Polylactosaminoglycan glycomics: enhancing the detection of high-molecular-weight N-glycans in matrix-assisted laser desorption ionization time-of-flight profiles by matched filtering.

For over 30 years, protocols based on the mass spectrometry (MS) of permethylated derivatives, complemented by enzymatic degradations, have underpinned glycomic experiments aimed at defining the structures of individual glycans present in the complex mixtures that are characteristic of biological samples. Both MS instrumentation and sample handling have improved markedly in recent years, enabling greater sensitivity and better signal-to-noise ratios, thereby facilitating the detection of glycans at much higher masses than could be achieved in the past. The latter is especially important for the characterization of the biologically important class of N-glycans that carry polylactosaminoglycan chains. Such advances in data acquisition heighten the need for informatics tools to assist in glycan structure assignment. Here, utilizing mouse lung tissue as a model system, we present evidence of polylactosaminoglycan-containing N-glycans with permethylated molecular weights exceeding 13 kDa. We show that antennae branching patterns and lengths can be successfully determined at these high masses via MS/MS experiments, even when MS ion counts are very low. We also describe the development and application of a matched filtering algorithm for assisting high-molecular-weight glycan detection and structure assignment.

N-linked glycan profiling of GGTA1/CMAH knockout pigs identifies new potential carbohydrate xenoantigens.

BACKGROUND: The temporary or long-term xenotransplantation of pig organs into people would save thousands of lives each year if not for the robust human antibody response to pig carbohydrates. Genetically engineered pigs deficient in galactose α1,3 galactose (gene modified: GGTA1) and N-glycolylneuraminic acid (gene modified: CMAH) have significantly improved cell survival when challenged by human antibody and complement in vitro. There remains, however, a significant portion of human antibody binding. METHODS: To uncover additional xenoantigens, we compared the asparagine-linked (N-linked) glycome from serum proteins of humans, domestic pigs, GGTA1 knockout pigs, and GGTA1/CMAH knockout pigs using mass spectrometry. Carbohydrate structures were determined with assistance from GlycoWorkbench, Cartoonist, and SimGlycan software by comparison to existing database entries and collision-induced dissociation fragmentation data. RESULTS: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis of reduced and solid-phase permethylated glycans resulted in the detection of high-mannose, hybrid, and complex type N-linked glycans in the 1000-4500 m/z ion range. GGTA1/CMAH knockout pig samples had increased relative amounts of high-mannose, incomplete, and xylosylated N-linked glycans. All pig samples had significantly higher amounts of core and possibly antennae fucosylation. CONCLUSIONS: We provide for the first time a comparison of the serum protein glycomes of the human, domestic pig, and genetically modified pigs important to xenotransplantation.

Benchmark study of automatic annotation of MALDI-TOF N-glycan profiles.

Human experts can annotate peaks in MALDI-TOF profiles of detached N-glycans with some degree of accuracy. Even though MALDI-TOF profiles give only intact masses without any fragmentation information, expert knowledge of the most common glycans and biosynthetic pathways in the biological system can point to a small set of most likely glycan structures at the "cartoon" level of detail. Cartoonist is a recently developed, fully automatic annotation tool for MALDI-TOF glycan profiles. Here we benchmark Cartoonist's automatic annotations against human expert annotations on human and mouse N-glycan data from the Consortium for Functional Glycomics. We find that Cartoonist and expert annotations largely agree, but the expert tends to annotate more specifically, meaning fewer suggested structures per peak, and Cartoonist more comprehensively, meaning more annotated peaks. On peaks for which both Cartoonist and the expert give unique cartoons, the two cartoons agree in over 90% of all cases. This article is part of a Special Issue entitled: Computational Proteomics.