Identification of particular groups of microRNAs that positively or negatively impact on beta cell function in obese models of type 2 diabetes.

AIMS/HYPOTHESIS: MicroRNAs are key regulators of gene expression involved in health and disease. The goal of our study was to investigate the global changes in beta cell microRNA expression occurring in two models of obesity-associated type 2 diabetes and to assess their potential contribution to the development of the disease. METHODS: MicroRNA profiling of pancreatic islets isolated from prediabetic and diabetic db/db mice and from mice fed a high-fat diet was performed by microarray. The functional impact of the changes in microRNA expression was assessed by reproducing them in vitro in primary rat and human beta cells. RESULTS: MicroRNAs differentially expressed in both models of obesity-associated type 2 diabetes fall into two distinct categories. A group including miR-132, miR-184 and miR-338-3p displays expression changes occurring long before the onset of diabetes. Functional studies indicate that these expression changes have positive effects on beta cell activities and mass. In contrast, modifications in the levels of miR-34a, miR-146a, miR-199a-3p, miR-203, miR-210 and miR-383 primarily occur in diabetic mice and result in increased beta cell apoptosis. These results indicate that obesity and insulin resistance trigger adaptations in the levels of particular microRNAs to allow sustained beta cell function, and that additional microRNA deregulation negatively impacting on insulin-secreting cells may cause beta cell demise and diabetes manifestation. CONCLUSIONS/INTERPRETATION: We propose that maintenance of blood glucose homeostasis or progression toward glucose intolerance and type 2 diabetes may be determined by the balance between expression changes of particular microRNAs.

Transcriptomic and functional analysis of an autolysis-deficient, teicoplanin-resistant derivative of methicillin-resistant Staphylococcus aureus.

The molecular basis of glycopeptide-intermediate S. aureus (GISA) isolates is not well defined though frequently involves phenotypes such as thickened cell walls and decreased autolysis. We have exploited an isogenic pair of teicoplanin-susceptible (strain MRGR3) and teicoplanin-resistant (strain 14-4) methicillin-resistant S. aureus strains for detailed transcriptomic profiling and analysis of altered autolytic properties. Strain 14-4 displayed markedly deficient Triton X-100-triggered autolysis compared to its teicoplanin-susceptible parent, although microarray analysis paradoxically did not reveal significant reductions in expression levels of major autolytic genes atl, lytM, and lytN, except for sle1, which showed a slight decrease. The most important paradox was a more-than-twofold increase in expression of the cidABC operon in 14-4 compared to MRGR3, which was correlated with decreased expression of autolysis negative regulators lytSR and lrgAB. In contrast, the autolysis-deficient phenotype of 14-4 was correlated with both increased expression of negative autolysis regulators (arlRS, mgrA, and sarA) and decreased expression of positive regulators (agr RNAII and RNAIII). Quantitative bacteriolytic assays and zymographic analysis of concentrated culture supernatants showed a striking reduction in Atl-derived, extracellular bacteriolytic hydrolase activities in 14-4 compared to MRGR3. This observed difference was independent of the source of cell wall substrate (MRGR3 or 14-4) used for analysis. Collectively, our results suggest that altered autolytic properties in 14-4 are apparently not driven by significant changes in the transcription of key autolytic effectors. Instead, our analysis points to alternate regulatory mechanisms that impact autolysis effectors which may include changes in posttranscriptional processing or export.

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem.

Nuclear Factor I genomic binding associates with chromatin boundaries.

BACKGROUND: The Nuclear Factor I (NFI) family of DNA binding proteins (also called CCAAT box transcription factors or CTF) is involved in both DNA replication and gene expression regulation. Using chromatin immuno-precipitation and high throughput sequencing (ChIP-Seq), we performed a genome-wide mapping of NFI DNA binding sites in primary mouse embryonic fibroblasts. RESULTS: We found that in vivo and in vitro NFI DNA binding specificities are indistinguishable, as in vivo ChIP-Seq NFI binding sites matched predictions based on previously established position weight matrix models of its in vitro binding specificity. Combining ChIP-Seq with mRNA profiling data, we found that NFI preferentially associates with highly expressed genes that it up-regulates, while binding sites were under-represented at expressed but unregulated genes. Genomic binding also correlated with markers of transcribed genes such as histone modifications H3K4me3 and H3K36me3, even outside of annotated transcribed loci, implying NFI in the control of the deposition of these modifications. Positional correlation between + and - strand ChIP-Seq tags revealed that, in contrast to other transcription factors, NFI associates with a nucleosomal length of cleavage-resistant DNA, suggesting an interaction with positioned nucleosomes. In addition, NFI binding prominently occurred at boundaries displaying discontinuities in histone modifications specific of expressed and silent chromatin, such as loci submitted to parental allele-specific imprinted expression. CONCLUSIONS: Our data thus suggest that NFI nucleosomal interaction may contribute to the partitioning of distinct chromatin domains and to epigenetic gene expression regulation.NFI ChIP-Seq and input control DNA data were deposited at Gene Expression Omnibus (GEO) repository under accession number GSE15844. Gene expression microarray data for mouse embryonic fibroblasts are on GEO accession number GSE15871.

