SMARCAD1 Contributes to the Regulation of Naive Pluripotency by Interacting with Histone Citrullination.

Histone citrullination regulates diverse cellular processes. Here, we report that SMARCAD1 preferentially associates with H3 arginine 26 citrullination (H3R26Cit) peptides present on arrays composed of 384 histone peptides harboring distinct post-transcriptional modifications. Among ten histone modifications assayed by ChIP-seq, H3R26Cit exhibited the most extensive genomewide co-localization with SMARCAD1 binding. Increased Smarcad1 expression correlated with naive pluripotency in pre-implantation embryos. In the presence of LIF, Smarcad1 knockdown (KD) embryonic stem cells lost naive state phenotypes but remained pluripotent, as suggested by morphology, gene expression, histone modifications, alkaline phosphatase activity, energy metabolism, embryoid bodies, teratoma, and chimeras. The majority of H3R26Cit ChIP-seq peaks occupied by SMARCAD1 were associated with increased levels of H3K9me3 in Smarcad1 KD cells. Inhibition of H3Cit induced H3K9me3 at the overlapping regions of H3R26Cit peaks and SMARCAD1 peaks. These data suggest a model in which SMARCAD1 regulates naive pluripotency by interacting with H3R26Cit and suppressing heterochromatin formation.

Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions.

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).

Intronic non-CG DNA hydroxymethylation and alternative mRNA splicing in honey bees.

BACKGROUND: Previous whole-genome shotgun bisulfite sequencing experiments showed that DNA cytosine methylation in the honey bee (Apis mellifera) is almost exclusively at CG dinucleotides in exons. However, the most commonly used method, bisulfite sequencing, cannot distinguish 5-methylcytosine from 5-hydroxymethylcytosine, an oxidized form of 5-methylcytosine that is catalyzed by the TET family of dioxygenases. Furthermore, some analysis software programs under-represent non-CG DNA methylation and hydryoxymethylation for a variety of reasons. Therefore, we used an unbiased analysis of bisulfite sequencing data combined with molecular and bioinformatics approaches to distinguish 5-methylcytosine from 5-hydroxymethylcytosine. By doing this, we have performed the first whole genome analyses of DNA modifications at non-CG sites in honey bees and correlated the effects of these DNA modifications on gene expression and alternative mRNA splicing. RESULTS: We confirmed, using unbiased analyses of whole-genome shotgun bisulfite sequencing (BS-seq) data, with both new data and published data, the previous finding that CG DNA methylation is enriched in exons in honey bees. However, we also found evidence that cytosine methylation and hydroxymethylation at non-CG sites is enriched in introns. Using antibodies against 5-hydroxmethylcytosine, we confirmed that DNA hydroxymethylation at non-CG sites is enriched in introns. Additionally, using a new technique, Pvu-seq (which employs the enzyme PvuRts1l to digest DNA at 5-hydroxymethylcytosine sites followed by next-generation DNA sequencing), we further confirmed that hydroxymethylation is enriched in introns at non-CG sites. CONCLUSIONS: Cytosine hydroxymethylation at non-CG sites might have more functional significance than previously appreciated, and in honey bees these modifications might be related to the regulation of alternative mRNA splicing by defining the locations of the introns.

Comparative epigenomic annotation of regulatory DNA.

Despite the explosive growth of genomic data, functional annotation of regulatory sequences remains difficult. Here, we introduce "comparative epigenomics"-interspecies comparison of DNA and histone modifications-as an approach for annotation of the regulatory genome. We measured in human, mouse, and pig pluripotent stem cells the genomic distributions of cytosine methylation, H2A.Z, H3K4me1/2/3, H3K9me3, H3K27me3, H3K27ac, H3K36me3, transcribed RNAs, and P300, TAF1, OCT4, and NANOG binding. We observed that epigenomic conservation was strong in both rapidly evolving and slowly evolving DNA sequences, but not in neutrally evolving sequences. In contrast, evolutionary changes of the epigenome and the transcriptome exhibited a linear correlation. We suggest that the conserved colocalization of different epigenomic marks can be used to discover regulatory sequences. Indeed, seven pairs of epigenomic marks identified exhibited regulatory functions during differentiation of embryonic stem cells into mesendoderm cells. Thus, comparative epigenomics reveals regulatory features of the genome that cannot be discerned from sequence comparisons alone.

