Multivariate data analysis for the detection of human alpha-acid glycoprotein aberrant glycosylation in pancreatic ductal adenocarcinoma

Determination of Sialic Acid Isomers from Released N-Glycans Using Ion Mobility Spectrometry

Complex carbohydrates are ubiquitous in nature and represent one of the major classes of biopolymers. They can exhibit highly diverse structures with multiple branched sites as well as a complex regio- and stereochemistry. A common way to analytically address this complexity is liquid chromatography (LC) in combination with mass spectrometry (MS). However, MS-based detection often does not provide sufficient information to distinguish glycan isomers. Ion mobility-mass spectrometry (IM-MS)—a technique that separates ions based on their size, charge, and shape—has recently shown great potential to solve this problem by identifying characteristic isomeric glycan features such as the sialylation and fucosylation pattern. However, while both LC-MS and IM-MS have clearly proven their individual capabilities for glycan analysis, attempts to combine both methods into a consistent workflow are lacking. Here, we close this gap and combine hydrophilic interaction liquid chromatography (HILIC) with IM-MS to analyze the glycan structures released from human alpha-1-acid glycoprotein (hAGP). HILIC separates the crude mixture of highly sialylated multi-antennary glycans, MS provides information on glycan composition, and IMS is used to distinguish and quantify α2,6- and α2,3-linked sialic acid isomers based on characteristic fragments. Further, the technique can support the assignment of antenna fucosylation. This feature mapping can confidently assign glycan isomers with multiple sialic acids within one LC-IM-MS run and is fully compatible with existing workflows for N-glycan analysis.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

Altered Glycosylation of Human Alpha-1-Acid Glycoprotein as a Biomarker for Malignant Melanoma

A high-resolution HILIC-MS/MS method was developed to analyze anthranilic acid derivatives of N-glycans released from human serum alpha-1-acid glycoprotein (AGP). The method was applied to samples obtained from 18 patients suffering from high-risk malignant melanoma as well as 19 healthy individuals. It enabled the identification of 102 glycan isomers separating isomers that differ only in sialic acid linkage (α-2,3, α-2,6) or in fucose positions (core, antenna). Comparative assessment of the samples revealed that upregulation of certain fucosylated glycans and downregulation of their nonfucosylated counterparts occurred in cancer patients. An increased ratio of isomers with more α-2,6-linked sialic acids was also observed. Linear discriminant analysis (LDA) combining 10 variables with the highest discriminatory power was employed to categorize the samples based on their glycosylation pattern. The performance of the method was tested by cross-validation, resulting in an overall classification success rate of 96.7%. The approach presented here is significantly superior to serological marker S100B protein in terms of sensitivity and negative predictive power in the population studied. Therefore, it may effectively support the diagnosis of malignant melanoma as a biomarker.

Recent Advances in Mass Spectrometry-Based Glycomic and Glycoproteomic Studies of Pancreatic Diseases

Modification of proteins by glycans plays a crucial role in mediating biological functions in both healthy and diseased states. Mass spectrometry (MS) has emerged as the most powerful tool for glycomic and glycoproteomic analyses advancing knowledge of many diseases. Such diseases include those of the pancreas which affect millions of people each year. In this review, recent advances in pancreatic disease research facilitated by MS-based glycomic and glycoproteomic studies will be examined with a focus on diabetes and pancreatic cancer. The last decade, and especially the last five years, has witnessed developments in both discovering new glycan or glycoprotein biomarkers and analyzing the links between glycans and disease pathology through MS-based studies. The strength of MS lies in the specificity and sensitivity of liquid chromatography-electrospray ionization MS for measuring a wide range of biomolecules from limited sample amounts from many sample types, greatly enhancing and accelerating the biomarker discovery process. Furthermore, imaging MS of glycans enabled by matrix-assisted laser desorption/ionization has proven useful in complementing histology and immunohistochemistry to monitor pancreatic disease progression. Advances in biological understanding and analytical techniques, as well as challenges and future directions for the field, will be discussed.

Alterations in the Glycan Profile of Mouse Transferrin: New Insights in Collagen-Induced Arthritis

Transferrin purification from mice serum samples by immunoaffinity chromatography (IAC) was optimized in order to study the possible modifications occurring in its glycans in collagen-induced arthritis (CIA) samples. SDS-PAGE and nanoLC-MS/MS were used to monitor the IAC purification performance. Afterward, a relative quantification of mouse transferrin (mTf) glycan isomers using [¹²C₆]/[¹³C₆]-aniline was used to unequivocally detect alterations in the glycan profile of CIA mice. In addition, multivariate data analysis was applied to identify the most meaningful glycan isomers for the discrimination between control and pathological samples. Partial least-squares discriminant analysis (PLS-DA) revealed that five out of fifteen mTf glycan isomers could be potential biomarkers of CIA, most of them corresponding to highly sialylated structures (H6N5S3_2, H6N5S3_3, and H5N4S3_2). Moreover, some of these glycan isomers also seemed to be related with the progression of CIA, especially H6N5S2 and H6N5S3_2, as their overexpression increased with the clinical score of the pathology. Hence, the established methodology not only provides valuable information to find glycan-based biomarkers of CIA, but also leaves the door open to evaluate, in the future, glycosylation changes of many other inflammatory diseases, in which transferrin has been described to be altered.