Polyclonal Immunoglobulin G N-Glycosylation in the Pathogenesis of

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Polyclonal IgG N-glycosylation in the pathogenesis of plasma cell disorders Stefan Mittermayr, Giao Ngoc Le, Colin Clarke, Silvia Millán Martín, Anne-Marie Larkin, Peter O'Gorman, and Jonathan Bones J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00768 • Publication Date (Web): 18 Nov 2016 Downloaded from http://pubs.acs.org on November 21, 2016

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Journal of Proteome Research

Polyclonal IgG N-glycosylation in the pathogenesis of plasma cell disorders. Stefan Mittermayrǂ,§, Giao N. Lêǂ,¥,#,§, Colin Clarkeǂ, Silvia Millán Martínǂ, Anne-Marie Larkin#, Peter O’Gorman¥,*, and Jonathan Bonesǂ ,*

ǂ

NIBRT – The National Institute for Bioprocessing Research and Training, Foster Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland ¥

Department of Haematology, Mater Misericordiae University Hospital, Dublin, Ireland

#

National Institute for Cellular Biotechnology, Dublin City University, Dublin, Ireland

§

These authors contributed equally to this work

*Corresponding Authors: [email protected], tel: +353 1215 8105, fax: +353 1215 8116; [email protected], tel: +353 1850 0977

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ABSTRACT The pathological progression from benign monoclonal gammopathy of undetermined significance (MGUS) to smouldering myeloma (SMM) and finally to active myeloma (MM) is poorly understood. Abnormal IgG glycosylation in myeloma has been reported. Using a glycomic platform composed of hydrophilic interaction UPLC, exoglycosidase digestions, weak anion-exchange chromatography and mass spectrometry, polyclonal IgG N-glycosylation profiles from 35 patients [MGUS (n=8), SMM (n=5), MM (n=8), complete-response (CR) post treatment (n=5), relapse (n=4), healthy age-matched control (n=5)] was characterized to map glycan structures in distinct disease phases of multiple myeloma. N-glycan profiles from MGUS resembled normal control. The abundance of neutral glycans containing terminal galactose was highest in SMM, while agalactosylated glycans and fucosylated glycans were lowest in MM. Three afucosyl-biantennary-digalactosylated-sialylated species (A2G2S1, A2BG2S1, and A2BG2S2) decreased 2.38-, 2.4-, and 4.25-fold, respectively, from benign to active myeloma. Increased light chain sialylation was observed in a longitudinal case of transformation from MGUS to MM. Bisecting N-acetylglucosamine was lowest in the CR group, while highest in relapsed disease. Gene expression levels of FUT 8, ST6GAL1, B4GALT1, RECK, and BACH2 identified from publicly available GEP data supported the glycomic changes seen in MM compared to control. The observed differential glycosylation underlined the heterogeneity of the myeloma spectrum. This study demonstrates the feasibility of mapping glycan modifications on the IgG molecule and provides proof of principle that IgG glycosylation patterns can be successfully evaluated in multiple myeloma samples. KEYWORDS: multiple myeloma, plasma cell disorders, IgG N-glycosylation, polyclonal IgG, glycan analysis

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INTRODUCTION Multiple myeloma (MM) remains an incurable haematological malignancy with an average 5year survival rate of approximately 48.5%, and an annual incidence rate of 6.5 cases per 100,000.1 MM is characterized by the accumulation of malignant clonal plasma cells in the bone marrow resulting in end-organ damage such as renal impairment, hypercalcemia, anaemia and bone destruction. Along the spectrum of plasma cell disorders, progression to active MM from the pre-malignant state of monoclonal gammopathy of undetermined significance (MGUS) occurs at approximately 1% transformation rate per year,2,3 while the overall risk of progression from smouldering (asymptomatic) multiple myeloma (SMM) is 10% per year for the first 5 years, 3% per year for the next 5 years, and 1-2% per year for the next 10 years.3, 4 The biochemical feature of plasma cell disorders is the presence of a monoclonal immunoglobulin, also known as an M-protein, of which immunoglobulin G (IgG) is the most common. IgG comprises of two heavy chains and two light chains, linked together by disulfide bonds. Each heavy chain contains a covalently bound bi-antennary N-glycan at the highly conserved asparagine position 297 in the Fc region. This N-glycan is crucial for the structural stability and function of the immunoglobulin.5,6 Its deletion reduces the binding capacity to Fcγ receptors on effector cells such as macrophages.5 The antigen-binding fragment (Fab) may also be glycosylated with increased galactosylation and sialylation compared to the Fc.5 Differences in sialylation of IgG in MGUS compared to various stages of myeloma had been previously reported.7 Nonetheless, the role of glycosylation in the myeloma pathology remains largely, and surprisingly, underexplored, especially when considering that the produced monoclonal immunoglobulin is itself glycosylated.

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A recent study has shown characteristic differences in the glycome-signatures of normal and malignant plasma cells.8 Exploring the genomic background of IgG glycosylation, a metaanalysis of a genome-wide association study9 identified genetic loci containing genes for sialyl-, galactosyl-, fucosyl-, and N-acetylglucosaminyltransferase (ST6GAL1, B4GALT1, FUT8, MGAT3) that have significant association with specific IgG glycans. Furthermore, novel candidate genes that have not been previously implicated in glycan synthesis were also found to be associated with IgG glycosylation.9 Recently, high expression of the sialyltransferase ST3GAL6 was observed in both myeloma cell lines and patients, associating with inferior overall survival.10 With the recently published IMWG guideline11, the landscape of multiple myeloma management is emphasizing on identifying the small subgroups of ultra-high-risk smouldering myeloma patients who have >80% progression within 2 years. There is little understanding about the molecular biology that delineates MGUS from smouldering myeloma. Exploration of the immunoglobulin glycome can yield potential glycan markers to predict changes from the benign state to asymptomatic malignancy. In this study, a proof-of-principle characterization of IgG N-glycosylation profiles across the myeloma disease spectrum using hydrophilic interaction UPLC-fluorescence and UPLC-FLRtime of flight mass spectrometry (ToF-MS) was performed. The differences in the abundances of fucosylation, terminal galactosylation, bisecting N-acetylglucosamine and sialylation delineated disease pathologies ranging from the benign MGUS to the inactive SMM, to active myeloma, then remission and relapsed. Three specific biantennary digalactosylated sialylated glycan species showed differential reduction in abundances as the disease transformed from benign to malignant state, representing potential glycan-based markers for disease progression.

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EXPERIMENTAL PROCEDURES Chemicals and Reagents Reagent water was from a Satorius Arium Pro UV (Satorius Stedim Biotech, Gottingen, Germany). Acetonitrile was Sigma E Chromasolv (Sigma Aldrich, Dublin, Ireland) grade for HPLC-fluorescence and Fisher LC-MS Optima grade (Fisher Scientific, Dublin, Ireland) for MS analysis. All other chemicals used were purchased from Sigma Aldrich and were of the highest available quality. Serum Samples 35 serum samples from patients with newly-diagnosed MM (n=8), MGUS (n=8), SMM (n=5), relapse myeloma (n=4), complete response (CR, n=5), and healthy age-matched control (n=5) were included in this study (Table 1), approved by the Ethics Committee of the Mater Misericordiae University Hospital, Dublin, Ireland. Ethics approvals were in accordance with the Declaration of Helsinki. Disease status was defined according to the IMWG criteria.11 Newlydiagnosed MM patients were staged according to the International Staging System (ISS).12 Patients chosen for the control group did not have monoclonal immunoglobulins detected by serum protein electrophoresis (SPE), malignancies, active infection, or inflammatory disorders. Blood samples were collected and analysed as per standard hospital phlebotomy protocol. Blood samples (5 mL) were drawn into Sarstedt (Cruinn, Dublin, Ireland) serum-gel monovettes, allowed to clot upright for 30 minutes, separated by centrifugation at 1000 x g for 10 minutes at room temperature, then transferred to cryovials and stored at -80ºC.

