Research

Putative herbal drugs for targeting infection and inflammatory diseases: Network pharmacological application in case of Zika virus and Dengue virus infections

Raja Jeet ^1,#,*, Arwa A. Faizo ^2,3,#, Mai M El-daly ^2,3, Aiah M Khateb ^2,4, Sarah A. Altwaim ^2,5, Ahmad M. Ashshi ^2,6, Manal Tashkandi ⁷, Mohammad Mobashir ^2,8,*, and Esam I. Azhar ^2,3,*

¹ Botany Department, G D College, Begusarai, 851101, Bihar, India.

² Special Infectious Agents Unit – BSL3, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 128442, Saudi Arabia.

³ Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 128442, Saudi Arabia.

⁴ Medical Laboratory Technology Department, College of Applied Medical Sciences, Taibah University, Medina 42353, Saudi Arabia.

⁵ Department of Medical Microbiology & Parasitology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.

⁶ Department of Laboratory Medicine, Faculty of Applied Medical Sciences, University of Umm Al-Qura, Makkah, Saudi Arabia.

⁷ University of Jeddah, College of Science, Department of Biochemistry, Jeddah, Saudi Arabia.

⁸ Department of Biomedical Laboratory Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway.

*    Correspondence: rajajeet10@gmail.com (R.J.); mohammad.mobashir@ntnu.no (M.M.); eazhar@kau.edu.sa (E.I.A.)

Citation: Jeet, R. et al., Putative herbal drugs for targeting infection and inflammatory diseases: Network pharmacological application in case of Zika virus and Dengue virus infections . Glob. Jour. Bas. Sci. 2025, 1(9). 1-13. Received: April 09, 2025 Revised: June 19, 2025 Accepted: July 02, 2025 Published: July 03, 2025 doi: 10.63454/jbs20000046 ISSN: 3049-3315 Volume 1; Issue 9 Download PDF file

 Abstract: Inflammation is the primary cause of many human diseases, including the Zika virus, Dengue infection, COVID-19 infection, asthma, rheumatoid arthritis, inflammatory bowel disease, Crohn’s disease, and tendinitis. Other diseases that are closely related to Dengue and Zika are also carried by the same kind of mosquitoes. Zika epidemics in human populations have just recently happened, but dengue fever is well-known and has long impacted people globally. When the inflammatory process is under control, it helps keep the body from disintegrating catastrophically; when it is not, it damages the body and results in unintentional deterioration. Given the importance of the pathways linked to inflammation, we have thus chosen to do research that may enable us to understand the function of these pathways and their constituent parts in cases of Dengue and Zika virus infection, as well as possible targets for herbal remedies. For a very long time, people have employed herbal treatments to treat or prevent ailments, including inflammatory problems. They are also an invaluable source of chemical building blocks that have developed into medicines that are necessary for contemporary health. It is of potential interest to understanding the gene expression patterns and the altered biological functions as a result of infection and to predict the potential drugs and the targets. Thus, to target those pathways using herbal drugs, we may have primarily investigated the shared/common pathways and the genes that comprise those processes in this study. By using in-silico approach, we have compared gene expression profiling, pathway enrichment analysis, network-level comprehension, projected potential genes and pathways, and performed docking for the top genes/proteins to predict the potential herbal drugs and the targets in order to achieve this using an integrated computational approach. Based on our analysis, we conclude that a large fraction of ZIKV and DENV DEGs overlap, and that all of the DENV-induced altered pathways exclusively belong to the ZIKV altered pathway list in terms of biological functions. These findings suggest that ZIKV and DENV infections are highly similar in terms of gene expression and possibly altered biological function levels. Furthermore, we performed docking profiling of the proteins inferred to chosen DEGs based on network analysis against the herbal medicines in order to discover potential targets for ZIKV and DENV infections with herbal medications. Fisetin appears to be the most promising among the three drugs selected with respect to Querceting and apigenin.

Keywords: Infection; inflammation; ZIKV; DENV; pathways; genes; biological networks; herbal drugs

1. Introduction

The illnesses known as infectious diseases are brought on by microorganisms such as bacteria, viruses, fungus, or parasites. Our bodies are home to many species, many of which are beneficial or even safe. However, certain species have the potential to be harmful under specific conditions. There is a chance that certain infectious diseases will transmit from person to person[1, 2]. The twenty-first century has seen a number of significant infectious disease outbreaks, the most notable of which being the COVID-19 pandemic, which has had a devastating impact on people’s livelihoods and way of life worldwide. The 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013–2016 West African Ebola virus disease epidemic, and the 2015 Zika virus disease epidemic all caused significant morbidity and mortality while infecting people in multiple countries. The 2003 coronavirus outbreak associated with severe acute respiratory syndrome was also linked to the 2009 pandemic. Even though improved sanitation and access to healthcare have led to significant advancements worldwide, the current situation may help us better understand how much the recent changes in the globe have contributed to an increase in the risk of infectious disease outbreaks[3-6].

Common human diseases including tuberculosis, polio, smallpox, and diphtheria caused severe morbidity and mortality prior to the development of immunizations. Along trade routes and with wandering armies, animal diseases like rinderpest, which have devastating impacts on livestock and dependent human populations, also spread simultaneously. However, within the past 20 years, there has been a decrease in the overall mortality and morbidity linked to infectious disorders, particularly for diarrheal disease and lower respiratory tract infections. This is brought about by developments in medicine, more access to healthcare, and better cleanliness. The rapid development of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine is evidence of the efficacy of modern research in quickly thwarting threats from novel viruses. In low- and lower-middle-income nations, the burden of infectious diseases remains substantial, and high rates of death and morbidity are still linked to neglected tropical diseases such tuberculosis, HIV infection, and malaria. Furthermore, the mortality rate from newly and re-emerging infections has remained constant in the twenty-first century, in contrast to seasonal and endemic disorders. This suggests that a new era of infectious sickness may be upon us, one in which endemic, emerging, and reemerging illnesses spread quickly due to shifting geographic ranges brought on by climate change and increased global connectivity[7-10].

