DNA Sequence Motifs Research Articles

A fast weak motif-finding algorithm based on community detection in graphs.

BackgroundIdentification of transcription factor binding sites (also called ‘motif discovery’) in DNA sequences is a basic step in understanding genetic regulation. Although many successful programs have been developed, the problem is far from being solved on account of diversity in gene expression/regulation and the low specificity of binding sites. State-of-the-art algorithms have their own constraints (e.g., high time or space complexity for finding long motifs, low precision in identification of weak motifs, or the OOPS constraint: one occurrence of the motif instance per sequence) which limit their scope of application.ResultsIn this paper, we present a novel and fast algorithm we call TFBSGroup. It is based on community detection from a graph and is used to discover long and weak (l,d) motifs under the ZOMOPS constraint (zero, one or multiple occurrence(s) of the motif instance(s) per sequence), where l is the length of a motif and d is the maximum number of mutations between a motif instance and the motif itself. Firstly, TFBSGroup transforms the (l, d) motif search in sequences to focus on the discovery of dense subgraphs within a graph. It identifies these subgraphs using a fast community detection method for obtaining coarse-grained candidate motifs. Next, it greedily refines these candidate motifs towards the true motif within their own communities. Empirical studies on synthetic (l, d) samples have shown that TFBSGroup is very efficient (e.g., it can find true (18, 6), (24, 8) motifs within 30 seconds). More importantly, the algorithm has succeeded in rapidly identifying motifs in a large data set of prokaryotic promoters generated from the Escherichia coli database RegulonDB. The algorithm has also accurately identified motifs in ChIP-seq data sets for 12 mouse transcription factors involved in ES cell pluripotency and self-renewal.ConclusionsOur novel heuristic algorithm, TFBSGroup, is able to quickly identify nearly exact matches for long and weak (l, d) motifs in DNA sequences under the ZOMOPS constraint. It is also capable of finding motifs in real applications. The source code for TFBSGroup can be obtained from http://bioinformatics.bioengr.uic.edu/TFBSGroup/.

In vivo actions of peroxisome proliferator-activated receptors: glycemic control, insulin sensitivity, and insulin secretion.

Peroxisome proliferator–activated receptors (PPARs) form a family of nuclear hormone receptors involved in energy hemostasis and lipid metabolism (1,2) and include three isotypes encoded by different genes: PPARα (chromosome 22q12–13.1), PPARβ/δ (chromosome 6p21.2–21.1), and PPARγ (chromosome 3p25). PPARα was the first discovered and causes cellular peroxisome proliferation in rodent livers (3), giving this receptor family its name. Upon activation, PPARs interact with retinoid X receptor to create heterodimers, which bind to a specific DNA sequence motif termed peroxisome proliferator response element (4). Peroxisome proliferator response element usually appears in promoter regions and is constructed from repeats of nucleotide sequence AGGTCA separated by a single nucleotide. PPARα is widely expressed in tissues with high fatty acid catabolic activity: brown fat, heart, liver, kidney, and intestine (5). Upon activation by endogenous fatty acids and their derivatives, PPARα mediates fatty acid catabolism, gluconeogenesis, and ketone body synthesis, mainly in liver (6–9). In rodents, PPARα activation also influences immune modulation (10,11) and amino acid metabolism (12), reduces plasma triglyceride, reduces muscle and liver steatosis, and ameliorates insulin resistance (IR) (13,14). Pharmacologic PPARα activation is achieved by fibrates (7) and results in reduced (30–50%) triglyceride and VLDL levels by increasing lipid uptake, lipoprotein lipase–mediated lipolysis, and β-oxidation (15). This is accompanied by a modest increase in HDL cholesterol (5–20%), secondary to transcriptional induction of apolipoprotein A-I/A-II synthesis in liver (15). In man, the primary effect of PPARα is to reduce plasma triglyceride concentration; effects on plasma free fatty acid (FFA) concentration/FFA oxidation, muscle/liver fat content, and muscle/hepatic insulin sensitivity have not been demonstrated with current PPARα agonists such as fenofibrate (16,17). Fibrates are used to treat severe hypertriglyceridemia and combined hyperlipidemia (18–20). Clinical trials to establish a role for PPARα agonists (fenofibrate, gemfibrozil) …

Redox-sensitive DNA binding by homodimeric Methanosarcina acetivorans MsvR is modulated by cysteine residues

BackgroundMethanoarchaea are among the strictest known anaerobes, yet they can survive exposure to oxygen. The mechanisms by which they sense and respond to oxidizing conditions are unknown. MsvR is a transcription regulatory protein unique to the methanoarchaea. Initially identified and characterized in the methanogen Methanothermobacter thermautotrophicus (Mth), MthMsvR displays differential DNA binding under either oxidizing or reducing conditions. Since MthMsvR regulates a potential oxidative stress operon in M. thermautotrophicus, it was hypothesized that the MsvR family of proteins were redox-sensitive transcription regulators.ResultsAn MsvR homologue from the methanogen Methanosarcina acetivorans, MaMsvR, was overexpressed and purified. The two MsvR proteins bound the same DNA sequence motif found upstream of all known MsvR encoding genes, but unlike MthMsvR, MaMsvR did not bind the promoters of select genes involved in the oxidative stress response. Unlike MthMsvR that bound DNA under both non-reducing and reducing conditions, MaMsvR bound DNA only under reducing conditions. MaMsvR appeared as a dimer in gel filtration chromatography analysis and site-directed mutagenesis suggested that conserved cysteine residues within the V4R domain were involved in conformational rearrangements that impact DNA binding.ConclusionsResults presented herein suggest that homodimeric MaMsvR acts as a transcriptional repressor by binding Ma PmsvR under non-reducing conditions. Changing redox conditions promote conformational changes that abrogate binding to Ma PmsvR which likely leads to de-repression.

DNA Replication Origins

The onset of genomic DNA synthesis requires precise interactions of specialized initiator proteins with DNA at sites where the replication machinery can be loaded. These sites, defined as replication origins, are found at a few unique locations in all of the prokaryotic chromosomes examined so far. However, replication origins are dispersed among tens of thousands of loci in metazoan chromosomes, thereby raising questions regarding the role of specific nucleotide sequences and chromatin environment in origin selection and the mechanisms used by initiators to recognize replication origins. Close examination of bacterial and archaeal replication origins reveals an array of DNA sequence motifs that position individual initiator protein molecules and promote initiator oligomerization on origin DNA. Conversely, the need for specific recognition sequences in eukaryotic replication origins is relaxed. In fact, the primary rule for origin selection appears to be flexibility, a feature that is modulated either by structural elements or by epigenetic mechanisms at least partly linked to the organization of the genome for gene expression.

DNA-binding specificity changes in the evolution of forkhead transcription factors

The evolution of transcriptional regulatory networks entails the expansion and diversification of transcription factor (TF) families. The forkhead family of TFs, defined by a highly conserved winged helix DNA-binding domain (DBD), has diverged into dozens of subfamilies in animals, fungi, and related protists. We have used a combination of maximum-likelihood phylogenetic inference and independent, comprehensive functional assays of DNA-binding capacity to explore the evolution of DNA-binding specificity within the forkhead family. We present converging evidence that similar alternative sequence preferences have arisen repeatedly and independently in the course of forkhead evolution. The vast majority of DNA-binding specificity changes we observed are not explained by alterations in the known DNA-contacting amino acid residues conferring specificity for canonical forkhead binding sites. Intriguingly, we have found forkhead DBDs that retain the ability to bind very specifically to two completely distinct DNA sequence motifs. We propose an alternate specificity-determining mechanism whereby conformational rearrangements of the DBD broaden the spectrum of sequence motifs that a TF can recognize. DNA-binding bispecificity suggests a previously undescribed source of modularity and flexibility in gene regulation and may play an important role in the evolution of transcriptional regulatory networks.

CisExpress: motif detection in DNA sequences

One of the major challenges for contemporary bioinformatics is the analysis and accurate annotation of genomic datasets to enable extraction of useful information about the functional role of DNA sequences. This article describes a novel genome-wide statistical approach to the detection of specific DNA sequence motifs based on similarities between the promoters of similarly expressed genes. This new tool, cisExpress, is especially designed for use with large datasets, such as those generated by publicly accessible whole genome and transcriptome projects. cisExpress uses a task farming algorithm to exploit all available computational cores within a shared memory node. We demonstrate the robust nature and validity of the proposed method. It is applicable for use with a wide range of genomic databases for any species of interest. cisExpress is available at www.cisexpress.org.

