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Proceeding Paper

Effects of Delphinidin-3-Sambubiosid on Different Pathways of Human Cells According to a Bioinformatic Analysis †

Sergio R. Zúñiga-Hernández
Trinidad García-Iglesias
Monserrat Macías-Carballo
Alejandro Perez-Larios
4 and
Christian Martin Rodríguez-Razón
CUALTOS, Universidad de Guadalajara, Tepatitlán de Morelos 47620, Mexico
INICIA Instituto de Investigación de Cáncer en la Infancia y Adolescencia, Departamento de Fisiología del CUCS, Guadalajara 44340, Mexico
Instituto de Investigación en Ciencias Médicas, CUALTOS, Universidad de Guadalajara, Tepatitlán de Morelos 47620, Mexico
Laboratorio de Nanomateriales, Agua y Energia, Departamento de Ingenierias, Division de Ciancias Agropecuarias e Ingenierias, Centro Universitario de los Altos, Tepatitlán de Morelos 47620, Mexico
Laboratorio de Experimentación Animal (Bioterio), Departamento de Ciencias de la Salud, CUALTOS, Tepatitlán de Morelos 47620, Mexico
Authors to whom correspondence should be addressed.
Presented at the 3rd International Electronic Conference on Nutrients, 1–15 November 2023; Available online:
Biol. Life Sci. Forum 2023, 29(1), 13;
Published: 1 November 2023
(This article belongs to the Proceedings of The 3rd International Electronic Conference on Nutrients)


The use of food and its nutrients as a remedy for diseases is historically and culturally well rooted in plenty of societies. An example of this is the use of Hibiscus sabdariffa to treat conditions like hypertension or high blood glucose. Furthermore, the natural biocompounds present in this plant have been demonstrated by several authors to be hypotensive, antioxidant, anticarcinogenic, antiobesogenic, etc. One of these compounds is Delphinidin-3-Sambubiosid (DS3), the most representative anthocyanin of Hibiscus sabdariffa, and as such, it has been proposed to have the beneficial effects previously mentioned. However, little is known about the molecular targets of DS3. Therefore, we conducted an in silico analysis using different bioinformatic tools to determine the possible molecular targets of this molecule and the potential impact the modification of its targets could have on the proteins and/or pathways of humans. We used the Swiss Target Prediction site to identify all the molecular targets of DS3, and then ShinnyGo 0.77, KEGG, and Stringdb were used to identify key pathways and hub genes related to them. Also, a literature search was conducted in PubMed, where each of the hub genes was linked to DS3 so we could gather information that complemented the results of the bioinformatic tools. The results show that DS3 can modify the behavior of genes related to nitrogen and glucose metabolism, inflammation, angiogenesis, and cell proliferation. Additionally, DS3 has direct effects on the PI3K-AKT pathway, which could be a key finding promoting further research, especially to determine the implications associated with changes in the aforementioned pathway.

1. Introduction

Hibiscus sabdariffa is a highly popular plant in Asia and America. Therefore, it has been used in a wide array of products, from flavored water to facial creams. Its popularity can also be attributed to its potential beneficial effects on health, from hypotensive to anticancerogenic activities, especially since this plant is rich in plenty of biocompounds. One of these biocompounds is anthocyanins, which are a group of phenol-derived compounds quite common in many fruits, vegetables, plants, and especially in Hibiscus sabdariffa. They are generally responsible for such foods’ blue, red, or purple colors [1]. Structurally, they are aliphatic or aromatic compounds with three rings and one or more sugar molecules. As for the potential therapeutic effect of anthocyanins, there is substantial evidence that most if not all anthocyanins have an effect on different cells of mammals [2], with some of them directly linked to alterations in biological pathways. Multiple biological models have shown that anthocyanins are capable of changing several pathologies’ prognosis. One anthocyanin of particular interest is Delphinidn-3-Sambubiosid, which is found in particularly high quantities in Hibiscus sabdariffa. This anthocyanin (DS3) has shown potential therapeutic effects [3,4]; however, there is little evidence of how exactly D3S affects the cells and its targets. As such, the objective of this study is to use bioinformatic tools to determine probable targets of D3S in human cells as well as to determine possible effects in such pathways.