Previous studies have shown how human-caused changes in technology, demographics, and climate have altered the risk environment for infectious diseases over the last 20 years. Although not specifically discussed, the three factors—technology, demography, and climate—are greatly influenced by economic trends, particularly economic development. Both the Dengue Virus (DENV) and the Zika Virus (ZIKV) are dangerous human diseases that belong to the Flavivirus genus. Dengue Virus (DENV) and Zika Virus (ZIKV) are closely related flaviviruses that can cause serious, life-threatening, or incapacitating illness, despite the fact that most human infections are asymptomatic. Of all the flaviviruses, DENV, with its four serotypes (DENV-1-4), causes the greatest disease burden, infecting about 390 million people and killing 21,000 of them annually. While infection during pregnancy can infect the developing foetus, cause early pregnancy loss, or cause developmental and neurological disabilities in neonates, ZIKV is mono-serotypic and causes significantly fewer fatalities. The primary mosquito vectors of ZIKV and DENV are members of the Aedes genus. The principal vectors of the Dengue and Zika viruses are mosquitoes of the Aedes species, particularly Aedes aegypti, which is widely distributed in the tropics and subtropics[11]. Any of these viruses can induce an acute illness that includes infection along with fever, rash, myalgia, and arthralgia. Because they share a vector, both viruses co-circulate throughout tropical and sub-tropical regions of Africa, Asia, and the Americas. It is estimated that almost one-third of the world’s population lives in areas where DENV infection is prevalent[12]. Unbelievably, the global spread of both of these viruses has lately expanded dramatically due to factors like climate change and globalization. Not all viruses have a specific antiviral treatment at this time, even though DENV and ZIKV are serious threats to human health and have a substantial socioeconomic impact on many of the world’s least developed nations[4, 13-17].

The stages in the formation of novel diseases include emergence, local-scale transmission, cross-border movement, and possible global-scale dissemination. Global changes may have an impact on various aspects of the risk of emergence, the dynamics of illness within a local community, and the global transmission of diseases between populations. Herbal medicine, sometimes known as herbalism, is a field of traditional medicine that teaches using medicinal herbs[18-20]. Medicinal herbs have been used for illness prevention since ancient times[7-17]. Novel drugs have been developed using medicinal plants. The use of herbal medicine has grown in popularity recently because of its cost-effectiveness and beneficial effects. The anti-oxidant, anti-cancer, anti-mutagenic, antiviral, antibacterial, and anti-inflammatory qualities of gingerol, curcumin, and other active ingredients[9, 18-22]. Having observed such intricacy, we examined the association between the two most prevalent infectious diseases in humans, ZIKV and DENV infection, at both the functional and gene expression levels, with a focus on developing countries. Furthermore, we aimed to find the most appropriate putative herbal drugs to jointly target these two diseases. Thus, to predict herbal medications and their targets, we have also examined the most important genes and functions using a network biology approach and docking analysis.

2. Methods

Data collection and processing: To proceed towards achieving our designed goals we have been through the relevant datasets freely available on freely available database that is GEO. Here, we focused on the quality of datasets and the number of samples to select the dataset for analysis. In order to obtain the relevant data, we first chose the relevant dataset from the GEO database. Based on sample size and quality, we processed the data until normalisation, selecting GSE98889 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98889) for ZIKV and GSE51808 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51808) for DENV. All mapped genes had their log2 values estimated following normalisation to prevent noise and the lowering of too high intensity. We compared the target samples (infected with ZIKV or DENV) with the normal samples (DEGs) to produce the lists of differentially expressed genes for the relevant disease.

Normalization and gene expression calculation: In general, intensity calculations, normalisation, and raw file processing are the three core processes that comprise the overall steps for generating the important genes lists for the expression dataset generated by microarray chips. GCRMA, RMA, and EB are the three normalisation methods that are most commonly used. We proceed with our goal of understanding the patterns of gene expression and their assumed functions after normalisation. Here, GEO2R, the built-in GEO tool, has completed each of these tasks[23-27]. The pathway enrichment analysis and the DEGs both had threshold p-values of 0.05. For network and pathway analysis, we have written our own code and utilised the KEGG database[20, 26, 28-36].

DEGs network mapping and analysis: Throughout the entire experiment, the DEGs networks were obtained using FunCoup2.0, and the networks were shown using Cytoscape[37]. We have mostly used MATLAB for our programming and computations. FunCoup anticipates four fundamental types of functional coupling or links: protein complexes, physical interactions between proteins, metabolism, and signaling pathways, to mention a few. While there are other relevant network databases (StringDB, BioGRID, MINT, DIP, HPRD, etc.) and visualizing tools (Pajek), we have chosen FunCoup and Cytoscape above the others due to their ease of use and handling[36, 38-41].

Protein structure preparation for docking research and modelling of three-dimensional structures: After analyzing differentially expressed genes, network-level understanding, and the altered biological functions, we employed docking against the three selected herbal medicines (between diabetes and cancer) for the shared differentially expressed genes. For this, the structures for the three chosen herbal medications from the PubChem database were collected[35, 42]. These drugs were selected based on the previous published work.

