potential side effects in the form of gastrointestinal tract (GIT) disorders such as nausea, gastritis, abdominal pain, and digestive tract bleeding, as well as other side effects such as dizziness, hypertension, headache, and vertigo12-14. These side effects encourage the need for various studies on potential new drug sources with minimal side effects, such as phytoestrogens15-18. Phytoestrogens are a group of natural plant products with a structure and function similar to 17β-estradiol19-21.

Marsilea crenata Presl. is one of the plants used as the typical food by the local community in East Java, Indonesia22. Ethanol 96% extract of M. crenata leaves been tested by radioimmunoassay (RIA) and shows a high estrogen-like substance23. Other previous studies also have shown that this plant contains phytoestrogen group compounds, such as flavonoids, that can act as an anti-inflammatory and has other estrogenic activity for maintaining human body homeostasis24-28.

In this research, we cultivated M. crenata under external controlled factors to gain a standardized raw material of this plant. Cultivation was carried out in Kediri, East Java, Indonesia. This area is a lowland of 0-200 m above sea level (masl), with humidity of 60-90% and an average temperature of 23.8-30.7°C29. The plant was cultivated in a greenhouse under controlled nutrition, soil, irrigation, and some environmental factors, to produce better quality and quantity of the raw material of this plant30-33.

This study aimed to prove the antineuroinflammatory activity of the 96% ethanolic extract of the leaves of M. crenata grown under controlled environmental conditions in inhibiting HMC3 microglia cells. This inhibition was observed in the situation of M1 polarization of these cells, with a decrease of MHC II expression as an indicator. This study also aimed to predict phytoestrogen compounds that play a role in the antineuroinflammatory activity through metabolite profiling using ultra-performance liquid chromatography – quadrupole time of flight – mass spectrometry (UPLC-QToF-MS/MS) and in silico studies of those compounds on estrogen receptors β (ERβ), which, in this case using protein 3OLS from protein data bank (PDB).

Materials

Plant materials

The leaves of M. crenata were harvested from a controlled farming (greenhouse) in Kediri, East Java, Indonesia. The characteristics of the plants were two weeks old, the stem height was approximately 17 cm, the leaf width was approximately 2 cm, and the color of the leaves was dark green. The leaves were then identified at Unit Pelayanan Teknis (UPT) Materia Medika, Batu, East Java, Indonesia, with the determination key of 1a-17b-18a-1 and identification letter 074/368/102.7/2017. The specification of the cultivation area and the external factors are listed in Table I.

TableI. Specification of location and external factors in the cultivation of M. crenata

Chemicals

Fetal bovine serum (FBS), penicillin, streptomycin, Eagle's minimum essential medium (EMEM), dimethyl sulfoxide (DMSO), tween 80, phosphate-buffered saline (PBS), paraformaldehyde (PFA), bovine serum albumin (BSA), fluorescein isothiocyanate (FITC) anti-rabbit secondary antibody were purchased from Sigma-Aldrich (St. Louis, USA). MHC II anti-rabbit secondary antibody was purchased from Abcam (Cambridge, UK). Ethanol 96%, dichloromethane, methanol, acetonitrile, and formic acid were purchased from Merck (Darmstadt, Germany).

Hardware and software

The hardware used was an A416MA-EB422TS personal computer with Intel Celeron. The software used is the operating system Windows® 10. ChemDraw Ultra 12.0 was used in drawing 2D structures. Avogadro 1.0.1 was used in structural optimization. The docking process was done with AutoDock Vina using PyRx 0.8. Visualization of docking results was performed using Biovia Discovery Studio 2016.

Methods

Extraction

About 500 g of powdered leaves of M. crenata were extracted with 96% ethanol using the ultrasonic method with Soltec Sonica 5300EP S3 for 3 x 10 minutes, filtered, and evaporated at 50°C using a Heidolph G3 rotary evaporator. A 25 g of the 96% ethanolic extract was obtained and subjected to further test and analysis.

Cell culture

HMC3 microglia cells (ATCC® CRL-3304™) were cultured using complete media in 25 cm2-sized flasks which contained 10% of FBS and a mixture of 1% of penicillin-streptomycin in a ±5 mL media EMEM. Cells in the flask were then incubated in a 5% CO2 ThermoScientific Hera Cell 150i incubator at 37°C for a week. The cells were then placed into a 24-well microplate after the confluence was approximately 80%.