2. Methods

2.1. Bioinformatic Analysis

The site SwissTargetPrediction was used to determine possible molecular targets of the interaction of D3S. Once the list of possible targets of D3S was obtained, the ShinnyGo 0.77 site was used to obtain the Fold Enrichment (FE) of each one by FDR (cut-off of 0.05). Out of those, the ones with an FE higher than 5 were used in KEGG to identify the pathways where there could be a key interaction caused by D3S. Also, the website Stringdb site was used to obtain a hub of genes gathered from the FE data. Regarding these last ones, the Pubmed database was used in order to find information according to what the bioinformatics suggested.

2.2. Literature Search and Data Selection

A search was conducted using PubMed to identify relevant articles that have information about the genes obtained from the bioinformatic analysis against D3S; we achieved this with a simple search string: “Gene Name” AND “Delphinidin 3 Sambubiosid”. The search included terms appearing in titles, abstracts, or a combination of both. Finally, inclusion and exclusion criteria were used to determine which articles could be considered for the final discussion.

2.3. Inclusion and Exclusion Criteria

The inclusion criterion was any study that included any of the genes (or protein derived from them) obtained from the bioinformatic analysis with DS3. As for the exclusion criteria, studies with duplicated or overlapping data, papers that only presented abstracts, conferences, editorials, or author responses, articles without full text available, and systematic reviews were excluded.

2.4. Results

Data from the Swiss Target Prediction site. Figure 1 shows the top 15 target classes of molecules that DS3 could interact with, most of which are enzymes and lyases, followed closely by a family of G-protein-coupled receptors. Also, the full information on all the possible targets is shown in Supplementary Material S1 [5,6].

2.4.1. Enriched Analysis of Gene Ontology and Metabolic Pathways

The site ShinnyGo 0.77 was used to perform Gene Ontology and KEGG analysis [7]. The full results of the Gene Ontology analysis are shown in Table 1. The results of KEGG analysis are shown in Table 2, and images of each pathway with the signaling of the potential changes in the protein are displayed in Supplementary Material S2.

2.4.2. Protein–Protein Interaction Network

The STRING database was used to predict the associations between protein targets of DS3 [8,9,10,11,12,13,14,15,16,17,18,19,20]. The network was constructed with a medium confidence of 0.400. The interactome had 257 edges, 57 nodes, an average node degree of 9.02, and a PPI enrichment p-value of < 1.0 × 10−16. Figure 2 shows the interactome and Table 2 shows the hub genes with at least 10 interactions.
Next, we will discuss data collection from the evidence of the hub genes regarding DS3. The results from the research in the PubMed database for articles on investigating hub genes and DS3 are shown in Table 3. These results are presented separately if direct evidence is found at the RNA, protein, or pathway level.

