Procedimiento y variables

The motivation for this work stems from our incomplete understanding of the intersection of clocks, sleep, and metabolism, and the realization that new tools are necessary to unearth these connections and track inherently dynamic physiological processes. While gross metabolic parameters such as glucose homeostasis have been extensively studied vis-à-vis disrupted circadian rhythms and sleep, mechanistic detail cannot be obtained from these measures. Considerably deeper and more complex analyses are required to uncover the extent of diurnal variance in metabolism. Metabolomics has recently emerged as an approach to holistically probe metabolic processes and integrate both endogenous and exogenous influences on host physiology. As with other high-throughput ‘omic technologies, metabolomic analyses rely on instrumentation with multiple tunable parameters which impacts the accurate detection of numerous variables, and requires efficient and rational optimization procedures. In Chapter 2, new LC-MS metabolomics methodology was developed in an iterative, data-driven manner. Design of experiments is commonly employed in process design and engineering, however has never been leveraged to build new LC-MS metabolomics methodology.

These methods can facilitate holistic metabolite profiling in appropriate models of circadian rhythms, highlighted in Chapter 3 by metabolomic analyses in Drosophila. In this project, we discovered a dependency of circadian metabolite rhythms on intact genetic clocks, and a set of metabolite rhythms driven by environmental cues. Given cycles in physiology arise from a complex interaction of environmental cues and genetic factors, this study represents an important novel paradigm which separates the impact of daily light cycles and intrinsic clocks on metabolism. Additionally, metabolomic profiling in Drosophila at sufficiently high sampling resolution necessary for ultradian rhythm analysis had not been previously performed, and advances flies as a model to uncover new basic mechanisms in chronometabolism.

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LC-MS metabolomics methods can thus be effectively developed to discern metabolic cycles, however these methods do not provide clear insight into reaction kinetics, presenting an additional challenge in obtaining cogent metabolic mechanisms. The LC-MS methods described in Chapter 2 were modified to address this limitation through isotope enrichment detection from a novel 13_{C6 glucose bolus injection platform in Drosophila, described in Chapter 4. Current} technological limitations prevent quantitative metabolic flux modeling across a circadian time- series in vivo, however deep isotopolomic profiling provided relative pathway fluxes in amino acid metabolism and redox across circadian time and sleep status. This approach not only represents a new isotope-tracing tracing paradigm in Drosophila, but also an unprecedented scope of isotopologue analysis over prior metabolic flux study designs. Multivariate modeling on isotopologues had not been previously utilized to obtain a fluxomics assessment of an in vivo

system, which revealed a global shift in carbohydrate oxidation early in the active phase of flies. This platform can have broad applicability in defining the metabolic consequence of clock and sleep disruption, as well as translatable experimental designs regarding nutrient processing and feeding paradigms to promote metabolic health.

The interpretability of systems-level ‘omics analyses lag behind advancements in instrumentation. While Chapter 4 outlines a novel approach to gauge metabolic flux, the source of many of these isotopologues cannot be fully explained by predefined biochemical pathways. The new metabolic variables discovered in Chapter 4 require a new means of interpretation, presenting yet another challenge in deriving true metabolic phenotypes. Fortunately, with the advent of genome-scale reaction annotation, all reactions can be considered which produce these detectable isotopologues. A new computational approach is developed which enumerates all possible isotope transfer events with atomic detail from an input 13_{C6 glucose tracer, and} subsequently reconstructs all paths which connects the input tracer to the user-defined isotopologue of interest, outlined in Chapter 5. As a proof of concept, this algorithm calculated paths to notable isotopologues obtained in Chapter 4, including serine M+3, glutamine M+2, and GSH M+2. After a series of rational trimming procedures, a subset of those possible routes

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remained, which provide supporting evidence for suspected sources of each of these isotopologues, as well as propose alternative mechanisms that warrant further experimentation. This tool simulated downstream isotopologues from a 13_{C3 serine tracer, as an example of the} potential utility to guide the researcher to test specific hypotheses through appropriate tracer experiments and metabolomics methodology. This approach represents a new hypothesis- generating bioinformatic tool built in an open-source platform for widespread use. Notably, the extent of possibilities calculated with this algorithm presents a degree of complexity which may require a shift in perspectives to consider metabolomic datasets in terms of updated probabilities, as attributing observable metabolite data to a single source or reaction is likely to be false. As technology and computational tools continue to improve, a new era of discovery can form to truly shed light on the complex nature of chronometabolism. The efforts outlined in this thesis work formed a logical progression of problem-solving endeavors, ultimately producing new analytical methodology, metabolic modeling algorithms, and most importantly, novel biological insight. Much work remains to improve the largely underdetermined platforms with which we probe metabolism, however this thesis work represents but a small effort to “make measurable what is not so.”