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IgG Purification Polyclonal IgG was extracted from 50 µL of serum from each patient diluted 1:1 with PBS using Protein G affinity chromatography on a HiTrap Protein G HP 1mL column (GE Healthcare, Uppsala, Sweden). Purified IgG was eluted with 0.5 M acetic acid and buffer exchanged into 50 mM ammonium bicarbonate buffer, pH 7.8 (ABC) using 10 kDa MWCO spin-filters (Millipore, Bedford, MA, USA). Protein concentrations were measured using the Bio-Rad Bradford assay (Fannin, Dublin, Ireland). IgG purity was evaluated by CE-SDS on a Beckman Coulter PA 800 plus using the IgG Purity/Heterogeneity assays and the conditions provided by the vendor (AB Sciex, Brea, CA, USA). N-Glycan Release, Labelling, Clean-up, and Enzymatic Processing Proteins were reduced and alkylated in-solution by incubation with 1 mM DTT at 65 oC for 15 minutes followed by 5.5 mM IAA at RT for 30 minutes, respectively. N-glycans were enzymatically liberated from 50 µg of purified IgG via in-solution PNGaseF digestion using 0.5 IUB milliunits (New England Biolabs, Ipswich, MA, USA) at 37 oC overnight. Deglycosylated proteins were precipitated using ice-cold ethanol, centrifuged at 3,000 x g at 4oC and supernatant containing the released glycans was evaporated to dryness in a vacuum concentrator. Glycans were converted to reducing aldoses by reconstitution in 50 µL of 1% formic acid, dried down and subsequently derivatised with 10 µL 2-aminobenzamide (2-AB) via reductive amination with sodium cyanoborohydride in 30% v/v acetic acid in DMSO at 65oC for two hours. Excess labelling dye removal was carried out by frontal HILIC purification using a Thermo UltiMate3000 RS UHPLC system (ThermoFisher Scientific). Samples were loaded in 85% acetonitrile, 15% 50 mM ammonium formate pH 4.5 v/v onto a Glycan BEH Amide, 1.7

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µm, 2.1 x 50 mm column (Waters, Milford, MA, USA) at 0.5 mL/min for 2.5 minutes. Labelled glycans were eluted in 20% aqueous acetonitrile for 2.5 minutes, monitored by fluorescence detecttion, λex/em = 330/420 and evaporated to dryness. All exoglycosidase digestions were performed in 50 mM ammonium acetate buffer, pH 5.5 in a final volume of 10 µL at 37 oC overnight. Amounts of enzyme used per digestion were 1 µL of neuraminidase (P0720, New England Biolabs), 2 µL of β1-4 galactosidase (P0730), 1 µL of αFucosidase (GKX-5006, Prozyme, Hayward, CA, USA), 2 µL of β-N-Acetylhexosaminidase (GK80050, Prozyme). UPLC-fluorescence N-Glycan Profiling Labelled N-glycans were separated by hydrophilic interaction UPLC-FLR on a Waters Acquity™ I-Class instrument. 2-AB labelled glycans were separated using a linear gradient of 70-53 % acetonitrile at 0.561 mL/min in 16.5 minutes, (50 mM ammonium formate pH 4.5, buffer A), on a Glycan BEH Amide column, 1.7 µm, 2.1 x 100 mm at 40 oC.13 Collection of individual peaks was achieved by time-programmed collection using a Waters UPLC fraction manager. Samples were injected in 50 µL 80 % v/v acetonitrile and stored at 8 oC prior to injection. Glycan symbol nomenclature was adapted from Varki et al.14 IgG glycosylation localization using SDS-PAGE 20 µg of IgG from each patient were digested into Fc and F(ab‘)2 fragments using 1 unit/µg IdeS protease (Genovis, Lund, Sweden) at 37 oC for 1 hour. IgG IdeS digests and an additional aliquot of 20 µg reduced and alkylated IgG per patient were separated by SDS-PAGE using Invitrogen NuPAGE Novex 10% Bis-Tris Protein Gels (BioSciences, Dublin, Ireland) at constant 200 V. Bands corresponding to Fc and F(ab‘)2 as well as heavy chain and light chain fragments were

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visualised by Coomassie Brilliant Blue staining, excised and glycans liberated by in-gel PNGaseF digestions as previously reported.15 Anion exchange chromatography Fractionation of glycans based upon the degree of sialylation was performed by anion exchange chromatography (AEC) on a Waters BioSuite DEAE 10 µm AXC, 7.5 x 75 mm column A linear gradient of 100 mM acetate, pH 7.0 in 20% v/v acetonitrile at 0.75 mL/min was used for the elution of charged oligosaccharides. N-Glycan analysis using LC-MS Glycan samples were separated on a Waters Acquity™ UPLC system with online fluorescence detection using a glycan BEH Amide column, 1.7 µm, 1.0 x 150 mm at 0.15 mL/min and at 60 °C. A linear gradient of 72-57 % acetonitrile in 30 minutes was applied. 8 µL sample was injected in 75 % v/v acetonitrile. The UPLC system was hyphenated to a Waters Xevo G2 QToF mass spectrometer through an electrospray ionization interface. Negative ionisation mode with a capillary voltage of 1.80 kV was applied. Ion source and nitrogen desolvation gas (600 L/h flow rate) temperatures were set to 120 °C and 400 °C, respectively and the cone voltage was kept at 50 V. Full-scan MS data scan range was set to 450-2500 m/z. Data collection and processing was carried out using MassLynx 4.1. Sialic acid linkage specific LC-MS analysis AEC and subsequently HILIC fractionated glycans were incubated for 60 minutes at 80 °C after the addition of 80 µL of 100 mM solution of 4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4methylmorpholiniumchloride (DMT-MM) in methanol.16 Samples were evaporated to dryness and reconstituted in 75 % v/v acetonitrile in 0.1% v/v formic acid for LC-MS analysis.

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Frontal hydrophilic interaction UHPLC- MS/MS Glycans from pooled patient samples were sialidase-digested and subjected to time-based individual peak collection as outlined above. Fractions were injected into a Q Exactive Plus MS (Thermo Fisher Scientific) by frontal HILIC using an UltiMate3000 RS UHPLC system. Samples were loaded in 80% acetonitrile v/v onto an Accucore 150 Amide, 2.6 µm, 2.1 x 150 mm column at 400 µL/min of 15% 50 mM ammonium formate pH 4.5 for 2.5 minutes. Retained glycans were eluted in 45% acetonitrile for 1.8 minutes. ESI source temperature was 300 °C, sheath and auxiliary gas flow were 20 and 10 arb, respectively. Spray voltage was 3.8 kV, capillary temperature was 320 °C. Full MS scan (m/z 600-1500) was carried out using negative mode FTMS (35000 resolving power). In-source CID was set to 30 eV. MS/MS spectra were obtained using 3 microscans at 17500 resolving power with an isolation window of 2 m/z and a stepped normalized collision energy of 30, 42 and 70. Spectra annotation and structural identification was carried out by SimGlycan 4.52 (Premier Biosoft, Palo Alto, CA, USA), charge state 2, precursor and fragment ion m/z error tolerance 10 ppm, class glycoprotein, N-glycan. mRNA expression analysis To associate potential transcriptomic variations with glycomic changes across the plasma cell disorder spectrum, gene expression data from 5 previous studies was downloaded from NCBI’s GEO repository (http://www.ncbi.nlm.nih.gov/geo/). The raw microarray data from each study (Supporting Information Table S1)17,18,19,20,21, was normalised and summarised using the robust multi-array average (RMA) method.22 A further pre-processing step was employed to eliminate probesets with low expression on study-by-study basis. Each retained probeset had a signal intensity > log2 (100) in at least 1 sample. Following comparison of the transcriptomic profiles of selected disease groups, probesets exhibiting a fold change of ≥ 1.5 up or downregulated -9-



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between two groups with a Benjamini Hochberg adjusted p-value < 0.05, were considered to be differentially expressed. All microarray analyses were carried out in the R software environment utilizing Bioconductor packages specific for each microarray platform (e.g. affy,23 limma, 24 and oligo 25). Experimental Design and Statistical Rationale In brief, polyclonal IgG was extracted from patient serum samples and purity checked by CESDS. Aliquots of purified IgG from each patient were (1) partitioned to light and heavy chain by protein reduction, (2) digested using IdeS to yield Fc and F(ab’)2 fragments (respective fragment separation by SDS-PAGE in both cases) and (3) retained unprocessed. In all instances, N-glycans were enzymatically released, fluorescently labelled and profiled using hydrophilic interaction UPLC-FLR. Chromatographic features from technical triplicates were integrated throughout the 35 patient samples and relative abundance of individual glycan peaks used for statistical analysis using IBM SPSS Statistics software Version 20. Multivariate statistics with principal component analysis was used to map the pattern of separation among the patient groups. Subsequently, Kruskall-Wallis test was used to analyse both clinical characteristics and the relative abundances of glycan peaks among multiple patient groups to identify glycan species that significantly contribute to the glycomic differences in plasma cell dyscrasias. Glycan structural identification of chromatographic peaks was supported by a combination of mass spectrometry detection, charge based partitioning using anion-exchange chromatography, sequential exoglycosidase digests and sialic acid linkage specific derivatisation following individual HILIC peak fraction collection. Glycan features such as total sialylation or fucosylation were also compared among the patient groups. Mann-Whitney U test was employed to explore the glycomic differences between complete response (CR) and healthy control as well as changes in the transformation

