Nova Biotechnol Chim (2019) 18(2): 144-153 DOI: 10.2478/nbec-2019-0017  Corresponding author: deleibraheem2007@yahoo.com Nova Biotechnologica et Chimica In silico toxicological analyzes of selected toxic compounds from dumpsite or contaminated soils on human health Omodele Ibraheem1, , Toluwase Hezekiah Fatoki1, Jesupemi Mercy Enibukun2, Bolanle Christianah Faleye3 and Daniel Uwaremhevho Momodu4 1 Department of Biochemistry, Federal University Oye-Ekiti, PMB 373, Oye-Ekiti, Ekiti State, Nigeria 2 Department of Microbiology, Federal University of Technology, PMB 704 Akure, Nigeria 3 Department of Chemical Science, Joseph Ayo Babalola University, Ikeji-Arakeji, PMB 5006, Ilesa Osun State, Nigeria 4 Department of Industrial Chemistry, Federal University Oye-Ekiti, PMB 373, Oye-Ekiti, Ekiti State, Nigeria Article info Article history: Received: 17th July 2019 Accepted: 30th September 2019 Keywords: Gene expression network Molecular docking Toxicants Toxicokinetic Transcription factors Abstract The soil is a key component of natural ecosystems because environmental sustainability depends largely on a sustainable soil ecosystem. The objective of this study was to predict the impact of selected toxic compounds from dumpsite or contaminated soils on human health at the molecular level of biological processes. The in silico methods that were used include toxicokinetics and target gene prediction, molecular docking, and gene expressing network analysis. The result showed bisphenol A (BPA), 2,20-bis(p-chlorophenyl)-1,1-dichloroethane (DDD), 2,20-bis(p-chlorophenyl)-1,1-trichloroethane (DDT), diethylhexyl phthalate (DEHP), nonylphenol (NP) and tetrachlorodibenzodioxin (TCDD) as the active toxic compounds that can modulate biological system and are considered as potential cause of several diseases including cancer. The principal target genes include substance-P receptor (also known as Neurokinin 1 receptor), 5-hydroxytryptamine receptor, human serotonin transporter; estrogen receptor alpha; and aryl hydrocarbon receptor. These genes implicated SUZ12, STAT3, and TRIM28 as the major transcription factors while mitogen-activated protein kinases and cyclin-dependent kinases were the major kinases from the protein- protein interaction. All the six toxicants investigated showed good free binding energies (ΔG) which were below - 5.0 kcal.mol-1. These toxic compounds showed ligand efficiency greater than 0.25 kcal.mol-1. HA and would possibly cause fatal damage on human health. The order of in silico predicted toxicity of these compounds were BPA > DDD = DDT > TCDD > NP > DEHP. Our results identified potential threats, which the selected toxicants can pose to public health. More importantly, it provides basis for investigation of super bugs (microorganisms) that can remediate these toxicants in our environment. Environmental monitoring and modern wastes management system should be implemented and enforced in the affected countries in order to safeguard the health of the citizenry.  University of SS. Cyril and Methodius in Trnava Introduction The soil is a key component of natural ecosystems because environmental sustainability depends largely on a sustainable soil ecosystem (Lombi et al. 1998). The biogeochemical cycles of contaminants have been greatly accelerated by human activities. Typical contaminated sites Bereitgestellt von Slovenská poľnohospodárska knižnica | Heruntergeladen 28.02.20 12:35 UTC mailto:deleibraheem2007@yahoo.com Nova Biotechnol Chim (2019) 18(2): 144-153 145 may be classified as either potential or practical, which could be quantitatively described as suspected to cause or constitute harm to human health and the environment – biotic and