DynamicKit

Objectives:

Tuberculosis (TB) is one of the deadliest infectious diseases in humans and claims around 1.5 million lives worldwide every year. To successfully treat this lung disease, a mix of different drugs has to be administered for several months. This however is problematic, as the bacterial pathogens become resistant, and even in treatable bacterial populations, highly resistant subpopulations can be detected. In order to prevent the spread of the disease, it is therefore not only necessary to develop new antibiotics, but also to constantly find new combinations of active substances. Such combinations can so far only be identified empirically in expensive clinical studies.

The goal of this project is the analysis of the metabolic processes of the mycobacteria (the causative agent of TB) via dynamic proteomics. The technique of dynamic proteomics used here, is a novel top-down proteomics approach, in which a proteome of the intact proteins is determined from a bacterial culture. After addition of a stable isotope labeled source (tracer), the masses of the newly formed proteins shift to higher masses due to the integration of the heavier atoms. Those mass-shifts of the different proteins can be tracked with temporal resolution at freely selected times with high resolution using HPLC-MS.

Together with bioinformatics partners new methods are being developed for the automated analysis of dynamic proteome data, which, in combination with novel machine learning techniques, allows us to find new drug targets, to identify patterns of change in the therapy with known antibiotics in sensitive or resistant pathogens and to predict promising combination therapies.

Responsible:

Anja Dollinger (anja.dollinger@tum.de)

Funding:

Partners:

• Ludwig-Maximilians-Universität München
  - Max von Pettenkofer Institut
  - Institute for Computational Biology; Helmholtz Zentrum München
  - Department of Infectious Diseases and Tropical Medicine; Medical Faculty
• Technische Universität München, Institute for Computational Biology, Helmholtz Zentrum München