Erasmus+

Traineeship

Initial situation and project objective

Microbial natural products (NPs) have always been a major inspiration for development of anti-infectives and control agents. Considered as none-essential metabolites for vegetative growth and reproduction of a microbe, but rather providing an evolutionary fitness advantages to changing environmental conditions, genes for NP biosynthesis are not constitutively expressed under laboratory conditions. This phenomenon is a constant challenge in cultivation-dependent but also -independent NP discovery campaigns demanding for creative solutions. The requirement of an adequate stimulus that induces NP biosynthesis brought forth overcoming strategies that e.g. mimic the suitable environmental condition in the lab. Upon success, this eventually leads to expression of dormant biosynthetic genes and in consequence triggers NP production.

Solution and customer benefits

Within an Erasmus+ Traineeship in our Natural Product Department M.Sc. Isabella Tropiano systematically scanned actinobacterial genomes in silico for the presence of sequence motifs correlating to a given environmental stimulus within biosynthetic gene clusters. This genomic survey revealed a yet unknown gene cluster family that remain silent under typical fermentation conditions. Only under depletion of an upfront-predicted micronutrient, the identified producer strains gave access to a broad diversity of known and yet unknown metabolites. Within a very short time, this project established an elegant process, combining genomics prediction tools to manipulate NP profiles of microbes in a target-oriented manner and advanced metabolomics technologies to assign the biosynthetic gene clusters to a tremendous diversity of microbial NPs.

Project profile

PROJECT TITLE	From in silico prediction of environmental stimulus via hypothesis driven experimental design to awakening dormant gene clusters
DURATION	11/2020 –06/2021
FUNDING PROGRAM	Erasmus+
PROJECT LEADER	Dr. Marius Spohn
GOALS	In silico Identify silent biosynthetic gene clusters Computational prediction of their triggering environmental stimulus In vivo Induce BGC expression by mimicking environmental conditions Identify and characterized Natural Products