Caprin Controls Follicle Stem Cell Fate in the Drosophila Ovary

Adult stem cells must balance self-renewal and differentiation for tissue homeostasis. The Drosophila ovary has provided a wealth of information about the extrinsic niche signals and intrinsic molecular processes required to ensure appropriate germline stem cell renewal and differentiation. The factors controlling behavior of the more recently identified follicle stem cells of the ovary are less well-understood but equally important for fertility. Here we report that translational regulators play a critical role in controlling these cells. Specifically, the translational regulator Caprin (Capr) is required in the follicle stem cell lineage to ensure maintenance of this stem cell population and proper encapsulation of developing germ cells by follicle stem cell progeny. In addition, reduction of one copy of the gene fmr1, encoding the translational regulator Fragile X Mental Retardation Protein, exacerbates the Capr encapsulation phenotype, suggesting Capr and fmr1 are regulating a common process. Caprin was previously characterized in vertebrates as Cytoplasmic Activation/Proliferation-Associated Protein. Significantly, we find that loss of Caprin alters the dynamics of the cell cycle, and we present evidence that misregulation of CycB contributes to the disruption in behavior of follicle stem cell progeny. Our findings support the idea that translational regulators may provide a conserved mechanism for oversight of developmentally critical cell cycles such as those in stem cell populations

PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew’s correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew’s correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx