{"paper_id":"8ee644d1-e716-4d23-af3f-741bdb6becc7","body_text":"In Indian agronomy, quinalphos (QP, O, O‐diethyl‐O‐[2‐quinoxalinylphosphorothioate]) is one of the most commonly used organophosphate pesticides for pest control to protect crops [ 1 ]. In view of a few suitable properties, for example, efficiency in quick pest control, adequate biodegradability and diminutive bioaccumulation, with the World Health Organization’s (WHO) classification as moderately hazardous and as a yellow labelled (highly toxic) pesticide in India, QP is a popular choice among farmers. The common target of QP in Indian rice, cotton and rapeseed fields are  Scirpophaga incestuous ,  Pegomya hyoscyamine ,  Lipaphis erysimi ,  Helicoverpa zea ,  Stem borers ,  Cnaphalocrosis medinalis ,  Cicadella viridis and Dicladispa armigera . Some quinalphos formulations treat stored grains to prevent infestations by grain weevils and meal moths. QP is applied as a seed treatment to control corn rootworm and soybean aphids in field crops and as a nematicide to control poultry red mites and nematodes such as poultry lungworms. It is used as a dip for salmonids to control sea lice and several other fish parasites, including dory perch and rusty crayfish parasites. Production, transportation, and application of pesticides in the field certainly cause high exposure of the same to a significant number of humans through dermal contact, inhalation, and/or any other way [ 2 ,  3 ,  4 ,  5 ].\nPesticide contamination is inherently anthropogenic, arising from human practices focusing on quick profits. A realization of the problem arises from any combination of factors: population growth, economic motivations, lack of knowledge and a lack of awareness. As a result, non‐target organisms suffer the consequences of these practices and highlight the ecological costs associated with pesticide usage. The substantial purpose of a pesticide is to eliminate target species (pests and weeds). Therefore, pesticides are designed to possess toxic elements in their concentrated ability to broadly interfere with the nervous, metabolic, and reproductive systems of all pests. A common mechanism of organophosphate pesticide activity is inhibiting acetylcholinesterase (AChE) following the accumulation of acetylcholine and excessive stimulation of cholinergic neurons, and QP is no different from the above functionality [ 5 ,  6 ,  7 ,  8 ]. Being highly soluble in water (17.8 mg/L), quinalphos is readily transported through soils into groundwaters or surface waters. Therefore, eliminating quinalphos from the groundwater is a significant concern.\nAdditionally, the principal intermediates generated from photolytic degradation of QP are quinalphos oxon, 2 hydroxy quinoxalines, trialkaline phosphate and thiol metabolite, which are recognized as more toxic than parent compounds [ 9 ]. Aquatic organisms, especially freshwater fishes, are highly prone to the adverse effects of QP [ 3 ]. A decrease in serum and red blood cell cholinesterase activity is often considered an organophosphate poisoning indicator. Quinalphos acts as a neurotoxicant, inhibiting acetylcholinesterase, affecting the respiratory system and irritating the skin and eyes. Once it enters the human body, QP undergoes several biotransformation reactions, interacting with various biomolecules. Residual pesticides increase pesticide exposure folds among consumers. Comparative toxicogenomic database (CTD) recognized nearly 298 diseases associated with QP alone, including mild to various types of cancer, and diseases related to cardiovascular, blood, renal, skin, metabolic and neural systems are listed [ 2 ,  5 ].\nThe intermediates are responsible for oxidative stress, destruction of vitamin E, micronuclei formation, compensatory induction of anti‐oxidant enzymes, decrease of radical scavengers, chromosomal aberrations etc. [ 4 ,  5 ,  8 ]. QP is widely used in Indian agriculture, but investigation of the fate of QP at the cellular and molecular levels is limited. Systematic information about the QP‐influenced genes, respective regulatory factors (transcription factors), involved components, process, and function and their interaction is expected to provide suitable ways to control the impact of QPs on health and consequence knowledge to resolve the issue.\nThis study's primary objective is to better understand the toxic potential of QP, a commonly utilised organophosphate insecticide in agricultural pest management, gardening and veterinary applications. This study delivers a comprehensive investigation of QP interaction with people at the genetic level through network biology approaches, which goes beyond traditional toxicological assessments. The study examines alterations in the expression of specific genes, their control by transcription factors and microRNA, their correlation with diverse disorders and prospective pharmacological interventions, all of which influence human health. The research provides a systems‐level understanding of OP toxicity by mapping the alteration in gene expression, analysing their regulation by TFs and microRNAs, and correlating these changes with disease pathways and potential pharmacological intervention. Unlike previous studies that primarily document end‐point damage or biochemical shifts, this investigation constructs a detailed regulatory network that connects QP exposure to specific genetic disruption and health outcomes [ 10 ]. With omics data integration, this network biology clarifies the molecular underpinnings of QP‐induced toxicity. It identifies actionable targets for therapeutic intervention and risk mitigation, an advancement not previously achieved in OP research. It also helps to find suitable solutions to resolve the threat of QP to the biosphere.