Whole Genome Analysis of Gene Expression Reveals Coordinated Activation of Signaling and Metabolic Pathways during Pollen-Pistil Interactions in Arabidopsis

Plant reproduction depends on the concerted activation of many genes to ensure correct communication between pollen and pistil. Here, we queried the whole transcriptome of Arabidopsis (Arabidopsis thaliana) in order to identify genes with specific reproductive functions. We used the Affymetrix ATH1 whole genome array to profile wild-type unpollinated pistils and unfertilized ovules. By comparing the expression profile of pistils at 0.5, 3.5, and 8.0 h after pollination and applying a number of statistical and bioinformatics criteria, we found 1,373 genes differentially regulated during pollen-pistil interactions. Robust clustering analysis grouped these genes in 16 time-course clusters representing distinct patterns of regulation. Coregulation within each cluster suggests the presence of distinct genetic pathways, which might be under the control of specific transcriptional regulators. A total of 78% of the regulated genes were expressed initially in unpollinated pistil and/or ovules, 15% were initially detected in the pollen data sets as enriched or preferentially expressed, and 7% were induced upon pollination. Among those, we found a particular enrichment for unknown transcripts predicted to encode secreted proteins or representing signaling and cell wall-related proteins, which may function by remodeling the extracellular matrix or as extracellular signaling molecules. A strict regulatory control in various metabolic pathways suggests that fine-tuning of the biochemical and physiological cellular environment is crucial for reproductive success. Our study provides a unique and detailed temporal and spatial gene expression profile of in vivo pollen-pistil interactions, providing a framework to better understand the basis of the molecular mechanisms operating during the reproductive process in higher plants.

Transcriptional profiling in Saccharomyces cerevisiae relevant for predicting alachlor mechanisms of toxicity

Alachlor has been a commonly applied herbicide and is a substance of ecotoxicological concern. The present study aims to identify molecular biomarkers in the eukaryotic model Saccharomyces cerevisiae that can be used to predict potential cytotoxic effects of alachlor, while providing new mechanistic clues with possible relevance for experimentally less accessible eukaryotes. It focuses on genome-wide expression profiling in a yeast population in response to two exposure scenarios exerting effects from slight to moderate magnitude at phenotypic level. In particular, 100 and 264 genes, respectively, were found as differentially expressed on a 2-h exposure of yeast cells to the lowest observed effect concentration (110 mg/L) and the 20% inhibitory concentration (200 mg/L) of alachlor, in comparison with cells not exposed to the herbicide. The datasets of alachlor-responsive genes showed functional enrichment in diverse metabolic, transmembrane transport, cell defense, and detoxification categories. In general, the modifications in transcript levels of selected candidate biomarkers, assessed by quantitative reverse transcriptase polymerase chain reaction, confirmed the microarray data and varied consistently with the growth inhibitory effects of alachlor. Approximately 16% of the proteins encoded by alachlor-differentially expressed genes were found to share significant homology with proteins from ecologically relevant eukaryotic species. The biological relevance of these results is discussed in relation to new insights into the potential adverse effects of alachlor in health of organisms from ecosystems, particularly in worst-case situations such as accidental spills or careless storage, usage, and disposal.