THE ROLE OF TYROSINE PHOSPHORYLATION IN THE FUNCTIONS OF THE TUMOR SUPPRESSOR GPRC5A

The retinoic acid inducible G protein coupled receptor family C group 5 type A (GPRC5A) is expressed preferentially in normal lung tissue but its expression is suppressed in the majority of human non-small cell lung cancer cell lines and tissues. This differential expression has led to the idea that GPRC5A is a potential tumor suppressor. This notion was supported by the finding that mice with a deletion of the Gprc5a gene develop spontaneous lung tumors. However, there are various tumor cell lines and tissue samples, including lung, that exhibit higher GPRC5A expression than normal tissues and some reports by other groups that GPRC5A transfection increased cell growth and colony formation. Obviously, GPRC5A has failed to suppress the development of the tumors and the growth of the cell lines where its expression is not suppressed. Since no mutations were detected in the coding sequence of GPRC5A in 20 NSCLC cell lines, it’s possible that GPRC5A acts as a tumor suppressor in the context of some cells but not in others. Alternatively, we raised the hypothesis that the GPRC5A protein may be inactivated by posttranslational modification(s) such as phosphorylation. It is well established that Serine/Threonine phosphorylation of G protein coupled receptors leads to their desensitization and in a few cases Tyrosine phosphorylation of GPCRs has been linked to internalization. Others reported that GPRC5A can undergo tyrosine phosphorylation in the cytoplasmic domain after treatment of normal human mammary epithelial cells (HMECs) with epidermal growth factor (EGF) or Heregulin. This suggested that GPRC5A is a substrate of EGFR. Therefore, we hypothesized that tyrosine phosphorylation of GPRC5A by activation of EGFR signaling may lead to its inactivation. To test this hypothesis, we transfected human embryo kidney (HEK) 293 cells with GPRC5A and EGFR expression vectors and confirmed that GPRC5A can be tyrosine phosphorylated after activation of EGFR by EGF. Further, we found that EGFR and GPRC5A can interact either directly or through other proteins and that inhibition of the EGFR kinase activity decreased the phosphorylation of GPRA5A and the interaction between GPRC5A and EGFR. In c-terminal of GPRC5A, There are four tyrosine residues Y317, Y320, Y347, Y350. We prepared GPRC5A mutants in which all four tyrosine residues had been replaced by phenylalanine (mutant 4F) or each individual Tyr residue was replaced by Phe and found that Y317 is the major site for EGFR mediated phosphorylation in the HEK293T cell line. We also found that EGF can induce GPRC5A internalization both in H1792 transient and stable cell lines. EGF also partially inactivates the suppressive function of GPRC5A on cell invasion activity and anchorage-independent growth ability of H1792 stable cell lines. These finding support our hypothesis that GPRC5A may be inactivated by posttranslational modification- tyrosine phosphorylation.

What Have We Learned from Articles Published in the Family Preservation Journal?

This exploratory descriptive study presents a content analyses of all (N=22) Family Preservation Journal (FPJ) articles published from its inception (1995) until today. Three raters independently used an analysis template to ascertain trends from these articles and assessed information about their purposes, methods, and findings/ implications. The main findings were less than half of the articles were deemed as 'research'; few used standardized or outcome measures; none compared family preservation to another method; descriptive knowledge was more likely to be generated; and the articles were primarily targeted to practitioners and other researchers. Given the relatively short history ofFPJ, the majority of these findings were considered typical and consistent with the literature. The recommendations call for more comprehensive practice descriptions, more research, and more rigorous research-oriented studies.

Quality Data is Key to Improving Education

The Data Quality Campaign (DQC) has been focused since 2005 on advocating for states to build robust state longitudinal data systems (SLDS). While states have made great progress in their data infrastructure, and should continue to emphasize this work, t data systems alone will not improve outcomes. It is time for both DQC and states to focus on building capacity to use the information that these systems are producing at every level – from classrooms to state houses. To impact system performance and student achievement, the ingrained culture must be replaced with one that focuses on data use for continuous improvement. The effective use of data to inform decisions, provide transparency, improve the measurement of outcomes, and fuel continuous improvement will not come to fruition unless there is a system wide focus on building capacity around the collection, analysis, dissemination, and use of this data, including through research.

Identification of genes associated with quantitative traits involved in cardiovascular disease and lipoprotein metabolism

Cardiovascular disease (CVD) is a threat to public health. It has been reported to be the leading cause of death in United States. The invention of next generation sequencing (NGS) technology has revolutionized the biomedical research. To investigate NGS data of CVD related quantitative traits would contribute to address the unknown etiology and disease mechanism of CVD. NHLBI's Exome Sequencing Project (ESP) contains CVD related phenotypes and their associated NGS exomes sequence data. Initially, a subset of next generation sequencing data consisting of 13 CVD-related quantitative traits was investigated. Only 6 traits, systolic blood pressure (SBP), diastolic blood pressure (DBP), height, platelet counts, waist circumference, and weight, were analyzed by functional linear model (FLM) and 7 currently existing methods. FLM outperformed all currently existing methods by identifying the highest number of significant genes and had identified 96, 139, 756, 1162, 1106, and 298 genes associated with SBP, DBP, Height, Platelet, Waist, and Weight respectively. ^