Antagonistic interaction between abscisic acid and jasmonate-ethylene signaling pathways modulates defense gene expression and disease resistance in Arabidopsis

The plant hormones abscisic acid (ABA), jasmonic acid (JA), and ethylene are involved in diverse plant processes, including the regulation of gene expression during adaptive responses to abiotic and biotic stresses. Previously, ABA has been implicated in enhancing disease susceptibility in various plant species, but currently very little is known about the molecular mechanisms underlying this phenomenon. In this study, we obtained evidence that a complex interplay between ABA and JA-ethylene signaling pathways regulate plant defense gene expression and disease resistance. First, we showed that exogenous ABA suppressed both basal and JA-ethylene-activated transcription from defense genes. By contrast, ABA deficiency as conditioned by the mutations in the ABA1 and ABA2 genes, which encode enzymes involved in ABA biosynthesis, resulted in upregulation of basal and induced transcription from JA-ethylene responsive defense genes. Second, we found that disruption of AtMYC2 (allelic to JASMONATE INSENSITIVE1 [JIN1]), encoding a basic helix-loop-helix Leu zipper transcription factor, which is a positive regulator of ABA signaling, results in elevated levels of basal and activated transcription from JA-ethylene responsive defense genes. Furthermore, the jin1/myc2 and aba2-1 mutants showed increased resistance to the necrotrophic fungal pathogen Fusarium oxysporum. Finally, using ethylene and ABA signaling mutants, we showed that interaction between ABA and ethylene signaling is mutually antagonistic in vegetative tissues. Collectively, our results indicate that the antagonistic interactions between multiple components of ABA and the JA-ethylene signaling pathways modulate defense and stress responsive gene expression in response to biotic and abiotic stresses.

Oxygen-regulated gene expression in bovine blastocysts

Oxygen concentrations used during in vitro embryo culture can influence embryo development, cell numbers, and gene expression. Here we propose that the preimplantation bovine embryo possesses a molecular mechanism for the detection of, and response to, oxygen, mediated by a family of basic helix-loop-helix transcription factors, the hypoxia-inducible factors (HIFs). Day 5 compacting bovine embryos were cultured under different oxygen tensions (2%, 7%, 20%) and the effect on the expression of oxygen-regulated genes, development, and cell number allocation and HIFalpha protein localization were examined. Bovine in vitro-produced embryos responded to variations in oxygen concentration by altering gene expression. GLUT1 expression was higher following 2% oxygen culture compared with 7% and 20% cultured blastocysts. HIF mRNA expression (HIF1alpha, HIF2alpha) was unaltered by oxygen concentration. HIF2alpha protein was predominantly localized to the nucleus of blastocysts. In contrast, HIF1alpha protein was undetectable at any oxygen concentration or in the presence of the HIF protein stabilizer desferrioxamine (DFO), despite being detectable in cumulus cells following normal maturation conditions, acute anoxic culture, or in the presence of DFO. Oxygen concentration also significantly altered inner cell mass cell proportions at the blastocyst stage. These results suggest that oxygen can influence gene expression in the bovine embryo during postcompaction development and that these effects may be mediated by HIF2alpha.

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.

Differential gene expression in the developing mouse ureter

In many instances, kidney dysgenesis results as a secondary consequence to defects in the development of the ureter. Through the use of mouse genetics a number of genes associated with such malformations have been identified, however, the cause of many other abnormalities remain unknown. In order to identify novel genes involved in ureter development we compared gene expression in embryonic day (E) 12.5, E15.5 and postnatal day (P) 75 ureters using the Compugen mouse long oligo microarrays. A total of 248 genes were dynamically upregulated and 208 downregulated between E12.5 and P75. At E12.5, when the mouse ureter is comprised of a simple cuboidal epithelium surrounded by ureteric mesenchyme, genes previously reported to be expressed in the ureteric mesenchyme, foxC1 and foxC2 were upregulated. By E15.5 the epithelial layer develops into urothelium, impermeable to urine, and smooth muscle develops for the peristaltic movement of urine towards the bladder. The development of these two cell types coincided with the upregulation of UPIIIa, RAB27b and PPAR gamma reported to be expressed in the urothelium, and several muscle genes, Acta1, Tnnt2, Myocd, and Tpm2. In situ hybridization identified several novel genes with spatial expression within the smooth muscle, Acta1; ureteric mesenchyme and smooth muscle, Thbs2 and Co15a2; and urothelium, Kcnj8 and Adh1. This study marks the first known report defining global gene expression of the developing mouse ureter and will provide insight into the molecular mechanisms underlying kidney and lower urinary tract malformations. (c) 2005 Elsevier B.V. All rights reserved.

