Peptidomic and glycomic profiling of commercial dairy products: identification, quantification and potential bioactivities.

Peptidomics and glycomics are recently established disciplines enabling researchers to characterize functional characteristics of foods at a molecular level. Milk-derived bioactive peptides and oligosaccharides have garnered both scientific and commercial interest because they possess unique functional properties, such as anti-hypertensive, immunomodulatory and prebiotic activities; therefore, the objective of this work was to employ peptidomic and glycomic tools to identify and measure relative and absolute quantities of peptides and oligosaccharides in widely consumed dairy products. Specifically, we identified up to 2117 unique peptides in 10 commercial dairy products, which together represent the most comprehensive peptidomic profiling of dairy milk in the literature to date. The quantity of peptides, measured by ion-exchange chromatography, varied between 60 and 130 mg/L among the same set of dairy products, which the majority originated from caseins, and the remaining from whey proteins. A recently published bioactive peptide database was used to identify 66 unique bioactive peptides in the dataset. In addition, 24 unique oligosaccharide compositions were identified in all the samples by nano LC Chip QTOF. Neutral oligosaccharides were the most abundant class in all samples (66-91.3%), followed by acidic (8.6-33.7%), and fucosylated oligosaccharides (0-4.6%). Variation of total oligosaccharide concentration ranged from a high of 65.78 to a low of 24.82 mg/L. Importantly, characterizing bioactive peptides and oligosaccharides in a wider number of dairy products may lead to innovations that go beyond the traditional vision of dairy components used for nutritional purposes but that will rather focus on improving human health

Supramolecular Nanoassemblies for the Separation and Mass Spectrometric Analysis of Peptides and Modified Proteins

Protein post-translational modifications (PTMs) play key roles in cellular physiology and disease, and identifying their locations on proteins can be beneficial for understanding more deeply protein chemistry. The methods applied for PTM analysis are most often based on mass spectrometry (MS). In the past few years, considerable progress has been made in developing MS-based proteomics technologies for global PTM analysis. Novel mass spectrometric peptide sequencing and analysis technologies allow for modification site mapping at molecular level. However, detecting PTMs on proteins and peptides by MS is challenging because of their low abundance and heterogeneity. Therefore, separation prior to MS analysis is typically required. This dissertation describes the use of supramolecular nanoassemblies, formed by amphiphilic polymers, as novel enrichment tools for the detection and analysis of peptides and proteins that are phosphorylated and glycosylated. The selectivity of the amphiphilic nanoassemblies were changed by loading metal ions for the enrichment of phosphopeptides and by incorporating hydrazide functional groups for the enrichment of glycopeptides. In addition to developing new nanoassemblies for the enrichment of phosphopeptides and glycopeptides, we also explored how supramolecular systems could be tuned to enhance extraction selectivity and efficiency via structural variations to the amphiphilic polymers. The utility of these materials for the enrichment of phosphopeptides and glycopeptides from complexed samples is also demonstrated

The Effects of Diet on the Bovine Milk Proteome

Protein is an important fraction within bovine milk. This milk protein is not only vital for calf growth and development, but also includes bioactive proteins and peptides that have been shown to enhance the health of animals and humans. Research efforts are focusing on factors, such as nutrition, that can influence the quantity and profile of proteins within the bovine milk proteome. The research outlined herein investigated the impact of diet on the bovine milk proteome. The first experiment examined whether dietary inclusion of grape marc (GM), a condensed tannin (CT) containing by-product from the viticulture industry, could alter the bovine milk proteome through altered nitrogen (N) metabolism. In this experiment, 10 lactating Holstein cows were fed either 2.0 kg dry matter (DM)/ cow/ day of beet pulp: soy hulls in a 50% mixture (control), or 1.5 kg DM/ cow/ day of GM as part of a balanced dairy cow ration for a 28-d trial. Milk samples were obtained for analysis of the high- and low-abundance protein fractions. Skimmed milk samples collected for high-abundance protein analysis were measured using high performance liquid chromatography (HPLC), and liquid-chromatography tandem mass spectrometry (LC-MS/MS) was used to identify proteins in the low-abundance protein enriched fraction. Skimmed milk samples collected for low-abundance milk protein analysis were fractionated and enriched to remove higher abundance proteins. Enriched milk samples were then digested and labeled with isobaric tandem mass tags (TMT) prior to protein identification using LC-MS/MS analysis. There were no changes in the high-abundance protein fraction in response to diet; however, 16 of 127 low-abundance proteins were identified at different relative-abundances due to diet (P ≤ 0.05). While there were no alterations in the metabolic or N status of animals due to GM supplementation, the 12% change in the low-abundance milk protein fraction highlighted the potential for dietary alteration of the bovine milk proteome. A second experiment evaluated the inclusion of alternative forage crops (AFC) as a means to alter the bovine milk proteome. In this experiment, both the skimmed milk and milk fat globule membrane (MFGM) protein fractions were included in analysis. Milk samples were collected from 16 lactating Jersey cattle included in a 21-d grazing experiment, where cows were offered one of two diets. The control group (CON, n=8) grazed a grass-legume pasture mixture containing orchardgrass (Dactylis glomerata), timothy (Phleum pratense), Kentucky bluegrass (Poa pratensis), and white clover (Trifolium repens). The treatment group (AFC, n=8) grazed a similar base pasture that was strip-tilled with oat (Avena sativa), buckwheat (Fagopyrum esculentum), and chickling vetch (Lathyrus sativus) so that the AFC species comprised 10% of the AFC group’s pasture DM intake (DMI). Milk samples were collected for HPLC analysis of the high abundance milk proteins, and LC-MS/MS analysis of the low abundance protein enriched skim milk fraction and MFGM-associated protein fraction. Cows that grazed pastures containing AFC had higher αs1-CAS content (P = 0.005), and higher relative-abundances of 7 low-abundance proteins within the skim milk and MFGM fractions (P ≤ 0.05). While it is plausible that the inclusion of AFC in pasture increased nutrient availability to the mammary gland, the specific mechanisms that could have caused the shifts observed remain unclear. Further investigation is necessary to fully understand the role of diet and the milk protein profile

