Put3 Positively Regulates Proline Utilization in Candida albicans.

The zinc cluster transcription factor Put3 was initially characterized in Saccharomyces cerevisiae as the transcriptional activator of PUT1 and PUT2, two genes acting early in the proline assimilation pathway. We have used phenotypic studies, transcription profiling, and chromatin immunoprecipitation with microarray technology (ChIP-chip) to establish that unlike S. cerevisiae, which only uses proline as a nitrogen source, Candida albicans can use proline as a nitrogen source, a carbon source, or a source of both nitrogen and carbon. However, a C. albicans put3 null mutant cannot grow on proline, suggesting that as in S. cerevisiae, C. albicans Put3 (CaPut3) is required for proline catabolism, and because the C. albicans put3 null mutant grew efficiently on glutamate as the sole carbon or nitrogen source, it appears that CaPut3 also regulates the early genes of the pathway. CaPut3 showed direct binding to the CaPUT1 promoter, and both PUT1 and PUT2 were upregulated in response to proline addition in a Put3-dependent manner, as well as in a C. albicans strain expressing a hyperactive Put3. CaPut3 directs proline degradation even in the presence of a good nitrogen source such as ammonia, which contrasts with S. cerevisiae Put3 (ScPut3)-regulated proline catabolism, which only occurs in the absence of a rich nitrogen source. Thus, while overall proline regulatory circuitry differs between S. cerevisiae and C. albicans, the specific role of Put3 appears fundamentally conserved. IMPORTANCECandida albicans poses a significant threat to the lives of immunocompromised people. Historically, knowledge has been drawn from studies on Saccharomyces cerevisiae to understand the pathogen, and many Candida albicans genes are named after their S. cerevisiae orthologs. Direct studies on the pathogen have, however, revealed differences in the roles of some orthologous proteins in the two yeasts. We show that the Put3 transcription factor allows the pathogen to completely degrade proline to usable nitrogen and carbon by evading regulatory restrictions imposed on its S. cerevisiae ortholog, which mandates conditional use of proline only as a nitrogen source in the baker's yeast. The ability of Candida albicans to freely obtain nutrients from multiple sources may help it thrive as a commensal and opportunistic pathogen.

p38 Mitogen-Activated Protein Kinase Pathway Regulates Genes during Proliferation and Differentiation in Oligodendrocytes.

We have previously shown that p38 mitogen-activated protein kinase (p38 MAPK) is important for oligodendrocyte (OLG) differentiation and myelination. However, the precise cellular mechanisms by which p38 regulates OLG differentiation remain largely unknown. To determine whether p38 functions in part through transcriptional events in regulating OLG identity, we performed microarray analysis on differentiating oligodendrocyte progenitors (OLPs) treated with a p38 inhibitor. Consistent with a role in OLG differentiation, pharmacological inhibition of p38 down-regulated the transcription of genes that are involved in myelin biogenesis, transcriptional control and cell cycle. Proliferation assays showed that OLPs treated with the p38 inhibitor retained a proliferative capacity which could be induced upon application of mitogens demonstrating that after two days of p38-inhibition OLGs remained poised to continue mitosis. Together, our results suggest that the p38 pathway regulates gene transcription which can coordinate OLG differentiation. Our microarray dataset will provide a useful resource for future studies investigating the molecular mechanisms by which p38 regulates oligodendrocyte differentiation and myelination.

The transcription factor encyclopedia.

Here we present the Transcription Factor Encyclopedia (TFe), a new web-based compendium of mini review articles on transcription factors (TFs) that is founded on the principles of open access and collaboration. Our consortium of over 100 researchers has collectively contributed over 130 mini review articles on pertinent human, mouse and rat TFs. Notable features of the TFe website include a high-quality PDF generator and web API for programmatic data retrieval. TFe aims to rapidly educate scientists about the TFs they encounter through the delivery of succinct summaries written and vetted by experts in the field. TFe is available at http://www.cisreg.ca/tfe.

Towards resolving the transcription factor network controlling myelin gene expression.

In the central nervous system (CNS), myelin is produced from spirally-wrapped oligodendrocyte plasma membrane and, as exemplified by the debilitating effects of inherited or acquired myelin abnormalities in diseases such as multiple sclerosis, it plays a critical role in nervous system function. Myelin sheath production coincides with rapid up-regulation of numerous genes. The complexity of their subsequent expression patterns, along with recently recognized heterogeneity within the oligodendrocyte lineage, suggest that the regulatory networks controlling such genes drive multiple context-specific transcriptional programs. Conferring this nuanced level of control likely involves a large repertoire of interacting transcription factors (TFs). Here, we combined novel strategies of computational sequence analyses with in vivo functional analysis to establish a TF network model of coordinate myelin-associated gene transcription. Notably, the network model captures regulatory DNA elements and TFs known to regulate oligodendrocyte myelin gene transcription and/or oligodendrocyte development, thereby validating our approach. Further, it links to numerous TFs with previously unsuspected roles in CNS myelination and suggests collaborative relationships amongst both known and novel TFs, thus providing deeper insight into the myelin gene transcriptional network.

A regulatory toolbox of MiniPromoters to drive selective expression in the brain.

