Scholarship & Research
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Item Investigating the metalloproteome of bacteria and archaea(Montana State University - Bozeman, College of Letters & Science, 2024) Larson, James Daniel; Chairperson, Graduate Committee: Brian Bothner; This is a manuscript style paper that includes co-authored chapters.Metalloproteins are proteins that rely on a bound metal for activity and comprise 30-50% of all proteins which are responsible for catalyzing imperative biological functions. Understanding the interplay between essential and toxic metals in the environment and the metalloproteins from an organism (metalloproteome) is important for a fundamental understanding of biology. A challenge in studying the metalloproteome is that standard proteomic methods disrupt protein-metal interactions, therefore losing information about protein- metal bonds required for metalloprotein function. One of the focuses of my work has been to develop a non-denaturing chromatographic technique that maintains these non-covalent interactions. My approach for investigating the native metalloproteome together with leading- edge mass spectrometry methods was used to characterize microbial responses to evolutionarily relevant environmental perturbations. Arsenic is a pervasive environmental carcinogen in which microorganisms have naturally evolved detoxification mechanisms. Using Escherichia coli strains containing or lacking the arsRBC arsenic detoxification locus, my research demonstrated that exposure to arsenic causes dramatic changes to the distribution of iron, copper, and magnesium. In addition, the native arsRBC operon regulates metal distribution beyond arsenic. Two specific stress responses are described. The first relies on ArsR and leads to differential regulation of TCA-cycle metalloenzymes. The second response is triggered independently of ArsR and increases expression of molybdenum cofactor and ISC [Fe-S] cluster biosynthetic enzymes. This work provides new insights into the metalloprotein response to arsenic and the regulatory role of ArsR and challenges the current understanding of [Fe-S] cluster biosynthesis during stress. Iron is an essential and plentiful metal, yet the most abundant iron mineral on Earth, pyrite (FeS2), was thought to be unavailable to anaerobic microorganisms. It has recently been shown that methanogenic archaea can meet their iron (and sulfur) demands solely from FeS2. This dissertation shows that Methanosarcina barkeri employs different metabolic strategies when grown under FeS2 or Fe(II) and HS- as the sole source of iron and sulfur which changes the native metalloproteome, metalloprotein complex stoichiometry, and [Fe-S] cluster and cysteine biosynthesis strategies. This work advances our understanding of primordial biology and the different mechanisms of iron and sulfur acquisition dictated by environmental sources of iron and sulfur.Item Characterization of osteoarthritis metabolism: a mass spectrometry based-approach(Montana State University - Bozeman, College of Letters & Science, 2024) Welhaven, Hope Diane Aloha; Co-chairs, Graduate Committee: Brian Bothner and Ronald K. June II; This is a manuscript style paper that includes co-authored chapters.Osteoarthritis (OA) effects 7% of the global population, equating to more than 500 million people worldwide, and is the leading cause of disability. Its multifaceted etiology includes risk factors ranging from genetics, to aging, obesity, sex, race, and joint injury. OA manifests differently across the patient population where symptom severity, rate of progression, response to treatment, pain perception, as well as others vary person to person posing significant challenges for effective management and prevention. At the cellular level, imbalanced matrix catabolism and anabolism contribute to the breakdown of cartilage, underlying bone, and other tissues affected by OA. Leveraging mass spectrometry-based techniques, particularly metabolomics, offers a promising avenue to dissect OA metabolism across musculoskeletal tissues, while considering individual patient-specific risk factors. Therefore, the goals of this research were to: (1) comprehensively characterize OA phenotypes and endotypes and (2) explore OA pathogenesis within the context of disease-associated risk factors. The first area of research focuses on profiling OA phenotypes and endotypes across disease development. These results provide clear evidence of OA-induced metabolic perturbations in OA cartilage and bone and elucidate mechanisms that shift as disease progresses. Several metabolites and pathways associated with lipid, amino acid, matrix, and vitamin metabolism were differentially regulated between healthy and OA tissues and within OA endotypes. The second area of research focuses on the impact of OA risk factors -- sex, injury, obesity, loading -- on the metabolism of circulatory fluids (i.e., serum, synovial fluid) and chondrocytes. Identification of metabolic indicators of disease, such as cervonyl carnitine, and metabolic pathways associated with these risk factors holds potential for improving screening, monitoring disease progression, and guiding preventative strategies. Overall, this work contributes to our current understanding of OA, its diverse metabolic landscape, risk factors and their interactions. Moreover, it lays the groundwork for personalized medicine by providing detailed insights into individualized phenotypic profiles, thereby advancing the prospect of tailored treatment strategies for OA individuals.Item Development and analysis of lipidomics procedures for the causal investigation of Alzheimer's disease(Montana State University - Bozeman, College of Letters & Science, 2022) Koch, Max Richard; Chairperson, Graduate Committee: Edward Dratz; This is a manuscript style paper that includes co-authored chapters.Uncovering sets of molecular features which cause a healthy metabolic state to transition to one of disease, requires extensive experimentation and often presents a difficult analysis. In the case of neurodegenerative diseases, such as Alzheimer's Disease, simply obtaining suitable samples can be a challenging endeavor. Many current 'Omics' techniques excel at profiling a vast array of molecules, such as water-soluble metabolites, lipids, and proteins, in order to compare groups of samples from healthy and diseased organisms. Such approaches primarily use various associations between molecules and disease to identify biomarkers. However, these 'omics' experiments frequently result in intriguing biological hypotheses, but to date have rarely provided mechanistic explanations. How then, can mechanistic explanations be recovered from metabolite or lipid profile data? In our work, we applied these methods to 6 Alzheimer's diseased brain samples and 6 age matched controls. When analyzed via mass spectrometry, lipids which differed significantly between control and disease were identified, but this information was not able to'provide mechanistic insight. The beginning of any 'omics' based experiment starts with the extraction of the desired molecules. In order to assess the efficiency of three different lipid extraction methods, a lipid standard was extracted from a matrix composed of rat liver tissue and analyzed by mass spectrometry. The classic Folch extraction was found to be best at reproducibly extracting a wide range of lipids. Several of the lipids identified from human brains showed oxidative damage. Lastly, 5 statistical measures of dependence and 3 network algorithms were investigated for their ability to reconstruct mechanistic relationships in a dynamic model of arachidonic acid metabolism. Many of the metabolites of arachidonic acid are oxidation products. Under conditions of high noise and relatively few samples, standard measures of correlation, such as Pearson's correlation, Spearman's correlation and Kendall's Tau were found to perform the best. Metrics which incorporate nonlinear metabolic relations and network algorithms were found to be applicable, when sample size is large and the signal to noise ratio is close to l.Item Investigating the role of allostery through changes in protein stability and dynamics(Montana State University - Bozeman, College of Letters & Science, 2021) Patterson, Angela Jean; Chairperson, Graduate Committee: Brian Bothner; Faiz Ahmad and Jaigeeth Deveryshetty were authors and Jenna R. Mattice, Nilisha Pokhrel, Brian Bothner and Edwin Antony were co-authors of the article, 'Hydrogen-deuterium exchange reveals a dynamic DNA-binding map of replication protein A' in the journal 'Nucleic Acids Research' which is contained within this dissertation.; Zhongchao Zhao, Elizabeth Waymire, Adam Zlotnick, and Brian Bothner were co-authors of the article, 'Dynamics of hepatitis B virus capsid protein dimer regulates assembly through an allosteric network' in the journal 'ACS chemical biology' which is contained within this dissertation.; Paul B.G. van Erp was an author and Ravi Kant, Luke Berry, Sarah M. Golden, Brittney L. Forsman, Joshua Carter, Ryan N. Jackson, Brian Bothner and Blake Wiedenheft were co-authors of the article, 'Conformational dynamics of DNA binding and Cas 3 recruitment by the CRISPR RNA-guide cascade complex' in the journal 'ACS chemical biology' which is contained within this dissertation.; Aidan White, Elizabeth Waymire, Sophie Fleck, Sarah Golden, Royce Wilkinson, Blake Wiedenheft and Brian Bothner were co-authors of the article, 'Thermodynamics of CRISPR-anti-CRISPR interactions provides mechanistic insight into inhibition' which is contained within this dissertation.Allostery is the presence of a communication network that links functional sites