Doctor of Philosophy

dissertationSelectivity in chemical reactions is a matter of distinguishing between pathways of little energetic difference. From reactions affording no selectivity in product formation to those achieving selectivity levels of >99:1, the energy differences responsible for these disparate isomer ratios range from 0 to ~3 kcal mol-1, respectively. It is astounding that such a seemingly trivial amount of energy, on the order of the energetic barrier to carbon-carbon bond rotation in ethane (~2.9 kcal mol-1), can precipitate products in exquisitely high isomeric purity. Identifying the origin of the small energy differences that afford selectivity has, historically, been a daunting endeavor and predominantly characterized by empiricism. In recent years, the Sigman group has been developing a more efficient alternative to the typical guess-and-check approach to optimizing catalyst-substrate interactions for high site- and enantioselective outcomes. This methodology relies on the quantification and systematic modulation of various reaction features that putatively induce selectivity, ultimately enabling the identification of mathematical equations to describe these effects. Detailed herein is the process for developing reliably predictive mathematical constructs of reaction selectivity. In the context of three distinct reactions-iridium-catalyzed asymmetric hydrogenation (Chapter 2), rhodium-catalyzed site-selective C-H amination (Chapter 3), and rhodium-catalyzed asymmetric transfer hydrogenation (Chapter 4)-means for effective model development are put forth. Namely, this work describes the examination of the unconventional application of design of experiments principles, the identification of parameters capable of describing selectivity, and the process by which linear regression models are developed and validated. Through this approach, mathematical equations are developed that relate the differential free energy of selectivity to numerical depictions of steric, electronic, and hydrophobic effects. By identifying underlying predictive trends, developed models serve as a unique avenue by which mechanistic insight may be gained about selectivity engendering interactions. Consequently, these models enable the energetic optimization of substrate-catalyst interactions and the quantitative prediction of how such changes will influence reaction selectivity. Through the work of myself and my colleagues in the Sigman group, we are learning how reactions may be investigated and understood so as to make the ~3 kcal mol"1 energy range that is responsible for selectivity a vast window of opportunity for shaping reaction partners to achieve desired reaction outcomes

Quantifying mucosal hemodynamics in a murine model of Ulcerative Colitis with diffuse reflectance spectroscopy

Ulcerative colitis (UC) is a gastrointestinal, autoimmune disease that causes ulceration and inflammation of the colon with an incidence 10 out of every 100,000 people in North America and Western Europe. Though the exact etiology is uncertain, a number of studies have shown that inflammatory cells along with environmental factors, genetics, and lifestyle habits can contribute to the sustained inflammatory response. In order to determine the cellular mechanism behind relapse and remission of UC, researchers have frequently employed immunohistochemistry, western blotting and gene sequencing, but these destructive analysis methods require the removal of a sample, necessarily limiting these methods to non-living tissues. There is an emerging interest in using non-invasive techniques to study the in vivo, longitudinal effects of UC on the mucosa in the colon. Here we have developed a mouse model of UC using dextran sulfate sodium and a non-invasive spectroscopy monitoring modality to study the changes in the tissue hemodynamics during active UC

A widely distributed metalloenzyme class enables gut microbial metabolism of host- and diet-derived catechols.

Catechol dehydroxylation is a central chemical transformation in the gut microbial metabolism of plant- and host-derived small molecules. However, the molecular basis for this transformation and its distribution among gut microorganisms are poorly understood. Here, we characterize a molybdenum-dependent enzyme from the human gut bacterium Eggerthella lenta that dehydroxylates catecholamine neurotransmitters. Our findings suggest that this activity enables E. lenta to use dopamine as an electron acceptor. We also identify candidate dehydroxylases that metabolize additional host- and plant-derived catechols. These dehydroxylases belong to a distinct group of largely uncharacterized molybdenum-dependent enzymes that likely mediate primary and secondary metabolism in multiple environments. Finally, we observe catechol dehydroxylation in the gut microbiotas of diverse mammals, confirming the presence of this chemistry in habitats beyond the human gut. These results suggest that the chemical strategies that mediate metabolism and interactions in the human gut are relevant to a broad range of species and habitats

Emergence of Caenorhabditis elegans as a Model Organism for Dissecting the Gut-Brain Axis.

Accumulating evidence links the gut microbiome to neuronal functions in the brain. Given the increasing prevalence of brain disorders, there is a critical need to understand how gut microbes impact neuronal functions so that targeted therapeutic interventions can be developed. In this commentary, we discuss what makes the nematode Caenorhabditis elegans a valuable model for dissecting the molecular basis of gut microbiome-brain interactions. With a fully mapped neuronal circuitry, C. elegans is an effective model for studying signaling of the nervous system in a context that bears translational relevance to human disease. We highlight C. elegans as a potent but underexploited tool to interrogate the influence of the bacterial variable on the complex equation of the nervous system. We envision that routine use of gnotobiotic C. elegans to examine the gut-brain axis will be an enabling technology for the development of novel therapeutic interventions for brain diseases

Designer substrate library for quantitative, predictive modeling of reaction performance

Assessment of reaction substrate scope is often a qualitative endeavor that provides general indications of substrate sensitivity to a measured reaction outcome. Unfortunately, this field standard typically falls short of enabling the quantitative prediction of new substrates' performance. The disconnection between a reaction's development and the quantitative prediction of new substrates' behavior limits the applicative usefulness of many methodologies. Herein, we present a method by which substrate libraries can be systematically developed to enable quantitative modeling of reaction systems and the prediction of new reaction outcomes. Presented in the context of rhodium-catalyzed asymmetric transfer hydrogenation, these models quantify the molecular features that influence enantioselection and, in so doing, lend mechanistic insight to the modes of asymmetric induction

Distinctive <i>Meta</i>-Directing Group Effect for Iridium-Catalyzed 1,1-Diarylalkene Enantioselective Hydrogenation

An iridium-catalyzed asymmetric hydrogenation of 1,1-diarylkenes is described. Employing a novel, modular phosphoramidite ligand, PhosPrOx, in this transformation affords biologically relevant 1,1-diarylmethine products in good enantiomeric ratios (96.5:3.5 to 71:29). We propose that a <i>meta</i>-directing group, 3,5-dimethoxyphenyl, is responsible for the observed enantioselection, the highest reported, to date, for iridium-catalyzed hydrogenation of 1,1-diarylalkenes lacking <i>ortho</i>-directing groups