3. Discussion

The results obtained with the SwissTargetPrediction software (2019 version) (Figure 1) have their basis in the mathematical fundament of SwissTargetPrediction, which makes docking predictions with the software EADocks DSS (2019 version) [5]. EADocks DSS primarily uses an algorithm that determines targets of molecules on proteins by using a binding model within all possible 3D cavities. According to Grosdidier A., Zoete V., and Michielin O., in this task, this software has a success rate of close to 70% in correctly predicting binding models. Moreover, it also discriminates and filters its results thanks to other tools, such as the Chemistry at Harvard Macromolecular Mechanics (CHARMM) and fast analytical continuum treatment of solvation (FACTS) [25]; by employing this combination of tools, EADocks DSS can achieve a success rate of up to 96% in identifying ligands with fewer than 15 free dihedral angles and/or test complexes with adequately defined biding pockets. It is important to remark on how SwissTargetPrediction has been used in the determination of molecular targets of small molecules that come from plants or foods (not dissimilar to the one in this study). For example, in a 2022 study, a team of researchers from China led by Lili Yan [26] looked at Erianin (a biphenyl compound) regarding its predicted molecular targets in a specific site; then, they compared the matches of these targets (along with the results of other bioinformatic tools) with then-current information published by other authors, seeing plenty of overlap in their results. This shows how this tool (SwissTargetPrediction) has been used with good success to find information regarding molecular target information.
The results from the Gene Ontology analysis (Table 1) show that DS3 affected several processes involving metabolism and inflammation, in particular nitrogen metabolism, insulin resistance signaling, the PI3k-Akt pathway, metabolic pathways, the insulin signaling pathway, regulation of lipolysis, the TNF signaling pathway, lipid and atherosclerosis, and endocrine resistance. These results are supported by the database research as well as their statistical analysis. The use of the FE for each one by FDR (using a cut-off of 0.05) has been widely accepted as a tool in bioinformatics to delimit the possibility of false positives [27]. These processes are related to a significant number of the effects described for DS3 and plants with high quantities of this anthocyanin [2,3].
Regarding the KEGG analysis of the affected pathways (Table 1), the results indicate a dysregulation in the metabolism of nitrogen (which is key to the regulation of energy metabolism and protein metabolism) and glucose metabolism (especially in muscle and adipose tissue). Interestingly, glucose metabolism alterations seemed to mostly be attributed the PI3K-Akt pathway; this result agrees with multiple studies that have investigated the effects of DS3 on PI3K-AKT [21,22,23,24].
On the other hand, the protein-to-protein analysis also supported the idea that the PI3K-AKT pathway is a major target of DS3, with AKT being the most linked node in the whole analysis. Also, the information on the hub genes (Table 2) shows a trend of genes related to the metabolism of glucose and nitrogen, inflammation, and angiogenesis. This not only correlates with the previous results shown but also includes angiogenesis, a process related to the production of nitric oxide and therefore to blood pressure. This is of interest since another of the most reported effects of DS3 is its potential as a hypotensive [28].
As for the search for evidence, using the PubMed database, we looked for any information regarding the hub genes obtained from protein-to-protein analysis and DS3. According to the results of the Gene Ontology analysis (Table 1), most of the genes of interest are related to the PI3K-Akt pathway. Out of these, it is fascinating to see the effects on EGFR, for which there is more evidence of what happens when it is exposed to DS3: reduced expression of the gene, suppressed function of the protein transcribed from it, and, finally, an association with inhibiting the whole PI3K-AKT pathway in this condition [21,22,23,24]. However, it is important to remark on how most of the other genes do have some predicted alterations, but there was not much information to be found about their relationship with DS3 (if any); therefore, the importance of studying them a posteriori is suggested.

4. Conclusions

The predictive analysis indicated that DS3 has the potential to trigger changes in genes related to nitrogen and glucose metabolism, inflammation, angiogenesis, and cell proliferation. The information currently available suggests that these changes also can occur directly in the protein, not only in mRNA. Also, quite possibly the most important result according to the bioinformatic tools is the potential modification in the function of several metabolic pathways, in particular, the effects that DS3 has on the PI3K-AKT pathway; these results are also supported by the findings presented in Table 2 and Table 3. However, there are not enough published studies on DS3 and its other potential targets (suggested by the bioinformatic tools). The lack of research in this area opens up possibilities to conduct new studies with a high probability of having significant relevance, helping us to understand the mechanisms of action of DS3 in human cells.

Supplementary Materials

The following supporting information can be downloaded at:, Supplementary Material S1: Most likely targets for DS3; Supplementary Material S2: KEGG pathways for DS3.