abiotic processes respectively. The qualitative and quantitative description of potentially contaminated and contaminated sites have been reported (WHO 2013). The study on soil samples of an animal burial site showed to be characterized by acute toxicity of decayed matter while the area of open waste dumping was found the most dangerous based on the amount of the contaminants (Pasko and Mochalova 2014). Typical study has shown that more industrialized countries had higher bisphenol A (BPA) concentrations in landfill leachate than less industrialized countries (Teuten et al. 2009). Cases of environmental pollution and contamination in developing and developed countries have been reported (Egboka et al. 1989; SCU 2013). Dumpsite has been huge challenge to the global health, because it serves as habitat for carcinogenic chemicals and pathogenic microbes (Odeyemi et al. 2011; Eze and Amaeze 2016). Dumpsites serve as lead for marine and coastal pollution and will account for 8 – 10 % of anthropogenic greenhouse gas emission in 2025 (ISWA 2016). Soil microorganisms often serve as the catalysts or promoters of reactions in the subsurface. However, the degradation of any contaminant depends on geochemical conditions and on the presence of microorganisms that are capable of adaptation (Boulding and Barcelona 1991). Uptake of contaminants by organisms occurs by a variety of pathways, most commonly inhalation, dermal sorption and ingestion. Contaminant transfer to organisms may occur by any of these routes, and the major transport route will vary according to the organism and the physicochemical properties of the contaminant (Teuten et al. 2009). Likelihood that health effects will occur from any exposure to a contaminant depends on the toxicity of the contaminant, which can be determined through understanding of: how harmful, how much, how long and how often the exposure occurs. However, differences in the health status, age, diet, gender, family traits and lifestyle will also affect the outcome of the level of exposure to a particular contaminant (Shayler et al. 2009). The protection of human health has utilized biomonitoring as a vital tool to track and assess the level of exposure of the community to environmental pollutants as well provides measures for local and global health policies (SCU 2013). Characterization of a hazardous waste site provides the understanding to predict future site behavior based on past site behavior (Mercer and Spalding 1991). A basic assumption in performing remediation is that one cannot remediate what is not observed. Consequently, an understanding of what to observe and how to go about making the observations is of utmost importance (Boulding and Barcelona 1991). Almost all typical research of soil quality often carried out solely on the basis of chemical analyses at the oversight of the resulting effect of the level of toxicity on the organism within the ecosystem. Toxicokinetics is the kinetics of toxicant absorption, distribution, metabolism and excretion (ADME). The investigation of ADME profiling and toxicological (ADME/Tox) screenings during biomonitoring and remediation processes are therefore very important. In silico prediction of toxicokinetics and impact on biomolecules will serve as an additional assessment tool for screening toxic level of soil and foster understanding of impact of pollutants in the environment considering the huge cost and time involve in bioanalytical testing as well as increasing number of toxicants nowadays. In silico prediction of key biomolecular targets of the potential toxic compound will help to ascertain the limit and possible outcome of high exposure. The understanding will help