STRUCTURAL ANALYSIS OF PROTEIN THERAPEUTICS USING COVALENT LABELING – MASS SPECTROMETRY

Using mass spectrometry (MS) to obtain information about a higher order structure of protein requires that a protein’s structural properties are encoded into the mass of that protein. Covalent labeling (CL) with reagents that can irreversibly modify solvent accessible amino acid side chains is an effective way to encode structural information into the mass of a protein, as this information can be read-out in a straightforward manner using standard MS-based proteomics techniques. The differential reactivity of proteins under two or more conditions can be used to distinguish protein topologies, conformations, and/or binding sites. CL-MS methods have been effectively used for the structural analysis of proteins and protein therapeutics. This dissertation focuses on the use of a diethylpyrocarbonate (DEPC-based CL-MS method to characterize the higher-order structure of protein therapeutics. DEPC is a simple to use, commercially-available covalent labeling reagent that can readily react with a range of nucleophilic residues in proteins. We find that in intact proteins weakly nucleophilic side chains (Ser, Thr, and Tyr) can be modified by DEPC in addition to other residues such as His, Lys, and Cys, providing very good structural resolution. We hypothesize that the microenvironment around these side chains, as formed by a protein’s higher order structure, tunes their reactivity such that they can be labeled. To test this hypothesis, we compare DEPC labeling reactivity of Ser, Thr, and Tyr residues in intact proteins with peptide fragments from the same proteins. Results indicate that these residues almost never react with DEPC in free peptides, supporting the hypothesis that a protein’s local microenvironment tunes the reactivity of these residues. From a close examination of the structural features near the reactive residues, we find that nearby hydrophobic residues are essential, suggesting that the enhanced reactivity of certain Ser, Thr, and Tyr residues occurs due to higher local concentrations of DEPC. Monoclonal antibodies (mAbs) are among the fastest growing therapeutics in the pharmaceutical industry. Detecting higher-order structure changes of antibodies upon storage or mishandling, however, is a challenging problem. In this dissertation, we describe the use of DEPC-based CL-MS to detect conformational changes caused by heat stress, using rituximab as a model system. The structural resolution obtained from DEPC CL-MS is high enough to probe subtle conformation changes that are not detectable by common biophysical techniques. Results demonstrate that DEPC CL-MS can detect and identify sites of conformational changes at the temperatures below the antibody melting temperature (e.g., 55 ᴼC). The observed labeling changes at lower temperatures are validated by activity assays that indicate changes in the Fab region. At higher temperatures (e.g., 65 ᴼC), conformational changes and aggregation sites are identified from changes in CL levels, and these results are confirmed by complementary biophysical and activity measurements. Given the sensitivity and simplicity of DEPC CL-MS, this method should be amenable to the structural investigations of other antibody therapeutics. Reliable information about antibody higher-order structure can be obtained, though, only when the protein’s structural integrity is preserved during labeling. In this dissertation, we have evaluated the applicability of DEPC reaction kinetics for ensuring the structural integrity of mAbs during labeling. By monitoring the modification extent of selected proteolytic fragments as a function of DEPC concentration, we find that a common DEPC concentration can be used for different monoclonal antibodies in formulated samples without perturbing their higher-order structure. Under these labeling conditions, we find that the antibodies can accommodate up to four DEPC modifications without being structurally perturbed, indicating that multi-domain proteins can withstand more than one label, which contrasts to previously studied single-domain proteins. This more extensive labeling provides a more sensitive measure of structure, making DEPC-based CL-MS suitable for the higher-order structural analyses of mAbs

