205 research outputs found
A Systematic Framework to Derive N-glycan Biosynthesis Process and the Automated Construction of Glycosylation Networks
published_or_final_versio
Bioinformatics and molecular modeling in glycobiology
The field of glycobiology is concerned with the study of the structure, properties, and biological functions of the family of biomolecules called carbohydrates. Bioinformatics for glycobiology is a particularly challenging field, because carbohydrates exhibit a high structural diversity and their chains are often branched. Significant improvements in experimental analytical methods over recent years have led to a tremendous increase in the amount of carbohydrate structure data generated. Consequently, the availability of databases and tools to store, retrieve and analyze these data in an efficient way is of fundamental importance to progress in glycobiology. In this review, the various graphical representations and sequence formats of carbohydrates are introduced, and an overview of newly developed databases, the latest developments in sequence alignment and data mining, and tools to support experimental glycan analysis are presented. Finally, the field of structural glycoinformatics and molecular modeling of carbohydrates, glycoproteins, and protein–carbohydrate interaction are reviewed
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
Immunoglobulin G N-glycan biomarkers for autoimmune diseases: Current state and a glycoinformatics perspective
The effective treatment of autoimmune disorders can greatly benefit from disease-specific biomarkers that are functionally involved in immune system regulation and can be collected through minimally invasive procedures. In this regard, human serum IgG N-glycans are promising for uncovering disease predisposition and monitoring progression, and for the identification of specific molecular targets for advanced therapies. In particular, the IgG N-glycome in diseased tissues is considered to be disease-dependent; thus, specific glycan structures may be involved in the pathophysiology of autoimmune diseases. This study provides a critical overview of the literature on human IgG N-glycomics, with a focus on the identification of disease-specific glycan alterations. In order to expedite the establishment of clinically-relevant N-glycan biomarkers, the employment of advanced computational tools for the interpretation of clinical data and their relationship with the underlying molecular mechanisms may be critical. Glycoinformatics tools, including artificial intelligence and systems glycobiology approaches, are reviewed for their potential to provide insight into patient stratification and disease etiology. Challenges in the integration of such glycoinformatics approaches in N-glycan biomarker research are critically discussed
Experimental Methods Towards Controlling the Glycoform
The glycosylation profiles of biopharmaceuticals have a major impact on the bioactivity, bioavailability, biocompatibility and biosafety of the therapeutic. As modulation of the glycosylation profile can enhance or attenuate therapeutic properties of the drug, methods towards control of glycosylation therefore are of interest to the research, clinical and industrial communities. This work embarks on a multipronged approach towards achieving uniform, consistent and specified protein glycosylation through development of analytical methodologies to monitor attributes of glycosylation (Chapters 2-4), and by construction of a cell-free bioreactor for In-Vitro Glyco-Engineering (Chapter 5).
In objective (1) development of state-of-the-art analytical technologies demonstrated how Chinese Hamster Ovary (CHO) cells, subjected to high-flux substrates and cellular and protein engineering strategies, can be manipulated towards achieving control over the protein glycosylation pathway in mammalian cells. HPLC-based methods were employed to determine: amino acid composition, glycosylation patterns, nucleotide sugar distributions and titer. Modulation of the feed was shown to increase nucleotide sugars, specifically CMP-Neu5Ac, resulting in increased sialylation of proteins. This detailed representation facilitated fundamental insight into the physiology of cellular responses, by synergizing how the glycosylation profile is inextricably coupled to substrate availability, enzyme activities and cellular metabolism, thereby revealing the interplay of the glycosylation processes at the systems level.
In objective (2) implementation of a novel, transformative and scalable biofabrication process applying In-Vitro Glyco-Engineering of glycoproteins to produce structurally well-defined, homogeneous glycans. This chemo-enzymatic approach utilizes Endoglycosidase-H covalently coupled to a solid phase support of silicon nanowires to deglycosylate proteins. Select model glycoproteins: RNaseB, alpha-amlyase and IgG, showed the flexibility of the process towards different classes of biomolecules.
These efforts will lead to higher productivity, increased product homogeneity and greater therapeutic efficacy. Ultimately, to enable the cost effective and routine production of biopharmaceuticals to facilitate increased patient affordability and larger profit margins.