The Pleiades Promoter Project integrates genomewide bioinformatics with large-scale knockin mouse production and histological examination of expression patterns to develop MiniPromoters and related tools designed to study and treat the brain by directed gene expression. Genes with brain expression patterns of interest are subjected to bioinformatic analysis to delineate candidate regulatory regions, which are then incorporated into a panel of compact human MiniPromoters to drive expression to brain regions and cell types of interest. Using single-copy, homologous-recombination "knockins" in embryonic stem cells, each MiniPromoter reporter is integrated immediately 5' of the Hprt locus in the mouse genome. MiniPromoter expression profiles are characterized in differentiation assays of the transgenic cells or in mouse brains following transgenic mouse production. Histological examination of adult brains, eyes, and spinal cords for reporter gene activity is coupled to costaining with cell-type-specific markers to define expression. The publicly available Pleiades MiniPromoter Project is a key resource to facilitate research on brain development and therapies.

TFCat: the curated catalog of mouse and human transcription factors.

Unravelling regulatory programs governed by transcription factors (TFs) is fundamental to understanding biological systems. TFCat is a catalog of mouse and human TFs based on a reliable core collection of annotations obtained by expert review of the scientific literature. The collection, including proven and homology-based candidate TFs, is annotated within a function-based taxonomy and DNA-binding proteins are organized within a classification system. All data and user-feedback mechanisms are available at the TFCat portal (http://www.tfcat.ca).

oPOSSUM: integrated tools for analysis of regulatory motif over-representation.

The identification of over-represented transcription factor binding sites from sets of co-expressed genes provides insights into the mechanisms of regulation for diverse biological contexts. oPOSSUM, an internet-based system for such studies of regulation, has been improved and expanded in this new release. New features include a worm-specific version for investigating binding sites conserved between Caenorhabditis elegans and C. briggsae, as well as a yeast-specific version for the analysis of co-expressed sets of Saccharomyces cerevisiae genes. The human and mouse applications feature improvements in ortholog mapping, sequence alignments and the delineation of multiple alternative promoters. oPOSSUM2, introduced for the analysis of over-represented combinations of motifs in human and mouse genes, has been integrated with the original oPOSSUM system. Analysis using user-defined background gene sets is now supported. The transcription factor binding site models have been updated to include new profiles from the JASPAR database. oPOSSUM is available at http://www.cisreg.ca/oPOSSUM/

Improving the specificity of high-throughput ortholog prediction.

BACKGROUND: Orthologs (genes that have diverged after a speciation event) tend to have similar function, and so their prediction has become an important component of comparative genomics and genome annotation. The gold standard phylogenetic analysis approach of comparing available organismal phylogeny to gene phylogeny is not easily automated for genome-wide analysis; therefore, ortholog prediction for large genome-scale datasets is typically performed using a reciprocal-best-BLAST-hits (RBH) approach. One problem with RBH is that it will incorrectly predict a paralog as an ortholog when incomplete genome sequences or gene loss is involved. In addition, there is an increasing interest in identifying orthologs most likely to have retained similar function. RESULTS: To address these issues, we present here a high-throughput computational method named Ortholuge that further evaluates previously predicted orthologs (including those predicted using an RBH-based approach) - identifying which orthologs most closely reflect species divergence and may more likely have similar function. Ortholuge analyzes phylogenetic distance ratios involving two comparison species and an outgroup species, noting cases where relative gene divergence is atypical. It also identifies some cases of gene duplication after species divergence. Through simulations of incomplete genome data/gene loss, we show that the vast majority of genes falsely predicted as orthologs by an RBH-based method can be identified. Ortholuge was then used to estimate the number of false-positives (predominantly paralogs) in selected RBH-predicted ortholog datasets, identifying approximately 10% paralogs in a eukaryotic data set (mouse-rat comparison) and 5% in a bacterial data set (Pseudomonas putida - Pseudomonas syringae species comparison). Higher quality (more precise) datasets of orthologs, which we term "ssd-orthologs" (supporting-species-divergence-orthologs), were also constructed. These datasets, as well as Ortholuge software that may be used to characterize other species' datasets, are available at http://www.pathogenomics.ca/ortholuge/ (software under GNU General Public License). CONCLUSION: The Ortholuge method reported here appears to significantly improve the specificity (precision) of high-throughput ortholog prediction for both bacterial and eukaryotic species. This method, and its associated software, will aid those performing various comparative genomics-based analyses, such as the prediction of conserved regulatory elements upstream of orthologous genes.

Assessment and integration of publicly available SAGE, cDNA microarray, and oligonucleotide microarray expression data for global coexpression analyses.

Large amounts of gene expression data from several different technologies are becoming available to the scientific community. A common practice is to use these data to calculate global gene coexpression for validation or integration of other "omic" data. To assess the utility of publicly available datasets for this purpose we have analyzed Homo sapiens data from 1202 cDNA microarray experiments, 242 SAGE libraries, and 667 Affymetrix oligonucleotide microarray experiments. The three datasets compared demonstrate significant but low levels of global concordance (rc<0.11). Assessment against Gene Ontology (GO) revealed that all three platforms identify more coexpressed gene pairs with common biological processes than expected by chance. As the Pearson correlation for a gene pair increased it was more likely to be confirmed by GO. The Affymetrix dataset performed best individually with gene pairs of correlation 0.9-1.0 confirmed by GO in 74% of cases. However, in all cases, gene pairs confirmed by multiple platforms were more likely to be confirmed by GO. We show that combining results from different expression platforms increases reliability of coexpression. A comparison with other recently published coexpression studies found similar results in terms of performance against GO but with each method producing distinctly different gene pair lists.