Glycosylation of Human Plasma Clusterin Yields a Novel Candidate

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Glycosylation of Human Plasma Clusterin Yields a Novel Candidate Biomarker of Alzheimer’s Disease Hui-Chung Liang, Claire Russell, Vikram Mitra, Raymond Chung, Abdul Hye , Chantal Bazenet, Simon Lovestone, Ian Pike, and Malcolm Ward J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00892 • Publication Date (Web): 21 Oct 2015 Downloaded from http://pubs.acs.org on October 27, 2015

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Journal of Proteome Research

Glycosylation of Human Plasma Clusterin Yields a Novel Candidate Biomarker of Alzheimer’s Disease Hui-Chung Liang,†,* Claire Russell,† Vikram Mitra,† Raymund Chung,‡ Abdul Hye,‡ Chantal Bazenet,‡ Simon Lovestone,§ Ian Pike,† and Malcolm Ward†,* †

Proteome Sciences plc, Coveham House, Downside Bridge Road, Cobham KT11 3EP, UK

‡

Department of Old Age Psychiatry, Institute of Psychiatry, King's College London, De

Crespigny Park, London SE5 8AF, UK §

Department of Psychiatry, Medical Sciences Division, University of Oxford, Warneford

Hospital, Oxford, OX3 7JX, UK

1 ACS Paragon Plus Environment


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ABSTRACT Specific glycosylated peptides of clusterin are found associated with hippocampal atrophy. The glycosylation of clusterin from human plasma was comprehensively analyzed and characterized using mass spectrometry (MS)-based glycoproteomics analysis. All six known N-glycosylation sites are covered, three in the alpha subunit (α64N, α81N and α123N) and three in the beta subunit (β64N, β127N, and β147N). More detailed structural characterization of clusterin glycopeptides was also performed, demonstrating the presence of glycosylated peptides and their corresponding glycans. Using liquid chromatography–tandem mass spectrometry (LC-MS/MS), we have determined the differences in the glycoforms associated at each of the different glycosylation sites in plasma clusterin obtained from subjects of low hippocampal atrophy (n=13) and high hippocampal atrophy (n=14). In our pilot study, the β64N site shows the most significant regulations between clinical groups. Eight β64N glycoforms are significantly reduced in patients with high atrophy compared to those with low atrophy, which demonstrates the utility of clusterin isoforms as diagnostic and prognostic Alzheimer’s disease (AD) markers. These results provide a novel and robust workflow suitable for rapid verification of specific clusterin glycoforms with utility as AD biomarkers.

Keywords: Clusterin, Alzheimer’s disease, hippocampal atrophy, glycosylation, selected monitoring reaction, mass spectrometry, and biomarker.


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INTRODUCTION Biomarker discovery in AD and other neurogenerative disorders has been considerably challenging and no ideal biomarker has yet been found to meet clinical need. Clusterin, also named apolipoprotein J (apoJ), has been linked to the pathogenesis of Alzheimer’s disease (AD) for more than 20 years in genome-wide association studies.1-4 Recently, clusterin was shown to be associated specifically with the Aβ40 form of amyloid-β (Aβ) rather than Aβ42 in the AD brain, exhibiting an important pathogenic feature of Aβ aggregation in the AD brain and potentially profound implications for AD pathogenesis.5 In addition, clusterin has been associated with clinical progression of AD and described as a potential AD biomarker.611

Most recently, Hye et al. identified 10 plasma proteins including clusterin, which are

highly associated with disease severity and disease progression,12 with an accuracy of 87%. Using magnetic resonance imaging (MRI) and a multiplexed bead assay on samples from over one thousand subjects, clusterin was found associated with greater atrophy, and demonstrated strong correlation with all brain regions assessed.

Using two-dimensional gel electrophoresis (2DE), we previously identified that plasma clusterin isoforms were found to be associated with hippocampal atrophy and rapid clinical progression in AD.6 The use of unmodified peptides in selected reaction monitoring (SRM) assays fail to fully replicate the regulation seen in 2DE, presumably because they are blind to the specifically regulated post-translational event. Clusterin is a highly glycosylated protein, consisting of α- and β-subunits both of which are 35-40 kDa and linked together by five disulfide bonds.13 There are six N-linked glycosylation sites and the carbohydrate content at these sites represents 20-25% of the total mass of the mature clusterin.14 Glycosylation is one of the most common post-translational modifications and it is well known that glycoproteins



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have many functions in essential cellular processes.15 Additionally, research has shown that altered glycosylation may play a crucial role in disease mechanism and may present novel prognostic and/or diagnostic biomarkers.16 An ideal prognostic and/or diagnostic biomarker should be able to monitor disease progression and treatment effects, hence contribute to a more accurate and earlier diagnosis of AD. Altered glycosylation profile may provide important information of disease progression. A recent review summarized the roles of protein glycosylation in AD and the identified alternations in glycosylation on AD-related proteins, including amyloid precursor protein (APP), tau, β-site amyloid precursor proteincleaving enzyme 1 (BACE-1), acetylcholinesterase (AChE) and Nicastrin.17 For example, Nglycosylation is required for axonal transport and secretion of APP and sialylated N-glycans enhance the secretion of APP and Aβ peptides.18-20 Furthermore, N-glycans carrying bisecting GlcNAc residues are shown to protect AD brains from additional Aβ production.21 More importantly, clusterin N-glycans were also found sialylated.22 Several studies indicate alterations of protein sialylation are associated with AD.23, 24 Therefore, the study of clusterin glycosylation could potentially be highly important for the development of an improved biomarker for AD diagnosis and prognosis.

Clusterin levels in CSF have been shown to be significantly increased in AD patients.25, 26 However, since clusterin is a highly glycosylated protein, it has also been reported that the accurate detection of clusterin levels in the CSF is difficult due to its variable glycosylation level.25 In addition, the extensive glycosylation of clusterin has been demonstrated to hinder the antibody binding and affect the measurement accuracy of quantitative immunoassay, e.g. ELISA.25 De-glycosylation is therefore used to improved binding ability. Nevertheless, the diagnostic utility for measuring naturally glycosylated clusterin levels in CSF remains


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formidable. Although Sihlbom C et al. (2008) reported that no specific glycoform of clusterin in the CSF could be assigned to AD using a glycoproteomics approach,26 the extent and pattern of glycosylation of clusterin is known to be variable between both species and tissue types.27, 28 For example, it was reported that clusterin isolated from secretions of primary cultures of ram Sertoli-cell-enriched preparations was shown to have more sialic acids and lower isoelectric point than that isolated from proteins secreted by primary cultures of rat Sertoli-enriched preparations,29 Therefore, the lack of alteration in the glycopeptide structure of clusterin in the CSF of AD patients does not exclude the possibility that differences may be present in the plasma. Herein we present the studies of plasma clusterin from subjects with low and high hippocampal atrophy using an MS-based glycoprotein analysis approach. Additionally, SRM, as a targeted MS technique, provides high accuracy for the detection of candidate proteins across multiple samples, and is particularly useful for biomarker studies.30 We have also developed a glyco-SRM method to measure specific glycopeptides of clusterin, in readiness for further high throughput measurements within much larger cohorts of clinical samples.



