Quantitative nuclear magnetic resonance techniques to investigate bacterial metabolites and protein competition kinetics on various nanoparticle surfaces
AdvisorFitzkee, Nicholas C.
CommitteeEmerson, Joseph P.
Mlsna, Debra A.
Mlsna, Todd E.
Thornton, Justin A.
Embargo TypeVisible to MSU only for 2 years
Embargo Lift Date2022-05-15
Solution nuclear magnetic resonance (NMR) spectroscopy is a valuable analytical technique that is nondestructive, highly reproducible, and relatively quick to identify and quantify many chemical compounds. Quantitative NMR is a technique commonly used in many medical applications such as drug analysis, metabolomics, and protein-nanoparticle (P-NP) interactions. The most common technique used is the proton (1H) NMR experiment. The 1H NMR analysis provides a quick snapshot of the interested compounds in solution. However, as the compounds become more complex the spectrum becomes overpopulated. This dissertation focuses on various quantitative NMR techniques applied to metabolic and protein competition studies. Specifically, we investigated the effect of biochar on Escherichia coli (E. coli) growth to provide insight on how the metabolic pathways were influenced with the addition of biochar in the RPMI media. A 1H NMR spectrum was recorded at various time points to monitor the metabolic changes over time as E. coli grew in the presence and absence of biochar. The spectra were compared to an in-house metabolite library to identify and quantify the metabolic changes in E. coli. To enhance our metabolic library analysis, we utilized a pure shift analysis attached to the TOCSY pulse program to deconvolute spin systems by using a second dimension for analysis. DIPSI-PSYCHE TOCSY was applied to investigate a metabolite mixture sample and Streptococcus pneumoniae (S. pneumoniae) extracellular metabolites to better resolve the spin systems that significantly overlap each other in the 1H NMR spectra. Our novel approach suggests that adding a pure shift to the TOCSY pulse program is extremely beneficial to investigate various metabolic profiles. Finally, we investigated the protein competition to the AuNP surfaces using a 2D 1H-15N HSQC pulse program. Specifically, we used 1H-15N HSQC technique to quantify the binding capacity for each protein to the AuNP surface before we investigated the competition of two proteins, GB3-Ubq (model protein mixture) or AM-R2ab (biofilm forming protein mixture) to the surface. We also employed a model to study the kinetics of the protein competition to the surface. Our model suggests that GB3-Ubq does not specifically behave kinetically but AM-R2ab is strictly kinetically controlled.