Human Serum N-glycome Research Articles

Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery

Simple SummaryGlobally, there were around 2.1 million lung cancer cases and 1.8 million deaths in 2018. Hungary—where this study was carried out—had the highest rate of lung cancer in the same year. We developed a new analytical method which can be readily used to follow up the tumor surgery by investigating the glycan (sugar) structures of proteins. As the results of such investigations are very complex, computer-assisted machine learning methods were utilized for data interpretation.The human serum N-glycome is a valuable source of biomarkers for malignant diseases, already utilized in multiple studies. In this paper, the N-glycosylation changes in human serum proteins were analyzed after surgical lung tumor resection. Seventeen lung cancer patients were involved in this study and the N-glycosylation pattern of their serum samples was analyzed before and after the surgery using capillary electrophoresis separation with laser-induced fluorescent detection. The relative peak areas of 21 N-glycans were evaluated from the acquired electropherograms using machine learning-based data analysis. Individual glycans as well as their subclasses were taken into account during the course of evaluation. For the data analysis, both discrete (e.g., smoker or not) and continuous (e.g., age of the patient) clinical parameters were compared against the alterations in these 21 N-linked carbohydrate structures. The classification tree analysis resulted in a panel of N-glycans, which could be used to follow up on the effects of lung tumor surgical resection.

Modeling of the Desialylated Human Serum N-glycome for Molecular Diagnostic Applications in Inflammatory and Malignant Lung Diseases

Immunoglobulin G and A, transferrin, haptoglobin and alpha-1- antitrypsin represent approximately 85% of the human serum glycoproteome and their N-glycosylation analysis may lead to the discovery of important molecular disease markers. However, due to the labile nature of the sialic acid residues, the desialylated subset of the serum N-glycoproteome has been traditionally utilized for diagnostic applications. Creating a five-protein model to deconstruct the overall N-glycosylation fingerprints in inflammatory and malignant lung diseases. The N-glycan pool of human serum and the five high abundant serum glycoproteins were analyzed. Simultaneous endoglycosidase/sialidase digestion was followed by fluorophore labeling and separation by CE-LIF to establish the model. Pooled serum samples from patients with COPD, lung cancer (LC) and their comorbidity were all analyzed. Nine significant (>1%) asialo-N-glycan structures were identified both in human serum and the standard protein mixture. The core-fucosylated-agalacto-biantennary glycan differentiated COPD and LC and both from the control and the comorbidity groups. Decrease in the core-fucosylated-agalacto-biantennary-bisecting, monogalacto and bigalacto structures differentiated all disease groups from the control. The significant increase of the fucosylated-galactosylated-triantennary structure was highly specific for LC, to a medium extent for COPD and a lesser extent for comorbidity. Also, some increase in the afucosylated-galactosylated-biantennary structure in all three disease types and afucosylated-galactosylated-triantennary structures in COPD and LC were observed in comparison to the control group. Our results suggested that changes in the desialylated human serum Nglycome hold glycoprotein specific molecular diagnostic potential for malignant and inflammatory lung diseases, which can be modeled with the five-protein mixture.

Comparative analysis of the human serum N-glycome in lung cancer, COPD and their comorbidity using capillary electrophoresis

Lung cancer (LC) and chronic obstructive pulmonary disease (COPD) are prevalent ailments with a great challenge to distinguish them based on symptoms only. Since they require different treatments, it is important to find non-invasive methods capable to readily diagnose them. Moreover, COPD increases the risk of lung cancer development, leading to their comorbidity. In this pilot study the N-glycosylation profile of pooled human serum samples (90 patients each) from lung cancer, COPD and comorbidity (LC with COPD) patients were investigated in comparison to healthy individuals (control) by capillary gel electrophoresis with high sensitivity laser-induced fluorescence detection. Sample preparation was optimized for human serum samples introducing a new temperature adjusted denaturation protocol to prevent precipitation and increased endoglycosidase digestion time to assure complete removal of the N-linked carbohydrates. The reproducibility of the optimized method was <3.5%. Sixty-one N-glycan structures were identified in the pooled control human serum sample and the profile was compared to pooled lung cancer, COPD and comorbidity of COPD with lung cancer patient samples. One important finding was that no other sugar structures were detected in any of the patient groups, only quantitative differences were observed. Based on this comparative exercise, a panel of 13 N-glycan structures were identified as potential glycobiomarkers to reveal significant changes (>33% in relative peak areas) between the pathological and control samples. In addition to N-glycan profile changes, alterations in the individual N-glycan subclasses, such as total fucosylation, degree of sialylation and branching may also hold important glycobiomarker values.

Sample Preparation Scale-Up for Deep N-glycomic Analysis of Human Serum by Capillary Electrophoresis and CE-ESI-MS*

We introduce an efficient sample preparation workflow to facilitate deep N-glycomics analysis of the human serum by capillary electrophoresis with laser induced fluorescence (CE-LIF) detection and to accommodate the higher sample concentration requirement of electrospray ionization mass spectrometry connected to capillary electrophoresis (CE-ESI-MS). A novel, temperature gradient denaturing protocol was applied on amine functionalized magnetic bead partitioned glycoproteins to circumvent the otherwise prevalent precipitation issue. During this process, the free sugar content of the serum was significantly decreased as well, accommodating enhanced PNGase F mediated release of the N-linked carbohydrates. The liberated oligosaccharides were tagged with aminopyrene-trisulfonate, utilizing a modified evaporative labeling protocol. Processing the samples with this new workflow enabled deep CE-LIF analysis of the human serum N-glycome and provided the appropriate amount of material for CE-ESI-MS analysis in negative ionization mode.

Serum protein N-glycosylation changes in multiple myeloma

BackgroundMultiple myeloma is characterized by clonal proliferation of malignant plasma cells in the bone marrow that produce monoclonal immunoglobulins. N-glycosylation changes of these monoclonal immunoglobulins have been reported in multiple myeloma, but previous studies only detected limited serum N-glycan features. MethodsHere, a more detailed study of the human serum N-glycome of 91 multiple myeloma patients and 51 controls was performed. We additionally analyzed sequential samples from patients (n = 7) which were obtained at different time points during disease development as well as 16 paired blood serum and bone marrow plasma samples. N-glycans were enzymatically released and measured by mass spectrometry after linkage specific derivatization of sialic acids. ResultsA decrease in both α2,3- and α2,6-sialylation, galactosylation and an increase in fucosylation within complex-type N-glycans were found in multiple myeloma patients compared to controls, as well as a decrease in difucosylation of diantennary glycans. The observed glycosylation changes were present in all ISS stages, including the “low-risk” ISS I. In individual patients, difucosylation of diantennary glycans decreased with development of the disease. Protein N-glycosylation features from blood and bone marrow showed strong correlation. Moreover, associations of monoclonal immunoglobulin (M-protein) and albumin levels with glycan traits were discovered in multiple myeloma patients. Conclusions & general significanceIn conclusion, serum protein N-glycosylation analysis could successfully distinguish multiple myeloma from healthy controls. Further studies are needed to assess the potential roles of glycan trait changes and the associations of glycans with clinical parameters in multiple myeloma early detection and prognosis.

Healthy human serum N-glycan profiling reveals the influence of ethnic variation on the identified cancer-relevant glycan biomarkers.

BackgroundMost glycomics studies have focused on understanding disease mechanisms and proposing serum markers for various diseases, yet the influence of ethnic variation on the identified glyco-biomarker remains poorly addressed. This study aimed to investigate the inter-ethnic serum N-glycan variation among US origin control, Japanese, Indian, and Ethiopian healthy volunteers.MethodsHuman serum from 54 healthy subjects of various ethnicity and 11 Japanese hepatocellular carcinoma (HCC) patients were included in the study. We employed a comprehensive glycoblotting-assisted MALDI-TOF/MS-based quantitative analysis of serum N-glycome and fluorescence HPLC-based quantification of sialic acid species. Data representing serum N-glycan or sialic acid levels were compared among the ethnic groups using SPSS software.ResultsTotal of 51 N-glycans released from whole serum glycoproteins could be reproducibly quantified within which 33 glycoforms were detected in all ethnicities. The remaining N-glycans were detected weakly but exclusively either in the Ethiopians (13 glycans) or in all the other ethnic groups (5 glycans). Highest abundance (p < 0.001) of high mannose, core-fucosylated, hyperbranched/hypersialylated N-glycans was demonstrated in Ethiopians. In contrast, only one glycan (m/z 2118) significantly differed among all ethnicities being highest in Indians and lowest in Ethiopians. Glycan abundance trend in Ethiopians was generally close to that of Japanese HCC patients. Glycotyping analysis further revealed ethnic-based disparities mainly in the branched and sialylated structures. Surprisingly, some of the glycoforms greatly elevated in the Ethiopian subjects have been identified as serum biomarkers of various cancers. Sialic acid level was significantly increased primarily in Ethiopians, compared to the other ethnicities.ConclusionThe study revealed ethnic-specific differences in healthy human serum N-glycome with highest abundance of most glycoforms in the Ethiopian ethnicity. The results strongly emphasized the need to consider ethnicity matching for accurate glyco-biomarker identification. Further large-scale study employing various ethnic compositions is needed to verify the current result.

Selective enrichment of N-linked glycopeptides and glycans by using a dextran-modified hydrophilic material.

Glycosylation analysis of proteins from biological sources utilizing mass spectrometry based approaches is challenging due to the relatively low abundance of glycopeptides, the structural diversity of glycans, and the coexisting matrices. In this study, a customized dextran-bonded silica-based stationary phase was introduced for selective enrichment of glycopeptides and glycans from complex biological samples. This material has exhibited superior selectivity and broader glycosylation site coverage over commercial Sepharose in glycoproteomic evaluation. Additionally, the glycomic analysis of fetuin, α1 -acid glycoprotein, and human serum N-glycome also indicated the relatively higher sensitivity, selectivity, and glycoform coverage of dextran-bonded silica than that of Sepharose and porous graphitized carbon. Therefore, the dextran-bonded silica is expected to make contributions in the fields of glycoproteomics and glycomics.

Abstract 5544: New technologies to probe the systems glycobiology of cancer

Abstract A robotic 384 well based platform was developed to release and label the human serum N-glycome. Improved separation of the glycan pool, based on hydrophilic interaction UPLC chromatography combined with mass spectrometry (Waters Corporation) and computer assisted data interpretation (NIBRT Glycobase) enabled us to build a database after assigning the detailed structures of more than 170 N-glycans. The aim of the project was to use these technologies and databases to link glycosylation changes in individual patients’ serum with features of cancer and with changes in a range of other –omics data acquired from the same patients. Alterations in glycosylation in various breast cancers were mapped to changes in the serum glycomes and aligned with genetic, transcriptomic and proteomic data. Pathway analysis showed strong associations between these glycan changes and the –omics data. This revealed that many of the glycan changes are directly associated with pathways involved in cancer metastasis. Our next aim was to demonstrate the feasibility of collecting personalised data from individual patients from each of the –omics analyses to build up a dataset of the changing glycosylation over time. This opens up the possibility of linking these data to explore pathways of disease and, in particular, nodal points where the patient can no longer compensate for the effects of an altered pathway that is leading to disease. To this end, single glycoproteins from 8 controls and 26 ovarian cancer patients were sequentially purified from 5ul of serum on affinity plates and the released glycans were analysed. Rather than the conventional way of looking at markers which compare a patients’ data with averages, the control can now be the blood of the patient themselves taken at an earlier time point. The automation, sensitivity, quantitation and resolution of the new technology platforms coupled with dedicated software open up new possibilities for precision medicine and early intervention based on the biochemical profile of the patient.

Association of N-Glycosylation with Breast Carcinoma and Systemic Features Using High-Resolution Quantitative UPLC

An improved separation of the human serum N-glycome using hydrophilic interaction chromatography technology with UPLC is described, where more than 140 N-glycans were assigned. Using this technique, serum samples from 107 healthy controls and 62 newly diagnosed breast cancer patients were profiled. The most statistically significant alterations were observed in cancer patients compared with healthy controls: an increase in sialylation, branching, and outer-arm fucosylation and a decrease in high-mannosylated and biantennary core-fucosylated glycans. In the controls and cases combined systemic features were analyzed; serum estradiol was associated with increase in digalactosylated glycans, and higher mammographic density was associated with increase in biantennary digalactosylated glycans and with decrease in trisialylated and in outer-arm fucosylated glycans. Furthermore, particular glycans were altered in some features of the breast carcinomas; bisected biantennary nonfucosylated glycans were decreased in patients with progesterone receptor positive tumors, and core-fucosylated biantennary bisected monogalactosylated glycans were decreased in patients with the TP53 mutation. Systemic features show more significant associations with the serum N-glycome than do the features of the breast carcinomas. In conclusion, the UPLC-based glycan analysis technique described here reveals highly significant differences between healthy women and breast cancer patients. Significant associations with breast carcinoma and systemic features are described.