UBC Faculty Research and Publications

The relation between DNA methylation patterns and serum cytokine levels in community-dwelling adults:… Verschoor, Chris P; McEwen, Lisa M; Kohli, Vikas; Wolfson, Christina; Bowdish, Dawn M; Raina, Parminder; Kobor, Michael S; Balion, Cynthia Jun 21, 2017

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata


52383-12863_2017_Article_525.pdf [ 690.98kB ]
JSON: 52383-1.0362105.json
JSON-LD: 52383-1.0362105-ld.json
RDF/XML (Pretty): 52383-1.0362105-rdf.xml
RDF/JSON: 52383-1.0362105-rdf.json
Turtle: 52383-1.0362105-turtle.txt
N-Triples: 52383-1.0362105-rdf-ntriples.txt
Original Record: 52383-1.0362105-source.json
Full Text

Full Text

RESEARCH ARTICLE Open AccessThe relation between DNA methylationpatterns and serum cytokine levels incommunity-dwelling adults: a preliminarystudyChris P. Verschoor1,3,4,5* , Lisa M. McEwen4,5, Vikas Kohli1, Christina Wolfson5,6, Dawn ME. Bowdish1,3,4,5,Parminder Raina2,3,5, Michael S. Kobor4,5 and Cynthia Balion1,5AbstractBackground: The levels of circulating cytokines fluctuate with age, acute illness, and chronic disease, and arepredictive of mortality; this is also true for patterns of DNA (CpG) methylation. Given that immune cells areparticularly sensitive to changes in the concentration of cytokines in their microenvironment, we hypothesized thatserum levels of TNF, IL-6, IL-8 and IL-10 would correlate with genome-wide alterations in the DNA methylationlevels of blood leukocytes. To test this, we evaluated community-dwelling adults (n = 14; 48–78 years old) recruitedto a pilot study for the Canadian Longitudinal Study on Aging (CLSA), examining DNA methylation patterns inperipheral blood mononuclear cells using the Illumina HumanMethylation 450 K BeadChip.Results: We show that, apart from age, serum IL-10 levels exhibited the most substantial association to DNAmethylation patterns, followed by TNF, IL-6 and IL-8. Furthermore, while the levels of these cytokines were higher inelderly adults, no associations with epigenetic accelerated aging, derived using the epigenetic clock, were observed.Conclusions: As a preliminary study with a small sample size, the conclusions drawn from this work must beviewed with caution; however, our observations are encouraging and certainly warrant more suitably poweredstudies of this relationship.Keywords: Aging, DNA methylation, Serum cytokine, Epigenetics, Canadian Longitudinal Study on AgingBackgroundThere is a wealth of evidence relating the concentrationsof circulating cytokines to the severity and outcomes ofacute and chronic illness [1]. Some early examples includeserum tumour necrosis factor (TNF) with death duefollowing meningococcal meningitis and/or septicaemia[2] and interleukin (IL)-6 with graft rejection followingtransplantation [3]. A more recent study showed the levelsof more than a dozen serum cytokines were indicative ofdisease subtype for older adults with rheumatoid arthritis[4]. In addition to these pathological forms of stress, otherstudies have shown that changes in the levels of serumcytokines can also occur in response to physiological (eg.long distance running [5]) and psychological (eg. caregiv-ing [6]) forms of stress as well, which can be reproducedusing controlled rodent models [7].Given the vast array of acute and chronic stressors thatindividuals experience over their lifespan, it is not surpris-ing that chronological age is accompanied by increases inthe levels of circulating cytokine such as IL-6 [6] and TNF[8]. This process is often described within the broader term“inflammaging”, which represents a multi-dimensionalchronic inflammatory state that compounds over thetrajectory of aging and contributes to premature immuno-senescence, morbidity and mortality [9]. Interestingly, thesechanges in circulating cytokines often parallel alterations inthe functionality of circulating blood leukocytes. For* Correspondence: cversch@mcmaster.ca1Department of Pathology and Molecular Medicine, McMaster University,1280 Main St. W, MIP309A, Hamilton, ON, Canada3McMaster Institute for Research on Aging, McMaster University, Hamilton,ON, CanadaFull list of author information is available at the end of the article© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Verschoor et al. BMC Genetics  (2017) 18:57 DOI 10.1186/s12863-017-0525-3example, the production of TNF [10], IL-6 [11], and IL-10[12] are increased in monocytes from older individuals, andthe age-related increase in monocyte IL-6 production inmice can be reversed following the removal of circulatingTNF [13]. Although the sub-cellular mechanism mediatingthe relationship between circulating cytokines andleukocyte function has not been established, an intriguingmechanism for this relationship is the epigenetic regula-tion of gene expression via DNA methylation [14]. Thepresence or absence of a methyl group on the 5′ cytosineof cytosine-guanine dinucleotides (CpG) is a critical formof cellular regulation, in particular, the determination ofmyeloid [15] and lymphoid [16] cell lineage, and the in-nate immune responses of blood leukocytes [17]. Broadchanges to the DNA methylome also occur with age, andare related to epigenetic drift (trajectories that may not besimilar amongst individuals) or the epigenetic clock(trajectories that are consistent across the population)[18]. Indeed, both have been reported to be associatedwith syndromes such as frailty [19], and health deficitssuch poor physical [20] and cognitive function [20, 21]and coronary heart disease [22] in older adults.While there is considerable evidence relating DNAmethylation patterns with aging, age-related disease andleukocyte function, and aging with circulating cytokinelevels and leukocyte function, there are few reportsexamining the effects of circulating cytokines on DNAmethylation patterns, and vice-versa. Results from twoindependent, targeted studies of the TNF promoter suggestthat an age-related loss of DNA methylation increases cir-culating TNF levels via an increase in transcriptional activ-ity [23, 24]. Another more recent study of nearly 13,000individuals reported several loci whose DNA methylationlevels are significantly associated with changes in the levelsof the inflammatory marker, C-reactive protein (CRP) [25].For the current study, our primary objective was to identifygenome-wide DNA methylation patterns that were signifi-cantly associated with the levels of circulating cytokines incommunity-dwelling older adults recruited to a pilot studyfor the Canadian Longitudinal Study on Aging (CLSA)[26]. We show that the levels of serum TNF, IL-6, IL-8 andIL-10 are significantly associated, with varying degrees, togenome-wide DNA methylation patterns, independent ofage and sex. Additionally, as a preliminary study this workvalidates the sample and data collection, and experimentalprocedures of the CLSA, thereby supporting larger epigen-etic studies in the future.MethodsParticipants and serum cytokine analysisParticipants were community-dwelling adults from Hamilton,Ontario and Montreal, Quebec recruited to a pilot study forthe Canadian Longitudinal Study on Aging (CLSA) [26]. Forthe current study, 8 middle-aged (48–55 years old, 4 female)and 6 elderly (72–78 years old, 2 female) individuals wereselected from a total of 32 participants recruited for the pilotstudy. Whole blood was collected between December 2011and January 2012 and written, informed consent wasobtained from all participants. The study protocol andconsent procedures were approved by the McMasterResearch Ethics Board. Serum levels of IFN-γ, TNF, IL-1β,IL-6, IL-8, IL-10, and IL-12p70 were measured by MilliplexHigh Sensitivity multiplexed ELISA (Millipore, ON, CA) inNovember 2012. More than 70% of participants did not havedetectable levels of IFN-γ, IL-1β, and IL-12p70 (observed byothers [27–30]), so these cytokines were left out of furtheranalyses. Unless stated otherwise, serum cytokine levels werenatural-log transformed in order to minimize the effect ofextreme values.DNA methylation analysisDNA methylation analysis was performed on cryopreservedPBMCs, prepared between December 2011 and January2012. Briefly, blood was drawn into CPT vacutainers (BDBiosciences, ON, CA), gently inverted and centrifuged at1000 xg for 10 mins. Afterwards, the PBMC layer wascarefully aspirated, washed twice and resuspended in PBSfor storage at −80 °C overnight, and vapour phase liquidnitrogen thereafter. DNA was extracted using the DNeasyBlood Mini Kit (Qiagen, ON, CA) in November 2012.Approximately 750 ng of genomic DNA was then bisulfiteconverted using the EZ DNA Methylation™ Kit (ZymoResearch, CA, USA). Bisulfite converted DNA was thenprocessed using the Infinium HumanMethylation450 Bead-Chip per manufacturer’s instructions (Illumina, CA, USA).All processing