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End date: 2020 × Start date: 2010 × search.filters.applied.f.resourcesubtype: Dissertation ×

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Differential effects of molecular chaperones on various types of protein aggregates

2020-12-09 , Chhabria, Rakhee M.

Proteins have the intrinsic ability to convert from their native functional state into insoluble fibrous protein aggregates known as amyloids. The assembly of different misfolded proteins into amyloid fibrils is a key feature in a wide range of proteopathies, including Alzheimer's disease, Parkinson’s disease, Huntington’s disease, Amyotrophic lateral sclerosis and Creutzfeldt-Jakob disease to name a few. My work has focused on understanding how molecular chaperones and protein degradation machineries operate in a continuous system of checks and balances to maintain proteostasis in eukaryotic cells. Firstly, I have characterized evolutionarily conserved, essential eukaryotic members of the AAA+ superfamily, RuvbL1 and RuvbL2 (yeast homologs Rvb1 and Rvb2), as novel mammalian disaggregases capable of reversing heat shock damage as well as key chaperones in modulating the formation of the aggresome quality control compartment in yeast. Secondly, I have shown that depletion of Rvb1, Rvb2 or its adaptor protein, Tah1, has differential effects on the aggregation patterns of different amylogenic proteins such as Abeta and Tau (associated with Alzheimer’s disease in humans) and Sup35 (an endogenous yeast protein capable of forming the self-perpetuating amyloid state, termed [PSI+] prion). Lastly, I have characterized a novel process by which the ubiquitin-proteasome machinery exerts its effects on proteins containing an amyloid core. Our work has shown that the E3 ligase Rsp5, capable of ubiquitinating the chaperones Hsp104 and Hsp70-Ssb, modulates the effects of these chaperones on the propagation and formation self-perpetuating amyloid aggregates (prions) in yeast. Overall, our work provides new information on how molecular chaperones and protein degradation pathways cope with protein aggregation. These data can be applied to better understanding events causing human amyloid diseases.

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QUANTITATIVE METHODS TO UNDERSTAND REPRODUCTIVE ISOLATION THAT CONTRIBUTE TO SPECIATION

2020-10-29 , Long, Lijiang

Ever since Darwin’s qualitative theory of the origin of species, there is growing demand for quantitative methods to study mechanisms underlying the speciation process. One key component towards new species formation is reproductive isolation. Reproductive isolation can be either pre-mating (e.g. mating behavior difference) or post-mating (e.g. Dobzhansky-Muller sites). In this thesis, I will present novel quantitative methods developed to study two aspects of reproductive isolation: mating rituals difference in Lake Malawi cichlids and genetic incompatibilities by selfish genetic elements in C. elegans. In chapter 2, I will talk about cichlids bower behaviors where male fishes construct bowers to attract female mates by manipulating sand with their mouths thousands of times over the course of many days. Variations in bower type (‘pits’ and ‘castles’) is one mechanism to create nonrandom mating and maintain a large number of cichlids species in Lake Malawi. To enable quantitative comparisons of these behaviors in different species, an automatic behavior quantification pipeline was built. Specifically, pixel-based Hidden Markov Modeling was combined with density-based spatiotemporal clustering for action detection. Each action video clip was then classified into ten categories using a 3D Residual Network (3D ResNet). These ten categories distinguish spitting, scooping, fin swipes and spawning. I showed that this approach is accurate (> 76% accuracy) in distinguishing fish behaviors and animal intent can be determined from these clips, as spits and scoops performed during bower construction are classified independently from spits and scoops performed during feeding. I applied this approach to >700 hours of video recordings taken from seven independent trials encompassing multiple species and hybrid crosses, collectively containing hundreds of thousands of independent behavioral events. In chapter 3, I use quantitative methods to measure fitness combined with population modeling to study the evolutionary origin of selfish genetic elements and their ability to spread in populations. Previous research found that a toxin-antidote element called peel-zeel is under balancing selection. Here, I explore different models that could cause balancing selection on this locus, which make different predictions on the fitness effect of the peel-zeel locus in hermaphrodites. However, pair-wise competition assays showed the loss of the toxin gene peel-1 decreased fitness of hermaphrodites, contradicting my expectation that peel-1 will decrease animal fitness due to its toxicity. This fitness advantage is independent of the antidote gene zeel-1. This work showed that toxin-antidote systems can spread through populations independent of their selfish effects and suggests linked variants for dauer pheromone response could be responsible for the balancing selection. Finally, in chapter 4, I use simulation methods to study the effect of toxin-antidote elements on linked and unlinked genetic variation in the case of admixture. While both simulation and calculation showed toxin-antidote elements are able to quickly spread in a population without toxin-antidote element, the evolution trajectories of the rest of the genome depends on the initial frequency of the toxin-antidote haplotype in the admixed population. Using calculations and simulations, I showed that unlinked neutral genetic variants will increase their frequency when the initial frequency of peel-zeel is higher than 1/3 and decrease when the initial frequency of peel-zeel is lower than 1/3. My doctoral thesis with many quantitative methods will advance our understanding of the genetic basis of species evolution and evolutional dynamics of selfish genetic elements.

