Suzy Van Sanden

The cellular redox state as a modulator in cadmium and copper responses in Arabidopsis thaliana seedlings.

The cellular redox state is an important determinant of metal phytotoxicity. In this study we investigated the influence of cadmium (Cd) and copper (Cu) stress on the cellular redox balance in relation to oxidative signalling and damage in Arabidopsis thaliana. Both metals were easily taken up by the roots, but the translocation to the aboveground parts was restricted to Cd stress. In the roots, Cu directly induced an oxidative burst, whereas enzymatic ROS (reactive oxygen species) production via NADPH oxidases seems important in oxidative stress caused by Cd. Furthermore, in the roots, the glutathione metabolism plays a crucial role in controlling the gene regulation of the antioxidative defence mechanism under Cd stress. Metal-specific alterations were also noticed with regard to the microRNA regulation of CuZnSOD gene expression in both roots and leaves. The appearance of lipid peroxidation is dual: it can be an indication of oxidative damage as well as an indication of oxidative signalling as lipoxygenases are induced after metal exposure and are initial enzymes in oxylipin biosynthesis. In conclusion, the metal-induced cellular redox imbalance is strongly dependent on the chemical properties of the metal and the plant organ considered. The stress intensity determines its involvement in downstream responses in relation to oxidative damage or signalling.

FABIA: factor analysis for bicluster acquisition

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. FABIA is based on a multiplicative model, which accounts for linear dependencies between gene expression and conditions, and also captures heavy-tailed distributions as observed in real-world transcriptomic data. The generative framework allows to utilize well-founded model selection methods and to apply Bayesian techniques. Results: On 100 simulated datasets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. On these datasets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches. Availability: FABIA is available as an R package on Bioconductor (http://www.bioconductor.org). All datasets, results and software are available at http://www.bioinf.jku.at/software/fabia/fabia.html Contact: hochreit@bioinf.jku.at Supplementary information: Supplementary data are available at Bioinformatics online.

Oxidative stress-related responses at transcriptional and enzymatic levels after exposure to Cd or Cu in a multipollution context

The physiological effects of Cd and Cu have been highlighted in several studies over the last years. At the cellular level, oxidative stress has been reported as a common mechanism in both stress situations. Nevertheless, because of differences in their redox-related properties, the origin of the stress and regulation of these effects can be very different. Our results show a specific Cd-related induction of NADPH oxidases, whereas both metals induced lipid peroxidation via the activation of lipoxygenases. With respect to the antioxidative defense system, metal-specific patterns of superoxide dismutases (SODs) were detected, whereas gene expression levels of the H2O2-quenching enzymes were equally induced by both metals. Because monometallic exposure is very unusual in real-world situations, the metal-specific effects were compared with the mechanisms induced when the plants are exposed to both metals simultaneously. Combined exposure to Cd and Cu enhanced some of the effects that were induced when only one metal was applied to the medium. Other specific monometallically induced effects, such as a copper zinc superoxide dismutase (CSD2) downregulation due to Cd, were also sustained in a multipollution context, irrespective of the other monometallic effects. Furthermore, specific multipollution effects were unravelled, as iron superoxide dismutase 1 (FSD1) upregulation in the leaves was significant only when both Cu and Cd were applied. Additional relationships between these treatments and the common and specific stress induction mechanisms are discussed.

Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach

The strength and weakness of microarray technology can be attributed to the enormous amount of information it is generating. To fully enhance the benefit of microarray technology for testing differentially expressed genes and classification, there is a need to minimize the amount of irrelevant genes present in microarray data. A major interest is to use probe-level data to call genes informative or noninformative based on the trade-off between the array-to-array variability and the measurement error. Existing works in this direction include filtering likely uninformative sets of hybridization (FLUSH; Calza et al., 2007) and I/NI calls for the exclusion of noninformative genes using FARMS (I/NI calls; Talloen et al., 2007; Hochreiter et al., 2006). In this paper, we propose a linear mixed model as a more flexible method that performs equally good as I/NI calls and outperforms FLUSH. We also introduce other criteria for gene filtering, such as, R2 and intra-cluster correlation. Additionally, we include some objective criteria based on likelihood ratio testing, the Akaike information criteria (AIC; Akaike, 1973) and the Bayesian information criterion (BIC; Schwarz, 1978 ).Based on the HGU-133A Spiked-in data set, it is shown that the linear mixed model approach outperforms FLUSH, a method that filters genes based on a quantile regression. The linear model is equivalent to a factor analysis model when either the factor loadings are set to a constant with the variance of the latent factor equal to one, or if the factor loadings are set to one together with unconstrained variance of the latent factor. Filtering based on conditional variance calls a probe set informative when the intensity of one or more probes is consistent across the arrays, while filtering using R2 or intra-cluster correlation calls a probe set informative only when average intensity of a probe set is consistent across the arrays. Filtering based on likelihood ratio test AIC and BIC are less stringent compared to the other criteria.

Using Linear Mixed Models for Normalization of cDNA Microarrays

Microarrays are a tool for measuring the expression levels of a large number of genes simultaneously. In the microarray experiment, however, many undesirable systematic variations are observed. Correct identification and removal of these variations is essential to allow the comparison of expression levels across experiments. We describe the use of linear mixed models for the normalization of two-color spotted microarrays for various sources of variation including printtip variation. Normalization with linear mixed models provides a parametric model of which results compare favorably to intensity dependent normalization LOWESS methods. We illustrate the use of this technique on two datasets. The first dataset contains 24 arrays, each with approximately 600 genes, replicated 3 times per array. A second dataset, coming from the apo AI experiment, was used to further illustrate the methods. Finally, a simulation study was done to compare between methods.

