Efficient analysis of the toxicity and mechanisms of Hexaconazole and two other triazole fungicides: insights from integrated network toxicology, molecular docking and bioinformatics data

doi:10.21203/rs.3.rs-5318182/v1

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Efficient analysis of the toxicity and mechanisms of Hexaconazole and two other triazole fungicides: insights from integrated network toxicology, molecular docking and bioinformatics data

https://doi.org/10.21203/rs.3.rs-5318182/v1

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Background: The safety of food grains is crucial for human health. Hexaconazole, Propiconazole, and Prothioconazole are widely used triazole fungicides primarily employed in agriculture for the control of plant diseases, aiming to enhance crop yield and quality.

Objective: This research aims to elucidate the potential toxic targets and underlying molecular mechanisms of brain and liver damage induced by exposure to the three fungicides through network toxicology combined with molecular docking and bioinformatics data analysis.

Method and results: Toxicity analysis through the ADMETlab database showed that the toxicity of the three fungicides was mainly associated with neurotoxicity and liver injury. Systematically utilizing GeneCards, OMIM and other databases and through Cytoscape tools, we identified potential and core targets (including EGFR, CASP3, ESR1, PPARG, TP53, HSP90AA1, and PTGS2) associated with fungicides and brain injury or liver injury. GO and KEGG enrichment analyses indicate that these targets are associated with pathways related to cancer, the PI3K-Akt signaling pathway, organic cyclic compounds, and organic nitrogen compounds. Molecular docking simulations conducted using AutoDock confirmed the high-affinity binding interactions between the fungicides and key target proteins.

Conclusions: This study provides a theoretical foundation for understanding the molecular mechanisms underlying the neurotoxicity and hepatotoxicity induced by Hexaconazole, Propiconazole, and Prothioconazole, while establishing a foundational framework for the development of prevention and treatment strategies related to triazole pesticide-associated brain and liver injuries. Our findings underscore the potential risks these three pesticides pose to brain and liver health, highlighting the need for further epidemiological and clinical research in the future.

Hexaconazole

Propiconazole

Prothioconazole

Network toxicology

Molecular docking

Brain injury

Liver injury

Triazole fungicides represent a significant class of agricultural chemicals widely utilized for the control of various fungal pathogens affecting crops [1]. Characterized by their five-membered ring structure containing three nitrogen atoms, triazoles function primarily by inhibiting the biosynthesis of ergosterol, an essential component of fungal cell membranes [2]. Among the most commonly used triazole fungicides are Hexaconazole (HA), Propiconazole (PP) and Prothioconazole (PT) which exhibit broad-spectrum efficacy against numerous fungal diseases, including rusts, powdery mildews, and blights [c]. These compounds are particularly valued for their systemic properties, allowing them to be absorbed and translocated within plant tissues, thereby providing effective protection against both foliar and soil-borne pathogens [4].

Research on the toxicity of triazole fungicides has garnered increasing attention due to their widespread use in agriculture and potential environmental and health impacts. Triazoles, including well-known compounds such as Hexaconazole, Tebuconazole, and Propiconazole, have been demonstrated to possess varying degrees of toxicity to non-target organisms, including aquatic life, terrestrial invertebrates, and mammals [5–7].Furthermore, significant efforts have been made to evaluate the ecological risks associated with triazole fungicides. Toxicological assessments, including acute and chronic exposure studies, have revealed adverse effects on aquatic organisms such as fish and invertebrates, as well as terrestrial species like bees and earthworms [8, 9]. Recent studies have highlighted the neurotoxic potential of triazole compounds, linking exposure to cognitive impairments and neurodegenerative conditions [10–12]. Moreover, the involvement of triazoles in disrupting hepatic metabolic pathways raises concerns about their long-term effects on liver health [13, 14]. However, the precise biochemical pathways through which triazole fungicides induce neurotoxicity and hepatotoxicity remain poorly characterized. Therefore, the mechanism of brain injury and liver injury induced by triazole fungicides still needs further study.