The UniProt database (https://www.uniprot.org/) provided the protein sequences of the proteins that were used for docking and the 3D protein structures were predicted from the protein sequence by using swissmodel. The ligands Apigenin (52804431), Fisetin (52816141), Quercetin (52803431), and Resveratrol (4451541) were obtained in SDF format from the PubChem database, which may be accessed at https://pubchem.ncbi.nlm.nih.gov. We used PyMol to visualize the structure of ligands and proteins. The Swiss Model website, https://swissmodel.expasy.org/, was used to model the homology of the aforementioned proteins. The GMQE, QMEANDisCo, and QMEAN Z-score analyses were used to select the simulated structures. Greater values indicate better anticipated quality. The global QMEANDisCo and GMQE models provide an overall model quality range of 0 to 1. After hydrogen atoms were added, the Swiss PDB Viewer was used to lower the energy of the three-dimensional structure of proteins while keeping all other parameters at their default values[43-46].

Prediction of active binding sites: The total number of active sites, together with details about their average cavity volume, amino acid sequence, and cavity placements, were found using a search engine. Consequently, the binding pocket of every protein described above was estimated by utilizing Discovery Studio and the CASTp server with the standard probe radius of 1.4[47-49].

Molecular docking studies: PyRx (AutoDock Vina) was used to conduct the molecular docking experiment. For docking, the atomic coordinates of the ligand and protein were transformed to pdbqt files. Using AutoDock Vina and grid box measurements with preset size and spacing pointing in the x, y, and z dimensions, the binding pocket was built. The docking investigations were conducted using the predefined default settings. The compounds with the lowest binding free energy (∆G), the most hydrogen bonds, and other hydrophobic interactions were also shown to have the best binding arrangements. PyMol and Discovery Studio were used to analyze the complex interactions, which included halogen bonds, alkyl, pi-alkyl, pi—pi T-shaped, pi-sigma, pi-sulfur, and hydrogen links as well as carbon-hydrogen bonds and van der Waals contacts[36, 44, 50-55].

3. Results

Comparative gene expression and functional profiling: For the selected diseases, ZIKV and DENV, we have conducted comparative gene expression investigations. The expression datasets for these studies were gathered from the Gene Expression Omnibus (GEO), a publicly accessible database. We utilized the built-in tool GEO2R to analyze the gene expression datasets. First, we divided the samples into two groups (control and targets/ZIKV or DENV infected samples), and then we carried out the analysis that resulted in the creation of a list of differentially expressed genes. The fold changes, p-values, and additional statistical information are included in this gene list. The cut-off for p-values and fold changes has been applied here.

Following the creation of the list of genes that were differentially expressed, we looked into the implications of altered gene expression at the functional level, utilizing pathway enrichment analysis. We obtain a list of routes along with their p-values by doing pathway enrichment analysis, and we utilize this list to determine statistical significance. Venn diagrams were produced for the enriched pathways and list of DEGs at various ZIKV infection time points (12 hours, 24 hours, 48 hours, 72 hours, and 9 days). For all time points in the gene expression profiling, only 19 DEGs were frequent. The DEGs for 12 hours, 24 hours, 48 hours, 72 hours, and 9 days, respectively, were 37, 625, 239, 349, and 219 (Figure 1a). For 12 and 24 hours, 12 and 48 hours, 12 and 72 hours, 12 hours, and 9 days, respectively, 57, 32, 34, and 25 DEGs were prevalent. For 24 and 48 hours, 24 and 72 hours, and 24 hours and 9 days, respectively, 426, 179, and 132 DEGs were prevalent. For 48 and 72 hours, 48 hours and 9 days, and 72 hours and 9 days, respectively, 198, 145, and 156 DEGs were prevalent. In a similar vein, some DEGs were shared by three or four distinct infection time points.

Figure 1. Gene expression profiling and pathways enrichment analysis. (a) and (b) venn diagram to display the common and exclusive DEGs and enriched pathways. (c) Enriched pathways at 12hrs of ZIKV infection. (d) Enriched pathways at 24hrs.

We can see that two enhanced pathways were present at every infection time point based on the enriched pathway venn diagram analysis. For 24 hours, 48 hours, 72 hours, and 9 days, respectively, the 15, 3, 2, and 1 enriched pathways were specific. There were two common enriched routes between 12 and 24 hours, 12 and 48 hours, 12 and 72 hours, and 12 hours and 9 days, in that order. There were common enriched pathways between 24 and 48 hours, 24 and 72 hours, 24 hours and 9 days, 48 and 72 hours, 48 hours and 9 days, and 72 hours and 9 days, corresponding to 57, 58, 18, 50, 17, and 17 enriched pathways (Figure 1b). The 12 hour infection period was associated with enriched cytokine-cytokine receptor interaction and TNF signaling pathways, which were likewise enriched for all other ZIKV infection time points and are well-established for the majority of infection cases (Figure 1c). Among the highly enriched pathways at 24 hours of ZIKV infection were MAPK signaling, cytokine-cytokine receptor interaction, PI3K-AKT, TNF signaling, NK cell-mediated cytotoxicity, apoptosis, FoxO signaling, neurotrophin signaling, NF-kB, TLR, cell cycle, and neuroactive ligand-receptor interaction (Figure 1d). For the majority of illnesses, all of these routes seem to be very relevant. Among the substantially enriched pathways for 48 hours of ZIKV infection were TNF signaling, MAPK, PI3K-AKT, cytokine-cytokine receptor interaction, NF-kB, FoxO, TLR, CAMs, NK cell-mediated cytotoxicity, antigen presentation and processing, JAK-STAT, cell cycle, and apoptosis. Most of the significantly enriched pathways were common with 24 and 48 hours after infection for 72 hours and 9 days (Figure 2a—2c). Based on these findings, we can conclude that the length of the infection may increase the number of altered biological processes, but overall the major pathways stay the same for the whole period of the infection, with the exception of the first 12 hours.