\n\nThe comparative toxicogenomic database (CTD;  http://ctdbase.org/ ) provides high‐quality content that is contextualised better to understand the relationship between chemical exposures and human health and to assist in comprehending diseases caused by environmental factors [ 11 ]. It manually curates the scientific literature to identify chemical genes, chemical diseases, gene diseases, chemical phenotypes and chemical exposure correlations across all species. CTD users are provided with tools for analysis and visualisation that can assist them in formulating hypotheses that can subsequently be tested [ 12 ,  13 ].\nThe model's cross‐species comparative framework also supports the translational perspective from model organisms to humans. In addition, the exposure science module of CTD's system of tools constructs relevance by integrating actual molecular outcome measures, which is key for toxicological risk assessments and regulatory evaluations. Even with limitations such as bias in the literature or needing experimental support, CTD's strong, integrative data system framing environment data streams in an ontology is conducive to forming advanced theories and using design logic reasoning in environmental health research [ 14 ]. Gene interaction search features identified genes affected by exposure to quinalphos (QP). Only those genes that demonstrated a strong relationship according to the exposure studies were considered in the research. The comparative toxicogenomics database (CTD) lists 16 genes impacted by the chemical quinalphos, as shown in Table  1 .\nList of genes affected by exposure to QP.\nFor pesticide docking, we have selected PDB IDs (4EY7, 5V0L, 1AO6, 2Q7I, 6SAM, 1DGF, 4I8V, 3S79, 1FAH, 3OS8, 1YYE, 6E08, 2GRT, 1HXH, 1ILH and 3P0L) that are coded by differentially expressed genes by the AutoDock 4.2.6 . Molecular docking uses these proteins with the quinalphos pesticide to determine the estimated free energy of binding (ΔG). The lower the energy (negative), the greater the binding affinity between the protein and the drug. The protein is prepared for docking by removal of the water molecule, addition of polar hydrogen and distribution of charge on each residue of the protein molecule. The ligand molecules are retrieved in the ‘sd’ format from PubChem and then prepared by detecting the root of the torsion tree, setting the number of torsions between l and 6. This ligand and proteins are also saved in their ‘pdbqt format’. For blind docking, the entire molecule is covered with an imaginary three‐dimensional grid box. The grid points are standard dimensionless points depending on the grid spacing value. The grid parameter file is created by selecting the appropriate ligand and macromolecule. Multiple iterations of gridding and docking are carried out with a different ligand to locate the binding site. Initially, all amino acids are used for blind docking.\nGene functional interaction involves two genes in the same biochemical reaction as an input, catalyst, activator, or inhibitor. The Cytoscape ReactomeFIViz (ReactomeFI) plugin has generated and annotated a gene functional interaction network. ReactomeFIViz finds disease‐related network pathways. This plugin enables performing pathway enrichment analysis for a list of genes, visualising hit pathways using manually laid‐out pathway diagrams, and investigating functional relationships among genes in found pathways [ 22 ]. In the function interaction network analysis, 16 genes are selected based on the literature and create the functional interaction network.\nThe iRegulon plugin in Cytoscape is used for the TFs Network [ 23 ]. Transcription factors (TFs) can effectively quantify gene expression regulation. The GeneSingDB curated gene signature and the MsingDB molecular signature database predict the regulatory transcript factors (TFs) for the differentially expressed genes using an iRegulon plugin for Cytoscape [ 24 ]. We have predicted regulators and targets with the help of some pre‐defined parameters.\nThe manually curated data of genes is retrieved, and the interaction network uses the Reactome database. Similarly, individual networks, TF‐gene regulatory, miRNA‐TFs and gene interaction are merged into a composite multi‐layer by mapping the target node, Gene to miRNAs, TFs to genes and TFs to miRNAs, where nodes represent genes, TFs and miRNAs, and edges represent functional interactions. The CyTargetLinker plugin expands the network with two selected data link sets named ‘miRTarBase’ and ‘TargetScan.’ These databases are based on ‘microRNA‐target gene’ interaction types. To identify targets and regulators, we search for gene‐miRNA targets and cross‐validation using the microRNA data integration portal (mirDIP) in 30 database sources by applying the high integrated score (< 0.5) and more.\nWe have used the DisGeNET plugin in Cytoscape to analyse the 16 genes identified as responsible for a disease. DisGeNET returns a list of genes associated with the investigated disease using different identifiers, namely, disease name, gene and variants [ 25 ].\nThe drug–gene interaction dataset was extracted from the STITCH and DGIdb databases. These databases are specialised for analysis and retrieving the drug–gene interaction and the relationship analysis. The STITCH is a search tool and resource for the interaction of chemicals and proteins. Chemical–chemical and chemical–protein associations are integrated from the pathway, experimental databases and literature [ 26 ]. In contrast, DGIdb mainly focuses on clinically relevant drug–gene interactions. It is a valuable resource for identifying the potential therapeutic targets and supporting precision medicine efforts [ 27 ]. The STITCH web tools are used to identify 16 gene drug interactions with a minimum required interaction score of 0.150 (Low confidence). The DGIdb database is used for purging and enriching the gene‐drug interaction by applying the threshold value 0.1 (interaction score) to retrieve more information.\nCytoscape's MCODE plugin [ 28 ] is used for module analysis. The analysis standards are: MCODE cutoff > 5, degree cutoff = 2, node score cutoff = 0.2, maximum depth = 100 and k‐core = 2. These parameters eliminate weakly linked or unassociated nodes, directing the focus towards densely connected clusters that are more likely to encapsulate critical biological processes and functional groups. Consequently, fourteen top‐level modules were displayed; the first four top‐level modules interacted most closely [ 23 ].\n\nQuinalphos is a broad‐spectrum organophosphate insecticide widely used in households and crop fields. Epidemiology studies indicated quinalphos could affect the activity of specific proteins in the body, including AHR, AR, CYP19A1, ESR1, ESR2, NR1I2 (increased expression); ACHE, ALB, BCHE, CAT, CYP1A1, CYP26B1, GSR, HSD3B3, STAR (decreased expression) and G6PD (affects activity) genes. These genes involve various biological processes, such as metabolism, stress response and hormone regulation. Quinalphos decreases the activity of these proteins, which can disrupt their normal functions. Quinalphos inactivates acetylcholinesterase (AChE), a key enzyme for producing and storing neurotransmitters in the nervous system. Quinalphos binds to AChE's esteratic site, phosphorylating the serine residue and rendering the enzyme inactive [ 29 ]. Consequently, AChE accumulation causes muscarinic (e.g., bradycardia and miosis), nicotinic (e.g., muscle fasciculations) and central nervous system effects [ 30 ]. Losing a chemical group from the OP–enzyme complex prevents further enzyme reactivation. After this process (termed ‘ageing’) has taken place, new enzymes must be synthesised before their function can be restored [ 31 ,  32 ]. BChE inhibition by quinalphos is not directly evidenced in the provided studies. However, organophosphates generally inhibit both AChE and BChE, with BChE gaining relevance in advanced neurodegenerative conditions. The long‐term effects of quinalphos on these proteins are still unclear, and more research is needed to understand how quinalphos affects the body's physiology. Quinalphos exposure leads to significant hepatic (liver) damage and metabolic disturbances. In animal studies, sub‐lethal doses of quinalphos caused oxidative stress in the liver, as evidenced by decreased ferric reducing ability of plasma (FRAP), indicating reduced antioxidant capacity [ 33 ]. Quinalphos has a strong binding affinity for the aryl hydrocarbon receptor (AHR) protein, an intracellular receptor that plays a role in the body's response to environmental contaminants [ 16 ,  34 ,  35 ]. The binding of quinalphos to AHR results in increased protein activity. Recent research has shown that quinalphos can significantly affect animal ALB protein expression [ 36 ,  37 ]. ALB protein is a key component of the liver and plays an important role in various metabolic processes [ 38 ]. In studies involving mice exposed to quinalphos, researchers observed a marked decrease in the expression of the ALB protein, which led to several health problems. This decrease is most pronounced in the liver, where ALB is produced [ 39 ]. A recovery period can return levels of ALB to normal. Quinalphos disrupts antioxidant defence mechanisms by affecting GSR activity, impairing the glutathione recycling and detoxification capacity. Significant decreases in CAT activity are observed in multiple tissues (liver, kidney and testis) after quinalphos treatment, reducing the breakdown of hydrogen peroxide and increasing susceptibility to oxidative damage (Padmanabha et al. 2015). In a study in hamsters, quinalphos showed marked decrease in testicular antioxidant enzyme (superoxide dismutase, SOD and catalase, CAT) activities with an increase in lipid peroxidation (MDA) level. Similarly, increased AR protein expression has been reported in dihydrotestosterone (DHT), a powerful androgen hormone synthesised from testosterone, with an important role in male sexual development, and can negatively affect overall health [ 40 ]. DHT has 5–10 times higher affinity for AR than testosterone. AR‐bound DHT upregulates hair development inhibitory factors, such as DKK‐1, IL‐6 and TGF‐β, encouraging hair follicle regression [ 41 ]. Quinalphos can interfere with hormone receptors such as the androgen receptor (AR), oestrogen receptor alpha (ESR1) and oestrogen receptor beta (ESR2) [ 42 ] According to various animal studies, OP insecticides may act as endocrine disruptors. Quinalphos‐induced testicular damage and oxidative stress impair testosterone synthesis, leading to reduced testosterone levels and negatively affecting male reproductive health [ 43 ]. Additionally, disruption of the hypothalamic–pituitary–gonadal (HPG) axis by quinalphos can alter luteinizing hormone (LH) secretion, which is essential for testosterone production and spermatogenesis [ 44 ]. Serum LH levels decreased in parathion‐exposed quails but recovered to normal levels 24 h later. In rats, chlorpyrifos–methyl binds to androgen receptors and exerts anti‐androgenic effects. Sublethal doses of quinalphos increased LH, prolactin and testosterone levels in rats, whereas dimethoate lowered LH levels in sheep [ 45 ]. Butyrylcholinesterase (BCHE), another important enzyme, exhibits significant activity decreases after exposure to quinalphos [ 18 ,  46 ]. It is involved in the breakdown of acetylcholine, a neurotransmitter that plays an important role in the nervous system [ 47 ,  48 ,  49 ]. An experiment with a mouse model, with 2 weeks of exposure to quinalphos, specified decreased activity of the catalase CAT protein, essential for protecting cells from oxidative damage, and the findings suggest that exposure to quinalphos leads to an increased risk of oxidative damage [ 15 ,  19 ].\nIncreased activity of the ESR1 and ESR2 proteins, known to mediate the effects of oestradiol, a primary female reproductive hormone, is reported due to quinalphos exposure [ 50 ]. These results confirm that quinalphos acts as an endocrine disruptor, which can interfere with normal hormone production and signalling pathways [ 51 ]. Similarly, quinalphos can increase the expression of CYP19A1 mRNA (upregulation), an enzyme responsible for oestrogen production in humans and other mammals, increasing the production of oestrogens [ 1 ].\nQuinalphos affects G6PD protein activity, the active gene encoding in red blood cells (RBCs) in either tetrameric or dimeric form [ 21 ]. The mutation type influences the level of activity of the protein in G6PD. In cases of a family history, a mutation is passed down through the family chain [ 52 ,  53 ]. A mutation in the gene G6PD results in a lower functional glucose‐6‐phosphate dehydrogenase, which in turn results in lower levels of the coenzyme NADPH and depletion of the anti‐oxidant glutathione, needed for the protection of the cells' haemoglobin and their cell walls (red cell membranes) from highly reactive oxygen radicals (oxidative stress). Individuals with a deficiency of G6PD cannot regenerate reduced glutathione (GSH) and are thus not protected from oxidative stress. The increased requirement of reduced equivalents by oxidative stress acting either on NADP/NADPH or the GSH system results in increased saturation of G6PD with NADP, increased intracellular G6PD activity and increased PPC flux [ 54 ,  55 ].\nGlutathione reductase (GR) or glutathione disulfide reductase (GSR), is an enzyme encoded by the GSR gene in humans. Glutathione reductase deficiency is a rare reductase activity condition, absent in erythrocytes, leucocytes or both. The activity of glutathione reductase is used as an indicator of oxidative stress. Quinalphos affects the activity of the GSR protein by altering the activity of GSR in rat tissues [ 21 ].\nStudies reveal that QP decreases testosterone levels in mice, resulting in less viable sperm. Its toxicity is caused by free radical production, indicated by increased lipid peroxidation (LPO) and decreased antioxidant enzyme levels in testicular tissue. Increased serum cholesterol and decreased testicular cholesterol levels indicate impaired transport and biosynthesis. The lack of cholesterol in testicular tissue hinders steroidogenesis by downregulating 3‐HSD, 17‐HSD and StAR, thereby reducing testosterone levels. In steroidogenic cells, hormone‐induced cholesterol transport is controlled by a protein complex containing the steroidogenic acute regulatory (StAR) protein. StAR protein functions as a key determinant of steroidogenesis via cholesterol transport from the outer mitochondrial membrane (OMM) to Cyp11a1 in the inner mitochondrial membrane (IMM) [ 56 ,  57 ].\nPregnane X receptor, also known as NR1I2, is a transcription factor activated by ligands and classed as a nuclear receptor superfamily member. In humans, rabbits, rats and mice, it is found in large concentrations in the animals' colon, small intestine and liver. Quinalphos has been shown to interact with the NR1I2 protein, leading to increased activity [ 58 ].\nQuinalphos demonstrates high‐affinity binding to several neurologically, hormonally and metabolically critical human proteins, suggesting a mechanistic basis for its toxicity. As mentioned in Table  2 , it binds strongly to acetylcholinesterase (ACHE, ΔG = −8.2 kcal/mol), a key enzyme in neurotransmission, potentially causing neurotoxicity through cholinergic overstimulation. Similarly, its interactions with the androgen receptor (AR, ΔG = −7.1), oestrogen receptor alpha (ESR1, ΔG = −5.8) and 3β‐hydroxysteroid dehydrogenase type 3 (HSD3B3, ΔG = −7.68) indicate endocrine–disruptive potential by interfering with steroid hormone signalling and biosynthesis. In addition, quinalphos shows moderate binding to oxidative stress‐regulating enzymes such as catalase (CAT), glutathione reductase (GSR) and glucose‐6‐phosphate dehydrogenase (G6PD), implicating it in the disruption of redox homoeostasis and induction of oxidative stress. Furthermore, its affinity toward cytochrome P450 enzymes (e.g., CYP1A1 and CYP19A1) raises concerns about impaired xenobiotic metabolism and detoxification.