2.2. ADA and TNFa SNPs genotyping

Given the pleiotropic nature of TNF-a and the complex regulating its expression and action [17], it is also conceivably potential interactions with other functional polymorphisms genes involved in the inflammatory pathway may modulate biological function. In this frame, adenosine deaminase (ADA, map locus 20q13.11) seems to be an excellent modifier gene TNF-a physiological actions. In fact, adenosine levels, which icantly exert a down-regulating action on TNF-a [18–20], are enced by ADA. Moreover, the ADA gene displays a G>A transition nucleotide 22 of exon 1 leading to an Asp (allele 1⁄) to Asn 2⁄) amino acid substitution at position 8 of the mature which influences ADA activity. Thus, ADA 22⁄A is (cid:3)35% lower that of the ADA 22⁄G allele [21] and consequently, carriers of 22⁄A allele show a higher level of both circulating and adenosine. Recently, we have found a very significant of ADA 22⁄A with TNF-a (cid:2)308⁄A allele in males aged >50 (O.R. = 5.16, P = 0.001) further suggesting a potential between these two genes [22]. It is also noteworthy that and TNF-a genetic polymorphisms were found associated human longevity only in males [8,23].

Genomic DNA extraction was carried out from peripheral blood through standardized salting-out method and DNA was stored at (cid:2)20 (cid:3)C until gene analysis.

ADA (22G>A) and TNF-a ((cid:2)308G>A; (cid:2)238G>A) SNPs were gen- otyped according to RFLP–PCR methods previously published [23,26]. Briefly, for ADA, PCR was performed with primers flanking the 22G>A polymorphic region: 50-GCCCGGCCCGTTAAGAAGAGC-30 as sense primer, and 50-GGTCAAGTCAGGGGCAGAAGCAGA-30 as antisense primer. The PCR products were digested with TaqI endo- nuclease for 90 min at 65 (cid:3)C, and the samples were electrophore- sed in a 1.8% agarose gel. The ADA 22⁄A allele was identified by the lack of a TaqI restriction site. For TNF-a SNPs, PCR was carried out using 50-GGGACACACAAGCATCAAGG-30 and 50-ATCTGGAGGAA GCGGTAGTG-30 as primers sense and 50-AATAGGTTTTGAGGGCC ATG-30, 50-AGAAGACCCCCCTCGGAACC-30 as primers antisense for (cid:2)308G>A and (cid:2)238G>A, respectively. The PCR products were digested 90 min at 37 (cid:3)C with NcoI to detect the TNF-a (cid:2)308⁄A allele and with MspI to detect the TNF-a (cid:2)238⁄A allele following electrophoresis in a 2% agarose gel.

2.3. Statistical analysis

The present study was thus undertaken: to replicate and extend previous findings, by testing the association of TNF-a ((cid:2)308G>A; (cid:2)238G>A) and ADA (22G>A) singular SNPs and TNF-a ((cid:2)308G>A; (cid:2)238G>A) promoter diplotype with human longevity/survival. Finally, a gene–gene interaction analysis between TNF-a ((cid:2)308G>A; (cid:2)238G>A) and ADA (22G>A) SNPs was performed.

Hardy–Weinberg equilibrium was tested by comparing, using the v2 test, observed and expected frequencies of the various genotypes. A dominant genetic model (G⁄/G⁄ vs. carriers of A⁄) was applied to test the genotypic association of singular SNPs with life-expectancy between the age classes. The differences in geno- types distributions between the groups were evaluated by the Fisher’s exact test and the results were expressed as odds ratios (ORs) with 95% confidence intervals (C.I.). Logistic regression mod- els were also performed using SPSS v.16.0 (SPSS Inc., Chicago, IL). TNF-a promoter diplotype association analysis was performed using UNPHASED [27]. Haploview v4.2 [28] was used to estimate the Linkage Disequilibrium (LD) among TNF-a promoter SNPs, both D0 and r2 were calculated separately in each gender-specific age class.

2. Materials and methods

2.1. Study subjects

males and 569 females), 18–106 years old, randomly recruited from the same geographical area of Central Italy (Marche region) on the eastern side of the Apennines. The whole population studied is composed of Caucasian individuals and is ethnically homoge- neous: this geographical area is characterized by low immigration rates and was settled by an ancient pre-Roman Italian population called the Piceni [24]. The same donors provided information concerning their health condition so as to ascertain that no patho- logical condition existed (e.g. cancer, diabetes, heart diseases, hypertension, obesity, and chronic inflammatory diseases). Subject’s age is self-reported. Almost all subjects were recruited in blood donor centres and through family physicians. The popula- tion under study was divided into three sex-specific age classes: for men (women), the first class consists of individuals <66 years old (<73 years old), the second class of individuals 66–88 years old (73–91 years old), and the third class of individuals >88 years old (>91 years old). These gender-specific age classes were defined according to demographic information and account for the different survivals of men and women in the Italian population. Briefly, the age classes ranging from 18 to 106 years were obtained on the basis of a synthetic survival curve con- structed using historic mortality data taken from the Italian