Identification of the protein kinases Pyk3 and Phg2 as regulators of the STATc-mediated response to hyperosmolarity

Cellular adaptation to changes in environmental osmolarity is crucial for cell survival. In Dictyostelium, STATc is a key regulator of the transcriptional response to hyperosmotic stress. Its phosphorylation and consequent activation is controlled by two signaling branches, one cGMP- and the other Ca(2+)-dependent, of which many signaling components have yet to be identified. The STATc stress signalling pathway feeds back on itself by upregulating the expression of STATc and STATc-regulated genes. Based on microarray studies we chose two tyrosine-kinase like proteins, Pyk3 and Phg2, as possible modulators of STATc phosphorylation and generated single and double knock-out mutants to them. Transcriptional regulation of STATc and STATc dependent genes was disturbed in pyk3(-), phg2(-), and pyk3(-)/phg2(-) cells. The absence of Pyk3 and/or Phg2 resulted in diminished or completely abolished increased transcription of STATc dependent genes in response to sorbitol, 8-Br-cGMP and the Ca(2+) liberator BHQ. Also, phospho-STATc levels were significantly reduced in pyk3(-) and phg2(-) cells and even further decreased in pyk3(-)/phg2(-) cells. The reduced phosphorylation was mirrored by a significant delay in nuclear translocation of GFP-STATc. The protein tyrosine phosphatase 3 (PTP3), which dephosphorylates and inhibits STATc, is inhibited by stress-induced phosphorylation on S448 and S747. Use of phosphoserine specific antibodies showed that Phg2 but not Pyk3 is involved in the phosphorylation of PTP3 on S747. In pull-down assays Phg2 and PTP3 interact directly, suggesting that Phg2 phosphorylates PTP3 on S747 in vivo. Phosphorylation of S448 was unchanged in phg2(-) cells. We show that Phg2 and an, as yet unknown, S448 protein kinase are responsible for PTP3 phosphorylation and hence its inhibition, and that Pyk3 is involved in the regulation of STATc by either directly or indirectly activating it. Our results add further complexities to the regulation of STATc, which presumably ensure its optimal activation in response to different environmental cues

Texture analysis-and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction:a preliminary study

We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status

The Solute Carrier Families Have a Remarkably Long Evolutionary History with the Majority of the Human Families Present before Divergence of Bilaterian Species

The Solute Carriers (SLCs) are membrane proteins that regulate transport of many types of substances over the cell membrane. The SLCs are found in at least 46 gene families in the human genome. Here, we performed the first evolutionary analysis of the entire SLC family based on whole genome sequences. We systematically mined and analyzed the genomes of 17 species to identify SLC genes. In all, we identified 4,813 SLC sequences in these genomes, and we delineated the evolutionary history of each of the subgroups. Moreover, we also identified ten new human sequences not previously classified as SLCs, which most likely belong to the SLC family. We found that 43 of the 46 SLC families found in Homo sapiens were also found in Caenorhabditis elegans, whereas 42 of them were also found in insects. Mammals have a higher number of SLC genes in most families, perhaps reflecting important roles for these in central nervous system functions. This study provides a systematic analysis of the evolutionary history of the SLC families in Eukaryotes showing that the SLC superfamily is ancient with multiple branches that were present before early divergence of Bilateria. The results provide foundation for overall classification of SLC genes and are valuable for annotation and prediction of substrates for the many SLCs that have not been tested in experimental transport assays

Loss of Dictyostelium HSPC300 causes a scar-like phenotype and loss of SCAR protein

<p>Abstract</p> <p>Background</p> <p>SCAR/WAVE proteins couple signalling to actin polymerization, and are thus fundamental to the formation of pseudopods and lamellipods. They are controlled as part of a five-membered complex that includes the tiny HSPC300 protein. It is not known why SCAR/WAVE is found in such a large assembly, but in <it>Dictyostelium </it>the four larger subunits have different, clearly delineated functions.</p> <p>Results</p> <p>We have generated <it>Dictyostelium </it>mutants in which the HSPC300 gene is disrupted. As has been seen in other regulatory complex mutants, SCAR is lost in these cells, apparently by a post-translational mechanism, though PIR121 levels do not change. HSPC300 knockouts resemble <it>scar </it>mutants in slow migration, roundness, and lack of large pseudopods. However <it>hspc300</it>-colonies on bacteria are larger and more similar to wild type, suggesting that some SCAR function can survive without HSPC300. We find no evidence for functions of HSPC300 outside the SCAR complex.</p> <p>Conclusion</p> <p>HSPC300 is essential for most SCAR complex functions. The phenotype of HSPC300 knockouts is most similar to mutants in <it>scar</it>, not the other members of the SCAR complex, suggesting that HSPC300 acts most directly on SCAR itself.</p

Developments in unsteady pipe flow friction modelling

This paper reviews a number of unsteady friction models for transient pipe flow. Two distinct unsteady friction models, the Zielke and the Brunone models, are investigated in detail. The Zielke model, originally developed for transient laminar flow, has been selected to verify its effectiveness for "low Reynolds number" transient turbulent flow. The Brunone model combines local inertia and wall friction unsteadiness. This model is verified using the Vardy's analytically deduced shear decay coefficient C* to predict the Brunone's friction coefficient k rather than use the traditional trial and error method for estimating k. The two unsteady friction models have been incorporated into the method of characteristics water hammer algorithm. Numerical results from the quasi-steady friction model and the Zielke and the Brunone unsteady friction models are compared with results of laboratory measurements for water hammer cases with laminar and low Reynolds number turbulent flows. Conclusions about the range of validity for the three friction models are drawn. In addition, the convergence and stability of these models are addressed.Anton Bergant, Angus Ross Simpson, John Vìtkovsk