Joshua A. Klein

Integrated omics and computational glycobiology reveal structural basis for Influenza A virus glycan microheterogeneity and host interactions

Despite sustained biomedical research effort, influenza A virus remains an imminent threat to the world population and a major healthcare burden. The challenge in developing vaccines against influenza is the ability of the virus to mutate rapidly in response to selective immune pressure. Hemagglutinin is the predominant surface glycoprotein and the primary determinant of antigenicity, virulence and zoonotic potential. Mutations leading to changes in the number of HA glycosylation sites are often reported. Such genetic sequencing studies predict at best the disruption or creation of sequons for N-linked glycosylation; they do not reflect actual phenotypic changes in HA structure. Therefore, combined analysis of glycan micro and macro-heterogeneity and bioassays will better define the relationships among glycosylation, viral bioactivity and evolution. We present a study that integrates proteomics, glycomics and glycoproteomics of HA before and after adaptation to innate immune system pressure. We combined this information with glycan array and immune lectin binding data to correlate the phenotypic changes with biological activity. Underprocessed glycoforms predominated at the glycosylation sites found to be involved in viral evolution in response to selection pressures and interactions with innate immune-lectins. To understand the structural basis for site-specific glycan microheterogeneity at these sites, we performed structural modeling and molecular dynamics simulations. We observed that the presence of immature, high-mannose type glycans at a particular site correlated with reduced accessibility to glycan remodeling enzymes. Further, the high mannose glycans at sites implicated in immune lectin recognition were predicted to be capable of forming trimeric interactions with the immune-lectin surfactant protein-D.

A review of methods for interpretation of glycopeptide tandem mass spectral data.

Despite the publication of several software tools for analysis of glycopeptide tandem mass spectra, there remains a lack of consensus regarding the most effective and appropriate methods. In part, this reflects problems with applying standard methods for proteomics database searching and false discovery rate calculation. While the analysis of small post-translational modifications (PTMs) may be regarded as an extension of proteomics database searching, glycosylation requires specialized approaches. This is because glycans are large and heterogeneous by nature, causing glycopeptides to exist as multiple glycosylated variants. Thus, the mass of the peptide cannot be calculated directly from that of the intact glycopeptide. In addition, the chemical nature of the glycan strongly influences product ion patterns observed for glycopeptides. As a result, glycopeptidomics requires specialized bioinformatics methods. We summarize the recent progress towards a consensus for effective glycopeptide tandem mass spectrometric analysis.

Microfluidic Capillary Electrophoresis–Mass Spectrometry for Analysis of Monosaccharides, Oligosaccharides, and Glycopeptides

Glycomics and glycoproteomics analyses by mass spectrometry require efficient front-end separation methods to enable deep characterization of heterogeneous glycoform populations. Chromatography methods are generally limited in their ability to resolve glycoforms using mobile phases that are compatible with online liquid chromatography-mass spectrometry (LC-MS). The adoption of capillary electrophoresis-mass spectrometry methods (CE-MS) for glycomics and glycoproteomics is limited by the lack of convenient interfaces for coupling the CE devices to mass spectrometers. Here, we describe the application of a microfluidics-based CE-MS system for analysis of released glycans, glycopeptides and monosaccharides. We demonstrate a single CE method for three different modalities, thus contributing to comprehensive glycoproteomics analyses. In addition, we explored compatible sample derivatization methods. We used glycan TMT-labeling to improve electrophoretic migration and enable multiplexed quantitation by tandem MS. We used sialic acid linkage-specific derivatization methods to improve separation and the level of information obtained from a single analytical step. Capillary electrophoresis greatly improved glycoform separation for both released glycans and glycopeptides over that reported for chromatography modes more frequently employed for such analyses. Overall, the CE-MS method described here enables rapid setup and analysis of glycans and glycopeptides using mass spectrometry.

Comparison of Collisional and Electron-Based Dissociation Modes for Middle-Down Analysis of Multiply Glycosylated Peptides

AbstractAnalysis of singly glycosylated peptides has evolved to a point where large-scale LC-MS analyses can be performed at almost the same scale as proteomics experiments. While collisionally activated dissociation (CAD) remains the mainstay of bottom-up analyses, it performs poorly for the middle-down analysis of multiply glycosylated peptides. With improvements in instrumentation, electron-activated dissociation (ExD) modes are becoming increasingly prevalent for proteomics experiments and for the analysis of fragile modifications such as glycosylation. While these methods have been applied for glycopeptide analysis in isolated studies, an organized effort to compare their efficiencies, particularly for analysis of multiply glycosylated peptides (termed here middle-down glycoproteomics), has not been made. We therefore compared the performance of different ExD modes for middle-down glycopeptide analyses. We identified key features among the different dissociation modes and show that increased electron energy and supplemental activation provide the most useful data for middle-down glycopeptide analysis. Graphical Abstract

pymzML v2.0: introducing a highly compressed and seekable gzip format

Motivation: In the new release of pymzML (v2.0), we have optimized the speed of this established tool for mass spectrometry data analysis to adapt to increasing amounts of data in mass spectrometry. Thus, we integrated faster libraries for numerical calculations, improved data retrieving algorithms and have optimized the source code. Importantly, to adapt to rapidly growing file sizes, we developed a generalizable compression scheme for very fast random access and applied this concept to mzML files to retrieve spectral data. Results: pymzML performs at par with established C programs when it comes to processing times. However, it offers the versatility of a scripting language, while adding unprecedented fast random access to compressed files. Additionally, we designed our compression scheme in such a general way that it can be applied to any field where fast random access to large data blocks in compressed files is desired. Availability and implementation: pymzML is freely available on https://github.com/pymzML/pymzML under GPL license. pymzML requires Python3.4+ and optionally numpy. Documentation available on http://pymzml.readthedocs.io.