Advisor: Michael Betenbaugh
Reader: Kevin Yarem
Automated identification of pathways from quantitative genetic interaction data
We present a novel Bayesian learning method that reconstructs large detailed gene networks from quantitative genetic interaction (GI) data.The method uses global reasoning to handle missing and ambiguous measurements, and provide confidence estimates for each prediction.Applied to a recent data set over genes relevant to protein folding, the learned networks reflect known biological pathways, including details such as pathway ordering and directionality of relationships.The reconstructed networks also suggest novel relationships, including the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated
Assessment of the interactions between bioprocess conditions and protein glycosylation in antibody- producing mammalian cell cultures
The pharmaceutical industry is going through a rather turbulent period. Many
blockbuster drugs have fallen off patent over the past two years and many more are
expected to do so in the near future. In response, pharmaceutical companies have
continued searching for products that will replace those that have lost patent
protection. However, drug development and approval is extremely time-consuming and
costly. So that this critical issue is addressed, industry experts and regulatory agencies
have jointly proposed the implementation of Quality by Design (QbD) principles in the
development and manufacture of all new drugs. Adoption of QbD is expected to reduce
drug development cost and approval time. It is also expected to encourage innovation
by developing drugs, and the processes used to manufacture them, around the
mechanisms that relate process inputs with end product quality. Within this context,
monoclonal antibodies (mAbs) are currently the highest-selling products of the
biopharmaceutical industry and are projected to account for nearly half of the world’s
top-selling drugs by 2018. All currently commercialized mAbs contain N-linked glycans
(complex carbohydrates) bound to their protein backbone. These carbohydrates, in
turn, have been widely reported to impact the safety and efficacy of mAbs. Furthermore,
it has widely been reported that bioprocess conditions heavily impact the composition
and distribution of these glycans. For these reasons, mAb glycosylation is considered a
critical quality attribute (CQA) of these therapeutic proteins under the QbD scope.
Based on QbD principles, the objective of this thesis was to generate a mathematical
model that mechanistically relates the effect of nutrient availability throughout cell
culture with the glycan profile of a mAb. The model was constructed from three
individual ones. The first model describes the N-linked glycosylation process which
occurs in the Golgi apparatus. The second model is unstructured and describes cell
culture dynamics. The third and final model describes the biosynthetic pathway for
nucleotide sugars. All three models were developed independently, but were adapted
with features so that they could be interconnected. The glycosylation model
approximates the Golgi apparatus to a single plug flow reactor where resident proteins
(glycosylation enzymes and transport proteins) are recycled from distal portions of the
Golgi space to proximal ones. Optimisation-based methods were developed to estimate
unknown parameters of the model. The cell culture dynamics model was developed to
represent cell growth, nutrient consumption and mAb synthesis. It was originally based
on Monod kinetics, but was adapted to include experimentally-encountered complexity.
The model for nucleotide metabolism was heuristically reduced from 35 constituting
reactions to 7. Additional mechanistic features were adapted or included to ensure
model fidelity. Experimentally, batch cultures were performed with hybridoma (CRL-1606 from
ATCC). Data for viable cell density, glucose, glutamine, lactate, ammonia and mAb titre
were collected. Intracellular samples were produced by perchloric acid extraction.
These samples were then analysed for nucleotide sugar content using a high
performance anion exchange chromatographic method which was optimized to quantify
eight nucleotide sugars and four nucleotides in 30min. mAb bound glycans were
analysed by MALDI mass spectrometry. The experimental data was used to estimate the
unknown parameters of the models.
The models – along with their associated parameters – were then combined to produce
a coupled model that mechanistically relates nutrient availability with mAb
glycosylation-associated quality. With further validation, such a model could be used for
bioprocess design, control and optimization
Algorithms for integrated analysis of glycomics and glycoproteomics by LC-MS/MS
The glycoproteome is an intricate and diverse component of a cell, and it plays a key role in the definition of the interface between that cell and the rest of its world. Methods for studying the glycoproteome have been developed for released glycan glycomics and site-localized bottom-up glycoproteomics using liquid chromatography-coupled mass spectrometry and tandem mass spectrometry (LC-MS/MS), which is itself a complex problem.
Algorithms for interpreting these data are necessary to be able to extract biologically meaningful information in a high throughput, automated context. Several existing solutions have been proposed but may be found lacking for larger glycopeptides, for complex samples, different experimental conditions, different instrument vendors, or even because they simply ignore fundamentals of glycobiology. I present a series of open algorithms that approach the problem from an instrument vendor neutral, cross-platform fashion to address these challenges, and integrate key concepts from the underlying biochemical context into the interpretation process.
In this work, I created a suite of deisotoping and charge state deconvolution algorithms for processing raw mass spectra at an LC scale from a variety of instrument types. These tools performed better than previously published algorithms by enforcing the underlying chemical model more strictly, while maintaining a higher degree of signal fidelity. From this summarized, vendor-normalized data, I composed a set of algorithms for interpreting glycan profiling experiments that can be used to quantify glycan expression. From this I constructed a graphical method to model the active biosynthetic pathways of the sample glycome and dig deeper into those signals than would be possible from the raw data alone. Lastly, I created a glycopeptide database search engine from these components which is capable of identifying the widest array of glycosylation types available, and demonstrate a learning algorithm which can be used to tune the model to better understand the process of glycopeptide fragmentation under specific experimental conditions to outperform a simpler model by between 10% and 15%. This approach can be further augmented with sample-wide or site-specific glycome models to increase depth-of-coverage for glycoforms consistent with prior beliefs
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