Virtual screening of Dioscorea alata active compound in the Sphingolipid metabolic pathway in endometriosis-related genes

 

Sri Nabawiyati Nurul Makiyah¹*, Ivanna Beru Brahmana¹, Mulyoto Pangestu²,

Ahmad Hafidul Ahkam³

¹School of Medicine, Faculty of Medicine and Health Sciences, Universitas Muhammadiyah Yogyakarta,

Daerah Istimewa Yogyakarta, Indonesia.

²Monash Institute of Reproduction and Development–Faculty of Medicine,

Monash University Australia.

³Master Student at Department of Pharmacology, Faculty of Pharmacy,

University of Padjajaran, Bandung, West Java, Indonesia.

 

ABSTRACT:

Endometriosis is a medical condition characterized by the growth of endometrial tissue outside the uterus, causing symptoms of pain and reproductive disorders in women. Expensive medical treatments open opportunities to explore herbal therapies with the potential for higher efficacy, lower side effects, and more affordable costs. Dioscorea alata is a food and herbal plant that has been used in several places. Therefore, this research aimed to evaluate the Dioscorea alata secondary metabolite potential for affecting endometriosis-related genes. The method used was to evaluate the differentially expressed genes (DEGs) from endometriosis samples, and then evaluate the potential of secondary metabolites of Dioscorea alata in influencing DEGs related to endometriosis. As a result, SGPP2 is known to be an endometriosis-related gene that plays a role in sphingolipid metabolism. Secondary metabolites of Dioscorea alata, namely diosgenin and prosapogenin, have high binding affinity and have the potential to interact with SGPP2. In conclusion, secondary metabolites of Dioscorea alata have a high potential to interact with SGPP2 and potentially influence its activity, which is an endometriosis-related gene. However, we recommend further research regarding SGPP2 as a marker for endometriosis and the potential of secondary metabolites of Dioscorea alata as SGPP2 agonists or inhibitors.

 

 

 

 

INTRODUCTION:

Endometriosis is a benign tumor condition with cancer-like features and a high prevalence rate, affecting one in ten women of reproductive age (15–49 years), or approximately 176 million people worldwide^1,2. Symptoms include dysmenorrhea, dyspareunia, dysuria, persistent abdominal discomfort, pelvic pain, and painful bowel movements.  Endometriosis increases the risk of ovarian cancer and infertility and requires long-term management. Common treatments include surgery and medication, but they are expensive, provide only pain relief, and require ongoing treatment because they can recur^3,4.

 

Therefore, herbal therapies, which are natural, cost-effective, and have fewer side effects, are needed. Dioscorea alata, or "big sweet potato" and "purple sweet potato," is a tropical plant rich in carbohydrates known for its nutritional and medicinal value, especially its anthocyanin content, which provides health benefits⁵. This plant is a staple food and substitute for medicine in many tropical areas^6,7, containing complex carbohydrates, fiber, vitamins (C and B), and minerals (manganese and potassium). Dioscorea alata has antioxidant, anti-inflammatory, antihypertensive, anticancer, immunomodulatory, and antihyperglycemic properties, indicating its potential for drug development^8–12.

 

In the modern era, drug development from natural materials has received increasing attention, with bioinformatics analysis playing a key role¹³. Bioinformatics helps accelerate and improve the efficiency of traditional medicine research by analyzing and predicting the activity of active herbal compounds^14,15, including their chemical structures, genomic and proteomic information^16,17, and potential biological activities.^18,19 This study aims to evaluate the potential of secondary metabolites of Dioscorea alata on endometriosis-related genes using virtual screening.

 

MATERIALS AND METHODS:

Gene expression omnibus related to endometriosis

Gene set enrichment (GSE) was accessed from the Gene Expression Omnibus (GEO) database (https:// www.ncbi.nlm.nih.gov/geo/) and obtained for three GSEs related to endometriosis patients, namely GSE232713, GSE226575, and GSE135485. GSEs were analyzed with the GEO2R tool by dividing gene samples into normal groups (healthy samples) and endometriosis samples. Other analysis parameters used were p-value adjustment using Benjamini and Hochberg (False Discovery Rate/FDR), a significance level cut-off of 0.05, a logFC cut-off greater than 1.5, and expression profiling by high-throughput sequencing. (Figure 1)

 

Network analysis to evaluate the differentially expressed genes between each GSE

From the three GSEs obtained, the differentially expressed gene (DEG) list for each GSE was tabulated with MS Excel and identified the same DEGs from each GSE via online Venn software²⁰. DEGs from the intersection of two and three GSEs were analyzed for their relationship in the protein network by the STRING tools (https://string-db.org/) and Cytoscape ver.3.10, and the potential bioactivity that might occur using MCODE clustering by the ClusterViz tool²¹. The parameters used in the molecular complex detection (MCODE) analysis²² include a degree threshold of two, a node score threshold of 0.2, a K-core threshold of two, and a max depth of 100. Meanwhile, DEGs from the three GSEs are genes that have been analyzed and used as targets for molecular docking studies with the active compound Dioscorea alata.

