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<p>    </p> <p>Domestication is the process by which a previously wild population is managed by humans, thereby being subjected to a different set of selective pressures than experienced in its natural setting. Its opposite, feralization, is therefore when a domesticate escapes or is released from a captive setting, reasserting natural selective pressures. The genomics underpinning both domesti- cation and feralization have not been studied in insects; the Western honey bee (<em>Apis mellifera </em>L.) is a good model for this system, as honey bees exist in both a managed and feral state, and have extensive historic and genomic resources to document population changes. My goal in this thesis was to 1) improve upon our understanding of honey bee importation and genetics to the United States to support demographic assertions, and 2) to sequence managed and feral stocks of honey bees to identify the population structure and 3) genetic differences underpinning domestication. Ultimately, I reconstructed 400 years of honey bee importation and management history, creating the most comprehensive understanding to date of importation dates and locations, historical man- agement practices, and genetic bottlenecks. Additionally, I summarized thirty years of honey bee genome sequencing to provide a road map for future studies. Then, I conducted whole genome pooled sequencing on six managed and three feral stocks of honey bees from the United States. The mitochondrial and whole genome ancestry of feral colonies holds relics from their importation history, while managed colonies show evidence of more recent importation events. The managed stocks in my sample set have higher overall genetic diversity, but exhibit little differentiation, but feral stocks exhibit varying levels of differentiation, indicating different levels of ferality likely dictated by the level of reproductive isolation from managed colonies. </p>

Breast cancer is the most common type of cancer among women worldwide. Multiple studies have reported the role of tumor-immune interactions and mechanisms that the immune system uses to combat tumor cells. Therapies based on the immune response are evolving by time, but more research is required to understand and identify the patterns and relationships within the tumor microenvironment. This study aims to characterize immune cell expression patterns using a multiplex method and to investigate the way different subpopulations in breast cancer patients’ tissue samples are correlated with clinicopathological characteristics. The results of this study indicate that there must be an association within immune cell composition and clinicopathological characteristics (Estrogen Receptor Status (ER+/ER-), Progesterone Receptor (PR+/PR-), Grade (I,II,III), which is a way to characterize the cancer cells on how similar they look to normal ones, Menopause, Tumor size, Nodal status, HR status, HER2) but validation in larger patient population is required in order to evaluate the role of the immune infiltration as a predictive / prognostic biomarker in early breast cancer.

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Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by breathlessness due to airflow obstruction. COPD is the third leading cause of death worldwide. So far, none of the existing pharmacological treatments for COPD can stop the progressive decline in lung function. Drug repurposing is the application of existing approved therapeutic compounds for new disease indications, which may reduce the cost and time of new drug development. So far, there is not any systematic multi-omics data integration for drug repurposing in COPD. The goal of this project is to develop a systems biology pipeline for the identification of biological-relevant gene targets with drug repurposing potential for COPD treatment using multi-omics integration. Here we implemented a computational methodology to identify drug repurposing targets for COPD. We integrated multi-omics COPD data including, genome, transcriptome, proteome, metabolome, interactome data, and drug-target information. A distance-based network model was created to rank the potential candidate genes. Fifty genes were prioritized as COPD signature genes for their overall proximity to signature genes identified at all omics levels. Forty of them may be considered as “druggable” targets. Literature search reported CRCX4 – Plerixafor as one prioritized targets-gene pair for drug repurposing. The bone marrow stimulant Plerixafor is currently being evaluated for COPD treatment in clinical trials, suggesting that our pipeline is finding promising drug repurposing targets. Our work, for the first time, applied a systematic approach integrating multiple omics data to find drug repurposing targets for COPD. / Pharmaceutical Sciences

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Allopolyploids form through the hybridization of two or more diploid genomes. A challenge to reproduction in allopolyploids is that pairing can occur between homologous chromosomes or homeologous chromosomes (i.e.different subgenomes.). Crossover between homeologous chromosomes can result in chromosome rearrangements that lower fertility and overall fitness. Rearrangements can alter the dosage of either entire chromosomes or just parts of chromosomes. Understanding the frequency and extent of rearrangements will help to explain the evolution and genome stabilization of agriculturally important allopolyploid species. Pyrosequencing is a useful tool in the study dosage changes in allopolyploids because it allows quantification of the relative contribution from each progenitor species at any given locus. Here we use pyrosequencing to analyze resynthesized Brassica napus allopolyploids and their progeny. Targets for pyrosequencing were identified using a bioinformatic approach taking advantage of recently-released Brassica genome sequence. SNPs identified through bioinformatics were confirmed through molecular biology. Markers along the A3/C3 homeolog pair were used to identify the occurrence of novel homeologous exchanges during meiosis in the parent plant, and segregation patterns arising from dosage changes in the parent. We identify a higher frequency of homeologous rearrangements at the distal end of the chromosomes. We also observe that the presence of a dosage change in a parent increases the likelihood that the chromosome bearing the dosage change will undergo subsequent rearrangements in neighboring loci.

The amount of data and analysis being published and archived in the biomedical research community is more than can feasibly be sifted through manually, which limits the information an individual or small group can synthesize and integrate into their own research. This presents an opportunity for using automated methods, including Natural Language Processing (NLP), to extract important information from text on various topics. Named Entity Recognition (NER), is one way to automate knowledge extraction of raw text. NER is defined as the task of identifying named entities from text using labels such as people, dates, locations, diseases, and proteins. There are several NLP tools that are designed for entity recognition, but rely on large established corpus for training data. Biomedical research has the potential to guide diagnostic and therapeutic decisions, yet the overwhelming density of publications acts as a barrier to getting these results into a clinical setting. An exceptional example of this is the field of breast cancer biology where over 2 million people are diagnosed worldwide every year and billions of dollars are spent on research. Breast cancer biology literature and research relies on a highly specific domain with unique language and vocabulary, and therefore requires specialized NLP tools which can generate biologically meaningful results. This thesis presents a novel annotation tool, that is optimized for quickly creating training data for spaCy pipelines as well as exploring the viability of said data for analyzing papers with automated processing. Custom pipelines trained on these annotations are shown to be able to recognize custom entities at levels comparable to large corpus based recognition.