I studied the molecular mechanisms of ultraviolet radiation mitigation (UVR) in the terrestrial cyanobacterium Nostoc punctiforme ATCC 29133, which produces the indole-alkaloid sunscreen scytonemin and differentiates into motile filaments (hormogonia). While the early stages of scytonemin biosynthesis were known, the late stages were not. Gene deletion mutants were interrogated by metabolite analyses and confocal microscopy, demonstrating that the ebo gene cluster, was not only required for scytonemin biosynthesis, but was involved in the export of scytonemin monomers to the periplasm. Further, the product of gene scyE was also exported to the periplasm where it was responsible for terminal oxidative dimerization of the monomers. These results opened questions regarding the functional universality of the ebo cluster. To probe if it could play a similar role in organisms other than scytonemin producing cyanobacteria, I developed a bioinformatic pipeline (Functional Landscape And Neighbor Determining gEnomic Region Search; FLANDERS) and used it to scrutinize the neighboring regions of the ebo gene cluster in 90 different bacterial genomes for potentially informational features. Aside from the scytonemin operon and the edb cluster of Pseudomonas spp., responsible for nematode repellence, no known clusters were identified in genomic ebo neighbors, but many of the ebo adjacent regions were enriched in signal peptides for export, indicating a general functional connection between the ebo cluster and biosynthetic compartmentalization. Lastly, I investigated the regulatory span of the two-component regulator of the scytonemin operon (scyTCR) using RNAseq of scyTCR deletion mutants under UV induction. Surprisingly, the knockouts had decreased expression levels in many of the genes involved in hormogonia differentiation and in a putative multigene regulatory element, hcyA-D. This suggested that UV could be a cue for developmental motility responses in Nostoc, which I could confirm phenotypically. In fact, UV-A simultaneously elicited hormogonia differentiation and scytonemin production throughout a genetically homogenous population. I show through mutant analyses that the partner-switching mechanism coded for by hcyA-D acts as a hinge between the scytonemin and hormogonia based responses. Collectively, this dissertation contributes to the understanding of microbial adaptive responses to environmental stressors at the genetic and regulatory level, highlighting their phenomenological and mechanistic complexity.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide and exhibits a male-bias in occurrence and mortality. Previous studies have provided insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and mortality. This study uses pathway analysis to add insight into the biological processes that drive sex-differences in HCC etiology as well as a provide additional framework for future studies on sex-biased cancers. Gene expression data from normal, tumor adjacent, and HCC liver tissue were used to calculate pathway scores using a tool called PathOlogist that not only takes into consideration the molecules in a biological pathway, but also the interaction type and directionality of the signaling pathways. Analysis of the pathway scores uncovered etiologically relevant pathways differentiating male and female HCC. In normal and tumor adjacent liver tissue, males showed higher activity of pathways related to translation factors and signaling. Females did not show higher activity of any pathways compared to males in normal and tumor adjacent liver tissue. Work suggest biologic processes that underlie sex-biases in HCC occurrence and mortality. Both males and females differed in the activation of pathways related apoptosis, cell cycle, signaling, and metabolism in HCC. These results identify clinically relevant pathways for future research and therapeutic targeting.

Human leukocyte antigen (HLA) is a group of proteins that the human immune system uses to detect pathogens. HLA is highly polymorphic, especially in the peptide-binding groove, which allows the binding of a diverse range of peptides including peptides produced by pathogens. Hepatitis B virus (HBV), is a pathogen that can cause liver disease. Chronic HBV infection, if left untreated, can lead to hepatocellular carcinoma, the most common form of liver cancer. In this paper, the association of Class I and II HLA with HBV-mediated liver cancer in patients of East Asian and European ancestry was studied. Results showed that, in the initial combined ancestry analysis, some alleles from all HLA types are associated with HBV-mediated liver cancer. However, once stratified by population ancestry, most of the alleles are no longer significant but still associate with HBV-mediated liver cancer in the same directions. In contrast, HLA-DP is the only HLA with haplotypes that are significantly different before and after stratification by ancestry. Notably, DPA10103-DPB10401, a previously known protective haplotype in the Asian population, is associated negatively with HBV-mediated liver cancer in both East Asian and European populations. Additionally, DPA10202-DPB10501, a known risk haplotype in the Asian population, is associated positively with HBV-mediated liver cancer patients of European ancestry. To understand how HLA-DP is associated with HBV-mediated liver cancer, the binding affinity of HLA-DP to all peptides generated from HBV coding sequences of genotypes A-H was predicted. It was speculated that an individual with HLA types that can bind strongly to HBV peptides will be more likely to clear viral infection whereas an individual with HLA types that fail to bind strongly to HBV peptides will be less likely to clear viral infection, thus developing chronic infection. Results showed that DPA10103-DPB10401 binds strongly to HBV peptides (<50nM) whereas DPA10202-DPB10501 does not bind strongly to any HBV peptides (>50nM), consistent with the speculation that the binding affinity of HBV peptides to HLA will influence the association of HLA with HBV-mediated liver cancer.