procedures were performed using the Rpackage ‘minfi’ [31]. Initial probe filtering included removalof the following: control probes designed to interrogate singlenucleotide polymorphisms (n = 65), those targeted to the Xor Y chromosomes (n = 11,648), polymorphic (n = 20,696)and cross-reactive probes (n = 40,590) [32], and those withlow detection (detection p-value >0.01 on more than twochips; n = 434). The final data set included 414,999 probes.Following probe filtering, raw data background correctionand dye-bias equalization was performed using Noob [33]and normalization using stratified quantile normalization[31]. Batch effects, namely sentrix ID, were corrected forusing the ‘ComBat’ function in the package ‘sva’ [34], and cellmixture effects (relative frequency of monocytes, CD4+ andCD8+ lymphocytes, NK-cells and B-Cells – see below) wereremoved from normalized and adjusted beta values using aregression-based approach [35]. Principal component ana-lysis (PCA) was performed using the R package ‘FactoMi-neR’; the first 11 principal components were selected foranalysis following qualitative assessment of the PCA screeplot. DNA methylation age and accelerated aging was calcu-lated using the Horvath DNA methylation age calculator([36]; https://labs.genetics.ucla.edu/horvath/dnamage/).Verschoor et al. BMC Genetics  (2017) 18:57 Page 2 of 7Whole blood immunophenotypingPeripheral blood leukocyte frequency was measured bymulticolour flow cytometry in cryopreserved whole bloodin October 2016. For whole blood preparation, blood wasdrawn into acid-citrate dextrose (ACD) vacutainers (BDBiosciences, ON, CA), gently inverted and mixed 1:1 with20% DMSO in RPMI (10% DMSO, 50% RPMI final).Aliquots were placed into a CoolCell controlled-rate freez-ing container (BioCision, CA, USA), stored at −80 °Covernight, and vapour phase liquid nitrogen thereafter.We have previously shown that cryopreserved wholeblood is a valid sample type for immunophenotyping [37].The multicolour stain employed included the followingconjugated antibodies: CD45-Alexa Fluor700, CD3-PacificBlue, and CD14-PE eFluor610 (eBioscience, CA, USA), andCD4-APC Cy7, CD8-Brilliant Violet 510, CD56-PE, NKp46-PE, HLA-DR-PE Cy7, and CD19-FITC (Biolegend, CA,USA). This allowed for the enumeration (relative to CD45+leukocytes) of monocytes (HLA-DR+CD14+), CD4+ and CD8+ T-lymphocytes (CD3+), natural killer (NK) cells (CD56+NKp46+), and B-lymphocytes (CD19+). Following antibodystaining, the samples were fixed and red blood cells lysedwith 1× Fix/Lyse Buffer (eBioscience, CA, USA) and washedtwice prior to analysis. Samples were analyzed immediatelyusing a Beckman Coulter Gallios flow cytometer (BeckmanCoulter, ON, CA), and subsequent gating performed usingKaluza Analysis v1.3 (Beckman Coulter, ON, CA).StatisticsAll statistics were performed in R version 3.2.3 (R Founda-tion for Statistical Computing, AUT). Comparison of serumcytokine levels between age groups were performed usingthe Wilcoxon rank-sum test. Associations with principalcomponent scores and individual CpG methylation lociwas performed by multiple linear regression. For regressionanalyses against principal component scores, age group andsex were assessed together in a single model without serumcytokines, followed by individual models that each includeda natural log-transformed cytokine. The methylation levelsof individual sites were tested using the R package ‘limma’[38]. In both cases, M-values were used as opposed to betamethylation values; they represent logit transformed betavalues (the ratio of the methylated probe intensity and theoverall intensity), and exhibit markedly reduced heterosce-dasticity as compared to beta values [39]. For individualCpG sites, adjusted p-values were obtained, which repre-sent p-values adjusted using Benjamini-Hochberg’s proced-ure for controlling false discovery rate (FDR). Limma wasemployed for multivariate analysis as its Bayesian approachfor adjusting probe-wise variance has been shown to besuperior to traditional linear regression in minimizing falsediscovery rate [38], and Benjamini-Hochberg’s procedure isthe most commonly used approach for minimizing family-wise type I error rate in large-scale “omics” studies. Post-hoc simulations indicated that we were powered to detect(β = 80%, α = 1 × 10−6) an M-value regression coefficient of2.0 when comparing age groups and a coefficient of1.4 for correlations with natural-log transformed cyto-kines (data not shown).ResultsHigh sensitivity, multiplexed bead-ELISAs were performedto measure TNF, IL-6, IL-8 and IL-10 in participant serum(n = 14): the median (25th–75th percentile) of TNF was0.73 (0.65–1.28) pg/ml; IL-6, 0.14 (0.06–0.16) pg/ml; IL-8,1.16 (0.62–1.73) pg/ml; and IL-10, 1.08 (0.58–1.91) pg/ml.The levels of all cytokines were higher in elderly (n = 6;72–78 years old) as compared to middle-aged (n = 8; 48–55 years old) adults, the most significant of which being IL-6 (p = 0.03) (Fig. 1).Fig. 1 Concentrations of serum cytokines TNF, IL-6, IL-8 and IL-10 in middle-aged (MID; n = 8, 48–55 years old) and elderly (ELD; n = 6; 72–78 years old)community-dwelling adults. Significance determined by Wilcoxon rank-sum testVerschoor et al. BMC Genetics  (2017) 18:57 Page 3 of 7In order to assess patterns in genome-wide DNA methy-lation levels, we partitioned our dataset using principalcomponent analysis (PCA) and used linear regression totest the association between serum cytokine levels and thescores of the first 11 components. Principal component 1represented 22% of the overall DNA methylation variance,PC2 represented 12%, PC3 to PC10 represented between9.9 and 5.2%, and PC11 represented 1.5%. Interleukin-10was associated with principal component (PC) 4 (un-adjusted p = 0.042) and PC5 (p = 0.029), TNF with PC5(p = 0.033), and IL-6 with PC10 (p = 0.014) (Fig. 2a). Agegroup was associated with PC2 (p = 0.048; Fig. 2b), whilesex did not associate with any PCs.To determine if individual DNA methylation sites wereassociated with age or the concentration of serumcytokines, we performed linear regression against themethylation levels of each of the 414,999 probes in our finaldataset. This approach yielded few significant observationsat an FDR-adjusted p-value threshold of 0.05; hence, an un-adjusted p-value threshold of 1 × 10−4 was chosen arbitrar-ily as a reference point to compare the frequency ofsignificant tests related to each of our variables of interest.As expected, the greatest number of sites were obtainedwhen elderly and middle-aged groups were compared: 129loci were p < 10−4 (Fig. 3; Additional file 1: Table S1). Onlyone loci, cg04267345 (~1kB from the transcriptional startsite of Nuclear Factor of Activated T-Cells 4 (NFATC4)),was significantly different between age groups at an FDR-adjusted p < 0.05 (unadjusted p = 1.08 × 10−7); the differ-ence in average methylation frequencies (Δbeta) for this lociwas 10.8% (middle-aged = 14.6%, elderly = 3.8%). Regardingserum cytokines, the greatest number of significantly asso-ciated sites were obtained for IL-10: 51 loci with p < 10−4(zero with an adjusted p < 0.05). The effects of the otherthree cytokines were much lower: TNF, 10 with p < 10−4;IL-6, 2 with p < 10−4; and IL-8, 5 with p < 10−4 (Additionalfile 1: Table S1). P-values for TNF, IL-6 and IL-8 did notfollow a uniform distribution, evident by the fewer thanexpected loci at significance levels below p = 0.30 (Fig. 3).We also measured DNA methylation age, a representativemeasure of one’s biological age, or the speed at which one isaging. DNA methylation age was highly correlated tochronological age (Spearman’s rho = 0.90) in our partici-pants, however, none of the serum cytokines were associatedwith DNA methylation age, or age acceleration (ie. rate ofaging = DNA methylation age minus chronological age).DiscussionIn the current study we tested whether variation in theDNA methylation patterns of blood immune cells corre-lated with serum cytokine levels, namely, TNF, IL-6, IL-8,and IL-10. These molecules can be readily found at detect-able levels in older adults (each of these cytokines wereidentified in >70% of participants in our study), are knownto change with age and/or disease state [40–42], and allplay instrumental roles in the regulation of inflammation,an important component of several age-related morbidities[9]. We hypothesized that the age-related rise in circu-lating inflammatory mediators is predicated by a grad-ual dysregulation of blood immune cell productionand secretion of many of those same molecules, aphenomenon that is supported by a number ofhuman ex vivo and rodent experiments from ourgroup and others [10–13].Comparing serum cytokine levels to DNA methylationpatterns, IL-10 exhibited the greatest degree of correlation,followed by TNF, IL-6 and IL-8. As the only cytokineFig. 