The Greatwall kinase safeguards the genome integrity by affecting the kinome activity in mitosis.

Progression through mitosis is balanced by the timely regulation of phosphorylation and dephosphorylation events ensuring the correct segregation of chromosomes before cytokinesis. This balance is regulated by the opposing actions of CDK1 and PP2A, as well as the Greatwall kinase/MASTL. MASTL is commonly overexpressed in cancer, which makes it a potential therapeutic anticancer target. Loss of Mastl induces multiple chromosomal errors that lead to the accumulation of micronuclei and multilobulated cells in mitosis. Our analyses revealed that loss of Mastl leads to chromosome breaks and abnormalities impairing correct segregation. Phospho-proteomic data for Mastl knockout cells revealed alterations in proteins implicated in multiple processes during mitosis including double-strand DNA damage repair. In silico prediction of the kinases with affected activity unveiled NEK2 to be regulated in the absence of Mastl. We uncovered that, RAD51AP1, involved in regulation of homologous recombination, is phosphorylated by NEK2 and CDK1 but also efficiently dephosphorylated by PP2A/B55. Our results suggest that MastlKO disturbs the equilibrium of the mitotic phosphoproteome that leads to the disruption of DNA damage repair and triggers an accumulation of chromosome breaks even in noncancerous cells.

The RNA-binding protein HuR/ELAVL1 regulates IFN-ß mRNA abundance and the type I IFN response.

Secretion of type I interferon (IFN) is the first cellular reaction to invading pathogens. Despite the protective function of these cytokines, an excessive response to their action can contribute to serious pathologies, such as autoimmune diseases. Transcripts of most cytokines contain adenylate-uridylate (A/U)-rich elements (AREs) that make them highly unstable. RNA-binding proteins (RBPs) are mediators of the regulatory mechanisms that determine the fate of mRNAs containing AREs. Here, we applied an affinity proteomic approach and identified lethal, abnormal vision, drosophila-like 1 (ELAVL1)/Hu antigen R (HuR) as the predominant RBP of the IFN-ß mRNA ARE. Reduced expression or chemical inhibition of HuR severely hampered the type I IFN response in various cell lines and fibroblast-like synoviocytes isolated from joints of rheumatoid arthritis patients. These results define a role for HuR as a potent modulator of the type I IFN response. Taken together, HuR could be used as therapeutic target for diseases where type I IFN production is exaggerated.

A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence.

MOTIVATION: Predicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders. RESULTS: We developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain-peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction. AVAILABILITY AND IMPLEMENTATION: The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinity.