Research Mission and Profile
Molecular Signal Processing
Bioorganic Chemistry
Biochemistry of Plant Interactions
Cell and Metabolic Biology
Independent Junior Research Groups
Program Center MetaCom
Publications
Good Scientific Practice
Research Funding
Networks and Collaborative Projects
Symposia and Colloquia
Alumni Research Groups
Publications
Publications - Cell and Metabolic Biology
Publications
A bottleneck in the development of new anti‐cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti‐proliferative drug candidates, focusing on human prostate cancer cells (PC‐3). As proof of concept, 38 drugs are studied with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC‐MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. The transferability of MoA predictions based on PC‐3 cell treatments is validated with two other cancer cell models, i.e., breast cancer and Ewing\'s sarcoma, and show that correct MoA predictions for alternative cancer cells are possible, but still at some expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, it is predicted that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as confirmed by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, this approach offers new opportunities, including the optimization of combinatorial drug applications.
Publications
Genetic resources for the model plant Arabidopsis comprise mutant lines defective in almost any single gene in reference accession Columbia. However, gene redundancy and/or close linkage often render it extremely laborious or even impossible to isolate a desired line lacking a specific function or set of genes from segregating populations. Therefore, we here evaluated strategies and efficiencies for the inactivation of multiple genes by Cas9-based nucleases and multiplexing. In first attempts, we succeeded in isolating a mutant line carrying a 70 kb deletion, which occurred at a frequency of ~ 1.6% in the T2 generation, through PCR-based screening of numerous individuals. However, we failed to isolate a line lacking Lhcb1 genes, which are present in five copies organized at two loci in the Arabidopsis genome. To improve efficiency of our Cas9-based nuclease system, regulatory sequences controlling Cas9 expression levels and timing were systematically compared. Indeed, use of DD45 and RPS5a promoters improved efficiency of our genome editing system by approximately 25–30-fold in comparison to the previous ubiquitin promoter. Using an optimized genome editing system with RPS5a promoter-driven Cas9, putatively quintuple mutant lines lacking detectable amounts of Lhcb1 protein represented approximately 30% of T1 transformants. These results show how improved genome editing systems facilitate the isolation of complex mutant alleles, previously considered impossible to generate, at high frequency even in a single (T1) generation.
This page was last modified on 27 Jan 2025 .
