A systems oriented approach of cyanobacterial metabolic adaptations to and diversity in highly fluctuating environments

Phototrophic organisms play an essential role as primary producers in the global carbon and nitrogen cycle on Earth. Amongst them, cyanobacteria are an ancient group of prokaryotes that perform oxygenic photosynthesis to convert solar energy into organic matter. Thus, cyanobacteria have been used for decades as models to study oxygenic photosynthesis as well as other, plant-like processes in a bacterial background.

Being prokaryotic and thus lacking organelles, these organisms are also well suited for metabolomics and systemic analyses. Amongst the bacteria, the monophyletic group of cyanobacteria comprises a huge diversity in terms of morphology, physiology, metabolic and molecular properties, which seem to be important factors for their evolutionary and ecological success. As a result, cyanobacteria can survive and thrive in richly diverse environments.

With the ever growing number of fully sequenced cyanobacterial genomes a first glance on their genetic capabilities can be gathered. Although this may facilitate the elucidation of genetic key factor that permitted them to withstand evolutionary environmental changes, many rather functional aspects might be overseen. Furthermore, still little is known about the metabolic diversity of cyanobacteria especially regarding the metabolite composition associated or potentially required to survive in highly fluctuating environments.

Thus, we are focusing on cyanobacteria as an ideal and simple system to functionally understand metabolic adaptation and diversification in detail. For this purpose, we are using the following strategies:

A comparative metabolomics approach to elucidate metabolic key factors and associated genes that enable cyanobacteria to adapt and thrive in richly diverse environments. Our focus lays here on the identification of transferable biochemical modules for targeted metabolic engineering.
A systems oriented approach to mechanistically understand the reprogramming of transcriptional and metabolic responses of a selected cyanobacterial strain under fluctuating environmental conditions. This will also enable a better host understanding for targeted metabolic engineering.
A reverse metabolic engineering approach to discover novel metabolites and metabolic pathways by using random shotgun engineered prokaryotes.