Wilms’ tumor (WT), one of the rare childhood tumor and their incidence is 1: 100,000 between 1 to 5 years age group [1,2]. WT occurs both in hereditary and sporadic forms and WTI gene associated with tumors and is mapped on chromosome 11p13. The protein encoded by the WT1 gene contains an amino terminus rich in proline and glutamine residues and a carboxy terminus containing four zinc fingers protein act as transcription factor to regulates gene expression [3-5].

The transforming growth factor beta (TGF-β) is polypeptides of highly conserved and abundant dimeric proteins of 25 kd, ubiquitously expressed in eukaryotes that modulate the function of glomerular cell which is responsible to increases the production of collagen and fibronectin in mesenchymal epithelial cells [6-8]. TGF-β signalling pathway regulates the cellular proliferation, differentiation, migration and apoptosis in tumours [9,10]. There are three TGF-β isoforms (TGF-β1, TGF-β2 and TGF-β3) are expressed in epithelium and each gene is encoded in tissue-specific manner. TGF-βR1 based protein kinase activity results in interaction with other transcription factors to promote angiogenesis and immunosuppressive activity [11,12].

TGF-βRI can also participate in variety of cellular functions including invasion, extracellular matrix (ECM) formation and migration of cancer cells [13,14]. TGF-βRI is rich in serine/threonine kinase receptor that is a member of the TGF-β signalling pathway and exhibits metastatic properties by invading surrounding cells [15]. It is still not clear whether TGF-β bind to ligands either type-I or type-II receptor during signalling at the time of angiogenesis. The present study explores for the first time India, a comprehensive role of TGF-βR1 gene mutation and their frequency after DNA sequencing includes the substitution, deletion and insertion in WT cases. The bioinformatics tools (docking) were used to decode amino acids and to predict the 3-D structural and functional role of TGF-βR1 and their interaction with methotrexate after the molecular docking to ligand. Therefore, the present study become relevant to understand the mechanism of oncogenesis through gene-protein interaction causing dysregulation of signal transduction and in future enhance effective management of WT cases.

In the present study clinically diagnosed patient of WT and age matched controls referred to genetics laboratory of department of Pathology/Lab Medicine at All India Institute of Medical Sciences- Patna, India. Blood sample (1.0 ml) were collected (n=48) from cases of WT and controls, after written consent from the parent. The study was approved by Institute Ethical Committee. The median age group consist of 3.5 years and none of the proband have family history of cancer or exposure of radiation or drug previously.

Genomic DNA was isolated, quantified by spectrophotometer subjected to RT PCR analysis using syber green as florescence dye. Initially the protocol consisted of an denaturation step (94°C for 3 min) followed by amplification and quantification steps repeated for 40 cycles (94°C for 20 s, 56°C for 10 s, ‘72°C for 20 s with a single fluorescence measurement at the end of the elongation step at 72° curve analysed the data and reaction was terminated by cooling to 40°C. GAPDH gene was used as positive control of homeobox region.

Melting curves were constructed by lowering the temperature to 65°C and later increasing the temperature by 0.2"C/s to 98°C to measuring the change fluorescence consistently. Tm values were assigned to develop plot generated by the RT-PCR of the negative derivation of fluorescence versus temperature (dF/dT) of the melting curve for amplification products measured at 530 nm TGF ßR1 gene amplify with initial denaturation at 94ºC for 4min, annealing at 56 ºC followed by 35 cycles and final extension at 72ºC for 10 min, using specific forward and reverse primers, 5’-TTTCGCCTTAGCGCCCACTG -3’5’- GAAGTTGGCATGGTAGCCCTT-3’ respectively of 414 bp to evaluate the frequency of mutation, copy number variations (CNV) and genetic heterogeneity of TGF-βR1.