\nEstimated binding affinity for free binding energy for various target proteins with quinalphos.\nQuinalphos (QP) demonstrates multisystem molecular toxicity in humans through high‐affinity binding to a wide range of proteins, disrupting neurological, endocrine, metabolic and detoxification pathways. QP targets acetylcholinesterase (ACHE), leading to the disruption of synaptic neurotransmission. This inhibition can result in neurobehavioral deficits and may contribute to neurodegeneration (link). It also exhibits oxidative stress, leading to testicular damage, including shrinkage of seminiferous tubules and degeneration of the germinal epithelium, which impairs spermatogenesis and testosterone synthesis. The depletion of protein content in organs such as the liver under quinalphos stress is attributed to impaired protein synthesis machinery and increased proteolysis, reflecting altered gene expression in protein metabolism and stress response. These effects are mediated through both direct protein targeting and downstream gene regulatory disturbances, significantly increasing the risk of disease and multisystem dysfunction in exposed individuals.\nThe database CTD has enlisted 16 affected genes, where there are 9 top‐interacting genes (CAT, GSR, STAR, ACHE, BCHE, CYP26B1, G6PD, HSD3B3, NR1I2 and AHR) with quinalphos. Quinalphos exposure results in differential expression of genes (DEGs) (from the identified 16 nos.) as in a decrease in activity (down‐regulation) in 9 nos. (ACHE, ALB, BCHE, CAT, CYP1A1, CYP26B1, GSR, HSD3B3 and STAR) and increases in activity (up‐regulation) in 6 Nos. (AHR, AR, CYP19A1, ESR1, ESR2 and NR1I2). These genes are interrelated in their designated functions, as seen in the Figure  1  derived using Cytoscape software. This indicates the interaction of these differentially expressed genes associated with various regulatory factors, pathways and functions in the human body. Knowing their relationship allows the discovery of therapeutic targets (receptors, antagonists and enzyme modulators) that can reduce QP‐induced toxicity and block its interaction with significant pathways [ 59 ]. The gene regulatory network is created using the Reactome database to analyse the interaction between the affected expression of genes and exposure to quinalphos. Studies have identified significant associations between these genes and other genes, such as ESR1, AR and CREB1. ESR1 belongs to the nuclear receptor target family within the steroidal subfamily. Several pesticides exhibit oestrogenic and/or anti‐androgenic properties via the oestrogen receptor (ER) and/or androgen receptor (AR), and countless additional synthetic compounds may also contain similar oestrogenic and anti‐androgenic effects. The aryl hydrocarbon receptor (AHR) is a regulatory protein that governs the transcription of genes involved in the processing of exogenous compounds and the proliferation and differentiation of cells. The activity of the molecule is contingent upon the presence of binding molecules.\nGene regulatory network (red coloured nodes: increasing the affect, blue coloured nodes: decreasing the affect, green coloured nodes: linker genes).\nThe AutoDock‐based blind docking (BD) technique determines the appropriate protein‐coding genes with the maximum binding affinity with the quinalphos drug. The scoring function calculates a score for each position to rank the different poses and ligands. The docking process can be divided into two steps, namely, pose creation and scoring. Pose creation refers to the techniques used to generate different conformations of proteins and ligands and align different ligand conformations within the protein‐binding region. The scoring component is necessary to quantitatively assess posture quality during the docking process. Because docking is often used to screen large chemical libraries, quick pose creation and quality evaluation techniques that require minimal computational cost are necessary. Several streamlining measures are needed for the entire docking procedure to achieve this. To investigate the potential binding of pesticides, a thorough docking analysis of the genes is carried out and listed in Table  2 , focusing on the expression of the differential proteins. The prepared compounds are subjected to docking with the target protein using AutoDock 4.2, generating hundreds of 10 conformations for each molecule. The “analysis” command in the docking parameter file prompts AutoDock to do a cluster analysis of the various docked conformations, explicitly focusing on the minimal energy obtained in each iteration. The coordinates are expressed in a modified PDB format, where four decimal values are added after the x‐, y‐, and z‐coordinates. These values represent the vdW + h bond + desolvation energy of the atom's interaction, the electrostatic