 

Figure 1. An outline of this research was carried out

 

Data mining of Dioscorea alata metabolites and DEG protein sequences

A list of secondary metabolites of Dioscorea alata was obtained from databases and literature from previous research^23,24 to widely open up the potential of secondary metabolites that have been reported to be contained in Dioscorea alata variants in every location. The 3D structure of each secondary metabolite of Dioscorea alata was obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) or ChemSpider database (https://www.chemspider.com/ Default.aspx), including the 3D structure of letrozole and anastrozole, which were used as control ligands. Meanwhile, the SGPP2 (Sphingosine-1-Phosphate Phosphatase 2) sequence was obtained from the Uniprot database (https://www.uniprot.org/) with entry Q8IWX5 (SGPP2_HUMAN) and a length of 399 residues.

 

Modelling and structure evaluation of protein:

Due to the limitations of the 3D structure of SGPP2 from laboratory experimental results, 3D structure modeling was carried out to obtain the tertiary structure of the SGPP2 sequence. Modeling for the two SGPP2 sequences was carried out using SWISSMODEL (https://swissmodel.expasy.org/), and then the modeling results were evaluated for tertiary structure via PDBsum (https://www.ebi.ac.uk/ thornton-srv/databases/pdbsum/) by looking at the Ramachandran plot and the results of the distribution of residues from each modeling result.

 

Molecular docking analysis and visualization:

Molecular docking studies were carried out using the structure of SGPP2 and SGPP2 isoform as receptors and the secondary metabolites of Dioscorea alata as ligands. Ligands were prepared by minimizing energy and converted from sdf to pdbqt format using the Open Babel tool²⁵ in PyRx 0.9^9,26. Then, molecular docking was carried out using PyRx 0.9 with the principle of targeted docking, with the binding residue determined based on the cavity analysis results from the COACH-D tool (https://yanglab.qd.sdu.edu.cn/COACH-D/)^26,27.

 

Table 1. List of GSE related to endometriosis

GSE series	Experiment Type	Platform	Sample Count	Sample Analysed
GSE232713	Expression profiling by high throughput sequencing	GPL16791: Illumina HiSeq 2500 (Homo sapiens)	14, (7 controls and 7 endometriosis)	14, (7 controls and 7 endometriosis)
GSE226575	Expression profiling by high throughput sequencing	GPL24676: Illumina NovaSeq 6000 (Homo sapiens)	9, (4 controls and 5 endometriosis)	9, (4 control and 5 endometrial)
GSE135485	Expression profiling by high throughput sequencing	GPL21290: Illumina HiSeq 3000 (Homo sapiens)	58, (4 controls and 54 endometriosis)	8, (4 controls and 4 endometriosis)

 

 

RESULT:

List of GSE and list of DEG related to Endometriosis:

Based on search results from the GEO database with the keyword endometriosis and using a sequencing platform in Homo sapiens, three GSEs were obtained, specifically GSE232713²⁸, GSE226575²⁹, and GSE135485³⁰, with details of each GSE in Table 1. Each GSE has several patient samples, so we took some of these samples for analysis. From each sample to be analyzed, the sample is then normalized to align the gene distribution, and the normalized gene expression is displayed in a volcano plot and marked with blue and red dots, which indicates the presence of significantly different expressions based on the parameters used. (Figure 2)

 

The colored genes in the volcano plot are DEGs from each GSE. Based on GSE232713, 385 unique DEGs were obtained; 18 DEGs were the same as GSE226575, and 23 DEGs were the same as GSE135485. Meanwhile, in GSE 226575, 320 unique DEGs were obtained, and 68 DEGs were similar to GSE 135485, while in GSE 135485 itself, 771 unique DEGs were obtained. In total, 104 DEGs are similar, including a DEG that is the same as the three GSEs. The protein network of the 104 DEGs was built, and the potential bioactivity was analyzed based on the MCODE clustering results obtained. (Figure 2B)

 

Based on the network formed from STRING (medium confidence 0.4), 46 proteins form the main network, 10 forms a secondary network, 4 forms another network, 6 proteins form 3 pairs, and 38 proteins do not form a network. Clustering results revealed three networks: 17 proteins in cluster 1, 5 in cluster 2, and 3 in cluster 3. SGPP2, a DEG from each GSE, was not included in these networks. Therefore, we built a high-confidence (0.7) network with proteins potentially interacting with SGPP2 (Figure 2C-E).

