Project description:Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.
Project description:An Adeno-Associated Virus capsid fitness landscape reveals a frameshifted viral gene and in vivo design principles, enabling machine-guided engineering.
Project description:In recent years the clinical success of T cell-based immunotherapy approaches has revolutionized treatment of solid tumors and hematological malignancies. However, still some patients do not respond to available therapies at all, others for limited time only. A promising low side-effect approach is peptide-based immunotherapy, which relies on specific immune recognition of tumor-associated human leucocyte antigen (HLA)-presented peptides. In this study, we developed a workflow for the immunopeptidome-guided design of off-the shelf warehouses for personalized peptide vaccines using the example of chronic lymphocyte leukemia (CLL). The so defined warehouses could provide the basis for different T cell-based immunotherapy approaches such as TCR-engineered T cell transfer or multi-peptide vaccinations. The warehouse approach enables a fast and cost-effective way to provide a personalized T cell-based immunotherapeutic approach. The here defined peptide warehouse is already utilized for a personalized multi-peptide vaccine trial (iVAC-XS15-CLL01, NCT04688385).
Project description:Inhibition of epigenetic regulators by small molecules is an attractive strategy for cancer treatment. Recently, we characterised the role of lysine methyltransferase 9 (KMT9) in prostate, lung, and colon cancer. Our observation that the enzymatic activity was required for tumour cell proliferation identified KMT9 an attractive therapeutic target. Here, we report the development of the first-in-class, potent and selective KMT9 inhibitor (compound 4, KMI169) through structure-based drug design. KMI169 functions as a bi-substrate inhibitor targeting the SAM and substrate binding pockets of KMT9 and exhibits high potency, selectivity, and cellular target engagement. KMT9 inhibition selectively downregulates target genes involved in transcription and cell cycle regulation and impairs proliferation of tumours cells including castration- and enzalutamide-resistant prostate cancer cells. Together, KMI169 represents a valuable tool to probe cellular KMT9 functions and paves the way for the development of clinical inhibitors as therapeutic options to treat malignancies such as therapy-resistant prostate cancer.
Project description:An experiment was designed to use a computer program to create lithography masks using a pseudo-random pattern generator. The data in this file are results from immunosignaturing 8 different monoclonals using a 10,000 peptide random-sequence microarray. Peptides were synthesized by Sigma Aldrich, and printed onto glass slides and used to test several different parameters.