Browsing by Author "Crawford, Gregory E"

Despite a largely static DNA sequence, our genomes are incredibly malleable. Comparative studies of chromatin features between different cell types, tissues, and species have revealed tremendous differences in how the genome is accessed, transcribed, and replicated. However, how the dynamics of chromatin accessibility contribute to development, environmental response, and disease status has only begun to be appreciated. In this work we identified chromatin accessibility changes by DNase-seq in three diverse processes: in granule neurons of the developing cerebellum, with intestinal epithelial cells in the absence of a normal microbiota, and with myelogenous leukemia cells in response to histone deacetylase inhibitor treatments. In all cases, we coupled these analyses with RNA-seq assays to identify concurrent transcriptional changes. By mapping the changes to these genome-wide signals we defined the contribution of local chromatin structure to the transcriptional programs underlying these processes, and improved our understanding of their relation to other chromatin changes like histone modifications. Furthermore we demonstrated use of the strongest accessibility changes to identify transcription factors critical for these processes by finding enrichment of their binding motifs. For a few of these key factors, depletion or overexpression of the protein was sufficient to regulate the expression of predicted target genes or exert limited chromatin accessibility changes, demonstrating the functional significance of these proteins in these processes. Together these studies have informed our understanding of the role chromatin accessibility changes play in development and environmental responses while also proving their utility for key regulator identification.

Chromatin structure plays an important role in gene regulation, especially in differentiating the diverse cell types in humans. In this dissertation, we analyze the nucleosome positioning and open chromatin profiles genome-wide and investigate the relationship with transcription initiation, the activity of regulatory elements, and expression levels. We mainly focus on the results of DNase-seq experiments, but also employ annotations from MNase-seq, FAIRE-seq, ChIP-seq, CAGE, and RNA microarrays. Our methods are based on computational approaches including managing large data sets, statistical analysis, and machine learning. We find that different transcription initiation patterns lead to distinct chromatin structures, suggesting diverse regulatory strategies. Moreover, we present a tool for comparing genome-wide annotation tracks and evaluate DNase-seq against a unique assay for detecting open chromatin. We also demonstrate how DNase-seq can be used to successfully predict rotationally stable nucleosomes that are conserved across cell types. We conclude that DNase-seq can be used to study genome-wide chromatin structure in an effort to better understand how it regulates gene expression.

Deciphering the molecular mechanisms driving the phenotypic differences between humans and primates remains a daunting challenge. Mutations found in protein coding DNA alone has not been able to explain these phenotypic differences. The hypothesis that mutations in non-coding regulatory DNA are responsible for altered gene expression leading to these phenotypic changes has now been widely supported by differential gene expression experiments. Yet, comprehensive identification of all regulatory DNA elements across different species has not been performed. To identify the genetic source of regulatory change, genome-wide DNaseI hypersensitivity assays, marking all types of active gene regulatory element sites, were performed in human, chimpanzee, macaque, orangutan, and mouse. Many DNaseI hypersensitive (DHS) sites were conserved among all 5 species, but we also identified hundreds of novel human- and chimpanzee-specific DHS gains and losses that showed signatures of positive selection. Species-specific DHS gains were enriched in distal non-coding regions, associated with active histone modifications, and positively correlated with increased expression - indicating that these are likely to be functioning as enhancers. Comparison to mouse DHS data indicate that human or chimpanzee DHS gains are likely to have been a result of single events that occurred primarily on the human- or chimpanzee-specific branch, respectively. In contrast, DHS losses are associated with events that occurred on multiple branches. At least one mechanism contributing to DHS gains and losses are species-specific variants that lead to sequence changes at transcription factor binding motifs, affecting the binding of TFs such as AP1. These variants were functionally verified by DNase footprinting and ChIP-qPCR analyses.

