Philipp Eser

Learn More
Genetically engineered mouse models (GEMMs) have dramatically improved our understanding of tumor evolution and therapeutic resistance. However, sequential genetic manipulation of gene expression and targeting of the host is almost impossible using conventional Cre-loxP-based models. We have developed an inducible dual-recombinase system by combining(More)
To decrypt the regulatory code of the genome, sequence elements must be defined that determine the kinetics of RNA metabolism and thus gene expression. Here, we attempt such decryption in an eukaryotic model organism, the fission yeast S. pombe. We first derive an improved genome annotation that redefines borders of 36% of expressed mRNAs and adds 487(More)
During the cell cycle, the levels of hundreds of mRNAs change in a periodic manner, but how this is achieved by alterations in the rates of mRNA synthesis and degradation has not been studied systematically. Here, we used metabolic RNA labeling and comparative dynamic transcriptome analysis (cDTA) to derive mRNA synthesis and degradation rates every 5 min(More)
Pancreatic ductal adenocarcinoma (PDAC) is a fatal disease with poor patient outcome often resulting from late diagnosis in advanced stages. To date methods to diagnose early-stage PDAC are limited and in vivo detection of pancreatic intraepithelial neoplasia (PanIN), a preinvasive precursor of PDAC, is impossible. Using a cathepsin-activatable(More)
Pancreatic ductal adenocarcinoma (PDAC) remains a dismal disease with a poor prognosis and targeted therapies have failed in the clinic so far. Several evidences point to the phosphatidylinositol 3-kinase (PI3K)-mTOR pathway as a promising signaling node for targeted therapeutic intervention. Markers, which predict responsiveness of PDAC cells towards PI3K(More)
3 Estimation of RNA metabolism rates from 4tU-Seq data 3 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.2 Junction Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.3 Exon model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .(More)
  • 1