Browsing by Subject "chromatin accessibility"
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Item Open Access Deciphering genome-wide chromatin occupancy, dynamics, and their connections to gene regulation(2022) Mitra, SnehaThe genomic DNA is bound by a myriad of proteins to form the chromatin inside the nucleus of the cell. The proteins can bind to the genome in different combinations leading to a combinatorial explosion in the number of possible chromatin configurations. The differences in the chromatin configurations for the same genome sequence give rise to distinct cell types. Likewise, cells of the same type also undergo changes in chromatin configurations under different environmental conditions. Key changes to the occupancy profile of the chromatin may dictate changes in gene regulation or vice versa. Therefore, it is important to decipher the chromatin occupancy profiles of the genome and understand how these configurations are related to the transcription of genes.
In this dissertation, we analyze chromatin using chromatin accessibility data sets, particularly MNase-seq and ATAC-seq, that describe the protein-bound and unbound regions of the genome. We first describe a state-space model that uses chromatin accessibility data to jointly infer the occupancy profile of hundreds of proteins binding to the genome. We apply our model to the yeast genome to study the occupancy profile of transcription factors and nucleosomes. We further extend our model to study chromatin dynamics of yeast cells subjected to cadmium stress. In doing so, we identify genomic regions exhibiting changes in the occupancy profile of transcription factors and nucleosomes. Upon comparing with available gene expression data we find that key changes in chromatin configuration occur around gene bodies that are differentially regulated during cadmium stress. Our analyses highlight how specific changes to the occupancy profiles relate to gene expression.
Building upon the interrelatedness of chromatin and transcription, we describe a regression-based approach that predicts transcription from chromatin accessibility data sets. We find that the chromatin accessibility in specific parts of the genome is highly correlated to gene expression. These genomic regions are potential regulatory regions that can lie far away from the gene body and interact with the genes due to the looping of the DNA. Our model identifies these regulatory regions in a gene-specific manner that helps us further understand the connections between chromatin and transcription.
Item Open Access Integrated Chromatin Analyses Offer Insights Into Trans-factor Function In Cancer Cell Lines(2012) Tewari, AlokUnderstanding 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.
Item Open Access Novel Methods to Identify Chromatin Accessibility Differences Across Primates(2019) Edsall, Lee ElizabethOne 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).