Our mission is to Map, Understand and Engineer Metabolic Regulation in Bacteria. In the wet-lab, we use methods like CRISPR genome editing, CRISPR interference, transcriptomics, proteomics and metabolomics. In the dry-lab, we integrate these data with tools like metabolic control analysis, flux balance analysis and kinetic models. Metabolomics methods are especially important for us, and we are innovating novel mass spectroscopy tools and data analysis methods for untargeted and targeted metabolomics.
Research Area 1: Mapping and Understanding Metabolic Regulation
Understanding the mutual feedback between metabolism and transcription is one of our main research goals.
The main challenge in this project is to identify regulatory interactions between metabolites and transcriptional regulators at a very large-scale (reviewed in Donati et al. 2018). The gold standard for testing the effects of metabolites on transcriptional regulators is still in vitro biochemistry. However, most in vitro assays are low-throughput, feasible for only certain compounds, and combinatorial effects cannot be assayed. We have now developed an approach to infer metabolite-transcription interactions directly from metabolomics and transcriptomics data (Lempp et al. 2019). Next, we use this method to map complete metabolic-genetic networks of bacteria. For this purpose, we use CRISPR interference to perturb hundreds of metabolic genes and measure the cellular responses at scale (Donati et al., 2021).
Research Area 2: Engineering Dynamic Control of Metabolic Pathways
Engineering metabolic valves and new feedback regulation is our second research goal.
Overproduction of chemicals is a burden for bacteria. In a recent study, we could show that the metabolic burden of engineered E. coli is caused by metabolome changes, which then led to a false response at the transcriptional level (Wang et al. 2021). Control of enzyme levels in production pathways can avoid such responses, especially if enzyme expression is feedback regulated. This was also the reason why we found that partial feedback-dysregulation is better for arginine overproducing E. coli than complete dysregulation (Sander et al. 2019).
In the ERC funded MapMe project, we engineer E. coli strains that switch between growth and overproduction of metabolites. In a pilot study we designed metabolic switches with temperature-sensitive enzymes and used them to overproduce citrulline (Schramm et al. 2020). The signal for the switch was a temperature shift of 6°C, which is easy to achieve in bioreactors, even at an industrial scale. Currently, we use CRISPR to create thermo-switches en masse.
Our current library contains 252 temperature-sensitive enzymes, each of which overproduces a different metabolite (Schramm et al., in preparation).