Human Plasma N-glycosylation as Analyzed by Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance-MS Associates with Markers of Inflammation and Metabolic Health*

Glycosylation is an abundant co- and post-translational protein modification of importance to protein processing and activity. Although not template-defined, glycosylation does reflect the biological state of an organism and is a high-potential biomarker for disease and patient stratification. However, to interpret a complex but informative sample like the total plasma N-glycome, it is important to establish its baseline association with plasma protein levels and systemic processes. Thus far, large-scale studies (n >200) of the total plasma N-glycome have been performed with methods of chromatographic and electrophoretic separation, which, although being informative, are limited in resolving the structural complexity of plasma N-glycans. MS has the opportunity to contribute additional information on, among others, antennarity, sialylation, and the identity of high-mannose type species. Here, we have used matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-MS to study the total plasma N-glycome of 2144 healthy middle-aged individuals from the Leiden Longevity Study, to allow association analysis with markers of metabolic health and inflammation. To achieve this, N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-aminobenzoic acid, and following purification analyzed by negative ion mode intermediate pressure MALDI-FTICR-MS. In doing so, we achieved the relative quantification of 61 glycan compositions, ranging from Hex4HexNAc2 to Hex7HexNAc6dHex1Neu5Ac4, as well as that of 39 glycosylation traits derived thereof. Next to confirming known associations of glycosylation with age and sex by MALDI-FTICR-MS, we report novel associations with C-reactive protein (CRP), interleukin 6 (IL-6), body mass index (BMI), leptin, adiponectin, HDL cholesterol, triglycerides (TG), insulin, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and smoking. Overall, the bisection, galactosylation, and sialylation of diantennary species, the sialylation of tetraantennary species, and the size of high-mannose species proved to be important plasma characteristics associated with inflammation and metabolic health.

Glycosylation is a ubiquitous co-and post-translational protein modification of functional relevance to the processing and activity of the conjugate. Examples include quality control during protein folding, regulation of circulatory half-life, and modulation of receptor interactions by either providing the recognition motif or by affecting protein conformation (1)(2)(3)(4)(5)(6)(7). Consequentially, glycosylation has been associated with a multitude of diseases and states thereof, among which the progression and metastasis of cancer and the remission of rheumatoid arthritis (8 -11). Because the process of glycosylation is not template-defined, glycosylation integrates a large series of cellular conditions such as glycosidase/glycosyltransferase abundance and activity, endoplasmic reticulum (ER)/Golgi localization and nucleotide sugar availability, and reflects the intricate biological state of an organism (1,2,12). To establish glycosylation as biomarker for (early detection of) disease and patient stratification, analysis of an easily obtainable biofluid such as plasma is of great interest (13,14). Observed effects in a total plasma N-glycome (TPNG) 1 , i.e. the released N-glycans from all plasma proteins, are highly informative but difficult to comprehend because of the complex contributions from relative protein glycoforms and overall glycoprotein abundances (15,16).
To interpret the TPNG in a context of human health and disease it is of importance to establish the behavior of Nglycans, and groups of N-glycans, in relation to plasma protein levels and systemic processes such as inflammation and metabolism. Previous studies of suitable size (n Ͼ 200) have performed this to various degrees, finding plasma N-glycans to be highly associated with e.g. age, sex, inflammation, body mass index (BMI), cholesterol and lipid levels (17)(18)(19)(20)(21)(22)(23)(24). However, these studies have either been performed on single proteins, immunoglobulin G (IgG) being a particularly wellstudied example, or predominantly by methods of liquid chromatographic and electrophoretic separation (e.g. (ultra)-highperformance liquid chromatography (U)HPLC and capillary gel electrophoresis with laser-induced fluorescence detection (CGE-LIF)). Although of high analytical value-the techniques can separate analytes that are the same in monosaccharide composition (e.g. Hex 4 HexNAc 4 dHex 1 ) but differ in glycan structure (e.g. ␣1,3-branch galactosylation versus ␣1,6branch galactosylation)-the complexity of the TPNG means that observed signals generally comprise a variety of distinct compositions (25)(26)(27)(28). Mass spectrometry provides orthogonal information from these methodologies, as it does not distinguish isomers but instead unambiguously evaluates glycans on a compositional level (29 -31). To date, a large mass spectrometric TPNG study remains to be performed for revealing associations with markers of inflammation and metabolic health (32)(33)(34).
