J Gerontol a Biol Sci Med Sci-2014

8112019 J Gerontol a Biol Sci Med Sci-2014

httpslidepdfcomreaderfullj-gerontol-a-biol-sci-med-sci-2014 111



780 KRIŠTI Ć ET AL

of genes (4) Epigenetic regulation of gene expression isexpected to affect protein glycosylation and several publi-cations recently reported this effect (5ndash8) Changes in gly-cosylation with age have been shown over 20 years ago (9)and have also replicated in recent large population studies(10ndash13)

Immunoglobulin G (IgG) is an excellent model glyco-protein because its glycosylation has been well defined(Figure 1) and many important functional effects of alterna-tive IgG glycosylation have been described (14) For exampleglycosylation acts as a switch between pro- and anti-inflam-matory IgG functionality Most of the IgG molecules are notsialylated and are proinflammatory Terminal α26-sialylationof IgG glycans decreases the ability of IgG to bind to acti-vating FcγRs and promotes recognition by DC-SIGN whichincreases expression of inhibitory FcγRIIB and is anti-inflam-matory (15) Another fascinating example is the role of corefucose in the modulation of antibody-dependent cellular cyto-toxicity IgG-containing glycans that lack core fucose have100-fold increased affinity for FcγRIIIA and are thereforemuch more efficient in activating antibody-dependent cel-lular cytotoxicity than fucosylated glycoforms of the same

molecule (16) On average 95 of the IgG population iscore fucosylated (12) thus most of the immunoglobulinshave a ldquosafety switchrdquo which prevents them from activatingantibody-dependent cellular cytotoxicity Malfunction of thissystem appears to be associated with autoimmune diseasesas indicated by both pleiotropic effects of genes that asso-

ciate with IgG glycosylation on different inflammatory andautoimmune diseases and the observed alterations in IgGglycosylation in systemic lupus erythematous (17) and manyinflammatory diseases (18)

Interindividual variability of IgG glycosylation in a popu-lation is large (12) and it appears to be affected by both vari-ation in DNA sequence (19) and environmental factors (11)Most of the studies that investigated glycosylation changeswith age were either of limited size or were performed onthe total plasma glycome thus in addition to changes inglycosylation the observed differences reflected changesin the concentration of individual plasma proteins In thisstudy we focused on glycosylation of IgG and analyzedmore than 5000 individuals from four different Europeanpopulations to provide definitive data about changes in IgGglycosylation through the lifetime

Figure 1 UPLC analysis of immunoglobulin G (IgG) glycosylation Each IgG contains one conserved N-glycosylation site on Asn197

of its heavy chain Differentglycans can be attached to this site and the process seems to be highly regulated UPLC analysis can reveal composition of the glycome attached to a populationof IgG molecules by separating total IgG N-glycome into 24 chromatographic glycan peaks (GP1ndashGP24) mostly corresponding to individual glycan structures






of samples as the ldquotraining setrdquo and one third (678) of sam-ples as the ldquovalidation setrdquo In 1000 replicas regressioncoefficients were estimated on the training set and goodnessof fit was obtained by applying the model estimated on the

training set to the validation set Final model estimates werecalculated as median values of 1000 iterations of randomsubsampling The resulting GlycanAge index is composedof three glycan variables

Predicted male age GP GP

GP

= + times times

times

139 9 85 1 6 5 2 6

34 6 14

2 ( ) ( )

( )

ndash ndash

++ times( )11 8 15GP

Predicted female age GP

GP G

= + times

times times

110 1 164 5 6

46 7 6 22 42

( )

( ) (

ndash

ndash PP

GP

14

1 9 15

) ndash

( )times

All three glycan structures selected by the optimal modelwere shown to be strongly associated with age but eachof the three glycan structures revealed different patternsbetween genders The model explained 58 of variation in

chronological age (with 95 of replicas giving R2

between54 and 61) with correlation between age and predictedage of 76 (73ndash78) (Table 3) When applied to the fullOrkney data set the standard deviation (SD) of residualswas 97 years (Figure 4A) The predictive power of ourGlycanAge index was better for women (64 of varianceexplained) than for men (49 of variance explained)

The same model was tested on the remaining three popu-lations (Vis = 906 Korcula = 915 and TwinsUK = 1261)Samples from these populations were processed in thesame way as samples from Orkney and used to predict agewith a model that was trained on the Orkney cohort The

KOR ORCA TWINS VIS

10

20

30

2

4

6

8

10

5

10

15

20

08

12

16

20

GP 4

GP 6

GP 1 4

GP 1 5

25 50 75 100 25 50 75 100 40 60 80 20 40 60 80

Age

P e r c e n t a

g e o f G l y c a n G r o u p

Sex

Female

Male

Figure 2 Relationship between age and selected glycan structures Plots indicate associations between the individual contributions of four selected glycanstructures to the total immunoglobulin G glycome and chronological age in four different human populations Blue and red curves are fitted local regression modelsdescribing gender-specific relationship between age and glycan structure The shaded region is a pointwise 95 confidence interval on the fitted values (there is 95confidence that the true regression curve lies within the shaded region)