2 Associations between genome-wide DNA methylation patterns,partitioned using principal component analysis, and age group, sexand serum cytokine levels. Tests were performed by linear regressionagainst the scores for the first 11 principal components. a) Natural-log(Ln) transformed serum cytokines were each tested in independentmodels that also adjusted for age group (middle-aged or elderly) andsex, and b) age group and sex were tested together in a single modelwithout serum cytokines. Principal component (PC) 1 represented 22%of the overall DNA methylation variance, PC2 represented 12%, PC3 toPC10 represented between 9.9 and 5.2%, and PC11 represented 1.5%.Significance indicated by colour: dark blue, p < 0.05; light blue,p < 0.10; grey, p > 0.10Verschoor et al. BMC Genetics  (2017) 18:57 Page 4 of 7having a primary role in the dampening of inflammatoryresponses, the observation that its effects were unlike theother three inflammatory cytokines might not be notsurprising. Furthermore, IL-10 exhibiting the strongestcorrelation suggests that circulating leukocytes may exhibitenhanced sensitivity to anti-inflammatory signals, possiblyas a protective mechanism against potentially damagingand energy demanding inflammatory responses; this type ofmechanism is well described in studies of the intestinalmicroenvironment [43], as well as in the circulation [44].As compared to a recent study reporting DNA methylationsites that were significantly correlated with circulating levelsof the inflammatory acute-phase protein CRP [25], thereare many commonalities to our own results. Of the 58 sitesreported by Ligthart and colleagues to be associated toCRP, 25 were identified as being associated (unadjustedp < 0.05) with TNF, IL-6, IL-8 and IL-10 in our study. Inter-estingly, the overwhelming majority of common CpG sitesrelated to inflammatory cytokines in our study (TNF, 7/9sites; IL-6, 6/6 sites; IL-8, 2/2 sites) exhibited similarrelationships (ie. positive or negative correlation) as previ-ously reported for CRP. Regarding the anti-inflammatorycytokine IL-10, of the 5 common sites identified, 4 showedthe opposite relationship as to what was previously re-ported for CRP. These trends suggest that alterations to theleukocyte methylome may occur via inflammatory andanti-inflammatory cues from the microenvironment, asopposed to specific cytokine signaling networks.Clearly, as a preliminary study with a small sample size,the substantiveness of our findings is limited. The effect ofa small sample size is evident in the relatively few CpG lociapproaching FDR-adjusted significance as well as the lackof uniformativity of p-value distributions for TNF, IL-6 andIL-8. This has been previously observed [45, 46] and maybe related to the overestimation of the variance of certainCpG loci due to the small sample size, leading to overlyconservative tests and inflated p-values [47, 48]. It is worth-while to note that despite this we were still able to identifyat least one DNA methylation site with an FDR adjusted p-value <0.05, cg04267345, which showed increased methyla-tion with age. Methylation at a nearby position (~500 bp)has also been shown to increase with age [49], and its clos-est gene, NFATC4, is prominently involved in the differenti-ation and development of a number of cell types includingneurons, endothelial cells and adipocytes [50].ConclusionsIn summary, we have provided evidence that the levelsof circulating cytokines correlate with the DNA methyla-tion patterns of blood immune cells. Our study providesimpetus for future studies on large human aging cohortssuch as the Canadian Longitudinal Study on Aging inorder to identify correlations between circulatingcytokines and DNA methylation patterns with a greaterdegree of confidence, and importantly, to infer causalrelationships via longitudinal analyses.Fig. 3 Distribution of significance for each individual DNA methylation site tested against age and serum cytokines levels. Histograms describethe distribution of p-values resulting from linear regression tests against DNA methylation M-values, adjusting for age group and sex. Age groupwas tested as either middle-aged or elderly groups, and serum cytokines were natural-log (Ln) transformed. Dotted lines represent the uniformdistribution of p-values, in other words, the number of sites expected to be obtained at a given significance