 

SGPP2, present in the DEGs of the three GSEs, was selected for molecular docking analysis. Bioactivity evaluation showed that SGPP2 and related proteins are involved in phospholipid dephosphorylation, lipid modification, diol metabolic processes, and specifically sphingoid and sphingosine metabolism. SGPP2 is located in organelle membranes, including the endoplasmic reticulum and cell membranes, where it plays a role in catalytic activity, particularly sphingosine-1-phosphate phosphatase (Figure 2F).

 

SGPP2 as the DEG of each GSE and target receptor in molecular docking analysis.:

SGPP2 was chosen as the receptor, and 28 secondary metabolites of Dioscorea alata and two control drugs as ligands were used in molecular docking analysis. The list of ligands can be seen in Table 2. The results of the SGPP2 modeling structure evaluation show that both structures have a Global Model Quality Estimate (GMQE) score close to 1, which means they have normal quality for each amino acid residue compared to the total length of the protein structure. Based on the Ramachandran plot analysis, the SGPP2 modeling structure has a model that is less than ideal because the most favored region score formed is lower than 90%. However, the SGPP2 modeling G-factor score shows an ideal structure, as indicated by a score of more than -0.5. The SGPP2 modeling  isoform  (SGPP2b) shows an ideal model where the GMQE score is higher than the SGPP2 GMQE score, the most favored region is more than 90%, and the G-factor score is more than -0.5. (Figure 3A, Table 3)

 

 

Table 2. List of ligands used in the molecular docking analysis.

 

 

Table 3. Structure profile of the SGPP2 model as the receptor

 

The localization of SGPP2 was known to be in organelle membranes or cell membranes, which a transmembrane protein. This was supported by the modeling results or localization predictions from GeneCard via the membrane confidence score of 3–5. Based on the binding site predictions, the binding location was located on the outer side of the protein membrane in both the SGPP2 and SGPP2b models. The predicted binding residue in the SGPP2 model was located at Lys136, Arg143, Leu156, Glu159, Ser164, Thr165, His166, and Arg207. Meanwhile, in the SGPP2b model, it was located at Lys8, Arg15, Leu28, Ser36, Thr37, His38, Arg79, and His85. (Figure 3B-D)

 

Docking visualization was carried out on three secondary metabolites with the highest binding affinity and on drug compounds, namely anastrozole and letrozole. Compounds with the highest binding affinity were compounds that have a high potential for interacting with receptors, which can be seen from the low binding energy required  to interact. In the SGPP2 model, the five secondary metabolites with the highest binding affinity are diosgenin, naringenin, prosapogenin, myricetin, and leucopelargonidin. Meanwhile, in the SGPP2 model, the five secondary metabolites with the highest binding affinity are prosapogenin, diosgenin, allantoin, chlorogenic acid, and myricetin. (Figure 3E-F)

 

Figure 2. Evaluation and determination of GSE and list of DEG related to endometriosis. A) DEG evaluation process for each GSE. B) The number of DEGs and similar DEGs in each GSE. C) Protein network of 104 DEGs (STRING, medium confidence), of which 46 proteins form the main network and the remaining 38 proteins do not form a network. D) Protein MCODE clustering results. E) Protein network related to SGPP2. F) Potential gene ontology of a protein network related to SGPP2.

 

Figure 3. Modeling results of two SGPP2 structures and their binding energy of each ligand. Isoform structure using the code SGPP2b. A) Ramachandran plot of both structures. It can be seen that the structure formed from residues Ala53, Arg44, Glu49, and Val48 in the SGPP2 model is in the Generously allowed region category, and the structure formed from Leu51 and His50 is in the disallowed region category, which is a non-ideal structure of the protein. B) Models of SGPP2 and SGPP2b, which are transmembrane proteins. C) Confidence score of cellular localization of SGPP2 from GeneCard. D) Binding positions of the five ligands with the highest binding affinity. E) The binding energy of each ligand was sorted based on their lowest binding energy on the SGPP2 model. The bluer the color of the bar, the lower the binding energy and the higher the binding affinity. F) 2D visualization of the three secondary metabolites with the lowest binding energy, anastrozole and letrozole.