GLYCANS ARE A BIOMARKER OF AGE 785


FEV

= + times times

times +

49 6 9 8 6 2 2 14

1 2 1

2

2

( ) ( )

( )

ndash ndash

(( )0 1timesSystolicBP

Predicted female age GP GP

FEV

= + times times

times

53 9 8 3 6 2 4 14

1 9 1

2

2

( ) ( )

(

ndash

ndash )) ( )+ times0 1 SystolicBP

The model was tested on the Korcula cohort and thecorrelation between age and age predicted with the modeltrained on the Orkney cohort was 80 The model trained onthe Korcula cohort explained 65 of variation in age in thatcohort (70 in women and 57 in men) with a correlationof chronological and predicted age of 81 (83 for womenand 76 for men) An overview of all results together withcorresponding 95 range of replicas can be seen in Table 3

D983145983155983139983157983155983155983145983151983150

Fine details of glycan structures attached to IgG Fc promotebinding of IgG to different receptors and in this way modulateaction of the immune system (1421) Individual variabilityin IgG glycosylation is large (12) and this variability in gly-cosylation contributes to individual differences in function ofthe immune system (2223) As it was clearly demonstrated

for intravenous immunoglobulin therapy even slight changesin IgG glycosylation can direct pro- and anti-inflammatoryactions of immunoglobulins (24) IgG Fc glycosylation mod-ulates inflammation (2526) and through promotion or sup-pression of inflammation it may significantly contribute tothe process of biological aging (27ndash29) This hypothesis isfurther supported by the observed decrease in galactosylationin some premature ageing syndromes (1330)

Up to 50 of plasma glycome variability is estimatedto be heritable (1231) The remaining variability is appar-ently caused by environmental factors but the observedlong-term stability of the glycome (32) argues in favor of

long-term epigenetic regulation of the glycan biosynthesispathways (33) As we have shown in this study a large partof this nongenetic variability can be explained by age andphysiological variables related to age In the total plasmaglycome as opposed to the IgG glycome age was reportedto have only minor effects on most of the glycans (31)

Nearly all IgG glycans (21 out of 24 directly measured)however were significantly affected by age (Table 1) Theheavily influenced feature of IgG glycosylation was galac-tosylation which decreased to less than 50 of its maximalvalue through lifetime The decrease in IgG galactosyla-tion with age was initially reported more than 25 years ago(9) and replicated in a number of subsequent studies (34)Recently we reported that in childhood and adolescencethe galactosylation of IgG glycans actually increases withage (35) Indeed from the population studies it appears thattotal galactosylation is increasing until early adulthood (midto late 20s) and decreases afterward However the situationis apparently more complex because the pattern of changesin men and women differ considerably (Figure 2) Alsogalactosylated glycans with (GP15) and without bisectingGlcNAc (GP14) have different patterns of change

Galactosylation strongly decreases proinflammatoryfunction of IgG (15) thus both age-related and individualvariation in the decrease of IgG galactosylation with ageis affecting inflammation and can actively contribute tothe deterioration of an aging organism through the inflam-mation-based process named inflammaging (2736) Thedecrease in expression of the corresponding galactosyl-transferase (β4-GalT1) is clearly involved (37) but the rea-son and molecular mechanisms underlying such dramatic

age changes in IgG galactosylation are not known Ourrecent genome-wide association study of IgG glycosyla-tion identified polymorphisms in IL6ST SMARCD3 HLA-

DQA2B2 and BACH2 that affect IgG galactosylation (17)None of these genes was previously reported to be involvedin IgG glycosylation thus their functional role in this pro-cess is unknown Recent study of genome-wide methyla-tion profiles identified a number of genes whose expressionchanged significantly with aging (38) and BACH2 was oneof them The combined observations that (i) BACH2 affectsIgG galactosylation and (ii) that epigenetic regulation ofBACH2 expression is changing with age suggest a new and

intriguing hypothesis that changes in IgG galactosylationmay actually represent a by-product of some other age-driven processes In our data set array methylation data for

BACH2 and glycans were available for only 310 individualsbut even on this small data set and despite the ldquomethylationnoiserdquo due to genetic and lifestyle factors and DNA fromother types of leukocytes several significant associationsbetween glycans and BACH2 promoter methylation wereobserved (Supplementary Table S4) With current knowl-edge it is not possible to speculate whether associations of

BACH2 promoter methylation changes with age and IgGglycosylation is causal or just a secondary by-product of

Table 2 Association of GlycanAge Index With Biochemical andPhysiological Traits After Correcting for Chronological Age and Sex

Orkney Vis and Korcula

Beta p Beta p

Insulin 00755 922E-08 00402 350E-01Fibrinogen 00157 198E-06 00167 883E-05

HbA1c 01106 263E-06 00084 316E-03BMI 00585 167E-04 00344 104E-02Triglycerides 00092 175E-04 00140 120E-04Glucose 00113 209E-04 00091 477E-02Waist circumference 01468 208E-04Calcium 00010 235E-04 00002 704E-01D-dimer 29670 824E-04Cholesterol 00036 307E-01 00201 551E-08LDL 00031 326E-01 00146 608E-06Uric acid 10773 402E-02 07620 968E-04

Note HbA1c = glycosylated hemoglobin BMI = body mass indexLDL = low-density lipoprotein p = p value beta = regression coefficient




BACH2 function If changes in IgG glycosylation would becaused by changes in BACH2 promoter methylation status

with aging they would contribute to the deterioration of theorganism with age by promoting inflammation as proposedby Franceschi and colleagues (2829)

The accurate prediction of chronological and biologi-cal ages from biochemical parameters is a priority in theaging field (39ndash41) Here we introduce a novel GlycanAgeindex that combines one nongalactosylated glycan (GP6)and two digalactosylated glycans (GP14 and GP15) Thisindex predicts chronological age with error of 97 years andimportantly explains 58 of variation in chronological ageand sex The explanation of nearly 60 of variance of ageis impressive compared with other so-called age biomarkerslike telomere length where most studies show 15ndash25

(42) thus our new GlycanAge index appears to be moreclosely related to age than telomere lengths After correct-ing for chronological age the GlycanAge index correlatedstrongly with physiological parameters associated withmeasurements related to biological age (Table 2) thus IgGglycosylation appears to be closely linked with both chron-ological and biological ages We have further supported thisby including forced expiratory volume in first second andsystolic blood pressure in our prediction model which sig-nificantly improved prediction and cumulatively explained71 of variation in chronological age (Figure 4B)

The GlycanAge index which includes only three gly-

cans seems to be a practical way to address both chron-ological and biological ages of an individual The abilityto measure human biological aging using molecular pro-filing has practical applications for diverse fields such asdisease prevention and treatment or forensics The evalua-tion of aging immune system is here particularly interestingbecause glycosylation strongly affects function of immu-noglobulins (14) and the immune system in general (43)Whether aging causes changes in glycosylation of IgG ordo glycosylation changes in IgG contribute to aging by pro-moting inflammation is very difficult to distinguish withoutlongitudinal studies Even though both of these aspects are

probably partly true it is clear that changes that occur inIgG glycosylation with aging are closely regulated and inte-

grated with other physiological processes in the body

M983137983156983141983154983145983137983148983155 983137983150983140 M983141983156983144983151983140983155

Human SamplesThis study was based on plasma samples obtained from

906 individuals (377 men and 529 women) from Croatianisland Vis 915 individuals (320 men and 595 women) fromCroatian island Korcula (44) 2035 individuals (797 men and1238 women) from the Orkney Islands in Scotland and 1261female twins (560 monozygotic and 698 dizygotic) from theTwinsUK cohort (45) The participants from Vis cohort were

men and women between 18 and 88 years of age (median age560 and SD 152) and 18 and 93 years of age (median 565SD 161) respectively Male participants of Korcula cohortwere aged between 20 and 90 (median age 578 SD 144) andfemale participants between 18 and 98 years of age (median554 SD 140) In Orkney cohort men were aged between16 and 100 (median 537 SD 151) and women between 17and 97 years of age (median 529 and SD 153) TwinsUKcohort consisted of women aged between 27 and 83 yearswith mean age of 586 and SD of 95

A subset of samples from the Croatian island Vis (n = 26)were sampled twice first time in 2003 and second time in2013 All human participants included in this study signed

an informed consent and the study was approved by theappropriate Ethics Committee of the University of SplitMedical School Institute for Anthropological ResearchResearch Ethics Committees in Orkney and Aberdeen andKings College London This study conformed to ethicalguidelines of the 1975 Declaration of Helsinki

Analysis of IgG glycans

Isolation of IgG from human plasmamdashThe IgG was iso-lated using protein G monolithic plates (BIA Separations

Table 3 Goodness-of-Fit and Spearmanrsquos Correlations of Chronological Age and Age Predicted by Various Models

Training Test

Population Female Male

R2 Correlation R2 Correlation R2 Correlation

GlycanAge IndexOrkney Orkney 578 [541ndash613] 76 [73 to 78] 640 [600ndash681] 80 [77 to 082] 494 [419ndash567] 70 [64 to 75]Orkney Korcula 413 64 506 71 252 50