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from benign (MGUS) to inactive malignant state (SMM). Association analyses between significant glycotraits contributory to the differences in disease states and clinical indices were performed using Spearman’s correlation model. P value 99% purity of IgG in the studied samples. The distribution of chromatic features (Figure 1) were markedly different between control sample (CTL5) and diseased samples (SMM7, MM1, MM4, REL4), as well as within the newly–diagnosed group (MM1 and MM4). In contrast, the glycan profile from an MGUS sample (MGUS4) most resembled that of normal control; while the two profiles of newly-diagnosed myeloma showed an increase in early eluting small (potentially associated with afucosyl) or large (potentially sialylated) structures in MM1 or MM4, respectively. For peak area based quantitation, a total of 28 chromatographic peaks were integrated throughout 105 chromatograms from 35 patient samples. Initially, principal component analysis (PCA) was used to investigate pathological grouping based on glycosylation patterns in the patient glycan profiles. The PCA plot (Supporting Information Figure S1) shows central clustering of control, MGUS and CR groups, whilst SMM, MM and relapse are well scattered with no distinct grouping. Next, univariate statistics were used to probe for chromatographic features that may contribute to the observed glycomic

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differences. Differentiation between control, MGUS, SMM and newly-diagnosed groups was observed based upon 7 statistically significant chromatographic peaks, presented in Table 2A. A statistically significant downward trend was noted for glycan peaks 18 (Figure 2A), 19 and 25, with the lowest abundances seen in the newly-diagnosed MM group. Glycomic differentiation in patients with active disease, remission post anti-myeloma treatment and disease reactivation was seen in Peaks 3 and 18 (Table 2B). The abundance level of peak 3 was reduced in newlydiagnosed MM, became elevated in patients with CR and decreased again at relapse (Figure 2B). Conversely, peak 18 continued to increase as the disease pathology changed from newlydiagnosed to relapse. Furthermore, reversion of IgG profiles from remission to the pre-cancerous state was investigated by comparing the CR group to control. 11 differentiating glycan peaks were identified (Figure 2C). Compared to control, the CR group had a 6.21×, 1.3×, and 5.7× increase in peak 1, peak 3, and peak 4, respectively. Exploration of potential glycomic contribution to the transformation from benign MGUS to inactive malignancy (SMM) also revealed 11 glycan peaks (Figure 2D). In particular, the MGUS state had a 1.4x decrease in the abundances of both peak 8 and peak 20 in comparison to SMM.

N-Glycan structure identification To ensure the presence of all of the aforementioned 28 chromatographic features, glycans from 40 μg IgG per patient were pooled for structure identification. Figure 3A shows the UPLC-FLR trace of the combined sample with the 28 chromatographic features, while the inset indicates neutral (N), mono-sialo (S1) and di-sialo (S2) fractions. Each fraction was collected and

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analysed individually by UPLC-FLR-ToF-MS in combination with sequential exoglycosidase digests. Mass losses upon stepwise addition of sialidase, fucosidase and galactosidase were monitored and are exemplified for fraction S2 in Figure 3B. Collated structural information is provided in Table 3 and all peak specific exoglycosidase induced mass shift data is available in Supporting Information Table S2. The distribution of identical m/z over multiple chromatographic peaks in S1 and S2 fractions, e.g. [M-2H]2- 1243.4 and 1344.9 were present in respective peak pairs 24/26 and 24/27 as shown in Figure 3B, indicated further structural complexity, potentially originating from sialic acid linkage isomerism. N, S1 and S2 fractions were each separated by UPLC-FLR and subjected to time-based individual peak collection. 16 neutral, 10 mono- and 5 di-sialo peaks were collected, as shown in Figure 4. All individual S1 and S2 peak fractions were derivatised with DMT-MM for sialic acid linkage identification. Precursor mass increased by 14 Da per α2-6 linked sialic acid as it became methylated, whereas α2-3 linked sialic acids caused a decrease of 18 Da due to dehydration and associated formation of a cyclic lactone. Figure 4 exemplary shows spectra for fractions S1_3 and S2_3 derivatised with DMT-MM, collated information for all fractions is presented in Supporting Information Table S3. For MS/MS based structural confirmation, pooled glycan sample was sialidase treated and 17 asialo fractions were collected using time-based individual peak collection (Supporting Information Figure S2). Selected precursor information is summarised in Table S4, fragment spectra are shown in Figures S3-21. Diagnostic fragment ions confirmed the tentative structural assignment of Peak 28 to be a bi-antennary tri-galactosylated N-glycan.

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Observed glycotraits from disease subgroups Based upon full structural characterisation, relative distributions of specific glycan features, i.e. sialic acid, fucose, terminal galactose and bisecting GlcNAc, with respect to pathological state were determined (Table 4). Compared to control, agalactosylation in neutral glycan species was lowest in newly-diagnosed MM (-1.97×), followed by smouldering myeloma (-1.68×), thus negatively correlated with M-protein (rho coefficient -0.438, p = 0.028) and serum free light chain ratio (rho coefficient -0.466, p = 0.019). In contrast, terminal galactosylation in neutral glycans was highest in the SMM group with a 1.4× increase from control. The proportion of structures with bisecting GlcNAc was lowest in the CR group, and highest in the relapse group with a 1.85× increase from CR to relapse. The total core fucosylation, also showing a negative correlation with M-protein (rho coefficient -0.51, p = 0.009), followed a downward trend initially from the highest proportion in the benign state (control, MGUS) to asymptomatic malignancy (SMM), to the lowest abundance in newly-diagnosed MM, then an upward projection to the relapse state.

Localisation of IgG Glycosylation Heavy (50 kDa) and light chain (25 kDa), as well as Fc (~25 kDa) and F(ab’)2 (~100 kDa) IgG protein fragments for each patient sample were separated by SDS-PAGE and respective bands excised. Selected gel lanes of heavy chain/light chain and Fc/F(ab’)2 separation are depicted in Supporting Information Figure S22A and B, respectively. Released glycan pools were analysed individually by UPLC-FLR. Chromatographic examples of localisation analysis from newlydiagnosed MM (MM5) and relapsed (REL1) patients are shown in Figure 5. MM5 exhibited almost identical distribution of chromatographic features within heavy chain (Hc) and Fc region,

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as well as light chain (Lc) and F(ab’)2 region pairs, thus indicating the absence of Hc glycosylation in the antigen binding fragment. Sialic acid contribution predominantly originated from the Lc for MM5, but from the Hc for patient sample REL1, in the respective Fab regions. Glycan species with high sialic acid content, i.e. the di-sialo (S2) fraction, were exclusively located in the antigen binding region within the control group. Control, MGUS and SMM patient samples lacked a neutral glycan portion on the IgG Lc, moreover, the SMM group showed no light chain glycosylation at all. The active disease state was divided into two groups in which light chain glycosylation was absent in patients MM3, 4 and 6, while present at high levels in patients MM1, 2 and 5. The CR group presented matching Hc/Fc and Lc/Fab pairs, thus lacking Hc glycosylation in the Fab region.

Analysis of differential gene expression in published studies Microarray data from five published studies were utilised to analyse the mRNA expression levels of 28 (Table 5) genes previously associated IgG glycosylation variation.9,26 Expression level of 3 glycosyltransferase genes (FUT8, B4GALT1, ST6GAL1), along with BACH2 which has been previously associated with autoimmune diseases, and a tumor suppressor gene RECK

9

were

found to be correlated with observed glycomic differentiation. Compared to control, myeloma plasma cells have lower FUT8 expression level, suggesting a potential decrease in fucosylated glycan species in active myeloma. Even though not statistically significant due to low patient number, our glycan analysis showed that the MM group had the lowest level of fucosylation (Table 4). ST6GAL1 expression was found to be upregulated in myeloma plasma cells from data set GSE39754, but down regulated in GSE47552, suggesting variation in sialic acid levels of IgG in myeloma. Similarly, we observed 2 distinct subgroups of high and low sialylation within the

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MM group, likely reflecting the heterogeneity of the disease. B4GALT1 and RECK mRNA expression was also found to be decreased in myeloma cells when compared to MGUS and control samples. The level of FA2G2S1 in our study was also lower in the MM group. Conversely, BACH2, reported to be associated with FA2[6]G1,9 was found to be upregulated in myeloma cells from data set GSE13591, as was the level of FA2[6]G1 from our analysis.

DISCUSSION In this study, we profiled the glycomes of polyclonal IgG in different disease subgroups across the spectrum of plasma cell disorders (MGUS, SMM, newly-diagnosed MM, remission, relapse) and compared them to healthy controls. Recent technological advances in both analytical separation and detection techniques have enabled the qualitative and quantitative capture of glycan’s structural finesse in complex biological samples. High- and ultra-performance liquid separation techniques now allow for differentiation between both positional- and linkageisomers,27 however, co-detection and associated difficulties in glycan structure specific quantitation remain challenging. The inherent structural complexity as well as the resemblance of glycans, where analogues are potentially associated with different functional roles, require case-specific elucidation. Thus, reliance on one-dimensional structure databases, literature or mass composition is applicable to large-scale comparative studies of healthy individuals,28,29 but is not pragmatic for monitoring aberrant glycosylation in the disease state with potential emergence of unique structures. Using de-novo sequencing, we confidently identified 54 glycan structures, including positional and linkage isomers, distributed across 28 chromatographic

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features occurring in the spectrum of the malignant plasma cell disorder. N-glycans derived from human IgG have been reported to predominantly carry α2,6-linked sialic acids.30 Recently, sialic acid linkage-specific derivatisation in combination with MALDI-ToF detection was employed to quantify N-glycans specific to Fc and Fab locations on IgG, confirming α2,3-linked sialic acid levels to be as low as 0.2%.31 In this study, we found that 22 out of 42 sialylated structures carried α2,3-linked sialic acids thus suggesting a more pronounced role in multiple myeloma. Site specific analysis of glycans derived from the fragment crystallizable region is crucial to derive implications related to antibody clearance,32 stability,33 effector function (e.g. ADCC),34 and receptor binding.35 Analysis on the glycan level using sensitive fluorescence detection provides isomer identification but often combines Fc and Fab fragments, whereas MS detection enables Fc glycopeptides fragment specific relative quantitation.28 However, glycopeptide ionization efficiency is affected by the respective structure and co-ionization of other peptides. Quantitative comparison across optical and mass detection techniques may therefore provide differential results.28,36 We employed individual analysis of N-glycans from antibody light/heavy chain and Fc/Fab fragments separated by SDS-PAGE. Issues arising from the generally much higher levels of glycosylation on the conserved N297 and potential cross-contamination when affinity-based separation is used, were bypassed. SDS-PAGE based fragment separation, however, was limited by protein loading capacity (20 µg) and associated downstream N-glycan relative quantitation. Genomically, each multiple myeloma case has complex subclonal variants driven by diverse paths of clonal evolution.37 Aberrant IgG glycosylation has been shown to occur in myeloma,38 with indications of each glycomic myeloma IgG profile being unique.39, 40, 41 Even though the residual polyclonal IgG glycan profiles from myeloma patients were shown to be different from

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healthy controls, instances of matching glycomic profiles of polyclonal (background) and monoclonal myeloma IgG have been reported, suggesting a far-reaching impact of the disease.39 We showed that the glycan profiles of total IgG in SMM, newly-diagnosed, and relapse myeloma do not resemble each other. The heterogeneity within and between the malignant groups was reflected in the scattering pattern observed in the PCA plot, suggesting the emergence of clonal dominance, even as early as smouldering myeloma. In MGUS, polyclonal immunoglobulins still dominate within the total IgG pool, as indicated by the low level of M-protein, thus displaying similar glycoforms to control. In the SMM, newly-diagnosed, and relapsed groups, the balance is shifted toward monoclonal immunoglobulin within the extracted total IgG, therefore the glycoforms reflect clonal heterogeneity across and within groups. A subgroup of patients (MM1, 2 and 5) within the newly-diagnosed myeloma exhibited a high proportion of sialylated glycans (44, 31 and 31%) which were found to be localized in the light chains, while Fc sialylation was similar to that of normal control. Aurer and colleagues38 on the other hand, reported stage dependent increase in sialylation of IgG heavy chain. However, similar to our study, they also noted higher variation in sialylation level among patients with active myeloma when compared to healthy control. Sialylated glycans located on the IgG light chain and an increase in its molecular weight in SDS-PAGE have been previously reported in multiple myeloma and associated with spontaneous precipitation.

42

We found heavier light

chains in patients MM1, MM2, MM5, REL3, MGUS4 and MGUS6 with all samples but MM2 being of the lambda subtype. The potential pathophysiological significance of light chain sialylation in myeloma is the effect on IgG circulating half-life. Binding of IgG to the human neonatal Fc receptor (FcRn) and associated prolonged half-life by preventing degradation

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through endocytosis 43 is reportedly unaffected by Fc glycans.44, 45However, sialylation at the Fab portion has been shown to increase serum half-life of monoclonal antibodies46,47 Core fucose at the Fc region of IgG is known to influence binding to Fc-gamma receptor (FcγR) resulting in a reduction of antibody-dependent cytotoxicity (ADCC).48, 49 Deletion of the core fucose at the Fc portion enhances the binding of therapeutic IgG to FcγRIIIA up to 50-fold and ADCC activity up to 100-fold.48,

49, 50

Competition for the FcγRIIIa binding sites between

endogenous IgG and therapeutic IgG can inhibit the ADCC activity of therapeutic IgG.51 However, this inhibitory effect of endogenous IgG can be evaded by nonfucosylated therapeutic IgG due to its high FcγRIIIa affinity.52 The observed decrease in total fucosylation of endogenous IgG in the newly-diagnosed myeloma group may have potential impact on FcγR binding and compete with therapeutic IgG, contributing to the biological and functional role of IgG glycosylation in myeloma. Thus, it is interesting for future research to investigate whether myeloma patients with high level of afucosylated IgG can lead to a decreased response to monoclonal antibody treatment. The extent of endogenous IgG core fucosylation may need to be considered in monoclonal antibody-based therapy in the treatment of myeloma to ensure therapeutic dose is achieved. Another finding in our study was the low total abundance of agalactosylated neutral glycans in the newly-diagnosed and SMM, suggesting a potential association with inflammatory changes. IgG glycans without terminal galactose are linked to complement activation and proinflammation, while galactosylation is thought to reduce the inflammatory potential.53,54 The statistically significant negative correlation between the total agalactosylation with the M-protein level and serum free light chain ratio suggested a decreased pro-inflammatory potential myeloma (both at the active and smouldering states). An investigation in cytokine levels of myeloma

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patients showed a significant reduction in pro-inflammatory cytokines (mainly interferon gamma and monokine induced by gamma-interferon), and an elevation in the expression of antiinflammatory cytokines, thus the anti-inflammatory phenotype protects the malignant plasma cells from immunosurveillance.55 Previously, IgG galactosylation, known to be associated with reducing inflammation, was reported to be decreased in myeloma,38,

56

inflammatory bowel

disease,57 and colorectal cancer.58 Our findings showed that the relapse myeloma group had the lowest abundance of total terminal galactose, while that of smouldering myeloma was highest. Thus, our observed variation in the abundances of IgG galactosylation in the myeloma spectrum implies a pathophysiological relevance in the myeloma inflammatory response, with proinflammatory potential being suppressed in SMM and active myeloma, while elevated in relapsed disease. Though our observation of total IgG agalactosylation and galactosylation are novel, further research exploring the glycomic changes in tandem with systemic cytokines variation in plasma cell disorder are required to solidify the link between inflammation and myeloma IgG glycosylation. We also demonstrated glycomic differentiation in disease progression from the benign to malignant state. A case example of disease transformation from an individual patient was shown in the glycan profiles of MGUS4 and MM1 (Figure 1), where prominent changes in the abundances of sialylated glycan species were predominantly observed in the patient’s light chain (Supporting Information Figure S22) as progression from benign to malignant occurred. The abundances of biantennary digalactosylated sialylated glycans (A2G2S1, A2BG2S1, A2BG2S2) were highest in the control group and lowest in the newly-diagnosed myeloma group. This decreasing trend negatively correlates with the M-protein rise in disease progression from MGUS to active myeloma, serving as potential markers for disease progression. Furthermore,

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pairwise comparison between MGUS and SMM showed glycomic alteration between the MGUS and smouldering states, specifically in the abundances of sialylated glycans with bisecting GlcNAc. The change in abundance of total bisecting GlcNAc and FA2 may have a potential role in disease reactivation from CR to relapse. Significant negative correlations between FA2 and M-protein, as well as serum free light chain ratio, implied that the abundance of FA2 increases after successful anti-myeloma therapy and decreases at relapse. Our findings also established that the polyclonal glycomes of patients in remission post treatment (CR group) differ from those of control patients, suggesting a likely permanent glycomic alteration in treated myeloma despite restoration of polyclonality. In comparison to the control group, the CR group had higher percentage of fucosylated agalactosylated neutral glycans (FA1 and FA2) and high mannose glycan (M5), while lower abundances of galactosylated and sialylated glycans containing bisecting GlcNAc. This differentiation suggests that patients in remission without detectable monoclonal protein should not be regarded as similar to those without plasma cell disorder. To support our glycomic findings, we found 28 genes involved in IgG glycosylation that were differentially expressed from publicly available GEP datasets. 5 of these genes (FUT8, ST6GAL1, B4GALT1, RECK, and BACH2) with identifiable association to specific chromatographic features of the IgG N-glycomes, were differentially expressed in comparison between MM plasma cells and control. In the presented analysis of the MM group, the variations in the glycans associated with these genes followed similar expression patterns observed in the published GEP data. It is acknowledged that definitive conclusions attributing specific glycosylation genes to different disease subgroups cannot be made due to limited sample size and lack of associated GEP. Future studies of whole exome sequencing of plasma cells in plasma

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cell disorder patient groups along with their IgG glycomic analysis will elucidate the linkage between genes and glycans in plasma cell disorders.

CONCLUSION In summary, in this proof-of-concept study we showed the applicability of glycomic platforms to confidently profile glycomes of polyclonal IgG across the spectrum of plasma cell disorders. The overall glycan profiles from MGUS patients most resembled that of normal control, while considerable glycomic heterogeneity was observed in malignant disease. Furthermore, marked variation in the level of sialic acids within the newly-diagnosed myeloma group was observed, where the glycans were localised to the light chains in the Fab region. Low level of agalactosylated neutral glycans in SMM and myeloma may have potential association with inflammatory changes. We showed that biantennary digalactosylated sialylated glycan species negatively correlated with increasing M-protein in disease progression from benign to malignant states. The low abundances of glycans bearing a bisecting GlcNAc residue and the FA2 glycan in remission compared to relapsed-disease suggested a potential avenue for disease monitoring. Glycomic differences between remission and control, both without detectable M-protein level, implied a permanent alteration in polyclonal IgG glycan profiles post myeloma-treatment. The ability to identify glycotraits that separate disease subgroups suggests that these glycans could act as markers of disease progression and early relapse detection.

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ASSOCIATED CONTENTS/SUPPORTING INFORMATION Figure S1: Principal Component Analysis showing clustering of Control, MGUS, CR groups, and scattering of SMM, MM, relapse groups. Figure S2: Fractionation of asialo pooled sample for MS/MS based structural confirmation Figures S3-23: Annotated MS/MS spectra of HILIC fractions Figure S24: SDS-PAGE separation of reduced (A) and IdeS digested (B) total IgG of selected patient samples. Figure S25: A case study of disease transformation from MGUS to MM where sialylated glycan species were predominantly observed in the patient’s light chain. Table S1: Summary of the publicly available microarray data. Table S2: Glycan structure confirmation from exoglycosidase induced mass shift Table S3: Fractionation of polyclonal IgG N-glycan pool using weak anion exchange, followed by derivatisation with DMT-MM and UPLC-FLR-ToF-MS analysis for sialic acid linkage identification. Table S4: Glycan structure identification of asialo fractions using MS/MS

ACKNOWLEDGMENTS The authors acknowledge funding from The Medici, 8 Berkeley Rd, Dublin 7, SM acknowledges EU Framework Programme 7 under Marie Curie Actions (FP7-PEOPLE-2012-ITN-316929), GNL acknowledges the funding support from the Irish Research Council Postgraduate Scholarship (GOIPG/2015/3379) and JB acknowledges the support of Science Foundation Ireland, grant reference 11/SIRG/B107. The authors kindly thank David Munoz, Gary Woffendin and Ken Cook for the technical assistance with MS/MS analyses, and Despina Bazou for editing the manuscript.

Conflict of interest The authors declare no conflict of interest.

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41. Mimura, Y.; Ashton, P. R.; Takahashi, N.; Harvey, D. J.; Jefferis, R., Contrasting glycosylation profiles between Fab and Fc of a human IgG protein studied by electrospray ionization mass spectrometry. Journal of Immunological Methods 2007, 326 (1–2), 116-126. 42. (a) Hashimoto, R.; Toda, T.; Tsutsumi, H.; Ohta, M.; Mori, M., AbnormalN-Glycosylation of the Immunoglobulin G κ Chain in a Multiple Myeloma Patient with Crystalglobulinemia: Case Report. International Journal of Hematology 2007, 85 (3), 203; (b) Toda T.; Nakamura, M.; Masaki, Y.; Nishine, T.; Torii, T.; Ikenaka, K.; Hashimoto, R.; Mori, M., Glycoproteomic analysis of abnormal Nglycosylation on the kappa chain of cryocrystalglobulin in a patient of multiple myeloma. J Electrophoresis 2009, 53, 1-6. 43. Roopenian, D. C.; Akilesh, S., FcRn: the neonatal Fc receptor comes of age. Nat Rev Immunol 2007, 7 (9), 715-725. 44. Hobbs, S. M.; Jackson, L. E.; J., H., Interaction of aglycosyl immunoglobulins with the IgG Fc transport receptor from neonatal rat gut: Comparison of deglycosylation by tunicamycim treatment and genetic engineering. Mol Immunol 1992, 29, 949-956. 45. Liu, L., Antibody Glycosylation and Its Impact on the Pharmacokinetics and Pharmacodynamics of Monoclonal Antibodies and Fc-Fusion Proteins. J Pharm Sci 2015, 104, 1866-1884. 46. van de Bovenkamp, F. S.; Hafkenscheid, L.; Rispens, T.; Rombouts, Y., The Emerging Importance of IgG Fab Glycosylation in Immunity. Journal of immunology 2016, 196 (4), 1435-41. 47. Bork, K.; Horstkorte, R.; Weidermann, W., Increasing the sialylation of therapeutic glycoproteins: the potential of the sialic acid biosynthetic pathyway. J Pharm Sci 2009, 98, 3499-3508. 48. Shields, R. L.; Lai, J.; Keck, R.; O'Connell, L. Y.; Hong, K.; Meng, Y. G.; Weikert, S. H. A.; Presta, L. G., Lack of Fucose on Human IgG1 N-Linked Oligosaccharide Improves Binding to Human FcγRIII and Antibody-dependent Cellular Toxicity. Journal of Biological Chemistry 2002, 277 (30), 26733-26740. 49. Masuda, K.; Kubota, T.; Kaneko, E.; Iida, S.; Wakitani, M.; Kobayashi-Natsume, Y.; Kubota, A.; Shitara, K.; Nakamura, K., Enhanced binding affinity for FcγRIIIa of fucose-negative antibody is sufficient to induce maximal antibody-dependent cellular cytotoxicity. Molecular Immunology 2007, 44 (12), 3122-3131. 50. Pučić, M.; Knežević, A.; Vidič, J.; Adamczyk, B.; Novokmet, M.; Polašek, O.; Gornik, O.; Šupraha-Goreta, S.; Wormald, M. R.; Redžić, I.; Campbell, H.; Wright, A.; Hastie, N. D.; Wilson, J. F.; Rudan, I.; Wuhrer, M.; Rudd, P. M.; Josić, D.; Lauc, G., High Throughput Isolation and Glycosylation Analysis of IgG–Variability and Heritability of the IgG Glycome in Three Isolated Human Populations. Molecular & Cellular Proteomics 2011, 10 (10), 1-15. 51. Preithner, S.; Elm, S.; Lippold, S.; Locher, M.; Wolf, A.; Silva, A. J. d.; Baeuerle, P. A.; Prang, N. S., High concentrations of therapeutic IgG1 antibodies are needed to compensate for inhibition of antibody-dependent cellular cytotoxicity by excess endogenous immunoglobulin G. Molecular Immunology 2006, 43 (8), 1183-1193. 52. Iida, S.; Misaka, H.; Inoue, M.; Shibata, M.; Nakano, R.; Yamane-Ohnuki, N.; Wakitani, M.; Yano, K.; Shitara, K.; Satoh, M., Nonfucosylated Therapeutic IgG1 Antibody Can Evade the Inhibitory Effect of Serum Immunoglobulin G on Antibody-Dependent Cellular Cytotoxicity through its High Binding to FcγRIIIa. Clinical Cancer Research 2006, 12 (9), 2879-2887. 53. Vuckovic, F.; Kristic, J.; Gudelj, I.; Teruel, M.; Keser, T.; Pezer, M.; Pucic-Bakovic, M.; Stambuk, J.; Trbojevic-Akmacic, I.; Barrios, C.; Pavic, T.; Menni, C.; Wang, Y.; Zhou, Y.; Cui, L.; Song, H.; Zeng, Q.; Guo, X.; Pons-Estel, B. A.; McKeigue, P.; Leslie Patrick, A.; Gornik, O.; Spector, T. D.; Harjacek, M.; Alarcon-Riquelme, M.; Molokhia, M.; Wang, W.; Lauc, G., Association of systemic lupus erythematosus with decreased immunosuppressive potential of the IgG glycome. Arthritis & rheumatology 2015, 67 (11), 2978-89. 54. Mihai, S.; Nimmerjahn, F., The role of Fc receptors and complement in autoimmunity. Autoimmunity Reviews 2013, 12 (6), 657-660.

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55. Zheng, M. M.; Zhang, Z.; Bemis, K.; Belch, A. R.; Pilarski, L. M.; Shively, J. E.; Kirshner, J., The Systemic Cytokine Environment Is Permanently Altered in Multiple Myeloma. PLoS ONE 2013, 8 (3), e58504. 56. Nishiura, T.; Fujii, S.; Kanayama, Y.; Nishikawa, A.; Tomiyama, Y.; Iida, M.; Karasuno, T.; Nakao, H.; Yonezawa, T.; Taniguchi, N.; Tarui, S., Carbohydrate Analysis of Immunoglobulin G Myeloma Proteins by Lectin and High Performance Liquid Chromatography: Role of Glycosyltransferases in the Structures. Cancer Res 1990, 50, 5345-5350. 57. Trbojevic Akmacic, I.; Ventham, N. T.; Theodoratou, E.; Vuckovic, F.; Kennedy, N. A.; Kristic, J.; Nimmo, E. R.; Kalla, R.; Drummond, H.; Stambuk, J.; Dunlop, M. G.; Novokmet, M.; Aulchenko, Y.; Gornik, O.; Campbell, H.; Pucic Bakovic, M.; Satsangi, J.; Lauc, G.; Consortium, I.-B., Inflammatory bowel disease associates with proinflammatory potential of the immunoglobulin G glycome. Inflammatory bowel diseases 2015, 21 (6), 1237-47. 58. Vuckovic, F.; Theodoratou, E.; Thaci, K.; Timofeeva, M.; Vojta, A.; Stambuk, J.; Pucic-Bakovic, M.; Rudd, P. M.; Derek, L.; Servis, D.; Wennerstrom, A.; Farrington, S. M.; Perola, M.; Aulchenko, Y.; Dunlop, M. G.; Campbell, H.; Lauc, G., IgG Glycome in Colorectal Cancer. Clinical Cancer Research 2016, 22 (12), 3078-3086.

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TABLES

Table 1: Clinical indices across the plasma cell disorder spectrum and control. Values are presented in median (IQR). β2M: beta-2-microglobulin, ISS: international staging system formultiple myeloma Control (n= 5) Gender (n) Age (years) Haemoglobin (g/dL) Creatinine (μmol/L) Albumin (g/L) M-protein (g/L)

Female (5) Male (0)

MM (n = 8) Female (4) Male (4)

SMM (n=5) Female (2) Male (3)

MGUS (n = 8)

Relapse (n = 4)

Female (5) Male (3)

Female (2) Male (2)

CR (n = 5)

P value

Female (3) Male (2)

69 (64-77)

72 (62-79)

11 (10.5-11.7)

9.8 (9.1-12.6)

71 (56-169)

95 (84-118)

76 (70-166)

75 (61-80)

95 (84-119)

80 (60-99)

NS

36 (35-39)

30 (29-31)

35 (32-39)

38 (34-41)

37 (32-39)

40 (39-41)

0.003

28.5 17 (16-44.5) (13.0-24.5)

4 (2.3-7.8)

17.5 (8.3-23.0)

none

0.001

none

62 (61-72)

73 (61-79)

72 (61-80)

65 (53-72)

NS

12.5 13.6 12.3 12.5 (11.2-13.9) (11.7-14.0) (11.0-13.6) (12.2-13.8)

0.038

SFLC ratio

1.4 (0.7-1.4)

46.9 (15.4-96)

20.6 (6.4-61.5)

0.9 (0.7-1.0)

16 (5.6-98.6)

0.8 (0.5-3.8)

0.002

β2M (g/L)

3.1 (2.0-4.3)

3.9 (3.3-4.6)

2.8 (1.7-3.9)

1.8 (1.5-2.5)

3.3 (2.0-4.0)

2 (1.7-2.5)

0.043

ISS (n)

ISS I (1) ISS II (6) ISS III (1)

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Page 30 of 43

Table 2A: Differential chromatographic features contributing to disease pathology from control to active MM Peak 3

Glycan FA2

Control MGUS 31.7 23 (23.70-33.94) (20.79-26.93)

SMM

MM 13.8 17.57 (8.41-24.60) (13.65-22.64)

P value 0.026

8

FA2[6]G1

15.44 (14.03-18.22)

16.47 23.08 19.74 (1.15-1.29) (19.87-26.23) (15.30-30.11)

0.046

18

A2G2S1

1.31 (1.15-1.59)

1.24 (1.15-1.29)

0.97 (0.56-1.21)

0.55 (0.20-0.84)

0.02

19

A2BG2S1

0.6 (0.54-0.64)

0.59 (0.54-0.74)

0.31 (0.20-0.38)

0.25 (0.10-0.46)

0.016

20

FA2G2S1

4.22 (3.63-7.67)

6.11 (4.87-6.87)

8.81 (6.45-14.95)

3.68 (2.57-6.80)

0.045

21

FA2BG2S1

2.56 (2.30-2.85)

3.06 (2.51-3.67)

1.59 (0.93-1.66)

1.78 (0.25-3.56)

0.049

25

A2BG2S2

0.34 (0.27-0.39)

0.31 (0.30-0.34)

0.14 (0.07-0.20)

0.08 (0.03-0.32)

0.031

Table 2B: Differential chromatographic features between active MM, remission, and reactivation of disease at relapse. Peak 3 18

Glycan FA2 A2G2S1

MM CR Relapse 17.57 41.45 22.70 (13.65-22.64) (34.65-42.40) (14.58-43.97) 0.55 (0.20-0.84)

1.06 (0.87-1.36)

-30-

1.33 (0.56-2.51)


P value 0.016 0.044

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Table 3: Glycomic structural identification of UPLC-FLR peaks from polyclonal IgG of patients with plasma cell disorders

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Page 32 of 43

Table 4: Relative abundances of glycotraits from disease subgroups in comparison to control. Expressed in median (IQR). Glycan Feature Total Sialylation

Total Fucosylation

Agalactosylation in neutral glycans (G0) Galactosylation in neutral glycans (G1 + G2) Total bisecting GlcNAc

Control (n = 5) 16.41 (15.3121.01) 72.56 (70.8574.06) 41.7 (29.5645.62) 37.88 (34.6645.2) 21.01 (16.5022.13)

MGUS (n = 8) 20.62 (17.3422.00) 70.3 (67.6671.73) 30.14 (28.1135.66) 43.88 (40.9147.05) 20.06 (18.2721.26)

SMM (n = 5) 18.49 (13.9224.35) 68.11 (61.3168.63) 24.87 (12.8630.96) 53.53 (47.0560.03) 16.03 (15.6116.67)

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MM CR (n = 8) (n = 5) 11.7 7.31 (6.54- (6.14-9.92) 30.91) 62.67 70.63 (52.81(70.8572.29) 74.06) 21.14 50.42 (18.43(44.6239.91) 54.94) 44.6 35.66 (37.19(30.2462.79) 40.54) 21.2 12.46 (14.96(11.8027.78) 14.93)


Relapse (n = 4) 26.48 (10.4040.18) 71.87 (67.2778.74) 31.56 (23.5549.50) 26.4 (23.2555.55) 23.02 (14.0742.11)

P value 0.076

0.053

0.009

0.023

0.036

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Statistically significant differential expression of 28 IgG glycosylation genes from publicly available datasets. Arrows indicate increased/decreased expression, followed by fold change in log2. Expression level of FUT8, B4GALT1, ST6GAL1, BACH2, and RECK were observed to be correlated with glycomic changes. (1) Dataset GSE211317, (2) Dataset GSE590018, (3) Dataset GSE1359119, (4) DatasetGSE3975420, (5) Dataset GSE4755221 MGUS vs CTRL

MGUS vs SMM

MGUS vs MM

SMM vs CTRL

Gene Symbol

Gene Name

LBR

lamin B receptor

MX1

MX dynamin-likeGTPase1

MDC1

mediator of DNA-damage checkpoint 1

ABCF2

ATP binding cassette subfamily F member 2

SEPHS1

selenophosphate synthetase 1

SMYD2

SET and MYND domain containing 2

ITGB3

integrin beta 3

ARID5B

AT rich interactive domain 5B (MRF1-like)

↑ 0.674 (2)

↑ 1.368 (5)

↓ -0.956 (3)

RELL1

RELT-like 1

↑ 0.595 (2)

↑ 0.831 (2)

↓ -0.685 (4)

FUT8

fucosyltransferase 8 (alpha (1,6) fucosyltransferase)

↑ 0.641 (2)

↓ -1.300 (5)

RECK

reversion-inducing-cysteine-rich protein with kazal motifs

EPAS1

endothelial PAS domain protein 1

↓ -0.922 (3)

SYNGR1

synaptogyrin 1

↓ -1.224 (3)

BACH2

BTB and CNC homology 1, basic leucine zipper transcription factor 2

ST6GAL1

ST6 beta-galactosamide alpha-2,6-sialyltranferase 1

PRICKLE2

prickle homolog 2

CYFIP2

cytoplasmic FMR1 interacting protein 2

PNOC TLR4 /// LOC105376244

prepronociceptin toll-like receptor 4 /// uncharacterized LOC105376244

B4GALT1

UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 1

PIK3AP1

phosphoinositide-3-kinase adaptor protein 1

TRIM5 /// TRIM22

tripartite motif containing 5 /// tripartite motif containing 22

ABCB9

ATP binding cassette subfamily B member 9

RALGAPA2

Ral GTPase activating protein, alpha subunit 2 (catalytic)

DOCK2

dedicator of cytokinesis 2

EPB41L2

erythrocyte membrane protein band 4.1-like 2

↓ -0.892 (5)

NSMAF

neutral sphingomyelinase activation associated factor

↓ -1.263 (5)

↓ -1.026 (1) ↑ 0.632 (2)

MM vs CTRL ↑ 1.320 (3)

↓ -1.368 (1); ↓ -1.226 (5)

↑ 0.788 (2)

↓ -0.775 (1)

↑ 0.786 (2)

↓ -0.831 (1); ↓ -0.628 (3)

↑ 0.675 (2)

↓ -0.872 (1)

↑ 2.283 (4) ↑ 0.729 (3) ↑ 0.705 (3)

↓ -0.665 (1) ↑ 0.632 (2)

↑ 0.688 (3)

↓ -1.035 (2)

↓ -1.426 (4)

↑ 0.864 (3)

↑ 1.000 (3) ↑ 2.890 (4); ↓ -1.003 (5) ↑ 0.950 (4) ↑ 1.186 (5)

↓ -1.066 (5)

↓ -0.860 (4); ↓ -1.307 (5) ↑ 0.591 (4) ↓ -1.497 (4) ↓ -1.288 (4), ↓ -1.198 (5)

↑ 0.702 (5)

↓ -0.720 (4); ↓ -0.833 (5) ↑ 0.736 (4) ↓ -0.943 (4) ↓ -1.316 (4); ↓ -0.727 (5)

↑ 0.596 (5)

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

FIGURE CAPTIONS

Schematic 1: An overview of sample preparation and analytical approach

Figure 1: UPLC-fluorescence glycan profiles of representative samples from control and each disease state showing the distribution of chromatographic features.

Figure 2: (A) Box-plots showing decreasing trend in A2G2S1 (peak 18) with negative Mprotein correlation as disease progressed from benign to active MM, while (B) FA2 (peak 3) was elevated at remission and decreased at relapse. (C) Differential glycan peaks between CR (shaded) vs. control (non-shaded), and (D) MGUS (shaded) vs. SMM (non-shaded).

Figure 3: (A) Polyclonal IgG N-glycan profile of the combined sample of 35 patients showing the 28 chromatographic features. The inset indicates fractionation (N, S1, S2) of the IgG Nglycan pool using weak anion exchange chromatography. (B) Exemplary profiling of the S2 fraction using UPLC-FLR-ToF-MS in combination with sequential exoglycosidase digests.

Figure 4: (Top panel) Structural identification of individual peak of neutral (N) fraction using UPLC-FLR-ToF-MS. Example of N_2 peak corresponding to FA2 and A2B glycan species. (Middle panel) Elucidation of the sialic linkage of the mono-sialylated glycan species in S1_3 peak from DMT-MM derivatisation followed by UPLC-FLR-ToF-MS. Mass shift between α(2,6) and α(2,3)-linked sialic acid is evident. (Bottom panel) Elucidation of the sialic linkage of the di-sialylated species in S2_3 peak.

Figure 5: UPLC-fluorescence of localisation analysis from newly-diagnosed MM (MM5) and relapsed (REL1) patients showing sialylated species present predominantly in the F(ab’)2 region

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For TOC only

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Journal of Proteome Research Serum

Page 36 of 43

IgG

AU

Co nt a min at i ng P

ro t

2

N

eins

4

8

n tei Pro G

Co ntro l

6

min

CE-SDS_

+

Purity

Reduction

IgG Extraction

SDS-PAGE

Buffer Exchange IdeS

D

PNGase F Digestion

Fluorescent Labelling 2-AB

Pur ifi

cat

ion HIL IC P has e

Analysis 400

300

500 200

+

+

bar

+

+ + + +

Digest

600 100

AEC

UPLC

M S2

+

TOF

1 2 3 4 5 6 250 150 7 100 8 75 9 50 10 11 35 12 13 25 kDa 14 15 250 16 150 17 100 75 18 19 50 20 35 21 22 25 23 kDa 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

MS

? DMT-MM ACS Paragon Plus Environment

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

CTRL5

MGUS4

Disease Progression

SMM7

MM1

MM4

CR2

REL4

ACS Paragon Plus Environment min 3

4

5

6

7

8

9

10

11

12

JournalAof Proteome M-Protein Research

%

Page B 38 %of 43

[g/L]

2.0

1 2 3 4 1.5 5 6 7 1.0 8 9 10 11 0.5 12 13 14 15 Peak 18 0 16 CRTL 17 18 3 19 % 20 40 21 22 30 23 20 24 6 25 26 5 27 28 4 29 30 3 31 32 2 33 34 1 35 36 0 37 4 10 38 1

40

50 40 40

30

30 30

20

20 20

10

10 10

0 MGUS

SMM

0

Peak 3

MM

MM

C

CR vs. CTRL

%

8

CR

REL

20

D

MGUS vs. SMM

30 20 10 5

0.5

4

0.4

3

0.3

2

0.2

1

0.1

ACS Paragon Plus Environment 11

15

19

21

25

26

27

0

19

21

24

26

27

22

23

25

28

0.0

3 A Page 39 of 43

N


1428.0344

0

28 10

11

4

8

12

16

20

24 min

12 min Peak 27 1344.980 [M-2H]2Peak 26 1243.446 [M-2H]2Peak 24 1344.973 [M-2H]2-

Peak 24 1170.415 [M-2H]2Peak 28 1426.002 [M-2H]2-

Peak 24 1243.446 [M-2H]2-

S2 - Neu5Ac2 1053.891 [M-2H]2952.351 [M-2H]2-

1135.9198

1134.9038 1135.3945

S2

820

- dHex1

821

823 m/z

980.862 [M-2H]2-

1063.3475 1063.8961

1061.849 [M-2H]2- Hex2 717.262 [M-2H]2-

818.805 [M-2H]2-

901.8304

901.3130 822

4.02e4 6.40 min

879.311 [M-2H]2-

900.8389

899.8419 900.3403

%

820.3087 820.8199 819

1134.910 [M-2H]2-

1062.9128

1061.8566

%

982.3728 982.8422

819.2986

819.8153

818.8055

% 818

879.323 [M-2H]2-

1062.3846

981.3508

%

981.8585

980.8497

1055.3829 1055.9028 1056.3961

1136.4108 1136.9501

%

%

1054.8899

S1

HILIC-UPLC-FLR-ToF MS

1426.0099 1426.5062 1426.9716 1427.4912

%

1053.8776 1054.3771

1346.9846 1347.4972

%

1346.4797

1345.9901

1344.9884 1345.4929

HILIC-UPLC-FLR

1 8 2 3 14 4 5 20 26 6 7 16 21 8 10 9 5 23 10 2 22 24 15 1112 4 67 25 17 18 19 11 1 13 12 2 3 4 5 6 7 8 9 13 Peak 27 B 14 Peak 28 100 100 15 16 17 18 19 20 21 22 23 24 25 1.20e3 4.06e4 20.26 min 18.66 min 26 1423 1424 1425 1426 1427 1428 m/z 1343 1344 1345 1346 1347 1348 m/z 27 28 100 100 29 30 31 32 33 34 35 36 37 38 1.28e5 4.54e3 39 11.76 min 13.77 min 1052 1053 1054 1055 1056 1057 m/z 1132 1133 1134 1135 1136 1137 m/z 40 41 100 100 42 43 44 45 46 47 48 49 50 51 52 3.87e4 1.09e3 10.63 min 12.69 min 53 1060 1061 1062 1063 1064 1065 m/z 979 980 981 982 983 984 m/z 54 55 100 100 56 57 58 59 60

WAX-HPLC-FLR

27

9


1.59e3 8.41 min 898 899 900 901 902 903 m/z

899.841 [M-2H]2min 4

6

8

10

12

14

16

18

20

22

24

818.8011 819.3005

%

4

5

819

%

1110.8771 1111.4177

S1_7

FA2BG[6]1S1 m/zmono1118.4131 [M-2H]2-

1327.5067

1118

7

822

m/z

N

FA2BG[6]1S(6)1

1328.4568

8

S2_2


10

11

12

13

1327

14

15

1328

16

m/z

TOF MS ES1.36e3

S1

FA2BG2S2 m/zmono1344.9872 [M-2H]2-

1328.9432

FA2G2S(6,6)2

m/z

1126

FA2BG2S(3,3)2

S2_1

1258.9482 1259.4364

1258

1122

1327.9406

S2_4

1326.9832

S1_10

S1_8 S1_9

1114

%

6

1254

821

1125.9266 1126.4089

FA2BG[6]1S(3)1

S2_3

S2_5

1250

820

1125.3962

1109.4065 1109.8851 1110.4050

S1_6 S1_5

100

TOF MS ES5.80e3

1246

818

S1_4 S1_2

1110

%

FA2G2S2 m/zmono1243.4476 [M-2H]2-

1242

817

m/z

1126.9188 1127.4014

N_15

N_16

792.3018

791.7877

820.2997

791.2967

%

N_13 1025.8667 1025.3605 1257.4626

100

1241.4325 1241.9172 1242.9448 1242.4454 1243.4225

794

S1_3

1024 m/z

FA2G2S(6,3)2

793

FA2G[3]1S(6)1

S1_1

1020

792

TOF MS ES866 TOF MS ES1.81e5

1257.9578

1016

791

1258.4457

1012

N_14

N_10 N_11 N_12 1024.8872

TOF MS ES3.85e3

790

A2B m/zmono818.8100 [M-2H]2-

100

1024.3680

1023.8752 FA2G[3]1S1 m/zmono1016.8735 [M-2H]2-

789

Page 40 of 43

819.8235

790.7943

N_7 N_8 N_9

N_3 N_4 N_5 N_6

N_1

FA2G[3]1S(3)1

1009.3763 1008.8546 1009.8590

%

100


FA2 m/zmono790.2993 [M-2H]2-

1007.8636 1008.3721

1 2 3 4 5 6 7 8 9 10 11 12 100 13 14 15 16 17 18 19 20 21 22 23 24 25 26 1008 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 3 44

790.2922

100

N_2

TOF MS ES998

1329

17

1330

1331

18

m/z

19

S2 min

Page 41 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 573


REL1 Relapse

MM5 Newly Diagnosed

Heavy Chain

Light Chain

Fc Region

F(ab’)2 Region


5

6

7

8

9

10

11

12

3

4

5

6

7

8

9

10

11

min

UPLC-FLR Journal of Proteome Research Symbolic Representation

Peak Glycan Structure 1

1 22 3 43 5 64 7 85 9 106 11 127 13 148 15 169 17 18 10 19 20 11 21 22 12 23 24 13 25 26 27 28 14 29 30 15 31 32 16 33 34 17 35 36 18 37 38 19 39 40 20 41 42 21 43 44 22 45 46 23 47 48 24 49 50 51 52 25 53 54 26 55 56 27 57 58 28 59

RT (min)

ExperimentalPage m/z [M-2H]2- (ppm)

42 of 43

FA[6]1

3.11

688.7596 (5.4)

A2

3.19

717.2659 (6.3)

A2B, FA2

3.72

818.8194 (11.5), 790.2937 (7.1)

MAN5

4.01

676.2626 (27.8)

FA2B, A2[6]G1 FA1[6]G1

4.13

891.8310 (9.0), 798.2912 (7.0) 769.7867 (0.9)

A2[3]G1

4.32

798.3028 (7.5)

A2B[6]G1, A2B[3]G1

4.51

899.8141 (24.9), 899.8265 (11.1)

FA2[6]G1, FA1[3]G1

4.74

871.3164 (10.7), 769.781 (6.5)

FA2[3]G1

4.92

871.3224 (3.8)

FA2B[6]G1

5.04

972.8662 (0.8)

FA2B[3]G1

5.23

972.8598 (5.8)

A2G2

5.45

879.3170 (7.1)

A2BG2, FA1G1S(3)1/S(6)1

5.68

A2G1S(6)1, A2B[6]G1S1

980.8561 (6.9), 915.3280 (6.2) 943.8369 (8.1), 1045.3739 (9.9)

FA2G2

6.02

952.3405 (12.2)

FA2BG2, FA2G[6]1S(3)1/S(6)1 A2B[3]G1S1

6.17

1053.8822 (9.1), 1016.8712 (2.2) 1045.4004 (15.5)

1

6.54

1126.3992 (-), 1016.8712 (2.2) 1118.3988 (12.8)

2

6.79

1227.942 (-)

A2G2S(3)1/S(6)1 FA2B[3]G1S(3)1/S(6)1

7.03

1024.8488 (21.6) 1118.4194 (5.6)

A2BG2S(3)1/S(6)1

7.35

1126.3716 (34.6)

FA2G2S(3)1/S(6)1

7.62

1097.8865 (12.1)

7.89

1199.4293 (8.5), 1271.9469 (-)

UNK1, FA2G[3]1S(3)1/S(6)1 FA2BG[6]1S(3)1/S(6)1 UNK2

FA2BG2S(3)1/S(6)1, UNK1 S(6)1

1

UNK2 S(6)1

2

8.01

1373.4972 (-)

UNK2 S(6)1

2

8.16

1373.5123 (-)

8.49

1280.4705 (3.6) 1170.4154 (2.7)

FA2BG3S(6)1 A2G2S(3,6)2/S(6,6)2

1271.9689 (8.3), 1243.446 (1.3) 1344.9808 (4.8)

A2BG2S(3,3)2, FA2G2S(3,3)2/S(3,6)2 FA2BG2S(3,6)2 A2BG2S(3,6)2/S(6,6)2

8.90

1271.9689 (8.3), 1243.446 (1.3)

FA2G2S(3,6)2/S(6,6)2 FA2BG2S(3,3)2

9.16

1243.446 (1.3) 1344.9808 (4.8)

FA2BG2S(3,3)/S(3,6)2/S(6,6)2

9.30

1243.446 (1.3), 1344.9808 (4.8)

FA2BG3S(6,6)2

10.04

1426.0022 (8.1)


Page 43 of 43

for TOC only

Relapse

Active myeloma

M protein g/L

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Relapse

SMM MGUS Asymptomatic

Therapy

Remission Symptomatic


Refractory relapse

Polyclonal Immunoglobulin G N-Glycosylation in the Pathogenesis of

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