Figure 2. Enriched pathways at (a) 48hrs, (b) 72hrs, and (c) 9 days of ZIKV infection.

Critical infectious and inflammatory pathways are dominantly affected as a result of ZIKV and DENV infection: We have carried out a comparative analysis of gene expression patterns and the enriched biological functions after our investigation of the gene expression patterns and the biological functions. In order to examine the precise function of the modified genes, we first analyzed each pathway at the individual gene level and then the pathway components. The bulk of the genes in this instance are associated with cytokine, TLR, NF-kB, and the ubiquitin proteasomal system, among other pathways that may also represent immune system components. We have also seen markedly changed pathways in this area. For the majority of ZIKV infection time durations after 12 hours, the highly enriched pathways included cytokine-cytokine receptor interaction, TNF signaling, NK cell-mediated cytotoxicity, apoptosis, FoxO signaling, neurotrophin signaling, NF-kB, TLR, inflammatory mediator regulation of TRP channel, TCR signaling, TGFB signaling, and neuroactive ligand-receptor interaction (Figures 1c, 1d, 2a, 2b, and 2c).

Following our investigation of the ZIKV infection gene expression patterns, we applied the same methodology to the DENV infection, comparing the enriched pathways and the DEGs associated with DF, DHF, convalescent, and overall infections (Figure 3a). In order to identify the shared DEGs and biological roles between ZIKV and DENV, we have finally created a list of all DEGs as well as the enriched pathways of both diseases (Figure 3b). In the event of a DENV infection, DF, DHF, convalescent, and overall DEGs shared a significant number of DEGs, and nearly all of the pathways were shared by all of them with very few exceptions (Figure 3a). When DEGs and enriched pathways between ZIKV and DENV were compared, it was found that 126 DEGs and 56 enriched pathways were shared by both illnesses (Figure 3b). There were 60 enriched pathways that were exclusive to ZIKV and no pathways that were specific to DENV. Of these, 1849 DEGs were specific to ZIKV and 696 DEGs were specific to DENV.

Docking profiling reveals potential putative targets against ZIKV and DENV: Lastly, we have looked into possible herbal remedies for the widespread conditions that are present in each of these illness cases. This study provides additional putative biomarkers in addition to the suspected herbal medications. Based on docking profiling, we find that IFNG has ∆G = -7.9 kcal/mol; NOS2 has ∆G = -9.2 kcal/mol; SLC11A1 has ∆G = -8.8 kcal/mol; IL4 has ∆G = -7.2 kcal/mol; TLR4 has ∆G = -7.1 kcal/mol; and IL1B has ∆G = -7.0 kcal/mol. Table 1 displays the majority of the target proteins that have a higher binding affinity with fisetin. Additionally, we delved deeply to find the precise binding pocket, as depicted in Figure 4. Docking investigations lead us to the conclusion that fisetin may be a viable herbal medicine intended to target ZIKV and/or DENV infection in humans.

Figure 3. Comparative analysis of gene expression and biological functions. (a) DEGs of DF, DHF, and convalescent and the enriched pathways of DF, DHF, and convalescent. (b) Comparison between the DEGs and the enriched pathways of ZIKV and DENV.

Figure 4. Docking profiling. (a)—(p) represents the docking profiling for the important proteins which could be the potential targets of the herbal drugs quercetin, apigenin, and fisetin.

 

Table 1: List of top enzyme-ligand complex showing remarkable binding energies

S. No.	Enzyme-ligand complex	Binding energy (Kcal/mol)	Number of HBs
1.	ARHGAP15-Quercetin	-7.0	2
2.	FOXO1-Apigenin	-6.4	1
3.	FOXO3-Apigenin	-6.4	2
4.	IFNG-Fisetin	-7.9	3
5.	IFNGR-Fisetin	-6.8	3
6.	IL1A-Fisetin	-5.8	2
7.	IL1B-Apigenin	-7.0	2
8.	IL4-Fisetin	-7.2	3
9.	IL10-Apigenin	-6.9	2
10.	IL13-Fisetin	-5.9	3
11.	MBP(MBL)-Resveratrol	-6.2	2
12.	NOS2-Fisetin	-9.2	2
13.	SLC11A1-Fisetin	-8.8	4
14.	TLR2-Apigenin	-7.9	1
15.	TLR4-Fisetin	-7.1	1
16.	TNFA-Quercetin	-6.2	2

 

 

 

4. Discussion

We used publicly accessible information to compare the gene expression patterns for human ZIKV and DENV infections, and we also carried out pathway enrichment analysis. After the DEGs and enriched pathways were identified as common and particular DEGs/biological activities in the cases of ZIKV and DENV infections, the DEGs and pathways were further compared for both cases. We find that a large fraction of ZIKV and DENV DEGs overlap, and that all of the DENV-induced altered pathways exclusively belong to the ZIKV altered pathway list in terms of biological functions. These findings suggest that ZIKV and DENV infections are highly similar in terms of gene expression and possibly altered biological function levels. Furthermore, we performed docking profiling of the proteins inferred to chosen DEGs based on network analysis against the herbal medicines in order to discover potential targets for ZIKV and DENV infections with herbal medications. This work offers a unique comparison of the important changes in gene expression and the functional changes that occurred after possible herbal medicines.