Elucidating Protein Aggregation in Neurodegeneration Diseases Using Computational Approaches

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggregation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute significantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both all-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders

Proteomic Analysis of Goat Milk

The advancement of electrophoresis and chromatography, along with technological developments in mass spectrometry, has widened the potential application of proteomics to study milk from smaller ruminants. The aim of this chapter is to provide an in-depth overview of the development and progress of proteomics applications in goat milk. After examining various proteomic approaches that are currently applied to this field, we narrow our focus on proteomic investigations of mastitis in goat milk. A summary of protein modulation in goat milk during experimentally-induced endotoxin mastitis is discussed. Because the molecular function of proteins is disrupted during disease due to changes in post-translational modifications, we also review the phosphorylation of caseins, which are the predominant phosphoproteins in milk, and discuss the implications of casein modifications during mastitis. These results offer new insights into the changes of protein expression in goat milk during infection

Development and application of quantitative proteomics strategies to analyze molecular mechanisms of neurodegeneration

Neurodegenerative disorders such as Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis or Prion diseases are chronic, incurable and often fatal. A cardinal feature of all neurodegenerative disorders is the accumulation of misfolded and aggregated proteins. Depending on the disease, these aggregated proteins are cell type specific and have distinct cellular localizations, compositions and structures. Despite intensive research, the contribution of protein misfolding and aggregation to the cell type specific toxicity is not completely understood. In recent years, quantitative proteomics has matured into an exceptionally powerful technology providing accurate quantitative information on almost all cellular proteins as well as protein interactions, modifications, and subcellular localizations, which cannot be addressed by other omics technologies. The aim of this thesis is to investigate key features of neurodegeneration such as misfolded proteins and toxic protein aggregates with cutting edge proteomics, presenting a technological “proof of concept” and novel insights into the (patho)physiology of neurodegeneration

Proteomic and metabolomic studies on milk during bovine mastitis

The principal objectives of the study presented in this thesis were to study the changes of milk proteomes, peptidomes and metabolomes during the course of bovine mastitis in comparison with normal milk samples and to discover new bovine mastitis biomarkers using various modern and up-to-date methodologies such as proteomics, peptidomics and metabolomics. Bovine mastitis caused by bacterial infection of the mammary gland of dairy cows is often associated with loss of milk production due to a reduction in milk composition and quality which in turns, lead to negative economic impact on dairy industry. Two important acute phase proteins (APPs) which serve as valuable biomarkers in bovine mastitis were investigated in every chapter using developed and validated enzyme linked immunosorbent assay (ELISA) for bovine milk haptoglobin and commercially available ELISA for bovine milk serum amyloid A3 (M-SAA3). These APPs were quantified alongside somatic cell counts (SCC) and California Mastitis Test (CMT) to confirm the disease status of each animal used in this study. Proteomic methodologies were applied including 1D gel electrophoresis, 2D gel electrophoresis, MALDI-TOF analysis and difference gel electrophoresis to investigate the changes of milk proteome in both subclinical and clinical mastitic milk samples in comparison with healthy milk samples. However these investigations did not reveal novel biomarkers for mastitis. Next, peptidomic methodologies were used to study the changes in milk peptidome and to detect the presence of any significant disease biomarkers in the presence of bovine mastitis by using CE-MS and LC-MS/MS. A total of 31 and 14 polypeptides can be used to discriminate control from infected groups and E. coli from S. aureus infected groups respectively. Lastly, metabolomic methodology was applied with an intention to study the changes in milk metabolome and ultimately to detect the presence of novel biomarkers in bovine mastitis. Di- and tri-peptides were found higher in S. aureus than in E. coli infected groups and based on metabolic pathways, arachidonic, arginine and galactose metabolites were seen increased in mastitic milk samples in comparison to healthy milk samples. Overall, the findings detailed in this thesis indicate that the use of advanced proteomic and metabolomic methodologies could deliver on their promise of the discovery of potential significant bovine mastitis biomarkers. Further studies are needed for validation of these proposed biomarkers and it was hoped that better prevention and treatment methods for bovine mastitis can be achieved in the future