of a protein that are distal from one another. The existence of an allosteric network can be observed through conformational change or a change in protein dynamics. These networks can be used to provide insight into the mechanistic function of proteins or protein complexes. In this thesis, four protein complexes were studied (RPA, HBV, Cascade, and Csy) and allosteric networks within the complexes were observed by monitoring the changes in protein dynamics upon an energy perturbation. To measure the changes in protein dynamics, hydrogen deuterium exchange mass spectrometry was used. This technique allows for the determination of how often the hydrogen bonding within a protein structure is broken. By tracking the longevity of the hydrogen bonding network that comprises the studied protein's structure, the dynamics of the protein can be studied. In this work, each of the proteins had changes in protein dynamics that were distal from the site of the energy perturbation that had functional impacts on each of the protein complexes. The combined presence of the distal changes in dynamics with an effect on protein function fits the definition of allostery. If allostery is present in these four diverse systems, is it possible that allostery is present in all proteins?Item Analysis of complex samples by mass spectrometry leads to insights into system dynamics(Montana State University - Bozeman, College of Letters & Science, 2021) Peach, Jesse Thomas; Chairperson, Graduate Committee: Brian Bothner; James Larson, Sutton Kanta, Eric Boltinghouse, Rebecca Mueller, Ganesh Balasubramanian, Mohammed Refai, Brent Peyton and Brian Bothner were co-authors of the article, 'Optimization of thermal small molecule and protein mass spectrometry analysis' submitted to the journal 'Analytical biochemistry' which is contained within this dissertation.; Rebecca Mueller, Dana Skorupa, Margaux Mesle, Sutton Kanta, Eric Boltinghouse, Bailey Sharon, Valerie Copie, Brian Bothner and Brent Peyton were co-authors of the article, 'Longitudinal meta-analysis of the Five Sisters Hot Springs in Yellowstone National Park reveals a dynamic thermoalkaline environment' submitted to the journal 'Environmental microbiology' which is contained within this dissertation.; Stephanie M. Wilson, Logan D. Gunderson, Lizzi Frothingham, Tan Tran, Seth T. Walk, Carl J. Yeoman, Brian Bothner and Mary P. Miles were co-authors of the article, 'Temporal metabolic response yields a dynamic biosignature of inflammation' submitted to the journal 'iScience' which is contained within this dissertation.Systems biology offers a holistic approach to biological science. In its most complete form, systems biology requires comprehensive data encompassing all of the parts or molecules across a set of hierarchical networks. To obtain and analyze the comprehensive and large datasets required for systems biology analysis, biologists have taken advantage of new technology and computational tools. Over the last few decades, advances in computational modeling and analysis technology has dramatically increased the efficacy of systems biology and the understanding of the natural world. However, systems biology is still an emerging discipline. The overwhelming scale of potential biological data that has yet to be described, coupled with interpretation and application obstacles, leaves much work to be accomplished. One aspect of systems biology that needs development is the interpretation and analysis of temporal biological data. Temporal data reveals more about biological phenomena than static data as biology is inherently dynamic. This dissertation explores the benefits of temporal profiling of complex samples to make time-resolved conclusions about complicated biological questions. Three research projects are the backbone of this document, with a chapter being devoted to each. Chapter 2 describes the development of a comprehensive method for extraction and mass spectrometry analysis of several different fractions from hot spring sediment. Chapter 3 delves into a multi-omics analysis tracking changes over the course of three years in a thermoalkaline spring system in Yellowstone National Park. It defines how specific extracellular small molecules correlate with microbial fitness. Specifically, how unique nitrogen and sulfur containing molecules in the sediment drive archaeal abundance and diversity. The final chapter introduces the concept of a 'dynamic biosignature', a set of metabolites that have similar responses to known biomarkers, in this case pro-inflammatory cytokines. A cohort of metabolites was identified that provided mechanistic insight into the inflammatory response. Overall, this dissertation provides examples of systems biology analysis and provides evidence that static, single time-point datasets fail to capture that which is the essence of biology - change.