Graduate Students Mini Symposium VIII 2020


16:00 Christoph Diehl, AG Erb

Optimization and extension of the CO2-fixation cycle CETCH

The crotonyl-coenzyme A (CoA)/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle is a new to nature CO2-fixation pathway, designed, realized and published by Schwander et al. (2016). The cycle is based on two NADPH dependent CO2-fixation reactions done by the crotonyl-CoA carboxylase/reductase, which is one of the fastest carboxylases known to date. The main goal of this project is to optimize the interplay of the different enzymes and components to increase the efficiency. Since the cycle is a manmade pathway, it did not evolve within a metabolic context. Therefore, we test combinations with other natural and non-natural pathways to extend the metabolic network.

16:30 Michelle Kuntz, AG Link

Mapping metabolite-transcription factor interactions at a genome-scale using CRISPRi knockdown library screenings

Allosteric binding of metabolites can have a strong influence on the activity and function of proteins. The current challenge still remains to detect functional metabolite-protein interactions in vivo at a very large-scale. We used multi-omics analysis and CRISPR interference (CRISPRi) to investigate how E. coli responds to decreases of single metabolic enzymes. We could see that CRISPRi knockdowns lead to very specific and local changes of metabolite levels in the cell. To study which influence these changing metabolite levels had on transcription factors, we combined a library of 7177 CRISPRi strains with fluorescent transcriptional reporters. This allowed us to recover known regulatory interactions of the arginine repressor ArgR and potentially new interactions of other transcription factors.

17:00 Niklas Farke, AG Link

Insights into metabolism and enzyme level regulation by kinetic modelling

The metabolic and the transcription regulation network in E. coli are well characterized. Both networks are intertwined: transcription factors control enzyme abundance, while metabolites can control enzyme and transcription factor activity. In order to guide data interpretation, mathematical models exist for both networks. However, there are only very few models that combine both networks. Here, we use small kinetic models that combine metabolism and enzyme level regulation together with multi-omics data to provide insights into metabolism. We present three case studies: 1) Branch point of arginine and pyrimidine biosynthesis: why does ornithine accumulate when carAB gets downregulated? 2) Amino acid biosynthesis: why is allosteric and enzyme level regulation required? 3) Predicting robust regulation for optimal glycerol production.

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