Post-translational modification of the RhoGTPase activating protein 21, ARHGAP21, by SUMO2/3

ARHGAP21 is a 217 kDa RhoGAP protein shown to modulate cell migration through the control of Cdc42 and FAK activities. In the present work a 250 kDa-ARHGAP21 was identified by mass spectrometry. This modified form is differentially expressed among cell lines and human primary cells. Co-immunoprecipitations and in vitro SUMOylation confirmed ARHGAP21 specific modification by SUMO2/3 and mapped the SUMOylation site to ARHGAP21 lysine K1443. Immunofluorescence staining revealed that ARHGAP21 co-localizes with SUMO2/3 in the cytoplasm and membrane compartments. Interestingly, our results suggest that ARHGAP21 SUMOylation may be related to cell proliferation. Therefore, SUMOylation of ARHGAP21 may represent a way of guiding its function. (C) 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Potential Contribution of Translational Factors to Triiodo-L-Thyronine-Induced Insulin Synthesis by Pancreatic Beta Cells

Background: Thyroid hormones (THs) are known to regulate protein synthesis by acting at the transcriptional level and inducing the expression of many genes. However, little is known about their role in protein expression at the post-transcriptional level, even though studies have shown enhancement of protein synthesis associated with mTOR/p70S6K activation after triiodo-l-thyronine (T3) administration. On the other hand, the effects of TH on translation initiation and polypeptidic chain elongation factors, being essential for activating protein synthesis, have been poorly explored. Therefore, considering that preliminary studies from our laboratory have demonstrated an increase in insulin content in INS-1E cells in response to T3 treatment, the aim of the present study was to investigate if proteins of translational nature might be involved in this effect. Methods: INS-1E cells were maintained in the presence or absence of T3 (10(-6) or 10(-8) M) for 12 hours. Thereafter, insulin concentration in the culture medium was determined by radioimmunoassay, and the cells were processed for Western blot detection of insulin, eukaryotic initiation factor 2 (eIF2), p-eIF2, eIF5A, EF1A, eIF4E binding protein (4E-BP), p-4E-BP, p70S6K, and p-p70S6K. Results: It was found that, in parallel with increased insulin generation, T3 induced p70S6K phosphorylation and the expression of the translational factors eIF2, eIF5A, and eukaryotic elongation factor 1 alpha (eEF1A). In contrast, total and phosphorylated 4E-BP, as well as total p70S6K and p-eIF2 content, remained unchanged after T3 treatment. Conclusions: Considering that (i) p70S6K induces S6 phosphorylation of the 40S ribosomal subunit, an essential condition for protein synthesis; (ii) eIF2 is essential for the initiation of messenger RNA translation process; and (iii) eIF5A and eEF1A play a central role in the elongation of the polypeptidic chain during the transcripts decoding, the data presented here lead us to suppose that a part of T3-induced insulin expression in INS-1E cells depends on the protein synthesis activation at the post-transcriptional level, as these proteins of the translational machinery were shown to be regulated by T3.

Computational framework to support integration of biomolecular and clinical data within a translational approach

Background The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information. Results We have implemented an extension of Chado – the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications. Conclusions Open-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different “omics” technologies with patient’s clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be accessed in http://dcm.ffclrp.usp.br/caib/pg=iptrans webcite.

Tracking the impact of translational research in psychiatry: state of the art and perspectives

Personalized treatments have become a primary goal in translational psychiatric research. They include the identification of neural circuits associated with psychiatric disorders and definition of treatment according to individual characteristics. Many new tools and technologies have been developed but further efforts are required to provide clues on how these scientific advances in psychiatry may be translated into more effective therapeutic approaches. Obstacles to the progress of translational psychiatry also involve numerous scientific, financial, ethical, logistics and regulatory aspects. Also, the goal of DSM-5 to expand “signs and symptoms” classification to incorporate biological measures may help the development of new multifactorial and dimensional models able to better understand the pathophysiology of psychiatric disorders and develop improved treatments. Finally, a better understanding on the significant response variability, cognitive functioning, role of comorbidities and treatment-resistant cases are critical for the development of prevention and intervention strategies that are more effective.