Advancements in toxicology have highlighted the importance of an integrated approach to elucidate the complex interactions between chemical agents and biological systems. Network toxicology offers a promising framework for this analysis, enabling researchers to examine the relationships between chemical exposure, biological pathways, and toxicological outcomes [15]. By leveraging bioinformatics tools, researchers can integrate large datasets, including gene expression profiles, protein interactions, and metabolic pathways, to identify key molecular targets and pathways affected by these fungicides.

Molecular docking techniques further enhance our understanding of the interactions between triazole fungicides and their biological targets [16]. This computational method allows for the prediction of binding affinities and interaction modes of fungicides with specific proteins, providing insights into their mechanistic action at the molecular level [17]. By applying molecular docking alongside network toxicology and bioinformatics, we can gain a comprehensive understanding of how Hexaconazole and its counterparts exert their toxic effects.

This study aims to efficiently analyze the toxicity and mechanisms of Hexaconazole, Propiconazole and Prothioconazole through an integrated approach that combines network toxicology, molecular docking, and bioinformatics data. By elucidating the key pathways and molecular interactions involved, this research will contribute to a better understanding of the risks associated with triazole fungicide use and inform future regulatory decisions and risk assessment strategies. Ultimately, our findings aim to promote safer agricultural practices and minimize the impact on human health of these widely used agrochemicals.

2.1 Initial network toxicology predictive analysis of HA, PP and PT

The terms “Hexaconazole (HA)”, “Propiconazole (PP)” and “Prothioconazole (PT)” were queried in the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) to obtain the standardized SMILES codes for these compounds. The SMILES codes of the three triazole compounds were then input into the ADMETlab 3.0 analysis tool (https://admetlab3.scbdd.com/). Utilizing the structural modeling capabilities of this platform, we predicted the toxicological properties of HA, PP, and PT, providing an initial understanding of their toxicological profiles. The predicted results were downloaded.

2.2 Targets collection of HA, PP and PT

First, a search was conducted in the PubChem database for “Hexaconazole”, “Propiconazole” and “Prothioconazole” to identify the best matches and verify the accuracy of their names and molecular formulas. Subsequently, the 2D structure SDF files were downloaded. The 2D structures are shown in Supplementary Fig. S1, with CAS numbers 103556-63-4, 60207-90-1, and 178928-70-6, respectively. Based on the SDF files, potential targets of "Homo sapiens" specific HA, PP,PT were searched from ChEMBL (https://www.ebi.ac.uk/chembl/) and Swiss Target Prediction database (http://www.swisstargetprediction.ch/) [18]. In addition, the Swiss-Target-Prediction tool is used to identify any ignored targets. The structural information throughout the search results is carefully cross-verified for consistency. Then open the Excel table and select the target with probability > 0 as the target of HA, PP and PT. Finally, integration, de-duplication and gene naming coordination were carried out by UniProt database (https://www.uniprot.org/) to establish the target library of HA, PP and PT.

2.3 Collection of targets related to brain and liver injury

We conducted a comprehensive search of both domestic and international literature, as well as utilizing the GeneCards (https://www.genecards.org/) and OMIM (https://omim.org/) databases, using the keywords “brain injury” and “liver injury” to identify relevant targets. To ensure that the genes obtained were highly associated with brain and liver injuries, we set the “score” threshold at the median value, selecting genes with scores above the median to establish a library of neurotoxic targets. We considered the intersection of targets related to HA, PP, and PT with brain and liver injury targets as potential targets for pesticide-induced brain or liver damage. Venn diagrams were employed to filter the common potential targets.

2.4 Protein-protein interaction network construction and core targets screening

The intersection genes representing potential targets for brain or liver injury induced by the three pesticides were individually input into the STRING database (https://cn.string-db.org/). We limited the species to “Homo sapiens”, set the “minimum required interaction score (FDR)” to “medium confidence > 0.4” and selected “FDR strict value ≤ 0.05” for analysis. These parameters ensured a robust analysis of the active target proteins corresponding to our target genes.

