School of Mathematics and Statistics - Research Publications
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ItemEffects of Salt Supplementation on the Albuminuric Response to Telmisartan With or Without Hydrochlorothiazide Therapy in Hypertensive Patients With Type 2 Diabetes Are Modulated by Habitual Dietary Salt Intake(AMER DIABETES ASSOC, 2009-08)OBJECTIVE This prospective randomized double-blind placebo-controlled crossover study examined the effects of sodium chloride (NaCl) supplementation on the antialbuminuric action of telmisartan with or without hydrochlorothiazide (HCT) in hypertensive patients with type 2 diabetes, increased albumin excretion rate (AER), and habitual low dietary salt intake (LDS; <100 mmol sodium/24 h on two of three consecutive occasions) or high dietary salt intake (HDS; >200 mmol sodium/24 h on two of three consecutive occasions). RESEARCH DESIGN AND METHODS Following a washout period, subjects (n = 32) received 40 mg/day telmisartan for 4 weeks followed by 40 mg telmisartan plus 12.5 mg/day HCT for 4 weeks. For the last 2 weeks of each treatment period, patients received either 100 mmol/day NaCl or placebo capsules. After a second washout, the regimen was repeated with supplements in reverse order. AER and ambulatory blood pressure were measured at weeks 0, 4, 8, 14, 18, and 22. RESULTS In LDS, NaCl supplementation reduced the anti-albuminuric effect of telmisartan with or without HCT from 42.3% (placebo) to 9.5% (P = 0.004). By contrast, in HDS, NaCl supplementation did not reduce the AER response to telmisartan with or without HCT (placebo 30.9%, NaCl 28.1%, P = 0.7). Changes in AER were independent of changes in blood pressure. CONCLUSIONS The AER response to telmisartan with or without HCT under habitual low salt intake can be blunted by NaCl supplementation. By contrast, when there is already a suppressed renin angiotensin aldosterone system under habitual high dietary salt intake, the additional NaCl does not alter the AER response.
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ItemPredicting qualitative phenotypes from microarray data - the Eadgene pig data set.(Springer Science and Business Media LLC, 2009-07-16)BACKGROUND: The aim of this work was to study the performances of 2 predictive statistical tools on a data set that was given to all participants of the Eadgene-SABRE Post Analyses Working Group, namely the Pig data set of Hazard et al. (2008). The data consisted of 3686 gene expressions measured on 24 animals partitioned in 2 genotypes and 2 treatments. The objective was to find biomarkers that characterized the genotypes and the treatments in the whole set of genes. METHODS: We first considered the Random Forest approach that enables the selection of predictive variables. We then compared the classical Partial Least Squares regression (PLS) with a novel approach called sparse PLS, a variant of PLS that adapts lasso penalization and allows for the selection of a subset of variables. RESULTS: All methods performed well on this data set. The sparse PLS outperformed the PLS in terms of prediction performance and improved the interpretability of the results. CONCLUSION: We recommend the use of machine learning methods such as Random Forest and multivariate methods such as sparse PLS for prediction purposes. Both approaches are well adapted to transcriptomic data where the number of features is much greater than the number of individuals.
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ItemFeasibility, design and conduct of a pragmatic randomized controlled trial to reduce overweight and obesity in children: The electronic games to aid motivation to exercise (eGAME) study.(Springer Science and Business Media LLC, 2009-05-19)BACKGROUND: Childhood obesity has reached epidemic proportions in developed countries. Sedentary screen-based activities such as video gaming are thought to displace active behaviors and are independently associated with obesity. Active video games, where players physically interact with images onscreen, may have utility as a novel intervention to increase physical activity and improve body composition in children. The aim of the Electronic Games to Aid Motivation to Exercise (eGAME) study is to determine the effects of an active video game intervention over 6 months on: body mass index (BMI), percent body fat, waist circumference, cardio-respiratory fitness, and physical activity levels in overweight children. METHODS/DESIGN: Three hundred and thirty participants aged 10-14 years will be randomized to receive either an active video game upgrade package or to a control group (no intervention). DISCUSSION: An overview of the eGAME study is presented, providing an example of a large, pragmatic randomized controlled trial in a community setting. Reflection is offered on key issues encountered during the course of the study. In particular, investigation into the feasibility of the proposed intervention, as well as robust testing of proposed study procedures is a critical step prior to implementation of a large-scale trial.
