Elucidating the role of protein load for the physiology of Enterococcus faecalis byintegrating proteomics data and genome-scale computational models

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Understanding microbial growth has high biotechnological relevance and is one key aspect in understanding how to combat microbial pathogens. Although gene and protein expression as well as metabolic processes are studied in detail, still microbial growth cannot be understood in its entirety and complexity. In the last years, theoretical and experimental studies revealed that protein load - the total amount of cellular proteins - is limited and thus constrains microbial growth. On the one hand, it has been shown that the total amount of proteins is limited by the amount and the catalytic efficiency ofribosomes and RNA polymerases. On the other hand, we and others found extremely high cellular concentrations of for instance glycolytic enzymes which indicate that the protein load must be close to the physical limit of the bacterial cell, meaning that significantly higher concentrations would lead to crystal formation. Consequently, when protein load is at its physical limit, e.g., expression of specific proteins for environmental adaptation can only be achieved by a reshuffling of the protein composition. A limiting protein load has also practical implications for molecular engineering strategies or biotechnological approaches. E.g., constraints on the amount of total protein will affect yieldoptimizations and the overexpression of proteins. Thus, it is becoming increasingly clear that physics and (bio)chemistry impose strong constraints on adaptation and evolution of cells. Such constraints have an impact on how a cell should partition that limited resource over its growth processes to optimize fitness (cellular economics). Accumulating data and theory have established that proper resource allocation isan important driver for such adaptations. All these findings imply that tight and sensitive regulatory mechanisms control protein expression on a cell-wide scale. Up to now, the protein load and its impact on cellular processes have not been studied systematically. Thus, in this proposed project we aim on investigating the effect of protein load oncellular behavior in a comprehensive and cell-wide approach, combining classical microbiological experiments, the latest quantitative whole-cell proteomic technique and computational modeling. We will use E. faecalis as a model organism for this study because (a) it is of major relevance in industry, (b) it is relatively well described in terms of genetics, and partially also in its metabolism and growth, (c) it has a relatively small genome, (d) we have a genome-scale metabolic model at hand, and (e) we were able to show feasibility of the integration of proteome data into the genome-scale model for thisorganism. We aim to learn how the intracellular protein load is balanced and maintained in E. faecalis, how it responds to artificially induced hyper expression of single proteins and how this response is controlled.