DNA sequencing study was performed using Sangers method to find out nucleotide changes (new mutation) like substitution, deletion and insertion and compare the same with controls. Gene coded protein sequences searched in Biological database (https://www.ncbi.nlm.nih.gov/protein) and mutational aspect were obtained by searching Ensemble genome databases (http://www.ensembl.org/index.html). The prospective TGFβ-R1 sequences were confirmed using the Basic Local Alignment Search Tool (BLAST; http://blast.ncbi.nlm.nih.gov/Blast.cgi). Identification of functional significance of TGFβ-R1 gene somatic mutations were extracted from the Ensemble genome databases as described previously [15,16]. Mutations of TGFβ-R1 gene were obtained from the Catalogue of somatic cancer database (http://www.cancer.sanger.ac.uk/cosmic) and protein structure 5E8S.pdb was obtained from structural database (https://www.rcsb.org/).

Identification of the binding site

Structure - based design begins with the identification of the target molecule to pocket with a variety of potential hydrogen bond donors and acceptors, hydrophobic characteristics, and molecular surface sizes. These are the active site for enzyme, act as assembly site for protein during binding to ligand which may vary for a disease state [17,18]. The ligand binding site predictions of a protein are based on relevant template library, selected for alignment of sequence and evaluation by Raptor X (http://raptorx.uchicago.edu/BindingSite/) [19]. Methotrexate (MTX) is an inhibitor of tetrahydrofolate dehydrogenase and prevents the formation of tetrahydrofolate, necessary for synthesis of thymidylate, an essential component of DNA synthesis MTX is entered into the S-phase of the cell-cycle affecting rapidly dividing cells of the growing foetus, germ cells, liver and bone marrow leads to inhibit DNA replication and finally cell-death (https://www.drugbank.ca/drugs/DB00563). Molecular docking is commonly used for predicting binding modes and energies of ligands to proteins. It help to determine accurate complex geometry and binding energy estimation during calculations of partial charges. AutoDock software was widely used docking programs help for the calculation of van der Waals and the electrostatic forces between protein and ligand [20,21].

Figure 1A, showing mutation of TGFβ-R1 frequency (18.18%), while, RT PCR revealed Ct mean value 23.63, S.D. 0.94 and C.I. at 95% 0.717- 1.280 and P=0.53 value showing lack of significant in WT cases with controls. Calculated mean Tm value was observed 90.70 which shifted to 91.0, showing significant differences (P=0.24) with S.D.4.28 and C.I. at 95% varying between 2.09-7.09 using student‘t’- test, GAPDH were used as positive control. CNVs also showing S.D.=0.37 and C.I. at 95% 0.337- 0.906 having significant difference (P=0.351) (Figures 1B-1E). Cytogenetic locus of TGFβ-R1 gene assigned on chromosome-9q22.33 and DNA sequencing data showing changes in nucleotides as substitution, insertion and deletion (represented in red) as documented in Figures 2A and 2B. The detailed spectrum of TGFβ-R1 nucleotide changes and their encoded corresponding amino acids after analysis of bioinformatics tools are depicted in Tables 1A and 1B.

Figure 1: (A) PCR based analysis of TGF βR1 showing disappereance of 414 bp band in lane 1,2, using specific primers (forward/reverse) on 1.5% agrose gel after staining withethedium bromide and bands were visualized on Gel Doc system, (B) Ct value of the cases (blue) 25 cycle showing lack of significant differences with respect to control 23 cycle, (C) Melt peak analysis showing significant differences in cases of WT and changes in Tm value with respect to controls, (D) Copy number variations also showing significance difference, (E) intensity of bands further analysed on agarose gel showing different intensity correlated to CNVs.

Figure 2: (A) Represent cytogenetic loci of TGFβ-R1 on Chromosome 9q22.33, (B) TGF-βR1 sequencing data represents in red colour substitution, insertion and deletion.

Table 1: (A) Detailed mutational spectra of TGFβR1 after DNA sequencing showing Substitution, (B) Deletion and Insertion with corresponding amino acids.

