We report a catalog of the mouse embryonic pituitary gland transcriptome consisting of five cDNA libraries including wild type tissue from E12.5 and E14.5, Prop1(df/df) mutant at E14.5, and two cDNA subtractions: E14.5 WT-E14.5 Prop1(df/df) and E14.5 WT-E12.5 WT. DNA sequence information is assembled into a searchable database with gene ontology terms representing 12,009 expressed genes. We validated coverage of the libraries by detecting most known homeobox gene transcription factor cDNAs. A total of 45 homeobox genes were detected as part of the pituitary transcriptome, representing most expected ones, which validated library coverage, and many novel ones, underscoring the utility of this resource as a discovery tool. We took a similar approach for signaling-pathway members with novel pituitary expression and found 157 genes related to the BMP, FGF, WNT, SHH and NOTCH pathways. These genes are exciting candidates for regulators of pituitary development and function.

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes.

In studies of their transcriptional activity, genomes have shown a high order of organization. We assessed the question of how genomically neighboring genes are transcriptionally coupled across tissues and what could be the driving force behind their coupling. We focused our analysis on the transcriptome information for 13 tissues of Mus musculus and 79 tissues of Homo sapiens. The analysis of coexpression patterns of genomically adjacent genes across tissues revealed 2619 and 1275 clusters of highly coexpressed genes, respectively. Most of these clusters consist of pairs and triplets of genes. They span a limited genomic length and are phylogenetically conserved between human and mouse. These clusters consist mainly of nonparalogous genes and show a decreased functional and similar regulatory relationship to one another compared to general genomic neighbors. We hypothesize that these clusters trace back to large-scale, qualitative, persistent reorganizations of the transcriptome, while transcription factor regulation is likely to handle fine-tuning of transcription on shorter time scales. Our data point to so far uncharacterized cis-acting units and reject cofunctionality as a driving force.