Poly(A) motif prediction using spectral latent features from human DNA sequences

Motivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA.Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.Results: We propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance.We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ∼30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before.Availability: http://sfb.kaust.edu.sa/Pages/Software.aspxContact: lsong@cc.gatech.edu or xin.gao@kaust.edu.saSupplementary information: Supplementary data are available at Bioinformatics online.

Resistance gene analogues in mango against mango malformation

Mango malformation is one of the most important diseases limiting its cultivation. However, disease resistance is known in some mango cultivars and is a desirable trait that can be utilized for developing mango varieties resistant to malformation. Resistance genes cloned from different plant species have revealed several similarities in DNA sequence and structural motifs. This provides the possibility of isolating resistance genes by polymerase chain reaction (PCR) with degenerate oligonucleotide primers designed from highly conserved regions of the nucleotide binding site (NBS). In the present study, we used eight combinations of oligonucleotide primers designed on the basis of P-loop and hydrophobic domains of conserved NBS-leucine rich repeat (LRR) protein sequences for amplifying resistance gene analogues (RGAs) in eight mango cultivars and hybrids showing a variable degree of resistance to mango malformation disease. A single band of about 500 bp in all mango cultivars was obtained from the s2+as2 primer combination. RGAs isolated from mango showed 73% similarity with RGAs in databases. It confirms that RGAs were actually isolated from mango. The obtained sequence can be used for isolating full length R-genes.

An Experimentally Tested Scenario for the Structural Evolution of Eukaryotic Cys2His2 Zinc Fingers from Eubacterial Ros Homologs

The exact evolutionary origin of the zinc finger (ZF) domain is unknown, as it is still not clear from which organisms it was first derived. However, the unique features of the ZF domains have made it very easy for evolution to tinker with them in a number of different manners, including their combination, variation of their number by unequal crossing-over or tandem duplication and tuning of their affinity for specific DNA sequence motifs through point substitutions. Classical Cys2His2 ZF domains as structurally autonomous motifs arranged in multiple copies are known only in eukaryotes. Nonetheless, a single prokaryotic Cys2His2 ZF domain has been identified in the transcriptional regulator Ros from Agrobacterium tumefaciens and recently characterized. The present work focuses on the evolution of the classical ZF domains with the goal of trying to determine whether eukaryotic ZFs have evolved from the prokaryotic Ros-like proteins. Our results, based on computational and experimental data, indicate that a single insertion of three amino acids in the short loop that separates the β-sheet from the α-helix of the Ros protein is sufficient to induce a structural transition from a Ros like to an eukaryotic-ZF like structure. This observation provides evidence for a structurally plausible and parsimonious scenario of fold evolution, giving a structural basis to the hypothesis of a horizontal gene transfer (HGT) from bacteria to eukaryotes.

Population Genomics and Transcriptional Consequences of Regulatory Motif Variation in Globally Diverse Saccharomyces cerevisiae Strains

Noncoding genetic variation is known to significantly influence gene expression levels in a growing number of specific cases; however, the patterns of genome-wide noncoding variation present within populations, the evolutionary forces acting on noncoding variants, and the relative effects of regulatory polymorphisms on transcript abundance are not well characterized. Here, we address these questions by analyzing patterns of regulatory variation in motifs for 177 DNA binding proteins in 37 strains of Saccharomyces cerevisiae. Between S. cerevisiae strains, we found considerable polymorphism in regulatory motifs across strains (mean π = 0.005) as well as diversity in regulatory motifs (mean 0.91 motifs differences per regulatory region). Population genetics analyses reveal that motifs are under purifying selection, and there is considerable heterogeneity in the magnitude of selection across different motifs. Finally, we obtained RNA-Seq data in 22 strains and identified 49 polymorphic DNA sequence motifs in 30 distinct genes that are significantly associated with transcriptional differences between strains. In 22 of these genes, there was a single polymorphic motif associated with expression in the upstream region. Our results provide comprehensive insights into the evolutionary trajectory of regulatory variation in yeast and the characteristics of a compendium of regulatory alleles.

ApiAP2 transcription factor restricts development of the Toxoplasma tissue cyst

Cellular differentiation leading to formation of the bradyzoite tissue cyst stage is the underlying cause of chronic toxoplasmosis. Consequently, mechanisms responsible for controlling development in the Toxoplasma intermediate life cycle have long been sought. Here, we identified 15 Toxoplasma mRNAs induced in early bradyzoite development that encode proteins with apicomplexan AP2 (ApiAP2) DNA binding domains. Of these 15 mRNAs, the AP2IX-9 mRNA demonstrated the largest expression increase during alkaline-induced differentiation. At the protein level, we found that AP2IX-9 was restricted to the early bradyzoite nucleus and is repressed in tachyzoites and in mature bradyzoites from 30-d infected animals. Conditional overexpression of AP2IX-9 significantly reduced tissue cyst formation and conferred alkaline pH-resistant growth, whereas disruption of the AP2IX-9 gene increased tissue cyst formation, indicating AP2IX-9 operates as a repressor of bradyzoite development. Consistent with a role as a repressor, AP2IX-9 specifically inhibited the expression of bradyzoite mRNAs, including the canonical bradyzoite marker, bradyzoite antigen 1 (BAG1). Using protein binding microarrays, we established the AP2 domain of AP2IX-9 binds a CAGTGT DNA sequence motif and is capable of binding cis-regulatory elements controlling the BAG1 and bradyzoite-specific nucleoside triphosphatase (B-NTPase) promoters. The effect of AP2IX-9 on BAG1 expression was direct because this factor inhibits expression of a firefly luciferase reporter under the control of the BAG1 promoter in vivo, and epitope-tagged AP2IX-9 can be immunoprecipitated with the BAG1 promoter in parasite chromatin. Altogether, these results indicate AP2IX-9 restricts Toxoplasma commitment to develop the mature bradyzoite tissue cyst.

Interplay between DNA sequence motifs and the human epigenome

Epigenomic modification is important in establishing cell-type specific gene expression; however, the regulation of the epigenome is not well understood. To investigate the role of DNA motifs in regulating the epigenome we have established an analysis pipeline that identifies DNA motifs that are predictive of epigenomic modification. We apply our strategy to a dataset of six histone modifications in five cell-types. Comparison of the identified motifs to known motifs identified interplay between known factors and the epigenome. Motif location preference analysis revealed that motifs occur at the edge of modification peaks and suggests they may function by establishing barriers.

An Appraisal of Human Mitochondrial DNA Instability: New Insights into the Role of Non-Canonical DNA Structures and Sequence Motifs

Mitochondrial DNA (mtDNA) deletion mutations are frequently observed in aged postmitotic tissues and are the cause of a wide range of human disorders. Presently, the molecular bases underlying mtDNA deletion formation remain a matter of intense debate, and it is commonly accepted that several mechanisms contribute to the spectra of mutations in the mitochondrial genome. In this work we performed an extensive screening of human mtDNA deletions and evaluated the association between breakpoint density and presence of non-canonical DNA elements and over-represented sequence motifs. Our observations support the involvement of helix-distorting intrinsically curved regions and long G-tetrads in eliciting instability events. In addition, higher breakpoint densities were consistently observed within GC-skewed regions and in the close vicinity of the degenerate sequence motif YMMYMNNMMHM. A parallelism is also established with hot spot motifs previously identified in the nuclear genome, as well as with the minimal binding site for the mitochondrial transcription termination factor mTERF. This study extends the current knowledge on the mechanisms driving mitochondrial rearrangements and opens up exciting avenues for further research.

A Discriminative Approach for Unsupervised Clustering of DNA Sequence Motifs

Algorithmic comparison of DNA sequence motifs is a problem in bioinformatics that has received increased attention during the last years. Its main applications concern characterization of potentially novel motifs and clustering of a motif collection in order to remove redundancy. Despite growing interest in motif clustering, the question which motif clusters to aim at has so far not been systematically addressed. Here we analyzed motif similarities in a comprehensive set of vertebrate transcription factor classes. For this we developed enhanced similarity scores by inclusion of the information coverage (IC) criterion, which evaluates the fraction of information an alignment covers in aligned motifs. A network-based method enabled us to identify motif clusters with high correspondence to DNA-binding domain phylogenies and prior experimental findings. Based on this analysis we derived a set of motif families representing distinct binding specificities. These motif families were used to train a classifier which was further integrated into a novel algorithm for unsupervised motif clustering. Application of the new algorithm demonstrated its superiority to previously published methods and its ability to reproduce entrained motif families. As a result, our work proposes a probabilistic approach to decide whether two motifs represent common or distinct binding specificities.