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MATERIALS AND METHODS Patients Sample cohort details Plasma samples from AD and mild cognitively impaired subjects (MCI) were selected from two independent studies: AddNeuroMed a multi-center European study31, 32 and Kings Health Partners-Dementia Case Register a UK clinic and population based study.33 There were 16 female patients and 11 male patients collected, which age ranged from 63 to 87. All clinical information was acquired as previously reported.12 The hippocampal volumes are based on voxel-based measurements and then image analysis was carried out using FreeSurfer (version 5.1) as previously described.34 All volumetric measures from each subject were normalized by the subject's intracranial volume. Mean hippocampus was obtained by the average amount of right and left hippocampal volume. For the evaluation of hippocampal atrophy, the MRI data were stratified into high and low atrophy based on their median volumetric measures. Plasma clusterin concentration was assayed by a commercially available human clusterin ELISA kit (RD194034200R, Biovendor, CZ) as previously reported.6

Experimental Procedures Clusterin purification from human plasma samples Plasma samples have been stored in -80°C freezer. Human clusterin from pooled clinical samples, and low and high atrophy samples was enriched by immunoprecipitation (IP) from albumin/immunoglobulin G (IgG)-depleted plasma (Sigma, Poole, UK), using a monoclonal anti-clusterin antibody (clone 41D, α-chain human, Millipore, USA). 150 µl of undepleted plasma from each patient was used. For IP, 1 mg of depleted plasma was required to bind 10


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µg of antibody overnight. Immunoprecipitated proteins were first analyzed by Western blotting as a quality control, and then separated by one dimensional sodium dodecyl sulfatepolyacrylamide gel electrophoresis (SDS-PAGE). The bands of interest were excised, reduced using dithiothreitol (DTT), 55°C, 1 hour (Sigma, UK), alkylated using iodoacetic acid (IAA), room temperature, dark, 1 hour (Sigma, UK) and digested in-gel with trypsin at 37°C for overnight (Promega, USA) prior to analysis by MS. For SRM studies, standard human plasma (Dade-Behring, Germany) was used and the proteins were depleted and fractionated using a gel-10 approach (10 gel bands/lane) prior to SRM analysis.

DDA-NanoLC- MS/MS-Orbitrap analyses An Orbitrap Velos mass spectrometer coupled online with a Proxeon EASY-nLC II system (Thermo Scientific, UK) and an Orbitrap Fusion Tribrid mass spectrometer coupled online with an EASY-nLC 1000 (Thermo Scientific, UK) were used for LC-MS/MS analyses. The trypsin digested samples were reconstituted in 50 mM ammonium bicarbonate buffer and separated with a guard column (C18, L 2 cm, ID 100 µm, 5 µm, 120 Å, Thermo Scientific, UK) and an analytical column (C18, L 10 cm, ID 75 µm, 3 um, 120 Å, Thermo Scientific, UK). Gradient elution was performed from 5% B to 40% B (acetonitrile with 0.1% formic acid) over 40 minutes, then 40% to 80% B for five minutes, holding at 80% B for five minutes to wash the column before equilibrating column with 5% B. Flow rate was 300 nL/min. MS analyses were performed full fourier transform (FT)-MS scan (scan range m/z 400-1800) and collision induced dissociation (CID) MS/MS scan with a Top20 CID method (Orbitrap Velos) and a Top3 Speed method (Orbitrap Fusion). Since the Orbitrap Fusion instrument provided much better sensitivity, we focused our efforts on the analysis of the data files acquired on this instrument.



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NanoLC-SRM-TSQ analysis The targeted SRM method was designed to use a triple quadrupole mass spectrometer, TSQ Vantage (Thermo Scientific, UK) coupled online with nanoflow reverse phase chromatography (C18, EASY-nLC II, Thermo Scientific). Gradient elution was performed from 5% B to 40% B over 20 minutes, 40% B to 80% B for five minutes, holding at 80% B for five minutes before column equilibration with 5% B. Flow rate is 300 nL/min. This newly established glyco-SRM workflow used eight glycoforms of clusterin β64N glycopeptides as precursors (Table 2), and two glycan-specific oxonium ion fragments at m/z 366.14 and m/z 657.24 as their transitions. Additionally, the ion at m/z 574.56 representing [HN*STGCLR]2+ where N* = Asparagine residue + HexNAc, was included to serve as the third transition ion providing confirmation of site-specific information. Cycle time was set for 2 seconds for all 24 transition ions.

Data analysis Data were analyzed using Xcalibur 2.1 (Thermo Fisher, UK). The clusterin data was searched against the human uniprot_sprot database using Mascot in Proteome Discover 1.3 (Thermo Scientific, UK). Glycopeptides were manually identified by the presence of glycan-specific oxonium ion fragments, m/z 204.08 for N-acetylhexosamine, [HexNAc]+, m/z 366.14 for hexose-N-acetylhexosamine, [Hex-HexNAc]+, and m/z 657.24 for sialic acid-hexose-Nacetylhexosamine [SA-Hex-HexNAc]+ in the MS/MS spectra.

Bioinformatics

Sum scaling data normalization was applied to all the glycopeptide intensities in order to remove experimental bias. The process involved summing of intensity values for all the 8 ACS Paragon Plus Environment

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measured glycopeptides in a given sample and then calculating the median value across all the samples of the summed values. The median value was then divided by each summed value to create a correction factor which was then multiplied to the original intensity values of the glycopeptides to give the normalized sum scaled measurement. In order to measure if any of the glycopeptides showed differential levels of modulation between low and high atrophy groups, a two sample one-tailed type 2 (two-sample assuming equal variance) T Test was applied to every single glycopeptide. The Excel function 'TTEST' was used to determine the significance levels (p-values) between the two atrophy levels. Sum scaled intensity values were checked for normality. The density distribution of log10-scaled normalized intensity values were plotted using a one dimensional density function with a Gaussian kernel. These plots were created using ggplot2 plotting package for R. A two sample Wilcoxon rank sum test was performed for every analyte to obtain an exact p-value. A p-value of ≤ 0.05 reflected significant regulation of an analyte between high and low atrophy.



