Screening of cell cycle fusion proteins to identify kinase signaling networks

<p>Kinase signaling networks are well-established mediators of cell cycle transitions. However, how kinases interact with the ubiquitin proteasome system (UPS) to elicit protein turnover is not fully understood. We sought a means of identifying kinase-substrate interactions to better understand signaling pathways controlling protein degradation. Our prior studies used a luciferase fusion protein to uncover kinase networks controlling protein turnover. In this study, we utilized a similar approach to identify pathways controlling the cell cycle protein p27^Kip1. We generated a p27^Kip1-luciferase fusion and expressed it in cells incubated with compounds from a library of pharmacologically active compounds. We then compared the relative effects of the compounds on p27^Kip1-luciferase fusion stabilization. This was combined with <i>in silico</i> kinome profiling to identify potential kinases inhibited by each compound. This approach effectively uncovered known kinases regulating p27^Kip1 turnover. Collectively, our studies suggest that this parallel screening approach is robust and can be applied to fully understand kinase-ubiquitin pathway interactions.</p

FAIR LINCS Data and Metadata powered by the CEDAR Framework

The Library of Integrated Network-based Signatures (LINCS) program generates a wide variety of cell-based perturbation-response signatures using diverse assay technologies. For example, LINCS includes large-scale transcriptional profiling of genetic and small molecule perturbations, and various proteomics and imaging datasets. We have developed data processing pipelines, and supporting informatics infrastructure to access, standardize and harmonize, register and publish LINCS datasets and metadata from all Data and Signature Generating Centers (DSGC’s). Metadata standards specifications provide a foundation for harmonizing and integrating LINCS data. Here we introduce a CEDAR-based LINCS Community Metadata Environment, to support end-to-end metadata management framework that supports authoring, curation, validation, management, and sharing of LINCS metadata, while building upon the existing LINCS metadata standards and data-release workflows. Following this initial validation, our goal is to create reusable metadata modules with user friendly templates for each of the LINCS metadata categories and to make our suite of tools compatible with the CEDAR metadata technologies. This should further simplify metadata handling in the LINCS consortium and facilitate a global metadata repository at CEDAR. As other projects apply the same approach, many more datasets will become cross-searchable and can be linked optimizing the metadata pathway from submission to discovery

Additional file 1: Table S1. of Drug target ontology to classify and integrate drug discovery data

SPARQL query results to identify kinase domains in the KINOMEscan assay with gatekeeper annotations. Shown are TDL classification, DTO ID, kinase domain description, and protein name. (XLSX 28 kb