in creating public awareness that is grounded of scientific propositions. The objective of this study was to predict the potential impact of selected toxic compounds from dumpsite or contaminated soils on human health at the molecular level of biological processes. Experimental In silico preparation of toxic ligands Among the several known toxicants found in typical dumpsites or contaminated soils from Bereitgestellt von Slovenská poľnohospodárska knižnica | Heruntergeladen 28.02.20 12:35 UTC Nova Biotechnol Chim (2019) 18(2): 144-153 146 Table 1. List of 13 selected toxicants among severally found in dumpsites or contaminated soils, which have been implicated in human chronic ailments, among which 6 toxicants (in bold) showed significant predicted targets. Serial No. Type of contaminants SMILES format 1 Bisphenol A (BPA) CC(C)(C1=CC=C(C=C1)O)C2=CC=C(C=C2)O 2 2,20-bis(p-chlorophenyl)-1,1- dichloroethane (DDD) C1=CC(=CC=C1C(C2=CC=C(C=C2)Cl)C(Cl)Cl)Cl 3 2,20-bis(p-chlorophenyl)-1,1- dichloroethylene (DDE) C1=CC(=CC=C1C(=C(Cl)Cl)C2=CC=C(C=C2)Cl)Cl 4 2,20-bis(p-chlorophenyl)-1,1- trichloroethane (DDT) C1=CC(=CC=C1C(C2=CC=C(C=C2)Cl)C(Cl)(Cl)Cl)Cl 5 diethylhexyl phthalate (DEHP) CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC 6 Hexachlorocyclohexane (HCH) C1(C(C(C(C(C1Cl)Cl)Cl)Cl)Cl)Cl 7 Octylphenol (OP) CCCCCCCCCC1=CC=C(C=C1)O 8 Perfluorooctanesulfonic acid (PFOS) C(C(C(C(C(F)(F)S(=O)(=O)O)(F)F)(F)F)(F)F)(C(C(C(F)(F)F)(F)F)(F)F)(F)F 9 Nonylphenol (NP) CCCCCCCCCC1=CC=C(C=C1)O 10 Perfluorooctanoic acid (PFOA) C(=O)(C(C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)O 11 Trinonylphenylphosphine (TNPP) CCCCCCCCC[P+](CCCCCCCCC)(CCCCCCCCC)C1=CC=CC=C1 12 Tetrachlorodibenzodioxin (TCDD) C1=C2C(=CC(=C1Cl)Cl)OC3=CC(=C(C=C3O2)Cl)Cl 13 Toxaphene (SONATOX) C1C2C(C(C(C1(Cl)Cl)(C2(CCl)CCl)CCl)Cl)Cl literatures (Teuten et al. 2009; Valentin et al. 2013; Yao et al. 2015), 13 toxicants (Table 1), which were known to be critically implicated in implicated in human chronic ailments such as cancer, chronic cough, and neurological disorder, were selected and used for this study. Available structures of these compounds were obtained from the PubChem Compound Database in structure data file (sdf) and canonical Simplified Molecular Input Line Entry Specification format (SMILES). All file conversion to protein data bank (pdb) format were performed using PyMol v2.0.7. In silico targets prediction and toxicokinetics In silico targets prediction for the toxicants were done on SwissTargetPrediction server, where Homo sapiens was selected as target organism (Diana et al. 2019). Among the 13 toxicants, 6 toxicants showed significant predicted targets (i.e. active ligands; Table 1), and these were then subjected to in silico Absorption-Distribution- Metabolism-Excretion (ADME) screening on SwissADME server (Diana et al. 2017). ADME screening was performed at default parameters. Molecular docking The molecular docking studies for the 6 toxicants against the 6 targets were carried out according to the method described by Fatoki et al. (2018a). The three-dimension (3D) structures of selected 6 targets were then obtained from RCSB Protein Data Bank (PDB). Briefly, all water molecules, hetero atoms, and multichains were removed from the crystal structure of the prepared targets using PyMol v2.0.7. The Gasteiger partial charges were added to the ligand atoms prior to docking. The docking parameter of each prepared ligand and each prepared target, were setup using AutoDock Tools (ADT) v1.5.6 (Morris et al. 2009) and saves the output file in pdbqt format. Molecular docking program AutoDock Vina v1.1.2 (Trott and Olson 2010) was employed to perform the docking experiment from