Subsequently, the results generated by STRING were imported into the network biology visualization application Cytoscape (version 3.10.0), which computes parameters for each node in the network diagram and displays molecular connections [19]. This allowed for the calculation of the topological properties of the network nodes and edges, resulting in a protein-protein interaction (PPI) network diagram. The criteria for screening core targets were as follows: nodes corresponding to targets that simultaneously met the conditions of betweenness centrality (BC) > median, closeness centrality (CC) > median, and degree centrality (DC) > median were selected as core targets for brain or liver injury induced by HA, PP and PT. Additionally, we utilized the MCODE plugin to validate the core genes within these PPI networks.

2.5 Functional enrichment and pathway analysis of targets for diseases induced by HA, PP and PT

2.5.1 GO and KEGG analysis of potential targets

To analyze the enrichment of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we imported the targets associated with brain or liver damage induced by HA, PP, and PT into the Metascape database (https://metascape.org/gp/index.html). This database serves as a comprehensive resource for gene functions, encompassing extensive information on biological processes (BP), cellular components (CC), and molecular functions (MF) [20]. The selection criteria included a minimum overlap of 3 and a minimum enrichment factor of 1.5, with a significance level set at P < 0.01. The overlapping targets were uploaded to the Metascape database, where human species were selected, and the results were submitted. The top 10 GO terms and the top 20 KEGG pathways were selected based on ascending p-values. GO and KEGG diagrams were generated using the Microbiotics website (http://www.bioinformatics.com.cn/) and R software. The results of the bioinformatics analyses produced online KEGG pathway enrichment maps and GO term enrichment maps to visualize the interactions among pathways related to brain or liver damage induced by the three pesticides.

2.5.2 KEGG analysis of core targets

We performed KEGG pathway enrichment analysis of the core targets associated with brain and liver damage induced by HA, PP, and PT using the Metascape database. The enrichment results were visualized to effectively interpret and present our findings. This multifaceted approach aims to comprehensively investigate the signaling pathways and biological processes mediated by the core targets related to brain and liver damage induced by these three pesticides, thereby elucidating and emphasizing the key mechanisms involved in this context.

2.6 Drug-target-pathway network of HA, PP and PT

The drug-target-pathway network provides a clear overview of how drugs affect pathways through specific targets and illustrates the relationships between these key targets and pathways, allowing for the identification of the most critical targets and pathways. First, an attribute table was constructed in Excel, containing the core targets associated with the three triazole pesticides and all pathways corresponding to each core target. Next, Cytoscape was opened, and the attribute table was uploaded in the Network file to load the locations, set the starting points, and visualize the results.

2.7 Molecular docking of key targets with HA, PP and PT

To investigate the molecular interactions and binding patterns between HA, PP, PT, and the identified key target proteins, we conducted molecular docking simulations. This theoretical structure-based approach enabled the predictive modeling of receptor-ligand binding geometries and affinities. The crystal structures of the core target proteins were obtained from the RCSB PDB database (https://www.rcsb.org/) and preprocessed using PyMOL 3.0 (https://www.pymol.org/) by removing solvent water molecules and any pre-existing ligands bound to the target proteins. The processed protein structures were then imported into AutoDock Tools 1.5.6 (https://autodock.scripps.edu/) for hydrogen addition, charge calculation, and non-polar hydrogen merging to generate receptor models. The structures of HA, PP, and PT compounds were downloaded from the PubChem database. The mechanical structures were optimized using Chem3D (https://revvitysignals.com/products/research/chemdraw) to minimize energy and designated as ligands. The docking range was determined by adjusting the center coordinates and grid box size parameters. Following this, a genetic algorithm was selected, and molecular docking simulations were executed via command line using AutoDock Vina. Finally, the predicted binding modes and affinities were visualized and analyzed using PyMOL 3.0.

2.8 Collection and analysis of bioinformatics data

2.8.1 Identification and validation of brain and liver cancer-related DEGs

To further elucidate the mechanistic impact of central targets associated with brain and liver damage induced by three pesticides on cancer, we obtained transcriptomic datasets from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/), selecting GSE30563 and GSE117361 for detailed analysis. The GEOexplorer tool (https://geoexplorer.rosalind.kcl.ac.uk/) was employed to determine differentially expressed genes (DEGs) between brain and liver tumors with normal tissue samples. Multichip analysis was conducted using the SVA and limma packages in R software (https://www.r-project.org/), allowing for batch correction and calculation of fold-change (FC) and adjusted p-values. The thresholds for identifying DEGs were set at |log (FC)| > 1 and P < 0.05. Furthermore, the overlapping DEGs filtered from both datasets were classified as cancer-related targets.

2.8.2 Analysis of brain and liver cancer-related target genes

Enrichment analysis (GO and KEGG) of differentially expressed genes (DEGs), commonly upregulated genes, and commonly downregulated genes was performed using the Metascape database (https://metascape.org/gp/index.html). The selection criteria included a minimum overlap of 3, a minimum enrichment factor of 1.5, and a significance level of P < 0.01.

Additionally, we employed the Gene Expression Profiling Interactive Analysis (GEPIA) tool (http://gepia.cancer-pku.cn/) to perform survival analysis on five key expressed genes. Our objective was to investigate the genetic alterations affecting each target gene and to test hypotheses regarding the recurrence patterns of genetic alteration events and their genomic context in brain tumors and liver tumors [21].

3.1 Toxicity analysis of HA, PP and PT

Predictions from the ADMETlab 3.0 database revealed that the co-toxicity of HA, PP, and PT is primarily associated with neurotoxicity and liver damage (Table 1). Additionally, the database provided predictions regarding the genotoxicity, respiratory toxicity, and skin irritation potential of these three pesticides. A comprehensive review of both the database and relevant literature was conducted to predict the molecular mechanisms underlying these two diseases.

Table 1

The prediction toxicity of Hexaconazole, Propiconazole and Prothioconazole.
Ingredient	Property	Probability
Hexaconazole	Drug-induced Neurotoxicity	0.875 (++)
Hexaconazole	Human Hepatotoxicity	0.600 (+)
Hexaconazole	Genotoxicity	0.564 (+)
Hexaconazole	Respiratory toxicity	0.749 (++)
Hexaconazole	Skin sensitization	0.710 (++)
Propiconazole	Drug-induced Neurotoxicity	0.907 (+++)
Propiconazole	Human Hepatotoxicity	0.685 (+)
Propiconazole	Genotoxicity	0.872 (++)
Propiconazole	Respiratory toxicity	0.672 (+)
Propiconazole	Skin sensitization	0.658 (+)
Prothioconazole	Drug-induced Neurotoxicity	0.821 (++)
Prothioconazole	Human Hepatotoxicity	0.477 (-)
Prothioconazole	Genotoxicity	0.999 (+++)
Prothioconazole	Respiratory toxicity	0.723 (++)
Prothioconazole	Skin sensitization	0.505 (+)
Classification endpoints of the prediction probability values: 0-0.1 (---), 0.1–0.3 (--), 0.3–0.5 (-), 0.5–0.7 (+), 0.7–0.9 (++), 0.9–1.0 (+++). Larger probability represents a greater risk of toxicity.

3.2 Targets analysis of HA, PP and PT induce diseases

We initially integrated data from the ChEMBL and Swiss-Target-Prediction databases, removing duplicate entries to identify 120 HA targets, 32 PP targets, and 118 PT targets. Following the removal of duplicates, we also identified 5,464 targets highly associated with brain injury and 5,071 targets highly associated with liver injury from the GeneCards and OMIM databases. The list of these target genes can be found in Appendix A.

Using a comprehensive Venn diagram approach, we identified a total of 94 HA cross-targets related to brain injury (Fig. 1A), 28 PP cross-targets related to brain injury (Fig. 1B), 84 PT cross-targets related to brain injury (Fig. 1C), 92 HA cross-targets related to liver injury (Fig. 1D), 24 PP cross-targets related to liver injury (Fig. 1E), and 84 PT cross-targets related to liver injury (Fig. 1F). These targets are considered potential candidates associated with brain or liver injuries induced by the three triazole pesticides.

