Reverse genetic screens by RNA interference (RNAi) in model organisms such as the nematode Caenorhabditis elegans have provided numerous insights into gene function, thereby connecting genotype to phenotype. However, genes that contribute only subtly are often missed because relatively large numbers of measurements and reliable quantification are required to overcome experimental and biological noise that may mask subtle phenotypic effects. Here, we address this challenge by focusing on two phenotypes in C. elegans: growth and fat storage. We carried out comprehensive RNAi knockdown of transcription factors (TFs), as these are known important regulators of biological processes during development and the maintenance of homeostasis. Microscopy images of TF knockdown animals stained with Oil Red O (ORO) were captured, and body size (proxy for growth) and ORO staining intensity (proxy for fat storage) were precisely quantified using a newly developed imaging tool we named IPPOME (Image Processing for Precise and Objective MEasurement). We found that a surprisingly large proportion of TFs contribute to growth and fat storage, but that most TFs have only subtle, yet significant effects. This study provides a blueprint for studies of other genes and phenotypes in C. elegans.

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.

Metabolic network rewiring is the rerouting of metabolism through the use of alternate enzymes to adjust pathway flux and accomplish specific anabolic or catabolic objectives. Here, we report the first characterization of two parallel pathways for the breakdown of the short chain fatty acid propionate in Caenorhabditis elegans. Using genetic interaction mapping, gene co-expression analysis, pathway intermediate quantification and carbon tracing, we uncover a vitamin B12-independent propionate breakdown shunt that is transcriptionally activated on vitamin B12 deficient diets, or under genetic conditions mimicking the human diseases propionic- and methylmalonic acidemia, in which the canonical B12-dependent propionate breakdown pathway is blocked. Our study presents the first example of transcriptional vitamin-directed metabolic network rewiring to promote survival under vitamin deficiency. The ability to reroute propionate breakdown according to B12 availability may provide C. elegans with metabolic plasticity and thus a selective advantage on different diets in the wild.

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.

Gene expression is controlled through the binding of transcription factors (TFs) to regulatory genomic regions. First introns are longer than other introns in multiple eukaryotic species and are under selective constraint. Here we explore the importance of first introns in TF binding in the nematode Caenorhabditis elegans by combining computational predictions and experimentally derived TF-DNA interaction data. We found that first introns of C. elegans genes, particularly those for families enriched in long first introns, are more conserved in length, have more conserved predicted TF interactions and are bound by more TFs than other introns. We detected a significant positive correlation between first intron size and the number of TF interactions obtained from chromatin immunoprecipitation assays or determined by yeast one-hybrid assays. TFs that bind first introns are largely different from those binding promoters, suggesting that the different interactions are complementary rather than redundant. By combining first intron and promoter interactions, we found that genes that share a large fraction of TF interactions are more likely to be co-expressed than when only TF interactions with promoters are considered. Altogether, our data suggest that C. elegans gene regulation may be additive through the combined effects of multiple regulatory regions.

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.

BACKGROUND: Copy number variation (CNV), an important source of diversity in genomic structure, is frequently found in clusters called CNV regions (CNVRs). CNVRs are strongly associated with segmental duplications (SDs), but the composition of these complex repetitive structures remains unclear. RESULTS: We conducted self-comparative-plot analysis of all mouse chromosomes using the high-speed and large-scale-homology search algorithm SHEAP. For eight chromosomes, we identified various types of large SD as tartan-checked patterns within the self-comparative plots. A complex arrangement of diagonal split lines in the self-comparative-plots indicated the presence of large homologous repetitive sequences. We focused on one SD on chromosome 13 (SD13M), and developed SHEPHERD, a stepwise ab initio method, to extract longer repetitive elements and to characterize repetitive structures in this region. Analysis using SHEPHERD showed the existence of 60 core elements, which were expected to be the basic units that form SDs within the repetitive structure of SD13M. The demonstration that sequences homologous to the core elements (>70% homology) covered approximately 90% of the SD13M region indicated that our method can characterize the repetitive structure of SD13M effectively. Core elements were composed largely of fragmented repeats of a previously identified type, such as long interspersed nuclear elements (LINEs), together with partial genic regions. Comparative genome hybridization array analysis showed that whereas 42 core elements were components of CNVR that varied among mouse strains, 8 did not vary among strains (constant type), and the status of the others could not be determined. The CNV-type core elements contained significantly larger proportions of long terminal repeat (LTR) types of retrotransposon than the constant-type core elements, which had no CNV. The higher divergence rates observed in the CNV-type core elements than in the constant type indicate that the CNV-type core elements have a longer evolutionary history than constant-type core elements in SD13M. CONCLUSIONS: Our methodology for the identification of repetitive core sequences simplifies characterization of the structures of large SDs and detailed analysis of CNV. The results of detailed structural and quantitative analyses in this study might help to elucidate the biological role of one of the SDs on chromosome 13.