Dengue and Zika are mosquito-borne flaviviruses that are closely related in terms of their cycles of transmission, prevalence in the tropics, and sickness symptoms. The primary flaviviruses carried by Aedes mosquitoes in the tropics and subtropics are the four Dengue viruses; however, the recent emergence of the Zika virus has confounded the selection and interpretation of diagnostic tests. The principal vectors of the Dengue and Zika viruses are mosquitoes of the Aedes species, particularly Aedes aegypti, which is widely distributed in the tropics and subtropics. Any of these viruses can induce an acute illness that manifests as an infection along with symptoms like fever, rash, myalgia, and arthralgia. A small percentage of hospitalized patients would experience fewer than 5% case-fatality rate due to clinical management of severe Dengue, which can be fatal in some cases. Dengue has been more common during the past few decades, doubling every ten years, with an estimated 58 million cases of symptomatic illnesses and 13,000 deaths worldwide in 2013[4-6, 26, 56-68].

Infections with the Dengue and Zika viruses usually show no symptoms. It takes a few days to two weeks from the moment of infection to the point at which symptomatic individuals begin to develop the disease. The best times to find serum RNA from the Dengue and Zika viruses are two days prior to and one week following the onset of sickness. It could take longer for certain people, especially pregnant women, to recognize the Zika virus’s nucleic acid. Zika virus RNA has also been found in a number of body fluids, including as breast milk, urine, saliva, amniotic fluid, whole blood, and semen. Some data indicate that viral RNA might be present in some of these specimens for extended periods of time or at higher concentrations. Similar to Dengue viral RNA detection, the incidence and duration of serum NS1 (Dengue virus nonstructural protein-1) antigen detection are comparable[68-80].

Numerous human disorders, including asthma, rheumatoid arthritis, inflammatory bowel disease, Crohn’s disease, and tendinitis, are known to be significantly influenced by inflammation. We also see that many immune system and inflammation-related pathways were changed in this instance[21, 31, 50-56]. Chronic inflammatory response has a critical role in the development of Alzheimer’s disease, diabetes, atherosclerosis, cancer, and obesity. When the inflammatory process is in control, it helps keep the body from collapsing catastrophically; when it is not, it damages the body and results in unintentional deterioration. As a result, inflammation may have two negative effects. For a very long time, people have employed herbal treatments to treat or prevent ailments, including inflammatory problems. They are also an invaluable source of chemical building blocks that have developed into medicines that are necessary for contemporary health. Their advantages in treating inflammatory diseases, however, have not yet been thoroughly examined. These days, chemical pharmaceuticals are frequently found on pharmacy shelves. We might take these advanced chemical treatments for granted. But from ancient times, herbal medicine has been utilized as a rich source of medicinal compounds. Herbal medicine plays a significant role in both traditional and modern medicine, and this trend is probably going to continue. Since inflammation is a major factor in the development of many human diseases, it is imperative that we increase our scientific understanding of the beneficial effects of herbal medicines on inflammatory disorders. It is also believed that further research is necessary to elucidate the precise molecular actions of herbal remedies. But with its use and application growing quickly, it’s important to recognize the role and promise of herbal therapy in the inflammatory process.

The cytokine-cytokine receptor interaction and TNF signaling pathways were considerably enriched across the various temporal DEGs study for most of the ZIKV infection time points. These pathways are well-established for the majority of infection patients. After 12 hours of infection, these pathways were enriched (Figure 1c). FoxO signaling, neurotrophin signaling, apoptosis, PI3K-AKT, TNF signaling, cytokine-cytokine receptor interaction, NF-kB, TLR, cell cycle, and neuroactive ligand-receptor interaction were among the most highly enriched pathways. We have examined potential herbal remedies for the infections that are so prevalent in these two ailments. This work offers hypothetical herbal remedies as well as potential biomarkers. IFNG has a ∆G value of -7.9 kcal/mol with fisetin, IL4 has a ∆G value of -7.2 kcal/mol with fisetin, TLR4 has a ∆G value of -7.1, and NOS2 has a ∆G value of -9.2 kcal/mol with fisetin, according to docking profiling. Among the proteins with higher binding affinities, fisetin was the most often seen target (Table 1).

As a result, this comparative study will provide an overview of the important research reports regarding the biological processes linked to inflammation and infection, as well as potential herbal remedies for disorders associated with inflammation. Such work could involve anything from basic research to investigate the potential benefits of herbal remedies for inflammatory conditions to clinical trials to evaluate those benefits, combinatorial applications of herbal remedies with traditional inflammatory disease treatments, or analyses of the bioactive compounds in medicinal plant extracts for those conditions. As a result, we view this work as a significant advancement in our knowledge of the immune system’s function, as well as the inflection and inflammatory pathways, and potential herbal remedies for addressing the particular pathways and their essential elements.

5. Conclusions

In this study, we looked at the components of signaling pathways for ZIKV and DENV infection and identified potential targets and herbal remedies that might be able to impede the signaling pathways that have been triggered. After dividing the patients into multiple subgroups with similar DEGs, we determined the upstream and downstream signaling pathways of DEGs in each category. Lastly, we determined which herbal remedies focus on both upstream and downstream signaling. These results suggest that, although the main pathways remain largely same, the length of the infection may increase the number of biological processes that are altered, with the exception of the first phase (i.e., 12 hours). Docking investigations may lead us to believe that fisetin is a promising herbal medicine to treat human ZIKV and/or DENV infections.

