Molecular and in-silico analysis of single nucleotide polymorphism targeting human TP53 gene exon 5-8 in Sudanese esophageal cancer patients

Sampling

Sections of 30–40 µm thickness from 50 formalin fixed paraffin embedded (FFPE) tissue samples were obtained from esophageal cancer patients representing different hospitals and clinics in Khartoum State, Sudan, from July 2013 to June 2017. All patients have been previously diagnosed with squamous cell carcinoma (SCC) and adenocarcinoma (AC).

DNA extraction

Genomic DNA for PCR analysis was extracted from FFPE tissue blocks. Using commercial DNA extraction kits for fast isolation of genomic DNA from FFPE samples as per manufacturer’s instructions. Extraction procedure is based on combination of an efficient lysis step with a subsequent binding of genomic DNA on a Spin Filter surface followed by washing of the bound DNA and finally eluting of the DNA (845-BP-0020250, black PREP FFPE DNA Kit, Analytik Jena Company).

PCR amplification

For amplification of exon 5, 6,7 and 8 of TP53 gene, four pairs of primers (catalogue numbers: 171002-009_D5, 171002-009_D6, 171002-009_D7, 171002-009_D8, 171002-009_D9, 171002-009_D10, 171002-009_D11, 171002-009_D12; Macrogen, Korea) were used^15,16 (Table 1). A total of 2–5 µl of genomic DNA, 0.5 µl forward and 0.5 µl reverse primer and 25 μl double distilled water (DDW) was combined to make up the final reaction volume. The mixture was amplified using Heal force thermal cycler (Model No: K960) with the following amplification conditions; 95°C for 5 min, followed by 37 cycles at 95°C for 45 sec, primer-specific annealing temperature for 45 sec, 72°C for 45 sec and a final extension at 72°C for 5 min. 5 μl of the PCR products were applied on 2 % agarose gel and remaining PCR products were sequenced by BGI company (China).

Dataset collection

The SNP information SNP ID, mRNA accession number NM_000546, and Protein accession number NP_000537 of the human TP53 gene used in our computational analysis were retrieved from the National Center for Biotechnology Information (NCBI) database and catalogue of somatic mutation in cancer (COSMIC) database (TP53_ENST00000269305). The nucleotide and amino acid sequence of the p53 protein were obtained and investigated using nucleotide (NG_017013), Gene (Gene ID: 7157) database NCBI and UniProt database (P04637).

Data analysis using different bioinformatics tools

Codon code aligner. Sequences were assembled into contigs end clipped and edited using Codon Code Aligner software version 8.0.1 (Dedham, MA, USA). Sequence data are available at GenBank under accession numbers MH366303 to MH366483¹⁷.

SIFT Program. SIFT (Sorting Intolerant from Tolerant) tool uses sequence homology to calculate the probability of affecting protein function in case of amino acid change. It uses the concept of evolutionarily conserved regions which is less tolerant to mutations, and therefore amino acid change or frame shift mutations in these regions are expected to affect protein function the most. SIFT tool works by introducing a query protein into SIFT program to be searched against protein database aligned with homologous protein sequences. Then the program calculates SIFT score based on amino acid changes in that position. A SIFT score ranges from 0 to 1. Score less than 0.05 is predicted to affect protein function and considered functionally deleterious, whereas any score more than or equal to 0.05 represents a neutral substitution^18–20.

PolyPhen -2. PolyPhen-2 (Polymorphism Phenotyping version 2) is a structural and functional predicting tool that predicts the effect of an amino acid change on protein characteristics based on SNPs functional annotations, protein structural properties with sequence annotation, and finally predict if the coding non-synonymous SNPs are considered damaging or not^21,22.

PolyPhen-2 workflow requires protein sequence, mutational position, and substitution. The PolyPhen output is represented with a score that ranges from 0 to 1, with zero score indicating a neutral effect of amino acid substitution on protein function and a higher score representing a mutation that is more likely to be damaging²³.

I-Mutant 3.0. I-Mutant 3.0 is a support vector machine (SVM) based tool, which was used to calculate the stability changes of specific SNP upon protein sequence. Information of wild and mutated residue, protein sequence, temperature, and pH was used as input parameters to this server, and finally, the outputs reports if a point mutation is stable or not. The program categorizes the prediction into: neutral mutation (DDG = 0.5 kcal/mol), large decrease of stability (0.5 kcal/mol). The output is a free Gibbs energy change value (ΔΔG) of protein before and after mutation^24–27.

PhD-SNP. PhD-SNP (Predictor of Human Deleterious Single Nucleotide Polymorphisms) software is a prediction tool that predicts disease association of nsSNP by dividing those SNPs into disease-related or neutral polymorphism based on a score ranged from (0-1); SNPs with a score above 0.5 are considered disease associated according to the program algorithm. PhD-SNP outputs depend on a number of sequences aligned, conservation index of SNP position, frequencies of wild and mutant residues^19,20,28.

Project HOPE. Structural and biochemical analysis for mutations was accomplished using Project HOPE is a web-server used to give a comprehensive report on the effect of the specific mutation on the 3D structure of the native protein and the variant model using different software and sources. The user can submit a protein sequence or an accession number of specific protein after specifying the wild-type residue and the new mutant form to create the report^29,30.

Mutation Taster. Mutation Taster calculates the pathogenic consequences of variations in DNA sequence. It predicts the functional impact of amino acid alterations, intronic and synonymous substitutions, in addition to INDEL mutations and variants covering intron-exon connection region. Mutation Taster prediction system divides alterations as; Disease-causing: which is probably deleterious, Disease-causing automatic: the alteration here is known to be deleterious, Polymorphism: probably harmless alteration and polymorphism automatic: known to be harmless^31–33.

FATHMM. FATHMM (Functional Analysis Through Hidden Markov Models) is a web-server predicts the functional significances of both coding and non-coding variants. We selected the cancer option to display predictions that can distinguish between cancer-promoting/driver mutations and other germline polymorphisms. It uses a default prediction threshold of -0.75. Predictions with scores less than this indicate that the mutation is potentially cancer associated³⁴.

Results of TP53 gene mutations in esophageal carcinomas

Esophageal squamous cell carcinoma cases represent 43 (86%) of all cases, whereas adenocarcinoma made up 7 cases (14%). Mutation analysis results demonstrate a higher TP53 mutation rate in esophageal adenocarcinoma compared to squamous cell carcinoma. This were illustrated in (Table 2) in the results section which describe Histopathological diagnosis, mutational status and exons affected in esophageal cancer patients.

Distribution of TP53 coding synonymous SNPs (sSNPs), coding non-synonymous SNPs (nsSNPs) and INDEL through exon 5-8 in P53 gene were represented by (Figure 1).

Figure 1. Distribution of TP53 coding synonymous SNPs (sSNPs), coding non-synonymous SNPs (nsSNPs) and INDEL through exon 5–8 in P53 gene.

TP53 gene SNPs were found in 20 out of 50 sample of esophageal carcinomas (40%). Six out of ten SNPs (60%) were missense SNPs leading to amino acid substitution, four SNPs (40%) were silent mutations without any amino acid change. Six out of ten (60%) TP53 SNPs occurred in exon 5 at the following codons 160, 161 (twice), 163, 164, 175. Three of them were missense and three were silent. Two SNPs (20%) were located in exon 6 at codon 215 (missense) and 222 (silent). Two SNPs (20%) were present in exon 8 at codon 298 (missense) and 305 (silent).

15 out of 43 (34.9%) SSC samples were found to be mutated, 13/15 (86.7%) of them existed in exon 5, 2/15 (13.3 %) in exon 6 and 1/15 (6.7%) in exon 8. Whereas adenocarcinoma had a higher rate of mutations 4/7 (57.1%) with 100% of SNPs occurring in exon 5.

The percentage of deleterious nsSNPs predicted by SIFT and PolyPhen was 66.7% (Figure 2), those SNPs were A161D, K164E, R175P and S215N according to SIFT and PolyPhen (Table 3–Table 4). I-Mutant suite also give the same percentage for deleterious nsSNPs which is 66.7% (Table 5) but in case of using I-Mutant suite the predicted SNPs to be deleterious were, A161D, R175P, S215N and M160V (Table 6). The PhD-SNP report defines 5/6 (83.3%) of nsSNPs as disease related polymorphism (Table 7).

Figure 2. Distribution of deleterious and benign non-synonymous single nucleotide polymorphisms (nsSNPs) by Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping v2 (PolyPhen), and I-Mutant Suite.

The magenta cylindrical bar indicates the percentage of nsSNPs that were found to be deleterious by SIFT, damaging (Possibly/Probably) by PolyPhen, and largely unstable by I-Mutant Suite. The pink cylinder indicates the percentage of nsSNPs that were found to be tolerated by SIFT, benign by PolyPhen, and largely stable/neutral by I-Mutant Suite.

The results reveal that SNPs in positions E298Q were predicted to be a neutral polymorphism which represent 10% of all mutation detected. All other SNPs M160V, A161D, A161A, Y163Y, K164E, R175P, S215N, P222P, and K305K representing 90% of all SNPs were predicted to be disease related according to MutationTaster software (Table 8).

Frequency of SNPs among different samples was shown in details in (Table 9). FATHMM server; cancer association predictions result of the non-synonymous changes found in TP53 gene exon 5-8 were demonstrated in (Table 10).

Different mutations with their position, wild type and mutant form in addition to alignment and chromatogram were illustrated in (Figure 3–Figure 8).

Figure 3. Mutation of alanine into aspartic acid at position 161.

Figure 4. Mutation of a serine into asparagine at position 215.

Figure 5. Mutation of arginine into proline at position 175.

Figure 6. Mutation of lysine into glutamic acid at position 164.

Figure 7. Mutation of a methionine into a valine at position 160.

Figure 8. Mutation of glutamic acid into glutamine at position 298.