PhD defence by Eftychia Eva Kontou: "Metabolomics for Specialized Metabolites"

Principal supervisor: Prof. Tilmann Weber
Co-supervisor: Prof. Thomas Ostenfeld Larsen & Timo Sachsenberg

Examiners:Prof. Ling Ding, Department DTU Bioengineering, Prof. Stephanie Grond, Eberhard Karls-University Tübingen, Institute of Organic Chemistry, Associate Prof. Thomas Tørring, Department of Biotechnological and Chemical Engineering Aarhus University

Chairperson at defence:Senior Researcher, Kai Blin

A copy of the PhD thesis is available for reading at the department

Summary of the PhD thesis:

Specialized – also known as “secondary” – metabolites (SMs) are small molecules produced by living organisms as a response to an ecological interaction (competition, pollination, predation, and others). They often have interesting biological activity and complex chemical structures. Many SM’s play key roles in medicine, nutrition, agriculture, and the cosmetic industry. Actinomycetes, a group of ubiquitous soil bacteria, are prolific natural product producers and, more importantly, producers of a large number of bioactive SMs. Genome mining data strongly suggests that their full biological potential still has not been fully exploited.

This Ph.D. project demonstrates the application of different omics strategies for the discovery of new SMs[. While this approach is successful and has resulted in the identification and characterization of various novel SMs, such as the project’s evident discoveries of two novel macrolides, epemicins A and B, and involvement to the activation of an otherwise silent gene cluster, it is limited to a small to medium number of strains. In order to analyze and process thousands of different biological samples derived from Actinomycete extracts, alternative experimental and computational methods were required. Using micro-scale fermentations and one hundred strains from our in-house collection grown in different media, the project generated 1250 raw LC-MS/MS data records. The challenge to analyze such data volumes and the lack of rapid data processing tools led to the conceptualization of a universal pipeline, UmetaFlow, that combines different tools and algorithms for high throughput untargeted metabolomic data processing and analysis. I anticipate that this workflow will be useful for many groups that generate high-throughput mass-spectrometry based metabolomics data.