Fournier, C. | Search bioRxiv

bioRxiv2018

EGF signalling in epithelial carcinoma cells utilizes preformed receptor homoclusters, with larger heteroclusters post activation

Epidermal growth factor (EGF) signalling regulates cell growth, differentiation and proliferation in epithelium and EGF receptor (EGFR) overexpression has been reported in several carcinoma types. Structural and biochemical evidence suggests EGF binding stimulates EGFR monomer-dimer transitions, activating downstream signalling. However, mechanistic details of ligand binding to functional receptors in live cells remain contentious. We report real time single-molecule TIRF of human epithelial carcinoma cells with negligible native EGFR expression, transfected with GFP-tagged EGFR, before and after receptor activation with TMR-labelled EGF ligand. Fluorescently labelled EGFR and EGF are simultaneously tracked to 40nm precision to explore stoichiometry and spatiotemporal dynamics upon EGF binding. Using inhibitors that block binding to EGFR directly, or indirectly through HER2, our results indicate that pre-activated EGFR consists of preformed homoclusters, while larger heteroclusters including HER2 form upon activation. The relative stoichiometry of EGFR to EGF after binding peaks at 2, indicating negative cooperativity of EGFR activation.

biophysics↗

bioRxiv2017

SARNAclust: Semi-Automatic Detection Of RNA Protein Binding Motifs From Immunoprecipitation Data

RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited, and in particular identification of the RNA motifs that bind proteins has long been a difficult problem. To address this challenge, we have developed a novel semi-automatic algorithm, SARNAclust, to computationally identify combined structure/sequence motifs from immunoprecipitation data. SARNAclust is, to our knowledge, the first unsupervised method that can identify RNA motifs at full structural resolution while also being able to simultaneously deconvolve multiple motifs. SARNAclust makes use of a graph kernel to evaluate similarity between sequence/structure objects, and provides the ability to isolate the impact of specific features through the bulge graph formalism. SARNAclust includes a key method for predicting RNA secondary structure at CLIP peaks, RNApeakFold, which we have verified to be effective on synthetic motif data. We applied SARNAclust to 30 ENCODE eCLIP datasets, identifying known motifs and novel predictions. Notably, we predicted a new motif for the protein ILF3 similar to that for the splicing factor hnRNPC, providing evidence for interaction between these two proteins. To validate our predictions, we performed a directed RNA bind-n-seq assay for two proteins: ILF3 and SLBP, in each case revealing the effectiveness of SARNAclust in predicting RNA sequence and structure elements important to protein binding. Availability: https://github.com/idotu/SARNAclust

bioinformatics↗