Metabolism provides the essential biochemical intermediates and energy that enable life and its growth. In this thesis we studied robustness of Escherichia coli metabolism, by perturbing it with different methods and measuring the response at a molecular level.
In Chapter 1, we introduce the latest insight into metabolic regulation and optimality in microbial model organisms. Overall, we identified and described two major gaps in knowledge: the limited amount of known metabolite-protein interactions and the unknown objectives towards which cells optimize their enzyme levels. Moreover, we provide a short introduction to the relevant methods utilized in this thesis.
In Chapter 2, we describe a series of experiments which confirmed that CRISPRi is a reliable tool to specifically perturb metabolism in E. coli. We showcase the advantage of using a CRISPRi system integrated in the genome, which is suitable to apply inducible knockdowns of essential genes. We demonstrate this by characterizing growth for a library of over 100 strains and verifying inducibility and specificity with proteomics data.
In Chapter 3 we applied the validated CRISPRi setup to perturb and study metabolism systematically. First, we used a pooled CRISPRi library to knock down all metabolic genes in E. coli. By following the appearance of growth defects with next generation sequencing, we show that metabolic enzymes are expressed at higher levels than strictly necessary. We then focused on a panel of 30 CRISPRi strains and characterize their response to lower enzyme levels with metabolomics and proteomics. We show that the metabolome can buffer perturbations of enzyme levels in two different stages: first, metabolites increase enzyme activity to maintain optimal growth and only later they activate gene regulatory feedbacks to specifically upregulate perturbed pathways.
In Chapter 4 we employed a different approach to perturb bacterial metabolism, by growing E. coli in different environmental conditions and measuring the response at the metabolome level. We could show that in exponentially growing cells key biosynthetic products as amino acids and nucleotides are kept at relatively stable levels across different environments. We compared our dataset to a matching published proteomics dataset, showing that unlike the proteome, metabolite levels are independent from growth effects.