The transcription factor ultraspiracle influences honey bee social behavior and behavior-related gene expression.

Behavior is among the most dynamic animal phenotypes, modulated by a variety of internal and external stimuli. Behavioral differences are associated with large-scale changes in gene expression, but little is known about how these changes are regulated. Here we show how a transcription factor (TF), ultraspiracle (usp; the insect homolog of the Retinoid X Receptor), working in complex transcriptional networks, can regulate behavioral plasticity and associated changes in gene expression. We first show that RNAi knockdown of USP in honey bee abdominal fat bodies delayed the transition from working in the hive (primarily "nursing" brood) to foraging outside. We then demonstrate through transcriptomics experiments that USP induced many maturation-related transcriptional changes in the fat bodies by mediating transcriptional responses to juvenile hormone. These maturation-related transcriptional responses to USP occurred without changes in USP's genomic binding sites, as revealed by ChIP-chip. Instead, behaviorally related gene expression is likely determined by combinatorial interactions between USP and other TFs whose cis-regulatory motifs were enriched at USP's binding sites. Many modules of JH- and maturation-related genes were co-regulated in both the fat body and brain, predicting that usp and cofactors influence shared transcriptional networks in both of these maturation-related tissues. Our findings demonstrate how "single gene effects" on behavioral plasticity can involve complex transcriptional networks, in both brain and peripheral tissues.

Towards an evolutionary model of transcription networks.

DNA evolution models made invaluable contributions to comparative genomics, although it seemed formidable to include non-genomic features into these models. In order to build an evolutionary model of transcription networks (TNs), we had to forfeit the substitution model used in DNA evolution and to start from modeling the evolution of the regulatory relationships. We present a quantitative evolutionary model of TNs, subjecting the phylogenetic distance and the evolutionary changes of cis-regulatory sequence, gene expression and network structure to one probabilistic framework. Using the genome sequences and gene expression data from multiple species, this model can predict regulatory relationships between a transcription factor (TF) and its target genes in all species, and thus identify TN re-wiring events. Applying this model to analyze the pre-implantation development of three mammalian species, we identified the conserved and re-wired components of the TNs downstream to a set of TFs including Oct4, Gata3/4/6, cMyc and nMyc. Evolutionary events on the DNA sequence that led to turnover of TF binding sites were identified, including a birth of an Oct4 binding site by a 2nt deletion. In contrast to recent reports of large interspecies differences of TF binding sites and gene expression patterns, the interspecies difference in TF-target relationship is much smaller. The data showed increasing conservation levels from genomic sequences to TF-DNA interaction, gene expression, TN, and finally to morphology, suggesting that evolutionary changes are larger at molecular levels and smaller at functional levels. The data also showed that evolutionarily older TFs are more likely to have conserved target genes, whereas younger TFs tend to have larger re-wiring rates.

Rewirable gene regulatory networks in the preimplantation embryonic development of three mammalian species.

Mammalian preimplantation embryonic development (PED) is thought to be governed by highly conserved processes. While it had been suggested that some plasticity of conserved signaling networks exists among different mammalian species, it was not known to what extent modulation of the genomes and the regulatory proteins could "rewire" the gene regulatory networks (GRN) that control PED. We therefore generated global transcriptional profiles from three mammalian species (human, mouse, and bovine) at representative stages of PED, including: zygote, two-cell, four-cell, eight-cell, 16-cell, morula and blastocyst. Coexpression network analysis suggested that 40.2% orthologous gene triplets exhibited different expression patterns among these species. Combining the expression data with genomic sequences and the ChIP-seq data of 16 transcription regulators, we observed two classes of genomic changes that contributed to interspecies expression difference, including single nucleotide mutations leading to turnover of transcription factor binding sites, and insertion of cis-regulatory modules (CRMs) by transposons. About 10% of transposons are estimated to carry CRMs, which may drive species-specific gene expression. The two classes of genomic changes act in concert to drive mouse-specific expression of MTF2, which links POU5F1/NANOG to NOTCH signaling. We reconstructed the transition of the GRN structures as a function of time during PED. A comparison of the GRN transition processes among the three species suggested that in the bovine system, POU5F1's interacting partner SOX2 may be replaced by HMGB1 (a TF sharing the same DNA binding domain with SOX2), resulting in rewiring of GRN by a trans change.

A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data.

BACKGROUND: How transcription factors (TFs) interact with cis-regulatory sequences and interact with each other is a fundamental, but not well understood, aspect of gene regulation. METHODOLOGY/PRINCIPAL FINDINGS: We present a computational method to address this question, relying on the established biophysical principles. This method, STAP (sequence to affinity prediction), takes into account all combinations and configurations of strong and weak binding sites to analyze large scale transcription factor (TF)-DNA binding data to discover cooperative interactions among TFs, infer sequence rules of interaction and predict TF target genes in new conditions with no TF-DNA binding data. The distinctions between STAP and other statistical approaches for analyzing cis-regulatory sequences include the utility of physical principles and the treatment of the DNA binding data as quantitative representation of binding strengths. Applying this method to the ChIP-seq data of 12 TFs in mouse embryonic stem (ES) cells, we found that the strength of TF-DNA binding could be significantly modulated by cooperative interactions among TFs with adjacent binding sites. However, further analysis on five putatively interacting TF pairs suggests that such interactions may be relatively insensitive to the distance and orientation of binding sites. Testing a set of putative Nanog motifs, STAP showed that a novel Nanog motif could better explain the ChIP-seq data than previously published ones. We then experimentally tested and verified the new Nanog motif. A series of comparisons showed that STAP has more predictive power than several state-of-the-art methods for cis-regulatory sequence analysis. We took advantage of this power to study the evolution of TF-target relationship in Drosophila. By learning the TF-DNA interaction models from the ChIP-chip data of D. melanogaster (Mel) and applying them to the genome of D. pseudoobscura (Pse), we found that only about half of the sequences strongly bound by TFs in Mel have high binding affinities in Pse. We show that prediction of functional TF targets from ChIP-chip data can be improved by using the conservation of STAP predicted affinities as an additional filter. CONCLUSIONS/SIGNIFICANCE: STAP is an effective method to analyze binding site arrangements, TF cooperativity, and TF target genes from genome-wide TF-DNA binding data.

Inferring gene regulatory networks by thermodynamic modeling.

BACKGROUND: To date, the reconstruction of gene regulatory networks from gene expression data has primarily relied on the correlation between the expression of transcription regulators and that of target genes. RESULTS: We developed a network reconstruction method based on quantities that are closely related to the biophysical properties of TF-TF interaction, TF-DNA binding and transcriptional activation and repression. The Network-Identifier method utilized a thermodynamic model for gene regulation to infer regulatory relationships from multiple time course gene expression datasets. Applied to five datasets of differentiating embryonic stem cells, Network-Identifier identified a gene regulatory network among 87 transcription regulator genes. This network suggests that Oct4, Sox2 and Klf4 indirectly repress lineage specific differentiation genes by activating transcriptional repressors of Ctbp2, Rest and Mtf2.

Selection of thermodynamic models for combinatorial control of multiple transcription factors in early differentiation of embryonic stem cells.

BACKGROUND: Transcription factors (TFs) have multiple combinatorial forms to regulate the transcription of a target gene. For example, one TF can help another TF to stabilize onto regulatory DNA sequence and the other TF may attract RNA polymerase (RNAP) to start transcription; alternatively, two TFs may both interact with both the DNA sequence and the RNAP. The different forms of TF-TF interaction have different effects on the probability of RNAP's binding onto the promoter sequence and therefore confer different transcriptional efficiencies. RESULTS: We have developed an analytical method to identify the thermodynamic model that best describes the form of TF-TF interaction among a set of TF interactions for every target gene. In this method, time-course microarray data are used to estimate the steady state concentration of the transcript of a target gene, as well as the relative changes of the active concentration for each TF. These estimated concentrations and changes of concentrations are fed into an inference scheme to identify the most compatible thermodynamic model. Such a model represents a particular way of combinatorial control by multiple TFs on a target gene. CONCLUSIONS: Applying this approach to a time-course microarray dataset of embryonic stem cells, we have inferred five interaction patterns among three regulators, Oct4, Sox2 and Nanog, on ten target genes.