interaction of the atom, the partial charge and the RMSD from the reference conformation. After completing 10 genetic algorithm runs, we calculated the mean value of the estimated free energy of binding (ΔG). The genes listed in ascending order of affinity are NR1I2, ESR2, ALB, AR, HSD3B3 and ACHE. Figure  2A–F  depict the molecular docking of the quinalphos pesticide. The six remaining genes exhibited reduced affinity towards quinalphos, with CYP19A1 and BCHE displaying the lowest affinities, resembling the behaviour of the negative control. These targets are implicated in hormone signalling, detoxification and cellular stress response. Knowledge of these interactions makes it possible to rationally design therapeutic agents (receptor antagonists and enzyme modulators) that can neutralise QP‐induced toxicity or inhibit its interaction with essential pathways.\nMolecular docking with quinalphos pesticide (A) NR1I2:  1ILH  (B) ESR2:  1YYE  (C) ALB:  1AO6  (D) AR:  2Q7I  (E) HSD3B3:  1HXH  (F) ACHE:  4EY7 .\nTo identify interactions of differentially upregulated and downregulated gene expression, a gene‐TFs regulatory network is created. Analysis of high‐degree nodes (those with a score of 10 or greater) identified 10 transcription factors (TFs), including MEF2B, HNF4A, CEBPG, USF1, BARX1, HOXB4 and FOXL2, as shown in Figure  3 . The CYP26B1, ESR1 and BCHE, is regulated by the BARX1, CEBPG, FOXL2, HNF4A, HOXB4, MEF2B and USF1 transcription factors. QP decreases cytochrome P450 expression, and the down‐regulation of Cytochrome P450 in QP‐exposed animals is related to elevated LPO and H 2 O 2  levels because free radicals react with lipids and increase lipid peroxidation. Cytochrome P450 enzymes in Leydig cells normally generate ROS during steroid hydroxylation. Environmental toxicants or metabolites act as pseudo‐substrates for CYP enzymes, resulting in enhanced ROS production via the CYP pseudo‐substrate‐O2–O 2  complex. Most steroid enzymes are CYPs or HSDs. Cytochrome P450 catalyses the initial step in steroidogenesis, cholesterol side‐chain breakage. The first rate‐limiting and hormonally regulated step in steroid hormone synthesis is the mitochondrial conversion of cholesterol to pregnenolone [ 19 ]. Pesticides interact with oestrogen and androgen receptors to exert oestrogenic and anti‐androgenic effects. Quinalphos is an ER agonist. Agonists activate a receptor by binding to one inside or on the cell's surface [ 17 ]. It inhibits butyrylcholinesterase, albeit some require cytochrome P450 metabolism to an oxon form. Inhibition of AChE causes overstimulation at cholinergic synapses in the autonomic nervous system, neuromuscular junction and CNS [ 18 ].\nGene TFs regulatory network (green nodes: regulators, purple nodes: regulated (targets), blue nodes: both regulators and targets).\nThe TF‐ mRNA regulatory network is a complex network that regulates gene expression. Transcription factors bind to the promoter region of a gene and activate or repress its transcription. miRNAs regulate gene expression by binding to complementary sequences in mRNA transcripts, inhibiting translation, or promoting the degradation of mRNA transcripts. As stated in the methodology, the authors used CyTargetLinker tools and the ‘miRTarBase’ and ‘TargetScan’ databases to retrieve the experimentally validated 1566 miRNA targets that are responsible for the regulation of differentially expressed gene expression of quinalphos. Similarly, 683 miRNAs (very high) from the mirDP database and 393 from the mortar base database have been identified that are experimentally validated by any two methods, such as reporter assay, western blot, qPCR (strong evidence), microarray and NGS. pSLIAC, CLIP‐Seq (less strong evidence). Present study considers only 41 miRNA experimentally validated miRNAs by reporter assay, western blot and qPCR (strong evidence), a total of 14 genes have been found to be the 41 miRNA target, including STAR (hsa‐miR‐153‐3p, hsa‐miR‐1‐3p), NR1I2 (hsa‐miR‐18a‐5p, hsa‐miR‐148a‐3p, hsa‐let‐7a‐5p), G6PD (hsa‐miR‐206, hsa‐miR‐1‐3p), ESR2 (hsa‐miR‐92a‐3p, hsa‐miR‐30a‐5p, hsa‐miR‐302c‐3p, hsa‐miR‐20b‐5p, hsa‐miR‐17‐5p), ESR1 (“hsa‐miR‐9‐5p, hsa‐miR‐4463, hsa‐miR‐302c‐3p, hsa‐miR‐29b‐3p, hsa‐miR‐26a‐5p, hsa‐miR‐222‐3p, hsa‐miR‐221‐3p, hsa‐miR‐22‐3p, hsa‐miR‐20b‐5p, hsa‐miR‐206, hsa‐miR‐19b‐3p, hsa‐miR‐19a‐3p, hsa‐miR‐193b‐3p, hsa‐miR‐18b‐5p, hsa‐miR‐18a‐5p, hsa‐miR‐130a‐3p, hsa‐miR‐129‐5p”), CYP26B1 (hsa‐miR‐15b‐5p, hsa‐miR‐15a‐5p), CYP1A1 (hsa‐miR‐200b‐3p), CYP19A1 (hsa‐miR‐4463, hsa‐miR‐19b‐3p), CAT (hsa‐miR‐30a‐5p), BCHE (hsa‐miR‐26b‐5p, hsa‐miR‐335‐5p), AR (hsa‐miR‐7‐5p, hsa‐miR‐488‐5p, hsa‐miR‐205‐5p, hsa‐miR‐1246, hsa‐miR‐124‐3p), ALB (hsa‐miR‐492), AHR (hsa‐miR‐375, hsa‐miR‐26a‐5p, hsa‐miR‐124‐3p, hsa‐let‐7a‐5p) and ACHE (hsa‐miR‐212‐3p) as shown in Figure  4 . Table  3  displays details on transcription regulators, miRNA and regulated genes.\nGene TF‐mRNA regulatory network (green nodes: genes, blue nodes: miRNA, purple nodes: targets).\nTranscription factor and gene regulation.