Dittmar, G. — Page 2 | Search bioRxiv

bioRxiv2017

Protein Interaction Screen on Peptide Matrix (PRISMA) reveals interaction footprints and the PTM-dependent interactome of intrinsically disordered C/EBPβ

CCAAT enhancer binding protein beta (C/EBP{beta}) is a pioneer transcription factor that specifies cell differentiation. C/EBP{beta} is intrinsically unstructured, a molecular feature common to many proteins involved in signal processing and epigenetics. The structure of C/EBP{beta} differs depending on alternative translation start site usage and multiple post-translational modifications (PTM). Mutation of distinct PTM sites in C/EBP{beta} alters designated protein interactions and cell differentiation, suggesting a C/EBP{beta} PTM indexing code determines epigenetic outcomes. Herein, we systematically explored the interactome of C/EBP{beta} using an array of spot-synthesised C/EBP{beta}-derived linear tiling peptides with and without PTM, combined with mass spectrometric proteomic analysis of protein interactions. We identified interaction footprints of ~1300 proteins in nuclear cell extracts, many with chromatin modifying, remodelling and RNA processing functions. The results suggest C/EBP{beta} acts as a multi-tasking molecular switchboard, integrating signal-dependent modifications and structural plasticity to orchestrate interactions with numerous protein complexes directing cell fate and function.\n\nHighlightsO_LIPeptide array based interaction proteomics map SLiM and PTM dependent C/EBP{beta} interactome\nC_LIO_LINovel links between C/EBP{beta}, RNA processing, transcription elongation, MLL, NuRD were revealed\nC_LIO_LIC/EBP{beta} structure organizes modular hub function for gene regulatory machinery\nC_LIO_LIPRISMA is suitable to resolve protein interactions and networks based on intrinsically disordered proteins\nC_LI

biochemistry↗

bioRxiv2017

Dr.Paso: Drug response prediction and analysis system for oncology research

The prediction of anticancer drug response is crucial for achieving a more effective and precise treatment of patients. Models based on the analysis of large cell line collections have shown potential for investigating drug efficacy in a clinically-meaningful, cost-effective manner. Using data from thousands of cancer cell lines and drug response experiments, we propose a drug sensitivity prediction system based on a 47-gene expression profile, which was derived from an unbiased transcriptomic network analysis approach. The profile reflects the molecular activity of a diverse range of cancer-relevant processes and pathways. We validated our model using independent datasets and comparisons with published models. A high concordance between predicted and observed drug sensitivities was obtained, including additional validated predictions for four glioblastoma cell lines and four drugs. Our approach can accurately predict anti-cancer drug sensitivity and will enable further pre-clinical research. In the longer-term, it may benefit patient-oriented investigations and interventions.

bioinformatics↗