Metabolism in bacteria occurs via a set of complex, dynamic, and interconnected metabolic steps (metabolic modules). The outputs of such metabolic systems depend on the interplay between the genetic circuits in the cell, which generate proteins, and the metabolic circuits, which generate flux and metabolite pools. Understanding how this complex system functions in the cell is the goal of integrated metabolism studies. Global """"""""omics""""""""-based approaches coupled to metabolic models and physiological insight are setting the stage for understanding how the metabolic network functions as an integrated system. Methylobacterium extorquens is a facultative methylotrophic bacterium, which has the property of two dramatically different modes of growth: growth on one-carbon (C1) compounds is reducing-power limited, while growth on multi-carbon compounds is energy-limited. In addition, through past NIH-funded work on this bacterium, a suite of computational and genetic tools and multivariate """"""""omics"""""""" datasets are available. Therefore, this bacterium is becoming an attractive model system in which to ask fundamental questions regarding metabolic integration, using comparative studies of different metabolic conditions. We propose to analyze how methylotrophic metabolism functions as an integrated system in M. extorquens. Our data to date show that at steady-state, the cells are in balance, while during perturbations, the metabolic network is shifted out of balance and the cells respond dramatically at the transcriptional, flux and metabolite levels. The response """"""""resets"""""""" the metabolic state at a different level, and the cells return to a balanced state. In this next project period, we will address how this response occurs at the metabolic module level, and carry out a set of manipulations to probe specific metabolic states and response scenarios.
The specific aims of this project are as follows: 1. Determine metabolic states of the cell. Linkages between central methylotrophic metabolism and other core metabolic functions that have been identified will be detailed, including growth rate, stress response, iron acquisition, and fatty acid metabolism. Metabolic states for all known modules will be determined by measuring transcripts, proteins, enzyme activities, fluxes, and metabolite pools. A set of criteria will be identified that define each metabolic state. 2. Analyze how the metabolic state changes in response to perturbations. Using the same set of measurements as in specific aim 1, we will determine how metabolic modules respond to changes in growth rate and flux of substrates and how they change when genetic modifications cause altered metabolic states, such as altered reducing power balance and altered flux through specific metabolic modules. The data generated in specific aim 1 and 2 will be analyzed to determine how the metabolic and genetic circuits are integrated. This study is expected to result in new principles in metabolic integration, as well new insights into C1 metabolism.
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