the command line. After docking, the ligands were analyzed and visualized using ADT and PyMol v2.0.7. The ligand efficiency (LE) was evaluated from the equation, LE = -ΔG/HA, where ΔG is the free energy of binding and HA is the number heavy atoms (non-hydrogen atoms) of the ligand (Padmanabhan et al. 2016). Target gene expression analyses The upstream regulatory networks from signatures of differentially expressed genes obtained from toxicants target prediction, were determined by Bereitgestellt von Slovenská poľnohospodárska knižnica | Heruntergeladen 28.02.20 12:35 UTC Nova Biotechnol Chim (2019) 18(2): 144-153 147 Table 2. Predicted targets of the dumpsites/contaminated soils active toxicants. Serial Targets Gene name UniProt ID Dumpsite toxicants No. A B C D E F 1 Estrogen receptor ESR1 P03372 **** **** 2 Estrogen receptor beta (by homology) ESR2 Q92731 **** 3 5-hydroxytryptamine receptor 6 HTR6 P50406 **** *** *** 4 FAD-linked sulfhydryl oxidase ALR GFER P55789 *** 5 Carbonic anhydrase 1, 2, 3, 5A, 5B, 7, 13 CA1, CA2, CA3, CA5A, CA5B, CA7, CA13 P00915, P00918, P07451, P35218, Q9Y2D0, P43166, Q8N1Q1 *** 6 Androgen receptor (by homology) AR P10275 *** ** 7 Arachidonate lipoxygenase ALOX5, ALOX12, ALOX15 P09917, P18054, P16050 *** 8 Alpha-2A adrenergic receptor ADRA2A P08913 *** 9 Microtubule-associated protein tau MAPT P10636 *** ** ** 10 Sodium-dependent noradrenaline transporter SLC6A2 P23975 *** *** **** 11 Sodium-dependent dopamine transporter SLC6A3 Q01959 *** *** 12 Sodium-dependent serotonin transporter SLC6A4 P31645 *** *** **** 13 Sodium-dependent proline transporter (by homology) SLC6A7 Q99884 *** *** 14 Sodium- and chloride-dependent glycine transporter 1 (by homology) SLC6A9 P48067 *** *** 15 Sodium- and chloride-dependent neutral and basic amino acid transporter B(0+) (by homology) SLC6A14 Q9UN76 *** *** 16 5-hydroxytryptamine receptor 2A, 2B, 2C (by homology) HTR2A, HTR2B HTR2C, HTR6 P28223, P41595, P28335, P50406 *** *** **** 17 Adrenergic receptor Alpha-2B, 2C (by homology) ADRA2B, ADRA2C P18089, P18825 *** *** **** 18 Adrenergic receptor Beta-1, Beta-2, Beta-3 ADRB1, ADRB2, ADRB3 P08588, P07550, P13945 **** 19 Protein kinase C alpha type, beta type, gamma type, theta type, delta type regulatory subunit PRKCA, PRKCB, PRKCG, PRKCQ, PRKCD P17252, P05771, P05129, Q04759, Q05655 ** 20 Tyrosine-protein phosphatase non- receptor type 1, type 2 PTPN1, PTPN2 P18031, P17706 ** 21 Opioid receptor Mu-type, Delta-type, Kappa-type OPRM1, OPRD1, OPRK1 P35372, P41143, P41145 ** 22 Adenosine receptor A3 ADORA3 P33765 **** 23 Substance-P receptor, K receptor TACR1, TACR2 P25103, P21452 **** 24 Testis-specific androgen-binding protein SHBG P04278 **** 25 Vascular endothelial growth factor receptor 1, 2, 3 FLT1, KDR, FLT4 P17948, P35968, P35916 **** 26 Aryl hydrocarbon receptor AHR P35869 **** * (20 – 40%), ** (40 – 60%), *** (60 – 80%), **** (80 – 100%) probability of binding on target. Probabilities have been computed based on a cross-validation. They may therefore not represent the actual probability of success for any new molecule. (A) = Bisphenol A (BPA) CC(C)(C1=CC=C(C=C1)O)C2=CC=C(C=C2)O (B) = 2,20-bis(p-chlorophenyl)-1,1-dichloroethane (DDD) C1=CC(=CC=C1C(C2=CC=C(C=C2)Cl)C(Cl)Cl)Cl (C) = 2,20-bis(p-chlorophenyl)-1,1-trichloroethane (DDT) C1=CC(=CC=C1C(C2=CC=C(C=C2)Cl)C(Cl)(Cl)Cl)Cl (D) = Diethylhexyl phthalate (DEHP) CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC (E) = Nonylphenol (NP) CCCCCCCCCC1=CC=C(C=C1)O (F) = Tetrachlorodibenzodioxin (TCDD) C1=C2C(=CC(=C1Cl)Cl)OC3=CC(=C(C=C3O2)Cl)Cl Bereitgestellt von Slovenská poľnohospodárska knižnica | Heruntergeladen 28.02.20 12:35 UTC Nova Biotechnol Chim (2019) 18(2): 144-153 148 