3.3 PPI network and core target acquisition

Potential targets associated with brain or liver injury induced by the three triazole pesticides were mapped to the protein-protein interaction (PPI) network using the STRING database (Supplementary Fig. S2), resulting in a highly interconnected network. Metrics such as the number of nodes, number of edges, average node degree, and PPI enrichment p-value are detailed in Appendix B. The topological analysis of the network, including degree and betweenness centrality metrics, was performed using Cytoscape. Additionally, we constructed a PPI network diagram to visually represent the results (Fig. 2).

Then, we employed multiple complementary algorithms, specifically the Betweenness Centrality (BC), Closeness Centrality (CC), and Degree Centrality (DC) algorithms from the CytoHubba plugin, to identify highly connected hub targets. The targets identified as hubs resulted in the selection of a subset of 35 target genes closely related to HA-induced brain injury, 34 target genes closely related to HA-induced liver injury, 9 target genes closely related to PP-induced brain injury, 8 target genes closely related to PP-induced liver injury, 26 target genes closely related to PT-induced brain injury, and 26 target genes closely related to PT-induced liver injury (Supplementary Tables S1-S6). A centralized PPI network diagram was constructed to illustrate the interactions among these hub targets (Fig. 3).

3.4 GO analysis

Gene Ontology (GO) analysis was conducted on potential targets associated with brain or liver injuries induced by three types of fungicides using the Metascape database. Our findings revealed significant GO terms (Appendix C). The top ten most enriched terms across the Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories were prioritized based on gene count and are visually represented in the enrichment analysis plot (Fig. 4). In terms of Biological Process (BP), the potential targets associated with brain and liver injuries induced by the three triazole fungicides exhibited significant enrichment in pathways related to cellular responses to organic cyclic compounds and organic nitrogen compounds. Regarding CC, the potential targets showed significant enrichment in protein kinase binding pathways for HA and PT, while for PP, they were significantly enriched in pathways related to DNA and organic compound binding. With respect to MF, the potential targets demonstrated significant enrichment in receptor complex pathways.

3.5 KEGG pathway analysis

KEGG pathway analysis was conducted using the Metascape database on potential targets associated with brain or liver injuries induced by three types of fungicides, revealing enriched pathways related to these targets (Appendix C). Overall, these targets were significantly enriched in pathways associated with cancer, the PI3K-Akt signaling pathway, and pathways related to chemical carcinogenesis. We generated a bubble plot (Fig. 4) displaying the top 20 most enriched KEGG signaling pathways, prioritized by gene count, to succinctly illustrate the interactions among these pathways and to highlight their significance in the pathogenesis and progression of brain and liver injuries caused by triazole fungicides.

Additionally, we performed KEGG analysis on the core targets associated with brain or liver injuries induced by the three fungicides using the Metascape database. This analysis produced a Sankey bubble plot (Fig. 5) illustrating the top 20 most enriched KEGG signaling pathways, also prioritized by gene count. These targets showed significant enrichment in pathways related to cancer, the PI3K-Akt signaling pathway, and protein processing.

3.6 Network analysis of diseases induced by HA, PP and PT

Using Cytoscape software, we constructed a target network diagram for HA, PP, and PT-induced brain and liver injuries, resulting in a "drug-target pathway" network diagram. As shown in Fig. 6, deep red or deep blue represents hub targets, while light red or light blue denotes pathways. This diagram provides insights into the core targets, pathways, and the extent of brain or liver injuries induced by the three pesticides.

3.7 Molecular docking analysis of HA, PP and PT and key targets

To further elucidate the interactions and relationships between HA, PP, and PT with the key targets (the top three core targets), we conducted comprehensive molecular docking simulations. Docking of each target was performed using AutoDock software, and the results indicated that all binding energies were below − 5.0 kcal/mol [22], demonstrating a strong binding affinity of HA, PP, and PT for these targets (Table 2). Our findings suggest that HA, PP, and PT can spontaneously bind to each key target, underscoring their critical roles in the molecular mechanisms underlying brain and liver injuries induced by these three pesticides. Visual representations of the lowest binding energy conformations between these pesticides and each key target were generated using Discovery Studio and PyMOL (Fig. 7). These results indicate that these three triazole pesticides may induce brain or liver injury through binding with the aforementioned targets. However, further experimental validation is required.

