Wheat is the most agronomically important crop with an annual production of approximately 680 million tonnes per year over the five year period of 2008-2012 (Shewry and Tatham 2016). Wheat typically contributes about 20% of the total calorie intake in Western Europe and between 50-70% in some countries in North Africa and in West and Central Asia. It is estimated that in order to meet the continuous growing global demand wheat production needs to increase by 50% by 2050. Wheat is most commonly consumed as bread, pasta and noodles however it is also used as a food ingredient in other types of foods such as sauces and condiments. The versatility of wheat is largely determined by the unique physiochemical properties of gluten (Bailey 1941). Gluten is one of the earliest proteins to be studied, and was first described by Beccari in 1728 (Bailey 1941) and is readily isolated from wheat flour as a viscoelastic mass. Gluten is a complex mixture of proteins which are the major seed storage proteins found in the cereal grains wheat, barley, rye and oats. Gluten accounts for 70-80% of the total protein content in wheat grains and is traditionally divided into two groups based on their solubility called gliadins and glutenins (Osborne 1907). In genetically pre-disposed patients gluten is able to elicit a non-IgE mediated T-cell response known as coeliac disease (CD). CD affects approximately 1% of the global population for which there is no cure. As no cure is available patients must adhere to a strict gluten-free diet which is often costly and socially excluding. The Codex Standard states that gluten-free foods must contain less than 20 ppm of gluten from wheat, barley, rye and oats and their crossbreeds (FAO/WHO 1983). The Codex Standard also recommends using immunobased methods (or alternative methods) that are able to achieve appropriate sensitivity and specificity for the detection and quantification of gluten with a 10 ppm limit of detection (FAO/WHO 1983). Consequently the current gold standard method for detection of gluten is enzyme linked immunosorbent assay (ELISA) utilising the R5 antibody, however this method is not without shortcomings. Proteomics by mass spectrometry has the potential to offer an alternative, complementary method to determine gluten proteins in foods but for the methodology to become fully validated and accepted it must also overcome similar challenges to immunoassay methods, such as effective extraction of samples and the identification of peptide targets with the requisite specificity. In this research a global approach is taken to aid the development of gluten detection methods using mass spectrometry. One of the major hurdles that has stunted the development of mass spectrometry methods for the detection and quantification for gluten is the lack of protein sequence databases which are required to undertake the MS data searching. In the first results chapter of the thesis a curated gluten protein sequence database was developed (GluPro), and investigated for its utility as a MS data searching tool. It was observed that utilising the GluPro database resulted in improved protein identifications. Following the development of the curated database, extensive method development was carried out to undertake the most extensive background characterisation of the gluten proteome to date using discovery proteomics. To ensure the most comprehensive profile was obtained a number of extraction protocols were investigated and two mass spectrometry platforms with intrinsic differences utilising different modes of acquisition were used. This resulted in the most comprehensive profile of the gluten proteome to date being obtained. In order to meet the continually growing global demand for wheat previous mentioned it is considered that this may be done through the use of genetically modified crops with improved traits such as pesticide resistance. Resulting in the very real possibility of GM crops being introduced into the food supply chain, however there is much widespread public concern regarding the toxicity and allergenicity of genetically modified crops. As wheat is already listed as one of the major eight allergens, it is crucial to be able to undertake safety assessments which are able to assess the toxicity and allergenicity and determine if the GM crop is substantially equivalent to the non-GM counterpart. In the third part of this thesis it is shown how the MS method developed in the previous chapters could be applied and has great potential to be used for safety assessment. Further to this, it is demonstrated how utilising the additional information gathered during the curation of the GluPro database was able to ground the results into in silico measure of toxicity. In the final part of this research all information gathered was interrogated to pick appropriate MRM target peptides, which were unique to a single gluten protein and reproducibly observed to be free from modification. The peptides were synthesised with heavy labels to develop a targeted method to replace current ELISA methods for the detection and quantification. Unexpectedly mass shifts were observed for the precursor ion corresponding to deamidation of the synthetic peptides. Further investigation was undertaken to understand the location and cause of the deamidation sites. This development leads into further recommendation for future development of MRM methods for the detection and quantification of gluten.

Thesis (Ph. D.)--Michigan State University. Chemistry, 2008. / Title from PDF t.p. (viewed on Aug. 17, 2009) Includes bibliographical references (p. 185-210). Also issued in print.