transcription factor enrichment analysis, protein- protein interaction network expansion and kinase enrichment analysis, using the 50 predicted target genes on eXpression2Kinases (X2K) webserver (Clarke et al. 2018). Results and Discussion Dumpsite/contaminated soil active toxicants and their predicted targets In this study, six toxicants from typical dumpsite or contaminated soil were found active in biological system at percentage probability greater than 20 % (Table 2), although octylphenol (data not shown) was also found to be about 20 % less active than nonylphenol. Fifty predicted targets (using gene name or UniProt ID) were obtained (Table 2) of which six were selected for further investigation based on their crucial implication on human health (Table 3). These were classified mainly into: (1) membrane proteins, such as substance-P receptor (also known as Neurokinin 1 receptor), 5-hydroxytryptamine receptor and human serotonin transporter; (2) nuclear proteins which include estrogen receptor alpha; and (3) transcription proteins which include aryl hydrocarbon receptor. The serotonin 2A receptor (5-HT2AR) is associated with diseases, such as bipolar disorder, depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease (Aznar and Hervig 2016). Structural elucidation has shown that 5-HT2AR possesses a unique side- extended cavity near the orthosteric binding site and possibly contributes to the high selectivity of ligands such pimavanserin (Kimura et al. 2019). Estrogen receptor (ER), an allosteric signaling protein and example of nuclear receptors, is a well validated therapeutic target for the treatment of estrogen receptor positive breast cancer (Burks et al. 2017; Fatoki et al. 2018a). Study on model compounds that targets ER has provided insight on the close structural similarity with tamoxifen, the FDA-approved drug which is a potent selective estrogen receptor modulator (Fatoki et al. 2018a). TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin) is the best known high-affinity ligand for aryl hydrocarbon receptor (AHR), and mediates its toxicity via activation of AHR. Increased levels of AHR and its target genes have been implicated in several belligerent tumors such as skin tumors, glioblastoma, or non-small cell lung cancer (Schulte et al. 2017). Predicted toxicokinetic properties of the toxicants The ADME parameter of the selected six toxicants (Table 3) showed that bisphenol A (BPA) and nonylphenol (NP) were soluble and moderately soluble (respectively), and both likely have high Table 3. Predicted toxicokinetic parameters of the dumpsites/contaminated soils active toxicants. Serial Parameters Selected dumpsite toxicants No. BPA DDD DDT DEHP NP TCDD 1 Molecular Weight 228.29 320.04 354.49 390.56 220.35 321.97 2 Heavy Atoms (HA) 17 18 19 28 16 18 3 Molar Refractivity 69.44 80.31 85.15 116.3 71.89 73.07 4 Total Polar Surface Area (Å2) 40.46 0 0 52.60 20.23 18.46 5 Consensus LogP 3.6 5.44 5.89 6.17 4.51 5.29 6 ESOL Class Soluble Moderately soluble Poorly soluble Poorly soluble Moderately soluble Poorly soluble 7 Gastrointestinal Absorption High Low Low High High Low 8 Blood Brain Barrier (BBB) Permeant Yes No No No Yes No 9 P-glycoprotein Substrate No No No Yes No Yes 10 Cytochrome P450 Inhibitor CYP1A2, CYP2D6 CYP1A2, CTP2C9, CYP2C19, CYP2D6 CTP2C9, CYP2C19 CTP2C9, CYP3A4 CYP1A2, CYP2C19, CYP2D6 CYP2C9 11 Skin permeation log Kp (cm.s -1) -5.34 -3.98 -3.56 -3.39 -3.55 -3.44 12 Lipinski Violation 0 1 1 1 0 1 13 Bioavailability Score 0.55 0.55 0.55 0.55 0.55 0.55 14 Synthetic Accessibility 1.43 2.37 2.37 4.12 1.63 2.71 Bereitgestellt von Slovenská poľnohospodárska knižnica | Heruntergeladen 28.02.20 12:35 UTC Nova Biotechnol