Role of Plant-Specific N-Terminal Domain of Maize CK2β1 Subunit in CK2β Functions and Holoenzyme Regulation

Protein kinase CK2 is a highly pleiotropic Ser/Thr kinase ubiquituous in eukaryotic organisms. CK2 is organized as a heterotetrameric enzyme composed of two types of subunits: the catalytic (CK2α) and the regulatory (CK2β). The CK2β subunits enhance the stability, activity and specificity of the holoenzyme, but they can also perform functions independently of the CK2 tetramer. CK2β regulatory subunits in plants differ from their animal or yeast counterparts, since they present an additional specific N-terminal extension of about 90 aminoacids that shares no homology with any previously characterized functional domain. Sequence analysis of the N-terminal domain of land plant CK2β subunit sequences reveals its arrangement through short, conserved motifs, some of them including CK2 autophosphorylation sites. By using maize CK2β1 and a deleted version (ΔNCK2β1) lacking the N-terminal domain, we have demonstrated that CK2β1 is autophosphorylated within the N-terminal domain. Moreover, the holoenzyme composed with CK2α1/ΔNCK2β1 is able to phosphorylate different substrates more efficiently than CK2α1/CK2β1 or CK2α alone. Transient overexpression of CK2β1 and ΔNCK2β1 fused to GFP in different plant systems show that the presence of N-terminal domain enhances aggregation in nuclear speckles and stabilizes the protein against proteasome degradation. Finally, bimolecular fluorescence complementation (BiFC) assays show the nuclear and cytoplasmic location of the plant CK2 holoenzyme, in contrast to the individual CK2α/β subunits mainly observed in the nucleus. All together, our results support the hypothesis that the plant-specific N-terminal domain of CK2β subunits is involved in the down-regulation of the CK2 holoenzyme activity and in the stabilization of CK2β1 protein. In summary, the whole amount of data shown in this work suggests that this domain was acquired by plants for regulatory purposes

Unbiased Functional Proteomics Strategy for Protein Kinase Inhibitor Validation and Identification of bona fide Protein Kinase Substrates: Application to Identification of EEF1D as a Substrate for CK2

bS Supporting Informatio

A co-registration investigation of inter-word spacing and parafoveal preview: Eye movements and fixation-related potentials

Participants’ eye movements (EMs) and EEG signal were simultaneously recorded to examine foveal and parafoveal processing during sentence reading. All the words in the sentence were manipulated for inter-word spacing (intact spaces vs. spaces replaced by a random letter) and parafoveal preview (identical preview vs. random letter string preview). We observed disruption for unspaced text and invalid preview conditions in both EMs and fixation-related potentials (FRPs). Unspaced and invalid preview conditions received longer reading times than spaced and valid preview conditions. In addition, the FRP data showed that unspaced previews disrupted reading in earlier time windows of analysis, compared to string preview conditions. Moreover, the effect of parafoveal preview was greater for spaced relative to unspaced conditions, in both EMs and FRPs. These findings replicate well-established preview effects, provide novel insight into the neural correlates of reading with and without inter-word spacing and suggest that spatial selection precedes lexical processing

Enhancing chemosensitivity to gemcitabine via RNA interference targeting the catalytic subunits of protein kinase CK2 in human pancreatic cancer cells

<p>Abstract</p> <p>Background</p> <p>Pancreatic cancer is a complex genetic disorder that is characterized by rapid progression, invasiveness, resistance to treatment and high molecular heterogeneity. Various agents have been used in clinical trials showing only modest improvements with respect to gemcitabine-based chemotherapy, which continues to be the standard first-line treatment for this disease. However, owing to the overwhelming molecular alterations that have been reported in pancreatic cancer, there is increasing focus on targeting molecular pathways and networks, rather than individual genes or gene-products with a combination of novel chemotherapeutic agents.</p> <p>Methods</p> <p>Cells were transfected with small interfering RNAs (siRNAs) targeting the individual CK2 subunits. The CK2 protein expression levels were determined and the effect of its down-regulation on chemosensitization of pancreatic cancer cells was investigated.</p> <p>Results</p> <p>The present study examined the impact on cell death following depletion of the individual protein kinase CK2 catalytic subunits alone or in combination with gemcitabine and the molecular mechanisms by which this effect is achieved. Depletion of the CK2α or -α' subunits in combination with gemcitabine resulted in marked apoptotic and necrotic cell death in PANC-1 cells. We show that the mechanism of cell death is associated with deregulation of distinct survival signaling pathways. Cellular depletion of CK2α leads to phosphorylation and activation of MKK4/JNK while down-regulation of CK2α' exerts major effects on the PI3K/AKT pathway.</p> <p>Conclusions</p> <p>Results reported here show that the two catalytic subunits of CK2 contribute differently to enhance gemcitabine-induced cell death, the reduced level of CK2α' being the most effective and that simultaneous reduction in the expression of CK2 and other survival factors might be an effective therapeutic strategy for enhancing the sensitivity of human pancreatic cancer towards chemotherapeutic agents.</p

Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins

<p>Abstract</p> <p>Background</p> <p>Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites.</p> <p>Results</p> <p>We characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D) structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information.</p> <p>Conclusion</p> <p>While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structure-based P-site prediction method has been made available at <url>http://phos3d.mpimp-golm.mpg.de</url>.</p

Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors

Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy

Detecting Clusters of Mutations

Positive selection for protein function can lead to multiple mutations within a small stretch of DNA, i.e., to a cluster of mutations. Recently, Wagner proposed a method to detect such mutation clusters. His method, however, did not take into account that residues with high solvent accessibility are inherently more variable than residues with low solvent accessibility. Here, we propose a new algorithm to detect clustered evolution. Our algorithm controls for different substitution probabilities at buried and exposed sites in the tertiary protein structure, and uses random permutations to calculate accurate P values for inferred clusters. We apply the algorithm to genomes of bacteria, fly, and mammals, and find several clusters of mutations in functionally important regions of proteins. Surprisingly, clustered evolution is a relatively rare phenomenon. Only between 2% and 10% of the genes we analyze contain a statistically significant mutation cluster. We also find that not controlling for solvent accessibility leads to an excess of clusters in terminal and solvent-exposed regions of proteins. Our algorithm provides a novel method to identify functionally relevant divergence between groups of species. Moreover, it could also be useful to detect artifacts in automatically assembled genomes