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 utilize 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 amount of time. eY1H assays will be an important tool for gene regulatory network mapping in Caenorhabditis elegans and other model organisms, as well as humans.

SUMMARY The human microbiota greatly affects physiology and disease. However, the contribution of bacteria to the response to chemotherapeutic drugs remains poorly understood. Caenorhabditis elegans and its bacterial diet provide a powerful system to study host-bacteria interactions. Here, we use this system to study how bacteria affect the C. elegans response to chemotherapeutics. We find that different bacterial species can increase the response to one drug yet decrease the effect of another. We perform genetic screens in two bacterial species using three chemotherapeutic drugs, 5-fluorouracil (5-FU), 5-fluoro-2′-deoxyuridine (FUDR) and camptothecin (CPT). We find numerous bacterial nucleotide metabolism genes that affect drug efficacy in C. elegans. Surprisingly, we find that 5-FU and FUDR act through bacterial ribonucleotide metabolism to elicit their cytotoxic effects in C. elegans, rather than by thymineless death or DNA damage. Our study provides a blueprint for characterizing the role of bacteria in the host response to chemotherapeutics.

SUMMARY Gene regulatory networks (GRNs) comprising interactions between transcription factors (TFs) and regulatory loci control development and physiology. Numerous disease-associated mutations have been identified, the vast majority residing in non-coding regions of the genome. As current GRN mapping methods test one TF at a time and require the use of cells harboring the mutation(s) of interest, they are not suitable to identify TFs that bind to wild type and mutant loci. Here, we use gene-centered yeast one-hybrid (eY1H) assays to interrogate binding of 1,086 human TFs to 246 enhancers, as well as to 109 non-coding disease mutations. We detect both loss and gain of TF interactions with mutant loci that are concordant with target gene expression changes. This work establishes eY1H assays as a powerful addition to the toolkit of mapping human GRNs and for the high-throughput characterization of genomic variants that are rapidly being identified by genome-wide association studies.

SUMMARY 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 degree 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 an unprecedented glimpse into the evolutionary dynamics that shaped TF networks.

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.