Single strand conformational polymorphism (SSCP) analysis is a genetic screening technique that allows detection of nucleotide substitutions in fragments of PCR amplified genomic DNA or cDNA (Orita et ah, 1989) (Vidal-Puig and Moller, 1994). The method involves the separation of denatured single strands of DNA on non-denaturing polyacrylamide gels. It is based on the relationship between electrophoretic mobility of single stranded DNA and its folded conformation that is dependent on the nucleotide sequence. A change in DNA sequence and therefore in the folded structure causes changes in the mobility within the gel that is identified as a different banding pattern (Kutach et al., 1999).
The sensitivity of SSCP depends on the length of the DNA fragment, with the optimal discrimination ranges between 150-250 bp (Hayashi, 1992; Kutach et al., 1999). Factors such as acrylamide content of the gel, additives (i.e. glycerol), running temperature can enhance the detection of some polymorphism. In this study essentially two different types of gel have been used, 10% acrylamide TBE precast gels (Novex) and 2x MDE gel (EMC Bioproducts, Maine, USA) depending on the gene explored.
2.2.5.1 Acrylamide gel electrophoresis
For analysis of SSCP using acrylamide gel electrophoresis, 2 pi of the PCR product was diluted in 4 pi of 0.1% SDS/lOmM EDTA and 5 pi of loading dye (99% formamide, 0.25% xylene cyanol, 0.25% Bromophenol blue) followed by dénaturation at 96° for 8 minutes and the samples were chilled on ice. Once cooled, 10 pi of the sample was load onto the gel and electrophoresis was
carried out. Electrophoresis with TBE (IX) buffer was performed at 4°C, 50 w for 12 hours, unless otherwise stated.
2.2.S.2 Silver Staining.
Once the electrophoresis had taken place, the gels were stained with a commercially available silver stain kit (Sigma). Briefly, the gels were fixed, wash, silver equilibrated, and developed following the manufacturers guidelines as outlined in Table 2.3..
Once the gels have been developed they were dried with Drying Solution (Promega) following the manufacturers guidelines and documented by photography using an UVP (Ultra Violet Productions Ltd. UK) and Grab-IT annotation image capture system package with Sony digital graphic printer (UP-D860D).
Table 2.3 Silver staining procedure.
Solution Time > .5 mm Time < .5 mm No. Changes Volume (ml)
1. Fixing 20 min. 10 min 3 300
2. dHzO 10 min. 5 min. 3 300
3. Silver Equilibration 30 min. 30 min 1 300
4. Rinse dHzO 10-20 sec. 10-20 sec. 1 300
5. Development sol. 5-8 min 5-08 min 2 150
6. Stop solution 5 min. 5 min. 1 300
7. Rinse dH2Û 10 min 5 min. 3 300
8. Reducer sol. 20 sec. 20 sec. 1 300
9. Rinse tap water 1 min. Imin. 1 Running water
2.3.6 D eterm ination o f autoantibodies
The identification of the autoantibodies present in the patients serum were carried out in the Department of Clinical Immunology of the Royal Free
Hospital (Dr. Chris Bunn). The techniques used are briefly described in the following section.
2.2.6.1.Anti-topoisoinerase 1 antibody
AT A were identified by counter immuno-electrophoresis using soluble extracts form human spleen and rabbit thymus acetone powder (Pelfreez Biologicals, Rogers, Arizona, USA) and antisera of confirmed specificity (Bunn and Tormey, 2000).
2.2.6.2 Anti-centromere antibody
The presence of anti-centromere antibodies was determined by indirect immunofluorescence of HEP-2 cells with rabbit anti-human polyvalent fluorescein isothiocyanate labelled antibodies.
2.2.Ô.3 Anti-RNA polymerase autoantibodies
Anti-RNA polymerase antibodies recognising RNA polymerases I, II and III were detected by immunoprécipitation of antigen form a radiolabelled HeLa cell extract using patient’s antibodies bound to protein A sepharose and visualized by autoradioraphy following separation on 8% polyacrylamide gels (Bunn et al., 1998).
2.3 Statistical Analysis
Genotype frequencies were determined by direct count of the genotypes divided by the total number of genotypes examined. Allele frequencies were calculated by direct counting of all alleles divided by the total number of alleles. Phenotype frequencies were calculated by counting for the presence or absence of an allele in each genotype and divided by the total number of genotypes.
Association between variables were analysed using a 2 x 2 o r 2 x « contingency tables. Chi square test with or without Yates’s correction and Fisher exact test were used where appropriate. A probability of error value (P) less than 0.05 was considered significant after the correction for multiple comparisons (Bonferroni method = Pcorr = 1-[1-P]") when n is the number of comparisons done.
Table2.4 General form of a contingency table Allele 1 (+) Allele 1 (-)
Disease group a b
Control group c d
The Chi square (%^) is non-parametric test of statistically significance that determines whether the differences observed between two groups are not just attributable to the chance alone. The operates by comparing the actual or observed frequencies to the frequencies we would expect if there were no relationship at all between the two variables in the population. The expected frequency for each cell in the table is the product of the row total multiplied by the column total divided by the sum total of all observations. The formula is: (0-E)^/E where O are the observed values and E the expected frequency. To interpret the y^ value it is necessary to compare the result with the critical values of y^ table, giving the degrees of freedom necessary to each specific table. The degree of freedom (df) is expressed by the following formula: df=(r-
l)(c-l), where r is the number of rows and c the number of columns.
When the sample size is small or the contingency table contains any number less than 5 the use of Yates’s continuity correction is recommended.
Yates’s continuity correction = [E( | O-E | -0.5)^/E].
When there is one or more expected frequency of less than five, the alternative approach for significance testing is the Fisher’s exact test. The result of this test is usually more or less the same as the result of the with Yates’s correction.
The odds ratio (OR) is the ratio of two odds for an event such as disease. In other words the CD is a way of comparing whether the probability of a certain event is the same for two groups in the presence and the absence of another event, such as exposure. An OR grater then 1 occurs when the disease is more common among the exposed than among the unexposed. When the OR is less than 1 indicates a negative association. The formula to calculate the OR is:
OR = ad/bc
Statistical calculations were performed using the program SPSS (SPSS Inc, II) on a personal computer.
The Knowledge Seeker programme (Angoss Software, Guildford, UK), a data minder programme, was used to analyse the relationship between genotype and clinical characteristics.