Thiol-Based redox regulation

In the presence of oxidative and nitrosative stress proteins can undergo oxidative modification. While unintended oxidative modifications most often lead to the damage of the affected proteins, oxidative modifications also play important roles in cellular redox sensing. Classical redox sensor proteins use reversible modifications of cysteine to change their activity in response to a changing redox environment. These redox sensors are involved in a wide variety of cellular processes ranging from central energy metabolism over protein quality control to the regulation of the oxidative stress response.

We study the role of these cysteine-based redox regulation in host-pathogen interactions. We expose bacteria to immune cells and identify changes in the oxidation profile of their proteins with proteomic methods. Our project is part of the DFG priority program SPP 1710 Thiol-Based Redox Switches in Cellular Physiology


Functional Metagenomics

Today the vast amount of newly published genomic data is generated by so-called metagenomic projects. The annotation of these sequences of organisms, whose existence was not even known before their DNA was extracted from environmental samples, has led to the identification of millions of new proteins. However, apart from their sequence, not much is known about these proteins.

In the ERC funded FuMe (Functional Metagenomics) project, we are trying to change this. We, therefore, characterize metagenomic proteins to learn about their function and to discover new biocatalysts. However, because metagenomic data is generated from environmental samples, we face several challenges: first and foremost, it is estimated that far more than 90% of the microbial species that are sequenced during a typical metagenomic data acquisition cannot be cultivated in the lab. But even if we were able to find the right conditions and convince all those species to grow, the experimental characterization of millions of proteins in the laboratory would still be an insurmountable task.

Thus, we use a short-cut. Instead of testing each protein individually, we group proteins that have similar sequences into so-called protein families. Often, we can group thousands of proteins into one family. Then, we create a phylogenetic tree of this family and select, based on this tree, a family member that best represents all the members of the family. And instead of trying to cultivate the original microbial species that originally harbors this protein, we use Escherichia coli as a host to express a synthesized version of the gene for us.