 

DISCUSSION:

Dioscorea alata is a plant with tubers that is used as a food garden in various regions and as a herbal plant in certain other areas^31,32. Currently, the literature regarding the potential of Dioscorea alata in endometriosis is still limited, although its potential as an anticancer^9,33, antioxidant¹², anti-inflammatory³⁴, and immunomodulator¹⁰ has been studied. Endometriosis is a condition where endometrial-like tissue grows outside the uterus. This condition can be caused by complex gynecological conditions and various possible factors³⁵. Based on other research, endometriosis has been reported to occur due to highly-invasive placentation³⁶, characteristic changes in endometrial cells into neoplasms and neoangiogenesis³⁷, endometrial cell reflux escaping immune surveillance and initiating a proinflammatory response³⁸, and hormonal disorders, especially excessive stimulation of estrogen expression³⁹.

According to estrogen expression, aromatase is an enzyme that increases the risk factors and severity of sufferers because it plays a role in stimulating the growth of endometrial tissue. Therefore, aromatase inhibitors are used as drugs to treat endometriosis, and anastrozole and letrozole are two of them. Anastrozole and letrozole are reported to have activity to inhibit endometriotic cell growth by inhibiting estradiol secretion⁴⁰. That is why these two drugs were used as ligand controls in this study. However, one of the limitations of this research is the unavailability of the 3D structure of the SGPP2 ligand obtained by laboratory experiments.

 

Furthermore, the 3D structure of SGPP2 has isoforms, and both were used in this study. The isoform is obtained based on the potential sequence of SGPP2 gene expressions, and the 3D structure is not yet available based on experimental results. Furthermore, the cause of the difference in modeling results scores for the two SGPP2 structures is the length of the amino acid residues in the two structures. The SGPP2 structure has 399 amino acids, where the tertiary structure of several amino acids is unknown and forms long loops, compared to the SGPP2b structure, which has 270 amino acids whose entire tertiary structure has been modeled successfully. However, the overall score for each model remains ideal and has a quantitative basis, as mentioned in the result.

 

The Sphingosine-1-phosphate phosphatase 2 (SGPP2) enzyme plays a crucial role in sphingolipid metabolism, particularly in the regulation of sphingosine-1-phosphate (S1P) levels, which has implications in various diseases and metabolic processes. S1P is a bioactive sphingolipid involved in signaling pathways that regulate immunity, inflammation, and inflammatory disorders⁴¹. SGPP2 is involved in the metabolism of S1P, and its dysregulation also has been associated with tumor progression and inflammatory responses⁴², regulation of proinflammatory factors and the inhibition of inflammation-induced signal transduction pathways⁴³, and disruption of mucosal integrity and ulcerative colitis in mice and humans, highlighting its role in intestinal inflammation⁴⁴. The role of SGPP2 in sphingolipid metabolism is further emphasized by its involvement in S1P signaling pathways, where it regulates the levels of S1P in cells, thereby influencing cell fate and function⁴⁵. SGPP2 plays a key role in the Sphingolipid signaling pathway by dephosphorylating S1P to sphingosine, which has implications in lung cell expression and lung function⁴⁶, and regulates endoplasmic reticulum stress and proliferation in pancreatic islet β- cells, indicating its significance in metabolic processes⁴⁷. Moreover, SGPP2 has been identified as a potential therapeutic target in various diseases, including inflammatory bowel diseases⁴⁸, and potential as a prognostic marker and its relevance in the tumor immune response⁴⁹. Therefore, its evaluation in endometriosis may reveal new basic information and potentially become a genetic marker of endometriosis or its severity.

 

Based on the docking results, the secondary metabolites of Dioscorea alata with the best binding affinity for SGPP2 were shown to be diosgenin and prosapogenin. These two secondary metabolites have better binding affinity than anastrozole, which shows a high potential to interact with SGPP2. However, the final limitation of this research is that the presence of diosgenin and prosapogenin in Dioscorea alata and the binding affinity score cannot be used as a reference for the occurrence of the intrinsic factor of SGPP2 or the inhibitory activity of SGPP2. We recommend further research to evaluate the intrinsic factor or inhibitory activity of secondary metabolites against SGPP2 at this binding site.

 

CONCLUSION:

In conclusion, secondary metabolites of Dioscorea alata have a high potential to interact with SGPP2 and potentially influence its activity which is an endometriosis-related gene. However, we recommend further research regarding SGPP2 as a marker for endometriosis and the potential of secondary metabolites of Dioscorea alata as SGPP2 agonists or inhibitors.

 

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Received on 30.12.2023      Revised on 26.06.2024 Accepted on 04.11.2024      Published on 27.03.2025 Available online from March 27, 2025 Research J. Pharmacy and Technology. 2025;18(3):1386-1393. DOI: 10.52711/0974-360X.2025.00200 © RJPT All right reserved
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