Author Contributions: Conceptualization, RJ, AAF, MME, AMK, SAA, AMA, MT, MM, and EAI; methodology, RJ, AAF, MME, AMK, SAA, AMA, MT, MM, and EAI software, EIA and MM; validation, RJ, MM, and EIA; formal analysis, RJ,  MM., MME, and EIA.; investigation, RJ, MM, and EIA.; resources,  MM, and EIA.; data curation,  MM and EIA; writing—original draft preparation, RJ, AAF, MME, AMK, AMA, MM, and EAI; writing—review and editing, RJ, AAF, MME, AMK, SAA, AMA, MT, MM, and EAI; visualization, EIA, and MM; supervision, MM and EIA; project administration, MM and EIA; funding acquisition, EIA. All authors have read and agreed to the published version of the manuscript.

Funding: This research was funded by Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, grant number FP-5-42 and The APC was funded by FP-5-42.

Acknowledgments: We are thankful to DSR, KAU and for providing us the resources and the facility to carry out the work to Special Infectious Agents Unit, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, Saudi Arabia, Medical Laboratory Sciences Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Data availability statement: All data generated or analyzed during this study are included in this published article.

References

1. Jones, K.E., et al., Global trends in emerging infectious diseases. Nature, 2008. 451(7181): p. 990-3.
2. Morens, D.M., G.K. Folkers, and A.S. Fauci, The challenge of emerging and re-emerging infectious diseases. Nature, 2004. 430(6996): p. 242-9.
3. Gorshkov, K., et al., Zika Virus: Origins, Pathological Action, and Treatment Strategies. Frontiers in Microbiology, 2019. 9: p. 3252.
4. Bardina, S.V., et al., Enhancement of Zika virus pathogenesis by preexisting antiflavivirus immunity. Science, 2017. 356(6334): p. 175-180.
5. Monath, T.P., Dengue: the risk to developed and developing countries. Proc Natl Acad Sci U S A, 1994. 91(7): p. 2395-400.
6. Clarke, T., Break-bone fever. Nature, 2002. 416(6882): p. 672-674.
7. Brown, S.-A., N. Sandhu, and J. Herrmann, Systems biology approaches to adverse drug effects: the example of cardio-oncology. Nature Reviews Clinical Oncology, 2015. 12(12): p. 718-731.
8. Hao, X., et al., Antitumor effect of luteolin proven by patient-derived organoids of gastric cancer. Phytother Res, 2023. 37(11): p. 5315-5327.
9. Huang, M.-Y., et al., Anticancer drug discovery from Chinese medicinal herbs. Chinese Medicine, 2018. 13(1): p. 35.
10. Khan, H.A., et al., Antiproliferative effect of Solanum nigrum L. water extract on breast can-cer cells: potential roles of apoptosis and oxidative stress. Cell Mol Biol (Noisy-le-grand), 2023. 69(10): p. 136-142.
11. Koosha, S., et al., An Association Map on the Effect of Flavonoids on the Signaling Pathways in Colorectal Cancer. International Journal of Medical Sciences, 2016. 13(5): p. 374-385.
12. Malabanan, J.W.T., et al., Enhancing Physicochemical Properties and Biocompatibility of Hollow Porous Iron Oxide Nanoparticles through Polymer-Based Surface Modifications. ACS Appl Bio Mater, 2023.
13. Mitra, S., et al., Piperlongumine: the amazing amide alkaloid from Piper in the treatment of breast cancer. Naunyn Schmiedebergs Arch Pharmacol, 2023.
14. Tascilar, M., et al., Complementary and alternative medicine during cancer treatment: beyond innocence. Oncologist, 2006. 11(7): p. 732-41.
15. Anwer, T., et al., Hepatoprotective potential of diosmin against hepatotoxic effect of isoniazid and rifampin in wistar rats. Hum Exp Toxicol, 2023. 42: p. 9603271221149199.
16. Huwait, E. and M. Mobashir, Potential and Therapeutic Roles of Diosmin in Human Diseases. Biomedicines, 2022. 10(5).
17. Lewinska, A., et al., Diosmin-induced senescence, apoptosis and autophagy in breast cancer cells of different p53 status and ERK activity.Toxicol Lett, 2017. 265: p. 117-130.
18. Mustafa, S. and M. Mobashir, LC-MS and docking profiling reveals potential difference between the pure and crude fucoidan metabolites.Int J Biol Macromol, 2020. 143: p. 11-29.
19. Adams, J.L., et al., Big opportunities for small molecules in immuno-oncology. Nature Reviews Drug Discovery, 2015. 14(9): p. 603-622.
20. Ahmed, S., et al., A Network-Guided Approach to Discover Phytochemical-Based Anticancer Therapy: Targeting MARK4 for Hepatocellular Carcinoma. Front Oncol, 2022. 12: p. 914032.
21. Bai, L. and S. Wang, Targeting Apoptosis Pathways for New Cancer Therapeutics. Annual Review of Medicine, 2013. 65(1): p. 139-155.
22. Szeto, G.L. and S.D. Finley, Integrative Approaches to Cancer Immunotherapy. Trends in Cancer, 2019. 5(7): p. 400-410.
23. Ackermann, M. and K. Strimmer, A general modular framework for gene set enrichment analysis. BMC Bioinformatics, 2009. 10(1): p. 47.
24. Alanni, R., et al., A novel gene selection algorithm for cancer classification using microarray datasets. BMC Medical Genomics, 2019. 12(1): p. 10.
