Retinoic Acid Modulates PTGDR Promoter Activity.

BACKGROUND AND OBJECTIVE: Vitamin A has been linked to the development of allergic diseases although its role is not fully understood, Retinoic acid (RA), a metabolite of Vitamin A, has been previously associated with the prostaglandin pathway, and PTGDR, a receptor of PGD2, has been proposed as a candidate gene in allergy and asthma. Considering the role of PTGDR in allergy, the goal of this study was to analyze the effect of RA on the activation of the promoter region of the PTGDR gene. METHODS: A549 lung epithelial cells were transfected with 4 combinations of genetic variants of the PTGDR promoter and stimulated with all-trans RA (ATRA); luciferase assays were performed using the Dual Luciferase Reporter System, and real-time quantitative polymerase chain reaction was used to measure the expression of PTGDR, CYP26A1, RARA, RARB, RARG, and RXRA in basal A549 cell cultures and after ATRA treatment. We also performed an in silico analysis. RESULTS: After ATRA treatment increased expression of CYP26A1 (12-fold) and RARB (4-fold) was detected. ATRA activated PTGDR promoter activity in transfected cells (P<.001) and RA response element sequences were identified in silico in this promoter region. CONCLUSIONS: RA modulated PTGDR promoter activity. Differential response to RA and to new treatments based on PTGDR modulation could depend on genetic background in allergic asthmatic patients.

Systematic review of the biological variation data for diabetes related analytes.

BACKGROUND: Objective interpretation of laboratory test results used to diagnose and monitor diabetes mellitus in part requires the application of biological variation data (BVD). The quality of published BVD has been questioned. The aim of this study was to quality assess publications reporting BVD for diabetes-related analytes using the Biological Variation Data Critical Appraisal Checklist (BIVAC); to assess whether published BVD are fit for purpose and whether the study design and population attributes influence BVD estimates and to undertake a meta-analysis of the BVD from BIVAC-assessed publications. METHODS: Publications reporting data for glucose, HbA1c, adiponectin, C-peptide, fructosamine, insulin like growth factor 1 (IGF-1), insulin like growth factor binding protein 3 (IGFBP-3), insulin, lactate and pyruvate were identified using a systematic literature search. These publications were assessed using the BIVAC, receiving grades A, B, C or D, where A is of highest quality. A meta-analysis of the BVD from the assessed studies utilised weightings based upon BIVAC grades and the width of the data confidence intervals to generate global BVD estimates. RESULTS: BIVAC assessment of 47 publications delivered 1 A, 3 B, 39C and 4 D gradings. Publications relating to adiponectin, C-peptide, IGF-1, IGFBP-3, lactate and pyruvate were all assessed as grade C. Meta-analysis enabled global BV estimates for all analytes except pyruvate, lactate and fructosamine. CONCLUSIONS: This study delivers updated and evidence-based BV estimates for diabetes-related analytes. There remains a need for delivery of new high-quality BV studies for several clinically important analytes.

p73 cooperates with Ras in the activation of MAP kinase signaling cascade.

The p73 gene is capable of inducing cell cycle arrest, apoptosis, senescence, differentiation and to cooperate with oncogenic Ras in cellular transformation. Ras can be considered as a branch point in signal transduction, where diverse extracellular stimuli converge. The intensity of the mitogen-activated protein kinase (MAPK) cascade activation influences the cellular response to Ras. Despite the fundamental role of p53 in Ras-induced growth arrest and senescence, it remains unclear how the Ras/MEK/ERK pathway induces growth arrest in the absence of p53. We report here that oncogenic Ras stabilizes p73 resulting in p73 accumulation and enhancement of its activity. p73, in turn, induces a sustained activation of the MAP kinase cascade synergizing with oncogenic Ras. We also found that inhibition of p73 function modifies the cellular outcome to Ras activation inhibiting Ras-dependent differentiation. Here, we show for the first time that there is a signaling loop between Ras-dependent MAPK cascade activation and p73 function.

Importance of implementing an analytical quality control system in a core laboratory.

INTRODUCTION: The aim of the clinical laboratory is to provide useful information for screening, diagnosis and monitoring of disease. The laboratory should ensure the quality of extra-analytical and analytical process, based on set criteria. To do this, it develops and implements a system of internal quality control, designed to detect errors, and compare its data with other laboratories, through external quality control. In this way it has a tool to detect the fulfillment of the objectives set, and in case of errors, allowing corrective actions to be made, and ensure the reliability of the results. OBJECTIVE: This article sets out to describe the design and implementation of an internal quality control protocol, as well as its periodical assessment intervals (6 months) to determine compliance with pre-determined specifications (Stockholm Consensus(1)). MATERIALS AND METHODS: A total of 40 biochemical and 15 immunochemical methods were evaluated using three different control materials. Next, a standard operation procedure was planned to develop a system of internal quality control that included calculating the error of the analytical process, setting quality specifications, and verifying compliance. RESULTS: The quality control data were then statistically depicted as means, standard deviations, and coefficients of variation, as well as systematic, random, and total errors. The quality specifications were then fixed and the operational rules to apply in the analytical process were calculated. Finally, our data were compared with those of other laboratories through an external quality assurance program. DISCUSSION: The development of an analytical quality control system is a highly structured process. This should be designed to detect errors that compromise the stability of the analytical process. The laboratory should review its quality indicators, systematic, random and total error at regular intervals, in order to ensure that they are meeting pre-determined specifications, and if not, apply the appropriate corrective actions.