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RESULTS Establishment of N-glycosylation profile from human plasma clusterin A representative pooled clinical plasma sample was used to develop methodology for the glycosylation profiling of clusterin. Immunoprecipitated proteins were first analyzed by Western blotting as a quality control (see Figure 1A), and then separated by SDS-PAGE (see Figure 1B). Lane 3 of Figure 1A showed that clusterin was pulled down by the antibody. The bands of interest (lane 3, Figure 1B) were excised, alkylated and digested with trypsin prior to analysis by MS. Using IP-Gel-LC-MS/MS, five out of six N-glycosylation sites of human plasma clusterin were initially identified, two in the α subunit (α64N and α81N), and three in the β subunit (β64N, β127N, and β147N). 41 distinct glycoforms associated with anticipated glycosylation consensus sites within the amino acid sequence were identified by the Orbitrap Velos instrument. For each glycopeptide, the m/z charge state and the retention time (RT) of the analyte were tabulated and summarized in the Clusterin GlycoMod database v1.0 (Table 3). Each glycosylation site has its corresponding glycoforms with distinct proportions, demonstrating the great diversity of clusterin glycosylation. Site β64N HN*STGCLR exhibited the greatest glycosylation diversity, with twelve types of oligosaccharide identified. Figure 2 shows the several glycoforms observed at β64N site. Most of the peaks were triple charges, [M+3H]3+ and the most abundant structure is bisialobiantennary N-glycan at m/z 1050.74.

Tri-antennary

and

tetra-antennary

structures

were

also

detected.

The

bisialobiantennary glycoform accounting for approximately 56% at β64N, 41%, 88%, 46% and 28% at α64N, β127N, β147N, α81N respectively showed its majority at five glycosylation sites. In general, we found clusterin glycopeptides are highly sialylated, and often contain fucose. These peripheral sugars add complexity to the glycosylation analysis of clusterin. Overall, although α123N was not observed in this initial experiment, our results


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show the major glycoforms including bisialobiantennary, trisialotriantennary, and bisialotriantennary N-glycans with the bisialobiantennary structure are dominant at every detected glycosylation sites.

MS/MS was used to demonstrate the structural information of glycosylated peptides. However, MS and MS/MS data only provided partial structural information of the glycopeptides that the branching and linkage details remain unknown. For example, a fucose residue may be attached to the terminal end generating Lewis X/Y or sialyl Lewis X/Y structure, or to the core structure as core-fucosylated. In addition, there is not sufficient data which can verify the position of the tri-antennae at Man3- or Man6-arm. Nevertheless, the glycosylated peptides were confirmed by the presence of [peptide+HexNAc+2H]2+. For example, bisialobiantennary N-glycan and corresponding peptide were identified by the peak at m/z 574.47 representing [HNSTGCLR+HexNAc+2H]2+ (Figure 3). β64N was not the only site to contain bisialobiantennary N-glycans, the structure was also found at other glycosylation sites, for example α64N and β127N. Therefore, the presence of glycopeptide fragment ion, [peptide+HexNAc+2H]2+ is important for the unambiguous structural determination of clusterin glycopeptides.

Having developed the IP-Gel-LC-MS/MS method for the characterization of clusterin glycosylation from pooled clinical samples, this established method was applied to subjects with low and high atrophy using the Orbitrap Fusion instrument. With the greater sensitivity and higher resolution provided by the Orbitrap Fusion, more accurate and precise quantification of relative abundances of each site-specific glycopeptides could most likely be obtained. All six known N-glycosylation sites of clusterin were identified: α64N, α81N, 11 ACS Paragon Plus Environment


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α123N, β64N, β127N, and β147N. High heterogeneity of carbohydrate components was evident with 42 distinct N-linked glycopeptides detected, which was updated as Clusterin GlycoMod database v1.1 (Table 4). Data were ostensibly similar between Velos and Fusion instruments. However, as expected, more glycosylated clusterin peptides were identified on the Orbitrap Fusion system and so all subsequent analysis was performed on this dataset.

Analysis of immunoprecipitated clusterin to compare individuals with low and high atrophy From the original small cohort of eight plasma samples from low atrophy (n=4) and high atrophy (n=4), we were able to perform relative quantification using IP-Gel-LC-MS/MS on the Fusion. All 42 glycoforms were quantified based on the integrated peak area from the extracted ion chromatogram. Whilst most glycosylation sites showed no regulation in glycan structures between high and low levels of hippocampal atrophy, three sites – α64N, β64N, and β147N - showed significant regulations between the clinical groups (Table 5). Six glycoforms at β64N glycosylation site HN*STGCLR were found significantly decreased (p≤0.05) in the four high atrophy samples compared to the four low atrophy samples of the discovery cohort when measured on the Orbitrap Fusion instrument.

Having identified the changes in clusterin glycosylation patterns which correlate to the extent of atrophy within a small cohort of clinical samples, we performed a further validation study on an additional cohort of AD patients with known levels of hippocampal atrophy; nine from low atrophy and ten from high atrophy (replication cohort). In the relatively larger replication cohort, three glycoforms of β64N glycopeptides were significantly reduced in high atrophy samples (Table 6). These include the SA1-(HexNAc-Hex)2-core, SA1-(HexNAc-Hex)3-core and SA2-(HexNAc-Hex)3-core glycoforms seen at m/z 953.71, 1075.42, 1172.45 in the


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spectra. All of these glycoforms were also seen reduced in high atrophy patients in the discovery cohort, further supporting their utility as prognostic biomarkers in patients with confirmed AD.

When the results of the two cohorts were combined, we again saw that changes in glycoforms found at site β64N correlated with atrophy, with four glycoforms significantly reduced over high atrophy, e.g. SA1-(HexNAc-Hex)2-core, SA2-(HexNAc-Hex)2-core, SA1-(HexNAcHex)3-core, and SA2-(HexNAc-Hex)3-core at m/z 953.71, 1050.74, 1075.42, and 1172.45 respectively (Figure 4). Figure 4A-4D shows all four glycoforms were correlated with atrophy. Figure 4E showed most of the glycosylation data across 27 samples were normally distributed. Overall, the significant change in the specific glycopeptides between the clinical groups was demonstrated in the discovery cohort, replication cohort, and also in the analysis of the combined cohort.

