Analysis of complex samples by mass spectrometry leads to insights into system dynamics

Abstract

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.