IQRray, a new method for Affymetrix microarray quality control, and the homologous organ conservation score, a new benchmark method for quality control metrics.

MOTIVATION: Microarray results accumulated in public repositories are widely reused in meta-analytical studies and secondary databases. The quality of the data obtained with this technology varies from experiment to experiment, and an efficient method for quality assessment is necessary to ensure their reliability. RESULTS: The lack of a good benchmark has hampered evaluation of existing methods for quality control. In this study, we propose a new independent quality metric that is based on evolutionary conservation of expression profiles. We show, using 11 large organ-specific datasets, that IQRray, a new quality metrics developed by us, exhibits the highest correlation with this reference metric, among 14 metrics tested. IQRray outperforms other methods in identification of poor quality arrays in datasets composed of arrays from many independent experiments. In contrast, the performance of methods designed for detecting outliers in a single experiment like Normalized Unscaled Standard Error and Relative Log Expression was low because of the inability of these methods to detect datasets containing only low-quality arrays and because the scores cannot be directly compared between experiments. AVAILABILITY AND IMPLEMENTATION: The R implementation of IQRray is available at: ftp://lausanne.isb-sib.ch/pub/databases/Bgee/general/IQRray.R. CONTACT: Marta.Rosikiewicz@unil.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Endogenous steroid profiling in the athlete biological passport.

The Athlete Biological Passport (ABP) is an individual electronic document that collects data regarding a specific athlete that is useful in differentiating between natural physiologic variations of selected biomarkers and deviations caused by artificial manipulations. A subsidiary of the endocrine module of the ABP, that which here is called Athlete Steroidal Passport (ASP), collects data on markers of an altered metabolism of endogenous steroidal hormones measured in urine samples. The ASP aims to identify not only doping with anabolic-androgenic steroids, but also most indirect steroid doping strategies such as doping with estrogen receptor antagonists and aromatase inhibitors. Development of specific markers of steroid doping, use of the athlete's previous measurements to define individual limits, with the athlete becoming his or her own reference, the inclusion of heterogeneous factors such as the UDPglucuronosyltransferase B17 genotype of the athlete, the knowledge of potentially confounding effects such as heavy alcohol consumption, the development of an external quality control system to control analytical uncertainty, and finally the use of Bayesian inferential methods to evaluate the value of indirect evidence have made the ASP a valuable alternative to deter steroid doping in elite sports. The ASP can be used to target athletes for gas chromatography/combustion/ isotope ratio mass spectrometry (GC/C/IRMS) testing, to withdraw temporarily the athlete from competing when an abnormality has been detected, and ultimately to lead to an antidoping infraction if that abnormality cannot be explained by a medical condition. Although the ASP has been developed primarily to ensure fairness in elite sports, its application in endocrinology for clinical purposes is straightforward in an evidence-based medicine paradigm.

Chemical profiling of different hashish seizures by gas chromatography-mass spectrometry and statistical methodology: a case report

Limited information is available regarding the methodology required to characterize hashish seizures for assessing the presence or the absence of a chemical link between two seizures. This casework report presents the methodology applied for assessing that two different police seizures were coming from the same block before this latter one was split. The chemical signature was extracted using GC-MS analysis and the implemented methodology consists in a study of intra- and inter-variability distributions based on the measurement of the chemical profiles similarity using a number of hashish seizures and the calculation of the Pearson correlation coefficient. Different statistical scenarios (i.e., a combination of data pretreatment techniques and selection of target compounds) were tested to find the most discriminating one. Seven compounds showing high discrimination capabilities were selected on which a specific statistical data pretreatment was applied. Based on the results, the statistical model built for comparing the hashish seizures leads to low error rates. Therefore, the implemented methodology is suitable for the chemical profiling of hashish seizures.