Table 2

The result of molecular docking.
Ligand	Target	Binding energy(kcal/mol)
Hexaconazole	CASP3	-6.5
Hexaconazole	EGFR	-6.0
Hexaconazole	ESR1	-7.2
Propiconazole	PPARG	-7.3
Propiconazole	TP53	-7.1
Propiconazole	HSP90AA1	-6.6
Prothioconazole	EGFR	-6.4
Prothioconazole	ESR1	-7.4
Prothioconazole	PTGS2	-7.3

3.8 Screening for brain and liver cancer-related DEGs and analysis of target genes

Building on our previous KEGG analysis, we revealed that the targets associated with brain and liver toxicity induced by the three triazole fungicides are enriched in pathways related to cancer. Therefore, we aimed to further elucidate the mechanistic impact of core targets for triazole fungicides-elicited brain and liver injury on brain and liver cancer.

To identify genes associated with brain tumors and liver tumors, we utilized two gene expression profiles: GSE30563 and GSE117361. To visually highlight the unique distribution and significance of these differentially expressed genes (DEGs), we employed volcano plots to visualize all DEGs across the two datasets (Figs. 8A-B). Detailed information regarding these datasets is provided in Appendix D. Venn diagrams were constructed to illustrate the overlap between the two sets of DEGs, revealing a total of 287 overlapping genes, of which 283 exhibited common expression patterns. Among these, 143 genes were significantly upregulated, while 140 genes were significantly downregulated (Fig. 8C). We conducted GO and KEGG enrichment analyses on the 287 overlapping gene targets to explore the differential expression patterns in brain tumors and liver tumors compared to normal tissues. Detailed information regarding the enrichment results is provided in Appendix E. The top ten most enriched terms in BP, CC, and MF categories were prioritized based on gene count and visually represented in a histogram (Fig. 8D). Then we generated a histogram of the top 20 most enriched KEGG signaling pathways, prioritized by gene count (Fig. 8E). To further investigate the functional roles of the DEGs that were commonly upregulated or downregulated, we performed GO and KEGG enrichment analyses on the 283 genes exhibiting common expression patterns. The results of the GO and KEGG enrichment analyses are visualized in Fig. 8F-G. In GO enrichment analysis,DEGs was significantly related to immune response and kinase binding pathways. In KEGG enrichment analysis, DEGs was significantly associated with immune disorders and infection-related pathways. This suggests that DEGs is highly correlated with immune responses in brain and liver tumors. Additionally, we selected key DEGs with a p-value less than 0.005, consisting of 15 genes with a common expression pattern. Among these, 5 genes were commonly upregulated, and 10 genes were commonly downregulated (Supplementary Fig. S4-S5). Kaplan-Meier analysis was performed on the 5 commonly upregulated genes, and the results were visualized (Supplementary Fig. S6). We observed a significant difference in survival times between the high-expression and low-expression groups for these 5 genes.

The adoption of triazole fungicides has significantly increased in modern agriculture due to their high efficacy, low application rates, and relatively favorable environmental profiles compared to traditional classes of fungicides. However, the triazole fungicides have been detected in a variety of food products, beverages, and human biomonitoring studies. Given the widespread human exposure to these compounds, comprehensive investigations are needed to evaluate their potential safety hazards [23, 24].

In our study, we integrated a comprehensive approach utilizing network toxicology, molecular docking, and multi-layered bioinformatics to explore the effects of exposure to the triazole fungicides HA, PP, and PT on the brain and liver, as well as their potential mechanisms of action. After applying network-based computational assessment tools, we identified potential targets associated with brain or liver damage induced by HA, PP, and PT using the ChEMBL, Swiss Target Prediction, GeneCards, OMIM, and DisGeNet databases. Utilizing the STRING database and Cytoscape software, we constructed an interaction network for these potential targets and extracted core targets, including EGFR, CASP3, ESR1, PPARG, TP53, HSP90AA1, and PTGS2. These targets were designated as critical for the induction of brain or liver damage by the three triazole fungicides.

Epidermal growth factor receptor (EGFR), a transmembrane receptor tyrosine kinase, plays an important role in maintaining normal tissue and cell signaling pathways [25]. Dysregulation of EGFR has been implicated in neurodegenerative diseases, where altered signaling pathways contribute to neuronal apoptosis and cognitive decline [26]. EGFR signaling plays a central role in the regenerative response after liver injury and is involved in cell transformation associated with chronic injury [27]. In addition, studies have shown that EGFR is also associated with apoptotic cell death signaling in various hepatocytes, mitochondrial dysfunction, and acute liver necrosis [28].

Caspase-3 (CASP3), a widely expressed member of the conserved protein family, plays a key role in regulating the growth and homeostasis maintenance of normal and malignant cells and tissues in multicellular organisms [29]. Activation of CASP3 is strongly associated with neurodegenerative diseases and traumatic brain injury, and its upregulation leads to neuronal apoptosis, exacerbating cognitive decline and dysfunction [30, 31]. CASP3 mediates hepatocyte apoptosis in response to hepatotoxic substances, leading to liver damage and fibrosis, cirrhosis and other conditions [32]. The study suggests that modulating CASP3 activity may provide a therapeutic pathway for alleviating cell death in brain and liver injury [33].

Estrogen receptor alpha (ESR1) is a key regulator that regulates the effects of estrogen on a variety of tissues, including the brain and liver [34]. Studies have shown that ESR1 signaling has a neuroprotective effect, as estrogen can enhance neuronal survival and reduce apoptosis after trauma or ischemic injury [35]. And ESR1 activation is associated with improved cognitive function and reduced neuroinflammation [36]. In liver injury, ESR1 plays a complex role in promoting liver cell regeneration and preventing oxidative stress [37]. However, dysregulated ESR1 signaling can contribute to the progression of liver diseases, including fatty liver and fibrosis [38].

Peroxisome proliferator activating receptor (PPARG) is a nuclear receptor that regulates genes involved in lipid metabolism, inflammation, and insulin sensitivity [39]. Activation of PPARG has been shown to have neuroprotective effects, enhancing neuronal survival and reducing neuroinflammation after trauma or ischemic injury [40]. Studies have shown that activating PPARG can improve cognitive outcomes and promote repair mechanisms in neurodegenerative diseases [41]. In addition, PPARG plays a key role in lipid homeostasis and inflammation resolution [42]. Activation of PPARG is associated with a reduction in steatosis and fibrosis in various liver diseases [43].

The TP53 gene encodes the tumor suppressor protein p53, which plays a crucial role in cellular stress responses, including apoptosis, cell cycle regulation, and DNA repair [44]. In brain injury, activation of TP53 is often associated with neurodegenerative diseases, and in response to oxidative stress and DNA damage, it mediates neuronal apoptosis, leading to cognitive decline and neuron loss [45]. In liver injury, p53 can promote apoptosis of damaged liver cells and prevent tumorigenesis, but excessive activation can lead to chronic inflammation and fibrosis [46].

The HSP90AA1 gene encodes heat shock protein 90α (Hsp90), an important companion involved in protein folding and cellular stress response [47]. Previous studies have shown that Hsp90 exhibits neuroprotective properties, promoting the stabilization of client proteins and preventing neuronal cell apoptosis during ischemic or traumatic events [48]. On the other hand, Hsp90 plays an important role in maintaining the integrity of hepatocytes under stress conditions such as oxidative damage and inflammation [49].

The enzyme protein prostaglandin-endoperoxide synthase 2 (PTGS2) is involved in inflammation and other physiological processes by catalyzing the conversion of arachidonic acid to prostaglandin [50]. For brain injury, COX-2 expression is upregulated in response to neuronal injury, leading to neuroinflammation and pain [51]. While COX-2 is involved in protective mechanisms that promote healing and regeneration, overactivation can exacerbate neuronal damage and lead to chronic neurodegenerative diseases [51]. For liver damage, PTGS2 is involved in the inflammatory response associated with liver disease, including non-alcoholic fatty liver disease and hepatitis [52]. Elevated COX-2 levels are associated with increased inflammation and apoptosis of liver cells, which worsens liver damage [53].

