Intestinal antiinflammatory effect of 5-aminosalicylic acid is dependent on peroxisome proliferator-activated receptor-gamma.

5-aminosalicylic acid (5-ASA) is an antiinflammatory drug widely used in the treatment of inflammatory bowel diseases. It is known to inhibit the production of cytokines and inflammatory mediators, but the mechanism underlying the intestinal effects of 5-ASA remains unknown. Based on the common activities of peroxisome proliferator-activated receptor-gamma (PPAR-gamma) ligands and 5-ASA, we hypothesized that this nuclear receptor mediates 5-ASA therapeutic action. To test this possibility, colitis was induced in heterozygous PPAR-gamma(+/-) mice and their wild-type littermates, which were then treated with 5-ASA. 5-ASA treatment had a beneficial effect on colitis only in wild-type and not in heterozygous mice. In epithelial cells, 5-ASA increased PPAR-gamma expression, promoted its translocation from the cytoplasm to the nucleus, and induced a modification of its conformation permitting the recruitment of coactivators and the activation of a peroxisome-proliferator response element-driven gene. Validation of these results was obtained with organ cultures of human colonic biopsies. These data identify PPAR-gamma as a target of 5-ASA underlying antiinflammatory effects in the colon.

Strategies to determine the biological function of microRNAs

MicroRNAs (miRNAs) are regulators of gene expression that control many biological processes in development, differentiation, growth and metabolism. Their expression levels, small size, abundance of repetitive copies in the genome and mode of action pose unique challenges in studies elucidating the function of miRNAs. New technologies for identification, expression profiling and target gene validation, as well as manipulation of miRNA expression in vivo, will facilitate the study of their contribution to biological processes and disease. Such information will be crucial to exploit the emerging knowledge of miRNAs for the development of new human therapeutic applications.

A Genomic Approach to Identify The Notch Pathway as a Putative Tumor Suppressor in Endometrial Cancer

Endometrial cancer is the most common gynecological malignancy and the fourth most frequently diagnosed cancer among women. The molecular changes that distinguish normal endometrium from endometrial carcinoma are not thoroughly understood. Identification of these changes could potentially aid in identifying at-risk women who are especially prone to develop endometrial cancer, such as obese women and women with Lynch Syndrome. A microarray analysis was performed using normal endometrium from thin and obese women and cancerous endometrium from obese women. We validated the differential expression of ten genes whose expression was significantly up-regulated or down-regulated using qRT-PCR. All of the genes had distinct expression levels depending on the endometrial carcinoma histotype. As a result, they could serve as molecular markers to distinguish between normal endometrium and endometrial cancer, as well as between low grade endometrial carcinomas and high grade endometrial carcinomas. Two of the ten genes validated, HEYL and HES1, are down-stream targets of the Notch signaling pathway. HEYL and HES1 were identified by microarray and qRT-PCR to have a significant decrease in expression in endometrial carcinomas compared to normal endometrium. We further analyzed the differential expression of other components of the Notch signaling pathway, Notch4 and Jagged1. They were also identified by qRT-PCR to be significantly down-regulated in endometrial carcinomas compared to normal endometrium. Therefore, we believe the Notch signaling pathway to act as a tumor suppressor in endometrial carcinomas.

Validation of reference genes for gene expression studies in the honey bee, Apis mellifera, by quantitative real-time RT-PCR

For obtaining accurate and reliable gene expression results it is essential that quantitative real-time RT-PCR (qRT-PCR) data are normalized with appropriate reference genes. The current exponential increase in postgenomic studies on the honey bee, Apis mellifera, makes the standardization of qRT-PCR results an important task for ongoing community efforts. For this aim we selected four candidate reference genes (actin, ribosomal protein 49, elongation factor 1-alpha, tbp-association factor) and used three software-based approaches (geNorm, BestKeeper and NormFinder) to evaluate the suitability of these genes as endogenous controls. Their expression was examined during honey bee development, in different tissues, and after juvenile hormone exposure. Furthermore, the importance of choosing an appropriate reference gene was investigated for two developmentally regulated target genes. The results led us to consider all four candidate genes as suitable genes for normalization in A. mellifera. However, each condition evaluated in this study revealed a specific set of genes as the most appropriated ones.

Single nucleotide polymorphism C/T-13910, located upstream of the lactase gene, associated with adult-type hypolactasia: Validation for clinical practice

Objectives: To validate C/T-13910 polymorphism associated with primary hypolactasia for clinical practice. Design and methods: Lactose breath test and PCR-RFLP for the C/T-13910 polymorphism were performed. Results: Twenty-seven of 28 patients with genotype CC had positive breath tests, all twenty-two patients with genotypes CT or TT had negative breath tests. Agreement of tests was high (p<0.0001; Kappa Index 0.96). Conclusion: C/T-13910 polymorphism detection may be a new tool for primary hypolactasia diagnosis. (C) 2008 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series.

PURPOSE: Recently, a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N(-)) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. EXPERIMENTAL DESIGN: Gene expression profiling of frozen samples from 198 N(-) systemically untreated patients was done at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were done with the genomic risk and adjusted for the clinical risk, defined by Adjuvant! Online. RESULTS: The actual 5- and 10-year time to distant metastasis were 98% (88-100%) and 94% (83-98%), respectively, for the good profile group and 76% (68-82%) and 73% (65-79%), respectively, for the poor profile group. The actual 5- and 10-year overall survival were 98% (88-100%) and 87% (73-94%), respectively, for the good profile group and 84% (77-89%) and 72% (63-78%), respectively, for the poor profile group. We observed a strong time dependence of this signature, leading to an adjusted hazard ratio of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for time to distant metastasis and overall survival, respectively. CONCLUSION: This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases.

Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer.

BACKGROUND: A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients. METHODS: Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from five European centers were divided into high- and low-risk groups based on the gene signature classification and on clinical risk classifications. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis-free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER]-positive patients) or 92% (for ER-negative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups. RESULTS: The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confidence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low- and high-clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively. CONCLUSIONS: The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer.

Validation of real-time methylation-specific PCR to determine O6-methylguanine-DNA methyltransferase gene promoter methylation in glioma.

Epigenetic silencing of the DNA repair protein O(6)-methylguanine-DNA methyltransferase (MGMT) by promoter methylation predicts successful alkylating agent therapy, such as with temozolomide, in glioblastoma patients. Stratified therapy assignment of patients in prospective clinical trials according to tumor MGMT status requires a standardized diagnostic test, suitable for high-throughput analysis of small amounts of formalin-fixed, paraffin-embedded tumor tissue. A direct, real-time methylation-specific PCR (MSP) assay was developed to determine methylation status of the MGMT gene promoter. Assay specificity was obtained by selective amplification of methylated DNA sequences of sodium bisulfite-modified DNA. The copy number of the methylated MGMT promoter, normalized to the beta-actin gene, provides a quantitative test result. We analyzed 134 clinical glioma samples, comparing the new test with the previously validated nested gel-based MSP assay, which yields a binary readout. A cut-off value for the MGMT methylation status was suggested by fitting a bimodal normal mixture model to the real-time results, supporting the hypothesis that there are two distinct populations within the test samples. Comparison of the tests showed high concordance of the results (82/91 [90%]; Cohen's kappa = 0.80; 95% confidence interval, 0.82-0.95). The direct, real-time MSP assay was highly reproducible (Pearson correlation 0.996) and showed valid test results for 93% (125/134) of samples compared with 75% (94/125) for the nested, gel-based MSP assay. This high-throughput test provides an important pharmacogenomic tool for individualized management of alkylating agent chemotherapy.

Selection and validation of reference genes for gene expression studies by reverse transcription quantitative PCR in Xanthomonas citri subsp citri during infection of Citrus sinensis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A statistical test vs. a validation experiment in Gene Expression Study

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Validation of reference housekeeping genes for gene expression studies in western corn rootworm (Diabrotica virgifera virgifera).

2014

Iis--integrated Interactome System: A Web-based Platform For The Annotation, Analysis And Visualization Of Protein-metabolite-gene-drug Interactions By Integrating A Variety Of Data Sources And Tools.

High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.

Selection And Validation Of Reference Genes For Functional Studies In The Calliphoridae Family.

The genera Cochliomyia and Chrysomya contain both obligate and saprophagous flies, which allows the comparison of different feeding habits between closely related species. Among the different strategies for comparing these habits is the use of qPCR to investigate the expression levels of candidate genes involved in feeding behavior. To ensure an accurate measure of the levels of gene expression, it is necessary to normalize the amount of the target gene with the amount of a reference gene having a stable expression across the compared species. Since there is no universal gene that can be used as a reference in functional studies, candidate genes for qPCR data normalization were selected and validated in three Calliphoridae (Diptera) species, Cochliomyia hominivorax Coquerel, Cochliomyia macellaria Fabricius, and Chrysomya albiceps Wiedemann . The expression stability of six genes ( Actin, Gapdh, Rp49, Rps17, α -tubulin, and GstD1) was evaluated among species within the same life stage and between life stages within each species. The expression levels of Actin, Gapdh, and Rp49 were the most stable among the selected genes. These genes can be used as reliable reference genes for functional studies in Calliphoridae using similar experimental settings.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.