Thesis (Ph. D.)--Ohio State University, 2007. / Full text release at OhioLINK's ETD Center delayed at author's request

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Prostate cancer (PCa) is the second most common cancer in men with more than 1 million new cases worldwide each year. While some of the genomic, genetic and molecular events characterizing PCa have been functionally associated with tumor onset, development and resistance to therapy, the meaning of many other molecular alterations remains poorly understood. Recent development of organoids technology and prostate organoid cultures has established an innovative and valuable model for the study of adult tissue homeostasis, physiology and disease. In this project we combined prostate organoids technology with genetic engineering and CLICK-chemistry coupled Mass Spectrometry approaches in order to better characterize molecular features of wild type and genetically engineered mouse prostate organoids modeling early steps of human prostate tumorigenesis. In details, by manipulating mPrOs to proxy ETS-related gene (ERG) precursor PIN/HGPIN lesions of human prostate, we identified possible novel pro-tumorigenic roles of ERG which unleashes cells proliferation from the tight control of growth stimuli, and, even more interesting, corrupts immune system components to escape immune surveillance. In conclusion, this project shows that coupling innovative biological systems and technological approaches can lead to significant improvements in the analysis and understanding of disease mechanisms.

Cell surface proteins play an important role in understanding cell-cell communication, cell signaling pathways, cell division and molecular pathogenesis in various diseases. Commonly used biotinylation regents for cell surface mapping have shown some potential drawbacks such as crossing the cell membrane, difficult recovery of biotinylated proteins from streptavidin/avidin beads, interference from endogenous biotin and nonspecific nature of streptavidin. With aim to solve these problems, we introduced sulfo-N-hydroxysuccinimidyl (NHS) ester functionalized magnetic nanoparticles containing cleavable groups to label solvent exposed primary amine groups of proteins. Silica coated iron oxide magnetic nanoparticles (Fe3O4@SiO2 MNPs) were linked to NHS ester groups via a cleavable disulfide bond. Additionally, the superparamagnetic properties of Fe3O4@SiO2 MNPs facilitate efficient separation of the labeled peptides and removal of the detergent without any extra step of purification. In the last step, the disulfide bond between the labeled peptides and MNPs was cleaved to release the labeled peptides. The disulfide linked NHS ester modified Fe3O4@SiO2 MNPs were tested using a small peptide, and a model protein (bovine serum albumin) followed by liquid chromatography-tandem mass spectrometry analysis (LC-MS/MS) of labeled peptides. In the next step, disulfide linked, NHS ester modified Fe3O4@SiO2 MNPs (150 nm) successfully labeled the solvent exposed cell surface peptides of Saccharomyces cerevisae. Electron microscopic analysis confirmed the cell surface binding of NHS ester modified Fe3O4@SiO2 MNPs. Mass spectrometric analysis revealed the presence of 30 unique proteins containing 56 peptides. Another MNPs based labeling reagent was developed to target solvent exposed carboxyl acid residues of peptides and proteins. The surface of Fe3O4@SiO2 MNPs was modified with free amine groups via a disulfide bond. Solvent exposed carboxyl groups of ACTH 4-11 and BSA were labeled by using1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) chemistry. Upon cleaving the disulfide bond, labeled peptides were analyzed by LC-MS/MS. The MNPs containing labeling reagents offers specific labeling under physiological conditions and rapid magnetic separation of labeled peptides prior to mass spectrometric analysis. The ability of large Fe3O4@SiO2 MNPs to specifically attach to cell surface makes them a potential candidate to study the surface of variety of different cell types and complex proteins surrounded by lipid bilayer.

Exponential data growth in life sciences demands cross discipline work that brings together computing and life sciences in a usable manner that can enhance knowledge and understanding in both fields. High throughput approaches, advances in instrumentation and overall complexity of mass spectrometry data have made it impossible for researchers to manually analyse data using existing market tools. By applying a user-centred approach to effectively capture domain knowledge and experience of biologists, this thesis has bridged the gap between computation and biology through software, PepTracker (http://www.peptracker.com). This software provides a framework for the systematic detection and analysis of proteins that can be correlated with biological properties to expand the functional annotation of the genome. The tools created in this study aim to place analysis capabilities back in the hands of biologists, who are expert in evaluating their data. Another major advantage of the PepTracker suite is the implementation of a data warehouse, which manages and collates highly annotated experimental data from numerous experiments carried out by many researchers. This repository captures the collective experience of a laboratory, which can be accessed via user-friendly interfaces. Rather than viewing datasets as isolated components, this thesis explores the potential that can be gained from collating datasets in a “super-experiment” ideology, leading to formation of broad ranging questions and promoting biology driven lines of questioning. This has been uniquely implemented by integrating tools and techniques from the field of Business Intelligence with Life Sciences and successfully shown to aid in the analysis of proteomic interaction experiments. Having conquered a means of documenting a static proteomics snapshot of cells, the proteomics field is progressing towards understanding the extremely complex nature of cell dynamics. PepTracker facilitates this by providing the means to gather and analyse many protein properties to generate new biological insight, as demonstrated by the identification of novel protein isoforms.