Author Contributions

Conceptualization, S.R.Z.-H. and C.M.R.-R.; methodology, S.R.Z.-H. and T.G.-I.; formal analysis, M.M.-C.; investigation, S.R.Z.-H. and M.M.-C.; data curation, A.P.-L.; writing—original draft preparation, S.R.Z.-H.; writing—review and editing, S.R.Z.-H., C.M.R.-R., T.G.-I., M.M.-C. and A.P.-L.; visualization, S.R.Z.-H.; supervision, C.M.R.-R.; project administration, C.M.R.-R.; funding acquisition, C.M.R.-R. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Top 15 molecular targets of DS3 according to the Swiss Target Prediction site.
Figure 1. Top 15 molecular targets of DS3 according to the Swiss Target Prediction site.
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Figure 2. PPI network. Each of the edges is a specific protein with a significant protein–protein association. The blue and purple borders are known interactions recognized by several databases (previously curated and experimentally curated). The predicted interactions of each neighborhood protein, protein-gene fusion, and protein-gene concurrence are highlighted in green, red, and navy blue. Other colors, such as grass green, black, and gray, represent text mining, coexpression, and protein homology, respectively.
Figure 2. PPI network. Each of the edges is a specific protein with a significant protein–protein association. The blue and purple borders are known interactions recognized by several databases (previously curated and experimentally curated). The predicted interactions of each neighborhood protein, protein-gene fusion, and protein-gene concurrence are highlighted in green, red, and navy blue. Other colors, such as grass green, black, and gray, represent text mining, coexpression, and protein homology, respectively.
Blsf 29 00013 g002
Table 1. Functional enrichment analysis of the genes predicted to interact with DS3.
Table 1. Functional enrichment analysis of the genes predicted to interact with DS3.
Enrichment FDRGenesPathway GenesFold EnrichmentPathwayGenes
4.20 × 10−181017141.151703Nitrogen metabolismCA2, CA9, CA14, CA6, CA1, CA3, CA4, CA7, CA5A CA13
1.49 × 10−91535410.1677074PI3K-Akt signaling pathwayGSK3B, PIK3CG, MET, IL2, FLT3, PKN1, KDR, IGF1R, AKT1, MCL1, PIK3R1, EGFR, SYK, PTK2, INSR
2.21 × 10−997927.3369754EGFR tyrosine kinase inhibitor resistanceGSK3B, MET, KDR, IGF1R, AKT1, PIK3R1, EGFR, AXL, SRC
2.73 × 10−92715274.24287044Metabolic pathwaysCD38, PTGS2, CA12, AKR1B1, HSD17B2, PYGL, CA2, SQLE, PIK3CG, CA9, ALOX12, ALDH2, CA14, GLO1, CA6, CA1, CYP19A1, PDE5A, XDH, ALOX15, CA3, CA4, CA7, PLA2G1B, CA5A, CA13, MAOA
1.98 × 10−789520.2069806Endocrine resistanceMMP2, MMP9, IGF1R, AKT1, PIK3R1, EGFR, PTK2, SRC
3.94 × 10−6710815.5528265Insulin resistanceNR1H3, GSK3B, PYGL, AKT1, PIK3R1, INSR, RPS6KA3
4.89 × 10−667020.5678196Central carbon metabolism in cancerHIF1A, MET, FLT3, AKT1, PIK3R1, EGFR
2.28 × 10−582148.97038859Lipid and atherosclerosisCAMK2B, GSK3B, MMP9, AKT1, PIK3R1, MMP3, PTK2, SRC
2.52 × 10−555621.424812Regulation of lipolysis in adipocytesPTGS2, AKT1, PIK3R1, ADORA1, INSR
0.000155482946.52946652MAPK signaling pathwayMET, FLT3, KDR, IGF1R, AKT1, EGFR, INSR, RPS6KA3
0.00042661511210.712406TNF signaling pathwayPTGS2, MMP9, AKT1, PIK3R1, MMP3
0.000878851378.7575874Insulin signaling pathwayGSK3B, PYGL, AKT1, PIK3R1, INSR
0.0013415651557.74057725Non-alcoholic fatty liver diseaseNR1H3, GSK3B, AKT1, PIK3R1, INSR
0.002440434715.3164614Carbohydrate digestion and absorptionSLC5A1, AKT1, PIK3R1
0.0037399541207.99859649AMPK signaling pathwayIGF1R, AKT1, PIK3R1, INSR
0.0195081631076.72779144Glucagon signaling pathwayCAMK2B, PYGL, AKT1
0.0283482424610.432952Type II diabetes mellitusPIK3R1, INSR
