Differential Gene Expression Profiles May Differentiate Responder and Nonresponder Patients with Rheumatoid Arthritis for Methotrexate (MTX) Monotherapy and MTX plus Tumor Necrosis Factor Inhibitor Combined Therapy

Objective. We aimed to evaluate whether the differential gene expression profiles of patients with rheumatoid arthritis (RA) could distinguish responders from nonresponders to methotrexate (MTX) and, in the case of MTX nonresponders, responsiveness to MTX plus anti-tumor necrosis factor-alpha (anti-TNF) combined therapy. Methods. We evaluated 25 patients with RA taking MTX 15-20 mg/week as a monotherapy (8 responders and 17 nonresponders). All MTX nonresponders received intliximab and were reassessed after 20 weeks to evaluate their anti-TNF responsiveness using the European League Against Rheumatism response criteria. A differential gene expression analysis from peripheral blood mononuclear cells was performed in terms of hierarchical gene clustering, and an evaluation of differentially expressed genes was performed using the significance analysis of microarrays program. Results. Hierarchical gene expression clustering discriminated MTX responders from nonresponders, and MTX plus anti-TNF responders from nonresponders. The evaluation of only highly modulated genes (fold change > 1.3 or < 0.7) yielded 5 induced (4 antiapoptotic and CCL4) and 4 repressed (4 proapoptotic) genes in MTX nonresponders compared to responders. In MTX plus anti-TNF nonresponders, the CCL4, CD83, and BCL2A1 genes were induced in relation to responders. Conclusion. Study of the gene expression profiles of RA peripheral blood cells permitted differentiation of responders from nonresponders to MTX and anti-TNF. Several candidate genes in MTX non-responders (CCL4, HTRA2, PRKCD, BCL2A1, CAV1, TNIP1 CASP8AP2, MXD1, and BTG2) and 3 genes in MTX plus anti-TNF nonresponders (CCL4, CD83, and BCL2A1) were identified for further study. (First Release July 1 2012; J Rheumatol 2012;39:1524-32; doi:10.3899/jrheum.120092)Fundacao de Amparo a Pesquisa do Estado de Sao PauloFundacao de Apoio ao Ensino Pesquisa e Assistencia do HC, Faculdade de Medicina de Ribeirao Preto, USPConselho Nacional de Desenvolvimento Cientifico e Tecnologic

South american guidelines for cardiovascular disease prevention and rehabilitation

Resumo não disponíve

PERINGKAT BANK BERDASARKAN UKURAN KINERJA INTELLECTUAL CAPITAL DAN BERDASARKAN RGEC (RISK PROFILE, GOOD CORPORATE GOVERNANCE, EARNINGS, CAPITAL)

Penelitian ini bertujuan untuk mengetahui peringkat bank berdasarkan ukuran kinerja Intellectual Capital menggunakan metode Value Added Intellectual Coefficient (VAICTM) dan berdasarkan RGEC (Risk Profile, Good Corporate Governance, Earnings, Capital yang berbasis risiko. Hasil penelitian menggunakan metode VAICTM diperoleh bahwa pada umumnya bank dikategorikan pada predikat top performers. Hasil RGEC diperoleh rasio NPL pada umumnya bank dikategorikan pada peringkat 2. Rasio LDR diperoleh 7 bank pada peringkat 1 dan 9 bank peringkat 2. Rasio ROA diperoleh 9 bank pada peringkat 1 dan 10 bank peringkat 2. Rasio NIM pada umumnya bank berada pada peringkat 1 dan hasil rasio CAR pada umunya bank berada pada peringkat 1. Hasil uji beda antara VAICTM dan RGEC dengan taraf nyata (α) = 5% = 0,05, diperoleh nilai probabilitas (sig.) < Level of Significant = 0,05, berarti ada perbedaan signifikan antara VAICTM dan RGEC

Influence of reservoir character and architecture on hydrocarbon distribution and production in the miocene of Starfak and Tiger Shoal fields, offshore Louisiana