Transcriptional profiling reveals divergent roles of PPARalpha and PPARbeta/delta in regulation of gene expression in mouse liver.

Little is known about the role of the transcription factor peroxisome proliferator-activated receptor (PPAR) beta/delta in liver. Here we set out to better elucidate the function of PPARbeta/delta in liver by comparing the effect of PPARalpha and PPARbeta/delta deletion using whole genome transcriptional profiling and analysis of plasma and liver metabolites. In fed state, the number of genes altered by PPARalpha and PPARbeta/delta deletion was similar, whereas in fasted state the effect of PPARalpha deletion was much more pronounced, consistent with the pattern of gene expression of PPARalpha and PPARbeta/delta. Minor overlap was found between PPARalpha- and PPARbeta/delta-dependent gene regulation in liver. Pathways upregulated by PPARbeta/delta deletion were connected to innate immunity and inflammation. Pathways downregulated by PPARbeta/delta deletion included lipoprotein metabolism and various pathways related to glucose utilization, which correlated with elevated plasma glucose and triglycerides and reduced plasma cholesterol in PPARbeta/delta-/- mice. Downregulated genes that may underlie these metabolic alterations included Pklr, Fbp1, Apoa4, Vldlr, Lipg, and Pcsk9, which may represent novel PPARbeta/delta target genes. In contrast to PPARalpha-/- mice, no changes in plasma free fatty acid, plasma beta-hydroxybutyrate, liver triglycerides, and liver glycogen were observed in PPARbeta/delta-/- mice. Our data indicate that PPARbeta/delta governs glucose utilization and lipoprotein metabolism and has an important anti-inflammatory role in liver. Overall, our analysis reveals divergent roles of PPARalpha and PPARbeta/delta in regulation of gene expression in mouse liver.

Illicit drug profiling, reflection on statistical comparisons

This paper presents reflexions about statistical considerations on illicit drug profiling and more specifically about the calculation of threshold for determining of the seizure are linked or not. The specific case of heroin and cocaine profiling is presented with the necessary details on the target profiling variables (major alkaloids) selected and the analytical method used. Statistical approach to compare illicit drug seizures is also presented with the introduction of different scenarios dealing with different data pre-treatment or transformation of variables.The main aim consists to demonstrate the influence of data pre-treatment on the statistical outputs. A thorough study of the evolution of the true positive rate (TP) and the false positive rate (FP) in heroin and cocaine comparison is then proposed to investigate this specific topic and to demonstrate that there is no universal approach available and that the calculations have to be revaluate for each new specific application.

MIMAS 3.0 is a Multiomics Information Management and Annotation System.

BACKGROUND: DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types. DESCRIPTION: MIMAS was originally conceived as a simple, convenient and local Microarray Information Management and Annotation System focused on GeneChips for expression profiling studies. MIMAS 3.0 enables users to manage data from high-density oligonucleotide SNP Chips, expression arrays (both 3'UTR and tiling) and promoter arrays, BeadArrays as well as UHTS data using MIAME-compliant standardized vocabulary. Importantly, researchers can export data in MAGE-TAB format and upload them to the EBI's ArrayExpress certified data repository using a one-step procedure. CONCLUSION: We have vastly extended the capability of the system such that it processes the data output of six types of GeneChips (Affymetrix), two different BeadArrays for mRNA and miRNA (Illumina) and the Genome Analyzer (a popular Ultra-High Throughput DNA Sequencer, Illumina), without compromising on its flexibility and user-friendliness. MIMAS, appropriately renamed into Multiomics Information Management and Annotation System, is currently used by scientists working in approximately 50 academic laboratories and genomics platforms in Switzerland and France. MIMAS 3.0 is freely available via http://multiomics.sourceforge.net/.

Protein-Binding Microarray Analysis of Tumor Suppressor AP2α Target Gene Specificity.

Cheap and massively parallel methods to assess the DNA-binding specificity of transcription factors are actively sought, given their prominent regulatory role in cellular processes and diseases. Here we evaluated the use of protein-binding microarrays (PBM) to probe the association of the tumor suppressor AP2α with 6000 human genomic DNA regulatory sequences. We show that the PBM provides accurate relative binding affinities when compared to quantitative surface plasmon resonance assays. A PBM-based study of human healthy and breast tumor tissue extracts allowed the identification of previously unknown AP2α target genes and it revealed genes whose direct or indirect interactions with AP2α are affected in the diseased tissues. AP2α binding and regulation was confirmed experimentally in human carcinoma cells for novel target genes involved in tumor progression and resistance to chemotherapeutics, providing a molecular interpretation of AP2α role in cancer chemoresistance. Overall, we conclude that this approach provides quantitative and accurate assays of the specificity and activity of tumor suppressor and oncogenic proteins in clinical samples, interfacing genomic and proteomic assays.

Gene expression databases and data mining.

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

Untargeted profiling of urinary steroid metabolites after testosterone ingestion: opening new perspectives for antidoping testing.

AIM: Antidoping procedures are expected to greatly benefit from untargeted metabolomic approaches through the discovery of new biomarkers of prohibited substances abuse. RESULTS: Endogenous steroid metabolites were monitored in urine samples from a controlled elimination study of testosterone undecanoate after ingestion. A platform coupling ultra-high pressure LC with high-resolution quadrupole TOF MS was used and high between-subject metabolic variability was successfully handled using a multiblock data analysis strategy. Links between specific subsets of metabolites and influential genetic polymorphisms of the UGT2B17 enzyme were highlighted. CONCLUSION: This exploratory metabolomic strategy constitutes a first step toward a better understanding of the underlying patterns driving the high interindividual variability of steroid metabolism. Promising biomarkers were selected for further targeted study.

The use of methylamphetamine chemical profiling in an intelligence-led perspective and the observation of inhomogeneity within seizures

This study focuses on methylamphetamine (MA) seizures made by the Australian Federal Police (AFP) to investigate the use of chemical profiling in an intelligence perspective. Correlation coefficients were used to obtain a similarity degree between a population of linked samples and a population of unlinked samples. Although it was demonstrated that a general framework can be followed for the use of any forensic case data in an intelligence-led perspective, threshold values have to be re-evaluated for each type of illicit drug investigated. Unlike the results obtained in a previous study on 3,4-methylenedioxymethylamphetamine (MDMA) seizures, chemical profiles of MA samples coming from the same seizure showed relative inhomogeneity, limiting their ability to link seizures. Different hypotheses were investigated to obtain a better understanding of this inhomogeneity although no trend was observed. These findings raise an interesting discussion in regards to the homogeneity and representativeness of illicit drug seizures (for intelligence purposes). Further, it also provides some grounds to discuss the initial hypotheses and assumptions that most forensic science studies are based on.

Molecular risk assessment of BIG 1-98 participants by expression profiling using RNA from archival tissue.

Background: The purpose of the work reported here is to test reliable molecular profiles using routinely processed formalin-fixed paraffin-embedded (FFPE) tissues from participants of the clinical trial BIG 1-98 with a median follow-up of 60 months. Methods: RNA from fresh frozen (FF) and FFPE tumor samples of 82 patients were used for quality control, and independent FFPE tissues of 342 postmenopausal participants of BIG 1-98 with ER-positive cancer were analyzed by measuring prospectively selected genes and computing scores representing the functions of the estrogen receptor (eight genes, ER_8), the progesterone receptor (five genes, PGR_5), Her2 (two genes, HER2_2), and proliferation (ten genes, PRO_10) by quantitative reverse transcription PCR (qRT-PCR) on TaqMan Low Density Arrays. Molecular scores were computed for each category and ER_8, PGR_5, HER2_2, and PRO_10 scores were combined into a RISK_25 score. Results: Pearson correlation coefficients between FF- and FFPE-derived scores were at least 0.94 and high concordance was observed between molecular scores and immunohistochemical data. The HER2_2, PGR_ 5, PRO_10 and RISK_25 scores were significant predictors of disease free-survival (DFS) in univariate Cox proportional hazard regression. PRO_10 and RISK_25 scores predicted DFS in patients with histological grade II breast cancer and in lymph node positive disease. The PRO_10 and PGR_ 5 scores were independent predictors of DFS in multivariate Cox regression models incorporating clinical risk indicators; PRO_10 outperformed Ki-67 labeling index in multivariate Cox proportional hazard analyses. Conclusions: Scores representing the endocrine responsiveness and proliferation status of breast cancers were developed from gene expression analyses based on RNA derived from FFPE tissues. The validation of the molecular scores with tumor samples of participants of the BIG 1-98 trial demonstrates that such scores can serve as independent prognostic factors to estimate disease free survival (DFS) in postmenopausal patients with estrogen receptor positive breast cancer.