Measurement of MHC II expression

As much as 40 mg of the 96% ethanolic extract was suspended in the mixture of 0.5% DMSO and 0.5% Tween 80 to produce the mother liquor of 4,000 μg/mL; it was then diluted in the concentration of 62.5; 125; 250 μg/mL. About 40 μL of genistein was added with the culture media reaching 0.8 mL to produce genistein with the concentration of 50 μM, which was used as the positive control. Induction of IFN-γ was performed after cells were cultured on a 24-well microplate and reached 80% confluence. After induction of 10 ng IFN-γ for 24 hours, the cells were rinsed with PBS and then treated with 50 μM genistein for 48 hours. The cells were then fixated with 4% PFA, Triton X-100, and blocking buffer, and also primary and secondary antibodies were added. They were, afterward, rinsed using PBS and visualized MHC II with CLSM (Olympus Fluoview Ver. 4.2a.) 488 nm34.

Metabolite profiling

The process of metabolite profiling was conducted at Pusat Laboratorium Forensik Badan Reserse Kriminal Kepolisian Negara Republik Indonesia (Puslabfor Bareskrim Polri) by using UPLC-QToF-MS/MS instrument. 96% ethanolic extract was prepared using dichloromethane and methanol solvents through the solid phase extraction (SPE) method. After that, 5 µL was equally injected into ACQUITY UPLC® H-Class System (Waters, USA) with the detector of MS Xevo G2-S QToF (Waters, USA). Samples were separated in the column of ACQUITY BEH C18 (1.7 µm × 2.1 mm × 50 mm) with acetonitrile of + 0.05% of formic acid and water + 0.05% of formic acid as the mobile phase, with the flow rate of 0.2 mL/minute. The analysis result of UPLC-QToF-MS/MS was processed using MassLynx 4.1 software to get chromatogram data and m/z spectrum from each detected peak. The detected compounds were then confirmed using the online database of ChemSpider (http://www.chemspider.com), PubChem (https://pubchem.ncbi.nlm.nih.gov), and MassBank (https://massbank.eu/MassBank).

In silico study

X-ray protein from ERβ was attained from the protein data bank (http://www.rcsb.org) with the PDB ID 3OLS. The antineuroinflammatory impact to be evaluated in this study is the antineuroinflammatory effect that emerges from phytoestrogens in plants, replacing the role of estrogen in the ER-dependent pathway and not through other mechanisms. ERβ also has a higher amount than other receptors, is more sensitive to estrogen binding, and plays the most role in regulating nerve cell homeostasis35,36.

The initial preparation was performed to separate native ligand (17β-estradiol) from 3OLS protein using Biovia Discovery Studio Visualizer 2016 and saved in Sybyl mol2 format. Metabolite profiling compounds drawn 2D using ChemDraw Ultra 12.0 and saved in mol format. Internal validation was performed by adding 3OLS and 17-estradiol ligands and then docking them with PyRx 0.8 software. Native ligand and the result compounds of metabolite profiling were then optimized with Avogrado 1.0.1 using the MMFF94 method. Then, molecular docking was conducted using PyRx 0.8 with the AutoDock Vina method to simulate the docking process. All compound files were imported into the PyRx 0.8 software and converted to pdbqt format automatically. The determination of the grid box includes setting location according with grid center x = 11.1148, y = -35.813, and z = 12.1403 with dimension of 25 x 25 x 25 Å. Setting the exhaustiveness to number 8 and the work grid through Autogrid to 17-estradiol ligand completed the operation, yielding a binding affinity value and a molecular docking compound in the form of pdbqt. The result of complex visualization between receptor and ligand was observed using Biovia Discovery Studio Visualizer 2016 to see the interaction that occurred. To see the compound's potential as oral medicine, then the compounds which had the agonist interaction were processed with physicochemical analysis using the SwissADME web tool to find out the penetration capability.

Measurement of MHC II expression

Figure 1 was the visualization result using the CLSM instrument, which displayed MHC II fluorescence intensity in the figure. IFNγ induction for 24 hours can activate nuclear factor kappa B (NF-κB) through toll-like receptor 4 (TLR4), which can affect cell protein synthesis, so that it can activate the microglia in M1 polarization and change its morphology into amoeboid, which cause the appearance of the inflammatory mediator, one of them is MHC II16,17,37,38. IFNγ plays the role of activating the microglia and can increase MHC II molecule expression as the transcription activator. MHC II plays a role in producing the exogen antigen, activating the T helper cell through the receptor, and secreting several cytokines to manage the immune response39,40. The strongest intensity was seen on the negative control, and the weakest intensity was seen on the positive control. In the negative control, treatment was not given, so it caused the HMC3 microglia cells to stay active on M1 polarization and produced high MHC II fluorescence intensity. All treatment groups showed lower MHC II fluorescence intensity compared to the negative control.

