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Biology subjects

Franken, H.

Publications and source records attributed to Franken, H..

4 recordsLinked to original sources

High-throughput platforms for genetic perturbation screening using CRISPR/Cas9 in human iPSC-derived macrophages for drug discovery

Human induced Pluripotent Stem Cell (hiPSC) models have revolutionised drug discovery, offering high translational relevance for recapitulating disease biology and thereby the potential to help reduce drug attrition. Macrophages are pivotal for maintaining tissue homeostasis and orchestrating immune responses; their dysregulation underpins several diseases, including autoinflammatory disorders, neurodegeneration, and cancer. Therapeutically targeting this cell type presents an attractive strategy to simultaneously influence multiple cellular mediators and functions. We have established a scalable, semi-automated, and physiologically relevant hiPSC-derived macrophage model, rigorously characterised through deep comparative multi-omics. We have also integrated our hiPSC-derived macrophage platform with large-scale CRISPR screening workflows designed for parallel genetic interrogation of thousands of gene targets, in both arrayed and pooled formats. In this manuscript, we apply those genetic screening methods to hiPSC-derived macrophages and showcase how genetic perturbations alter pro- and anti-inflammatory transcriptional signatures and significantly impact functional phenotypes in this cell model. This integrated approach allows for the exploration of novel genetic insights linked to causal disease biology, advancing myeloid cell-associated target discovery across a broad spectrum of therapeutic areas.

immunology↗

Computational analysis of ligand dose range thermal proteome profiles

Detecting ligand-protein interactions in living cells is a fundamental challenge in molecular biology and drug research. Proteome-wide profiling of thermal stability as a function of ligand concentration promises to tackle this challenge. We present a statistical analysis method with reliable control of the false discovery rate and apply it to several datasets probing epigenetic drugs. We detect off-target drug engagement in unrelated protein families.

bioinformatics↗

Proteome thermal stability reflects organ physiology and identifies drug-target engagement in vivo

Studying biological processes at a molecular level and monitoring drug-target interactions is established for simple cell systems but challenging in vivo. We introduce and apply a methodology for proteome-wide thermal stability measurements to characterize organ physiology and activity of many fundamental biological processes across tissues, such as energy metabolism and protein homeostasis. This method, termed tissue thermal proteome profiling (tissue-TPP), also enabled target and off-target identification and occupancy measurements in tissues derived from animals dosed with the non-covalent histone deacetylase inhibitor, panobinostat. Finally, we devised blood-CETSA, a thermal stability-based method to monitor target engagement in whole blood. Our study generates the first proteome-wide map of protein thermal stability in tissue and provides tools that will be of great impact for translational research.

cell biology↗

Non-Parametric Analysis of Thermal Proteome Profiles Reveals Novel Drug-Binding Proteins

Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present non-parametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP datasets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. To facilitate access to a wide range of users, a freely available software implementation of NPARC is provided.

bioinformatics↗