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.
The general strategy to optimize a production pathway is controlling expression levels of enzymes using variation of promoter strength or ribosome affinity. Choosing the optimal expression levels “a priori” is key for the performance of a heterologous production pathway, because the pathway usually lacks regulatory mechanisms and is not integrated with regulatory mechanisms of the host. However, without mechanisms like feedback regulation the production pathway cannot respond to the cells growth phase and any deviations away from optimal conditions might cause premature decrease in the production rate. Therefore, it is highly desirable to implement metabolic feedback regulation in a production pathway which drains the bulk of resources from the host.
For example, we want to understand the consequences of removing regulation in native pathways and inserting heterologous pathways that are not under metabolic control. In a recent study, we have shown that partial feedback-dysregulation is better for arginine overproducing E. coli than complete dysregulation (Sander et al. 2019). In another project, we engineered an E. coli strain that switches between growth and overproduction of 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.