Pathway analysis highlights key insights into the molecular mechanisms of brain and liver injury induced by triazole fungicides. The significant enrichment of targets in cancer-related pathways suggests that exposure to these fungicides may promote carcinogenic processes in these organs, raising concerns about long-term health effects [54]. Specifically, the PI3K-Akt signaling pathway plays an important role in cell survival, proliferation, and metabolism [55]. Dysregulation of this pathway may lead to neuroinflammatory responses and hepatotoxicity, which may exacerbate tissue damage. In addition, the identification of pathways associated with chemical carcinogenesis highlights the potential of these fungicides to initiate or enhance malignant transformation.

In summary, the present study combined applied network toxicology, molecular docking, and multilevel bioinformatics data to systematically explore the toxicological and molecular mechanisms of three triazole fungicides, including Hexaconazole, Propiconazole, and Prothioconazole, in brain and liver injury. Our findings demonstrate that the toxicity profiles of these compounds are closely linked to their interactions with specific biological pathways, highlighting the potential risks they pose to human health and the environment. This integrated approach not only enhances our understanding of fungicide-induced toxicity but also offers valuable insights for future research aimed at developing safer agricultural practices and regulatory policies.

HA, Hexaconazole; PP, Propiconazole; PT, Prothioconazole; DEGs, differential gene expression; BP, biological processes; CC, cellular components; MF, molecular functions; GP: Genetic Information Processing; EI: Environmental Information Processing; CP: Cellular Processes; OR: Organismal Systems; HD: Human Diseases.

Funding:

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author Contribution

Author contributions: Qing Lv: Methodology, Conceptualization, Data analysis, Visualization, Writing – original draft. Xingke Zhu: Methodology, Conceptualization, Data analysis, Visualization, Writing – original draft, Writing – review & editing.Author Statement: By submitting this manuscript, we, the authors, affirm that we have contributed significantly to the research presented and are in agreement with the content and conclusions of the work. We confirm that this manuscript has not been published elsewhere and is not under consideration by any other journal. All authors have reviewed and approved the final version of the manuscript. We declare that there are no conflicts of interest related to this submission, and we have adhered to ethical standards in conducting our research. We agree to abide by the policies and procedures of the journal regarding the publication process.

Acknowledgments:

The author declares no financial conflicts of interest or financial relationships with any organizations or individuals that could potentially bias the results or interpretation of this study. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Ethics approval and consent to participate:

Not applicable.

Running Title

Toxicity and mechanisms of three triazole fungicides.

Availability of data and materials

Data will be made available on request. Further inquiries can be directed to the corresponding authors.

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Efficient analysis of the toxicity and mechanisms of Hexaconazole and two other triazole fungicides: insights from integrated network toxicology, molecular docking and bioinformatics data

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Abstract

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1. Introduction

2. Materials and methods

2.1 Initial network toxicology predictive analysis of HA, PP and PT

2.2 Targets collection of HA, PP and PT

2.3 Collection of targets related to brain and liver injury

2.4 Protein-protein interaction network construction and core targets screening

2.5 Functional enrichment and pathway analysis of targets for diseases induced by HA, PP and PT

2.5.1 GO and KEGG analysis of potential targets

2.5.2 KEGG analysis of core targets

2.6 Drug-target-pathway network of HA, PP and PT

2.7 Molecular docking of key targets with HA, PP and PT

2.8 Collection and analysis of bioinformatics data

2.8.1 Identification and validation of brain and liver cancer-related DEGs

2.8.2 Analysis of brain and liver cancer-related target genes

3. Results

3.1 Toxicity analysis of HA, PP and PT

3.2 Targets analysis of HA, PP and PT induce diseases

3.3 PPI network and core target acquisition

3.4 GO analysis

3.5 KEGG pathway analysis

3.6 Network analysis of diseases induced by HA, PP and PT

3.7 Molecular docking analysis of HA, PP and PT and key targets

3.8 Screening for brain and liver cancer-related DEGs and analysis of target genes

4. Discussion

5. Conclusion

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