Mass spectrometry (MS) has been a key player in biological investigation for some time and is the instrument of choice for high throughput proteomics. However, the generation of large, inherently rich, proteomics data sets has far outpaced our ability to utilize them to produce biological knowledge. The ultimate utility of MS proteomics is closely tied to our ability to interpret, integrate and validate this voluminous data. By way of introduction, I discuss the creation of the Open Proteomics Database, which aims to increase publicly available data and to encourage broader contribution from the statistical and bioinformatic communities. Next, I detail research efforts in the integration of mass spectrometry data sets to increase the number of quantifiable peptides. Comparing peptide quantities between experiments (or subsequent chromatographic fractions) in large numbers requires the chromatographic alignment of MS signals, a challenging problem. We use Dynamic Time Warping (DTW) and a bijective (one-to-one) interpolant to create a smooth warp function amenable to multiple alignment. We test a wide variety of alignment scenarios coupled with high confidence, overlapping peptide identifications to optimize and compare alignment parameters. We determine an optimal spectral similarity function, show the importance of penalizing gaps in the alignment path, and demonstrate the utility of our algorithm for multiple alignments. Then, we introduce a method to independently validate large scale proteomics data sets. We use known biases in sample constitution including amino acid content, transmembrane sequence content, and protein abundance to estimate peptide false identification rates (FIRs) in what we term sample bias validation (SBV). We use SBV to compare the false identification rate accuracy (FIRA) and recall capabilities of widely used techniques for error estimation in MS based proteomics. Finally, we describe the open source package mspire (mass spectrometry proteomics in Ruby). Mspire offers unified interfaces for working with a variety of file formats across the analytical pipeline, much needed converters between key formats, and tools for FIR determination. The package eases the burden of working with MS proteomics data, reducing the barrier of entry to developers and offering useful tools to analysts of MS proteomics data. / text

Apolipoprotein E (apoE) occurs on the majority of plasma lipoproteins and plays a major role in the lipid metabolism in the periphery and in the central nervous system. ApoE is a polymorphic protein with three common isoforms, apoE2, apoE3 and apoE4, derived from respective alleles '2, '3 and '4. The aim of this study was to develop a sample pretreatment protocol combined with rapid mass spectrometry (MS)-based assay for simultaneous apolipoprotein profiling and apoE phenotype identification. This assay was validated in 481 samples from patients with stable atherosclerotic cardiovascular disease (ASCVD) and applied to study association with mild cognitive impairment (MCI) in the LIFE Adult study, including overall 690 study subjects. Simultaneous quantification of 8–12 major apolipoproteins including apoA-I, apoB-100 and apoE could be performed within 6.5 min. Phenotyping determined with the developed MS assay had good agreement with the genotyping by real-time fluorescence PCR (97.5%). ApoE2 isoform was associated with the highest total apoE concentration compared to apoE3 and apoE4 (p < 0.001). In the subgroup of diabetic atherosclerotic cardiovascular disease (ASCVD) patients, apoE2 isoform was related to higher apoC-I levels (apoE2 vs. apoE3, p < 0.05), while in the subgroup of ASCVD patients under statin therapy apoE2 was related to lower apoB-100 levels (apoE2 vs. apoE3/apoE4, p < 0.05). A significant difference in apoE concentration observed between mild cognitive impairment (MCI) subjects and controls was confirmed for each apoE phenotype. In conclusion, this study provides evidence for the successful implementation of an MS-based apoE phenotyping assay, which can be used to assess phenotype effects on plasma lipid and apolipoprotein levels.

Cancers represent a group of unprecedented heterogeneous diseases and currently available anti-cancer therapies provide highly variable efficacy with unsatisfactory cure rates. A wide range of proteomic technologies are being used in quest for newer approaches which could significantly contribute to the discovery and development of selective and specific cancer biomarkers for monitoring the disease state and anti-cancer therapy success. Taking into consideration the above aspects, this research was undertaken to study cancer cell proteomes and their changes after anti-cancer treatment with specific focus on: (a) response to conventional anthracycline/anthracenedione drugs with respect to their different clinical efficacy and (b) identification of novel targets for therapy in cancer cells resistant to biological drugs such as inhibitors of (b1) cyclin-dependent kinases and (b2) Aurora kinases. This study identified several interesting key aspects related to the effects of daunorubicin, doxorubicin and mitoxantrone. With the main focus on early time intervals when the influence of apoptosis is minimised, changes common for all three drugs belonging mainly to metabolic and cellular processes were observed. More importantly, significant changes in proteins involved in the generation of precursor...