Orkney Vis 415 64 491 70 314 56Orkney TwinsUK 480 69 480 69 NA NAKorcula Korcula 429 [342ndash497] 65 [58 to 70] 511 [405ndash589] 71 [63 to 076] 268 [161ndash363] 51 [40 to 60]Vis Vis 430 [366ndash510] 65 [60 to 71] 498 [413ndash578] 70 [64 to 76] 346 [246ndash462] 58 [49 to 67]TwinsUK TwinsUK 495 [429ndash553] 70 [65 to 074] 495 [429ndash553] 70 [65 to 74] NA NACombined Glycan Age IndexOrkney Orkney 713 [682ndash739] 84 [83 to 086] 757 [727ndash784] 87 [85 to 89] 637 [571ndash692] 80 [76 to 83]Orkney Korcula 645 80 695 83 563 75Korcula Korcula 652 [597ndash700] 81 [77 to 084] 696 [638ndash748] 83 [80 to 86] 574 [465ndash670] 76 [68 to 82]

Note Numbers in brackets are 95 range of 1000 replicas of random subsampling validation




Ajdovščina Slovenia) as described previously (12) Briefly50ndash90 983221L of plasma was diluted 10times with 1times phosphate-buffered saline pH 74 applied to the protein G plate andinstantly washed with 1times phosphate-buffered saline pH74 to remove unbound proteins IgGs were eluted with1 mL of 01 M formic acid (Merck Darmstadt Germany)

and neutralized with 1 M ammonium bicarbonate (Merck)

Glycan release and labelingmdashGlycan release and labe-ling of Korčula and Vis IgG samples was performed essen-tially as described by Royle and coworkers (46) BrieflyIgG was immobilized in a block of sodium dodecyl sulfatendashpolyacrylamide gel and N-glycans were released by diges-tion with PNGase F (ProZyme Hayward CA) Each stepwas done in a 96-well microtiter plate to achieve the bestthroughput of sample preparation After deglycosylationN-glycans were labeled with 2-AB fluorescent dye Excessof label was removed by solid-phase extraction usingWhatman 3MM chromatography paper Finally glycanswere eluted with water and stored at minus20degC until usage

Orkney and TwinsUK IgG samples were first dena-tured with addition of 30 μL 133 sodium dodecyl sul-fate (wv) (Invitrogen Carlsbad CA) and by incubation at65degC for 10 min Subsequently 10 μL of 4 Igepal-CA630(SigmandashAldrich St Louis MO) and 125 mU of PNGaseF (ProZyme) in 10 μL 5times phosphate-buffered saline wereadded to the samples The samples were incubated over-night at 37degC for N-glycan release The released N-glycanswere labeled with 2-AB The labeling mixture was freshlyprepared by dissolving 2-AB (SigmandashAldrich) in dimethylsulfoxide (SigmandashAldrich) and glacial acetic acid (Merck)

mixture (8515 vv) to a final concentration of 48 mgmLA volume of 25 μL of labeling mixture was added to eachN-glycan sample in the 96-well plate Also 25 μL of freshlyprepared reducing agent solution (10696 mgmL 2-picolineborane [SigmandashAldrich] in dimethyl sulfoxide) was addedand the plate was sealed using adhesive tape Mixing wasachieved by shaking for 10 min followed by 2-hour incuba-tion at 65degC Samples (in a volume of 100 μL) were broughtto 80 acetonitrile (ACN) (vv) by adding 400 μL of ACN(JT Baker Phillipsburg NJ) Free label and reducing agentwere removed from the samples using hydrophilic interac-tion chromatographyndashsolid-phase extraction An amount of

200 μL of 01 gmL suspension of microcrystalline cellu-lose (Merck) in water was applied to each well of a 045 μmGHP filter plate (Pall Corporation Ann Arbor MI) Solventwas removed by application of vacuum using a vacuummanifold (Millipore Corporation Billerica MA) All wellswere prewashed using 5 times 200 μL water followed by equi-libration using 3 times 200 μL acetonitrilewater (8020 vv)The samples were loaded to the wells The wells were sub-sequently washed seven times using 200 μL acetonitrile water (8020 vv) Glycans were eluted two times with100 μL of water and combined eluates were stored at minus20degCuntil usage

