Functional modularity of nuclear hormone receptors in a Caenorhabditis elegans metabolic gene regulatory network

We present the first gene regulatory network (GRN) that pertains to post-developmental gene expression. Specifically, we mapped a transcription regulatory network of Caenorhabditis elegans metabolic gene promoters using gene-centered yeast one-hybrid assays. We found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs) compared with other gene-centered regulatory networks, and that these NHRs organize into functional network modules.The NHR family has greatly expanded in nematodes; C. elegans has 284 NHRs, whereas humans have only 48. We show that the NHRs in the metabolic GRN have metabolic phenotypes, suggesting that they do not simply function redundantly.The mediator subunit MDT-15 preferentially interacts with NHRs that occur in the metabolic GRN.We describe an NHR circuit that responds to nutrient availability and propose a model for the evolution and organization of NHRs in C. elegans metabolic regulatory networks

A first version of the Caenorhabditis elegans Promoterome

An important aspect of the development of systems biology approaches in metazoans is the characterization of expression patterns of nearly all genes predicted from genome sequences. Such localizome maps should provide information on where (in what cells or tissues) and when (at what stage of development or under what conditions) genes are expressed. They should also indicate in what cellular compartments the corresponding proteins are localized. Caenorhabditis elegans is particularly suited for the development of a localizome map since all its 959 adult somatic cells can be visualized by microscopy, and its cell lineage has been completely described. Here we address one of the challenges of C. elegans localizome mapping projects: that of obtaining a genome-wide resource of C. elegans promoters needed to generate transgenic animals expressing localization markers such as the green fluorescent protein (GFP). To ensure high flexibility for future uses, we utilized the newly developed MultiSite Gateway system. We generated and validated version 1.1 of the Promoterome: a resource of approximately 6000 C. elegans promoters. These promoters can be transferred easily into various Gateway Destination vectors to drive expression of markers such as GFP, alone (promoter::GFP constructs), or in fusion with protein-encoding open reading frames available in ORFeome resources (promoter::ORF::GFP)

Sequence variation in the human transcription factor gene POU5F1

<p>Abstract</p> <p>Background</p> <p>POU5F1 expression is required to maintain stem cell pluripotency and for primordial germ cells to retain proliferative capability in embryonic development. Recent evidence suggests that <it>POU5F1 </it>may also be a testicular germ cell carcinoma (TGCC) oncogene, and <it>POU5F1 </it>variation may influence TGCC risk. As an important first step to a genetic association study, we sought to identify all common sequence variants in an 11.3 kb region containing <it>POU5F1</it>, and to describe the linkage disequilibrium patterns, using DNA from individuals of African-descent (AD) and European-descent (ED).</p> <p>Results</p> <p>A higher number of polymorphisms was observed in the AD (n = 102) versus ED (n = 82) population. Among the 41 observed haplotypes, 21 (51%) and 12 (29%) were unique to the AD and ED populations, respectively, while 8 (20%) were observed in both. The number of tagging polymorphisms necessary to explain at least 80% of common variation (minor allele frequency ≥ 0.10) due to the remaining untyped polymorphisms was 17 for an AD and 10 for an ED population, providing a 4.0- and 7.0-fold gain in genotyping efficiency for characterizing nucleotide variation, respectively.</p> <p>Conclusion</p> <p><it>POU5F1 </it>is highly polymorphic, however a smaller subset of polymorphisms can tag the observed genetic variation with little loss of information.</p

Generation of the Brucella melitensis ORFeome version 1.1.

The bacteria of the Brucella genus are responsible for a worldwide zoonosis called brucellosis. They belong to the alpha-proteobacteria group, as many other bacteria that live in close association with a eukaryotic host. Importantly, the Brucellae are mainly intracellular pathogens, and the molecular mechanisms of their virulence are still poorly understood. Using the complete genome sequence of Brucella melitensis, we generated a database of protein-coding open reading frames (ORFs) and constructed an ORFeome library of 3091 Gateway Entry clones, each containing a defined ORF. This first version of the Brucella ORFeome (v1.1) provides the coding sequences in a user-friendly format amenable to high-throughput functional genomic and proteomic experiments, as the ORFs are conveniently transferable from the Entry clones to various Expression vectors by recombinational cloning. The cloning of the Brucella ORFeome v1.1 should help to provide a better understanding of the molecular mechanisms of virulence, including the identification of bacterial protein-protein interactions, but also interactions between bacterial effectors and their host's targets

Transcription factor modularity in a gene-centered C. elegans core neuronal protein–DNA interaction network

Transcription regulatory networks play a pivotal role in the development, function, and pathology of metazoan organisms. Such networks are comprised of protein–DNA interactions between transcription factors (TFs) and their target genes. An important question pertains to how the architecture of such networks relates to network functionality. Here, we show that a Caenorhabditis elegans core neuronal protein–DNA interaction network is organized into two TF modules. These modules contain TFs that bind to a relatively small number of target genes and are more systems specific than the TF hubs that connect the modules. Each module relates to different functional aspects of the network. One module contains TFs involved in reproduction and target genes that are expressed in neurons as well as in other tissues. The second module is enriched for paired homeodomain TFs and connects to target genes that are often exclusively neuronal. We find that paired homeodomain TFs are specifically expressed in C. elegans and mouse neurons, indicating that the neuronal function of paired homeodomains is evolutionarily conserved. Taken together, we show that a core neuronal C. elegans protein–DNA interaction network possesses TF modules that relate to different functional aspects of the complete network

C. elegans ORFeome Version 3.1: Increasing the Coverage of ORFeome Resources With Improved Gene Predictions

The first version of the Caenorhabditis elegans ORFeome cloning project, based on release WS9 of Wormbase (August 1999), provided experimental verifications for ∼55% of predicted protein-encoding open reading frames (ORFs). The remaining 45% of predicted ORFs could not be cloned, possibly as a result of mispredicted gene boundaries. Since the release of WS9, gene predictions have improved continuously. To test the accuracy of evolving predictions, we attempted to PCR-amplify from a highly representative worm cDNA library and Gateway-clone ∼4200 ORFs missed earlier and for which new predictions are available in WS100 (May 2003). In this set we successfully cloned 63% of ORFs with supporting experimental data (“touched” ORFs), and 42% of ORFs with no supporting experimental evidence (“untouched” ORFs). Approximately 2000 full-length ORFs were cloned in-frame, 13% of which were corrected in their exon/intron structure relative to WS100 predictions. In total, ∼12,500 C. elegans ORFs are now available as Gateway Entry clones for various reverse proteomics (ORFeome v3.1). This work illustrates why the cloning of a complete C. elegans ORFeome, and likely the ORFeomes of other multicellular organisms, needs to be an iterative process that requires multiple rounds of experimental validation together with gradually improving gene predictions

A C. elegans genome-scale microRNA network contains composite feedback motifs with high flux capacity

MicroRNAs (miRNAs) and transcription factors (TFs) are primary metazoan gene regulators. Whereas much attention has focused on finding the targets of both miRNAs and TFs, the transcriptional networks that regulate miRNA expression remain largely unexplored. Here, we present the first genome-scale Caenorhabditis elegans miRNA regulatory network that contains experimentally mapped transcriptional TF → miRNA interactions, as well as computationally predicted post-transcriptional miRNA → TF interactions. We find that this integrated miRNA network contains 23 miRNA ↔ TF composite feedback loops in which a TF that controls a miRNA is itself regulated by that same miRNA. By rigorous network randomizations, we show that such loops occur more frequently than expected by chance and, hence, constitute a genuine network motif. Interestingly, miRNAs and TFs in such loops are heavily regulated and regulate many targets. This “high flux capacity” suggests that loops provide a mechanism of high information flow for the coordinate and adaptable control of miRNA and TF target regulons

Matrix and Steiner-triple-system smart pooling assays for high-performance transcription regulatory network mapping

Yeast one-hybrid (Y1H) assays provide a gene-centered method for the identification of interactions between gene promoters and regulatory transcription factors (TFs). To date, Y1H assays have involved library screens that are relatively expensive and laborious. We present two Y1H strategies that allow immediate prey identification: matrix assays that use an array of 755 individual Caenorhabditis elegans TFs, and smart-pool assays that use TF multiplexing. Both strategies simplify the Y1H pipeline and reduce the cost of protein-DNA interaction identification. We used a Steiner triple system (STS) to create smart pools of 4-25 TFs. Notably, we uniplexed a small number of highly connected TFs to allow efficient assay deconvolution. Both strategies outperform library screens in terms of coverage, confidence and throughput. These versatile strategies can be adapted both to TFs in other systems and, likely, to other biomolecules and assays as well