textReservoir properties and production performance have been assumed to be controlled by their sequence stratigraphic position and the systems tract in which they are found. This hypothesis has been tested in a dataset from two mature gas fields, located ten miles offshore Louisiana. Available data included several decades of production history, various geophysical logs of 155 wells, sidewall core and special core analyses, which provide full information on hydrocarbon distribution and type, reservoir lithology, porosity, permeability, and production characteristics. The systems tracts of the Miocene study interval (60 fourth-order and 10 third-order sequences) were characterized with thickness and net sand maps, facies interpretation, and production evaluation. The three systems tracts of the fourth-order sequences (lowstand, transgressive, and highstand) show different trends in rock properties, such as porosity, permeability, or sand content. Each systems tract can be characterized with a typical range of values for its reservoir properties. These reservoir characteristics are clearly different for each of the three fourth-order systems tracts. In contrast, the data of the third-order sequences show high variability. The values of porosity of the three systems tracts do not differ significantly from each other and prediction is impossible with high confidence. The sequence stratigraphic framework was linked to production information by (1) analyzing the average monthly production rate per perforated foot of fourth-order systems tracts and by (2) correlating to producing facies of each fourth-order systems tract. The strongest producers were found in facies of lowstand and highstand systems tracts and ranked highest. The incorporation of depositional facies interpretation into the fourth-order framework allows more detailed production estimation. Facies data also confirm the general picture of the systems tracts regarding rock properties, but they reveal differences among the facies types that could not be detected during the analysis of entire fourth-order systems tracts. One end product of this research is a database that stores all the information collected during the research, with customized search options that present a quick overview of systems tracts information. The application of this database is limited to stratigraphic units produced by similar depositional environments.Geological Science

Dietary isoflavones act on bone marrow osteoprogenitor cells and stimulate ovary development before influencing bone mass in pre-pubertal piglets

International audienceFood containing soybeans provide isoflavone phytoestrogens that can preserve bone mass in postmenopausal women, and prevent bone loss in ovariectomized rats. But their effects on bone remain unclear, particularly on bone formation during growth. Two groups of eight pre-pubertal piglets were fed a basal or an isoflavone-enriched (S800) diet for 6 weeks. The S800 diet contained 800 mg SoyLife (TM) /kg, providing 2.8 mg isoflavones/kg body weight/day. Several bones were collected and tested for bone strength and density. Bone marrow was collected from humeri together with blood samples and genital tracts. The plasma concentrations of isoflavones were increased in the pigs fed S800, but growth rate, body weight, plasma bone markers, bone mineral density, and strength were all unaffected. In contrast, cultured stromal cells from S800 pigs had more alkaline phosphatase-rich cells and mineralized nodules, secreted more osteocalcin, osteoprotegerin and RANK-L, synthesized more osteoprotegerin, and RANK-L. Cultured mononucleated nonadherent bone marrow cells from S800 pigs developed fewer tartrate-resistant acid phosphatase mononucleated cells (osteoclast progenitors) when cultured with 1,25(OH)(2)D-3, and resorbed a smaller area of dentine slices. Freshly isolated bone marrow osteoclast progenitors from S800 pigs had more caspase-3 cleavage activity, and synthesized less RANK. Both osteoclast and osteoblast progenitors had ER alpha and ERP, whose syntheses were stimulated by the S800 diet. The S800 piglets had heavier ovaries with more follicles, but their uterus weight was unaffected. We conclude that dietary isoflavones have no detectable effect on the bone mass of growing female piglets, but act on bone marrow osteoprogenitors via ERs - mainly ER beta and stimulate ovary development

Integrative analysis of the transcriptome profiles observed in type 1, type 2 and gestational diabetes mellitus reveals the role of inflammation

Abstract Background Type 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures. Methods Samples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays. Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool. Results The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin. Conclusion This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients

The added value of WES reanalysis in the field of genetic diagnosis: lessons learned from 200 exomes in the Lebanese population

International audienceBackground:The past few decades have witnessed a tremendous development in the field of genetics. Theimplementation of next generation sequencing (NGS) technologies revolutionized the field of molecular biologyand made the genetic information accessible at a large scale. However, connecting a rare genetic variation to acomplex phenotype remains challenging. Indeed, identifying the cause of a genetic disease requires amultidisciplinary approach, starting with the establishment of a clear phenotype with a detailed family history andending, in some cases, with functional assays that are crucial for the validation of the pathogenicity of a mutation.Methods:Two hundred Lebanese patients, presenting a wide spectrum of genetic disorders (neurodevelopmental,neuromuscular or metabolic disorders, etc.), sporadic or inherited, dominant or recessive, were referred, over thelast three and a half years, to the Medical Genetics Unit (UGM) of Saint Joseph University (USJ). In order to identify the genetic basis of these diseases, Whole Exome Sequencing (WES), followed by a targeted analysis, was performed for each case. In order to improve the genetic diagnostic yield, WES data, generated during the first 2 years of this study, were reanalyzed for all patients who were left undiagnosed at the genetic level. Reanalysis was based on updated bioinformatics tools and novel gene discoveries.Results:Our initial analysis allowed us to identify the specific genetic mutation causing the disease in 49.5% of the cases, in line with other international studies. Repeated WES analysis enabled us to increase the diagnostics yield to 56%.Conclusion:The present article reports the detailed results of both analysis and pinpoints the contribution of WES data reanalysis to an efficient genetic diagnosis. Lessons learned from WES reanalysis and interpretation are also shared