25. Allen, T., Detecting Differential Gene Expression Using Affymetrix Microarrays. The Mathematica Journal, 2013. 15.
26. Bajrai, L.H., et al., Gene Expression Profiling of Early Acute Febrile Stage of Dengue Infection and Its Comparative Analysis With Streptococcus pneumoniae Infection. Front Cell Infect Microbiol, 2021. 11: p. 707905.
27. Bransburg-Zabary, S., et al., Individual and meta-immune networks. Physical Biology, 2013. 10(2): p. 025003.
28. Postdoctoral Associate (Computational Biology) with Stony Brook University | 374684.pdf.
29. Anwer, S.T., et al., Synthesis of Silver Nano Particles Using Myricetin and the In-Vitro Assessment of Anti-Colorectal Cancer Activity: In-Silico Integration. Int J Mol Sci, 2022. 23(19).
30. El-Kafrawy, S.A., et al., Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma. Front Genet, 2022. 13: p. 880440.
31. Eldakhakhny, B.M., et al., In-Silico Study of Immune System Associated Genes in Case of Type-2 Diabetes With Insulin Action and Resistance, and/or Obesity. Front Endocrinol (Lausanne), 2021. 12: p. 641888.
32. Helmi, N., D. Alammari, and M. Mobashir, Role of Potential COVID-19 Immune System Associated Genes and the Potential Pathways Linkage with Type-2 Diabetes. Comb Chem High Throughput Screen, 2022. 25(14): p. 2452-2462.
33. Kamal, M.A., et al., Gene expression profiling and clinical relevance unravel the role hypoxia and immune signaling genes and pathways in breast cancer: Role of hypoxia and immune signaling genes in breast cancer. Journal of Internal Medicine: Science & Art, 2020. 1.
34. Khouja, H.I., et al., Multi-staged gene expression profiling reveals potential genes and the critical pathways in kidney cancer. Sci Rep, 2022. 12(1): p. 7240.
35. Krishnamoorthy, P.K.P., et al., In-silico study reveals immunological signaling pathways, their genes, and potential herbal drug targets in ovarian cancer. Informatics in Medicine Unlocked, 2020. 20: p. 100422.
36. Mobashir, M., et al., An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation. Cells, 2022. 11(24).
37. Shannon, P., et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003. 13(11): p. 2498-504.
38. Alexeyenko, A., et al., Comparative interactomics with Funcoup 2.0. Nucleic Acids Research, 2012. 40(D1): p. D821-D828.
39. Alexeyenko, A. and E.L.L. Sonnhammer, Global networks of functional coupling in eukaryotes from comprehensive data integration.Genome Research, 2009. 19(6): p. 1107-1116.
40. Schmitt, T., C. Ogris, and E.L.L. Sonnhammer, FunCoup 3.0: database of genome-wide functional coupling networks. Nucleic Acids Research, 2014. 42(D1): p. D380-D388.
41. Studham, M.E., et al., Functional association networks as priors for gene regulatory network inference. Bioinformatics, 2014. 30(12): p. i130-i138.
42. Kim, S., et al., PubChem 2023 update. Nucleic Acids Res, 2023. 51(D1): p. D1373-D1380.
43. Kumari, J.L.J. and C. Sudandiradoss, Computational investigation of theoretical models of cleavable and uncleavable mucin 1 isoforms.Molecular BioSystems, 2013. 9(10): p. 2473-2488.
44. Bardwell, A.J., M. Abdollahi, and L. Bardwell, Docking sites on mitogen-activated protein kinase (MAPK) kinases, MAPK phosphatases and the Elk-1 transcription factor compete for MAPK binding and are crucial for enzymic activity. Biochemical Journal, 2003. 370(3): p. 1077-1085.
45. Bardwell, A.J., et al., A Conserved Docking Site in MEKs Mediates High-affinity Binding to MAP Kinases and Cooperates with a Scaffold Protein to Enhance Signal Transmission*. Journal of Biological Chemistry, 2001. 276(13): p. 10374-10386.
46. Vargiu, A.V. and H. Nikaido, Multidrug binding properties of the AcrB efflux pump characterized by molecular dynamics simulations. Proc Natl Acad Sci U S A, 2012. 109(50): p. 20637-42.
47. Gfeller, D., et al., The multiple‐specificity landscape of modular peptide recognition domains. Molecular Systems Biology, 2011. 7(1): p. 484-484.
48. Gfeller, D., O. Michielin, and V. Zoete, Shaping the interaction landscape of bioactive molecules. Bioinformatics, 2013. 29(23): p. 3073-9.
49. Grosdidier, A., V. Zoete, and O. Michielin, EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins, 2007. 67(4): p. 1010-25.
50. Akl, M.A., et al., Design, spectral, molecular modeling, antimitotic, analytical and mechanism studies of phenyl isothiocyanate Girard’s T derived metal complexes. BMC Chem, 2023. 17(1): p. 153.
51. Bairy, S.K., et al., Three‐Dimensional Quantitative Structure–Activity Relationship Studies on c‐Src Inhibitors Based on Different Docking Methods. Chemical Biology & Drug Design, 2009. 73(4): p. 416-427.
52. Campbell, S.J., et al., Ligand binding: functional site location, similarity and docking. Current Opinion in Structural Biology, 2003. 13(3): p. 389-395.
53. Ferreira, L.G., et al., Molecular Docking and Structure-Based Drug Design Strategies. Molecules, 2015. 20(7): p. 13384-13421.
