Regulatory regions of plant genes tend to be more compact than those of animal genes, but the complement of transcription factors encoded in plant genomes is as large or larger than that found in those of animals. Plants therefore provide an opportunity to study how transcriptional programs control multicellular development. We analyzed global gene expression during development of the reference plant Arabidopsis thaliana in samples covering many stages, from embryogenesis to senescence, and diverse organs. Here, we provide a first analysis of this data set, which is part of the AtGenExpress expression atlas. We observed that the expression levels of transcription factor genes and signal transduction components are similar to those of metabolic genes. Examining the expression patterns of large gene families, we found that they are often more similar than would be expected by chance, indicating that many gene families have been co-opted for specific developmental processes.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Pancreatic ductal adenocarcinoma (PDAC) is known for its very poor overall prognosis. Accurate early diagnosis and new therapeutic modalities are therefore urgently needed. We used 377 feature microRNA (miRNA) arrays to investigate miRNA expression in normal pancreas, chronic pancreatitis, and PDAC tissues as well as PDAC-derived cell lines. A pancreatic miRNome was established comparing the data from normal pancreas with a reference set of 33 human tissues. The expression of miR-216 and -217 and lack of expression of miR-133a were identified as characteristic of pancreas tissue. Unsupervised clustering showed that the three pancreatic tissues types can be classified according to their respective miRNA expression profiles. We identified 26 miRNAs most prominently misregulated in PDAC and a relative quantitative reverse transcriptase-polymerase chain reaction index using only miR-217 and -196a was found to discriminate normal pancreas, chronic pancreatitis and cancerous tissues, establishing a potential utility for miRNAs in diagnostic procedures. Lastly, comparing differentially expressed genes from PDAC with predicted miRNA target genes for the top 26 miRNAs, we identified potential novel links between aberrant miRNA expression and known target genes relevant to PDAC biology. Our data provides novel insights into the miRNA-driven pathophysiological mechanisms involved in PDAC development and offers new candidate targets to be exploited both for diagnostic and therapeutic strategies.