Understanding the mechanisms whereby the sequence of the human genome is interpreted into diverse cellular phenotypes is a critical endeavor in modern biology. A major determinant of cellular phenotype is the spatial and temporal pattern gene expression, which is regulated in part by epigenomic properties such as histone post-translational modifications, DNA methylation, chromatin accessibility and the 3-dimensional architecture of the genome within the nucleus. These properties regulate the dynamic assembly of transcription factors and their co-regulatory proteins upon chromatin. To properly understand the interplay between the epigenomic framework of a cell and transcription factors, integrated analysis of transcription factor-DNA binding, chromatin status, and transcription is required. This work integrates information about chromatin accessibility, as measured by DNaseI hypersensitivity, transcription factor binding, as measured by chromatin immunoprecipitation, and transcription, as measured by microarray or transcriptome sequencing, to further understand the functional role of two important transcription factors, the androgen receptor (AR) and CTCF, in cancer cell line models. Data gathered from a prostate cancer cell line model demonstrate that the AR does not exclusively bind accessible chromatin upon ligand-activation, and induces significant changes in chromatin accessibility upon binding. Regions of quantitative change in chromatin accessibility contain motifs corresponding to potential collaborators for AR function, and are also significantly associated with AR-regulated transcriptional changes. Furthermore, base pair resolution of the DNaseI cleavage profile revealed three distinct patterns of AR-DNA interaction, suggesting multiple modes of AR interacting with the genome. A novel role for the nuclear receptor REV-ERBα in AR-mediated transcription was explored within the same model system. Though preliminary, results thus far indicate that REV-ERBα is required for AR-induced increases in target gene transcription in a manner that is likely dependent on HDAC3. Genetic knockdown of REV-ERBα resulted in notable changes in chromatin accessibility around AR-target genes both before and after AR activation. The function of CTCF was interrogated using stable knockdown in a breast cancer cell line model. CTCF knockdown led to widespread changes in chromatin accessibility that were dependent on DNA sequence. Further analysis suggested that AP-1 and FOXA1 are involved in CTCF function. Together, the work presented in this dissertation offers novel insight into the behavior of two critical transcription factors in cancer cell lines, and describe a framework of analysis that can be extended and applied to any transcription factor within any desired cellular context.

One of the aims of evolutionary biology is to identify gene regulatory regions (and the resulting level of expression) that evolved between species. The conventional method of analysis for this is to perform pairwise comparisons on data generated for each species. Software programs for this approach are mature and work well when there are only two species of interest. These same programs can be used when there are three species of interest. However, the analysis becomes more cumbersome and the statistical significance (p-value) difficult to calculate. Performing pairwise comparisons when there are more than three species have significant limitations. One is the exponential increase in the number of tests performed, greatly reducing the sensitivity after false discovery rate correction. For n species, (n-1) tests are performed on each region. Another limitation is the lack of a principled way to identify and classify genes (or regulatory regions) containing changes in multiple species.

To address these limitations, we developed a novel method of jointly modelling the data from all of the species using a negative binomial generalized linear model. In addition to providing a principled way of identifying and classifying sites with multiple changes, our method is more sensitive largely due to a substantial decrease in the number of tests performed. Our method jointly models all of the data in a single test, regardless of the number of species. As a result, the correction for number of independent tests performed is (n-1) times larger for the multiple pairwise method than for the joint modelling approach.

We applied this joint modelling approach to DNase-seq data generated from skin fibroblast cells from five primate species; human, chimpanzee, gorilla, orangutan, and rhesus macaque. We identified 89,744 DNase I Hypersensitive sites (DHS sites) that were comparable across all species, of which 41% (36,666) were classified as differential in one or more species. 30% of the differential sites (11,095) are likely due to a single change in chromatin accessibility in one species. Changes that likely occurred on the internal human-chimpanzee branch or human-chimpanzee-gorilla branch account for 15% (5,385) of the differential sites. 16% (6,034) of the differential sites contain changes that happened on either the human-chimpanzee-gorilla-orangutan internal branch or the rhesus macaque species branch. 32% (11,698) of the differential sites are due to multiple changes in chromatin accessibility (e.g., independent changes on the human and orangutan species branches).

The accuracy of this new approach was demonstrated by a high degree of concordance with an earlier study from our laboratory that analyzed data from human, chimpanzee, and rhesus macaque. Additionally, we performed a conventional pairwise analysis of the DHS sites from the five species and classified only 33% as differential, indicating decreased sensitivity compared to the joint modelling approach. Together, these results indicate that this novel joint modelling approach provides an improved method for comparative analysis of DNase-seq data.

Although we developed this method for DNase-seq data, we expect that it can be applied to other count-based data types such as ChIP-seq, ATAC-seq, and RNA-seq. We also expect that it can be applied to other experimental designs such as time-series, multi-tissue comparisons, and multiple developmental stage comparisons. The R script for performing the joint modelling analysis and instructions for modifying the script for use by other investigators are available in a GitHub repository (http://github.com/ledsall/2019primate).

After finishing a human genome reference sequence in 2002, the genomics community has

turned to the task of interpreting it. A primary focus is to identify and characterize not only

protein-coding genes, but all functional elements in the genome. The effort has identified

millions of regulatory elements across species and in hundreds of human cell-types. Nearly

all identified regulatory elements are found in non-coding DNA, hypothesizing a function

for previously unannotated sequence. The ability to identify regulatory DNA genome-wide

provides a new opportunity to understand gene regulation and to ask fundamental questions

in diverse areas of biology.

One such area is the aim to understand the molecular basis for phenotypic differences

between humans and other primates. These phenotypic differences are partially driven

by mutations in non-coding regulatory DNA that alter gene expression. This hypothesis

has been supported by differential gene expression analyses in general, but we have not

yet identified specific regulatory variants responsible for differences in transcription and

phenotype. I have worked to identify regulatory differences in the same cell-type isolated

from human, chimpanzee, and macaque. Most regulatory elements were conserved among

all three species, as expected based on their central role in regulating transcription. How-

ever, several hundred regulatory elements were gained or lost on the lineages leading to

modern human and chimpanzee. Species-specific regulatory elements are enriched near

differentially expressed genes, are positively correlated with increased transcription, show

evidence of branch-specific positive selection, and overlap with active chromatin marks.

ivSpecies-specific sequence differences in transcription factor motifs found within this regu-

latory DNA are linked with species-specific changes in chromatin accessibility. Together,

these indicate that species-specific regulatory elements contribute to transcriptional and

phenotypic differences among primate species.

Another fundamental function of regulatory elements is to define different cell-types in

multicellular organisms. Regulatory elements recruit transcription factors that modulate

gene expression distinctly across cell-types. In a study of 112 human cell-types, I classified

regulatory elements into clusters based on regulatory signal tissue specificity. I then used

these to uncover distinct associations between regulatory elements and promoters, CpG-

islands, conserved elements, and transcription factor motif enrichment. Motif analysis

identified known and novel transcription factor binding motifs in cell-type-specific and

ubiquitous regulatory elements. I also developed a classifier that accurately predicts cell-

type lineage based on only 43 regulatory elements and evaluated the tissue of origin for

cancer cell-types. By correlating regulatory signal and gene expression, I predicted target

genes for more than 500k regulatory elements. Finally, I introduced a web resource to

enable researchers to explore these regulatory patterns and better understand how expression

is modulated within and across human cell-types.

Regulation of gene expression is fundamental to life. This dissertation uses identified

regulatory DNA to better understand regulatory systems. In the context of either evolution-

ary or developmental biology, understanding how differences in regulatory DNA contribute

to phenotype will be central to completely understanding human biology.

Abstract

Transcription factors control numerous vital processes in the cell through their ability to control gene expression. Dysfunctional regulation by transcription factors lead to disorders and disease. Transcription factors regulate gene expression by binding to DNA sequences (motifs) on the genome and altering chromatin. DNase-seq footprinting is a well-established assay for identification of DNA sequences that bind to transcription factors. We developed computational techniques to analyze footprints and predict transcription factor binding. These transcription factor specific predictive models are able to correct for DNase sequence bias and characterize variation in DNA binding sequence. We found that DNase-seq footprints are able to identify cell-type or condition specific transcription factor activity and may offer information about the type of the interaction between DNA and transcription factor. Our DNase-seq footprint model is able to accurately discover high confidence transcription factor binding sites and discover alternative interactions between transcription factors and DNA. DNase-seq footprints can be used with ChIP-seq data to discover true binding sites and better understand transcription regulation.