The 3D-structure of TGFβ-R1, after changes of amino acid residues as compared with normal structure showing substitution (red) and insertion (green) as shown in Figure 2B. Non-polar hydrogen atoms were merged with rotatable bonds and the interaction of protein with ligand binding sites with polar and hydrophobic bonding is documented in Figures 3A and 3B. Free energy binding sites with their respective minimum interacting energy is shown in Table 2 obtained using docking calculations to maintain accuracy for ligand and protein that can be visualized with amino acid residues (ASP 351, LEU340, LEU 240, VAL 219 and ILE 211). The interaction between MTX and protein after calculation of root mean square deviation (RMSD) using docking calculations are shown in Figures 3C and 3D. HB plot presenting protein structure as shown in Figure 4 with new approach for description of the three-dimensional folding and flexibility of a protein. Docking simulations were performed using the Lamarckian genetic algorithm (LGA) and the Solis & Wets method [21]. Thus, it can be concluded that good geometry prediction help to contribute accurate binding energy estimation. Further, our results were compared to the experimentally designed complex structures of different ligand binding sites suggested that our predicted 3-D model of TGF-βR1 derived the best energy based ligand binding sites [22].

Figure 3: (A) 3D Helical normal structure showing active binding sites structure of TGF-βR1, (B) Showing mutated structure showing change in the amino acid residues sites represents in three different colours red, green and pink, (C) Interaction of amino acid with ligand in ball & stick model and (D) Interaction of amino acid residues with ligand binds with polar, hydrophobic, electrostatic force and VDW forces.

Table 2: Showing rank 1 most stable docking position on the basis of total estimated energies.

Figure 4: HB plot representation of a protein containing parallel and antiparallel beta sheets.

In this study, we have demonstrated that the TGF-β has been associated with development of WT. It is well known that WT1 expression is essential for podocytes function, and TGF-β is able to repress its expression [23]. TGF-β superfamily consists of more than 60 secreted proteins that play critical roles in regulating diverse biological processes during embryonic development and in adults [24]. WTI protein has been designated as a transcription factor because it contains two discrete functional domains, one comprising the glutamine and proline rich amino terminal domain responsible for transcriptional repression and four-zinc finger DNA binding domain. Mutations in TGF-βR1 have been identified in cell lines and decreased TGF-βR1 expression has been shown to decrease tumorigenicity [25,26]. In accordance, previous studies also demonstrated a significantly higher frequency of mutation of 6A allele in cancer patients as compared to controls, suggesting its role in genetic susceptibility [27]. This allele is located on the human TGFβ-R1 promoter, which is required for WTl-mediated repression of transcription. Mutation of several nucleotides in the WTl/Egr-1 response element that prevent or partially prevent WTl mediated transcriptional repression and mediating down regulation of TGFβ-R1 promoter activity by Egr-1 and play role in angiogenesis of tumor [28].

Previous findings suggested that the DNA binding domain of WTI is inactivated in tumours along with its transcriptional activities as a result, the target genes may be over expressed and the cell may lose control of normal cellular proliferation and differentiation [29,30]. WTl has been shown to be modified by alternative splicing and one alternative spliced product generates a protein with a 17-amino acid insertion N-terminal to the zinc finger domain; this protein retains its ability to repress the TGFβ-R1 promoter [31]. A second form of human WTl protein contains an insertion of three amino acids (Lys-Thr-Ser) between zinc fingers protein does not regulate the TGFβ-R1 promoter and probably binds to a sequence distinct from Egr-I /WTl [32].

Accumulating evidence has demonstrated that the TGFβ-R1 gene mutation plays vital role in progression of WT cases. Furthermore, such mutations might depress TGFβ-R1 activity, thereby leading to increased TGFβ-R1 expression and, subsequently, increased collagen production [33]. Hence, our findings are with the agreement of previous studies that WTl might play a significant role in regulating TGFβ-R1 expression during matrix production in WT cases. The bioinformatics tools were used for prediction of TGFβ-R1 3D structures based on identified mutations help in providing knowledge of ligand binding sites in the gene coded amino acid residues (protein) with MTX as model anticancer drug [34]. Present study reveals the best interaction between protein and ligand with lowest binding energy. Interestingly, the mutation of TGFβ-R1 genes becomes relevant that how to reduce the mutagenicity either by changes in structural remodelling or decoding or supplementation of amino acids. Hence, the present study has widened the scope of developing new derivatives based on gene protein drug interaction for pharmacogenomics and personalized medicine for the management of the disease like Wilms’ tumor.

The present findings demonstrate a significant role of TGFβ-R1 gene mutations as major determinants in the WT cases, suggesting the significance of genetic alterations based on DNA sequencing increase high risk in cancer patients. Further, the realistic approach to provide better understanding of structural and functional genomics interactions (protein or drug) make the study more relevant. Hence this study has explored the scientific arena to develop derivatives of promising drug for disease like tumour, otherwise the study will be remaining incomplete.

AKS thankfully acknowledges to the Director, AIIMS Patna for valuable suggestions, and financial support is provided by the Department of Biotechnology (Govt. India) DST/SSTP/Bihar/444 to carry out this research work.

All the authors have equally participated during preparation of the manuscript. There is no conflict of interest between the authors.

AKS, Aprajita is responsible for genetics analysis, VS, PS responsible for clinical diagnosis and management MT and AK is responsible structural analysis during preparation of the manuscript.

1. Hohenstein P, Pritchard-Jones K, Charlton J (2015) The yin and yang of kidney development and Wilms' tumors. Genes Dev 29: 467-482.
2. Apoznanski W, Sawicz-Birkowska K, Palczewski M, Szydelko T (2015) Extrarenal nephroblastoma. Central Eur J Urol 68: 153-156.
3. Rose EA, Glaser T, Jones C, Smith CL, Lewis WH, et al. (1990) Complete physical map of the WAGR region of 11 pl3 localizes a candidate Wilms’ tumor gene. Cell 60: 495-508.
4. Call KM, Glaser T, Ito CY, Buckler AJ, Pelletier J, et al. (1990) Isolation and characterization of a zinc finger polypeptide gene at the human chromosome 11 Wilms’ tumor locus. Cell 60: 509-520.
5. Gessler M, Poustka A, Cavenee W, Neve RL, Orkin SH, et al. (1990) Homologous deletion in Wilms’ tumors of a zinc finger gene identified by chromosomal jumping. Nature 343: 774-778.
6. Kreidberg JA, Sariola H, Loring JM, Maeda M, Pelletier J, et al. (1993) WT‑1 is required for early kidney development. Cell 74: 679‑691.
7. Rauscher FJ, Morris JF, Tournay OE, Cook DM, Curran T (1990) Binding of the Wilms’ tumor locus zinc finger protein to the EGR-1 consensus sequence. Science 250: 1259-l262.
8. Madden SL, Cook DM, Morris JF, Gashler A, Sukhatme VP, et al. (1991) Transcriptional repression mediated by the WTl Wilms’ tumor gene product. Science 253: 1550-l553.
9. Mackay K, Striker LJ, Stauffer JW, Doi T, Agodoa LY, et al. (1989) Transforming growth factor- β: murine glomerular receptors and responses of isolated glomerular cells. J Clin Invest 83: 1661-l666.
10. Roberts AB, McCune BK, Sporn MB (1992) TGF-beta: Regulation of extracellular matrix. Kidney Int 41: 557-559.
11. Massague J, Blain SW, Lo RS (2000) TGFβ signaling in growth control, cancer, and heritable disorders. Cell 103: 295-309.
12. Akhurst RJ (2004) TGF beta signaling in health and disease. Nat Genet 36: 790-792.
13. Elliott RL, Blobe GC (2005) Role of transforming growth factor beta in human cancer. J Clin Oncol 23: 2078-2093.
14. Roman C, Saha D, Beauchamp R (2001) TGF-beta and colorectal carcinogenesis. Microsc Res Tech 52: 450-457.
15. Ding Z, Yang HW, Xia TS, Wang B, Ding Q (2015) Integrative genomic analyses of the RNA‑binding protein, RNPC1, and its potential role in cancer prediction. Int J Mol Med 36: 473-484.
16. Wang B, Chen K, Xu W, Chen D, Tang W, et al. (2014) Integrative genomic analyses of secreted protein acidic and rich in cysteine and its role in cancer prediction. Mol Med Rep 10: 1461-1468.
17. Lind KE, Du Z, Fujinaga K, Peterlin BM, James TL (2002) Structure-based computational database screening, in vitro assay, and NMR assessment of compounds that target TAR RNA. Chem Biol 9 :185-193.
18. Filikov A, Mohan V, Vickers T, Griffey R, Cook P, et al. (2000) Identification of ligands for RNA targets via structure-based virtual screening: HIV-1 TAR. J Computer Aided Mol Des 14: 593-610.
19. Källberg M, Wang H, Wang S, Peng J, Wang Z, et al. (2012) Template-based protein structure modeling using the RaptorX web server. Nature Protocols 7: 1511-1522.
20. Bikadi Z, Hazai E (2009) Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock. J Cheminform 11: 1758-2946.
21. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, et al. (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem19: 1639-1662.
22. Bikadi, Z, Demko L, Hazai E (2007) Functional and structural characterization of a protein based on analysis of its hydrogen bonding network by hydrogen bonding plot. Arch Biochem Biophys 461: 225-234.
23. Sakairi T, Abe Y, Kopp JB (2011) TGF-beta1 reduces Wilms tumor suppressor gene expression in podocytes. Nephrol Dial Transplant 26: 2746-2752.
24. Massagué J (2000) How cells read TGF-beta signals. Nat Rev Mol Cell Biol 1: 169-178.
25. Ku JL, Park SH, Yoon KA, Shin YK, Kim KH, et al. (2007) Genetic alterations of the TGF-beta signaling pathway in colorectal cancer cell lines: A novel mutation in Smad3 associated with the inactivation of TGF-beta-induced transcriptional activation. Cancer Lett 247: 283-292.
26. Wang J, Han W, Zborowska E, Liang J, Wang X, et al. (1996) Reduced expression of transforming growth factor beta type I receptor contributes to the malignancy of human colon carcinoma cells. J Biol Chem 271: 17366-17371.
27. Pasche B, Kolachana P, Nafa K, Satagopan J, Chen YG, et al. (1999) TβR-I(6A) is a candidate tumor susceptibility allele. Cancer Res 59: 5678-5682.
28. Baxter SW, Choong DY, Eccles DM, Campbell IG (2002) Transforming growth factor beta receptor 1 polyalanine polymorphism and exon 5 mutation analysis in breast and ovarian cancer. Cancer Epidemiol. Biomarkers Prev 11: 211-214.
29. Haber DA, Buckler AJ, Glaser T, Call KM, Pelletier J, et al. (1990) An internal deletion within an 11 pl3 zinc finger gene contributes to the development of Wilms’ tumor. Cell 61: 1257-1269.
30. Bickmore WA, Oghene K, Little MH, Seawright A, Van Heyningen V, et al. (1992) Modulation of DNA binding specificity by alternative splicing of the Wilms’ tumor wtl gene transcript. Science 257: 235-237.
31. Haber DA, Sohn RL, Buckler AJ, Pelletier J, Call KM, et al. (1991) Alternative splicing and genomic structure of the Wilms’ tumor gene WTl. Proc Natl Acad Sci USA 88: 9618-9622.
32. Sukhatme VP (1992) The Egr transcription factor family: From signal transduction to kidney differentiation. Kidney Int 41: 550-553.
33. Border WA, Okuda S, Languino LR, Sporn MB, Ruoslahti E (1990) Suppression of experimental glomerulonephritis by antiserum against transforming growth factor-p1. Nature 346: 371-374.
34. Saxena AK, Singh D, Singh G (2009) Structural interaction between drug – DNA protein – A novel approach for bioinformatics in medicine. Biomed Res 20: 28-34.