Inferring Contagion in Regulatory Networks

Several gene regulatory network models containing concepts of directionality at the edges have been proposed. However, only a few reports have an interpretable definition of directionality. Here, differently from the standard causality concept defined by Pearl, we introduce the concept of contagion in order to infer directionality at the edges, i.e., asymmetries in gene expression dependences of regulatory networks. Moreover, we present a bootstrap algorithm in order to test the contagion concept. This technique was applied in simulated data and, also, in an actual large sample of biological data. Literature review has confirmed some genes identified by contagion as actually belonging to the TP53 pathway.

GRASSIUS: A Platform for Comparative Regulatory Genomics across the Grasses

Transcription factors (TFs) are major players in gene regulatory networks and interactions between TFs and their target genes furnish spatiotemporal patterns of gene expression. Establishing the architecture of regulatory networks requires gathering information on TFs, their targets in the genome, and the corresponding binding sites. We have developed GRASSIUS (Grass Regulatory Information Services) as a knowledge-based Web resource that integrates information on TFs and gene promoters across the grasses. In its initial implementation, GRASSIUS consists of two separate, yet linked, databases. GrassTFDB holds information on TFs from maize (Zea mays), sorghum (Sorghum bicolor), sugarcane (Saccharum spp.), and rice (Oryza sativa). TFs are classified into families and phylogenetic relationships begin to uncover orthologous relationships among the participating species. This database also provides a centralized clearinghouse for TF synonyms in the grasses. GrassTFDB is linked to the grass TFome collection, which provides clones in recombination-based vectors corresponding to full-length open reading frames for a growing number of grass TFs. GrassPROMDB contains promoter and cis-regulatory element information for those grass species and genes for which enough data are available. The integration of GrassTFDB and GrassPROMDB will be accomplished through GrassRegNet as a first step in representing the architecture of grass regulatory networks. GRASSIUS can be accessed from www.grassius.org.

Fur controls iron homeostasis and oxidative stress defense in the oligotrophic alpha-proteobacterium Caulobacter crescentus

In most bacteria, the ferric uptake regulator (Fur) is a global regulator that controls iron homeostasis and other cellular processes, such as oxidative stress defense. In this work, we apply a combination of bioinformatics, in vitro and in vivo assays to identify the Caulobacter crescentus Fur regulon. A C. crescentus fur deletion mutant showed a slow growth phenotype, and was hypersensitive to H(2)O(2) and organic peroxide. Using a position weight matrix approach, several predicted Fur-binding sites were detected in the genome of C. crescentus, located in regulatory regions of genes not only involved in iron uptake and usage but also in other functions. Selected Fur-binding sites were validated using electrophoretic mobility shift assay and DNAse I footprinting analysis. Gene expression assays revealed that genes involved in iron uptake were repressed by iron-Fur and induced under conditions of iron limitation, whereas genes encoding iron-using proteins were activated by Fur under conditions of iron sufficiency. Furthermore, several genes that are regulated via small RNAs in other bacteria were found to be directly regulated by Fur in C. crescentus. In conclusion, Fur functions as an activator and as a repressor, integrating iron metabolism and oxidative stress response in C. crescentus.