Mechanisms of Protein Binding to DNA: Statistical Interactions are Important

Protein molecules binding to DNA initiate sequences of biochemical transitions that control and regulate all major processes in living cells (1). It has long been understood that the efficient transfer of genetic information is possible when genome-regulating protein molecules are found to be strongly bound to special sequences on DNA, known as specific binding sites. It was later realized that finding these target sequences might be complicated by the existence of a large number (≃106–109 per DNA) of other nonspecific binding sites and because of low concentrations of relevant protein molecules (2–4). Although significant experimental and theoretical efforts to investigate mechanisms of protein binding to DNA have been made, the role and functioning of nonspecific interactions has not been fully understood.

Chromosome Engineering Allows the Efficient Isolation of Vertebrate Neocentromeres

SummaryCentromeres are specified by sequence-independent epigenetic mechanisms in most organisms. Rarely, centromere repositioning results in neocentromere formation at ectopic sites. However, the mechanisms governing how and where neocentromeres form are unknown. Here, we established a chromosome-engineering system in chicken DT40 cells that allowed us to efficiently isolate neocentromere-containing chromosomes. Neocentromeres appear to be structurally and functionally equivalent to native centromeres. Chromatin immunoprecipitation sequencing (ChIP-seq) analysis with 18 neocentromeres revealed that the centromere-specific histone H3 variant CENP-A occupies an ∼40 kb region at each neocentromere, which has no preference for specific DNA sequence motifs. Furthermore, we found that neocentromeres were not associated with histone modifications H3K9me3, H3K4me2, and H3K36me3 or with early replication timing. Importantly, low but significant levels of CENP-A are detected around endogenous centromeres, which are capable of seeding neocentromere assembly if the centromere core is removed. In summary, our experimental system provides valuable insights for understanding how neocentromeres form.

Chromatin and higher-order chromosome organization shape the recombination landscape in C. elegans

Meiotic recombination generates crossovers, which physically link homologous chromosomes, and direct their segregation to opposite poles. Despite the benefits, crossovers are restricted both in number as well as spatially. Crossovers in the nematode C. elegans occur most frequently within the distal regions (or “arms”) of the chromosomes, which comprise ~50% of the genome, but ~90% of the genetic map length. Meiotic recombination initiates with the formation of programmed double strand breaks (DSBs) catalyzed by the SPO-11 enzyme. It has been unclear whether the crossover bias in C. elegans is a consequence of DSB distribution, or instead reflects a mechanism that biases the downstream repair outcome of DSBs. In order to address this, we generated a genome-wide map of meiotic DSBs by mapping the distribution of the sole C. elegans RecA-like recombinase, RAD-51, by ChIP-seq. We have found that the RAD-51 binding pattern is strikingly similar to the known recombination pattern in C. elegans. The majority of RAD-51 binding sites occur on chromosome arms. Comparing RAD-51 distribution relative to functional genomic elements revealed that RAD-51 is particularly enriched in intronic DNA while coding sequences are underrepresented in RAD-51 occupied regions. We find that monomethylation of histone H3 lysine 36 (H3K36me1), which has not previously been associated with meiotic recombination, is highly correlated with break site preference. H3K36me1 is particularly enriched over active genes with high intron content, suggesting that the specific chromatin environment associated with these genes favors DSB formation in C. elegans. We have also identified a sequence motif that is highly overrepresented in DSB sites. Although the DSB pattern is highly correlated with crossover frequency, there are interesting exceptions. In particular, the sub-telomeric regions are completely devoid of crossovers but are active DSB sites. We have also identified a DNA sequence motif that appears to be enriched within DSB sites suggesting that sequence specific factors may have a role in DSB specification in C. elegans. In a parallel study, we have developed an Illumina Golden Gate SNP array to rapidly measure genome-wide recombination rates in C. elegans. Using this array we measured meiotic recombination rates in met-2(n4256) mutant animals. MET-2 is one of the two histone methyltransferases responsible for H3K9 methylation, a histone mark associated with heterochromatin. We find that the genetic map is significantly altered in met-2(n4256) mutant animals. In particular, regions that are normally enriched for H3K9 methylation show elevated recombination rates in met-2(n4256) mutants. We are currently investigating whether this change in the genetic map is the result of altered DSB distribution. Taken together, our data provide new insights into the role of chromatin in meiotic recombination. Because meiotic recombination generates genetic diversity, our data implicates chromatin as a key player in the evolution of genomes.

Convergence rate of Markov chain methods for genomic motif discovery

We analyze the convergence rate of a simplified version of a popular Gibbs sampling method used for statistical discovery of gene regulatory binding motifs in DNA sequences. This sampler satisfies a very strong form of ergodicity (uniform). However, we show that, due to multimodality of the posterior distribution, the rate of convergence often decreases exponentially as a function of the length of the DNA sequence. Specifically, we show that this occurs whenever there is more than one true repeating pattern in the data. In practice there are typically multiple such patterns in biological data, the goal being to detect the most well-conserved and frequently-occurring of these. Our findings match empirical results, in which the motif-discovery Gibbs sampler has exhibited such poor convergence that it is used only for finding modes of the posterior distribution (candidate motifs) rather than for obtaining samples from that distribution. Ours are some of the first meaningful bounds on the convergence rate of a Markov chain method for sampling from a multimodal posterior distribution, as a function of statistical quantities like the number of observations.

Distinct transcriptional regulatory modules underlie STAT3’s cell type-independent and cell type-specific functions

Transcription factors (TFs) regulate gene expression by binding to short DNA sequence motifs, yet their binding specificities alone cannot explain how certain TFs drive a diversity of biological processes. In order to investigate the factors that control the functions of the pleiotropic TF STAT3, we studied its genome-wide binding patterns in four different cell types: embryonic stem cells, CD4+ T cells, macrophages and AtT-20 cells. We describe for the first time two distinct modes of STAT3 binding. First, a small cell type-independent mode represented by a set of 35 evolutionarily conserved STAT3-binding sites that collectively regulate STAT3’s own functions and cell growth. We show that STAT3 is recruited to sites with E2F1 already pre-bound before STAT3 activation. Second, a series of different transcriptional regulatory modules (TRMs) assemble around STAT3 to drive distinct transcriptional programs in the four cell types. These modules recognize cell type-specific binding sites and are associated with factors particular to each cell type. Our study illustrates the versatility of STAT3 to regulate both universal- and cell type-specific functions by means of distinct TRMs, a mechanism that might be common to other pleiotropic TFs.

Genome-wide analysis of condensin binding in Caenorhabditis elegans

BackgroundCondensins are multi-subunit protein complexes that are essential for chromosome condensation during mitosis and meiosis, and play key roles in transcription regulation during interphase. Metazoans contain two condensins, I and II, which perform different functions and localize to different chromosomal regions. Caenorhabditis elegans contains a third condensin, IDC, that is targeted to and represses transcription of the X chromosome for dosage compensation.ResultsTo understand condensin binding and function, we performed ChIP-seq analysis of C. elegans condensins in mixed developmental stage embryos, which contain predominantly interphase nuclei. Condensins bind to a subset of active promoters, tRNA genes and putative enhancers. Expression analysis in kle-2-mutant larvae suggests that the primary effect of condensin II on transcription is repression. A DNA sequence motif, GCGC, is enriched at condensin II binding sites. A sequence extension of this core motif, AGGG, creates the condensin IDC motif. In addition to differences in recruitment that result in X-enrichment of condensin IDC and condensin II binding to all chromosomes, we provide evidence for a shared recruitment mechanism, as condensin IDC recruiter SDC-2 also recruits condensin II to the condensin IDC recruitment sites on the X. In addition, we found that condensin sites overlap extensively with the cohesin loader SCC-2, and that SDC-2 also recruits SCC-2 to the condensin IDC recruitment sites.ConclusionsOur results provide the first genome-wide view of metazoan condensin II binding in interphase, define putative recruitment motifs, and illustrate shared loading mechanisms for condensin IDC and condensin II.