Mapping combinatorial stress responses in bacteria using chimeric proteins and probabilistic modelling

Living systems adapt to changing environments in order to survive and to grow and reproduce. Features of an adaptive response have to date been studied in a fairly one dimensional way, yet we know that the cell operates using networks of interactions between the key players that are responsible for the cells growth and viability. These players can sense stress, some will cause new players to appear in the cell, and some of these will work to overcome the stress in different ways. However the relationships between these different players and the levels at which they operate are largely unknown. In particular whether or not a single unique solution to a set of imposed conditions is all that can reasonably operate in the cell is a major unknown. Knowledge of the boundary conditions acceptable to a cell will greatly help advance work where special properties of a cell are desirable, as for example in many biotechnological and synthetic biology settings. By studying two important but relatively experimentally amenable single cell bacteria we will study what cell components change when the cells respond to stress and how their patterns of response amount to an integrated response to stress. To do so we will collect data across several different areas of cell activity, and will perturb cells using novel control proteins to redirect responses away from particular imposed stresses. Data analysis coupled to mathematical modelling will be conducted in order to integrate and describe the observed cellular behaviour, and to help explain how the processes contributing to the cell's responses work as a whole. The collection of data is targeted directly at informing the development and evaluation of mechanistic models of cell response. We have chosen to conduct the same experimental programme in two different bacterial organisms. This comparative dimension to the proposed research project allows us to explore the evolutionary aspects underlying the response to stresses that are intimately linked to bacterial pathogenesis. This also has the potential to inform future analyses in synthetic biology or attempts to direct microorganismal evolution. At the end of the research program we expect to able to better predict how cells cope with large changes in their environments, through a knowledge of which activities within the cell are key to achieving adaptation to stress. Outcomes of the work should provide insights into how cells might be forward evolved for particular purposes, and identify where particular vulnerabilities might exist that may suggest new targets for remedial therapies such as new antibiotic targets.

Bacteria respond to changing environments by redirecting gene expression to cope with the applied stress, during infection and in the environment as nutrient and abiotic conditions vary. How the complex signal transduction pathways, the associated metabolic factors and protein factors inter-relate to achieve the necessary adaptive changes in the cell has not been well studied at an integrated level. In particular how combinatorial stresses cause adaptive change in the cell is unknown. We plan to use non-native bio-synthetic regulatory proteins to rewire signal transduction pathways to reprogram E. coli and M. tuberculosis to elicit specific gene expression changes, uncoupled from the cognate native cues of gene expression. The main strength of synthetic proteins to study complex biological systems is that they can be made to function independent from the native physiological context. Hence the complex control feedback mechanisms that regulatory systems usually employ to regulate genetic and metabolic flow can be separated out. We plan to produce and characterise bio-synthetic domain exchanged (chimeric) transcription activators of the bacterial RNA polymerase to control genetic flow under defined sets of stress conditions. These chimera will be used to dissect genetic and metabolic control of nitrogen regulation and other stresses and to gauge the relative contributions of genetic and metabolic factors to cell adaptation. Methodologies include quantitative proteomics of key players, metabolic profiling and array technologies to measure protein-DNA interactions and transcript responses linked to advanced modelling approaches. Results of the project are anticipated to contribute significantly to biotechnology, infection research and emerging fields of synthetic and systems biology. Synthetic biology can be applied to uncover design principles of complex genetic networks through dissecting the functional performance of modular system components of the cell.