Cryptosporidium parvum vaccine candidates are incompletely modified with O-linked-N-acetylgalactosamine or contain N-terminal N-myristate and S-palmitate

Cryptosporidium parvum (studied here) and Cryptosporidium hominis are important causes of diarrhea in infants and immunosuppressed persons. C. parvum vaccine candidates, which are on the surface of sporozoites, include glycoproteins with Ser- and Thr-rich domains (Gp15, Gp40, and Gp900) and a low complexity, acidic protein (Cp23). Here we used mass spectrometry to determine that O-linked GalNAc is present in dense arrays on a glycopeptide with consecutive Ser derived from Gp40 and on glycopeptides with consecutive Thr derived from Gp20, a novel C. parvum glycoprotein with a formula weight of ~20 kDa. In contrast, the occupied Ser or Thr residues in glycopeptides from Gp15 and Gp900 are isolated from one another. Gly at the N-terminus of Cp23 is N-myristoylated, while Cys, the second amino acid, is S-palmitoylated. In summary, C. parvum O-GalNAc transferases, which are homologs of host enzymes, densely modify arrays of Ser or Thr, as well as isolated Ser and Thr residues on C. parvum vaccine candidates. The N-terminus of an immunodominant antigen has lipid modifications similar to those of host cells and other apicomplexan parasites. Mass spectrometric demonstration here of glycopeptides with O-glycans complements previous identification C. parvum O-GalNAc transferases, lectin binding to vaccine candidates, and human and mouse antibodies binding to glycopeptides. The significance of these post-translational modifications is discussed with regards to the function of these proteins and the design of serological tests and vaccines.

Negative Electron Transfer Dissociation Sequencing of 3- O -Sulfation-Containing Heparan Sulfate Oligosaccharides

AbstractAmong dissociation methods, negative electron transfer dissociation (NETD) has been proven the most useful for glycosaminoglycan (GAG) sequencing because it produces informative fragmentation, a low degree of sulfate losses, high sensitivity, and translatability to multiple instrument types. The challenge, however, is to distinguish positional sulfation. In particular, NETD has been reported to fail to differentiate 4-O- versus 6-O-sulfation in chondroitin sulfate decasaccharide. This raised the concern of whether NETD is able to differentiate the rare 3-O-sulfation from predominant 6-O-sulfation in heparan sulfate (HS) oligosaccharides. Here, we report that NETD generates highly informative spectra that differentiate sites of O-sulfation on glucosamine residues, enabling structural characterizations of synthetic HS isomers containing 3-O-sulfation. Further, lyase-resistant 3-O-sulfated tetrasaccharides from natural sources were successfully sequenced. Notably, for all of the oligosaccharides in this study, the successful sequencing is based on NETD tandem mass spectra of commonly observed deprotonated precursor ions without derivatization or metal cation adduction, simplifying the experimental workflow and data interpretation. These results demonstrate the potential of NETD as a sensitive analytical tool for detailed, high-throughput structural analysis of highly sulfated GAGs. Graphical Abstract

Application of network smoothing to glycan LC-MS profiling

Motivation: Glycosylation is one of the most heterogeneous and complex protein post‐translational modifications. Liquid chromatography coupled mass spectrometry (LC‐MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolution mass spectrometry. Mass spectrometry data contains intricate sub‐structures that encode mass and abundance, requiring several transformations before it can be used to identify biological molecules, requiring automated tools to analyze samples in a high throughput setting. Existing tools for interpreting the resulting data do not take into account related glycans when evaluating individual observations, limiting their sensitivity. Results: We developed an algorithm for assigning glycan compositions from LC‐MS data by exploring biosynthetic network relationships among glycans. Our algorithm optimizes a set of likelihood scoring functions based on glycan chemical properties but uses network Laplacian regularization and optionally prior information about expected glycan families to smooth the likelihood and thus achieve a consistent and more representative solution. Our method was able to identify as many, or more glycan compositions compared to previous approaches, and demonstrated greater sensitivity with regularization. Our network definition was tailored to N‐glycans but the method may be applied to glycomics data from other glycan families like O‐glycans or heparan sulfate where the relationships between compositions can be expressed as a graph. Availability and implementation Built Executable: http://www.bumc.bu.edu/msr/glycresoft/ and Source Code: https://github.com/BostonUniversityCBMS/glycresoft. Supplementary information: Supplementary data are available at Bioinformatics online.