Development and preliminary testing of a SRM method for eight glycoforms of clusterin in human plasma We further hypothesized that the overall glycopeptide profile might also be used as a diagnostic tool, since the difference in glycosylation patterns associated with distinct glycosylation sites may prove relevant to disease status. To explore this possibility, we used a more sensitive and easier approach to compare the clusterin glycoform distribution in plasma obtained from a cohort of low atrophy (n=13) and high atrophy (n=14) patients.

In previous studies (data not shown), we were able to extract clusterin glycopeptides from human plasma without prior IP. Given the potential sensitivity gains offered by SRM 13 ACS Paragon Plus Environment


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methods, we followed a more straightforward Gel-LC method for clusterin enrichment which would be more compatible with high throughput analysis such as would be required for a clinical diagnostic test. Initially, to identify the location of clusterin in a SDS-PAGE experiment, the standard human plasma was depleted from albumin and IgG, and proteins were subjected to SDS-PAGE. Figure 5 shows the gel separation of albumin/IgG-depleted plasma. All ten bands were excised, reduced, alkylated, and trypsin-digested prior to MS analysis on the Orbitrap Fusion instrument. Clusterin was identified in bands #5, #6, and #7 (data not shown), but mainly observed in band #7 with 45% protein coverage.

In readiness for higher throughput measurements within much larger numbers of clinical samples, we have also developed a targeted glyco-SRM method to measure specific glycopeptides of clusterin. The details of this newly established glyco-SRM method are described in Table 2. According to the IP-Gel-LC-MS/MS analysis of the combined cohort, the four glycoforms found correlated with atrophy are all attached to β64N, therefore only β64N glycopeptides were selected for the development of glyco-SRM assay. In addition, only non-fucosylated β64N glycopeptides (n=8) were chosen for the SRM assay. This is because rearrangements of fucose residues have been observed during CID of protonated N-glycans and fucose readily fragments, which give rise to misleading fragments.35, 36

All ten tryptic-digested gel bands were submitted for analysis using the newly-developed glyco-SRM method to confirm the suitability of the Gel-LC method for sample preparation. Figure 6 shows clusterin glycopeptides were found in bands #5, #6 and #7, with the majority of clusterin identified in band #7. The Gel10-glyco-SRM results were therefore consistent with the previous Orbitrap discovery data. 14 ACS Paragon Plus Environment

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When the SRM data files were examined for the extracted ion chromatograms (XIC) of eight β64N glycopeptide precursors (Figure 7), we were able to determine that the majority of them eluted between 7-8 minutes of the 30 minute-gradient. The SRM analysis is consistent with the Orbitrap results as β64N glycopeptides eluted around minute 11 of the 60 minute-gradient, both of which showing β64N glycopeptides are eluted at 16-17% B. Precise identification of elution time allows subsequent scheduling of SRM or adjustment of elution buffers to improve separation of closely related glycan structures and isoforms. The Gel10-glyco-SRM was also found reproducible measuring replicates (n=6) from human standard plasma. The CV varies among different analytes, ranging from 1.9% to 20.3%. Typically, the most abundant analyte containing the structure of SA2-(HexNAc-Hex)2-core was shown least CV and most stable for this assay.

To further validate our targeted biomarker glycopeptides, the clusterin glyco-SRM method was applied to evaluate immunoprecipitated clusterin from the discovery cohort and replication cohort. As expected, the SRM method gave tighter quantitative results and this improved precision resulted in higher levels of significance for the reduction of specific glycoforms in AD patients with higher levels of hippocampal atrophy (Figure 8). When the results for the discovery and replication cohorts were combined, all eight glycoforms attained statistical significance for reduced concentrations in the high atrophy group compared to those with low hippocampal atrophy (Figure 8). Box plots and p-values for each β64N glycopeptide showing significant reduction over hippocampal atrophy are provided in Figure 8A-8H. Again, Figure 8I showed the glycosylation SRM data from all 27 samples were mostly normally distributed.



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DISCUSSIONS A useful biomarker must meet the criteria of high sensitivity and specificity, however, AD biomarkers with ideal sensitivity and specificity are difficult to find to date. With recent technological advances in MS-based analysis and immunological approaches, the selection of biomarkers should be more carefully assessed and more able to correctly identify true positive/negative rate from large sample sizes. In this study, we employed an extended LCMS/MS discovery and glyco-SRM targeted approach, as well as established a staged workflow to facilitate biomarker discovery and validation. The carbohydrate content of plasma clusterin consists of highly sialylated glycans, with peripheral fucose, resulting in high heterogeneity and thus complexity of detection. The glycosylation pattern of clusterin has previously been analyzed by Kapron JT et al. (1997), Nilseld A-M et al. (2006) and Tousi F et al. (2012)37 from serum, CSF and kidney respectively. O-glycosylation is not detected on plasma clusterin, supporting the previous studies using serum or CSF clusterin.22,

25

Our

results show that site-specific distribution of oligosaccharides at all six N-glycosylation sites and site β64N HN*STGCLR of plasma clusterin exhibited the greatest glycosylation diversity, with twelve types of oligosaccharide identified. Interestingly, this is different from Kapron JT et al. (1997), who indicated β64N of serum clusterin exhibited the least glycosylation diversity and β147N exhibited the greatest diversity. This indicates the glycosylation of clusterin can be spatially and temporally regulated. Additionally, Nilseld AM found that some CSF clusterin glycoforms carry sialic acids, however, in our study all 42 clusterin glycopeptides were detected carrying sialic acids. This might be due to different antibodies used for the purification. Although extensive numbers of glycosylated peptides were obtained, our IP-gel-LC-MS/MS approach is likely limited to certain glycoforms as IP may favor specific forms and excised gel bands may contain only a subset of the clusterin glycoforms. It is possible that more glycoforms could be obtained if bands #5, #6 and #7


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were pooled (Figure 5) for the Orbitrap analysis as clusterin was identified in all these bands. Furthermore, it was reported that the difference between the molecular weight of clusterin from human brain and serum is mostly due to differential glycosylation.27, 28 In fact, plasma clusterin is secreted from a number of tissues, including liver, kidney, lung and heart. Although it is likely dominated by clusterin produced by the liver, specific oligosaccharide structure may be found associated with clusterin from different types of tissue source.22, 38 Therefore, the comprehensive glycosylation profile of plasma clusterin from various subjects presented here provides a molecular basis for developing a better understanding of clusterin structure-function relationships and the role clusterin glycosylation plays in physiological function. Additionally, using IP-gel-LC-MS/MS and glyco-SRM workflow, it is possible that different types and levels of glycosylation can be distinguished and used for further evaluation of species- and tissue-specific glyco-regulation, which is likely to improve biomarker selection.

Using the Orbitrap LC-MS/MS system, three N-glycosylation sites - α64N, β64N, and β147N are found to be significantly regulated between high and low atrophy samples. In particular, a number of the β64N glycoforms show a significant reduction in the plasma of patients with high hippocampal atrophy compared to those with low hippocampal atrophy. With the advantages of high sensitivity, precision and reproducibility, our SRM technique demonstrates eight specific N-linked glycopeptides at β64N of human clusterin that can differentiate between high hippocampal atrophy and low hippocampal atrophy. Using glycan oxonium ions and the [peptide+HexNAc] fragment ion as transition ions, this technique is capable of studying protein glycosylation. Importantly, although site-specific information can be obtained by the Y-1 ion [peptide+HexNAc], e.g. m/z 574.47 representing [HNSTGCLR+HexNAc], any N-glycosylated peptide generating the same m/z of Y-1 ion 17 ACS Paragon Plus Environment


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and oxonium ions would yield a SRM signal and interfere the result. Therefore, retention time was used as a secondary identifier. Figure 7 showed that most of clusterin β64N glycopeptides eluted approximately 7-8 minutes of the 30 minute-gradient, which was consistent with the Orbitrap results as β64N glycopeptides are found eluted at 16-17% B. Additionally, the MS/MS of the most abundant glycoform of clusterin β64N at m/z 1050.74 was also obtained by TSQ product ion MS/MS analysis demonstrating the same fragmentation profile as seen in Figure 3 which retention time was also around 7 minute of the 30 minute-gradient (data not shown). Using transition ions and retention time, risk of capturing false positives from complex peptide mixtures can be reduced. In addition, a particular advantage over the present method is that the integrated peak area for each monitored species can be used for label-free quantification using sum-scaling data normalization. Furthermore, this Gel10-glyco-SRM method does not require IP and clusterin glycopeptides can be detected within 30 minutes by a TSQ-MS, instead of analysis using the Orbitrap, which typically employs a longer gradient elution time. Hence the SRM method provides a more efficient and faster way to verify the potential biomarker glycopeptide of clusterin. Larger studies are needed to investigate the usefulness of the reported β64N glycopeptides as biomarkers for AD. In addition, since two other glycosylation sites were also found to correlate with hippocampus atrophy, further investigation using SRM will be required to validate the correlations of α64N and β147N glycoforms with disease status.

In order to validate our data that β64N glycopeptides are really correlated with atrophy, sum scaling normalization and Wilcoxon rank sum test were first conducted for the confirmation of data normality. Figure 4E and 8I showed the distributions of the sum-scaling normalized data by LC-MS/MS and SRM. In Table 7, the data normality was confirmed by Wilcoxon


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rank sum tests and more importantly, all eight β64N glycopeptides were shown again significantly correlated with atrophy (p≤0.05). This is consistent to sum-scaling normalized results. Since all glycoforms are down regulated in high atrophy compared to low atrophy samples, the quantities of eight β64N glycopeptides were also normalized to a nonglycosylated clusterin peptide ELDESLQVAER for the correction of any potential analytical variability, such as pipetting, digestion efficiency, and gel excision. It was shown the same result that eight analytes were significantly decreased in high atrophy samples compared to low atrophy samples (data not shown). However, it is arguably that the deregulation of β64N glycoforms might be related to protein regulation of clusterin itself instead of atrophy. In order to demonstrate the relationship between the level of β64N glycopeptides and clusterin level, eight glycoforms were plotted against clusterin level, shown in Figure 9A-9H. All scatter diagrams indicated there is no correlation between glycosylation level and clusterin level. Therefore, it is most likely the correlation between atrophy and clusterin glycopeptides is predominantly due to its site-specific glycosylation status. More significantly, there is possible benefit of measuring site-specific clusterin glycopeptides over non-glycosylated peptides or glycans for the study of AD biomarkers.

Furthermore, the best performing biomarker in this study is best described by the difference in glycosylation level and glycoform distribution associated at specific glycosylation site, rather than a specific glycoform, This may explain findings from previous glycoproteomics analysis by Sihlborn et al. (2008) where no specific glycoforms could be assigned to AD.26 Since the difference in site-specific glycosylation pattern is shown to be related to disease status, overall glycopeptide profile is likely to be used as a diagnostic and prognostic tool for AD. Also, it is possible that alteration of glycosylation distribution changes the properties of



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the target glycoprotein, for example, significant reduction of β64N glycopeptides of clusterin may affect its binding to Aβ peptides and fibrils and its function in lipid trafficking.39

Although most researches study AD biomarkers in CSF, plasma is less invasive and easier to obtain. In addition, the concentration of clusterin in plasma is higher than in CSF, 35-105 µg/mL and 1.2-3.6 µg/mL respectively.40,

41

As previously stated, a number of plasma

proteins, including clusterin, are reported to be highly associated with disease severity and disease progression.12 Plasma proteins may also be involved in early detection of AD, with specific inflammatory proteins serving as potential markers of MCI conversion to AD.42 Interestingly, clusterin is reported to prevent the inflammatory host response, and its sialylation may enhance the immune masking process.3 With further optimization, the targeted SRM method may not only provide the basis for a routine clinical test to assess hippocampal atrophy, based on the detection of specific glycosylation levels in an individual patient and comparing this to levels known to represent specific levels of hippocampal atrophy, but could also be expanded to incorporate other plasma glycoproteins, such as α2macroglobulin and α1-acid glycoprotein that act as potential AD biomarkers.43

Taken together, we have employed LC-MS/MS to analyze the various N-glycan distributions on all six sites of clusterin in plasma and we have established a novel Gel10-glyco-SRM assay to explore the utility of β64N specific glycoforms in further clinical cohorts. The ability of these eight clusterin glycopeptides to differentiate patients based on their hippocampal volume provides a minimally invasive means to predict the progression of AD. In addition, due to the role of glycosylation in neurodegenerative diseases, our glyco-SRM approach will likely be applicable to the analysis of Parkinson’s Disease or Huntington’s Disease.44 20 ACS Paragon Plus Environment

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CONCLUSION With the aim to explore differential clusterin glycosylation in AD progression and discover potential AD biomarkers, LC-MS/MS-based approaches were developed and performed in our pilot study for the characterization and quantitation of clusterin glycopeptides from the plasma of the patients with high and low hippocampal atrophy. Site-specific N-glycosylated peptides of plasma clusterin were obtained using DDA-NanoLC- MS/MS-Orbitrap analyses. Several glycoforms, particularly β64N glycopeptides were shown correlated with atrophy using both discovery and replication cohorts. Our established Gel10-glyco-SRM assay demonstrated targeted glycosylation analysis from clsuterin β64N and validated eight sialylated β64N glycopeptides are down regulated in high atrophy compared low atrophy samples. Larger sample cohorts and more clinically friendly assay format will be required to enhance accuracy and to facilitate verification and validation in order to meet clinical need.



