Elsevier

Journal of Proteomics

Volume 213, 20 February 2020, 103616
Journal of Proteomics

Comprehensive analysis of human IgG Fc N-glycopeptides and construction of a screening model for colorectal cancer

https://doi.org/10.1016/j.jprot.2019.103616Get rights and content

Highlights

  • We developed a strategy for the analysis of IgG N-glycopeptides in plasma samples.

  • Application of CS@PGMA@IDA allowed purification of N-glycopeptides from plasma.

  • We performed a proof-of-principle study using plasma from CRC patients.

  • A predictive model was constructed to identify CRC but requires further validation.

Abstract

Currently, analyzing intact glycopeptides remains a challengeable task. Considerable progress has been achieved in the knowledge of immunoglobulin G (IgG) glycans in patients with colorectal cancer (CRC), whereas data on IgG Fc N-glycopeptides are scarce in the literature. To fill this gap in knowledge, we developed a rapid and effective method to obtain and analyze IgG Fc N-glycopeptides in the plasma from 46 CRC patients and 67 healthy individuals using chitosan@poly (glycidyl methacrylate) @iminodiacetic acid (CS@PGMA@IDA) nanomaterial in combination with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS). A total of 29 N-glycopeptides were detected and analyzed. Compared with healthy individuals, CRC patients had increased levels of N-acteylglucosamine, yet decreased levels of galactosylation, fucosylation and sialylation. Further, a multivariate logistic regression model was developed using the levels of IgG Fc N-glycopeptides to distinguish CRC patients from healthy individuals, and the prediction performance was good, with an average AUC of the ROC curves of 0.893.

Significance

In this study, we proposed a strategy for obtaining and analyzing IgG glycopeptides using CS@PGMA@IDA nanomaterial in combination with MALDI-TOF-MS. Using this strategy, IgG Fc N-glycopeptides were analyzed in the plasma of CRC patients, and our findings indicated that glycosylation levels in the IgG Fc region were closely related to CRC. By using the IgG N-glycopeptide enrichment method and screening model designed in this study, early large-scale colorectal cancer screening can be implemented easily and fast.

Introduction

Colorectal cancer (CRC) currently ranks as one of the most fatal types of cancer worldwide [1]. The CRC prevalence rate differs geographically, with the highest incidence in Australia, New Zealand, Europe and North America, and the relatively low incidence in Africa and South-Central Asia. Additionally, in most parts of the world, men have higher CRC incidence rate than women [2]. The CRC incidence rate is significantly high in people over the age of 50 [3]. It is hence of clinical and public importance for targeted prevention and early detection in the control of CRC. At present, early screening methods include screening colonoscopy, fecal occult blood testing, sigmoidoscopy, computed tomography colonography and double cecum barium enema [2,[4], [5], [6]]. Except for colonoscopy, the misdiagnosis rates of these screening methods are high [2,7,8]. Even if colonoscopy is a highly sensitive screening method, it is invasive, complex and costly [4]. Therefore, it is essential to find an accurate and feasible method for the early screening of CRC.

Glycosylation is an important post-translational modification of proteins in normal physiological process. It is well reported that changes in protein glycosylation are associated with the occurrence of several diseases, as well as invasion and metastasis of malignant cells [9,10]. Glycoproteins with specific N-glycan epitopes have been treated as cancer biomarkers, which act as criteria for diagnosing and prognosing malignant tumors [[11], [12], [13]]. For instance, ɑ-fetoprotein (AFP), prostate-specific antigen (PSA) and carbohydrate antigen (CA)15–3 are currently used as the biomarkers of hepatocellular carcinoma, prostate cancer and breast cancer respectively [[14], [15], [16]]. Recently, a number of studies have focused on analyzing glycosylation characteristics of individual proteins [[17], [18], [19]], especially immunoglobulin G (IgG) [[20], [21], [22], [23]].

IgG is a prominent glycoprotein in circulation that plays a key role in humoral immune response [24]. The conformation of the Fc region is altered significantly by the variation in IgG glycosylation, which influences the interaction with receptors and modulates the biological functions of IgG [9,25,26]. As reported by some previous studies, certain changes of IgG glycosylation may be correlated with CRC and its progression using glycan analysis [[27], [28], [29]]. With the development of glycopeptide enrichment technology, research on IgG N-glycopeptides has gained much attention. Subtle differences in IgG glycopeptides have been found to reflect different physiological and pathological states, along with pathogenesis and progression of diseases, including cancers [[30], [31], [32], [33], [34], [35]]. Compared with glycan analysis, the glycopeptide analysis method could provide information about the amino acid sequence besides glycan information. Therefore, it could distinguish between Fab and Fc glycans. The glycans derived from different IgG subclasses could also be discriminated.

Recently, our group has designed the chitosan@poly (glycidyl methacrylate) @iminodiacetic acid (CS@PGMA@IDA) nanomaterial, which was previously demonstrated able to recognize the glycosylation site of IgG and enrich glycopeptides efficiently [36]. Compared with other glycopeptide enrichment methods, this nanomaterial showed great capacity to purify N-glycopeptide from a complex plasma system and could bind N-glycopeptides from digested proteins within 20 min, which could greatly shorten the enrichment time with extremely high sensitivity and specificity. In this study, we designed a workflow based on this nanomaterial to assess the differences in IgG Fc N-glycopeptides in plasma under different physiological states and further developed multivariate logistic regression models that could identify CRC patients in an easy and efficient way.

Section snippets

Study cohort

In this study, a total of 113 subjects were enrolled, including 46 CRC patients and 67 normal subjects (Table 1). After the patients were diagnosed with CRC by pathological biopsy, the plasma was centrifuged for 10 min, and the supernatant was collected and placed in a test tube and inverted several times. The protease inhibitor (cOmplete, Mini, EDTA-free, EASYpa; Roche, Basel, Switzerland) was added to the supernatant in a ratio of 1:500 (mass ratio), mixed and transferred to a − 80 °C freezer

Strategy for IgG N-glycopeptide analysis with MALDI-TOF-MS

Purifying IgG with protein G beads from plasma is a pretreatment step in analyzing the glycosylation features of IgG. After purification, a huge number of the IgG complexes were detected, among which 9 glycoproteins seriously interfered with the IgG N-glycosylation analysis (Fig. S1, Table S1). In the glycopeptide analysis method developed by Li et al., to obtain pure IgG N-glycopeptides, polyacrylamide gel electrophoresis was performed and the bands of interest were then excised [33]. The

Discussion

IgG N-glycan analysis methods and IgG N-glycopeptide analysis methods, which were used to discover the glycosylation profiling of CRC patients, vary in many aspects. First, the experimental workflows were different. In the glycan analysis workflow, after the common IgG enrichment step, IgGs were digested by PNGase F. Then the released glycans were labeled and purified by HILIC-SPE, followed by detection via HILIC-UPLC (Fig. 1a). In contrast, the glycopeptide analysis workflow was much simpler.

Conclusion

In this study, we attempted to uncover the characteristics of IgG Fc N-glycopeptides in the plasma of CRC patients. By analyzing IgG N-glycosylation in the plasma of CRC patients and healthy controls, we found that the levels of 11 N-glycopeptides such as IgG1 G0N in CRC patients significantly varied (p < .01). With machine learning, we used 11 N-glycopeptides as features to distinguish between CRC patients and healthy controls and the average AUC value of the ROC curves reached 0.893, which is

Declaration of Competing Interest

The authors declare that they have no conflicts of interest.

Acknowledgements

This work was supported by the Seeding Grant for Medicine and Information Sciences of Peking University (Grant number: 2014-MI-21), the National Natural Science Foundation of China (Grant numbers: 21572010 and 21772005), and the Qinghai Department of Science & Technology (Grant number: 2015-ZJ-742).

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    These authors contributed equally.

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