Aurelie Labbe | PubMed Health

PubMed2006

Transcriptional profiling implicates novel interactions between abiotic stress and hormonal responses in Thellungiella, a close relative of Arabidopsis.

Thellungiella, an Arabidopsis (Arabidopsis thaliana)-related halophyte, is an emerging model species for studies designed to elucidate molecular mechanisms of abiotic stress tolerance. Using a cDNA microarray containing 3,628 unique sequences derived from previously described libraries of stress-induced cDNAs of the Yukon ecotype of Thellungiella salsuginea, we obtained transcript profiles of its response to cold, salinity, simulated drought, and rewatering after simulated drought. A total of 154 transcripts were differentially regulated under the conditions studied. Only six of these genes responded to all three stresses of drought, cold, and salinity, indicating a divergence among the end responses triggered by each of these stresses. Unlike in Arabidopsis, there were relatively few transcript changes in response to high salinity in this halophyte. Furthermore, the gene products represented among drought-responsive transcripts in Thellungiella associate a down-regulation of defense-related transcripts with exposure to water deficits. This antagonistic interaction between drought and biotic stress response may demonstrate Thellungiella's ability to respond precisely to environmental stresses, thereby conserving energy and resources and maximizing its survival potential. Intriguingly, changes of transcript abundance in response to cold implicate the involvement of jasmonic acid. While transcripts associated with photosynthetic processes were repressed by cold, physiological responses in plants developed at low temperature suggest a novel mechanism for photosynthetic acclimation. Taken together, our results provide useful starting points for more in-depth analyses of Thellungiella's extreme stress tolerance.

Arabidopsis↗

PubMed2005

Hierarchical inverse Gaussian models and multiple testing: application to gene expression data.

Detecting differentially expressed genes in microarray experiments is a topic that has been well studied in the literature. Many hypothesis testing methods have been proposed that rely on strong distributional assumptions for the gene intensities. However, the shape of microarray data may vary substantially from one experiment to another, and model assumptions may be seriously violated in many cases. The literature on microarray data is mainly based on two distributions: the log-normal and the gamma distributions, that often appear to be effective when used in a Bayesian hierarchical framework. However, if a model that fits the data well in a global manner seems attractive, two points should be regarded with attention: the ability of the model to fit the tail of the observed distribution, and its robustness to a wrong specification of the model, in terms of error rates for the hypothesis tests. In order to focus on these aspects, we propose to use Bayesian models involving the inverse Gaussian distribution to describe gene expression data. We show that these models can be good competitors to the traditional Bayesian or random effect gamma or log-normal models in some situations. A multiple testing procedure is then proposed, based on an asymptotic property of the posterior probability of the one-sided alternative hypothesis. We show that the asymptotic property is well approximated for inverse Gaussian models, even when the number of observations available for each test is very small.

Journal Article↗