The Pathways of Distinction Analysis (PoDA) program calculates relationships between a given group of genes contained within a pathway, and a disease state. It was used here to investigate liver cancer, and to explore how genetic variability may contribute to the different rates of development of the disease in males and females. The goal of the study was to identify germline variation that differs by sex in hepatocellular carcinoma. Using the program, multiple pathways and genes were identified to have significant differences in their relationship to liver cancer in males and females. In animal studies, the genes which were identified using the PoDA analysis have been shown to impact liver cancer, often with different results for males and females. While these genes are often the focus in animal models, they are absent from current Genome Wide Association Studies (GWAS) catalogs for humans. By working to bridge the results of animal studies and human studies, the results help to identify the causes of liver cancer, and more specifically, the reason the disease affects males at much higher rates. The differences in pathways identified to be significant for the two sexes indicate the germline variance may play sex-specific roles in the development of hepatocellular carcinoma. Additionally, these results reinforce the capacity of the PoDA analysis to identify genes that may be missed by more traditional GWAS methods. This study lays the groundwork for further investigations into the identified genes and pathways, and how they behave differently within males and females.

The family Cactaceae is extremely diverse and has a near global distribution yet very little has been described regarding the community of viruses that infect or are associated with cacti. This research characterizes the diversity of viruses associated with Cactaceae plants and their evolutionary aspects. Five viruses belonging to the economically relevant plant virus family Geminiviridae were identified, initially, two novel divergent geminiviruses named Opuntia virus 1 (OpV1) and Opuntia virus 2 (OpV2) and Opuntia becurtovirus, a new strain within the genus Becurtovirus. These three viruses were also found in co-infection. In addition, two known geminiviruses, the squash leaf curl virus (SLCV) and watermelon chlorotic stunt virus (WCSV) were identified infecting Cactaceae plants and other non-cactus plants in the USA and Mexico. Both SLCV and WCSV are known to cause severe disease in cultivated Cucurbitaceae plants in the USA and Middle East, respectively. This study shows that WCSV was introduced in the America two times, and it is the first identification of this virus in the USA, demonstrating is likely more widespread in North America. These findings along with the Opuntia becurtovirus are probable events of spill-over in agro-ecological interfaces. A novel circular DNA possibly bipartite plant-infecting virus that encodes protein similar to those of geminiviruses was also identified in an Opuntia discolor plant in Brazil, named utkilio virus, but it is evolutionary distinct likely belonging to a new taxon. Viruses belonging to the ssDNA viral family Genomoviridae are also described and those thus far been associated with fungi hosts, so it is likely the ones identified in plants are associated with their phytobiome. Overall, the results of this project provide a molecular and biological characterization of novel geminiviruses and genomoviruses associated with cacti as well as demonstrate the impact of agro-ecological interfaces in the spread of viruses from or to native plants. It also highlights the importance of viral metagenomics studies in exploring virus diversity and evolution given then amount of virus diversity identified. This is important for conservation and management of cacti in a global scale, including the relevance of controlled movement of plants within countries.

Mayer-Rokitansky-Küster-Hauser (MRKH) is a rare Disorder of Sexual Development (DSD) that results in the lack of a uterus and vagina in women. Receiving this diagnosis during adolescence can cause various forms of psychological distress in patients and families.<br/>Specifically, this condition could affect a women’s gender identity, body image, romantic relationships, family relationships, and psychological wellbeing. Parents are also put in a stressful<br/>position as they now have to navigate the healthcare system, disclosure, and the relationship with their child. This study aims to expand the knowledge of psychosocial adjustment by studying body<br/>image, gender identity, and mental health in individuals living with MRKH as well as parental disclosure, parental support systems, and parental perceptions of their child’s mental health.