Figure1. MHC II fluorescence intensity on HMC3 microglia cells (a) negative control; (b) 62.5 μg/mL; (c) 125 μg/mL; (d) 250 μg/mL; and (e) genistein.

Furthermore, Figure 2 shows MHC II expressions comparison in the concentration of 62.5; 125; 250 μg/mL with negative control and positive control. In all concentrations, MHC II expression was lower compared to the negative control. The result of ANOVA test showed significant difference in MHC II expression reduction among all concentrations on negative control (p=0.000; p=0.000; and p=0.000) and on positive control (p=0.000; p=0.000; and p=0.000).

Figure2. Expression of MHC II in 96% ethanol extract of M. crenata leaves. The * sign indicates a significant difference from the negative control (K-), while the ** sign indicates a significant difference from the positive control (K+).

In Figure 2, concentration increase was not accompanied by MHC II expression decrease. This probably happened because of the Non-Monotonic Dose Response (NMDR) phenomenon in HMC3 microglia cells, which was indicated by various slope values in the given concentration ranges41. NMDR often occurred in research by using hormone treatment or sample used as the hormone substitute, in this case, was phytoestrogen compound in 96% ethanol extract of the environmental controlled growth M. crenata leaves.

The affinity level difference between the hormone or sample that substituted the hormone and the receptor could cause difficulty in predicting the response following the increased concentration. Another factor causing NMDR was receptor downregulation and receptor desensitization; this may happen because the increasing concentration on the sample could cause the compound to bond with other receptors except for ER or make ER insensitive in bonding with the compound. However, the moment the concentration was continuously increased, it could increase the number of ER degraded and unequal to the produced ER, which caused the cell to produce ER massively and could increase the bond of the compound with ER and the activity response41,42.

A 62.5 μg/mL concentration was the optimum concentration for reducing MHC II fluorescence intensity. This was because the concentration treatment showed a significant difference from the negative control and a big fluorescence intensity difference between the negative control value and the MHC II expression value of 97.458 Arbitrary Unit (AU). Concentrations 62.5 and 250 μg/mL were the concentrations that also could reduce MHC II expression well but did not differ from each other significantly in statistics, so 62.5 μg/mL was chosen as the most optimum concentration because the small concentration could produce a similar effect with the concentration of 250 μg/mL. The result showed that giving 96% ethanol extract to the controlled environmental growth of M. crenata leaves treatment reduced the number of MHC II, which was observed from the expression reduction.

This in vitro activity test was carried out to know the activity potential of a plant that was given to a cell43. The result of the in vitro test showed that the 96% ethanol extract of M. crenata leaves had antineuroinflammatory activity, which was indicated by a significant MHC II expression decrease. Then, to predict the compound contained in 96% ethanol extract of M. crenata leaves, metabolite profiling was conducted44, and to predict the compound which had antineuroinflammatory activity, in silico analysis was carried out45.

Metabolite profiling

The metabolite profiling result of 96% ethanol extract of the controlled environmental growth of M. crenata leaves by using UPLC-QToF-MS/MS instrument on dichloromethane and methanol solvents in the form of total ion chromatogram (TIC) can be seen in Figure 3. In contrast, the value of retention time (RT), % area, m/z, molecule formula, and the compound's name can be overviewed in Tables II and III.

Figure3. TIC of 96% ethanol extract of M. crenata leaves in solvent (a) dichloromethane and (b) methanol.

TableII. Prediction of compounds in 96% ethanol extract of M. crenata leaves in dichloromethane solvent

TableIII. Prediction of compounds in 96% ethanol extract of M. crenata leaves in methanol solvent

The result of metabolite profiling showed a total of 79 compounds in dichloromethane and methanol solvents which consisted of 54 known and 25 unknown compounds. The use of the two solvents aimed at eluting the extract optimally in the column of UPLC-QToF-MS/MS27. The compound peak analyzed from the result of metabolite profiling was the RT peak between 0 and 14 minutes. The RT peak above 14 minutes could not be considered because the peak was generally impure, like the peak produced from solvent or degradant. From the total of detected 79 compounds, not all peaks in TIC could be identified in the process of metabolite profiling. This was shown by the appearance of 25 unknown compounds. Unknown compounds could not be identified in the database; these compounds could be in the form of impure compounds or degradant which were still detected by the instrument or new compounds which were still not listed in the database, especially unknown compounds which had high content27,46.