Chim (2019) 18(2): 144-153 149 Table 4. Docking parameters implemented in AutoDockTool for selected 6 targets. Serial No. Selected Target Center grid box [points] Size [points] Spacing [Å] 1 Adrenergic receptor alpha-2A 171.339 x 21.415 x 21.578 126 x 108 x 112 0.675 (PDB ID: 5UIG, Chain A) 2 Aryl hydrocarbon receptor -8.185 x 37.203 x 215.837 126 x 126 x 126 0.375 (PDB ID: 5NJ8, Chain A) 3 Estrogen receptor -33.601 x 18.875 x -21.737 120 x 126 x 122 0.375 (PDB ID: 5T97, Chain A) 4 Sodium-dependent serotonin transporter 36.842 x 183.210 x 142.913 126 x 100 x 126 0.475 (PDB ID: 5I6Z, Chain A) 5 Substance-P receptor 2.367 x 28.422 x -26.136 70 x 80 x 126 0.875 (PDB ID: 6HLL, Chain A) 6 5-hydroxytryptamine receptor 2A 43.501 x -0.464 x 60.742 126 x 66 x 76 0.775 (PDB ID: 6A93, Chain A) gastrointestinal absorption (GA), may permeate blood-brain barrier (BBB) and could inhibit cytochrome P450 (CYP1A2 and CYP2D6). On the other hand, diethylhexyl phthalate (DEHP) and tetrachlorodibenzodioxin (TCDD) were identified to possibly have high and low GA respectively, and both are poorly soluble, may not permeate blood-brain barrier (BBB), and serve as P-glycoprotein substrates as well as inhibitors of cytochrome P450 (CYP2C9). The best bioavailability score and synthetic accessibility (SA) score is 1.0 which is an indication of the amount of the compound that could reach the active site and extent of ease of synthesis of the compound, respectively (Diana et al. 2017; Fatoki et al. 2018b). The SA of the toxicants in this study ranged between 1.43 and 4.12, and showed that they were from or could make up synthetic materials. Usually, not all contaminant found in soil are biologically available. Bioavailability has been described as the physicochemical access that a toxicant has to the biological processes of an organism (Allen 2002). The bioavailability of a contaminant depends on the characteristics of the soil and of the site (Shayler et al. 2009). Binding energy and efficiency of the toxicants The parameter for docking analysis and predicted binding site amino acid residues are shown in Table 4. All the six toxicants investigated in this study show good free binding energies below –5.0 kcal.mol-1 as shown in Table 5. DDT and BPA were found to have highest predicted free binding energy in four and two of the targets respectively. The interaction of some of the ligands with the protein targets are shown in Fig. 1. The ligand efficiency (LE) is a useful metric to assess binding affinity of compounds with respect to the number of non-hydrogen atoms (Schultes et al. 2010). The toxic compounds showing LE greater than 0.25 kcal.mol-1. HA will possibly cause fatal damage on human health. Based on the ligand efficiency result (Table 6), the order of toxicity of the compounds investigated in this study is BPA > DDD = DDT > TCDD > NP > DEHP. Table 5. Docking score for the binding free energy between the selected 6 targets and 6 active toxicants. Serial Selected Target Binding energy [kcal.mol-1] of the selected toxicants No. BPA DDD DDT DEHP NP TCDD 1 Estrogen receptor (PDB ID: 5T97) - 9.1 - 8.5 - 7.3 - 7.5 - 6.7 - 7.4 2 Sodium-dependent serotonin transporter (PDB ID: 5I6Z) -9.5 - 8.2 - 9.8 - 8.2 - 7.5 - 8.2 3 Adrenergic receptor alpha-2A (PDB ID: 5UIG) - 11.9 - 11.1 - 9.2 - 7.6 - 6.8 - 10.3 4 Aryl hydrocarbon receptor (PDB ID: 5NJ8) - 8.7 - 8.3 - 9.5 - 7.2 - 7.0 - 6.2 5 Substance-P receptor (PDB ID: 6HLL) - 9.0 - 8.2 - 9.9 - 7.5 - 7.1 - 9.2 6 5-hydroxytryptamine receptor 2A (PDB ID: 6A93) - 9.2 - 8.6 - 10.5 - 8.3 - 7.8 - 8.8 Bereitgestellt von Slovenská poľnohospodárska knižnica | Heruntergeladen 28.02.20 12:35 UTC Nova Biotechnol Chim (2019) 18(2): 144-153 150 Fig. 