\nOne of the most critical aspects of this research is the identification of genes associated with a given disease. This process is called enrichment analysis and can be performed by examining a list of genes (Table  1 ) associated with the disease in question. The goal of gene enrichment analysis is to identify a subset of genes that are overrepresented in each set. For example, when a gene associated with breast cancer is investigated, one needs to identify those that appear more frequently than expected from the reference gene list and then look for an explanation for why this might occur. In the present analysis with the quinalphos exposure database, gene–disease enrichment analysis revealed that the DEGs are responsible for 14 human diseases, as shown in Figure  5a . This figure shows a gene–disease interaction network, where blue nodes represent genes and pink nodes represent diseases. The connecting lines indicate known associations between genes and various diseases based on the literature evidence. Similarly, Figure  5b  shows the number of genes associated with multiple diseases. The highest association with the disease included  obesity, neoplasm metastasis, myocardial infarction, liver neoplasms, adenocarcinoma and endometriosis.  The aryl hydrocarbon receptor (AHR) is a ligand‐activated transcription factor highly expressed in hepatocytes. AHR deficiency significantly reduces weight gain and adiposity and increases multilocular lipid droplet formation within perigonadal white adipose tissue [ 60 ]. Similarly, a study investigated the role of PCB‐77, a ligand of the aryl hydrocarbon receptor (AhR), in reducing obesity. It found that AhR‐deficient female mice were resistant to diet‐induced obesity. During weight loss, PCB‐77 negatively affected glucose tolerance in male mice. However, in obese males losing weight, the absence of the AhR gene reversed the negative impact of PCB‐77 on glucose tolerance, whereas in females, it worsened glucose intolerance. Additionally, in female mice, IRS2 mRNA levels in adipose tissue were lower than those in vehicle (VEH)‐treated controls [ 61 ]. Another genome‐wide association study (GWAS) identified > 250 loci for body mass index (BMI). Most GWAS loci represent clusters of common noncoding variants. In the present analysis, data from 718,734 individuals are combined to identify rare and low‐frequency coding variants associated with BMI. From the 14 coding variants in 13 genes, of which 8 variants are in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants are in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants are in GIPR [ 62 ].\nDisease enrichment network (a) number of genes with disease association (purple nodes: disease, blue nodes: gene IDs) (b) bar graph of the number of genes associated with various diseases.\nTo obtain an overview of the molecular functions and processes of the 16 DEGs in the gene‐disease association network, we performed functional and pathway enrichment analyses using ClueGO. Gene ontology (GO) considers three distinct aspects of gene function. They are divided into three main categories: biological processes (BP), molecular functions (MF) and cellular components (CC). Network‐based pathway enrichment analysis is used to identify regulatory pathways or gene ontologies with statistically significant associations with a given gene set. The idea behind enrichment analyses is that if a gene set linked to a pathological condition is enriched in a given pathway or gene ontology, it may suggest a connection between the pathway and the disease. Among the GO BP terms having the highest significance, we find some linked to genitalia development, intracellular oestrogenic receptor signalling pathway, ligand‐activated transcription factor activity, nuclear receptor activity, prostate gland development, prostate gland growth and response to vitamin A, as shown in Figure  6A . Quinalphos‐induced genes ESR1, ESR2, AR (Upregulated), STAR and CYP1A1 (downregulated) are associated with oestrogen signal pathway and metabolic process, respectively. The activation of oestrogen receptors (ERs) and signal transduction is the principal focus of the oestrogen signal pathway. The binding of oestrogen to ERs or ER elements along with specific NRs influences the activity of the target gene regulatory region, enhancing regulation of gene expression and Oestrogen signalling pathway, and also various diseases, including breast cancer, gastric cancer and atherosclerosis, are related [ 63 ,  64 ,  65 ,  66 ], vitamin A is allied with many molecular mechanisms such as the regulation core of gene expression, activation of kinase cascades, RA signalling associated with NRs, nucleic acids response elements, retinoic acid (RA) response elements, multiple coregulators etc, and therefore essential throughout the initiation of life [ 67 ,  68 ,  69 ]. BCHE, CYP1A1 (downregulated) and NR1I2 (upregulated) genes are involved in the drug metabolic process. Issues of pregnancy complications, oxidative stress, proliferation inhibition, DNA damage and many more are related to drug metabolisms to differential expression of these genes [ 65 ,  70 ,  71 ,  72 ,  73 ].\nGene ontology analysis. (A) Functionally grouped network with terms as nodes linked based on their kappa score level (≥ 0.3), where only the label of the most significant term per group is shown. The node size represents the term enrichment significance. (B) Biological pathway.\nThe present study adopted a novel approach to examine the association of genes with well‐described signalling for metabolic and regulatory pathways. ClueGO retrieved associations between genes from curated databases such as KEGG [ 74 ] and REACTOME [ 75 ]. The analysis detected 3 pathways that showed significant disease enrichment, as shown in Figure  6B . Interestingly, the pathways belonged to several categories linked to Cytochrome P450—arranged by substrate type, nuclear receptor transcription pathway and SUMOylation of intracellular receptors. Nuclear receptors (NRs) are prevalent in preventing several diseases caused by foreign elements through their detoxification sense and response. NRs are, in general, DNA‐binding bifunctional transcription factors (TFs) that link to specific cellular molecules such as hormones, vitamins etc. It is observed that quinalphos affected genes, namely ESR2, ESR1, AR and NR1I2, are actively associated with nuclear receptor transcription activities and pathways. In contrast, the latter three genes are associated with SUMOylation (activity of small ubiquitin‐like modifier proteins) of the intracellular receptor [ 76 ] quantitative investigations have demonstrated that quinalphos exposure in humans is associated with a statistically significant suppression of total T3 levels ( p  < 0.01), a marginal decrease in T4 (about 7%), and elevated TSH levels (about 28% higher than controls), indicating impaired thyroid function [ 77 ]. In animal models, quinalphos induces oxidative stress and liver injury, which can disrupt the metabolism of vitamins A and D [ 67 ,  70 ,  71 ,  78 ]. SUMOylation is responsible for several activities associated with NRs, such as transcriptional repression and altered NR signalling [ 3 ,  65 ,  76 ,  79 ]. The association is shown in Figure  6B , where the most significant pathways are depicted with the largest node size.\nThe present study includes investigations of 16 different protein‐coding genes to predict interactions between drugs and proteins. The analysis identified 1445 drugs for 15 genes; the network was pruned by applying the threshold value 0.1, and finally, 446 drug therapeutic targets were obtained, as shown in Figure  7 . Gene HSD3B3 has not been predicted to regulate any drug expression. The drug has been categorised into two groups to assess the translation relevance of gene–drug interaction: approved and not approved (experimental and investigational) compounds. The ACHE gene exhibited a higher number of drug interactions ( n  = 64), with 31 approved and 33 non‐approved drugs for 7 diseases. Similarly, the ESRI found extensive interaction ( n  = 90), but a larger fraction ( n  = 55) was unapproved for 13 diseases, indicating a research‐heavy focus. In contrast, genes such as G6PD and GSR demonstrated a higher proportion of approved drugs (71% and 79%, respectively), suggesting more established therapeutic applications. A summary of the approved and non‐approved drugs is presented in Table  4 .\nGene–drug–disease interaction network regulated by several drugs. In this network (orange nodes: disease, red nodes: drug, green node: gene).\nList of genes and their predicted drug for the disease.\n\nQuinalphos is a widely applied organophosphate pesticide due to its high efficacy in pest control. However, it also carries potential health risks to humans, as exposure to quinalphos has been associated with alterations in the expression of 16 genes, which are linked to several health issues such as obesity, neoplasm metastasis, myocardial infarction, liver neoplasms, adenocarcinoma, endometriosis and so on. It has been demonstrated that 26 transcription factors mainly regulate the expression of these genes and could be targeted by 41 miRNAs. Additionally, the expression of genes is influenced by changes in the methylation status of DNA in the gene promoter region, known as CpG islands, which have a high methylation status and are less likely to be expressed than regions with a low methylation status. In light of this evidence, further research might help to understand the effects of quinalphos exposure on human health. These results suggest that quinalphos has the potential to cause significant health issues in humans and should be used with caution. Future research on these AI and deep learning approaches could significantly enhance the resolution and predictive power of QP toxicity studies. By leveraging large environmental and biomedical datasets, future research can move toward more precise risk assessment, early biomarker discovery and identification of novel therapeutic targets.\n\nJyoti Kant Choudhari:  conceptualization, data curation, investigation, methodology, resources, software, validation, visualization, writing – original draft, writing – review and editing.  Biju Prava Sahariah:  conceptualization, data curation, investigation, methodology, writing – original draft, writing – review and editing.  Anand Kumar Jayapal:  conceptualization, methodology, writing – original draft, writing – review and editing.  Jyotsna Choubey:  conceptualization, investigation, methodology, writing – original draft, writing – review and editing.  Abhishek Tripathi:  methodology, writing – original draft, writing – review and editing.\n\nThe authors have nothing to report.\n\nThe authors have nothing to report.\n\nAll authors have given their consent for publication.\n\nThe authors declare no conflicts of interest.\n\nThe authors have nothing to report.","source_license":"CC-BY-4.0","license_restricted":false}