level by chanceVerschoor et al. BMC Genetics  (2017) 18:57 Page 5 of 7Additional fileAdditional file 1: Table S1. Differentially methylated position (DMP)analysis of middle-aged and elderly participants. Description: Association ofeach CpG loci with the variables: age group, Ln IL-10, Ln TNF, Ln IL-6, andLn IL-8. Analysis performed using limma on M-values, and the difference(log fold-change or regression coefficient) and significance (unadjusted andFDR-adjusted p-value, and log-odds difference (B)) presented for all probeswith an unadjusted p-value <0.05 (XLSX 12493 kb)AbbreviationsACD: Acid-citrate dextrose; CD: Cluster of differentiation; CLSA: CanadianLongitudinal Study on Aging; CpG: Cytosine-guanine dinucleotides; CRP: C-reactive protein; DMP: Differentially methylated position; ELD: Elderly;FDR: False discovery rate; IL: Interleukin; Ln: Natural-log; MID: Middle-aged;NFATC4: Nuclear Factor of Activated T-Cells 4; NK: Natural killer;PCA: Principal component analysis; TNF: Tumour necrosis factor;Δbeta: Difference in average methylation frequenciesAcknowledgmentsDMEB is the Canada Research Chair in Aging and Immunity, PR is theRaymond and Margaret Labarge Chair in Research and KnowledgeApplication for Optimal Aging and the Canada Research Chair inGeroScience, and MSK is the Canada Research Chair in Social Epigenetics,Senior Fellow of the Canadian Institute for Advanced Research, and SunnyHill BC Leadership Chair in Child Development. LMM is supported by a CIHRFrederick Banting and Charles Best Doctoral Research Award.FundingFunding for the Canadian Longitudinal Study on Aging (CLSA) is providedby the Government of Canada through the Canadian Institutes of HealthResearch (CIHR) and the Canada Foundation for Innovation.Availability of data and materialsAll related data are available within the manuscript and its additional file.Authors’ contributionsCPV prepared biological materials, analyzed and interpreted the data andprepared the manuscript. VK performed experiments. LMM interpreted thedata and reviewed the manuscript. CW, DMEB, PR and CB reviewed themanuscript. MSK supervised the epigenetic analyses and reviewed themanuscript. All authors have read and approved of the final manuscript.Competing interestsNot applicable.Consent for publicationNot applicable.Ethics approval and consent to participateWritten, informed consent was obtained from all participants, and the studyprotocol and consent procedures were approved by the McMaster ResearchEthics Board.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1Department of Pathology and Molecular Medicine, McMaster University,1280 Main St. W, MIP309A, Hamilton, ON, Canada. 2Department of HealthResearch Methods, Evidence, and Impact, McMaster University, Hamilton, ON,Canada. 3McMaster Institute for Research on Aging, McMaster University,Hamilton, ON, Canada. 4Department of Medical Genetics, Centre forMolecular Medicine and Therapeutics, BC Children’s Hospital ResearchInstitute, University of British Columbia, Vancouver, BC, Canada. 5CanadianLongitudinal Study on Aging, Hamilton, ON, Canada. 6Department ofEpidemiology, Biostatistics and Occupational Health, McGill University,Montreal, QC, Canada.Received: 15 April 2017 Accepted: 13 June 2017References1. Stepanova M, Rodriguez E, Birerdinc A, Baranova A. Age-independent rise ofinflammatory scores may contribute to accelerated aging in multi-morbidity. Oncotarget. 2015;6:1414–21.2. Waage A, Halstensen A, Espevik T. Association between tumour necrosisfactor in serum and fatal outcome in patients with meningococcal disease.Lancet Lond Engl. 1987;1:355–7.3. Yoshimura N, Oka T, Kahan BD. Sequential determinations of serum interleukin6 levels as an immunodiagnostic tool to differentiate rejection fromnephrotoxicity in renal allograft recipients. Transplantation. 1991;51:172–6.4. Kuller LH, Mackey RH, Walitt BT, Deane KD, Holers VM, Robinson WH, et al.Rheumatoid arthritis in the Women’s health initiative: methods and baselineevaluation. Am J Epidemiol. 2014;179:917–26.5. Rowlands DS, Pearce E, Aboud A, Gillen JB, Gibala MJ, Donato S, et al.Oxidative stress, inflammation, and muscle soreness in an 894-km relay trailrun. Eur J Appl Physiol. 