T-cell differentiation measurements

Single gene analysis of gene expression data

Differences in expression level between long-lived siblings, their offspring and the partners of their offspring were assessed using linear regression. In these analyses, expression level was the dependent variable and the two groups of individuals (either nonagenarians vs. controls or offspring vs. controls) were included in the model as a categorical variable together with age (in offspring vs. controls only), gender and their interaction as covariates. To take into account dependencies within sibships, robust standard errors were used, that is, the variance was computed from the between family variation. P-values were also based on these robust standard errors. Analyses were performed using the software package STATA/SE 11.0 (DPC Software, StataCorp 2009).

Data on distribution of different T-cell subsets were generated in 71 offspring and 73 controls, of whom also gene expression data were available, as described previously (Derhovanessian et al., 2010). Briefly, PBMCs viably cryopreserved with DMSO were thawed and treated with human Ig, GAMUNEX (Bayer, Leverkusen, Germany) and ethidium monoazide (EMA) (Invitrogen, Karlsruhe, Germany). Cells were first stained indirectly for KLRG-1 using a primary antibody kindly provided by Prof. Hans-Peter Pircher, Freiburg, Germany and a Pacific-Orange-conjugated secondary antibody (Invitrogen). After blocking with mouse serum (Chemicon/Millipore, Schwalbach, Ger- many), the following directly conjugated monoclonal antibodies were added: CD3-PE (Calltag; Invitrogen), CD4- PerCP, CD8-allophycocya- nin-Cy7, CCR7-PE-Cy7 (BD Biosciences, Heidelberg, Germany), CD27- allophycocyanin, CD45RA-Pacific Blue, CD28- Alexa Fluor 700, (Bio- Legend, San Diego, CA, USA) and CD57-FITC (Immunotools, Freiburg, Germany). After 20 min incubation on ice, cells were washed and analysed immediately on an LSR II cytometer with FACSDiva software (BD Biosciences). The spectral overlap between all channels was calculated automatically by the BD FACSDiva software, after measur- ing negative and single-colour controls. Data were analysed using FLOWJO software (Tree Star, Portland, OR, USA). For data analysis, EMA-positive dead cells were excluded. T cells were characterized as na¨ıve (CD45RA+CCR7+ CD27+ CD28+), central memory (CD45RA(cid:1) CCR7+ CD27+ CD28+), effector memory (CD45RA(cid:1)CCR7(cid:1)CD27(cid:1) CD28(cid:1)) and ‘terminally differentiated’ effector memory (TEMRA; CD45RA+ CCR7(cid:1)CD27(cid:1)CD28(cid:1)) T cells according to previously published models (Koch et al., 2008).

Sample and SNP collection for genetic association analysis

The SNP set analysis was performed in the same manner as described in Deelen et al. (2011b). We used the genotype data of 417 unrelated long-lived individuals and 476 young controls from the LLS, typed with Illumina HumanOmniExpress BeadChips (Supplementary Table S1). We removed SNPs with a SNP call rate <0.95, MAF < 0.01 or P < 10(cid:1)4 in HWE nonagenarian cases and controls and 1018 analysed SNPs within a 10-kb window around the 40 genes from the MTOR pathway (Fig. 1) using the PLINK set-based test.

PLINK set-based statistical analysis

In the PLINK set-based test [–set-test, http://pngu.mgh.harvard.edu/ purcell/plink (Purcell et al., 2007)], a SNP analysis of the pathway or gene SNP set is performed. For the SNP set, a mean SNP statistic is calculated from the single SNP statistics of a maximum amount (–setmax) of independent SNPs below a certain P-value threshold (–set-P). If SNPs are not independent, that is in case linkage disequilibrium (r2) is above a certain threshold (–set-r2), the SNP with the lowest P-value in the single SNP analysis is selected. The same analysis is performed with a certain amount (–mperm) of simulated SNP sets in which the phenotype status of the individuals is permuted. An empirical P-value for the SNP set is computed by calculating the number of times the test statistic of the simulated SNP sets exceeds that of the original SNP set. For the analysis in this study, the parameters were set to –setP 0.05 –set-r2 0.5, –set-max 99 999 and –mperm 10 000. Bonferroni correction for the pathway analysis is not necessary as it contains just one test. When SNP sets were tested per gene (40 SNP sets), the significance level for P-values after Bonferroni correction for multiple testing is 0.00125.

Association of gene expression with insulin-related phenotypes

Since the mTOR pathway has been implicated in insulin-related phenotypes, their effect on the expression of the longevity-related genes was further investigated. Blood samples were taken at baseline for the determination of non-fasted serum parameters. For the serum measurements, the Hitachi Modular or the Cobas Integra 800, both from Roche, Almere, the Netherlands were applied. CVs of these measurements were all below 5%. Information on medical history was requested from the participants’ general practitioners. As described before, differences in expression level between offspring and controls were assessed using linear regression using the same model, but excluding known Type 2 diabetes patients. The association of glucose and insulin levels with expression levels of the seven longevity- associated genes was also analysed using linear regression in controls only. To investigate the influence of glucose and insulin levels on the association with longevity of these seven genes, linear regression between offspring and controls was performed again, including one of these parameters as covariate.

Blood cell subtypes

To further investigate the candidate genes, their expression level was again tested for association with familial longevity, but now adjusted for

Guertin DA, Sabatini DM (2005) An expanding role for mTOR in cancer. Trends

Acknowledgements