Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Identification of cellulolytic hot spring organisms through bioorthogonal labeling(Montana State University - Bozeman, College of Letters & Science, 2021) Reichart, Nicholas John; Chairperson, Graduate Committee: Roland Hatzenpichler; This is a manuscript style paper that includes co-authored chapters.Microbial physiology is the study of the metabolism and function of microorganisms. The recent expansion of genomic diversity has outpaced the description of physiology. To better understand microbial metabolisms and environmental processes, more detailed research is needed for both novel and undescribed microbes. While many new methods are being developed to describe in situ microbial activity, this dissertation implements bioorthogonal non-canonical amino acid tagging as a proxy to track metabolic activity of microbes under close to environment conditions. Using differential analyses on hot spring microbial communities, we were able to show that certain microbial taxa had preferential activity towards specific incubation amendments. Previous activity-based studies had shown that hot springs were a unique environment for discovering cellulolytic microbes that could be used in industrial processing of plant biomass. Herein, we used computational analysis to screen publicly available metagenomic datasets to identify the enzymatic potential of hot springs worldwide. The wide diversity of taxa and biomass degrading enzymes were investigated and hot springs were further highlighted as a system that could be used to find improvement for the industry of plant biomass degradation and processing. To build upon the cellulolytic potential found in hot spring metagenomic datasets, bioorthogonal non-canonical amino acid tagging coupled with fluorescence-activated cell sorting was applied to the biotechnological relevant field of plant biomass degradation to identify microbes involved in the cellulolytic process. Examination of the active microbes revealed difference in the community when supplemented with cellulose. Taken together, the work in this dissertation served to expand and apply the recent development of activity-based studies used to describe environmental microbial populations, with a focus on plant biomass degradation.Item Development of a protein-based sensor assay for rapid classification of complex biological samples(Montana State University - Bozeman, College of Letters & Science, 2016) Hamerly, Timothy Kyle; Chairperson, Graduate Committee: Brian Bothner; Joshua Heinemann, Monika Tokmina-Lukaszewska, Elizabeth R. Lusczek, Kristine E. Mulier, Greg J. Beilman and Brian Bothner were co-authors of the article, 'Bovine serum albumin as a molecular sensor for the discrimmination of complex metabolite samples' in the journal 'Analytica chemica acta' which is contained within this dissertation.; Brian Bothner was a co-author of the article, 'Adding metrics to the aging of whiskey using a protein sensor assay' which is contained within this dissertation.; Brian Bothner was a co-author of the article, 'Analysis of wine using the protein sensor assay' which is contained within this dissertation.; Brian Bothner was a co-author of the article, 'Investigations into the use of a protein sensor assay for metabolite analysis' in the journal 'Applied biochemistry and biotechnology' which is contained within this dissertation.Metabolomics, one of the core 'omics' fields within the umbrella of systems biology, is the study of the small molecules which can be used to characterize the state of an organism. Metabolites are constantly being transformed inside a cell in direct response to stimuli around them. This makes the metabolome the most dynamic of all the omics fields and is considered to be a direct readout of the cells state at any given time. Although highly informative, the metabolome is inherently difficult to study, with thousands of known metabolites, any of which could be important for classifying a cell into a healthy or diseased state. Techniques such as mass spectrometry are well suited to study the metabolome and have been used to successfully classify cells by identify markers for a given disease state. However, current methods require lengthy analysis times due in part to the complexity of the metabolome. The research presented in this dissertation highlights a new and promising methodology which improves classification and speeds marker discovery. Making use of a protein found in animals which has evolved to selectively bind metabolites, an assay was developed which better classified samples compared to current methods used in the field of metabolomics. This improved classification was achieved with an overall decrease in analysis time. The implementation of this method in the study of complex biological systems would have an immediate impact in academic and medical research.