Tommy Kaplan

Dynamics of Replication-Independent Histone Turnover in Budding Yeast

Chromatin plays roles in processes governed by different time scales. To assay the dynamic behavior of chromatin in living cells, we used genomic tiling arrays to measure histone H3 turnover in G1-arrested Saccharomyces cerevisiae at single-nucleosome resolution over 4% of the genome, and at lower (∼265 base pair) resolution over the entire genome. We find that nucleosomes at promoters are replaced more rapidly than at coding regions and that replacement rates over coding regions correlate with polymerase density. In addition, rapid histone turnover is found at known chromatin boundary elements. These results suggest that rapid histone turnover serves to functionally separate chromatin domains and prevent spread of histone states.

Modeling dependencies in protein-DNA binding sites

The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and characterizing the binding sites of DNA-binding proteins, such as transcription factors. A common representation of transcription factor binding sites is a position specific score matrix (PSSM). This representation makes the strong assumption that binding site positions are independent of each other. In this work, we explore Bayesian network representations of binding sites that provide different tradeoffs between complexity (number of parameters) and the richness of dependencies between positions. We develop the formal machinery for learning such models from data and for estimating the statistical significance of putative binding sites. We then evaluate the ramifications of these richer representations in characterizing binding site motifs and predicting their genomic locations. We show that these richer representations improve over the PSSM model in both tasks.

Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing

Defining the transcriptome, the repertoire of transcribed regions encoded in the genome, is a challenging experimental task. Current approaches, relying on sequencing of ESTs or cDNA libraries, are expensive and labor-intensive. Here, we present a general approach for ab initio discovery of the complete transcriptome of the budding yeast, based only on the unannotated genome sequence and millions of short reads from a single massively parallel sequencing run. Using novel algorithms, we automatically construct a highly accurate transcript catalog. Our approach automatically and fully defines 86% of the genes expressed under the given conditions, and discovers 160 previously undescribed transcription units of 250 bp or longer. It correctly demarcates the 5′ and 3′ UTR boundaries of 86 and 77% of expressed genes, respectively. The method further identifies 83% of known splice junctions in expressed genes, and discovers 25 previously uncharacterized introns, including 2 cases of condition-dependent intron retention. Our framework is applicable to poorly understood organisms, and can lead to greater understanding of the transcribed elements in an explored genome.

Large-scale discovery of enhancers from human heart tissue

Development and function of the human heart depend on the dynamic control of tissue-specific gene expression by distant-acting transcriptional enhancers. To generate an accurate genome-wide map of human heart enhancers, we used an epigenomic enhancer discovery approach and identified ∼6,200 candidate enhancer sequences directly from fetal and adult human heart tissue. Consistent with their predicted function, these elements were markedly enriched near genes implicated in heart development, function and disease. To further validate their in vivo enhancer activity, we tested 65 of these human sequences in a transgenic mouse enhancer assay and observed that 43 (66%) drove reproducible reporter gene expression in the heart. These results support the discovery of a genome-wide set of noncoding sequences highly enriched in human heart enhancers that is likely to facilitate downstream studies of the role of enhancers in development and pathological conditions of the heart.

Structure and function of a transcriptional network activated by the MAPK Hog1

Cells regulate gene expression using a complex network of signaling pathways, transcription factors and promoters. To gain insight into the structure and function of these networks, we analyzed gene expression in single- and multiple-mutant strains to build a quantitative model of the Hog1 MAPK-dependent osmotic stress response in budding yeast. Our model reveals that the Hog1 and general stress (Msn2/4) pathways interact, at both the signaling and promoter level, to integrate information and create a context-dependent response. This study lays out a path to identifying and characterizing the role of signal integration and processing in other gene regulatory networks.

Extensive Divergence of Transcription Factor Binding in Drosophila Embryos with Highly Conserved Gene Expression

To better characterize how variation in regulatory sequences drives divergence in gene expression, we undertook a systematic study of transcription factor binding and gene expression in blastoderm embryos of four species, which sample much of the diversity in the 40 million-year old genus Drosophila: D. melanogaster, D. yakuba, D. pseudoobscura and D. virilis. We compared gene expression, measured by mRNA-seq, to the genome-wide binding, measured by ChIP-seq, of four transcription factors involved in early anterior-posterior patterning. We found that mRNA levels are much better conserved than individual transcription factor binding events, and that changes in a genes expression were poorly explained by changes in adjacent transcription factor binding. However, highly bound sites, sites in regions bound by multiple factors and sites near genes are conserved more frequently than other binding, suggesting that a considerable amount of transcription factor binding is weakly or non-functional and not subject to purifying selection.

Establishment of regions of genomic activity during the Drosophila maternal to zygotic transition

We describe the genome-wide distributions and temporal dynamics of nucleosomes and post-translational histone modifications throughout the maternal-to-zygotic transition in embryos of Drosophila melanogaster. At mitotic cycle 8, when few zygotic genes are being transcribed, embryonic chromatin is in a relatively simple state: there are few nucleosome free regions, undetectable levels of the histone methylation marks characteristic of mature chromatin, and low levels of histone acetylation at a relatively small number of loci. Histone acetylation increases by cycle 12, but it is not until cycle 14 that nucleosome free regions and domains of histone methylation become widespread. Early histone acetylation is strongly associated with regions that we have previously shown to be bound in early embryos by the maternally deposited transcription factor Zelda, suggesting that Zelda triggers a cascade of events, including the accumulation of specific histone modifications, that plays a role in the subsequent activation of these sequences. DOI: http://dx.doi.org/10.7554/eLife.03737.001

Zelda binding in the early Drosophila melanogaster embryo marks regions subsequently activated at the maternal-to-zygotic transition.

The earliest stages of development in most metazoans are driven by maternally deposited proteins and mRNAs, with widespread transcriptional activation of the zygotic genome occurring hours after fertilization, at a period known as the maternal-to-zygotic transition (MZT). In Drosophila, the MZT is preceded by the transcription of a small number of genes that initiate sex determination, patterning, and other early developmental processes; and the zinc-finger protein Zelda (ZLD) plays a key role in their transcriptional activation. To better understand the mechanisms of ZLD activation and the range of its targets, we used chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-Seq) to map regions bound by ZLD before (mitotic cycle 8), during (mitotic cycle 13), and after (late mitotic cycle 14) the MZT. Although only a handful of genes are transcribed prior to mitotic cycle 10, we identified thousands of regions bound by ZLD in cycle 8 embryos, most of which remain bound through mitotic cycle 14. As expected, early ZLD-bound regions include the promoters and enhancers of genes transcribed at this early stage. However, we also observed ZLD bound at cycle 8 to the promoters of roughly a thousand genes whose first transcription does not occur until the MZT and to virtually all of the thousands of known and presumed enhancers bound at cycle 14 by transcription factors that regulate patterned gene activation during the MZT. The association between early ZLD binding and MZT activity is so strong that ZLD binding alone can be used to identify active promoters and regulatory sequences with high specificity and selectivity. This strong early association of ZLD with regions not active until the MZT suggests that ZLD is not only required for the earliest wave of transcription but also plays a major role in activating the genome at the MZT.

A High-Resolution Enhancer Atlas of the Developing Telencephalon

The mammalian telencephalon plays critical roles in cognition, motor function, and emotion. Though many of the genes required for its development have been identified, the distant-acting regulatory sequences orchestrating their in vivo expression are mostly unknown. Here, we describe a digital atlas of in vivo enhancers active in subregions of the developing telencephalon. We identified more than 4,600 candidate embryonic forebrain enhancers and studied the in vivo activity of 329 of these sequences in transgenic mouse embryos. We generated serial sets of histological brain sections for 145 reproducible forebrain enhancers, resulting in a publicly accessible web-based data collection comprising more than 32,000 sections. We also used epigenomic analysis of human and mouse cortex tissue to directly compare the genome-wide enhancer architecture in these species. These data provide a primary resource for investigating gene regulatory mechanisms of telencephalon development and enable studies of the role of distant-acting enhancers in neurodevelopmental disorders.

Quantitative Models of the Mechanisms That Control Genome-Wide Patterns of Transcription Factor Binding during Early Drosophila Development

Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ∼0.4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0.6–0.9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision.