Scott R. Kronewitter

Glycomics and disease markers

Glycomics is the comprehensive study of all glycans expressed in biological systems. The biosynthesis of glycan relies on a number of highly competitive processes involving glycosyl transferases. Glycosylation is therefore highly sensitive to the biochemical environment and has been implicated in many diseases including cancer. Recently, interest in profiling the glycome has increased owing to the potential of glycans for disease markers. In this regard, mass spectrometry is emerging as a powerful technique for profiling the glycome. Global glycan profiling of human serum based on mass spectrometry has already led to several potentially promising markers for several types of cancer and diseases.

High-mannose glycans are elevated during breast cancer progression

Alteration in glycosylation has been observed in cancer. However, monitoring glycosylation changes during breast cancer progression is difficult in humans. In this study, we used a well-characterized transplantable breast tumor mouse model, the mouse mammary tumor virus-polyoma middle T antigen, to observe early changes in glycosylation. We have previously used the said mouse model to look at O-linked glycosylation changes with breast cancer. In this glycan biomarker discovery study, we examined N-linked glycan variations during breast cancer progression of the mouse model but this time doubling the number of mice and blood draw points. N-glycans from total mouse serum glycoproteins were profiled using matrix-assisted laser desorption/ionization Fourier transform-ion cyclotron resonance mass spectrometry at the onset, progression, and removal of mammary tumors. We observed four N-linked glycans, m/z 1339.480 (Hex3HexNAc), 1485.530 (Hex3HexNAc4Fuc), 1809.639 (Hex5HexNAc4Fuc), and 1905.630 (Man9), change in intensity in the cancer group but not in the control group. In a separate study, N-glycans from total human serum glycoproteins of breast cancer patients and controls were also profiled. Analysis of human sera using an internal standard showed the alteration of the low-abundant high-mannose glycans, m/z 1419.475, 1581.528, 1743.581, 1905.634 (Man6–9), in breast cancer patients. A key observation was the elevation of a high-mannose type glycan containing nine mannoses, Man9, m/z 1905.630 in both mouse and human sera in the presence of breast cancer, suggesting an incompletion of the glycosylation process that normally trims back Man9 to produce complex and hybrid type oligosaccharides.

The development of retrosynthetic glycan libraries to profile and classify the human serum N-linked glycome.

Annotation of the human serum N‐linked glycome is a formidable challenge but is necessary for disease marker discovery. A new theoretical glycan library was constructed and proposed to provide all possible glycan compositions in serum. It was developed based on established glycobiology and retrosynthetic state‐transition networks. We find that at least 331 compositions are possible in the serum N‐linked glycome. By pairing the theoretical glycan mass library with a high mass accuracy and high‐resolution MS, human serum glycans were effectively profiled. Correct isotopic envelope deconvolution to monoisotopic masses and the high mass accuracy instruments drastically reduced the amount of false composition assignments. The high throughput capacity enabled by this library permitted the rapid glycan profiling of large control populations. With the use of the library, a human serum glycan mass profile was developed from 46 healthy individuals. This paper presents a theoretical N‐linked glycan mass library that was used for accurate high‐throughput human serum glycan profiling. Rapid methods for evaluating a patients glycome are instrumental for studying glycan‐based markers.

A versatile and scalable strategy for glycoprofiling bifidobacterial consumption of human milk oligosaccharides

Human milk contains approximately 200 complex oligosaccharides believed to stimulate the growth and establishment of a protective microbiota in the infant gut. The lack of scalable analytical techniques has hindered the measurement of bacterial metabolism of these and other complex prebiotic oligosaccharides. An in vitro, multi‐strain, assay capable of measuring kinetics of bacterial growth and detailed oligosaccharide consumption analysis by FTICR‐MS was developed and tested simultaneously on 12 bifidobacterial strains. For quantitative consumption, deuterated and reduced human milk oligosaccharide (HMO) standards were used. A custom software suite developed in house called Glycolyzer was used to process the large amounts of oligosaccharide mass spectra automatically with 13C corrections based on de‐isotoping protocols. High growth on HMOs was characteristic of Bifidobacterium longum biovar infantis strains, which consumed nearly all available substrates, while other bifidobacterial strains tested, B. longum bv. longum, B. adolescentis, B. breve and B. bifidum, showed low or only moderate growth ability. Total oligosaccharide consumption ranged from a high of 87% for B. infantis JCM 7009 to only 12% for B. adolescentis ATCC 15703. A detailed analysis of consumption glycoprofiles indicated strain‐specific capabilities towards differential metabolism of milk oligosaccharides. This method overcomes previous limitations in the quantitative, multi‐strain analysis of bacterial metabolism of HMOs and represents a novel approach towards understanding bacterial consumption of complex prebiotic oligosaccharides.

Glycomic Approach for Potential Biomarkers on Prostate Cancer: Profiling of N-Linked Glycans in Human Sera and pRNS Cell Lines

Prostate cancer is a leading cause of cancer death among men. Currently available screening test measures prostate-specific antigen (PSA) to detect prostate cancer. However, this test produces false positive values that often lead to negative biopsies. Therefore, a more reliable diagnostic tool is needed. Glycans in serum are of particular interest as around half of all proteins are glycosylated. In this study, N-linked glycans were enzymatically released by PNGase F from prostate epithelial cell lines (pRNS) expressing wild type or mutant androgen receptors and a small set of human serum samples. Released glycans were purified and partitioned into neutral and acidic components by solid phase extraction (SPE) using graphitized carbon cartridges. The SPE fractions were analyzed by matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI FT-ICR MS). Significant changes in some high-mannose and fucosylated biantennary complex N-linked glycans were observed in the serum of prostate cancer patients.

Human Serum Processing and Analysis Methods for Rapid and Reproducible N-Glycan Mass Profiling

Glycans constitute a new class of compounds for biomarker discovery. Glycosylation is a common post-translational modification and is often associated with transformation to malignancy. To analyze glycans, they are released from proteins, enriched, and measured with mass spectrometry. For biomarker discovery, repeatability at every step of the process is important. Locating and minimizing the process variability is key to establishing a robust platform stable enough for biomarker discovery. Understanding the variability of the measurement devices helps understand the variability associated with the chemical processing. This report explores the potential use of methods expediting the enzymatic release of glycans such as a microwave reactor and automation of the solid-phase extraction with a robotic liquid handler. The study employs matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance mass spectrometry but would be suitable with any mass spectrometry method. Methods for system-wide data analysis are examined because proper metrics for evaluating the performance of glycan sample preparation procedures are not well established.

Automated Assignments of N- and O-Site Specific Glycosylation with Extensive Glycan Heterogeneity of Glycoprotein Mixtures

Site-specific glycosylation (SSG) of glycoproteins remains a considerable challenge and limits further progress in the areas of proteomics and glycomics. Effective methods require new approaches in sample preparation, detection, and data analysis. While the field has advanced in sample preparation and detection, automated data analysis remains an important goal. A new bioinformatics approach implemented in software called GP Finder automatically distinguishes correct assignments from random matches and complements experimental techniques that are optimal for glycopeptides, including nonspecific proteolysis and high mass resolution liquid chromatography/tandem mass spectrometry (LC/MS/MS). SSG for multiple N- and O-glycosylation sites, including extensive glycan heterogeneity, was annotated for single proteins and protein mixtures with a 5% false-discovery rate, generating hundreds of nonrandom glycopeptide matches and demonstrating the proof-of-concept for a self-consistency scoring algorithm shown to be compliant with the target-decoy approach (TDA). The approach was further applied to a mixture of N-glycoproteins from unprocessed human milk and O-glycoproteins from very-low-density-lipoprotein (vLDL) particles.

The Glycolyzer: Automated Glycan Annotation Software for High Performance Mass Spectrometry and Its Application to Ovarian Cancer Glycan Biomarker Discovery

Human serum glycomics is a promising method for finding cancer biomarkers but often lacks the tools for streamlined data analysis. The Glycolyzer software incorporates a suite of analytic tools capable of identifying informative glycan peaks out of raw mass spectrometry data. As a demonstration of its utility, the program was used to identify putative biomarkers for epithelial ovarian cancer from a human serum sample set. A randomized, blocked, and blinded experimental design was used on a discovery set consisting of 46 cases and 48 controls. Retrosynthetic glycan libraries were used for data analysis and several significant candidate glycan biomarkers were discovered via hypothesis testing. The significant glycans were attributed to a glycan family based on glycan composition relationships and incorporated into a linear classifier motif test. The motif test was then applied to the discovery set to evaluate the disease state discrimination performance. The test provided strongly predictive results based on receiver operator characteristic curve analysis. The area under the receiver operator characteristic curve was 0.93. Using the Glycolyzer software, we were able to identify a set of glycan biomarkers that highly discriminate between cases and controls, and are ready to be formally validated in subsequent studies.

Detecting glycan cancer biomarkers in serum samples using MALDI FT-ICR mass spectrometry data

MOTIVATION The development of better tests to detect cancer in its earliest stages is one of the most sought-after goals in medicine. Especially important are minimally invasive tests that require only blood or urine samples. By profiling oligosaccharides cleaved from glycosylated proteins shed by tumor cells into the blood stream, we hope to determine glycan profiles that will help identify cancer patients using a simple blood test. The data in this article were generated using matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI FT-ICR MS). We have developed novel methods for analyzing this type of mass spectrometry data and applied it to eight datasets from three different types of cancer (breast, ovarian and prostate). RESULTS The techniques we have developed appear to be effective in the analysis of MALDI FT-ICR MS data. We found significant differences between control and cancer groups in all eight datasets, including two structurally related compounds that were found to be significantly different between control and cancer groups in all three types of cancer studied.

Analysis of MALDI FT-ICR mass spectrometry data: a time series approach.

Matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry is a technique for high mass-resolution analysis of substances that is rapidly gaining popularity as an analytic tool. Extracting signal from the background noise, however, poses significant challenges. In this article, we model the noise part of a spectrum as an autoregressive, moving average (ARMA) time series with innovations given by a generalized gamma distribution with varying scale parameter but constant shape parameter and exponent. This enables us to classify peaks found in actual spectra as either noise or signal using a reasonable criterion that outperforms a standard threshold criterion.