0.0292980524710.2109742Pyruvate metabolismALDH2, GLO1
Table 2. Hub genes with at least 10 interactions in humans were obtained from the predictions of interactions with DS3.
Table 2. Hub genes with at least 10 interactions in humans were obtained from the predictions of interactions with DS3.
Gene SymbolProtein NameProtein Function
AKT1RAC-alpha serine/threonine-protein kinaseRegulates many processes, including metabolism, proliferation, cell survival, growth, and angiogenesis
PTK2Focal adhesion Kinase 1Related to the increase in glucose uptake and glycogen synthesis in insulin-sensitive tissues.
IL2Interleukin-2 Required for T-cell proliferation and other cells of the immune system
PIK3R1 Phosphoinositide-3-kinase regulatory subunit alpha/beta/deltaNecessary for the insulin-stimulated increase in glucose uptake and glycogen synthesis
SYK Spleen-associated tyrosine kinaseRegulates biological processes including immunity, cell adhesion, vascular development, and others
PTGS2 Prostaglandin G/H synthase 2Plays a role in the production of inflammatory prostaglandins
MMP9 Matrix metalloproteinase-9Key role in local proteolysis of the extracellular matrix and leukocyte migration
HIF1A Hypoxia-inducible factor 1-alphaMaster transcriptional regulator in response to hypoxia
MMP2Matrix metalloproteinase-2 (gelatinase a)Involved in angiogenesis, tissue repair, tumor invasion, inflammation, and atherosclerotic plaque rupture
KDR Vascular endothelial growth factor receptor 2Essential in the regulation of angiogenesis, promotes the proliferation, survival, and migration of endothelial cells
MET Hepatocyte growth factor receptorRegulates processes like proliferation, scattering, morphogenesis, and survival
HGF Hepatocyte growth factorGrowth factor for a broad spectrum of tissues and cell types
EGFR Epidermal growth factor receptorConverts extracellular cues into appropriate cellular responses
IGF1RInsulin-like growth factor 1 receptorInvolved in cell growth and survival control
CA9 Carbonic anhydrase 9Involved in pH regulation
BLNKB-cell linker proteinImportant for the activation of NF-kappa-B and NFAT
Table 3. Evidence found for hub genes when tested against DS3.
Table 3. Evidence found for hub genes when tested against DS3.
GenesResults at the Gene Expression LevelResults at the Protein LevelResults of Pathway Impact
MET Syed, D. N. 2008: Suppress the phosphorylation of the protein [21]
IGF1R Teller et al., 2009: Inhibition of its kinase activity [22]
EGFRHarish Chandra Pal, et al., 2013: Reduction in the expression of the gen [23] Fredrich D, Et all, 2008: Suppress phosphorylation of the protein [24] Harish Chandra Pal, et al., 2013: Inhibition of the PI3K-Akt pathway [23]
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Zúñiga-Hernández, S.R.; García-Iglesias, T.; Macías-Carballo, M.; Perez-Larios, A.; Rodríguez-Razón, C.M. Effects of Delphinidin-3-Sambubiosid on Different Pathways of Human Cells According to a Bioinformatic Analysis. Biol. Life Sci. Forum 2023, 29, 13.

AMA Style

Zúñiga-Hernández SR, García-Iglesias T, Macías-Carballo M, Perez-Larios A, Rodríguez-Razón CM. Effects of Delphinidin-3-Sambubiosid on Different Pathways of Human Cells According to a Bioinformatic Analysis. Biology and Life Sciences Forum. 2023; 29(1):13.

Chicago/Turabian Style

Zúñiga-Hernández, Sergio R., Trinidad García-Iglesias, Monserrat Macías-Carballo, Alejandro Perez-Larios, and Christian Martin Rodríguez-Razón. 2023. "Effects of Delphinidin-3-Sambubiosid on Different Pathways of Human Cells According to a Bioinformatic Analysis" Biology and Life Sciences Forum 29, no. 1: 13.

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