Kenzie D. MacIsaac

Transcriptional regulatory code of a eukaryotic genome

DNA-binding transcriptional regulators interpret the genomes regulatory code by binding to specific sequences to induce or repress gene expression. Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation, but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeasts transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeasts transcriptional regulators.

Foxp3 occupancy and regulation of key target genes during T-cell stimulation.

Foxp3+CD4+CD25+ regulatory T (Treg) cells are essential for the prevention of autoimmunity. Treg cells have an attenuated cytokine response to T-cell receptor stimulation, and can suppress the proliferation and effector function of neighbouring T cells. The forkhead transcription factor Foxp3 (forkhead box P3) is selectively expressed in Treg cells, is required for Treg development and function, and is sufficient to induce a Treg phenotype in conventional CD4+CD25- T cells. Mutations in Foxp3 cause severe, multi-organ autoimmunity in both human and mouse. FOXP3 can cooperate in a DNA-binding complex with NFAT (nuclear factor of activated T cells) to regulate the transcription of several known target genes. However, the global set of genes regulated directly by Foxp3 is not known and consequently, how this transcription factor controls the gene expression programme for Treg function is not understood. Here we identify Foxp3 target genes and report that many of these are key modulators of T-cell activation and function. Remarkably, the predominant, although not exclusive, effect of Foxp3 occupancy is to suppress the activation of target genes on T-cell stimulation. Foxp3 suppression of its targets appears to be crucial for the normal function of Treg cells, because overactive variants of some target genes are known to be associated with autoimmune disease.

Tissue-specific transcriptional regulation has diverged significantly between human and mouse

We demonstrate that the binding sites for highly conserved transcription factors vary extensively between human and mouse. We mapped the binding of four tissue-specific transcription factors (FOXA2, HNF1A, HNF4A and HNF6) to 4,000 orthologous gene pairs in hepatocytes purified from human and mouse livers. Despite the conserved function of these factors, from 41% to 89% of their binding events seem to be species specific. When the same protein binds the promoters of orthologous genes, approximately two-thirds of the binding sites do not align.

Practical Strategies for Discovering Regulatory DNA Sequence Motifs

Many functionally important regions of the genome can be recognized by searching for sequence patterns, or “motifs.” Aside from the genes themselves, examples include CpG islands, often present in promoter regions, and splice sites that denote intron/exon boundaries. Other motifs of great interest correspond to sites bound by regulatory proteins. Differential expression of genes in response to environmental and developmental cues depends on the action of these proteins, which are also known as transcription factors. Identifying the regulatory motifs bound by transcription factors can provide crucial insight into the mechanisms of transcriptional regulation. However, the search for these sites is challenging because a single regulatory protein will often recognize a variety of similar sequences. In this tutorial, we review computational techniques, termed “motif discovery,” to learn representations of regulatory motifs from sequence data. In Figure 1, we present an overview of the basic workflow in a motif discovery analysis and some practical strategies for successfully mining sequence data for biologically important regulatory motifs. In the remainder of this tutorial, we discuss the main challenges associated with motif discovery in detail, and we review recent developments for addressing these challenges. Figure 1 Motif Discovery Workflow

High-resolution computational models of genome binding events

Direct physical information that describes where transcription factors, nucleosomes, modified histones, RNA polymerase II and other key proteins interact with the genome provides an invaluable mechanistic foundation for understanding complex programs of gene regulation. We present a method, joint binding deconvolution (JBD), which uses additional easily obtainable experimental data about chromatin immunoprecipitation (ChIP) to improve the spatial resolution of the transcription factor binding locations inferred from ChIP followed by DNA microarray hybridization (ChIP-Chip) data. Based on this probabilistic model of binding data, we further pursue improved spatial resolution by using sequence information. We produce positional priors that link ChIP-Chip data to sequence data by guiding motif discovery to inferred protein-DNA binding sites. We present results on the yeast transcription factors Gcn4 and Mig2 to demonstrate JBDs spatial resolution capabilities and show that positional priors allow computational discovery of the Mig2 motif when a standard approach fails.

A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data

MOTIVATION Genome-wide chromatin-immunoprecipitation (ChIP-chip) detects binding of transcriptional regulators to DNA in vivo at low resolution. Motif discovery algorithms can be used to discover sequence patterns in the bound regions that may be recognized by the immunoprecipitated protein. However, the discovered motifs often do not agree with the binding specificity of the protein, when it is known. RESULTS We present a powerful approach to analyzing ChIP-chip data, called THEME, that tests hypotheses concerning the sequence specificity of a protein. Hypotheses are refined using constrained local optimization. Cross-validation provides a principled standard for selecting the optimal weighting of the hypothesis and the ChIP-chip data and for choosing the best refined hypothesis. We demonstrate how to derive hypotheses for proteins from 36 domain families. Using THEME together with these hypotheses, we analyze ChIP-chip datasets for 14 human and mouse proteins. In all the cases the identified motifs are consistent with the published data with regard to the binding specificity of the proteins.

A Quantitative Model of Transcriptional Regulation Reveals the Influence of Binding Location on Expression

Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the functional significance of these binding sites remains unclear. We present a quantitative model of transcriptional regulation that suggests the contribution of each binding site to tissue-specific gene expression depends strongly on its position relative to the transcription start site. For three cell types, we show that, by considering binding position, it is possible to predict relative expression levels between cell types with an accuracy approaching the level of agreement between different experimental platforms. Our model suggests that, for the transcription factors profiled in these cell types, a regulatory sites influence on expression falls off almost linearly with distance from the transcription start site in a 10 kilobase range. Binding to both evolutionarily conserved and non-conserved sequences contributes significantly to transcriptional regulation. Our approach also reveals the quantitative, tissue-specific role of individual proteins in activating or repressing transcription. These results suggest that regulator binding position plays a previously unappreciated role in influencing expression and blurs the classical distinction between proximal promoter and distal binding events.

Sequence analysis of chromatin immunoprecipitation data for transcription factors.

Chromatin immunoprecipitation (ChIP) experiments allow the location of transcription factors to be determined across the genome. Subsequent analysis of the sequences of the identified regions allows binding to be localized at a higher resolution than can be achieved by current high-throughput experiments without sequence analysis and may provide important insight into the regulatory programs enacted by the protein of interest. In this chapter we review the tools, workflow, and common pitfalls of such analyses and recommend strategies for effective motif discovery from these data.