Protein kinases regulate a multitude of processes by reversible phosphorylation of target molecules. Induction of cell proliferation and differentiation are fundamental to development and rely on tightly controlled kinase activities. Vaccinia-Related Kinases (VRKs) have emerged as a multifunctional family of kinases with essential functions conserved, from nematodes and fruit flies, to humans. VRK substrates include chromatin and transcription factors, whereas deregulation of VRKs is implicated in sterility, cancer and neurological defects. In contrast to previous observations, we describe here that Caenorhabditis elegans VRK-1 is expressed in all cell types, including proliferating and post-mitotic cells. Despite the ubiquitous expression pattern, we find that vrk-1 mutants are particularly impaired in uterine development. Our data show that VRK-1 is required for uterine cell proliferation and differentiation. Moreover, the anchor cell, a specialized uterine cell, fails to fuse with neighboring cells to form the utse syncytium in vrk-1 mutants, thus providing further insight on the role of VRKs in organogenesis. ; We gratefully acknowledge funding from the Spanish Ministry of Economy and Competitiveness (BFU2013-42709P), the Autonomous Government of Andalusia (P08-CVI-3920) and the European Regional Development Fund to P.A. and a postdoctoral contract from the Autonomous Government of Andalusia to A.D. ; Peer reviewed
11 pages, 5 figures, 1 table.-- PMID: 19337973 [PubMed]. ; The Vaccinia-Related Kinases (VRKs) branched off early from the family of casein kinase (CK) I and compose a relatively uncharacterized family of the kinome. The VRKs were discovered due to their close sequence relation to the vaccinia virus B1R serine/threonine kinase. They were first described in phosphorylation of transcription factors that led to the discovery of an autoregulatory mechanism between VRK and the tumor suppressor transcription factor p53. The relevance of VRKs has broadened recently by introduction of its members as essential regulators in cell signaling, nuclear envelope dynamics, chromatin modifications, apoptosis and cellular stress response. Several phosphorylation substrates have been described, as well as the first positive and negative regulators of VRK. We provide an overview of the VRKs across species and discuss the wide diversity of cellular and organismal requirements for this kinase family. ; Work in the Askjaer laboratory is supported by the Spanish Ministry of Science and Innovation (BFU-2007-60116) and the Andalusian Regional Government (P07-CVI-02697). ; Peer reviewed
[Background] The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field. ; [Results] Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications. ; [Conclusions] The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data. ; The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007–2013) / ERC Grant Agreement n. 320737. The first author acknowledges the Spanish Government (Ministerio de Educación, Cultura y Deporte) for a predoctoral FPU scholarship. The authors acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). Grant support from Generalitat de Catalunya 2017-SGR-753 and Spanish Ministry of Economy, Industry and Competitiveness (project CTQ2015–66254-C2–1-P) is also acknowledged. ; Peer reviewed
