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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Functional Identification and Characterization of cis-Regulatory Elements

Transcription is regulated through interactions between regulatory proteins, such as transcription factors (TFs), and DNA sequence. It is known that TFs act combinatorially in some cases to regulate transcription, but in which situations and to what degree is unclear. I first studied the contribution of TF binding sites to expression in mouse embryonic stem (ES) cells by using synthetic cis-regulatory elements (CREs). The synthetic CREs were comprised of combinations of binding sites for the pluripotency TFs Oct4, Sox2, Klf4, and Esrrb. A statistical thermodynamic model explained 72% of the variation in expression driven by these CREs. The high predictive power of this model depended on five TF interaction parameters, including favorable heterotypic interactions between Oct4 and Sox2, Klf4 and Sox2, and Klf4 and Esrrb. The model also included two unfavorable homotypic interaction parameters. These homotypic parameters help to explain the fact that synthetic CREs with mixtures of binding sites for various TFs drive much higher expression than multiple binding sites for the same TF. I then found that the expression of these synthetic CREs largely changes as ES cells differentiate down the neural lineage. However, CREs with no repeat binding sites drove similar levels of expression, suggesting that heterotypic interactions may be similar in the two conditions. In a separate set of experiments I interrogated the determinants of expression driven by genomic sequences previously segmented into classes based on chromatin features. A set of these sequences was assayed in K562 cells. As expected, we found that Enhancers and Weak Enhancers drove expression over background, while Repressed elements and Enhancers from another cell type did not. Unexpectedly, we found that Weak Enhancers drove higher expression than Enhancers, possibly based on their lower H3K36me3 and H3K27ac, which we found to be weakly associated with lower expression. Using a logistic regression model, we showed that matches to TF binding motifs were best able to predict active sequences, but chromatin features contributed significantly as well. These results demonstrate that interactions between certain combinations of pluripotency TFs, but not all combinations, are important to transcriptional regulation. Furthermore, chromatin modifications can still contribute to predictions of expression even after accounting for binding site motifs. Better understanding of the process of cis-regulation will allow us to predict which sequences can drive expression and how perturbations affect this expression

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

Taking into account nucleosomes for predicting gene expression

The eukaryotic genome is organized in a chain of nucleosomes that consist of 145-147. bp of DNA wrapped around a histone octamer protein core. Binding of transcription factors (TF) to nucleosomal DNA is frequently impeded, which makes it a challenging task to calculate TF occupancy at a given regulatory genomic site for predicting gene expression. Here, we review methods to calculate TF binding to DNA in the presence of nucleosomes. The main theoretical problems are (i) the computation speed that is becoming a bottleneck when partial unwrapping of DNA from the nucleosome is considered, (ii) the perturbation of the binding equilibrium by the activity of ATP-dependent chromatin remodelers, which translocate nucleosomes along the DNA, and (iii) the model parameterization from high-throughput sequencing data and fluorescence microscopy experiments in living cells. We discuss strategies that address these issues to efficiently compute transcription factor binding in chromatin. © 2013 Elsevier Inc

Dissecting Transcriptional Network in Mouse Embryonic Stem Cells

Ph.DDOCTOR OF PHILOSOPH

Gene-set analysis identifies master transcription factors in developmental courses

AbstractTranscriptional regulation plays key roles in many biological processes. The regulation is dynamic in time and space. Identifying transcription factors that play major roles in a developmental time course is very important for understanding the regulation. This cannot be realized by studying the relation between the expression of individual genes. We developed a gene-set analysis approach to study master regulators and their actively regulated targets during a time course from gene expression data. We applied the method to a mouse liver development data and a mouse embryonic stem cell (mESC) development data, and identified 14 and 9 transcription factors that play major regulatory roles in the two development courses, respectively. Some transcription factors could not be identified as active in the process by studying their correlation with individual targets. The method was also extended for studying other regulation factors or pathways from time-course expression data

The MIR-17-92 Cluster Contributes to MLL Leukemia Development Through the Repression of the Meis1 Competitor Pknox1

Mixed lineage leukemias have a relatively poor prognosis and arise as a result of translocations between the MLL gene and one of multiple partner genes. Downstream targets of MLL are aberrantly upregulated and include the developmentally important HOX genes and MEIS1, as well as multiple miRNAs, including the miR-17-92 cluster and miR-196b. Here I utilize custom anti-miRNA oligonucleotides to examine the contribution of specific miRNAs to MLL leukemias both as individual miRNAs and in cooperation with other miRNAs. Combinatorial treatment with antagomirs against miR-17 and miR-19a of the miR-17-92 cluster dramatically reduces colony forming ability of MLL-fusion containing cell lines but not non-MLL AML controls. Further, I validated PKNOX1 as a target of both miR-17 and miR-19a. MEIS1 and PKNOX1 are TALE domain proteins that participate in ternary complexes with HOX and PBX proteins. Here I examine the competitive relationship between PKNOX1 and MEIS1 in PBX-containing complex formation and determine the antagonistic role of PKNOX1 to leukemia in a murine MLL-AF9 model. Collectively, these data implicate the miR-17-92 cluster as part of a regulatory mechanism necessary to maintain MEIS1/HOXA9 -mediated transformation in MLL leukemia. This approach represents a paradigm where targeting multiple non-homologous miRNAs may be utilized as a novel therapeutic regimen