54. Fischer, D., et al., A geometry-based suite of moleculardocking processes. Journal of Molecular Biology, 1995. 248(2): p. 459-477.
55. Liu, Y., et al., CB-Dock2: improved protein-ligand blind docking by integrating cavity detection, docking and homologous template fitting.Nucleic Acids Res, 2022. 50(W1): p. W159-W164.
56. Borchering, R.K., et al., Impacts of Zika emergence in Latin America on endemic dengue transmission. Nature Communications, 2019. 10(1): p. 5730.
57. Cattarino, L., et al., Mapping global variation in dengue transmission intensity. Science Translational Medicine, 2020. 12(528).
58. Chaturvedi, U.C., R. Nagar, and R. Shrivastava, Dengue and dengue haemorrhagic fever: implications of host genetics. FEMS Immunology & Medical Microbiology, 2006. 47(2): p. 155-166.
59. Chen, Y., et al., Dengue virus infectivity depends on envelope protein binding to target cell heparan sulfate. Nat Med, 1997. 3(8): p. 866-71.
60. Cordeiro, M.T., et al., Characterization of a dengue patient cohort in Recife, Brazil. The American journal of tropical medicine and hygiene, 2007. 77(6): p. 1128-34.
61. Dalrymple, N.A. and E.R. Mackow, Endothelial Cells Elicit Immune-Enhancing Responses to Dengue Virus Infection. Journal of Virology, 2012. 86(12): p. 6408-6415.
62. Dang, T.T., et al., Whole genome sequencing and genetic variations in several dengue virus type 1 strains from unusual dengue epidemic of 2017 in Vietnam. Virology Journal, 2020. 17(1): p. 7.
63. Datan, E., et al., Dengue-induced autophagy, virus replication and protection from cell death require ER stress (PERK) pathway activation.Cell Death & Disease, 2016. 7(3): p. e2127-e2127.
64. Guzman, M.G., et al., Dengue infection. Nature Reviews Disease Primers, 2016. 2(1): p. 16055.
65. Guzman, M.G., et al., Dengue: a continuing global threat. Nature Reviews Microbiology, 2010. 8(Suppl 12): p. S7-S16.
66. Kazmi, S.S., et al., A review on Zika virus outbreak, epidemiology, transmission and infection dynamics. Journal of Biological Research-Thessaloniki, 2020. 27(1): p. 5.
67. Martina, B.E.E., P. Koraka, and A.D.M.E. Osterhaus, Dengue Virus Pathogenesis: an Integrated View. Clinical Microbiology Reviews, 2009. 22(4): p. 564-581.
68. Morrison, A.C., et al., Epidemiology of Dengue Virus in Iquitos, Peru 1999 to 2005: Interepidemic and Epidemic Patterns of Transmission.PLoS Neglected Tropical Diseases, 2010. 4(5): p. e670.
69. OhAinle, M., et al., Dynamics of Dengue Disease Severity Determined by the Interplay Between Viral Genetics and Serotype-Specific Immunity. Science Translational Medicine, 2011. 3(114): p. 114ra128.
70. Pierson, T.C. and M.S. Diamond, The continued threat of emerging flaviviruses. Nature Microbiology, 2020. 5(6): p. 796-812.
71. Rodriguez-Barraquer, I., et al., Impact of preexisting dengue immunity on Zika virus emergence in a dengue endemic region. Science, 2019. 363(6427): p. 607-610.
72. Roth, A., et al., Concurrent outbreaks of dengue, chikungunya and Zika virus infections – an unprecedented epidemic wave of mosquito-borne viruses in the Pacific 2012–2014. Eurosurveillance, 2014. 19(41).
73. Rothman, A.L., Immunity to dengue virus: a tale of original antigenic sin and tropical cytokine storms. Nature Reviews Immunology, 2011. 11(8): p. 532-543.
74. Screaton, G., et al., New insights into the immunopathology and control of dengue virus infection. Nature Reviews Immunology, 2015. 15(12): p. 745-759.
75. Seema and S.K. Jain, Molecular mechanism of pathogenesis of dengue virus: Entry and fusion with target cell. Indian Journal of Clinical Biochemistry, 2005. 20(2): p. 92-103.
76. Sim, S. and M.L. Hibberd, Genomic approaches for understanding dengue: insights from the virus, vector, and host. Genome Biology, 2016. 17(1): p. 38.
77. Sun, P., et al., Sequential Waves of Gene Expression in Patients with Clinically Defined Dengue Illnesses Reveal Subtle Disease Phases and Predict Disease Severity. PLoS Neglected Tropical Diseases, 2013. 7(7): p. e2298.
78. Thomas, S.J., NS1: A corner piece in the dengue pathogenesis puzzle? Science Translational Medicine, 2015. 7(304): p. 304fs37.
79. Ubol, S., et al., Differences in Global Gene Expression in Peripheral Blood Mononuclear Cells Indicate a Significant Role of the Innate Responses in Progression of Dengue Fever but Not Dengue Hemorrhagic Fever. The Journal of Infectious Diseases, 2008. 197(10): p. 1459-1467.
80. Warsi, M.K., et al., Comparative Study of Gene Expression Profiling Unravels Functions Associated with Pathogenesis of Dengue Infection.Curr Pharm Des, 2020. 26(41): p. 5293-5299.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of Global Journal of Basic Science and/or the editor(s). Global Journal of Basic Science and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Copyright: © 2025 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).