Diverse transcription factor binding features revealed by genome-wide ChIP-seq in C. elegans

Abstract

Regulation of gene expression by sequence-specific transcription factors is central to developmental programs and depends on the binding of transcription factors with target sites in the genome. To date, most such analyses in Caenorhabditis elegans have focused on the interactions between a single transcription factor with one or a few select target genes. As part of the modENCODE Consortium, we have used chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) to determine the genome-wide binding sites of 22 transcription factors (ALR-1, BLMP-1, CEH-14, CEH-30, EGL-27, EGL-5, ELT-3, EOR-1, GEI-11, HLH-1, LIN-11, LIN-13, LIN-15B, LIN-39, MAB-5, MDL-1, MEP-1, PES-1, PHA-4, PQM-1, SKN-1, and UNC-130) at diverse developmental stages. For each factor we determined candidate gene targets, both coding and non-coding. The typical binding sites of almost all factors are within a few hundred nucleotides of the transcript start site. Most factors target a mixture of coding and non-coding target genes, although one factor preferentially binds to non-coding RNA genes. We built a regulatory network among the 22 factors to determine their functional relationships to each other and found that some factors appear to act preferentially as regulators and others as target genes. Examination of the binding targets of three related HOX factors-LIN-39, MAB-5, and EGL-5-indicates that these factors regulate genes involved in cellular migration, neuronal function, and vulval differentiation, consistent with their known roles in these developmental processes. Ultimately, the comprehensive mapping of transcription factor binding sites will identify features of transcriptional networks that regulate C. elegans developmental processes.

Mapping genome-wide transcription-factor binding sites using DAP-seq

“…Based on the genomes of well-characterized model systems, multicellular organisms dedicate a significant portion of their protein coding genes (6-8%) to the expression of between 1000-2500 DNA-binding transcription factors 3 . With the exception of data from ENCODE and modENCODE projects [4][5][6][7] , in vivo genome-wide TF location data is available for relatively few TFs. To expand this analysis for a wider range of organisms, scalable methods are needed for the low-cost and high-throughput examination of thousands of TFs.…”

SND1 Transcription Factor-Directed Quantitative Functional Hierarchical Genetic Regulatory Network in Wood Formation in Populus trichocarpa

“…TF functional hierarchical gene regulatory networks (hGRNs), consisting of three to five hierarchical layers, have been described for growth and development of Caenorhabditis elegans and Drosophila melanogaster Roy et al, 2010). Subsets of hGRNs with multiple hierarchical layers have also been described for human and mouse Niu et al, 2011).…”

“…For tree species that are amenable to genetic transformation, methods are technically demanding and slow, requiring 12 to 18 months of tissue culture (Merkle and Dean, 2000). To reveal a functional hGRN for wood formation, an efficient transgenic system, such as those developed for the cell cultures of yeast (Saccharomyces cerevisiae) and animals (Horak et al, 2002;Yu and Gerstein, 2006;Gerstein et al, 2010;Cheng et al, 2011;Niu et al, 2011), is needed where immediate transcriptome responses to TF perturbation can be induced, characterized, and quantified.…”

Universal transcriptomic signature of age reveals temporal scaling ofCaenorhabditis elegansaging trajectories

“…proteins interacting with CEY-1 [38] 7.1e-3 MUT-14, SMUT-1 (DEAD box) [39] 1.6e-2 Hox gene targets (LIN-39, MAB-5, EGL-5) [40] 3.3e-2 Table II: Transcription factors or RNA-binding proteins for which targets are significantly enriched in the identified direction of aging (327 genes). Over-representation analysis was performed using WormExp database [41].…”

“…The MetaWorm dataset is sufficiently large to generate the cross-validation ensemble of single-gene association tests using random resampling. We further reduced the number of candidate genes by thresholding the transcripts by the frequency Geneset FDR DAF-16 targets [30] 4.6e-7 CEC-3 targets (spr-5 mutant, generation 10) [31] 2.0e-4 small RNAs decreased by starvation at P0 [32] 1.3e-3 PQM-1 L3 targets [33] 1.4e-3 Rb/E2F pathway (DPL-1,EFL-1,LIN-35), intestine [34] 2.7e-3 PMK-1 targets down in Day 15 vs. Day 6 [35] 3.8e-3 age-regulated ELT-2 targets [36] 3.8e-3 up by CSR-1 and w/out CSR-1-bound 22G RNA [37] 7.1e-3…”