1. Binding Pose and Score of the 6 active toxicants, where (A) – BPA interaction with 5UIG (- 11.9 kcal.mol-1); (B) – DDD interaction with 5T97 (- 8.5 kcal.mol-1); (C) – DDT interaction with 6A93 (- 10.5 kcal.mol-1); (D) – DEHP interaction with 5I6Z (-8.2 kcal.mol-1); (E) – NP interaction with 5NJ8 (-7.0 kcal.mol-1); (F) – TCDD interaction with 6HLL (- 9.2 kcal.mol-1) visualized on PyMol and ADT, respectively. Bereitgestellt von Slovenská poľnohospodárska knižnica | Heruntergeladen 28.02.20 12:35 UTC Nova Biotechnol Chim (2019) 18(2): 144-153 151 Table 6. Ligand Efficiencies (LE) of docked scores of selected 6 targets and 6 active toxicants. Serial No. Selected Target Ligand efficiency of the selected toxicants [kcal.mol-1 HA] BPA DDD DDT DEHP NP TCDD 1 Estrogen receptor (PDB ID: 5T97) 0.54 0.47 0.38 0.27 0.42 0.41 2 Sodium-dependent serotonin transporter (PDB ID: 5I6Z) 0.56 0.46 0.52 0.29 0.47 0.46 3 Adrenergic receptor alpha-2A (PDB ID: 5UIG) 0.70 0.62 0.48 0.27 0.43 0.57 4 Aryl hydrocarbon receptor (PDB ID: 5NJ8) 0.51 0.46 0.50 0.26 0.44 0.34 5 Substance-P receptor (PDB ID: 6HLL) 0.53 0.46 0.52 0.27 0.44 0.51 6 5-hydroxytryptamine receptor 2A (PDB ID: 6A93) 0.54 0.48 0.55 0.30 0.49 0.49 Gene expression network modulated by the toxicants The overall expression network of the fifty predicted target genes obtained in this study as shown in Fig. 2, highly implicated SUZ12 (polycomb protein) with hypergeometric p-value of 5.44 × 10-10, as the most enriched transcription factors among others, which are associated with the active toxicants from the typical dumpsite while the mitogen-activated protein kinases (MAPKs) and cyclin-dependent kinases (CDKs) with maximum hypergeometric p-value of 2.70 × 10-15 were the major kinases from the protein-protein interaction (PPI) as shown in Fig. 2. Differential gene expression study has become one of the imperative methods to discover genes which are essential in diagnosis and prediction of diseases such cancer (Fatoki et al. 2018b). The transcription factors such as TRIM28, SUZ12 and STAT3 are associated with the proliferation of cancer cells. The result of this study was found similar with that of enrichment analysis of urethane-targeted genes which showed TRIM28 and SUZ12 as most Fig. 2. Overall network of target genes of active toxicants from typical dumpsite or contaminated soil generated by eXpression2Kinases server. Bereitgestellt von Slovenská poľnohospodárska knižnica | Heruntergeladen 28.02.20 12:35 UTC Nova Biotechnol Chim (2019) 18(2): 144-153 152 expressed transcription factors (Fatoki et al. 2018b). However, endometrial stromal tumors may be a result of a chromosomal aberration involving SUZ12. Conclusions This study has unravelled some of the chemical compounds from typical dumpsite or grossly contaminated soil that might actively modulate biological system and are potential cause of several ailments which include cancer, bipolar disorder, schizophrenia, neurodegenerative disease and others. This study has showed the threat which toxicants pose to public health. More importantly, it provides avenue for further investigation of super bugs (microorganisms) that can remediate these toxicants in our environment through bioaugmentation, which improves the biodegradative capacities of contaminated sites. Environmental monitoring and modern wastes management system should be implemented and enforced in order to safeguard the public health and prevent indiscriminate dumping of wastes in the communities and regularize human habitation far away from existing dumpsites. Conflict of Interest The authors declare that they have no conflict of interest. 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