Sandra V. Bennun

Coarse-grained modeling of lipids.

Molecular modeling of phospholipids on many scales has progressed significantly over the last years. Here we review several membrane models on intermediate to large length scales restricting ourselves to particle based coarse-grained models with implicit and explicit solvent. We explain similarities and differences as well as their connection to experiments and fine-grained models. We neglect any field descriptions on larger scales. We discuss then a few examples where we focus on studies of lipid phase behavior as well as supported lipid bilayers as these examples can only be meaningfully studied using large-scale models to date.

A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data

Effective representation and characterization of biosynthetic pathways of glycosylation can be facilitated by mathematical modeling. This paper describes the expansion of a previously developed detailed model for N-linked glycosylation with the further application of the model to analyze MALDI-TOF mass spectra of human N-glycans in terms of underlying cellular enzyme activities. The glycosylation reaction network is automatically generated by the model, based on the reaction specificities of the glycosylation enzymes. The use of a molecular mass cutoff and a network pruning method typically limits the model size to about 10,000 glycan structures. This allows prediction of the complete glycan profile and its abundances for any set of assumed enzyme concentrations and reaction rate parameters. A synthetic mass spectrum from model-calculated glycan profiles is obtained and enzyme concentrations are adjusted to bring the theoretically calculated mass spectrum into agreement with experiment. The result of this process is a complete characterization of a measured glycan mass spectrum containing hundreds of masses in terms of the activities of 19 enzymes. In addition, a complete annotation of the mass spectrum in terms of glycan structure is produced, including the proportions of isomers within each peak. The method was applied to mass spectrometric data of normal human monocytes and monocytic leukemia (THP1) cells to derive glycosyltransferase activity changes underlying the differences in glycan structure between the normal and diseased cells. Model predictions could lead to a better understanding of the changes associated with disease states, identification of disease-associated biomarkers, and bioengineered glycan modifications.

Integration of the Transcriptome and Glycome for Identification of Glycan Cell Signatures

Abnormalities in glycan biosynthesis have been conclusively linked to many diseases but the complexity of glycosylation has hindered the analysis of glycan data in order to identify glycoforms contributing to disease. To overcome this limitation, we developed a quantitative N-glycosylation model that interprets and integrates mass spectral and transcriptomic data by incorporating key glycosylation enzyme activities. Using the cancer progression model of androgen-dependent to androgen-independent Lymph Node Carcinoma of the Prostate (LNCaP) cells, the N-glycosylation model identified and quantified glycan structural details not typically derived from single-stage mass spectral or gene expression data. Differences between the cell types uncovered include increases in H(II) and Ley epitopes, corresponding to greater activity of α2-Fuc-transferase (FUT1) in the androgen-independent cells. The model further elucidated limitations in the two analytical platforms including a defect in the microarray for detecting the GnTV (MGAT5) enzyme. Our results demonstrate the potential of systems glycobiology tools for elucidating key glycan biomarkers and potential therapeutic targets. The integration of multiple data sets represents an important application of systems biology for understanding complex cellular processes.

Drying and rehydration of DLPC/DSPC symmetric and asymmetric supported lipid bilayers: a combined AFM and fluorescence microscopy study.

This work characterizes the impact of lipid symmetry/asymmetry on drying/rehydration reorganization in phase-separated dilauroylphosphatidylcholine (DLPC)/distearoylphosphatidylcholine (DSPC) supported lipid bilayers (SLBs) at the submicron and micron-scale. In addition the prevention of major drying/rehydration reorganization by the use of trehalose is demonstrated. Even though it was found using fluorescence microscopy that micrometer scale structure is preserved in the presence and absence of trehalose upon drying/rehydration, AFM and FRAP experiments successfully revealed major changes in the phase-separated structure such as defects, obstructions, lipid condensation, collapse structures, and complex incomplete DLPC-DSPC mixing/exchange in the absence of trehalose. In the presence of trehalose the membrane preserves its structure at the nanometer scale and mobility. We found that SLBs with asymmetric domain configurations underwent major rearrangements during drying and rehydration, whereas the symmetric domain configuration mainly rearranged during rehydration, that we hypothesize is related to lower transmembrane cohesiveness or lack of anchoring to the substrate in the case of the asymmetric domains.

Karhunen-Loeve analysis for pattern description in phase separated lipid bilayer systems

Karhunen-Loeve analysis, a special variant of principal component analysis, is used to describe and analyze the dynamics of self-assembled pattern formation in a mixed phospholipid bilayer. The dominant modes of the evolving heterogeneities in density and dynamics are elucidated. At low temperatures the evolution of patterns can be followed by the principal modes of the systems. We find that the higher modes only evolve after the dominant modes have been established. At high temperatures no such dominant modes are found. So, a clear descriptor of an evolving self-assembled pattern can be identified and its time evolution can be monitored. This analysis suggests, additionally, a new way of determining the equilibration decision in complex systems.

Model-based analysis of N-glycosylation in Chinese hamster ovary cells

The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products.

Towards Integrative Glycoinformatics for Glycan Based Biomarker Cancer Research and Discovery

Despite some recent successes in deciphering new cancer molecular makers, there is still a clear and continual need to develop new technologies that help characterizing existing biomarkers or facilitate discovery of new biomarkers. An important systems biology opportunity on this respect is provided by understanding the glycosylation changes associated with cancer. Indeed, interest in cancer glycosylation has expanded over the past decade and large amount of data relevant to cancer glycosylation has been accumulating rapidly. Furthermore, new and improved sophisticated glycoinformatics tools, methods and databases for glycan analysis now offer the opportunity to investigate this data for understanding the role that glycans play in cancer glycosylation. Here we summarize developments of glycoinformatics tools to support analysis of cancer glycosylation and experimental glycoproteomics approaches. In addition, we discuss challenges faced by glycoinformatics for the integration and interrogation of disparate high-throughput glycan data sets in order to assimilate technologies and better address cancer glycosylation. We also provide examples of integrative glycoinformatics approaches that lead to a better understanding of cancer glycosylation as a complex cellular process.

Glycoengineering and Modeling of Protein N-Glycosylation

Abstract Glycoproteins for treating human diseases have revolutionized the health care industry. However, controlling glycosylation has been a challenge as small variations in glycan structure can be responsible for significant changes in key therapeutic properties. Manipulation of glycan biosynthesis can be particularly complex since the process is not directly encoded on the genome but depends on multiple variables such as enzymes’ activity, selectivity, localization, expression host, and process parameters and conditions. Furthermore, a particular glycoprotein may include many different glycan structures due to differences in processing that occur for each individual molecule. The present chapter focuses on experimental and computational approaches to direct N-glycosylation in expression systems for generation of biotherapeutics of superior value. Glycoengineering-based manipulations of glycan structures using glycosyltransferases, modification of precursor biosynthetic pathways, and predictions of glycosylation patterns using mathematical models are described including examples from the literature as a means of optimizing glycoform distributions in cells.