Here, we have used high-resolution intermediate-pressure matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-mass spectrometry (MS) to profile the total plasma N-glycosylation of 2144 middle-aged individuals of the Leiden Longevity Study (LLS) (35). Although MALDI-MS is reported to lead to underestimation of sialylated glycan species because of in-source and metastable decay, a phenomenon particularly visible with reflectron-based MALDI-time-of-flight (TOF)-MS, the intermediate pressure of the here-presented method prevents the residue loss and allows for the repeatable analysis of species carrying up to four sialic acids (36 -41). The 61 plasma N-gly-can compositions we detected by the method, as well as 39 glycosylation traits mathematically derived thereof, showed to highly associate with not only age and sex, but also with clinical markers of inflammation, liver function, cholesterol, insulin, and lipid metabolism.

EXPERIMENTAL PROCEDURES
Participants-The LLS, described in detail previously (35,42), is a family-based study comprising 1671 offspring of 421 nonagenarians sibling pairs of Dutch descent, and the 744 partners of these offspring. A total of 2144 individuals with clinical blood parameters available were included in the current analysis. The study protocol was approved by the Leiden University Medical Center ethical committee and an informed consent was signed by all participants prior to participation in the study.
Carbon-SPE-Prior to analysis by MALDI-FTICR-MS, samples were additionally desalted using carbon SPE. To achieve this, 100 l of graphitic porous carbon (Grace, Deerfield, IL) was applied to each well of an OF1100 96-well polypropylene filter plate with a 10 m polyethylene frit (Orochem Technologies, Lombard, IL) using a 96well column loader (Millipore, Billerica, MA). The stationary phase was activated and conditioned with 2 ϫ 200 l ACN/water (80:20 v/v) and 3 ϫ 100 l 0.1% trifluoroacetic acid (TFA; Sigma-Aldrich) in water, respectively. Of the 2-AA labeled N-glycans, 100 l were loaded into the wells and washed using 3 ϫ 100 l 0.1% TFA in water. Slight vacuum was applied to facilitate the procedure. The 2-AA labeled N-glycans were eluted into a V-bottom microtiter plate (Nunc, Roskilde, Denmark) using 3 ϫ 30 l of freshly prepared ACN/water (80:20 v/v) containing 0.1% TFA by centrifugation at 500 rpm (154 mm rotational diameter).
The 9.4 T FTICR APEX-ultra mass spectrometer was equipped with a dual electrospray ionization (ESI)/MALDI ion source (Apollo II) incorporating a quadrupole mass filter and a smartbeam laser system. Before analysis, the instrument was externally calibrated by peptide calibration standard (Bruker Daltonics). All experiments used a laser spot size of ϳ150 m, laser fluence slightly above threshold, and a laser repetition rate of 200 Hz. To allow the semiquantitative analysis for the range of expected N-glycans, all samples were analyzed using two methods: one optimized for lower m/z ions (approximately m/z 1000 to 2500) and another optimized for higher m/z ions (approximately m/z 2200 to 4000). The quadrupole was operated in rf-only mode with the selection masses set to m/z 1650 and 2500 for lowmass and high-mass measurements respectively. A customized experiment sequence (pulse program) was used, in which the multiple ICR-fill parameter was reconfigured to approximate a "random-walk" functionality (38). Briefly, the ions produced from 50 laser shots were accumulated in a hexapole and then transferred through the rf-only quadrupole to the collision cell. The sample stage was then moved 200 m, and fresh sample interrogated with the next 50 laser shots. This cycle was performed nine times, effectively accumulating ions from 450 laser shots in the collision cell. The accumulated ions were then transferred to the ICR cell for a mass analysis scan. Each spectrum is the sum of eight such scans. All data were acquired using ApexControl 3.0.0 software (Bruker Daltonics) in expert mode, controlled by Hystar 3.8 software (Bruker Daltonics) for automatic measurement. In total, 20,736 spectra were recorded, these being for each biological sample a quadruplicate of low-as well as high-mass measurements.
Data processing-Following acquisition, representative low-and high-mass spectra were internally calibrated in DataAnalysis 4.2 (Bruker Daltonics) using a set of expected glycan masses (supplemental Table S1). Using the calibrated spectra, 37 glycan compositions were manually assigned within the low-mass measurements (H4N2 to H5N4S1), and 25 within the high-mass measurements (H5N4S1 to H7N6F1S4), using mass and parts-per-million (ppm) errors to validate the assignments (supplemental Table S1) (H ϭ hexose; n ϭ N-acetylhexosamine; F ϭ deoxyhexose (fucose); S ϭ N-acetylneuraminic acid). In addition, peak widths were assessed per composition to allow precise area integration (supplemental Table  S1). The resulting 61 compositions (H5N4S1 being present in both the low-and high-mass spectra) were in agreement with previously reported observations, as well as knowledge of the biological synthesis of N-glycans (2,27,45,46).
To achieve repeated extraction of the list of glycan compositions from the 20,736 MALDI-FTICR-MS measurements (10,368 low-mass and 10,368 high-mass), the spectra were converted to simple text based format (x,y) using msconvert from ProteoWizard 3.0.5622 (47). The raw mass spectrometric data has been made publicly available in the MassIVE repository (massive.ucsd.edu) titled "MALDI-FTICR-MS total plasma N-glycomics" with ID: MSV000080307. Spectrum calibration, analyte integration and spectrum curation was performed using MassyTools 0.1.5.0 (48). In short, spectra were calibrated by applying the least variance second degree polynomial fit through a set of calibration masses (supplemental Table S1). During this step, spectra were excluded from further analysis when not all calibrants were detected with intensities at least 3-fold higher than the maximum signal deviation within the local noise (MinMax; roughly corresponding to a root-mean-square (RMS) signal-to-noise ratio (S/N) of 9). This led to the exclusion of 470 low-mass and 372 high-mass spectra. Glycan compositions from the analyte list were subsequently integrated by summing and grouping the areas of 95% of the theoretical isotopic envelope belonging to that composition. Prior to summation, each isotope was integrated using the peak widths previously established, and an equal width local background (within a window of 50 Thomson) was subtracted from these. To further ensure data quality, spectra were removed if more than 5% of the total analyte area was below S/N 3 (MinMax), which led to the additional exclusion of 305 low-mass and 424 high-mass spectra. After additional curation of clinical samples with no available information on age and sex, we retained a total of 16,346 spectra (8194 low-mass and 8152 highmass), yielding glycosylation information on 2144 individuals.
To arrive at one set of glycan values per individual, the replicate spectra for the low-and high-mass spectra were averaged for that individual. In case of low-mass spectra, 1878 averages were constructed from 4 spectra, 170 from 3, 76 from 2 and 20 from 1 (supplemental Fig. S1). For the high-mass spectra, 1857 averages were constructed from 4 spectra, 177 from 3, 83 from 2 and 27 from 1. To reconstruct the overview of the total plasma N-glycome, the low-and high-mass averages were normalized on the value of their overlapping composition H5N4S1, and subsequently combined. The resulting combined pattern was normalized by dividing each glycan value by the sum of all glycan values (total area normalization). Hereof, we calculated derived glycosylation traits on basis of enzymatic steps and protein groupings (supplemental Table S2). Glycan and derived trait variation within the replicate low-mass and highmass measurements was assessed on the basis of mean, standard deviation (S.D.) and coefficient of variation (CV) (supplemental Fig. S2; supplemental Fig. S3). Mass spectrometric figures were exported from DataAnalysis 4.2 (Bruker Daltonics) and annotated with glycan depictions following the symbol nomenclature proposed by the Consortium for Functional Glycomics (CFG), created in GlycoWorkbench 2.1 build 146 (49,50).
Data Analysis-Throughout data analysis we employed R 3.1.2 in an environment of RStudio 0.98.1091 (RStudio Team, Boston, MA) (51). In case of significance testing, a study-wide significance threshold was maintained of ␣ ϭ 1.00 ⅐ 10 Ϫ5 , values below or equal being considered statistically significant. The value arises from being the lower bound of the order of magnitude of an ␣ ϭ 0.05 significance threshold Bonferroni corrected for the total number of regression tests throughout the study (number of tests ϭ 27 phenotypes ⅐ 100 glycan features ϩ 26 sex comparisons ϩ 25 age comparisons ϭ 2751; ␣ ϭ 0.05/2751 ϭ 1.82 ϫ 10 Ϫ5 Ϸ 1.00 ϫ10 Ϫ5 ).
To limit the experimental component within the sample variability, batch correction was performed on the glycan and derived trait variables using the R package ComBat, using sample plate as batch (52). To limit outlier influence, individual glycosylation values exceeding a 5 times S.D. value from the mean of that variable were excluded from statistical analysis. Insulin, hsCRP, IL-6, TG, adiponectin, leptin, ALT, AST, GGT, and DHEA-S levels were transformed to the natural logarithm because of nonnormal distribution of the data. In addition, to obtain interpretable estimates, the glycosylation variables were scaled before analysis (i.e. mean subtraction and division by S.D.).
Association of Variables with Age and Sex-Linear and logistic regression analyses were performed to establish the association between age and sex (female ϭ 0; male ϭ 1) as outcome variables, and nonglycan clinical variables (Table I), glycans (Table II) and derived glycosylation traits (Table III) as predictor variables. As no glycosylation differences were found between LLS offspring and partners (a grouping to test predisposition for longevity), these individuals were grouped for all analyses. Furthermore, because the LLS contains multiple offspring from the same family, within-family (between-siblings) dependence was taken into account by using a sandwich estimator for the standard errors (53).
Association of Glycosylation with Clinical Variables-To eliminate possible confounding effects, age, sex and the interaction thereof were included as covariates in further models. For these analyses, the remaining nonglycan variables were used as outcome (using linear and logistic regression for respectively continuous and dichotomous variables), whereas glycans and derived traits were used as predictor (model: nonglycan ϳ ␤ 1 ⅐age ϩ ␤ 2 ⅐sex ϩ ␤ 3 ⅐age*sex ϩ ␤ 4 ⅐glycan).
To visualize the association between nonglycans and (derived) glycan traits, the t-statistics (or Wald statistics in case of logistic regression, both ␤ 4 /S.E. 4 ) from the models were expressed in heatmap format. Sorting of the heatmap variables was performed using hierarchical clustering (Euclidean distance, complete linkage).

RESULTS
To investigate the association of plasma protein N-glycosylation with clinical markers of metabolic health and inflammation, we analyzed the total plasma N-glycomes of 2144 middle-aged individuals of the LLS. N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-AA, purified by HILIC-and carbon-SPE, and analyzed by intermediate pressure MALDI-FTICR-MS. The acidic tag 2-AA allowed the joint negative mode mass spectrometric detection and relative quantification of neutral and sialylated glycan species, whereas the intermediate pressure of the measurement limited the decay commonly observed for sialylated glycans with MALDI ( Fig. 1) (36 -41).
Measurement Variability-Following spectrum curation, we retained a total of 16,346 mass spectra originating from lowand high-mass measurements of MALDI spotting quadrupli- Whereas the glycan compositions could be established with high confidence, the displayed linkages are presumed based on literature knowledge (2,27,45,46). B, Area integration of the detectable N-glycan compositions, summing the isotopes within 95% of the isotopic envelope for each species. Each composition is represented as a fraction of the total spectrum area.
cates for each individual. In the measurement optimized for lower masses (m/z 1000 to 2500) 37 N-glycans could be detected, ranging from H4N2 to H5N4S1, with an average absolute ppm error of 2.24 (S.D. Ϯ 3.18). In the measurement optimized for higher masses (m/z 2200 to 4000) 25 additional N-glycans were detected, from the overlapping composition H5N4S1 to H7N6F1S4, the average absolute ppm error being 3.65 (S.D. Ϯ 3.98) (supplemental Table S1). Based on literature, the glycan compositions within the TPNG were presumed to have certain structural features (16,27,45,46). Examples of this are the antennarity, judged as the number of N-acetylhexosamines minus two unless bisected, and bisection, judged to be the case if the number of N-acetylhexosamines equaled five and the number of hexoses five or less. Although these structural assignments are expected to represent the majority of structures contributing to an MS signal, additional structural isomers are likely to be present in the signals. For example, a composition assigned as tetraantennary may instead contain diantennary structures with two N-acetyllactosamine repeats, and the bisected species could be triantennary with incomplete galactosylation.
Assessing repeatability, an example quadruplicate measurement from a single individual yielded an average CV of 6.52% (S.D. Ϯ 3.42%) for the 10 most abundant signals in the low-mass measurement (total area normalized for the mass range), and an average CV of 5.97% (S.D. Ϯ 2.53%) for the 10 most abundant signals in the high-mass measurement (supplemental Fig. S2A; supplemental Fig. S2B). Combining the measurements by the overlapping composition H5N4S1 yielded for the 20 must abundant species an average CV of 9.29% (S.D. Ϯ 5.88%) (supplemental Fig. S2C). Derived glycosylation traits, constructed to provide mathematical expressions of monosaccharide differences and groupings with structural similarity, showed a lower CV, a phenomenon previously observed for mass spectrometric plasma glycomics (54), i.e. on average 1.20% (S.D. Ϯ 0.84%) for the 20 most abundant members (supplemental Fig. S2D). For a listing of derived traits and their calculations see supplemental Table S2.
In total, the glycosylation analysis workflow allowed for 2144 individuals the relative quantification of 61 N-glycan compositions and 39 derived traits. The LLS provided an additional 27 clinical variables to facilitate association analysis. Next to age and sex, measures were included on liver function (GGT, ALT, AST, AST/ALT), glucose metabolism (glucose, insulin, glucose/insulin), lipid metabolism (cholesterol, LDL-C, HDL-C, cholesterol/HDL-C, TG, lipid lowering medication, leptin, adiponectin), inflammation (hsCRP, IL-6), blood pressure (hypertension, antihypertensive medication), adrenal function (DHEA-S), thyroid function (free T3), as well as information on BMI, smoking and CMV infection, and familial propensity for longevity (Table I).
Association Glycosylation With Inflammation and Metabolic Health-Regression analysis was used to establish the relationship between the glycosylation traits and clinical markers, adding age, sex and the interaction thereof as covariates to limit their confounding influence (supplemental Table S3). The t-statistics (or Wald-statistics; both ␤ 4 / S.E. 4 ) arising from the models were expressed in clustered heatmap format (Fig. 3; for a heatmap visualization of the results without age and sex adjustment see supplemental Fig. S4). Significantly associating with a selection of derived glycosylation traits were hsCRP (23 statistically significant associations out of a possible 39), GGT (14), BMI (13), leptin (13), smoking (10), TG (9), insulin (7), the ratio of total cholesterol and HDL (7), the ratio of glucose and insulin (5), adiponectin (5), HDL (4), IL-6 (3), ALT (2), the ratio of ASL and ALT (1) and lipid medication (1) (supplemental Table S4;  supplemental Table S5). Only individual N-glycan associations could be proven for AST, glucose, cholesterol, LDL and free T3, whereas no associations were found for hypertension, the usage of antihypertensive medication, DHEA-S and CMV infection.

FIG. 2. Scatterplots with local regression providing an overview of the three main derived glycosylation traits changing with age and differing by sex (males in blue
The liver marker GGT showed major associations with the total plasma N-glycome, whereas AST, ALT, and the ratio thereof were of only minor influence. GGT appeared similar to

FIG. 3. Heatmaps showing the t-(or Wald) statistics (␤/S.E.) of the associations between clinical markers of metabolic health and inflammation, and glycosylation.
A, Single total plasma N-glycans after total area normalization. B, Derived glycosylation traits. All models were adjusted for age, sex, the interaction thereof, and within-family dependence. Crosses (x) indicate a statistical significance of p Ͻ ϭ 1.0 ϫ 10 Ϫ5 thereby surpassing the study-wide significance threshold, whereas periods (.) indicate associations with a significance of p Ͻ ϭ 0.05.

DISCUSSION
Here we report the MALDI-FTICR-MS analysis of the total plasma N-glycomes of 2144 principally healthy individuals from the LLS, and the association thereof with clinical markers of inflammation and metabolic health. In doing so, we have confirmed expectations from literature by showing a decrease of galactosylation and increase in bisection of diantennary fucosylated glycans with increasing age (59 -61), and by men having a higher tri-and tetraantennary fucosylation and lower bisection than women (62,63). Adjusting for the age and sex effects as well as the literature-reported interaction of age and sex (18,21), we proved additional associations between clinical markers for metabolic health/inflammation and plasma N-glycosylation characteristics including antennarity, sialylation, bisection, and galactosylation of various diantennary subgroups, and the size of high-mannose species.
Methodology-MS analysis of plasma N-glycosylation is not without challenges, particularly when employing MALDI ionization. A downside of this technique is the in-source and metastable loss of sialic acid residues, which is problematic given that plasma N-glycans are often highly sialylated (a notable exception being those from the fragment crystallizable (Fc) region of IgG) (16,37). Although chemical derivatization prior to MALDI-MS has been found to solve these stabilization issues (e.g. by permethylation, methyl/ethyl esterification or amidation), this adds extra steps to the sample preparation workflow and often leads to byproducts (31, 64 -69). Instead, here we have used MALDI-FTICR-MS equipped with an intermediate pressure source to decrease sialic acid decay (36,38). The intermediate pressure source is known to promote ion integrity by cooling of the MALDI-generated ions (36, 39 -41). In our study this has facilitated the relative quantification of N-glycan species containing up to four N-acetylneuraminic acids.
Although similarly sized investigations have analyzed plasma N-glycosylation by separation techniques like (U) HPLC or CGE-LIF (18 -20, 23, 70), to our knowledge this is the largest study of its kind performed by MS (32)(33)(34). Glycan compositions as obtained by MS provide orthogonal information to chromatographic peaks. UHPLC, for example, can separate diantennary N-glycan isomers with ␣1,3versus ␣1,6-arm galactosylation and can likewise distinguish an antennary from a bisecting GlcNAc, whereas MS can provide more precise groups of di-, tri-, and tetraantennary compositions, number of sialic acids, and clear separation of highmannose type glycans (29,71). As such, the construction of derived glycosylation traits making use of these features, while still biased on a compositional level, is simple to perform and provides additional insight into the complexity of glycan changes.
One example of the added information of derived traits can be found in the association of glycosylation with hsCRP. On an individual glycan level, we can only observe a relative increase in tri-and tetraantennary compositions together with the inflammation marker, and a corresponding relative decrease in all other compositions (which may be because of the total area normalization). However, when we mathematically take several individual glycans with shared biological features out of the total plasma N-glycome and compare them relative to each other in the form of a derived trait, we now additionally reveal, for example, a decrease in galactosylation within the subset of glycan compositions predominantly occurring on IgG-Fc (nonsialylated fucosylated diantennary species; A2FS0), a finding expected from literature (16,72,73).
Additional aspects of the analytical methodology need to be considered to allow valid interpretation of the presented findings. First, N-glycans are released from their protein backbones, and thus information is lost whether an observed glycan change originates from protein glycosylation or from glycoprotein abundance. Second, mass spectrometry does not distinguish isomers, meaning that a given monosaccharide mass, e.g. a hexose, is assigned differently based on literature knowledge of its compositional context (e.g. as mannose for H5N2 and as galactose for H4N4F1) (16,27,45,46). Similarly, literature is the main source of information on the linkages between monosaccharides, for example presuming bisection for compositions having five N-acetylhexosamines but less than three galactoses (e.g. H5N5F1). Although this group indeed encompasses bisection, the information within will also be confounded by triantennary structures with incomplete galactosylation, even if these are not abundant in human plasma (16,27). Third, the methodology presented here will not provide biologically true relative ratios of glycan compositions, as the profiles will, for example, be skewed by the ionization advantage of sialylated species in negative ion mode MS. Nonetheless, the relative signal differences will still be representative for the biological directions of change, as well as providing an estimate of the magnitude.
Clinical Findings-With the help of literature, we can speculate on the biological background of the observed associations. Acute inflammation, represented in our study by hsCRP and IL-6, shows to confirm previous glycomics studies with regard to the increase in antennarity, fucosylation of triantennary species, and a decrease in galactosylation of IgG glycans which is particularly well-established (9,17,21,24,59,74). The first two may be explained by an inflammation-induced increase in plasma levels and glycosylation changes of acute phase proteins such as alpha-1-antitrypsin and alpha-1-acid glycoprotein (orosomucoid-1) (9,75). Not only are these carriers of N-glycans with two or more antennae at baseline conditions, they furthermore display increased antennarity and sialyl-Lewis X upon acute inflammation (76 -78). Additional observations include the increase of galactosylation and sialylation per galactose of diantennary fucosylated (A2F) species in general, which is notably different from the behavior of IgG. A contributor to this observation could be the level of IgM, an abundant immunoglobulin shown to increase with many autoimmune and inflammatory conditions and which carries the required highly galactosylated and sialylated A2F species (79,80).
Of particular interest is the highly significantly increased size of high-mannose glycans (MM) which is not only observed with increased hsCRP, but as well with increasing BMI, non-HDL cholesterol and TG. This glycosylation change appears to largely stem from the increase in the single glycan composition H9N2, an analyte difficult to assess by commonly used liquid chromatography with fluorescence detection (23,81). Within the total plasma N-glycome, this large high-mannose glycan may predominantly originate from apolipoprotein B, the main protein constituent of most non-HDL lipoproteins (e.g. VLDL, LDL) (16,82,83). The observed correlations with H9N2 and MM can represent differing apolipoprotein B levels or glycosylation thereof, and may be indicative of an unhealthy glycosylation profile with regard to lipid transport and metabolism. The association of MM with inflammatory marker hsCRP is likely a consequence of the connection between inflammation and obesity (84), as a model corrected for BMI no longer shows significant association between high mannose size and inflammation (data not shown). The effect size being less pronounced with hsCRP than with BMI is explainable by the aforementioned increase in IgM, which contains the smaller high-mannose compositions H5N2 and H6N2 at its Asn 279 site (80). On the other hand, the association between MM and BMI remains true in a model adjusted for CRP and leptin, indicating the glycosylation trait may have potential to discriminate healthy from unhealthy obese.
Although largely following an inflammatory glycosylation profile, BMI, leptin, non-HDL cholesterol and TG do show a unique negative association with the sialylation of nonfucosylated tetraantennary compositions (A4F0GS), a change mainly occurring because of the relative increase of the lowly sialylated H7N6S1 and H7N6S2. Many proteins may contribute to this decrease in sialylation, a notable one being alpha-1-acid glycoprotein (85)(86)(87), which is known to bind lipophilic compounds with affinity modulated by its degree of sialylation (88 -90). This effect also applies to binding of hydrophobic drugs, possibly explaining why we similarly observed trends of decreased A4F0GS with the usage of lipid-and antihypertensive medication (89,90). Additionally, increased levels of glycoproteins with exposed galactose residues can reflect a modulation of protein turnover, e.g. recycling by the liver-based asialoglycoprotein receptors (5,(91)(92)(93). Low sialylation would induce rapid turnover of lipid scavengers like alpha-1-acid glycoprotein, a situation which is beneficial when the blood needs to be cleared of high levels of lipophilic compounds.
Smoking proved the only phenotype to positively associate with the fucosylation of tri-and tetraantennary species (A3F and A4F), as well as with the bisection of IgG-Fc type glycans (A2FS0B) (20,94). Likely these reflect the chronic response to vascular injury obtained from smoking-induced shear stress and oxidative damage (95). Increased fucosylation of acute phase proteins, when antennary-linked in the form of sialyl-Lewis X or A, facilitates recruitment to sites of injury, e.g. by interactions with selectins presented on inflammation-activated endothelial cells (16,76,96).
Although it is invalid to interpret absence of statistical significance as an absence of association, we have nevertheless failed to identify derived traits or single glycans to be predictive of DHEA-S, CMV infection or the propensity for longevity. The latter is a peculiar absence, as plasma N-glycosylation analysis of the same cohort by HPLC had revealed two chromatographic peaks to be predictors of the phenotype (18). Reasons for this lack of biological reproduction may include the measurement error (HPLC tends to provide more robust measurements than mass spectrometry) (71), the chromatographic peaks representing a culmination of multiple mass spectrometric compositions that are not individually significant, the previous findings being incidental, or the inability of mass spectrometry to separate or detect the responsible analytes.
Glycosylation is of high interest for the development or improvement of biomarkers for disease and patient stratification (14,97). In particular, the findings within this study may be of benefit to the detection and discrimination of metabolic syndrome or inflammatory disorders, and may bolster the predictability of existing biomarkers such as the Framingham Risk Score (98). Important glycosylation phenotypes in this regard would then be the derived traits MM, A3F, A4F, A2F0G, A2F0B, A2FS0G, A2FGS, and A4F0GS, as well as the various individual glycans comprising these groups, e.g. most tri-and tetraantennary compositions, high-mannose compositions H5N2, H6N2, and H9N2, and the truncated N-glycans suggested by compositions H3N3, H4N4, and H4N4S1. Interestingly, although this last example, H4N4S1, is a glycan composition difficult to characterize in a derived trait, it does show to be the major single glycan to positively associate with most beneficial markers of metabolic health (e.g. glucose/ insulin, HDL-C, adiponectin) and negatively with most detrimental ones (e.g. hsCRP, BMI, GGT, hypertension, TG). The protein source of the N-glycan is as of yet unclear, but is of interest to study in more detail.
To summarize, we have reported a large number of associations between total plasma N-glycosylation as measured by MALDI-FTICR-MS and clinical markers of metabolic health and inflammation. By this, we have identified glycan compositions and derived traits indicative of overall metabolic health and inflammation, as well as finding glycosylation traits uniquely associating with single marker variables. With this knowledge, we hope to contribute to the interpretation of the plasma N-glycome as biomarker for health and disease, and to assist clinical translation of mass spectrometric glycosylation analysis. * This work was supported by the European Union Seventh Framework Programme projects HighGlycan (278535), MIMOmics (305280), and IDEAL (259679). In addition, financial support was provided by the Innovation-Oriented Research Program on Genomics (Senter-Novem IGE05007), the Centre for Medical Systems Biology and the Netherlands Consortium for Healthy Aging (grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, the Netherlands Organization for Scientific Research (NWO) and by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007).