Spatial gene expression in the T-stage mouse metanephros

The E11.5 mouse metanephros is comprised of a T-stage ureteric epithelial tubule sub-divided into tip and trunk cells surrounded by metanephric mesenchyme (MM). Tip cells are induced to undergo branching morphogenesis by the MM. In contrast, signals within the mesenchyme surrounding the trunk prevent ectopic branching of this region. In order to identify novel genes involved in the molecular regulation of branching morphogenesis we compared the gene expression profiles of isolated tip, trunk and MM cells using Compugen mouse long oligo microarrays. We identified genes enriched in the tip epithelium, sim-1, Arg2, Tacstd1, Crlf-1 and BMP7; genes enriched in the trunk epithelium, Innp1, Itm2b, Mkrn1, SPARC, Emu2 and Gsta3 and genes spatially restricted to the mesenchyme surrounding the trunk, CSPG2 and CV-2, with overlapping and complimentary expression to BMP4, respectively. This study has identified genes spatially expressed in regions of the developing kidney involved in branching morphogenesis, nephrogenesis and the development of the collecting duct system, calyces, renal pelvis and ureter. (c) 2006 Elsevier B.V. All rights reserved.

Genes and gene expression in the brains of human alcoholics

Chronic alcohol misuse by human subjects leads to neuronal loss in regions such as the superior frontal cortex (SFC). Propensity to alcoholism is associated with several genes. γ-Aminobutyric acid (GABA)A receptor expression differs between alcoholics and controls, whereas glutamate receptor differences are muted. We determined whether genotype differentiated the regional presentation of GABAA and glutamate-NMDA (N-methyl-d-aspartate) receptors in SFC. Autopsy tissue was obtained from alcoholics without comorbid disease, alcoholics with liver cirrhosis, and matched controls. ADH1C, DRD2B, EAAT2, and APOE genotypes modulated GABAA-β subunit protein expression in SFC toward a less-effective form of the receptor. Most genotypes did not divide alcoholics and controls on glutamate-NMDA receptor pharmacology, although gender and cirrhosis did. Genotype may affect amino acid transmission locally to influence neuronal vulnerability.

Dynamics of gene expression in an artificial genome

Complex systems techniques provide a powerful tool to study the emergent properties of networks of interacting genes. In this study we extract models of genetic regulatory networks from an artificial genome, represented by a sequence of nucleotides, and analyse how variations in the connectivity and degree of inhibition of the extracted networks affects the resulting classes of behaviours. For low connectivity systems were found to be very stable. Only with higher connectivity was a significant occurrence of chaos found. Most interestingly, the peak in occurrence of chaos occurs perched on the edge of a phase transition in the occurrence of attractors.

Selection bias in gene extraction on the basis of microarray gene-expression data

In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called. 632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.

Assessment of transient gene expression in plant tissues using the green fluorescent protein as a reference

Four different promoters (35S and enhanced 35S of the cauliflower mosaic virus, polyubiquitin of maize and actin1 of rice) were compared in a transient assay using maize leaves and particle bombardment. A gene encoding the jellyfish green fluorescent protein (GFP) driven by the 358 promoter was used as an internal standard to monitor the effectiveness of each bombardment. Normalisation of the transient expression assay using the GFP reference significantly reduced the variability between separate bombardments and allowed for a rapid and accurate evaluation of different promoters in microprojectile-bombarded leaves.