The evolutionarily conserved centromeric histone H3 variant (Cse4 in budding yeast, CENP-A in humans) is essential for faithful chromosome segregation. Mislocalization of CENP-A to non-centromeric chromatin contributes to chromosomal instability (CIN) in yeast, fly, and human cells and CENP-A is highly expressed and mislocalized in cancers. Defining mechanisms that prevent mislocalization of CENP-A is an area of active investigation. Ubiquitin-mediated proteolysis of overexpressed Cse4 (GALCSE4) by E3 ubiquitin ligases such as Psh1 prevents mislocalization of Cse4, and psh1Delta strains display synthetic dosage lethality (SDL) with GALCSE4. We previously performed a genome-wide screen and identified five alleles of CDC7 and DBF4 that encode the Dbf4-dependent kinase (DDK) complex, which regulates DNA replication initiation, among the top twelve hits that displayed SDL with GALCSE4. We determined that cdc7-7 strains exhibit defects in ubiquitin-mediated proteolysis of Cse4 and show mislocalization of Cse4. Mutation of MCM5 (mcm5-bob1) bypasses the requirement of Cdc7 for replication initiation and rescues replication defects in a cdc7-7 strain. We determined that mcm5-bob1 does not rescue the SDL and defects in proteolysis of GALCSE4 in a cdc7-7 strain, suggesting a DNA replication-independent role for Cdc7 in Cse4 proteolysis. The SDL phenotype, defects in ubiquitin-mediated proteolysis, and the mislocalization pattern of Cse4 in a cdc7-7 psh1Delta strain were similar to that of cdc7-7 and psh1Delta strains, suggesting that Cdc7 regulates Cse4 in a pathway that overlaps with Psh1. Our results define a DNA replication initiation-independent role of DDK as a regulator of Psh1-mediated proteolysis of Cse4 to prevent mislocalization of Cse4.

Transcription factors (TFs) play a central role in controlling spatiotemporal gene expression and the response to environmental cues. A comprehensive understanding of gene regulation requires integrating physical protein-DNA interactions (PDIs) with TF regulatory activity, expression patterns, and phenotypic data. Although great progress has been made in mapping PDIs using chromatin immunoprecipitation, these studies have only characterized ~10% of TFs in any metazoan species. The nematode C. elegans has been widely used to study gene regulation due to its compact genome with short regulatory sequences. Here, we delineated the largest gene-centered metazoan PDI network to date by examining interactions between 90% of C. elegans TFs and 15% of gene promoters. We used this network as a backbone to predict TF binding sites for 77 TFs, two-thirds of which are novel, as well as integrate gene expression, protein-protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.

A wealth of physical interaction data between transcription factors (TFs) and DNA has been generated, but these interactions often do not have apparent regulatory consequences. Thus, equating physical interaction data with gene regulatory networks (GRNs) is problematic. Here, we comprehensively assay TF activity, rather than binding, to construct a network of gene regulatory interactions in the C. elegans intestine. By manually observing the in vivo tissue-specific knockdown of 921 TFs on a panel of 19 fluorescent transcriptional reporters, we identified a GRN of 411 interactions between 19 promoters and 177 TFs. This GRN shows only modest overlap with physical interactions, indicating that many regulatory interactions are indirect. We applied nested effects modeling to uncover information flow between TFs in the intestine that converges on a small set of physical TF-promoter interactions. We found numerous cell nonautonomous regulatory interactions, illustrating tissue-to-tissue communication. Altogether, our study illuminates the complexity of gene regulation in the context of a living animal.

Biological networks can be used to functionally annotate genes on the basis of interaction-profile similarities. Metrics known as association indices can be used to quantify interaction-profile similarity. We provide an overview of commonly used association indices, including the Jaccard index and the Pearson correlation coefficient, and compare their performance in different types of analyses of biological networks. We introduce the Guide for Association Index for Networks (GAIN), a web tool for calculating and comparing interaction-profile similarities and defining modules of genes with similar profiles.

Gene duplication results in two identical paralogs that diverge through mutation, leading to loss or gain of interactions with other biomolecules. Here, we comprehensively characterize such network rewiring for C. elegans transcription factors (TFs) within and across four newly delineated molecular networks. Remarkably, we find that even highly similar TFs often have different interaction degrees and partners. In addition, we find that most TF families have a member that is highly connected in multiple networks. Further, different TF families have opposing correlations between network connectivity and phylogenetic age, suggesting that they are subject to different evolutionary pressures. Finally, TFs that have similar partners in one network generally do not in another, indicating a lack of pressure to retain cross-network similarity. Our multiparameter analyses provide unique insights into the evolutionary dynamics that shaped TF networks.

As we come to the end of 2011, Genome Biology has asked some members of our Editorial Board for their views on the state of play in genomics. What was their favorite paper of 2011? What are the challenges in their particular research area? Who has had the biggest influence on their careers? What advice would they give to young researchers embarking on a career in research?

A major challenge in systems biology is to understand the gene regulatory networks that drive development, physiology and pathology. Interactions between transcription factors and regulatory genomic regions provide the first level of gene control. Gateway-compatible yeast one-hybrid (Y1H) assays present a convenient method to identify and characterize the repertoire of transcription factors that can bind a DNA sequence of interest. To delineate genome-scale regulatory networks, however, large sets of DNA fragments need to be processed at high throughput and high coverage. Here we present enhanced Y1H (eY1H) assays that use a robotic mating platform with a set of improved Y1H reagents and automated readout quantification. We demonstrate that eY1H assays provide excellent coverage and identify interacting transcription factors for multiple DNA fragments in a short time. eY1H assays will be an important tool for mapping gene regulatory networks in Caenorhabditis elegans and other model organisms as well as in humans.