Alzheimer’s disease is a disease that can affect cognition, perception and behavior and is currently untreatable. It was discovered in the early 20th century and while significant scientific advancements have occurred, there is ambiguity that remains to be researched and understood. Latinos are the largest ethnic minority in the United States and while data still needs to be uncovered, possible risk factors for developing Alzheimer’s include heart issues, poverty and obesity, age and education level, to name a few. Poverty is linked to obesity, diabetes and a low education level, which in turn have been found to have an impact on Alzheimer’s and all factors impact cardiovascular and vascular health. Due to the collectivistic culture that is deeply rooted in Latinos, there is a strong sense of family that is upheld when caring for relatives who are afflicted and may be hesitant to receive the care that is needed. Other barriers include financial stability, linguistic and cultural barriers, underutilizing resources and health literacy. There are still research gaps that are yet to be filled like brain health and longitudinal studies for Latinos, but current treatments like diet and culturally competent professionals can help with the prognosis. Alzheimer’s is a complex disease, but with the numerous efforts made thus far, such as creating the LatinosAgainstAlzheimer’s Network, it will soon be able to be understood and hopefully eradicated.

Next-generation sequencing is a powerful tool for detecting genetic variation. How-ever, it is also error-prone, with error rates that are much larger than mutation rates.
This can make mutation detection difficult; and while increasing sequencing depth
can often help, sequence-specific errors and other non-random biases cannot be de-
tected by increased depth. The problem of accurate genotyping is exacerbated when
there is not a reference genome or other auxiliary information available.
I explore several methods for sensitively detecting mutations in non-model or-
ganisms using an example Eucalyptus melliodora individual. I use the structure of
the tree to find bounds on its somatic mutation rate and evaluate several algorithms
for variant calling. I find that conventional methods are suitable if the genome of a
close relative can be adapted to the study organism. However, with structured data,
a likelihood framework that is aware of this structure is more accurate. I use the
techniques developed here to evaluate a reference-free variant calling algorithm.
I also use this data to evaluate a k-mer based base quality score recalibrator
(KBBQ), a tool I developed to recalibrate base quality scores attached to sequencing
data. Base quality scores can help detect errors in sequencing reads, but are often
inaccurate. The most popular method for correcting this issue requires a known
set of variant sites, which is unavailable in most cases. I simulate data and show
that errors in this set of variant sites can cause calibration errors. I then show that
KBBQ accurately recalibrates base quality scores while requiring no reference or other
information and performs as well as other methods.
Finally, I use the Eucalyptus data to investigate the impact of quality score calibra-
tion on the quality of output variant calls and show that improved base quality score
calibration increases the sensitivity and reduces the false positive rate of a variant
calling algorithm.

Pathway analysis helps researchers gain insight into the biology behind gene expression-based data. By applying this data to known biological pathways, we can learn about mutations or other changes in cellular function, such as those seen in cancer. There are many tools that can be used to analyze pathways; however, it can be difficult to find and learn about the which tool is optimal for use in a certain experiment. This thesis aims to comprehensively review four tools, Cytoscape, PaxtoolsR, PathOlogist, and Reactome, and their role in pathway analysis. This is done by applying a known microarray data set to each tool and testing their different functions. The functions of these programs will then be analyzed to determine their roles in learning about biology and assisting new researchers with their experiments. It was found that each tools holds a very unique and important role in pathway analysis. Visualization pathways have the role of exploring individual pathways and interpreting genomic results. Quantification pathways use statistical tests to determine pathway significance. Together one can find pathways of interest and then explore areas of interest.

Analyzing human DNA sequence data allows researchers to identify variants associated with disease, reconstruct the demographic histories of human populations, and further understand the structure and function of the genome. Identifying variants in whole genome sequences is a crucial bioinformatics step in sequence data processing and can be performed using multiple approaches. To investigate the consistency between different bioinformatics methods, we compared the accuracy and sensitivity of two genotyping strategies, joint variant calling and single-sample variant calling. Autosomal and sex chromosome variant call sets were produced by joint and single-sample calling variants for 10 female individuals. The accuracy of variant calls was assessed using SNP array genotype data collected from each individual. To compare the ability of joint and single-sample calling to capture low-frequency variants, folded site frequency spectra were constructed from variant call sets. To investigate the potential for these different variant calling methods to impact downstream analyses, we estimated nucleotide diversity for call sets produced using each approach. We found that while both methods were equally accurate when validated by SNP array sites, single-sample calling identified a greater number of singletons. However, estimates of nucleotide diversity were robust to these differences in the site frequency spectrum between call sets. Our results suggest that despite single-sample calling’s greater sensitivity for low-frequency variants, the differences between approaches have a minimal effect on downstream analyses. While joint calling may be a more efficient approach for genotyping many samples, in situations that preclude large sample sizes, our study suggests that single-sample calling is a suitable alternative.