Albertha J. M. Walhout

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

In apparently scale-free protein–protein interaction networks, or ‘interactome’ networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the ‘hubs’, interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein–protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: ‘party’ hubs, which interact with most of their partners simultaneously, and ‘date’ hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes—or modules —to each other, whereas party hubs function inside modules.

GATEWAY recombinational cloning: application to the cloning of large numbers of open reading frames or ORFeomes.

Publisher Summary Complete genome sequences are available for three model organisms— Escherichia coli , Saccharomyces cerevisiae , and Caenorhabditis elegans —and for several pathogenic microorganisms such as Helicobacter pylori . Complete genome sequences are expected to become available soon for other model organisms and for humans. This information is expected to revolutionize the way biological questions can be addressed. Molecular mechanisms should now be approachable on a more global scale in the context of (nearly) complete sets of genes, rather than by analyzing genes individually. However, most open reading frames (ORFs) predicted from sequencing projects have remained completely uncharacterized at the functional level. The emerging field of functional genomics addresses this limitation by developing methods to characterize the function of large numbers of predicted ORFs simultaneously.

Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression

In any given cell, thousands of genes are expressed and work in concert to ensure the cells function, fitness, and survival. Each gene, in turn, must be expressed at the proper time and in the proper amounts to ensure the appropriate functional outcome. The regulation and expression of some genes are highly robust; their expression is controlled by invariable expression programs. For instance, developmental gene expression is extremely similar in a given cell type from one individual to another. The expression of other genes is more variable: Their levels are noisy and are different from cell to cell and from individual to individual. This can be highly beneficial in physiological responses to outside cues and stresses. Recent advances have enabled the analysis of differential gene expression at a systems level. Gene regulatory networks (GRNs) involving interactions between large numbers of genes and their regulators have been mapped onto graphic diagrams that are used to visualize the regulatory relationships. The further characterization of GRNs has already uncovered global principles of gene regulation. Together with synthetic network biology, such studies are starting to provide insights into the transcriptional mechanisms that cause robust versus stochastic gene expression and their relationships to phenotypic robustness and variability. Here, we discuss GRNs and their topological properties in relation to transcriptional and phenotypic outputs in development and organismal physiology.

The interplay between transcription factors and microRNAs in genome‐scale regulatory networks

Metazoan genomes contain thousands of protein‐coding and non‐coding RNA genes, most of which are differentially expressed, i.e., at different locations, at different times during development, or in response to environmental signals. Differential gene expression is achieved through complex regulatory networks that are controlled in part by two types of trans‐regulators: transcription factors (TFs) and microRNAs (miRNAs). TFs bind to cis‐regulatory DNA elements that are often located in or near their target genes, while miRNAs hybridize to cis‐regulatory RNA elements mostly located in the 3′ untranslated region of their target mRNAs. Here, we describe how these trans‐regulators interact with each other in the context of gene regulatory networks to coordinate gene expression at the genome‐scale level, and discuss future challenges of integrating these networks with other types of functional networks.

A protein–protein interaction map of the Caenorhabditis elegans 26S proteasome

The ubiquitin‐proteasome proteolytic pathway is pivotal in most biological processes. Despite a great level of information available for the eukaryotic 26S proteasome—the protease responsible for the degradation of ubiquitylated proteins—several structural and functional questions remain unanswered. To gain more insight into the assembly and function of the metazoan 26S proteasome, a two‐hybrid‐based protein interaction map was generated using 30 Caenorhabditis elegans proteasome subunits. The results recapitulate interactions reported for other organisms and reveal new potential interactions both within the 19S regulatory complex and between the 19S and 20S subcomplexes. Moreover, novel potential proteasome interactors were identified, including an E3 ubiquitin ligase, transcription factors, chaperone proteins and other proteins not yet functionally annotated. By providing a wealth of novel biological hypotheses, this interaction map constitutes a framework for further analysis of the ubiquitin‐proteasome pathway in a multicellular organism amenable to both classical genetics and functional genomics.

Genome-scale spatiotemporal analysis of Caenorhabditis elegans microRNA promoter activity

The Caenorhabditis elegans genome encodes more than 100 microRNAs (miRNAs). Genetic analyses of miRNA deletion mutants have only provided limited insights into miRNA function. To gain insight into the function of miRNAs, it is important to determine their spatiotemporal expression pattern. Here, we use miRNA promoters driving the expression of GFP as a proxy for miRNA expression. We describe a set of 73 transgenic C. elegans strains, each expressing GFP under the control of a miRNA promoter. Together, these promoters control the expression of 89 miRNAs (66% of all predicted miRNAs). We find that miRNA promoters drive GFP expression in a variety of tissues and that, overall, their activity is similar to that of protein-coding gene promoters. However, miRNAs are expressed later in development, which is consistent with functions after initial body-plan specification. We find that miRNA members belonging to families are more likely to be expressed in overlapping tissues than miRNAs that do not belong to the same family, and provide evidence that intronic miRNAs may be controlled by their own, rather than a host gene promoter. Finally, our data suggest that post-transcriptional mechanisms contribute to differential miRNA expression. The data and strains described here will provide a valuable guide and resource for the functional analysis of C. elegans miRNAs.

Chromatin immunoprecipitation (ChIP) coupled to detection by quantitative real-time PCR to study transcription factor binding to DNA in Caenorhabditis elegans

In order to determine how signaling pathways differentially regulate gene expression, it is necessary to identify the interactions between transcription factors (TFs) and their cognate cis-regulatory DNA elements. Here, we have outlined a chromatin immunoprecipitation (ChIP) protocol for use in whole Caenorhabditis elegans extracts. We discuss optimization of the procedure, including growth and harvesting of the worms, formaldehyde fixation, TF immunoprecipitation and analysis of bound sequences through real-time PCR. It takes ∼10–12 d to obtain the worm culture for ChIP; the ChIP procedure is spaced out over a period of 2.5 d with two overnight incubations.

A compendium of Caenorhabditis elegans regulatory transcription factors: a resource for mapping transcription regulatory networks

BackgroundTranscription regulatory networks are composed of interactions between transcription factors and their target genes. Whereas unicellular networks have been studied extensively, metazoan transcription regulatory networks remain largely unexplored. Caenorhabditis elegans provides a powerful model to study such metazoan networks because its genome is completely sequenced and many functional genomic tools are available. While C. elegans gene predictions have undergone continuous refinement, this is not true for the annotation of functional transcription factors. The comprehensive identification of transcription factors is essential for the systematic mapping of transcription regulatory networks because it enables the creation of physical transcription factor resources that can be used in assays to map interactions between transcription factors and their target genes.ResultsBy computational searches and extensive manual curation, we have identified a compendium of 934 transcription factor genes (referred to as wTF2.0). We find that manual curation drastically reduces the number of both false positive and false negative transcription factor predictions. We discuss how transcription factor splice variants and dimer formation may affect the total number of functional transcription factors. In contrast to mouse transcription factor genes, we find that C. elegans transcription factor genes do not undergo significantly more splicing than other genes. This difference may contribute to differences in organism complexity. We identify candidate redundant worm transcription factor genes and orthologous worm and human transcription factor pairs. Finally, we discuss how wTF2.0 can be used together with physical transcription factor clone resources to facilitate the systematic mapping of C. elegans transcription regulatory networks.ConclusionwTF2.0 provides a starting point to decipher the transcription regulatory networks that control metazoan development and function.

Yeast Two-Hybrid Systems and Protein Interaction Mapping Projects for Yeast and Worm

The availability of complete genome sequences necessitates the development of standardized functional assays to analyse the tens of thousands of predicted gene products in high‐throughput experimental settings. Such approaches are collectively referred to as ‘functional genomics’. One approach to investigate the properties of a proteome of interest is by systematic analysis of protein–protein interactions. So far, the yeast two‐hybrid system is the most commonly used method for large‐scale, high‐throughput identification of potential protein–protein interactions. Here, we discuss several technical features of variants of the two‐hybrid systems in light of data recently obtained from different protein interaction mapping projects for the budding yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans. Copyright

A stele-enriched gene regulatory network in the Arabidopsis root.

Tightly controlled gene expression is a hallmark of multicellular development and is accomplished by transcription factors (TFs) and microRNAs (miRNAs). Although many studies have focused on identifying downstream targets of these molecules, less is known about the factors that regulate their differential expression. We used data from high spatial resolution gene expression experiments and yeast one‐hybrid (Y1H) and two‐hybrid (Y2H) assays to delineate a subset of interactions occurring within a gene regulatory network (GRN) that determines tissue‐specific TF and miRNA expression in plants. We find that upstream TFs are expressed in more diverse cell types than their targets and that promoters that are bound by a relatively large number of TFs correspond to key developmental regulators. The regulatory consequence of many TFs for their target was experimentally determined using genetic analysis. Remarkably, molecular phenotypes were identified for 65% of the TFs, but morphological phenotypes were associated with only 16%. This indicates that the GRN is robust, and that gene expression changes may be canalized or buffered.