Computational Methods to Dissect Tissue-Specific Landscapes of Transcription Factor and DNA Interactions

The intricately ordered structure of the human genome is a product of dynamic interactions between DNA and proteins such as nucleosomes and transcription factors (TFs), which allow cells to respond to environmental changes while maintaining robustness of genetic programs. Changes in the non-coding genome can affect gene regulation and lead to increased disease predisposition, but the underlying mechanisms are not fully understood. Therefore, understanding how the genome is organized and regulated is a central question in biomedical research. My thesis aims to develop and apply novel computational methods to understand general biological mechanisms of genome regulation, with a focus on TF-DNA interactions. In the initial part of this thesis, I develop computational methods to quantify TF-DNA interaction patterns by applying information theory to high-throughput molecular profiles of chromatin accessibility data (using the assay for transposase-accessible chromatin followed by high-throughput sequencing, ATAC-seq) to measure a property which we name chromatin information. To circumvent the requirement of high-throughput molecular profiles of TF binding (chromatin immunoprecipitation followed by sequencing, ChIP-seq) to obtain chromatin information measurements, I develop BMO, a novel algorithm to predict TF binding from chromatin accessibility data that outperforms current state-of-the-art methods. Using BMO in combination with the information theoretical approach developed here, I quantify the chromatin information patterns of hundreds of TF motifs across different human tissues and cell lines. Only a subset of TFs (10-20%) have high chromatin information, and are therefore associated with organized chromatin. By integrating multiple layers of molecular profiles, I find that high chromatin information TFs have longer TF-DNA residence times, associate with nucleosome phasing, and are enriched to overlap regions associated with the genetic control of gene expression. I then use genetic data to find evidence that high chromatin information TFs associate with increased chromatin accessibility and may therefore act as pioneer TFs. In the last part of this thesis, I apply TF binding prediction algorithms to characterize the regulatory landscape associated with thymocyte development. The results from these analyses support that thymocyte development is a highly dynamic process and help prioritize novel candidate TFs and regulatory elements for future experimental validation. This work represents a novel fusion of two research domains -- information theory and genomics -- which allowed to capture properties of TF-chromatin interactions, with important implications for gene regulation, cell state dynamics, and understanding the pathological mechanisms associated with non-coding disease-associated genetic variants.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155279/1/albanus_1.pd

Turnera subulata Anti-Inflammatory Properties in Lipopolysaccharide-Stimulated RAW 264.7 Macrophages

In South America, particularly in the Northeastern regions of Brazil, Turnera subulata leaf extract is used as an alternative traditional medicine approach for several types of chronic diseases, such as diabetes, hypertension, chronic pain, and general inflammation. Despite its widespread use, little is known about the medicinal properties of the plants of this genus. In this study, we evaluate the antioxidant and anti-inflammatory of T. subulata leaf extract in an in vitro model of inflammation, using lipopolysaccharide-stimulated RAW-264.7 macrophage cell line. We observed that cotreatment with T. subulata leaf extract was able to reduce the oxidative stress in cells due to inflammatory response. More importantly, we observed that the leaf extract was able to directly modulate inflammatory response by altering activity of members of the mitogen-activated protein kinase pathways. Our results demonstrate for the first time that T. subulata have antioxidant and anti-inflammatory properties, which warrant further investigation of the medicinal potential of this species.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140112/1/jmf.2016.0047.pd

Interactions between genetic variation and cellular environment in skeletal muscle gene expression

From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis-acting genotype-environment interactions (GxE)-genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate environmental response expression quantitative trait loci (reQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.Peer reviewe

The genetic regulatory signature of type 2 diabetes in human skeletal muscle

Type 2 diabetes (T2D) results from the combined effects of genetic and environmental factors on multiple tissues over time. Of the 4100 variants associated with T2D and related traits in genome-wide association studies (GWAS), >90% occur in non-coding regions, suggesting a strong regulatory component to T2D risk. Here to understand how T2D status, metabolic traits and genetic variation influence gene expression, we analyse skeletal muscle biopsies from 271 well-phenotyped Finnish participants with glucose tolerance ranging from normal to newly diagnosed T2D. We perform high-depth strand-specific mRNA-sequencing and dense genotyping. Computational integration of these data with epigenome data, including ATAC-seq on skeletal muscle, and transcriptome data across diverse tissues reveals that the tissue-specific genetic regulatory architecture of skeletal muscle is highly enriched in muscle stretch/super enhancers, including some that overlap T2D GWAS variants. In one such example, T2D risk alleles residing in a muscle stretch/super enhancer are linked to increased expression and alternative splicing of muscle-specific isoforms of ANK1.Peer reviewe

Estudo da região flanqueadora dos genes que codificam as subunidades do antígeno B de Echinococcus granulosus.

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Estudo da região flanqueadora dos genes que codificam as subunidades do antígeno B de Echinococcus granulosus.

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Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program.

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits