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Gene expression analysis of mtor pathway: association with human longevity

Gene expression analysis of mTOR pathway: association withhuman longevity Willemijn M. Passtoors,1 Marian Beekman,1,2 Joris Deelen,1,2 present in two distinct multiprotein complexes called mTOR complex 1 Ruud van der Breggen,1 Andrea B. Maier,3 Bruno Guigas,4 (mTORC1) and mTOR complex 2 (mTORC2), each with different Evelyna Derhovanessian,5 Diana van Heemst,3 Anton J. M. de functions (Polak & Hall, 2009), although their exact respective roles Craen,3 David A. Gunn,6 Graham Pawelec5 and remain to be elucidated. The main known functions of mTORC1 include translation initiation, protein synthesis and autophagy, while those ofmTORC2 include cytoskeletal organization (Laplante & Sabatini, 2009).
1Section of Molecular Epidemiology, Leiden University Medical Center, In addition, the two complexes are connected in that mTORC2 regulates Leiden, The Netherlands2Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands Akt phosphorylation, part of an upstream pathway that controls 3Department of Gerontology and Geriatrics, Leiden University Medical activation of mTORC1 (Stanfel et al., 2009). Inhibition of the mTOR signalling pathway in yeast (Kaeberlein et al., 2005), worms (Vellai et al., 4Department of Molecular Cell Biology, Leiden University Medical Center, 2003), flies (Kapahi et al., 2004) and mice (Harrison et al., 2009) results in lifespan extension. In mouse models, inhibition of the mTOR signalling 5Center for Medical Research, University of Tu¨bingen, Tu¨bingen, Germany6 pathway provides cardiovascular benefits and improved metabolic Unilever Discover, Colworth, Sharnbrook, Bedfordshire, UK function (Stanfel et al., 2009). Additionally, a primary hallmark of calorie restriction (CR) in rodents, which leads to inhibition of themTORC1 pathway, is a dramatic reduction in age-associated cancer mTOR signalling is implicated in the development of disease andin lifespan extension in model organisms. This pathway has been incidence and growth rate (Spindler, 2005), implying that enhancedmTOR signalling plays a central role in cancer progression. In rats, the associated with human diseases such as diabetes and cancer, but mTOR pathway has also been implicated in both Type 1 and Type 2 has not been investigated for its impact on longevity per se. Here,we investigated whether transcriptional variation within the diabetes, with chronic activity of mTORC1 contributing to obesity andinsulin insensitivity (Newgard et al., 2009). Because reduced mTOR mTOR pathway is associated with human longevity using whole- signalling in model organisms extends lifespan and is associated with blood samples from the Leiden Longevity Study. This is a uniquecohort of Dutch families with extended survival across genera- better metabolic health, we postulated that a similar reduction in this tions, decreased morbidity and beneficial metabolic profiles in signalling pathway may also contribute to longevity and healthy aging inhumans.
middle-age. By comparing mRNA levels of nonagenarians and Thus far, mTOR signalling has been investigated in humans mainly by middle-aged controls, the mTOR signalling gene set was found toassociate with old age (P = 4.6 3 10À7). Single gene analysis studying disease. Its activity has been found to be elevated in differentcancers including lymphomas, melanomas, breast and prostate (Guertin showed that seven of 40 mTOR pathway genes had a significant & Sabatini, 2005; Stanfel et al., 2009). Elevated mTOR signalling in differential expression of at least 5%. Of these, the RPTOR(Raptor) gene was found to be differentially expressed also when human tumours is correlated with poor tumour prognosis, and inhibitorsof this pathway are showing promising results in clinical trials (Le et al., the offspring of nonagenarians was compared with their 2008; Strimpakos et al., 2008; Yap et al., 2008). Additionally, interme- spouses, indicating association with familial longevity in mid-dle-age. This association was not explained by variation between diate phenotype insulin resistance in human diabetes can be preventedby mTOR inhibitors (Krebs et al., 2007). Metformin, a commonly the groups in the prevalence of type 2 diabetes and cancer or prescribed anti-diabetic drug which inhibits mTORC1 downstream glucose levels. Thus, the mTOR pathway not only plays a role in pathways through activation of AMP kinase, has also been shown to the regulation of disease and aging in animal models, but also inhuman health and longevity.
reduce tumour growth (Dowling et al., 2007). Human CR studies usedas model for down-regulation of mTOR are much more short term in Key words: aging; longevity; mTOR; human; gene expression.
comparison with animal studies, but nevertheless, CR may reduce therisk of age-related diseases even in non-obese humans (Fontana et al.,2004; Holloszy & Fontana, 2007). Thus, accumulating evidence is showing that mTOR signalling can play an important role in human Mammalian target of rapamycin (mTOR) is an evolutionarily conserved disease. In contrast, it has not been investigated whether natural nutrient-sensing protein kinase that belongs to the phosphoinositide 3- variation in mTOR signalling could also contribute to disease risk.
kinase (PI3K)-related protein kinase family and acts as a central regulator Recently, we have identified 360 genes, the expression of which was of growth and cell division (Stanfel et al., 2009). The core kinase mTOR is associated with human longevity (Passtoors et al., 2012). MTOR (alsoknown as FRAP1) was among these, suggesting a role for the mTORsignalling pathway in human longevity. In the current study, weinvestigated whether common natural genetic and transcriptional variation among the 40 genes of the mTOR pathway (Fig. 1) was Professor Eline Slagboom, Department of Molecular Epidemiology, LeidenUniversity Medical Center, Postzone S-05-P, PO Box 9600, 2300 RC Leiden, associated with human longevity. To do this, we took advantage of the The Netherlands. Tel.: +31 71 526 9731; fax: +31 71 526 8280; Leiden Longevity Study (LLS), a Dutch cohort in which families are selected on the basis of nonagenarian sibling pairs (Schoenmaker et al.,2006). The middle-aged offspring of these nonagenarians have a Accepted for publication 27 September 2012 Aging Cell ª 2012 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland mTOR pathway in human longevity, W. M. Passtoors et al.
Fig. 1 mTOR signalling pathway of which genetic variation and expression of 40 genes are investigated for their association with human aging and longevity. mTORC1receives signals from growth factors, nutrients, energy status and a range of stressors and positively regulates cell growth and proliferation by promoting many anabolicprocesses, including biosynthesis of proteins, lipids and organelles, and by limiting catabolic processes such as autophagy (Sengupta et al., 2010). mTORC2 phosphorylatesand activates Akt, serum- and glucocorticoid-regulated kinase (sGK), RhoA and protein kinase C (PKC), which regulate cell survival, cell cycle progression and cytoskeletonorganization (Sarbassov et al., 2005; Garcia-Martinez & Alessi, 2008; Ikenoue et al., 2008).
decreased prevalence of myocardial infarction and type 2 diabetes First, we selected the two mTOR complexes mTORC1 and mTORC2 (Westendorp et al., 2009) and display healthier intrinsic metabolism including their downstream targets often described in the literature reflected by lipid profiles (Barzilai et al., 2001; Heijmans et al., 2006), (Laplante & Sabatini, 2009; Polak & Hall, 2009). Next, we included the glucose metabolism and preservation of insulin sensitivity (Rozing et al., direct activators and inhibitors of these two complexes. Figure 1 shows 2009; Wijsman et al., 2011) than age-matched controls. They also have the mTOR pathway as we investigated.
different immune profiles in that they fail to show a lower number ofCD8+ naive T cells and a higher number of CD8+ late-stage Gene expression in whole blood: nonagenarians vs. controls differentiated memory cells dependent on cytomegalovirus infectionoften seen as a hallmark of immune aging (Derhovanessian et al., We measured gene expression levels for each of the 40 mTOR signalling 2010). To investigate whether expression of mTOR signalling genes is genes using RT-qPCR on whole blood samples from 87 LLS nonagenar- associated with familial longevity, we compared the levels of mRNA for ians, 337 of their middle-aged offspring and 321 middle-aged LLS these genes in peripheral blood of nonagenarians from families controls (Table 1). When comparing long-lived individuals to younger enriched for longevity and younger controls. In addition, genetic controls differentially expressed genes may associate either with variation in mTOR pathway genes of LLS nonagenarians is compared calendar age, familial longevity or both. We tested the total gene set with that of younger controls. Next, mRNA levels of the top longevity- for such association using the globaltest method (Goeman & Oosting, associated genes were compared between the offspring of the 2011). We observed that the gene expression levels of the mTOR nonagenarians and similarly aged controls to exclude the possibility signalling gene set did associate significantly with age and/or familial that differential expression of these genes did not simply mark old age, longevity in these families (P = 4.6 9 10À7).
but are a defining characteristic of the long-lived families in middle-age.
Second, to investigate which genes are primarily responsible for this Finally, we examined whether the association of the expression of association, single gene analysis using linear regression was performed mTOR signalling genes with longevity is independent of metabolic on all 40 mTOR signalling genes (Table 2). After Bonferroni correction health parameters, namely plasma glucose levels, and the prevalence of for multiple testing, seven genes showed significant differential type 2 diabetes and cancer, because these factors are known expression with at least a 5% expression difference: FOXO1 and RPTOR characteristics of the long-lived families and are suggested to beinfluenced by mTOR signalling.
Table 1 Analysed samples: gene expression For the mTOR pathway, we selected 40 genes encoding proteins that belong to the well-described core of the mammalian mTOR pathway.
Aging Cell ª 2012 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland 26 mTOR pathway in human longevity, W. M. Passtoors et al.
Table 2 Linear regression results of gene expression of long-lived individuals genes simultaneously in one test (P-value < 0.05 for significance) (Supplementary Table S2). We observed a significant association ofgenetic variation in the mTOR pathway as a whole with familial longevity (P = 0.009). However, the single gene analysis (of 40 SNP sets), for which the significance level for P-values after Bonferronicorrection for multiple testing is 0.00125, did not result in significant associations. (Supplementary Table S3), suggesting that effects of single common variation in mTOR signalling genes on familial longevity is very Gene expression in whole blood: offspring of nonagenarians To investigate whether the differences in expression of the seven genes differentially expressed between nonagenarians and controls are a characteristic of the long-lived families and not just a marker of old age, we compared their expression in the middle-aged offspring of nonage- narians to the controls of similar age (Supplementary Table S5). While the association for six genes was not significant after correction for multiple testing, as compared to the controls, the offspring expressed significantly less RPTOR mRNA (Table 3). Thus, in two generations, the RPTOR gene expression was decreased as compared to controls. As association of PRR5L was borderline significant and displayed the largest effect size (FC = 0.74), we also included PRR5L for further follow-up in this article Gene expression levels of PRR5L were increased in the nonagenarians, but decreased in their offspring. The gene expression differences between offspring and control groups may be due to variation in gene expression level indeed or could (partially) be explained by differences in between the groups in blood cell subsets or in disease Relation of gene expression in whole blood with cell Whole blood consists of several cell subtypes and subsets of differen- tiated immune cells that may have different mRNA expression profiles.
To investigate whether the proportions of different blood cell types could explain differential expression levels of RPTOR and PRRL5, the comparisons between offspring and controls were corrected for the relative counts of leucocytes, thrombocytes, neutrophils, lymphocytes, monocytes, basophils and eosinophils present in the whole blood samples (see Experimental procedures). We observed that variationbetween cell counts in offspring and controls did not influence the Significance level for P-value after Bonferroni correction for multiple testing is associations of the gene expression with familial longevity.
0.00125. Genes significantly differentially expressed with at least 5% are depicted Recently, an increasingly important role for mTOR in directing T-cell activation and differentiation has become apparent (Derhovanessian Mean, mean relative expression level; SD, standard deviation of relative expression et al., 2010). We therefore examined whether the difference in expres- level; FC, fold change, above one indicated higher expression in long-livedindividuals. P, raw P-value from linear regression model.
sion levels of RPTOR and PRR5L between offspring and partners isinfluenced by the level of T-cell differentiation reflected in the distribution were expressed at a lower level, and EIF4EBP1, LAMTOR2, AKT1S1, of different T-cell phenotypes. To this end, the frequency of naı¨ve PRR5L and RHOA at a higher level in nonagenarians.
(CD45RA+ CCR7+ CD27+ CD28+), central memory (CD45RAÀCCR7+CD27+ CD28+), effector memory (CD45RAÀCCR7ÀCD27ÀCD28À)and late-stage differentiated (CD45RA+CCR7ÀCD27ÀCD28À) T cells Genetic analysis: nonagenarians vs. controls was assessed in 71 offspring and 73 controls. The largest effect was seen Since we had GWAS data available in the LLS study, we analysed the when correcting for the relative amount of effector memory T cells set of GWAS-based SNPs within a 10-kb window around the 40 mTOR present in this sample; it reduced the expression difference between signalling genes for differences in variation between 417 unrelated offspring and controls for PRR5L by about 60% (the beta changed from nonagenarian participants and 476 younger controls from the LLS À0.44 to À0.17, Supplementary Table S4). This indicated that differences (Supplementary Table S1) (Deelen et al., 2011a). Using the PLINK set- in the proportion of effector memory T cells in these groups explained based test, we investigated 1,018 SNPs in the 40 mTOR signalling about half of the differential gene expression effect for PRR5L. No Aging Cell ª 2012 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland mTOR pathway in human longevity, W. M. Passtoors et al.
middle-aged members of the longevity families as compared to similarly Table 3 Linear regression results of gene expression of offspring compared withcontrols aged controls. The significant differential expression of RPTOR andsuggestive differential PRR5L expression indicate that expression of these genes may not just mark old age but familial longevity per se.
We also compared nonagenarians and controls in a genetic approach.
Variation in the mTOR gene set associated with longevity in the LLS due to the joint contribution of small effects in several genes. This association could not pinpoint the main effector genes or SNPs, so we conclude that this finding must be replicated in much larger studies to test whether genetic variation in the pathway and specific genes truly associate with Decreased mTORC1 protein activity is associated with lower glucose Significance level for P-value after Bonferroni correction for multiple testing is levels and a lower prevalence of diabetes in humans (Liu et al., 2004; 0.00714. Genes significantly differentially expressed with at least 5% are depicted Krebs et al., 2007), while impaired mTORC2 is associated with glucose intolerance and insulin resistance (Lamming et al., 2012). The Mean, mean relative expression level; SD, standard deviation of relative expression offspring of the nonagenarian siblings in the LLS have a lower level; FC, fold change, above one indicated higher expression in offspring of prevalence of diabetes and lower glucose levels than similarly aged nonagenarians; P, raw P-value from linear regression model.
controls. Since adjustment for glucose levels and excluding diabeticpatients provides similar associations, differences in the prevalence of remarkable changes in effect size were found for RPTOR when correcting type 2 diabetes or plasma glucose levels between offspring and for the above-mentioned T-cell differentiation markers (Supplementary controls did not explain the association of RPTOR and PRR5L mRNA Our results could be explained if RPTOR gene expression in blood marks familial longevity and metabolic health in middle-age and would Association of gene expression in whole blood with disease thus be an early marker of familial longevity. The altered gene expression levels in the longevity family members may be the consequence of aging, Since in the literature expression levels of mTOR pathway proteins have or merely a trait shared by the long-lived families or may truly contribute been implicated in the pathogenesis of type 2 diabetes (Newgard et al., to human longevity. In view of the findings in animal models (Vellai 2009) and progression of cancer (Guertin & Sabatini, 2005; Stanfel et al., 2003; Kapahi et al., 2004; Kaeberlein et al., 2005; Harrison et al., et al., 2009) and since the prevalence of diabetes differed between LLS 2009), it is not unlikely that a familial and genetic background offspring and controls (Westendorp et al., 2009), we tested whether contributing to low mTORC1 signalling activity (and preservation of RPTOR and PRR5L expression were simply marking differences in disease low mTOR throughout life) contributes to metabolic health and an prevalence. We therefore performed the same analysis between extended life expectancy across species.
offspring and controls without known diabetics and cancer patients, In our study, we found RPTOR, AKT1S1 and EIF4EBP1 to be associated which yielded similar associations to those described above (Supple- with old age and/or familial longevity, and the directions of association indicate a transcriptional down-regulation of mTORC1. Even in middle- Because glucose levels differed between offspring and controls age, a down-regulation of RPTOR mRNA was associated with familial (Supplementary Table S5), we investigated whether the mRNA differ- longevity, providing another example of the highly conserved role mTOR ences of RPTOR and PRR5L depended on glucose levels in non-diabetic signalling has in lifespan regulation across different species.
participants. The association of familial longevity with RPTOR was not Not only mTORC1, but also mTORC2 activity has been linked to affected by adjustment for glucose levels, and the association with health and lifespan. Rictor, an important component of mTORC2, PRR5L expression gained significance (Supplementary Table S7). Thus, contributes to glucose homeostasis in murine muscle tissue (Kumar the expression level of the RPTOR gene in blood mark familial longevity et al., 2008), TORC2 C. elegans mutants show modulation of lifespan independent of the prevalence of type 2 diabetes and cancer and by affecting feeding and metabolism of different diets (Soukas et al., glucose levels. Due to the influence of T-cell differentiation, glucose 2009), TORC2 is a mediator of proliferative and survival signals in levels and diabetes prevalence on its gene expression, the association of cancer cells (Fang et al., 2012) and a study in mouse embryonic PRRL5 with familial longevity is more complex.
fibroblasts showed that mTORC2 regulates the TLR-mediated inflam-matory response via FoxO1 (Brown et al., 2011). In addition, PRR5L, which is part of mTORC2, has been suggested to play a role inapoptosis in HeLa cells, but whether it is pro-apoptotic or anti- By comparing mRNA levels in blood of nonagenarians and middle-aged apoptotic remains unclear (Thedieck et al., 2007). Recently, Lamming controls from the LLS, we found that the mTOR signalling gene is et al. showed that disruption of mTORC2 can result in glucose differentially expressed in old age. Single gene analysis showed that intolerance and insulin resistance in rodents and may be relevant in seven of 40 mTOR pathway genes had a significant differential the pathogenesis of type 2 diabetes and metabolic syndrome (Lamming expression of at least 5%, up to 1.81-fold. The expression of the genes et al., 2012). Our results regarding the association of PRR5L, RHOA EIF4EBP1, LAMTOR2, AKT1S1, PRR5L and RHOA were higher in and FOXO1 with old age and/or familial longevity suggest a transcrip- nonagenarians, whereas FOXO1 and RPTOR were lower. The directions tional up-regulation of mTORC2, which would be in concordance with of these differential gene expressions indicate separate effects on these earlier findings in animal models. As we provide the first evidence mTORC1 and mTORC2 complexes. Two of the seven differentially that mTORC2 is beneficial for healthy aging in humans, further expressed genes, RPTOR and PRR5L, were expressed to a lower level in research is required to replicate these findings.
Aging Cell ª 2012 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland 28 mTOR pathway in human longevity, W. M. Passtoors et al.
We observed that PRR5L expression was higher in nonagenarians, but lower in their offspring as compared to the controls. If expression of agene is associated with familial longevity in this population, we would For the mTOR pathway, we selected 40 genes encoding proteins that expect to find a similar direction in both long-lived nonagenarians as well belong to the well-described core of the mammalian mTOR pathway.
as their offspring. Late-differentiated memory T-cells accumulate with First, we selected the two mTOR complexes mTORC1 and mTORC2 age and this hallmark feature of immunosenescence is believed to including their downstream targets often described in the literature contribute to the weakened immune status in the elderly. It has also (Laplante & Sabatini, 2009; Polak & Hall, 2009). Next, we included the been shown that offspring of the long-lived nonagenarians have less direct activators and inhibitors of these two complexes. Figure 1 shows accumulation of these effector memory T cells in comparison with the mTOR pathway as we investigated.
controls (Derhovanessian et al., 2010). Therefore, varying proportions ofT-cell subsets might influence observed PRR5L expression levels in the different groups of the LLS. When testing for association with cellsubtype measurements in a subset of middle-aged LLS participants, we Eighty-seven non-related long-lived siblings, 337 offspring and 321 found that PRR5L expression is positively associated with the amount of partners belonging to 281 nuclear families were selected for the current CCR7ÀCD45RAÀCD27ÀCD28À late-differentiated effector memory study (Table 1 and Supplementary Table S5). These samples were cells. Data on T-cell subset counts are not available for the LLS randomly selected, but in such a way that age and gender were nonagenarians, but Koch and colleagues showed the same subset of balanced between the groups and age range was as large as possible.
effector memory cells to be increased in the elderly (Koch et al., 2008).
Additionally, individuals with outlying cell counts (beyond 3 SDs below This may explain the higher PRR5L expression levels we found in the or above the standard error of the mean) were excluded. This nonagenarians, while lower amounts of effector memory cells (Derhov- subpopulation is representative for the whole LLS regarding disease anessian et al., 2010) and also lower PRR5L expression was found in the prevalence and parameters involved in metabolic syndrome (Supple- middle-aged offspring compared with controls. From this, we conclude mentary Table S5) (Westendorp et al., 2009; Rozing et al., 2010). From that PRR5L expression may be a marker for the differentiation state of T these non-fasted individuals, peripheral blood was harvested using cells, indicating a more late-differentiated T-cell compartment in PAXgeneTM tubes (Qiagen, Venlo, the Netherlands). The tubes were nonagenarians as compared to younger controls, but a less-differenti- frozen and kept at À20 °C for ~3–5 years. After thawing at room ated phenotype in the offspring in middle-age. The link between the temperature for at least 2 h, total RNA was extracted from the mTOR pathway and T-cell activation and differentiation is perhaps not approximately 2.5 mL of peripheral blood in each tube following the surprising when it is considered that rapamycin was first identified and manufacturer’s recommended protocol (PAXgene Blood RNA Kit Hand- exploited as a T-cell-directed immunosuppressive agent in transplanta- book, Qiagen, Venlo, the Netherlands). The quality and integrity of the tion (Morath et al., 2007). The differential distribution of T-cell pheno- total RNA was evaluated on the 2100 Bioanalyzer (Agilent Technologies, types contributed approximately to 60% of the PRR5L effect, suggesting Amstelveen, the Netherlands), and the concentration was measured that PRR5L might have another role in longevity other than marking/ using a NanoDrop spectrophotometer (NanoDrop Technologies, Wil- influencing T-cell population subtypes; further research is required to mington, DE, USA). Quality criteria included a 28S/18S ratio as measured by the Bioanalyzer of at least 1.2, and a total RNA yield of at least 3 lg.
In conclusion, the expression level in blood of genes belonging to the two mTOR complexes 1 and 2 associated with old age showing down- and up-regulation in the long-lived respectively. RPTOR gene expressionwas further significantly associated with familial longevity in middle-age For all 40 mTOR signalling genes, the suggested Taqman® assay independent of glucose levels and the prevalence of type 2 diabetes and (Applied Biosystems, Bleiswijk, the Netherlands) was selected. Reverse cancer. At the level of suggestive evidence, this was also the case for transcription was performed with total RNA from blood of in total 790 PRRL5. These results suggest that the mTOR pathway may not only be samples, which passed QC using the First Strand cDNA Synthesis Kit involved in lifespan regulation in animal models, but also with the according to the manufacturer’s protocol (Roche Applied Science, metabolic health and extended lifespan of human longevity families.
Almere, the Netherlands). cDNA was amplified using the DNA EngineTetrad® 2 Peltier Thermal Cycler (Bio-Rad, Veenendaal, the Nether-lands). qPCR was then performed with the Taqman® method using the BiomarkTM 48.48 and 96.96 Dynamic Arrays (Fluidigm, Amsterdam,the Netherlands). Relative gene expression of the BioMarkTM Array data were calculated using the 2ÀDDCt method, in which Ct indicates The individuals investigated in this study are participants of the LLS. The cycle threshold, the fractional cycle number where the fluorescent families participating in this study have at least two siblings with a signal reaches the detection threshold (Livak & Schmittgen, 2001).
minimum age for men of 89 years and for women of 91 years YKT6 was used as internal control and commercially available human (Schoenmaker et al., 2006). The offspring of these long-lived individuals, total reference RNA (Clontech Laboratories, Mountain View, CA, USA) who have an increased potential to become long-lived individuals (30% reduced standardized mortality rate) were also included. In addition, thepartners of the offspring were included as population controls of similar age and environmental exposures as the offspring and as a youngcontrol group for the nonagenarian siblings. Blood samples were taken The Globaltest methodology was designed to determine whether the from all the participants. The LLS was approved by the Medical Ethical common expression pattern of genes within a pre-defined set is Committee of Leiden University Medical Centre, and all participants gave significantly related to clinical outcome (Goeman et al., 2004; Goeman et al., 2005). A generalized linear model is used to estimate a Aging Cell ª 2012 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland mTOR pathway in human longevity, W. M. Passtoors et al.
‘Q-statistic’ for each gene set, which describes the correlation between several blood cell counts. In the whole blood samples of the participants, gene expression profiles, X, and clinical outcomes, Y. The Q-statistic for a the following cell subtypes were counted using the automated Siemens gene set is the average of the Q-statistics for each gene in the set. The ADVIA 1200 system (SMSD, Tarrytown, NY, USA) in the Leiden Medical Globaltest method was used to perform geneset analysis comparing two Diagnostical Center: leucocytes, thrombocytes, neutrophils, lympho- groups of individuals (either nonagenarians vs. controls or offspring vs.
cytes, monocytes, basophils and eosinophils. Both longevity-associated controls) including age (in offspring vs. controls only) and gender and genes in middle-age were adjusted for each of these cell counts their interaction as covariates. Global test package in R (Goeman & separately when associating with familial longevity, which analysis is Oosting, 2011) has been used to perform analyses.
Single gene analysis of gene expression data Differences in expression level between long-lived siblings, their Data on distribution of different T-cell subsets were generated in 71 offspring and the partners of their offspring were assessed using linear offspring and 73 controls, of whom also gene expression data were regression. In these analyses, expression level was the dependent available, as described previously (Derhovanessian et al., 2010). Briefly, variable and the two groups of individuals (either nonagenarians vs.
PBMCs viably cryopreserved with DMSO were thawed and treated controls or offspring vs. controls) were included in the model as a with human Ig, GAMUNEX (Bayer, Leverkusen, Germany) and categorical variable together with age (in offspring vs. controls only), ethidium monoazide (EMA) (Invitrogen, Karlsruhe, Germany). Cells gender and their interaction as covariates. To take into account were first stained indirectly for KLRG-1 using a primary antibody dependencies within sibships, robust standard errors were used, that kindly provided by Prof. Hans-Peter Pircher, Freiburg, Germany and a is, the variance was computed from the between family variation.
P-values were also based on these robust standard errors. Analyses were blocking with mouse serum (Chemicon/Millipore, Schwalbach, Ger- performed using the software package STATA/SE 11.0 (DPC Software, 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- Sample and SNP collection for genetic association analysis Legend, San Diego, CA, USA) and CD57-FITC (Immunotools, Freiburg, The SNP set analysis was performed in the same manner as described in Germany). After 20 min incubation on ice, cells were washed and Deelen et al. (2011b). We used the genotype data of 417 unrelated analysed immediately on an LSR II cytometer with FACSDiva software long-lived individuals and 476 young controls from the LLS, typed with (BD Biosciences). The spectral overlap between all channels was Illumina HumanOmniExpress BeadChips (Supplementary Table S1). We calculated automatically by the BD FACSDiva software, after measur- removed SNPs with a SNP call rate <0.95, MAF < 0.01 or PHWE < 10À4 in ing negative and single-colour controls. Data were analysed using nonagenarian cases and controls and 1018 analysed SNPs within a 10-kb FLOWJO software (Tree Star, Portland, OR, USA). For data analysis, window around the 40 genes from the MTOR pathway (Fig. 1) using the EMA-positive dead cells were excluded. T cells were characterized as naı¨ve (CD45RA+CCR7+ CD27+ CD28+), central memory (CD45RAÀCCR7+ CD27+ CD28+), effector memory (CD45RAÀCCR7ÀCD27ÀCD28À) and ‘terminally differentiated’ effector memory (TEMRA; CD45RA+ CCR7ÀCD27ÀCD28À) T cells according to previously In the PLINK set-based test [–set-test, http://pngu.mgh.harvard.edu/ published models (Koch et al., 2008).
purcell/plink (Purcell et al., 2007)], a SNP analysis of the pathway orgene SNP set is performed. For the SNP set, a mean SNP statistic Association of gene expression with insulin-related 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 Since the mTOR pathway has been implicated in insulin-related disequilibrium (r2) is above a certain threshold (–set-r2), the SNP with phenotypes, their effect on the expression of the longevity-related the lowest P-value in the single SNP analysis is selected. The same genes was further investigated. Blood samples were taken at baseline analysis is performed with a certain amount (–mperm) of simulated SNP for the determination of non-fasted serum parameters. For the serum sets in which the phenotype status of the individuals is permuted. An measurements, the Hitachi Modular or the Cobas Integra 800, both empirical P-value for the SNP set is computed by calculating the from Roche, Almere, the Netherlands were applied. CVs of these number of times the test statistic of the simulated SNP sets exceeds measurements were all below 5%. Information on medical history was that of the original SNP set. For the analysis in this study, the requested from the participants’ general practitioners. As described parameters were set to –setP 0.05 –set-r2 0.5, –set-max 99 999 and before, differences in expression level between offspring and controls –mperm 10 000. Bonferroni correction for the pathway analysis is were assessed using linear regression using the same model, but not necessary as it contains just one test. When SNP sets were tested excluding known Type 2 diabetes patients. The association of glucose per gene (40 SNP sets), the significance level for P-values after and insulin levels with expression levels of the seven longevity- Bonferroni correction for multiple testing is 0.00125.
associated genes was also analysed using linear regression in controlsonly. To investigate the influence of glucose and insulin levels on theassociation with longevity of these seven genes, linear regression between offspring and controls was performed again, including one of To further investigate the candidate genes, their expression level was again tested for association with familial longevity, but now adjusted for Aging Cell ª 2012 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland 30 mTOR pathway in human longevity, W. M. Passtoors et al.
Guertin DA, Sabatini DM (2005) An expanding role for mTOR in cancer. Trends Harrison DE, Strong R, Sharp ZD, Nelson JF, Astle CM, Flurkey K, Nadon NL, We thank all participants of the Leiden Longevity Study. The research Wilkinson JE, Frenkel K, Carter CS, Pahor M, Javors MA, Fernandez E, Miller RA leading to these results has received funding from the European Union’s (2009) Rapamycin fed late in life extends lifespan in genetically heterogeneous Seventh Framework Programme (FP7/2007-2011) under grant agree- ment no. 259679. This study was supported by a grant from the Heijmans BT, Beekman M, Houwing-Duistermaat JJ, Cobain MR, Powell J, Blauw GJ, van der Ouderaa FJ, Westendorp RG, Slagboom PE (2006) Lipoprotein Innovation-Oriented Research Program on Genomics (SenterNovem particle profiles mark familial and sporadic human longevity. PLoS Med. 3, e495.
IGE05007), the Centre for Medical Systems Biology, the Netherlands Holloszy JO, Fontana L (2007) Caloric restriction in humans. Exp. Gerontol. 42, Consortium for Healthy Ageing (Grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, the Netherlands Ikenoue T, Inoki K, Yang Q, Zhou X, Guan KL (2008) Essential function of TORC2 in Organization for Scientific Research (NWO) and by Unilever Colworth.
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Table S1 Analysed samples: genetic variation.
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