In silico study

The 79 compounds from metabolite profiling of 96% ethanol extract of the controlled environmental growth M. crenata leaves were then analyzed through molecular docking using PyRx 0.8 software and AutoDock Vina as the docking simulator. Based on the native ligand test (17β-estradiol) using 3OLS protein, the value of RMSD 1.238 Å was retrieved, which showed that RMSD <2 Å means the docking protocol could be used in the docking process of resulting compounds from metabolite profiling using 3OLS protein47,48. After that, the bond between native ligand and compound towards 3OLS protein was visualized using Biovia Discovery Visualizer 2016 software. Based on the analysis of molecular docking 17β-estradiol result on 3OLS protein, it was found that the compound was categorized as an ERβ agonist compound if it met several parameters similar to 17β-estradiol. These parameters consisted of a pharmacophore cluster that bonded His 475 amino acid to Glu 305 amino acid or Arg 346 amino acid, which can be viewed in Figure 4. The type of bond to the amino acid also influences the bonds' stability. This interaction describes the binding strength of the ligand to the receptor. The hydrogen bonds can stabilize the interaction between ligands and receptors, in addition to van der walls and electrostatic bonds49. Besides, they also had a pharmacophore distance similar to 17β-estradiol, approximately 10.862 Å. The similarity in pharmacophore distance from pharmacophore cluster on 17β-estradiol could be used as a guideline to predict other compounds with the same pharmacological effects50-52. The analysis using Discovery Studio Visualizer 2016 of 79 compounds resulted from metabolite profiling of 96% ethanol extract of the controlled environmental growth of M. crenata leaves can be seen in Table IV.

Figure4. Interaction of 17β-estradiol with 3OLS protein.

TableIV. 17β-estradiol and compounds in 96% ethanol extract of M. crenata leaves which are agonists against ERβ

The result of in silico analysis showed that 19 compounds had the agonist characteristics towards 3OLS protein, which meant that those compounds belonged to phytoestrogen. To predict the compound potential as oral medicine, ERβ agonist compounds were then selected using the SwissADME web tool to identify the physicochemical properties of the compounds (The result of the SwissADME test can be seen on https://doi.org/10.5281/zenodo.6904891). The parameters used in the physicochemical analysis were molecule weight < 500 g/mol, HBD (hydrogen binding donors) < 5, HBA (hydrogen binding acceptors) < 10, TPSA ≤ 140 Å53, met Lipinski rule of five54, and BBB permeant "yes"55. The TPSA value showed the compounds' capability to penetrate the cell membrane, and the BBB permeant showed the compound's capability to penetrate the blood-brain barrier53,55. Lipinski's rule of five was used to predict the similarity of compounds and medicine, which had a specific biological activity designed for oral treatment53.

The analysis result of the TPSA parameter found 11 compounds that met those criteria. Based on the analysis of the Lipinski rule of five, it was found that 14 compounds met those criteria. At the same time, the analysis result of the BBB permeant parameter found that three compounds met those criteria. This indicates that these three compounds may influence the CNS. Thus, the result of the physicochemical analysis showed that three ERβ agonist compounds met all parameters: TPSA, Lipinski rule of five, and BBB permeant, which can be seen in Table V.

TableV. Agonist compound that met all parameters of physicochemical analysis

The result of the physicochemical analysis above implied that those compounds were categorized as phytoestrogen, which was indicated by the agonist interaction with 3OLS protein and had the potential to be developed as antineuroinflammatory medicine given orally, which was shown by meeting physicochemical analysis parameters56,57. The correlation of these research findings was to prove that 96% ethanol extract of the controlled environmental growth of M. crenata leaves had in vitro antineuroinflammatory activity, which was shown by a significant decrease in MHC II expression, and supported by the prediction of 19 secondary metabolite compounds as the result of metabolite profiling on 96% ethanol extract of the controlled environmental growth of M. crenata which had in silico antineuroinflammatory activity and three of those compounds had the potential to be developed as oral medicine. The correlation result showed that the use of cultivated M. crenata had the advantage of decreasing MHC II expression significantly and contained more active compounds because of external factors control which could affect the compound content of the plant58.

The 96% ethanol extract of the environmental-controlled growth of M. crenata has an antineuroinflammatory activity through MHC II expression inhibition on HMC3 microglia cells, with an optimum concentration of 62.5 μg/mL and a value of 97.458 AU. This extract was predicted to contain 19 phytoestrogen compounds with agonist characteristics on ERβ, and three met all parameters of physicochemical analysis, including BBB permeant.

None.

All authors have an equal contribution in carrying out this study.

The result of the SwissADME test can be seen on https://doi.org/10.5281/zenodo.6904891.

The authors declare there is no conflict of interest in this research.

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