2012;112:1839–48.6. Kiecolt-Glaser JK, Preacher KJ, MacCallum RC, Atkinson C, Malarkey WB,Glaser R. Chronic stress and age-related increases in the proinflammatorycytokine IL-6. Proc Natl Acad Sci U S A. 2003;100:9090–5.7. López-López AL, Jaime HB, Escobar Villanueva MDC, Padilla MB, Palacios GV,Aguilar FJA. Chronic unpredictable mild stress generates oxidative stressand systemic inflammation in rats. Physiol Behav. 2016;161:15–23.8. Dobbs RJ, Charlett A, Purkiss AG, Dobbs SM, Weller C, Peterson DW.Association of circulating TNF-alpha and IL-6 with ageing and parkinsonism.Acta Neurol Scand. 1999;100:34–41.9. Franceschi C, Bonafè M. Centenarians as a model for healthy aging.Biochem Soc Trans. 2003;31:457–61.10. Verschoor CP, Johnstone J, Millar J, Parsons R, Lelic A, Loeb M, et al.Alterations to the frequency and function of peripheral blood monocytesand associations with chronic disease in the advanced-age, frail elderly.PLoS One. 2014;9:e104522.11. Roubenoff R, Harris TB, Abad LW, Wilson PW, Dallal GE, Dinarello CA.Monocyte cytokine production in an elderly population: effect of age andinflammation. J Gerontol A Biol Sci Med Sci. 1998;53:M20–6.12. Mohanty S, Joshi SR, Ueda I, Wilson J, Blevins TP, Siconolfi B, et al.Prolonged proinflammatory cytokine production in monocytes modulatedby interleukin 10 after influenza vaccination in older adults. J Infect Dis.2015;211:1174–84.13. Puchta A, Naidoo A, Verschoor CP, Loukov D, Thevaranjan N, Mandur TS, etal. TNF drives monocyte dysfunction with age and results in impaired anti-pneumococcal Immunity. PLoS Pathog. 2016;12:e1005368.14. McEwen LM, Goodman SJ, Kobor MS, Jones MJ. The DNA Methylome: AnInterface Between the Environment, Immunity, and Ageing. In: Bueno V,Lord JM, Jackson TA, editors. The Ageing Immune System and Health.Springer International Publishing; 2017. p. 35–52. http://link.springer.com/chapter/10.1007/978-3-319-43365-3_3. Accessed 12 Apr 2017.15. Álvarez-Errico D, Vento-Tormo R, Sieweke M, Ballestar E. Epigenetic controlof myeloid cell differentiation, identity and function. Nat Rev Immunol.2015;15:7–17.16. Muegge K, Young H, Ruscetti F, Mikovits J. Epigenetic control duringlymphoid development and immune responses: aberrant regulation, viruses,and cancer. Ann N Y Acad Sci. 2003;983:55–70.17. Lam LL, Emberly E, Fraser HB, Neumann SM, Chen E, Miller GE, et al. Factorsunderlying variable DNA methylation in a human community cohort. ProcNatl Acad Sci U S A. 2012;109(Suppl 2):17253–60.18. Jones MJ, Goodman SJ, Kobor MS. DNA methylation and healthy humanaging. Aging Cell. 2015;14:924–32.19. Breitling LP, Saum K-U, Perna L, Schöttker B, Holleczek B, Brenner H. Frailty isassociated with the epigenetic clock but not with telomere length in aGerman cohort. Clin Epigenetics. 2016;8:21.20. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE, et al.The epigenetic clock is correlated with physical and cognitive fitness in theLothian birth cohort 1936. Int J Epidemiol. 2015;44:1388–96.21. Wang S-C, Oelze B, Schumacher A. Age-specific epigenetic drift in late-onset Alzheimer’s disease. PLoS One. 2008;3:e2698.22. Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, et al. Anepigenetic clock analysis of race/ethnicity, sex, and coronary heart disease.Genome Biol. 2016;17:171.Verschoor et al. BMC Genetics  (2017) 18:57 Page 6 of 723. Hermsdorff HH, Mansego ML, Campión J, Milagro FI, Zulet MA, Martínez JA.TNF-alpha promoter methylation in peripheral white blood cells:relationship with circulating TNFα, truncal fat and n-6 PUFA intake in youngwomen. Cytokine. 2013;64:265–71.24. Gowers IR, Walters K, Kiss-Toth E, Read RC, Duff GW, Wilson AG. Age-relatedloss of CpG methylation in the tumour necrosis factor promoter. Cytokine.2011;56:792–7.25. Ligthart S, Marzi C, Aslibekyan S, Mendelson MM, Conneely KN, Tanaka T, etal. DNA methylation signatures of chronic low-grade inflammation areassociated with complex diseases. Genome Biol. 2016;17:255.26. Raina PS, Wolfson C, Kirkland SA, Griffith LE, Oremus M, Patterson C, et al.The Canadian longitudinal study on aging (CLSA). Can J Aging Rev CanVieil. 2009;28:221–9.27. Breen EC, Reynolds SM, Cox C, Jacobson LP, Magpantay L, Mulder CB, et al.Multisite comparison of high-sensitivity multiplex cytokine assays▿. ClinVaccine Immunol CVI. 2011;18:1229–42.28. Kleiner G, Marcuzzi A, Zanin V, Monasta L, Zauli G. Cytokine levels in theserum of healthy subjects. Mediat Inflamm. 2013;2013:e434010.29. Marques-Vidal P, Bochud M, Bastardot F, Lüscher T, Ferrero F, Gaspoz J-M, etal. Levels and determinants of inflammatory biomarkers in a Swisspopulation-based sample (CoLaus study). PLoS One. 2011;6:e21002.30. Mitsunaga S, Ikeda M, Shimizu S, Ohno I, Furuse J, Inagaki M, et al. Serumlevels of IL-6 and IL-1β can predict the efficacy of gemcitabine in patientswith advanced pancreatic cancer. Br J Cancer. 2013;108:2063–9.31. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, etal. Minfi: a flexible and comprehensive Bioconductor package for the analysis ofInfinium DNA methylation microarrays. Bioinforma Oxf Engl. 2014;30:1363–9.32. Price ME, Cotton AM, Lam LL, Farré P, Emberly E, Brown CJ, et al. Additionalannotation enhances potential for biologically-relevant analysis of theIllumina Infinium HumanMethylation450 BeadChip array. EpigeneticsChromatin. 2013;6:4.33. Triche TJ, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of Illumina Infinium DNA methylation BeadArrays. NucleicAcids Res. 2013;41:e90.34. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package forremoving batch effects and other unwanted variation in high-throughputexperiments. Bioinforma Oxf Engl. 2012;28:882–3.35. Jones MJ, Islam SA, Edgar RD, Kobor MS. Adjusting for cell typecomposition in DNA methylation data using a regression-based approach.Methods Mol Biol Clifton NJ. 2017;1589:99–106.36. Horvath S. DNA methylation age of human tissues and cell types. GenomeBiol. 2013;14:R115.37. Verschoor CP, Kohli V, Balion C. A comprehensive assessment ofimmunophenotyping performed in cryopreserved peripheral whole blood.Cytometry B Clin Cytom. 2017;38. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powersdifferential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res. 2015;43:e47.39. Du J, Klein JD, Hassounah F, Zhang J, Zhang C, Wang XH. Aging increasesCCN1 expression leading to muscle senescence. J Physiol - Cell Physiol.2014;30640. Alvarez-Rodríguez L, López-Hoyos M, Muñoz-Cacho P, Martínez-TaboadaVM. Aging is associated with circulating cytokine dysregulation. CellImmunol. 2012;273:124–32.41. Baune BT, Smith E, Reppermund S, Air T, Samaras K, Lux O, et al.Inflammatory biomarkers predict depressive, but not anxiety symptomsduring aging: the prospective Sydney memory and aging study.Psychoneuroendocrinology. 2012;37:1521–30.42. Patel KD, Duggan SP, Currid CA, Gallagher WM, McManus R, Kelleher D, etal. High sensitivity cytokine detection in acute coronary syndrome revealsup-regulation of interferon gamma and interleukin-10 post myocardialinfarction. Clin Immunol Orlando Fla. 2009;133:251–6.43. Shouval DS, Ouahed J, Biswas A, Goettel JA, Horwitz BH, Klein C, et al.Interleukin 10 receptor signaling: master regulator of intestinal mucosalhomeostasis in mice and humans. Adv Immunol. 2014;122:177–210.44. Hickey MJ, Issekutz AC, Reinhardt PH, Fedorak RN, Kubes P. Endogenousinterleukin-10 regulates hemodynamic parameters, leukocyte-endothelialcell interactions, and microvascular permeability during endotoxemia. CircRes. 1998;83:1124–31.45. Arceo ME, Ernst CW, Lunney JK, Choi I, Raney NE, Huang T, et al.Characterizing differential individual response to porcine reproductive andrespiratory syndrome virus infection through statistical and functionalanalysis of gene expression. Front Genet. 2012;3:321.46. Stupar RM, Bhaskar PB, Yandell BS, Rensink WA, Hart AL, Ouyang S, et al.Phenotypic and transcriptomic changes associated with potatoautopolyploidization. Genetics. 2007;176:2055–67.47. Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM. Efficientexperimental design and analysis strategies for the detection of differentialexpression using RNA-sequencing. BMC Genomics. 2012;13:484.48. Ferguson JP, Palejev D. P-value calibration for multiple testing problems ingenomics. Stat Appl Genet Mol Biol. 2014;13:659–73.49. Acevedo N, Reinius LE, Vitezic M, Fortino V, Söderhäll C, Honkanen H, et al.Age-associated DNA methylation changes in immune genes, histonemodifiers and chromatin remodeling factors within 5 years after birth inhuman blood leukocytes. Clin Epigenetics. 2015;7:34.50. Horsley V, Pavlath GK. NFAT: ubiquitous regulator of cell differentiation andadaptation. J Cell Biol. 2002;156:771–4.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Verschoor et al. BMC Genetics  (2017) 18:57 Page 7 of 7


Citation Scheme:


Citations by CSL (citeproc-js)

Usage Statistics



Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            async >
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:


Related Items