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AUTHOUR INFORMATION Corresponding Author Tel: +44 207 848 5112 Fax: +44 207 848 5114 [email protected]

ACKNOWLEDGEMENTS The authors would like to formally acknowledge the scientific contribution of our colleagues at Proteome Science plc, and collaborators at Institute of Psychiatry, King’s College London.

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TABLES Table 1. Sample details associated with 13 (low hippocampal atrophy) vs 14 (high hippocampal atrophy) samples. Samples 4.1-4.8 were used as the discovery cohort and samples 10.1-10.19 were used as the replication cohort. Sample

Disease

Gender

number

group

4.1

AD

Female

4.2

MCI

4.3

Age

Mean Clusterin

Mean hippocampus

Atrophy

(ng/mL)

(mL)

82

87.00

1.35

High

Male

79

93.00

2.64

Low

MCI

Male

72

90.00

2.74

Low

4.4

MCI

Male

75

90.00

2.64

Low

4.5

AD

Female

66

153.00

1.61

High

4.6

AD

Female

78

84.00

1.64

High

4.7

AD

Male

69

90.00

1.06

High

4.8

MCI

Female

71

114.00

3.08

Low

10.1

AD

Male

79

154.32

1.12

High

10.2

AD

Female

76

337.70

1.24

High

10.3

AD

Male

77

422.11

1.26

High

10.4

AD

Male

69

252.19

2.33

Low

10.5

AD

Female

87

322.05

2.38

Low

10.6

AD

Female

70

253.82

2.34

Low

10.7

AD

Male

70

303.13

0.73

High

10.8

AD

Female

83

530.31

1.37

High

10.9

AD

Female

65

497.37

2.27

Low

10.10

AD

Female

77

404.60

2.36

Low

10.11

AD

Female

76

241.56

1.00

High

10.12

AD

Male

76

300.21

1.10

High


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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


10.13

AD

Female

67

323.54

1.35

High

10.14

AD

Female

72

280.50

2.28

Low

10.15

AD

Female

63

309.15

2.45

Low

10.16

AD

Male

83

423.18

1.27

High

10.17

AD

Female

71

7147.63

2.37

Low

10.18

AD

Female

68

307.14

1.40

High

10.19

AD

Male

79

351.02

2.38

Low



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Page 32 of 56

Table 2. Glyco-SRM method of TSQ analysis #

Parent

Product SRM collision

Start

Stop

energy

Time

Time

Polarity Trigger Reference

1

953.712

366.140

30

0

30

+

100

No

2

953.712

574.556

30

0

30

+

100

No

3

953.712

657.235

30

0

30

+

100

No

4

1050.744 366.140

33

0

30

+

100

No

5

1050.744 574.556

33

0

30

+

100

No

6

1050.744 657.235

33

0

30

+

100

No

7

1075.423 366.140

34

0

30

+

100

No

8

1075.423 574.556

34

0

30

+

100

No

9

1075.423 657.235

34

0

30

+

100

No

10 1172.454 366.140

37

0

30

+

100

No

11 1172.454 574.556

37

0

30

+

100

No

12 1172.454 657.235

37

0

30

+

100

No

13 1197.134 366.140

38

0

30

+

100

No

14 1197.134 574.556

38

0

30

+

100

No

15 1197.134 657.235

38

0

30

+

100

No

16 1269.487 366.140

41

0

30

+

100

No

17 1269.487 574.556

41

0

30

+

100

No

18 1269.487 657.235

41

0

30

+

100

No

19 1294.165 366.140

42

0

30

+

100

No

20 1294.165 574.556

42

0

30

+

100

No

21 1294.165 657.235

42

0

30

+

100

No

22 1391.532 366.140

45

0

30

+

100

No

23 1391.532 574.556

45

0

30

+

100

No

24 1391.532 657.235

45

0

30

+

100

No


Page 33 of 56

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


Table 3. The Clusterin GlycoMod database v1.0 (pooled clinical plasma). SA: Sialic Acid; HexNAc: N-acetylhexosamine; Hex: Hexose; DeoxyHex: Deoxyhexose; core: N-glycan structure, Manα1–6(Manα1–3)Manβ1–4GlcNAcβ1–4GlcNAcβ1. No

Clusterin Glycopeptide

Average m/z Retention time (min) (charge state)

HN*STGCLR (B64N) 1 2 3 4 5 6 7 8 9 10 11 12

SA1-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)2-core SA1-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)2-core DeoxyHex1-SA1-(HexNAc-Hex)3-core SA2-(HexNAc-Hex)3-core SA1-(HexNAc-Hex)4-core DeoxyHex1-SA2-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)3-core SA2-(HexNAc-Hex)4-core DeoxyHex1-SA3-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)4-core

13 14 15 16 17

SA2-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)3-core DeoxyHex1-SA3-(HexNAc-Hex)3-core

18 19 20 21 22 23 24

SA1-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)2-core SA1-(HexNAc-Hex)3-core SA2-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)3-core DeoxyHex1-SA3-(HexNAc-Hex)3-core

11.55 11.35 10.43 10.95 11.68 11.03 10.34 11.10 12.11 10.87 11.93 11.74

953.71 (3+) 1050.74 (3+) 1075.42 (3+) 1099.46 (3+) 1124.1 (3+) 1172.45 (3+) 1197.46 (3+) 1221.13 (3+) 1269.81 (3+) 1294.49 (3+) 1318.17 (3+) 1391.19 (3+)

14.11 14.08 13.91 15.07 14.88

1094.44 (3+) 1216.48 (3+) 1264.84 (3+) 1313.51 (3+) 1361.87 (3+)

16.57 14.56 15.97 14.56 14.36 14.49 14.30

1040.11 (3+) 1137.14 (3+) 1161.82 (3+) 1258.85 (3+) 1307.87 (3+) 1356.21 (3+) 1404.9 (3+)

19.08 19.66

1180.11 (3+) 1301.82 (3+)

26.97 29.85

1442.3 (3+) 1153.52 (4+)

25.38 26.35 25.87 26.23 27.18 26.17 26.97 26.17 27.26 27.08

1200.17 (3+) 1297.54 (3+) 1322.22 (3+) 1346.57 (3+) 1370.57 (3+) 1419.25 (3+) 1443.04 (3+) 1467.93 (3+) 1516.62 (3+) 1564.97 (3+)

30.39 31.09 31.09

1286.29 (4+) 1450.60 (4+) 1487.36 (4+)

KEDALN*ETR (A64N)

KKEDALN*ETR (A64N)

KKKEDALN*ETR (A64N) 25 SA2-(HexNAc-Hex)2-core 26 SA2-(HexNAc-Hex)3-core

MLN*TSSLLEQLNEQFNWVSR (B127N) 27 SA1-(HexNAc-Hex)2-core 28 SA2-(HexNAc-Hex)2-core

LAN*LTQGEDQYYLR (B147N) 29 30 31 32 33 34 35 36 37 38

SA1-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)2-core SA1-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)2-core DeoxyHex1-SA1-(HexNAc-Hex)3-core SA2-(HexNAc-Hex)3-core DeoxyHex1-SA3-(HexNAc-Hex)2-core DeoxyHex1-SA2-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)3-core DeoxyHex1-SA3-(HexNAc-Hex)3-core

LKELPGVCN*ETMMALWEECKPCLK (A81N) 39 SA2-(HexNAc-Hex)2-core 40 SA3-(HexNAc-Hex)3-core 41 DeoxyHex1-SA3-(HexNAc-Hex)3-core



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

Page 34 of 56

Table 4. The Clusterin GlycoMod database v1.1 (plasma of high and low atrophy) No Clusterin Glycopeptide

m/z (charge state)

HN*STGCLR (B64N) 1 2 3 4 5 6 7 8 9 10 11

SA1-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)2-core SA1-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)2-core DeoxyHex1-SA1-(HexNAc-Hex)3-core SA2-(HexNAc-Hex)3-core SA1-(HexNAc-Hex)4-core DeoxyHex1-SA2-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)3-core SA2-(HexNAc-Hex)4-core SA3-(HexNAc-Hex)4-core

12 13 14 15 16 17

SA2-(HexNAc-Hex)2-core DeoxyHex1-SA2-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)3-core DeoxyHex1-SA3-(HexNAc-Hex)3-core

18 19 20 21 22 23 24

SA1-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)2-core SA1-(HexNAc-Hex)3-core SA2-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)3-core DeoxyHex1-SA3-(HexNAc-Hex)3-core

953.71 (3+) 1050.74 (3+) 1075.42 (3+) 1099.46 (3+) 1124.10 (3+) 1172.45 (3+) 1197.46 (3+) 1221.13 (3+) 1269.81 (3+) 1294.49 (3+) 1391.19 (3+)

KEDALN*ETR (A64N) 1094.44 (3+) 1143.13 (3+) 1216.48 (3+) 1264.84 (3+) 1313.51 (3+) 1361.87 (3+)

KKEDALN*ETR (A64N) 1040.11 (3+) 1137.14 (3+) 1161.82 (3+) 1258.85 (3+) 1307.87 (3+) 1356.21 (3+) 1404.90 (3+)

KKKEDALN*ETR (A64N) 25 SA2-(HexNAc-Hex)2-core

1180.11 (3+)

MLN*TSSLLEQLNEQFNWVSR (B127N) 26 SA1-(HexNAc-Hex)2-core 27 SA2-(HexNAc-Hex)2-core

1442.30 (3+) 1153.53 (4+)

LAN*LTQGEDQYYLR (B147N) 28 29 30 31 32 33 34 35

SA1-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)2-core SA1-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)2-core SA2-(HexNAc-Hex)3-core DeoxyHex1-SA2-(HexNAc-Hex)3-core SA3-(HexNAc-Hex)3-core DeoxyHex1-SA3-(HexNAc-Hex)3-core

1200.17 (3+) 1297.54 (3+) 1322.22 (3+) 1346.57 (3+) 1419.25 (3+) 1467.93 (3+) 1516.62 (3+) 1564.97 (3+)

ELPGVCN*ETMMALWEECK(A81N) 36 SA2-(HexNAc-Hex)2-core 37 SA3-(HexNAc-Hex)3-core 38 DeoxyHex1-SA3-(HexNAc-Hex)3-core

1216.50(4+) 1390.56 (4+) 1427.33 (4+)

LKELPGVCN*ETMMALWEECKPCLK (A81N) 39 SA2-(HexNAc-Hex)2-core 40 SA3-(HexNAc-Hex)3-core 41 DeoxyHex1-SA3-(HexNAc-Hex)3-core

1286.55(4+) 1451.11(4+) 1487.37 (4+)

QLEEFLN*QSSPFYFWMWGDR (A123N) 42 SA2-(HexNAc-Hex)2-core

1179.48 (4+)


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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


Table 5. Significant changes in clusterin glycopeptides (4 vs 4) m/z (3+)

composition

p-value*

site

953.71

SA1-(HexNAc-Hex)2-core

0.0160

β64N

1050.74


0.0035

β64N

1075.42


0.0093

β64N

1172.45


0.0060

β64N

1269.49


0.0168

β64N

1391.53


0.0426

β64N

1297.54


0.0445

β147N

1356.21


0.0435

α64N

Table 6. Significant changes in clusterin glycopeptides (9 vs 10) m/z (3+)

composition

p-value

site

953.71


0.0353

β64N

1075.42


0.0185

β64N

1172.45


0.0429

β64N

1297.54


0.0445

β147N

1137.14


0.0161

α64N

1356.21


0.0073

α64N



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

Page 36 of 56

Table 7. Performance of glyco-SRM assay in combined Discovery and Replication cohort using Wilcoxon rank sum analysis. m/z (3+)

composition

p-value

site

953.71


0.0005

β64N

1050.74


6.9797E-07

β64N

1075.42


0.0015

β64N

1172.45


3.9884E-07

β64N

1197.13


0.0010

β64N

1269.49


0.0027

β64N

1294.17


0.0125

β64N

1391.53


0.0377

β64N


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FIGURE LEGENDS Figure 1. (A) Western blot image of the captured clusterin by IP. Lane 1: 0.25 ug of recombinant clusterin; lane 2: 10 ug of depleted human plasma; lane 3: 1/8 of the IP eluate. (B) SDS-PAGE of the IP-captured clusterin. Lane 1: SeeBlue® Plus Marker; lane 2: recombinant clusterin; lane 3: 7/8 of the IP eluate.

Figure 2. ESI-MS of glycopeptides from clusterin β64N. The N-glycopeptide ions shown are triply-charged. The possible β64N glycopeptide structures of clusterin are depicted, with the sugar components of GlcNAc, mannose, galactose, sialic acid.

Figure 3. MS/MS spectrum of m/z 1050.74 the [M+3H]3+ molecular ion for clusterin glycopeptide of molecular weight 3149.22 Da. The fragment ions enable the structure of the glycan to be deduced with the fragment ion at m/z 574.47 representing the [Peptide+HexNac+2H]2+ moiety. Hence the sequence of the “naked” peptide is HN*STGCLR and a fully sialylated biantennary glycan structure, SA2-(HexNAc-Hex)2-core, is attached to the asparagine residue (N*).

Figure 4. The box plots of significantly regulated clusterin β64N glycopeptides from combined discovery and replication cohorts by LC-MS/MS analysis. A) β64N_SA1(HexNAc-Hex)2-core, m/z 953.71; B) β64N_SA2-(HexNAc-Hex)2-core, m/z 1050.74; C) β64N_SA1-(HexNAc-Hex)3-core, m/z 1075.42; and D) β64N_SA2-(HexNAc-Hex)3-core, m/z 1172.45. The Y-axis represents integrated peak area. The p-value indicates significance of 37 ACS Paragon Plus Environment


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

Page 38 of 56

change between high and low atrophy groups. (E) Distribution profile of glycoforms from 27 samples based on the log10 peak intensity by LC-MS/MS analysis.

Figure 5. Gel image of albumin/IgG–depleted plasma. Lane A: SeeBlue® Plus marker; lane B: the standard human plasma depleted from albumin and IgG. Red bars represent cut points and numbers represent the band number used for Orbitrap analysis to identify clusterin glycopeptides. The majority of clusterin was consistently found in band 7.

Figure 6. Total ion chromatogram (TIC) of bands #4 - #9 from depleted plasma using Gel10glyco-SRM method. Eight clusterin β64N glycopeptides served as precursors (m/z 953.71, 1050.74, 1075.42, 1172.45, 1197.13, 1269.49, 1294.17, 1391.53), and fragment ions at m/z 366.14, 574.56, and 657.24 were set as transition ions for each precursor. The highlighted red box represents the clusterin peak.

Figure 7. Extracted ion chromatogram of band #7 showing various retention times and peak areas of eight glycoforms at site β64N.

Figure 8. Box plots of significantly regulated clusterin β64N glycopeptides from combined discovery and replication cohorts by SRM analysis. A) β64N_SA1-(HexNAc-Hex)2-core, m/z 953.71; B) β64N_SA2-(HexNAc-Hex)2-core, m/z 1050.74; C) β64N_SA1-(HexNAc-Hex)3core, m/z 1075.42; D) β64N_SA2-(HexNAc-Hex)3-core, m/z 1172.45; E) β64N_SA1(HexNAc-Hex)4-core, m/z 1197.13; F) β64N_SA3-(HexNAc-Hex)3-core, m/z 1269.49; G) β64N_SA2-(HexNAc-Hex)4-core, m/z 1294.17; and H) β64N_SA3-(HexNAc-Hex)4-core, m/z 1391.53. (I) Distribution profile of glycoforms from 27 samples according to the log10 peak intensity by SRM analysis. 38 ACS Paragon Plus Environment

Page 39 of 56

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 9. Scatter diagrams of glycosylation levels using peak area correlated against clusterin levels. All numbers were converted to Z score for the plot correlation. All eight analytes were shown not related to the clusterin level.



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

Page 40 of 56

Figure 1


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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 2



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

Page 42 of 56

Figure 3


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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


A) β64N_SA1-(HexNAc-Hex)2-core

B) β64N_SA2-(HexNAc-Hex)2-core



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

Page 44 of 56

C) β64N_SA1-(HexNAc-Hex)3-core

D) β64N_SA2-(HexNAc-Hex)3-core


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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


E) Data distribution

Figure 4



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

A

Page 46 of 56

B

Figure 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


#4

#5

#6

#7

#8 #9

Figure 6



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

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Figure 7


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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


A) β64N_SA1-(HexNAc-Hex)2-core

B) β64N_SA2-(HexNAc-Hex)2-core



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

Page 50 of 56

C) β64N_SA1-(HexNAc-Hex)3-core

D) β64N_SA2-(HexNAc-Hex)3-core


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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


E) β64N_SA1-(HexNAc-Hex)4-core

F) β64N_SA3-(HexNAc-Hex)3-core



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

Page 52 of 56

G) β64N_SA2-(HexNAc-Hex)4-core

H) β64N_SA3-(HexNAc-Hex)4-core


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(I)

Data distribution

Figure 8



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

Page 54 of 56


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Figure 9



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

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Table of Contents ABSTRACT ........................................................................................................................ 2 INTRODUCTION ............................................................................................................... 3 MATERIALS AND METHODS ........................................................................................ 6 Patients ................................................................................................................................................... 6 Sample cohort details ...................................................................................................................... 6

Experimental Procedures........................................................................................................................ 6 Clusterin purification from human plasma samples........................................................................ 6 DDA-NanoLC- MS/MS-Orbitrap analyses..................................................................................... 7 NanoLC-SRM-TSQ analysis .......................................................................................................... 8 Data analysis ................................................................................................................................... 8

Bioinformatics ......................................................................................................................................... 8

RESULTS .......................................................................................................................... 10 Establishment of N-glycosylation profile from human plasma clusterin .............................................. 10 Analysis of immunoprecipitated clusterin to compare individuals with low and high atrophy ............ 12 Development and preliminary testing of a SRM method for eight glycoforms of clusterin in human plasma ................................................................................................................................................... 13

DISCUSSIONS .................................................................................................................. 16 CONCLUSION ................................................................................................................. 21 AUTHOUR INFORMATION .......................................................................................... 22 ACKNOWLEDGEMENTS .............................................................................................. 22 REFERENCES.................................................................................................................. 22 TABLES ............................................................................................................................ 30 FIGURE LEGENDS ......................................................................................................... 37


Glycosylation of Human Plasma Clusterin Yields a Novel Candidate

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