Hydrophilic interaction chromatography-UPLCmdashFluorescently labeled N-glycans were separated by hydro-philic interaction chromatography on a Waters AcquityUPLC instrument (Waters Milford MA) consisting of aquaternary solvent manager sample manager and a FLRfluorescence detector set with excitation and emission

wavelengths of 330 and 420 nm respectively The instru-ment was under the control of Empower 2 software build2145 (Waters) Labeled N-glycans were separated on aWaters ethylene bridged hybrid (BEH) Glycan chroma-tography column 100 times 21 mm id 17 μm BEH parti-cles with 100 mM ammonium formate pH 44 as solventA and acetonitrile as solvent B Separation method usedlinear gradient of 75ndash62 acetonitrile (vv) at flow rateof 04 mLmin in a 25-minute analytical run Samples weremaintained at 5degC before injection and the separation tem-perature was 60degC The system was calibrated using anexternal standard of hydrolyzed and 2-AB-labeled glucoseoligomers from which the retention times for the individualglycans were converted to glucose units Data processingwas performed using an automatic processing method witha traditional integration algorithm after which each chroma-togram was manually corrected to maintain the same inter-vals of integration for all the samples The chromatogramswere all separated in the same manner into 24 peaks and theamount of glycans in each peak was expressed as percent-age of total integrated area In addition to 24 directly meas-ured glycan structures 53 derived traits were calculatedas described previously (12) These derived traits averageparticular glycosylation features (galactosylation fuco-sylation and sialylation) across different individual glycan

structures Consequently they are more closely related toindividual enzymatic activities and underlying genetic pol-ymorphisms (47)

Statistical AnalysisTo obtain normally distributed variables for 24 glycan

structures a log transformation was performed on glycanvariables Batch correction was performed for each glycangroup separately using linear mixed model In this modeldependent variable was log-transformed fraction of glycangroup in total glycome Age and gender were described

as fixed effects and technical sources of variation weredescribed as random effects To obtain corrected values forfractions of glycan groups estimated batch effect (techni-cal source of variation) was subtracted from each log-trans-formed glycan measurement

The predictive model of age (GlycanAge model) wasbuilt using multivariate analysis of covariance approachimplemented in ldquostatsrdquo package for R programming lan-guage (48) The maximal model included gender-specificlinear and quadratic terms for each of 24 glycan struc-turesmdash96 parameters in total Feature selection was per-formed using backward elimination implemented in ldquosteprdquo




function of ldquostatsrdquo package for R with Akaike informationcriterion (20) as optimization criteria Reported goodness offit correlations and regression coefficients were estimatedas median values of 1000 iterations of repeated randomsubsampling validation with two thirds (1357 samples) ofOrkney cohort as ldquotraining setrdquo and one third (678 samples)

as ldquovalidation setrdquoTo identify factors that may be responsible for the dif-ferences between chronological age and predicted age weperformed association analysis with all available biochemi-cal and physiological traits in our databases For each traitwe defined two models

predictedAge age sex~ +

predictedAge age sex trait~ + +

Likelihood ratio test was performed on those models tosee if trait variable significantly improves model predic-tion False discovery rate was controlled using BenjaminindashHochberg procedure

For combined biological and glycan age model bio-chemical and physical parameters that were present in atleast two cohorts were selected from available databasesThe list contained many biomarkers that were previouslyshown to be related to human aging (49) and their corre-lations with age may be seen in Supplementary Table S3Not all biomarkers were collected in all samples fromOrkney so resulting data set consisted of 1728 people Themost significant biomarkers for the analysis were absentin Croatian Vis cohort and TwinsUK so these populations

were excluded from the analysisThe predictive model of combined biological and gly-

can biomarkers was built in the same way as GlycanAgemodel A full model consisted of gender-specific polyno-mial of second degree for each of the 24 biological param-eters included in the analysis and three glycans included inGlycanAge resulting in 108 parameters Penalty for largenumber of parameters was selected based on 1000 itera-tions of 10-fold cross-validation on Orkneyrsquos training setwith robustness of goodness of fit (adjusted for the num-ber of predictors) as selection criteria The performanceof the model was additionally tested on Korcula cohort

Training and validating on Korcula cohort were performedin the same manner

S983157983152983152983148983141983149983141983150983156983137983154983161 M983137983156983141983154983145983137983148

Supplementary material can be found at httpbiomedgerontologyoxfordjournalsorg

F983157983150983140983145983150983143

The CROATIA-Vis study in the Croatian island of Vis was supported bygrants from the Medical Research Council (UK) the Ministry of ScienceEducation and Sport of the Republic of Croatia (108-1080315-0302) andthe European Union framework program 6 European Special PopulationsResearch Network project (contract LSHG-CT-2006-018947)ORCADES was supported by the Chief Scientist Office of the Scottish

Government the Royal Society and the European Union FrameworkProgramme 6 EUROSPAN project (contract LSHG-CT-2006-018947)Glycome analysis was supported by the Ministry of Science Educationand Sport of the Republic of Croatia (309-0061194-2023) and theEuropean Commission GlycoBioM (contract 259869) HighGlycan(contract 278535) MIMOmics (contract 305280) IBD-BIOM (con-tract 305479) and Integra-Life (contract 315997) grants The workof YA was supported by Russian Foundation for Basic Research grant

12-04-33182 The collection of samples at island Vis was supported bythe Ministry of Science Education and Sports of the Republic of Croatia(196-1962766-2751 to PR) The funders had no role in study designdata collection and analysis decision to publish or preparation of themanuscript