Performance of Gene Selection and Classification Methods in a Microarray Setting: A Simulation Study

In a previous article, we investigated the performance of several classification methods for cDNA-microarrays. Via simulations, various experimental settings could be explored without having to conduct expensive microarray studies. For the selection of genes, on which classification was based, one particular method was applied. Gene selection is, however, a very important aspect of classification. We extend the previous study by considering several gene selection methods. Furthermore, the stability of the methods with respect to distributional assumptions is examined by also considering data simulated from a symmetric and asymmetric Laplace distribution, in addition to normally distributed microarray data.

Translation of disease associated gene signatures across tissues

It has recently been shown that disease associated gene signatures can be identified by profiling tissue other than the disease related tissue. In this paper, we investigate gene signatures for Irritable Bowel Syndrome (IBS) using gene expression profiling of both disease related tissue (colon) and surrogate tissue (rectum). Gene specific joint ANOVA models were used to investigate differentially expressed genes between the IBS patients and the healthy controls taken into account both intra and inter tissue dependencies among expression levels of the same gene. Classification algorithms in combination with feature selection methods were used to investigate the predictive power of gene expression levels from the surrogate and the target tissues. We conclude based on the analyses that expression profiles of the colon and the rectum tissue could result in better predictive accuracy if the disease associated genes are known.

Evaluation of Laplace distribution-based ANOVA models applied to microarray data

In a microarray experiment, intensity measurements tend to vary due to various systematic and random effects, which enter at the different stages of the measurement process. Common test statistics do not take these effects into account. An alternative is to use, for example, ANOVA models. In many cases, we can, however, not make the assumption of normally distributed error terms. Purdom and Holmes [6] have concluded that the distribution of microarray intensity measurements can often be better approximated by a Laplace distribution. In this paper, we consider the analysis of microarray data by using ANOVA models under the assumption of Laplace-distributed error terms. We explain the methodology and discuss problems related to fitting of this type of models. In addition to evaluating the models using several real-life microarray experiments, we conduct a simulation study to investigate different aspects of the models in detail. We find that, while the normal model is less sensitive to model misspecifications, the Laplace model has more power when the data are truly Laplace distributed. However, in the latter situation, neither of the models is able to control the false discovery rate at the pre-specified significance level. This problem is most likely related to sample size issues.

The use of background signal in the transformation of cDNA-microarray measurements.

As the application field of microarrays grows, so does the need for appropriate statistical tools to analyse the signal intensity measurements. Normalisation procedures are required to make the signals from different channels and arrays comparable. One objective, which is also the focus of this report, is to remove the curvature seen on plots of the log ratio versus the mean log intensity values of two channels.A number of methods already exist that are based on the assumption of a shift between the measurements of the two channels. In this article, we explore the use of background measurements to estimate and correct for the shift. We compare our proposal with some well known methods by applying them to microarrays from two studies. These two studies investigate the effect of vegetable diets on the gene expression in colon and lung tissue of mice.Graphical illustrations and a robust summary statistic show that all transformations are an improvement to the raw data. Overall, the best results are obtained with our proposed transformation that takes the background measurements into account.

Impact of abiraterone acetate plus prednisone or enzalutamide on fatigue and cognition in patients with metastatic castration-resistant prostate cancer: Initial results from the observational AQUARiUS study

Introduction Abiraterone acetate plus prednisone (AAP) and enzalutamide (ENZ) are commonly prescribed for metastatic castration-resistant prostate cancer (mCRPC). Data comparing their effects on patient-reported outcomes (PROs) from routine clinical practice are limited. Methods AQUARiUS (NCT02813408) is an ongoing, two-cohort, prospective, observational, non-randomised, multicentre, phase IV European study assessing the effects of AAP and ENZ on PROs in 211 patients with mCRPC over 12 months. Patients receive AAP or ENZ per routine clinical practice. Data on cognition, fatigue, pain and health-related quality of life are measured using the Functional Assessment of Cancer Therapy-Cognitive Function, Brief Fatigue Inventory-Short Form, Brief Pain Inventory-Short Form and European Organization for Research and Treatment of Cancer Quality of Life-C30 questionnaires, respectively. Results This 3-month analysis was conducted in 105 patients; 46 received AAP and 59 received ENZ. There were statistically significant differences in mean change from baseline favouring AAP over ENZ at months 1, 2 and 3 for perceived cognitive impairments and cognitive functioning. At each time-point, ENZ-treated patients had a significantly higher risk of experiencing clinically meaningful worsening in perceived cognitive impairments versus those receiving AAP. Statistically significant differences in mean change from baseline favouring AAP over ENZ were seen for usual level of fatigue and fatigue interference at months 2 and 3 and for current fatigue and worse level of fatigue at month 3. Differences favouring AAP versus ENZ were seen for the fatigue scale of the QLQ-C30 questionnaire (months 1 and 3). There was a significantly higher risk of clinically meaningful worsening in usual level of fatigue with ENZ versus AAP at month 3. No significant differences between cohorts were observed for pain (BPI-SF) at any time-point. Conclusion This analysis suggests more favourable outcomes with AAP versus ENZ for cognition and fatigue in the first 3 months of treatment initiation for mCRPC. These findings require confirmation from future analyses of data from AQUARiUS from a larger number of patients with a longer follow-up period.