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ItemImproved Methodology for Assessment of mRNA Levels in Blood of Patients with FMR1 Related Disorders(BIOMED CENTRAL LTD, 2009)BACKGROUND: Elevated levels of FMR1 mRNA in blood have been implicated in RNA toxicity associated with a number of clinical conditions. Due to the extensive inter-sample variation in the time lapse between the blood collection and RNA extraction in clinical practice, the resulting variation in mRNA quality significantly confounds mRNA analysis by real-time PCR. METHODS: Here, we developed an improved method to normalize for mRNA degradation in a sample set with large variation in rRNA quality, without sample omission. Initially, RNA samples were artificially degraded, and analyzed using capillary electrophoresis and real-time PCR standard curve method, with the aim of defining the best predictors of total RNA and mRNA degradation. RESULTS: We found that: (i) the 28S:18S ratio and RNA quality indicator (RQI) were good predictors of severe total RNA degradation, however, the greatest changes in the quantity of different mRNAs (FMR1, DNMT1, GUS, B2M and GAPDH) occurred during the early to moderate stages of degradation; (ii) chromatographic features for the 18S, 28S and the inter-peak region were the most reliable predictors of total RNA degradation, however their use for target gene normalization was inferior to internal control genes, of which GUS was the most appropriate. Using GUS for normalization, we examined in the whole blood the relationship between the FMR1 mRNA and CGG expansion in a non-coding portion of this gene, in a sample set (n = 30) with the large variation in rRNA quality. By combining FMR1 3' and 5' mRNA analyses the confounding impact of mRNA degradation on the correlation between FMR1 expression and CGG size was minimized, and the biological significance increased from p = 0.046 for the 5' FMR1 assay, to p = 0.018 for the combined FMR1 3' and 5' mRNA analysis. CONCLUSION: Our observations demonstrate that, through the use of an appropriate internal control and the direct analysis of multiple sites of target mRNA, samples that do not conform to the conventional rRNA criteria can still be utilized to obtain biologically/clinically relevant data. Although, this strategy clearly has application for improved assessment of FMR1 mRNA toxicity in blood, it may also have more general implications for gene expression studies in fresh and archival tissues.
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ItemMolecular networks involved in mouse cerebral corticogenesis and spatio-temporal regulation of Sox4 and Sox11 novel antisense transcripts revealed by transcriptome profiling(BMC, 2009)BACKGROUND: Development of the cerebral cortex requires highly specific spatio-temporal regulation of gene expression. It is proposed that transcriptome profiling of the cerebral cortex at various developmental time points or regions will reveal candidate genes and associated molecular pathways involved in cerebral corticogenesis. RESULTS: Serial analysis of gene expression (SAGE) libraries were constructed from C57BL/6 mouse cerebral cortices of age embryonic day (E) 15.5, E17.5, postnatal day (P) 1.5 and 4 to 6 months. Hierarchical clustering analysis of 561 differentially expressed transcripts showed regionalized, stage-specific and co-regulated expression profiles. SAGE expression profiles of 70 differentially expressed transcripts were validated using quantitative RT-PCR assays. Ingenuity pathway analyses of validated differentially expressed transcripts demonstrated that these transcripts possess distinctive functional properties related to various stages of cerebral corticogenesis and human neurological disorders. Genomic clustering analysis of the differentially expressed transcripts identified two highly transcribed genomic loci, Sox4 and Sox11, during embryonic cerebral corticogenesis. These loci feature unusual overlapping sense and antisense transcripts with alternative polyadenylation sites and differential expression. The Sox4 and Sox11 antisense transcripts were highly expressed in the brain compared to other mouse organs and are differentially expressed in both the proliferating and differentiating neural stem/progenitor cells and P19 (embryonal carcinoma) cells. CONCLUSIONS: We report validated gene expression profiles that have implications for understanding the associations between differentially expressed transcripts, novel targets and related disorders pertaining to cerebral corticogenesis. The study reports, for the first time, spatio-temporally regulated Sox4 and Sox11 antisense transcripts in the brain, neural stem/progenitor cells and P19 cells, suggesting they have an important role in cerebral corticogenesis and neuronal/glial cell differentiation.
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ItemIllumina WG-6 BeadChip strips should be normalized separately(BMC, 2009-11-11)BACKGROUND: Illumina Sentrix-6 Whole-Genome Expression BeadChips are relatively new microarray platforms which have been used in many microarray studies in the past few years. These Chips have a unique design in which each Chip contains six microarrays and each microarray consists of two separate physical strips, posing special challenges for precise between-array normalization of expression values. RESULTS: None of the normalization strategies proposed so far for this microarray platform allow for the possibility of systematic variation between the two strips comprising each array. That this variation can be substantial is illustrated by a data example. We demonstrate that normalizing at the strip-level rather than at the array-level can effectively remove this between-strip variation, improve the precision of gene expression measurements and discover more differentially expressed genes. The gain is substantial, yielding a 20% increase in statistical information and doubling the number of genes detected at a 5% false discovery rate. Functional analysis reveals that the extra genes found tend to have interesting biological meanings, dramatically strengthening the biological conclusions from the experiment. Strip-level normalization still outperforms array-level normalization when non-expressed probes are filtered out. CONCLUSION: Plots are proposed which demonstrate how the need for strip-level normalization relates to inconsistent intensity range variation between the strips. Strip-level normalization is recommended for the preprocessing of Illumina Sentrix-6 BeadChips whenever the intensity range is seen to be inconsistent between the strips. R code is provided to implement the recommended plots and normalization algorithms.
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ItemDifferential splicing using whole-transcript microarrays(BMC, 2009-05-22)BACKGROUND: The latest generation of Affymetrix microarrays are designed to interrogate expression over the entire length of every locus, thus giving the opportunity to study alternative splicing genome-wide. The Exon 1.0 ST (sense target) platform, with versions for Human, Mouse and Rat, is designed primarily to probe every known or predicted exon. The smaller Gene 1.0 ST array is designed as an expression microarray but still interrogates expression with probes along the full length of each well-characterized transcript. We explore the possibility of using the Gene 1.0 ST platform to identify differential splicing events. RESULTS: We propose a strategy to score differential splicing by using the auxiliary information from fitting the statistical model, RMA (robust multichip analysis). RMA partitions the probe-level data into probe effects and expression levels, operating robustly so that if a small number of probes behave differently than the rest, they are downweighted in the fitting step. We argue that adjacent poorly fitting probes for a given sample can be evidence of differential splicing and have designed a statistic to search for this behaviour. Using a public tissue panel dataset, we show many examples of tissue-specific alternative splicing. Furthermore, we show that evidence for putative alternative splicing has a strong correspondence between the Gene 1.0 ST and Exon 1.0 ST platforms. CONCLUSION: We propose a new approach, FIRMAGene, to search for differentially spliced genes using the Gene 1.0 ST platform. Such an analysis complements the search for differential expression. We validate the method by illustrating several known examples and we note some of the challenges in interpreting the probe-level data.Software implementing our methods is freely available as an R package.
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ItemMicroarray background correction: maximum likelihood estimation for the normal-exponential convolution(OXFORD UNIV PRESS, 2009-04)Background correction is an important preprocessing step for microarray data that attempts to adjust the data for the ambient intensity surrounding each feature. The "normexp" method models the observed pixel intensities as the sum of 2 random variables, one normally distributed and the other exponentially distributed, representing background noise and signal, respectively. Using a saddle-point approximation, Ritchie and others (2007) found normexp to be the best background correction method for 2-color microarray data. This article develops the normexp method further by improving the estimation of the parameters. A complete mathematical development is given of the normexp model and the associated saddle-point approximation. Some subtle numerical programming issues are solved which caused the original normexp method to fail occasionally when applied to unusual data sets. A practical and reliable algorithm is developed for exact maximum likelihood estimation (MLE) using high-quality optimization software and using the saddle-point estimates as starting values. "MLE" is shown to outperform heuristic estimators proposed by other authors, both in terms of estimation accuracy and in terms of performance on real data. The saddle-point approximation is an adequate replacement in most practical situations. The performance of normexp for assessing differential expression is improved by adding a small offset to the corrected intensities.
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ItemSparse canonical methods for biological data integration: application to a cross-platform study(BIOMED CENTRAL LTD, 2009-01-26)BACKGROUND: In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines. RESULTS: We compare the results obtained with two other sparse or related canonical correlation approaches: CCA with Elastic Net penalization (CCA-EN) and Co-Inertia Analysis (CIA). The latter does not include a built-in procedure for variable selection and requires a two-step analysis. We stress the lack of statistical criteria to evaluate canonical correlation methods, which makes biological interpretation absolutely necessary to compare the different gene selections. We also propose comprehensive graphical representations of both samples and variables to facilitate the interpretation of the results. CONCLUSION: sPLS and CCA-EN selected highly relevant genes and complementary findings from the two data sets, which enabled a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same phenomenons with a different priority. They outperformed CIA that tended to select redundant information.
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ItemDrug and Cell Type-Specific Regulation of Genes with Different Classes of Estrogen Receptor β-Selective Agonists(PUBLIC LIBRARY SCIENCE, 2009-07-17)Estrogens produce biological effects by interacting with two estrogen receptors, ERalpha and ERbeta. Drugs that selectively target ERalpha or ERbeta might be safer for conditions that have been traditionally treated with non-selective estrogens. Several synthetic and natural ERbeta-selective compounds have been identified. One class of ERbeta-selective agonists is represented by ERB-041 (WAY-202041) which binds to ERbeta much greater than ERalpha. A second class of ERbeta-selective agonists derived from plants include MF101, nyasol and liquiritigenin that bind similarly to both ERs, but only activate transcription with ERbeta. Diarylpropionitrile represents a third class of ERbeta-selective compounds because its selectivity is due to a combination of greater binding to ERbeta and transcriptional activity. However, it is unclear if these three classes of ERbeta-selective compounds produce similar biological activities. The goals of these studies were to determine the relative ERbeta selectivity and pattern of gene expression of these three classes of ERbeta-selective compounds compared to estradiol (E(2)), which is a non-selective ER agonist. U2OS cells stably transfected with ERalpha or ERbeta were treated with E(2) or the ERbeta-selective compounds for 6 h. Microarray data demonstrated that ERB-041, MF101 and liquiritigenin were the most ERbeta-selective agonists compared to estradiol, followed by nyasol and then diarylpropionitrile. FRET analysis showed that all compounds induced a similar conformation of ERbeta, which is consistent with the finding that most genes regulated by the ERbeta-selective compounds were similar to each other and E(2). However, there were some classes of genes differentially regulated by the ERbeta agonists and E(2). Two ERbeta-selective compounds, MF101 and liquiritigenin had cell type-specific effects as they regulated different genes in HeLa, Caco-2 and Ishikawa cell lines expressing ERbeta. Our gene profiling studies demonstrate that while most of the genes were commonly regulated by ERbeta-selective agonists and E(2), there were some genes regulated that were distinct from each other and E(2), suggesting that different ERbeta-selective agonists might produce distinct biological and clinical effects.
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