TwinsUK The study was funded by the Wellcome Trust EuropeanCommunityrsquos Seventh Framework Programme (FP72007-2013) Thestudy also receives support from the National Institute for Health Research(NIHR) BioResource Clinical Research Facility and Biomedical ResearchCentre based at Guyrsquos and St Thomasrsquo NHS Foundation Trust and KingrsquosCollege London

A983139983147983150983151983159983148983141983140983143983149983141983150983156983155

JK TK IB MP-B MN and KT performed experiments MMPR NN JS SM IK OP and CH provided samples and resourcesFV CM and LK analyzed data IR HC YA JFW OG VZ

TS and GL planned experiments and AV TS VZ and GL wrotethe manuscript Tim Spector is holder of an ERC Advanced PrincipalInvestigator award SNP Genotyping was performed by The WellcomeTrust Sanger Institute and National Eye Institute via NIHCIDR

C983151983150983142983148983145983139983156 983151983142 I983150983156983141983154983141983155983156

GL declares that he is a founder and owner and JK FV IBMP-B LK MN and DP declare that they are employees of GenosLtd which offers commercial service of glycomic analysis and has severalpatents in this field

R983141983142983141983154983141983150983139983141983155 1 Fontana L Partridge L Longo VD Extending healthy life spanmdash

from yeast to humans Science 2010328321ndash326 doi101126 science1172539

2 Blair SN Kohl HW 3rd Paffenbarger RS Jr Clark DG Cooper KHGibbons LW Physical fitness and all-cause mortality A prospectivestudy of healthy men and women JAMA 19892622395ndash2401

3 Walt D Aoki-Kinoshita KF Bendiak B et al TransformingGlycoscience A Roadmap for the Future Washington DC NationalAcademies Press 2012

4 Lauc G Rudan I Campbell H Rudd PM Complex genetic regulationof protein glycosylation Mol Biosyst 20106329ndash335 doi101039 b910377e

5 Horvat T Mužinić A Barišić D Bosnar MH Zoldoš V Epigeneticmodulation of the HeLa cell membrane N-glycome Biochim Biophys Acta 201218201412ndash1419 doi101016jbbagen201112007

6 Zoldoš V Horvat T Novokmet M et al Epigenetic silencing ofHNF1A associates with changes in the composition of the humanplasma N-glycome Epigenetics 20127164ndash172 doi104161

epi7218918 7 Saldova R Dempsey E Peacuterez-Garay M et al 5-AZA-2prime-deoxycytidine

induced demethylation influences N-glycosylation of secreted gly-coproteins in ovarian cancer Epigenetics 201161362ndash1372doi104161epi61117977

8 Lauc G Vojta A Zoldoš V Epigenetic regulation of glycosyla-tion is the quantum mechanics of biology Biochim Biophys Acta2013184065ndash70 doi101016jbbagen201308017

9 Parekh R Roitt I Isenberg D Dwek R Rademacher T Age-relatedgalactosylation of the N-linked oligosaccharides of human serum IgG J Exp Med 19881671731ndash1736 doi101084jem16751731

10 Ruhaak LR Uh HW Beekman M et al Plasma protein N-glycan pro-files are associated with calendar age familial longevity and health JProteome Res 2011101667ndash1674 doi101021pr1009959




11 Knezevic A Gornik O Polasek O et al Effects of aging body massindex plasma lipid profiles and smoking on human plasma N-glycansGlycobiology 201020959ndash969 doi101093glycobcwq051

12 Pucić M Knezević A Vidic J et al High throughput isolation andglycosylation analysis of IgG-variability and heritability of the IgGglycome in three isolated human populations Mol Cell Proteomics201110M111010090 doi101074mcpM111010090

13 Vanhooren V Dewaele S Libert C et al Serum N-glycan profile shiftduring human ageing Exp Gerontol 201045738ndash743 doi101016jexger201008009

14 Gornik O Pavić T Lauc G Alternative glycosylation modulates func-tion of IgG and other proteinsmdashimplications on evolution and dis-ease Biochim Biophys Acta 201218201318ndash1326 doi101016jbbagen201112004

15 Karsten CM Pandey MK Figge J et al Anti-inflammatory activity ofIgG1 mediated by Fc galactosylation and association of FcγRIIB anddectin-1 Nat Med 2012181401ndash1406 doi101038nm2862

16 Masuda K Kubota T Kaneko E et al Enhanced binding affinity forFcgammaRIIIa of fucose-negative antibody is sufficient to induce maximalantibody-dependent cellular cytotoxicity Mol Immunol 2007443122ndash3131 doi101016jmolimm200702005

17 Lauc G Huffman JE Pučić M et al Loci associated with N-glycosylation

of human immunoglobulin G show pleiotropy with autoimmune dis-eases and haematological cancers PLoS Genet 20139e1003225doi101371journalpgen1003225

18 Gornik O Lauc G Glycosylation of serum proteins in inflamma-tory diseases Dis Markers 200825267ndash278 doihttpdxdoiorg1011552008493289

19 Lauc G Huffman J Pučić M et al Loci associated with N-glycosylationof human immunoglobulin G show pleiotropy with autoimmune dis-eases and haematological cancers PLoS Genet 20139e1003225doi101371journalpgen

20 Akaike H A new look at the statistical model identification IEEE Trans Automat Contr 197419716ndash723 doi101109TAC19741100705

21 Anthony RM Nimmerjahn F The role of differential IgG gly-cosylation in the interaction of antibodies with FcgammaRs invivo Curr Opin Organ Transplant 2011167ndash14 doi101097

MOT0b013e328342538f 22 Anthony RM Wermeling F Ravetch JV Novel roles for the IgG

Fc glycan Ann N Y Acad Sci 20121253170ndash180 doi101007 s10875-010-9405-6

23 Johnson JL Jones MB Ryan SO Cobb BA The regulatory powerof glycans and their binding partners in immunity Trends Immunol201334290ndash298 doi101016jit201301006

24 Nimmerjahn F Ravetch JV Anti-inflammatory actions of intravenousimmunoglobulin Annu Rev Immunol 200826513ndash533 doi101146 annurevimmunol26021607090232

25 Albert H Collin M Dudziak D Ravetch JV Nimmerjahn F In vivoenzymatic modulation of IgG glycosylation inhibits autoimmune dis-ease in an IgG subclass-dependent manner Proc Natl Acad Sci U S A200810515005ndash15009 doi101073pnas0808248105

26 Lux A Aschermann S Biburger M Nimmerjahn F The pro and

anti-inflammatory activities of immunoglobulin G Ann Rheum Dis201069(suppl 1)i92ndashi96 doi101136ard2009117101

27 Dallrsquoolio F Vanhooren V Chen CC Slagboom PE Wuhrer MFranceschi C N-glycomic biomarkers of biological aging and lon-gevity a link with inflammaging Ageing Res Rev 201312685ndash698doi101016jarr201202002

28 De Martinis M Franceschi C Monti D Ginaldi L Inflamm-ageingand lifelong antigenic load as major determinants of ageing rateand longevity FEBS Lett 20055792035ndash2039 doi101016jfebslet200502055

29 Franceschi C Bonafegrave M Valensin S et al Inflamm-aging An evo-lutionary perspective on immunosenescence Ann N Y Acad Sci2000908244ndash254 doi101111j1749-66322000tb06651x

30 Vanhooren V Desmyter L Liu XE et al N-glycomic changes inserum proteins during human aging Rejuvenation Res 200710521ndash531a doi101089rej20070556

31 Knezević A Polasek O Gornik O et al Variability heritability andenvironmental determinants of human plasma N-glycome J Proteome Res 20098694ndash701 doi101021pr800737u

32 Gornik O Wagner J Pucić M Knezević A Redzic I Lauc G Stability

of N-glycan profiles in human plasma Glycobiology 2009191547ndash1553 doi101093glycobcwp134 33 Zoldoš V Novokmet M Bečeheli I Lauc G Genomics and epigenom-

ics of the human glycome Glycoconj J 20133041ndash50 doi101007 s10719-012-9397-y

34 Yamada E Tsukamoto Y Sasaki R Yagyu K Takahashi N Structuralchanges of immunoglobulin G oligosaccharides with age in healthyhuman serum Glycoconj J 199714401ndash405 doi101023A1018582930906

35 Pucic M Muzinic A Novokmet M et al Changes in plasma andIgG N-glycome during childhood and adolescence Glycobiology201222975ndash982 doi101093glycobcws062

36 OrsquoNeill LA Hardie DG Metabolism of inflammation limitedby AMPK and pseudo-starvation Nature 2013493346ndash355doi101038nature11862

37 Keusch J Lydyard PM Delves PJ The effect on IgG glycosyla-tion of altering beta1 4-galactosyltransferase-1 activity in B cellsGlycobiology 199881215ndash1220 doihttpdxdoiorg101093 glycob8121215

38 Hannum G Guinney J Zhao L et al Genome-wide methylationprofiles reveal quantitative views of human aging rates Mol Cell201349359ndash367 doi101016jmolcel201210016

39 Mitnitski A Rockwood K Biological age revisited J Gerontol A BiolSci Med Sci in press doi101093geronaglt137

40 Sanders JL Minster RL Barmada MM et al Heritability of andmortality prediction with a longevity phenotype The Healthy AgingIndex J Gerontol A Biol Sci Med Sci in press doi101093gerona glt117

41 Nakamura E Miyao K A method for identifying biomarkers of agingand constructing an index of biological age in humans J Gerontol

A Biol Sci Med Sci 2007621096ndash1105 42 Muumlezzinler A Zaineddin AK Brenner H A systematic review

of leukocyte telomere length and age in adults Ageing Res Rev201312509ndash519 doihttpdxdoiorg101016jarr201301003

43 Rudd PM Elliott T Cresswell P Wilson IA Dwek RA Glycosylationand the immune system Science 20012912370ndash2376 doi 101126 science29155122370

44 Rudan I Marusić A Janković S et al ldquo10001 Dalmatiansrdquo Croatialaunches its national biobank Croat Med J 2009504ndash6 doi103325 cmj2009504

45 Moayyeri A Hammond CJ Valdes AM Spector TD Cohort profileTwinsUK and healthy ageing twin study Int J Epidemiol 20134276ndash85 doi101093ijedyr207

46 Royle L Campbell MP Radcliffe CM et al HPLC-based analy-sis of serum N-glycans on a 96-well plate platform with dedicated

database software Anal Biochem 20083761ndash12 doi101016jab200712012

47 Lauc G Essafi A Huffman JE et al Genomics meets glycomicsThe first GWAS study of human N-glycome identifies HNF1alphaas a master regulator of plasma protein fucosylation PLoS Genet 20106e1001256 doi101371journalpgen1001256

48 R Core Team (2013) R A language and environment for statisticalcomputing R Foundation for Statistical Computing Vienna AustriaISBN 3-900051-07-0 httpwwwR-projectorg Accessed May2013

49 Crimmins E Vasunilashorn S Kim JK Alley D Biomarkers relatedto aging in human populations Adv Clin Chem 200846161ndash216doi101016S0065-2423(08)00405-8




of genes (4) Epigenetic regulation of gene expression isexpected to affect protein glycosylation and several publi-cations recently reported this effect (5ndash8) Changes in gly-cosylation with age have been shown over 20 years ago (9)and have also replicated in recent large population studies(10ndash13)

Immunoglobulin G (IgG) is an excellent model glyco-protein because its glycosylation has been well defined(Figure 1) and many important functional effects of alterna-tive IgG glycosylation have been described (14) For exampleglycosylation acts as a switch between pro- and anti-inflam-matory IgG functionality Most of the IgG molecules are notsialylated and are proinflammatory Terminal α26-sialylationof IgG glycans decreases the ability of IgG to bind to acti-vating FcγRs and promotes recognition by DC-SIGN whichincreases expression of inhibitory FcγRIIB and is anti-inflam-matory (15) Another fascinating example is the role of corefucose in the modulation of antibody-dependent cellular cyto-toxicity IgG-containing glycans that lack core fucose have100-fold increased affinity for FcγRIIIA and are thereforemuch more efficient in activating antibody-dependent cel-lular cytotoxicity than fucosylated glycoforms of the same

molecule (16) On average 95 of the IgG population iscore fucosylated (12) thus most of the immunoglobulinshave a ldquosafety switchrdquo which prevents them from activatingantibody-dependent cellular cytotoxicity Malfunction of thissystem appears to be associated with autoimmune diseasesas indicated by both pleiotropic effects of genes that asso-

ciate with IgG glycosylation on different inflammatory andautoimmune diseases and the observed alterations in IgGglycosylation in systemic lupus erythematous (17) and manyinflammatory diseases (18)

Interindividual variability of IgG glycosylation in a popu-lation is large (12) and it appears to be affected by both vari-ation in DNA sequence (19) and environmental factors (11)Most of the studies that investigated glycosylation changeswith age were either of limited size or were performed onthe total plasma glycome thus in addition to changes inglycosylation the observed differences reflected changesin the concentration of individual plasma proteins In thisstudy we focused on glycosylation of IgG and analyzedmore than 5000 individuals from four different Europeanpopulations to provide definitive data about changes in IgGglycosylation through the lifetime

Figure 1 UPLC analysis of immunoglobulin G (IgG) glycosylation Each IgG contains one conserved N-glycosylation site on Asn197

of its heavy chain Differentglycans can be attached to this site and the process seems to be highly regulated UPLC analysis can reveal composition of the glycome attached to a populationof IgG molecules by separating total IgG N-glycome into 24 chromatographic glycan peaks (GP1ndashGP24) mostly corresponding to individual glycan structures









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GP 6

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Male










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KOR ORCA TWINS VIS

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GP 4

GP 6

GP 1 4

GP 1 5

25 50 75 100 25 50 75 100 40 60 80 20 40 60 80

Age

P e r c e n t a


Sex

Female

Male










FEV

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times +

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Training Test


































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Training Test


































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J Gerontol a Biol Sci Med Sci-2014

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Transcript of J Gerontol a Biol Sci Med Sci-2014