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Fine-mapping of human genetic regulatory variants

2020-05-13 , Tian, Ruoyu

The majority of GWAS (Genome-Wide Association Study) identified common genetic variants map to regulatory regions of gene, and are likely to influence disease risk by affecting gene expression. One of the most important challenges is to experimentally fine-map causal regulatory variants that typically lie in credible intervals of 100 or more variants. Another large proportion of genetic variants, rare variants, are expected to have large effects causing disease in individual, but are not detectable in GWAS. Herein, I provide both experimental and computational approaches for fine-mapping common and rare genetic variants accounting for medium and large effect on population or individual. First, I describe a single cell clone-based strategy for targeted single-nucleotide polymorphism (SNP) evaluation wherein microindels are introduced by CRISPR/Cas9. Multiple constraints, including the variability in mutability, clonal genotype and expense, render this approach infeasible for fine-mapping 10%-20% moderate effect size expression SNPs (eSNPs), which is also validated in a simulation study. Subsequently, I switch to a moderate-throughput parallel screening tool that characterizes multiplexed CRISPR/Cas9 perturbed transcriptomes by single-cell RNA-seq, called “expression CROP-seq”. Two causal SNPs, rs2251039 and rs35675666, are identified that significantly alter the expression of CISD1 and PARK7, respectively. The sites overlap with chromatin accessibility peaks and are risk loci of inflammatory bowel disease. Expression CROP-seq reduces the variability identified in previous method and is powerful to screen genetic regulatory variants within credible intervals. Finally, to extend its application to rare variants, I develop a novel gene categorization system according to gene intolerance to promoter polymorphism and depletion of rare regulatory variants with GTEx v8 data. 49 GTEx tissues are clustered into functional groups with gene features. It supports the use of tissue-gene genomic annotation for prioritization of GWAS tagged risk loci. In summary, this work comprehensively describes and evaluates two CRISPR/Cas9-based eSNP screening systems. The use of rare regulatory variants in gene classification with tissue information demonstrates its potential in rare disease diagnoses. Both researches inevitably contribute to the genetic interpretation of human complex disease and personalized medicine in post-GWAS era.

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MOLECULAR MECHANISMS OF RNA-MEDIATED DNA REPAIR AND MODIFICATION

2020-01-13 , Meers, Chance

Genomic stability is essential in maintaining the accurate inheritance of genetic material from mother to daughter cell. However, endogenous and exogenous stresses are continuously attacking and damaging genetic information. In responses, cells developed mechanisms to prevent and correct these damages. DNA can be damaged in a variety of different ways, with DNA double-stranded breaks being one of the most dangerous lesions. We recently showed that RNA can be used as a template for repair in budding yeast. Here, we investigate the molecular mechanisms by which RNA can template the repair of DNA damage. We determine the role of endogenous retrotransposons and characterize molecular components that are required for repair by reverse transcribed transcript-RNA. To better understand the role of RNA in directly templating DNA repair, we eliminated components required for retrotransposon-mediated repair events to find that RNA can directly template double-strand break repair. We show that this process is strongly reliant on translesion DNA polymerase ζ (Zeta). Remarkably, we uncover a role of RNA directly modifying genomic DNA in the absence of induced DNA damage, revealing a novel role of translesion DNA synthesis.

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Star-shaped bubbles and cubic feces: geometry through soft matter

2020-12-07 , Lee, Alexander Bo-Ping

In this thesis, we consider how mammals use soft tissue to generate geometric shapes out of non-living materials. The star-nosed mole sniffs for prey underwater by rapidly exhaling and inhaling bubbles without letting the bubbles pinch off. The bare-nosed wombat forms cubic feces, displaying 6 flat sides and 8 rounded corners. We develop mathematical models supported by simple table-top experiments to better understand how these mammals accomplish such amazing feats. These species control the fluids through interactions with solid tissue. Understanding these interactions could lead to innovations in chemical sensing and manufacturing.

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Bioinformatic platforms and methods for worldwide polygenic risk scores

2020-08-20 , Chande, Aroon T.

Genetic diversity underpins much of observed human phenotypic diversity and plays an important role in human health and disease. This dissertation is focused on exploring the genetic architecture of phenotypic diversity among global populations and studying common complex disease in genetically diverse but geographically close communities. This work is motivated by prevalent health disparities that disproportionately affect disadvantaged populations across the world, and in particular, those in the Americas. I utilize thousands of genomes from diverse populations worldwide, along with hundreds of genome-wide association studies (GWAS) on thousands of human traits, to address three overarching questions: (1) which phenotypes vary among populations, and what explains that variance?; (2) is it possible to predict and stratify risk for common complex diseases across diverse populations?; and (3) can we apply already discovered genetic associations to risk prediction in new and ancestrally distinct populations? Polygenic risk scores (PGS) are increasingly used to quantify individuals’ genetic predisposition for disease. I developed the first of its kind web platform for PGS computation and visualization, GADGET, The Global Distribution of Genetic Traits webserver (https://gadget.biosci.gatech.edu/). GADGET enables biomedical researchers to easily test hypotheses and generate publication-ready visualizations of PGS for thousands of individuals in 27 global populations. I also developed a specialized, country and population-specific PGS server, the Colombian Phenotype-Genotype Browser (CPGB; https://map.chocogen.com/), to support precision public health efforts in Colombia. Next, I leveraged the PGS curation from GADGET to explore the differentiation of single loci and polygenic traits between neighboring populations of Afro-Colombians in Chocó and Euro-Colombians in Antioquia. I developed PGS and found that they largely reflect the observed health disparities for seven high-cost and high-burden common complex diseases in Colombia. Interestingly, PGS for type 2 diabetes (T2D) significantly over-predicted risk in Afro-Colombians. Further analysis of T2D in Colombia revealed the importance of environmental and lifestyle effects on T2D. In Colombia, in contrast to much of the developed world, low socioeconomic status was correlated with decreased prevalence for T2D. My final study brings the focus back to the US and developed a correction method for applying already ascertained SNP-trait associations, again for T2D, in diverse populations. I predicted T2D risk in Mexican-Americans and European-Americans and validated my predictions at the population level using epidemiological data. A simulation-based correction method utilizing the derived allele frequency spectrum for trait-associated variants was used to correct PGS bias between ancestrally divergent populations. Together, these studies underscore how genetic diversity contributes to global phenotypic variance. Differences in population PGS distributions are generally an accurate indicator of relative disparities between populations in a country; although, differences in ancestry impact the accuracy of individuals’ PGS. In cases where predictions do not match observed disparities, there are significant socioeconomic and environmental effects that mediate the genetic component of disease risk. Finally, simulation-based controls showed promise for helping to account for and correct bias in PGS when transferring associations between populations with distinct ancestry.

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Chemotherapy Induced Sensory Neuropathy Depends on Non-Linear Interactions with Cancer

2020-03-30 , Housley, Stephen N.

For the constellation of neurological disorders known as chemotherapy induced neuropathy, mechanistic understanding, and treatment remain deficient. In project one, I leveraged a multi-scale experimental approach to provide the first evidence that chronic sensory neuropathy depends on non-linear interactions between cancer and chemotherapy. Global transcriptional profiling of dorsal root ganglia revealed amplified differential expression, notably in regulators of neuronal excitability, metabolism and inflammatory responses, all of which were unpredictable from effects observed with either chemotherapy or cancer alone. Systemic interactions between cancer and chemotherapy also determined the extent of deficits in sensory encoding in vivo and ion channel protein expression by single mechanosensory neurons, with the potassium ion channel Kv3.3 emerging as candidate mechanisms explaining sensory neuron dysfunction. The sufficiency of this novel molecular mechanism was tested in an in silico biophysical model of mechanosensory function. Finally, validated measures of sensorimotor behavior in awake behaving animals confirmed that dysfunction after chronic chemotherapy treatment is exacerbated by cancer. Notably, errors in precise fore-limb placement emerged as a novel behavioral deficit unpredicted by our previous study of chemotherapy alone. These original findings identify novel contributors to peripheral neuropathy, and emphasize the fundamental dependence of neuropathy on the systemic interaction between chemotherapy and cancer across multiple levels of biological control. In project two, I extend study to multiple classes of mechanosensory neurons that are necessary for generating the information content (population code) needed for proprioception. I first tested the hypothesis that exacerbated neuronal dysfunction is conserved across multiple classes of mechanosensory neurons. Results revealed co-suppression of specific signaling parameters across all neuronal classes. To understand the consequences of corrupt population code, I employed a long-short-term memory neural network (LSTM), a deep-learning algorithm, to test how decoding of spatiotemporal features of movement are altered after chemotherapy treatment of cancer. Results indicate that spiking activity from the population of neurons in animals with cancer, treated by chemotherapy contain significantly less information about key features of movement including, e.g. timing, magnitudes, and velocity. I then modeled the central nervous systems (CNS) capacity to compensate for this information loss. Even under optimal learning conditions, the inability to fully restore predictive power suggests that the CNS would not be able to compensate and restore full function. Our results support our proposal that lasting deficits in mobility and perception experienced by cancer survivors can originate from sensory information that is corrupted and un-interpretable by CNS neurons or networks. Collectively, I present the first evidence that chronic cancer neuropathy cannot be explained by the effects of chemotherapy alone but instead depend on non-linear interactions with cancer. This understanding is a prerequisite for designing future studies and for developing effective treatments or preventative measures.

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Prediction of disease risks across multiple populations using evolutionary genetics

2020-11-30 , Kim, Michelle

Complex diseases tend to be polygenic and allele frequencies at disease-associated loci vary widely across the globe. Interestingly, while some disease risks are similar across continents, the incidence and mortality rates of prostate cancer vary greatly across global populations. This dissertation is focused on exploring the genetic architecture of phenotypic diversity among global populations. The motivation behind this work is to bridge the gap between evolutionary genetics and genetic epidemiology with the following questions: (1) Why do we observe risk allele frequency differences across populations? (2) How well do GWAS signals replicate across populations with different evolutionary histories? (3) How well can discovered genetic associations predict risk across different populations? Thousands of genome-wide association studies (GWAS) have successfully identified genetic associations with common diseases and other traits. However, the vast majority of published GWAS have used samples of European ancestry and genotyping arrays as opposed to whole genome sequencing. By simulating GWAS with different study populations, I found that non-African cohorts (bottlenecked populations) yield disease associations that have biased allele frequencies and that African cohorts yield disease associations that are relatively free of bias. In addition, I found empirical evidence that genotyping arrays and SNP ascertainment bias contribute to continental differences in risk allele frequencies. Next, I studied the replicability of trait-associations in a European cohort from the UK Biobank to examine whether continental ancestry impacts the results. By comparing GWAS results from the UK Biobank to the novel sub-Saharan Africa dataset, I found that trait associations from European GWAS poorly replicate in sub-Saharan Africa. Notably, the converse was not valid: the top hits from African GWAS were enriched for low p-values in the UK Biobank. GWAS do not always replicate well across populations, and this can cause polygenic risk scores (PRS) to poorly predict disease risks. PRS quantify an individual’s chances of having a disease by summing up the number of risk-increasing alleles in each individual’s genome. Here, I quantified how well genetic predictions of prostate cancer work in different continental populations using PRS that was originally generated from GWAS of European Americans. Using African individuals genotyped using the Men of African Descent and Carcinoma of the Prostate (MADCaP) Array and British individuals from the UK Biobank, I found that genetic predictions of case vs. control status were much more effective European than African individuals. Similarly, genetic prediction of height performs poorly when European results are generalized to African samples. In most PRS calculations, additive effects of risk-increasing alleles are used. Thus, an additional aspect of my dissertation work explores how non-additive models of disease influence risk predictability. Here I incorporated dominance coefficients into PRS calculations, and explored models that ranged from complete recessivity of risk alleles (h = 0) to complete dominance of risk alleles (h = 1). In general, additive models (h = 0.5) work well, but genetic predictions are marginally improved by allowing individual risk-increasing alleles to have different dominance coefficients. Together, these studies underscore how genetic variation contributes to health and disease, as well as the benefits of an evolutionary perspective and correcting for ascertainment bias. As presently calculated, PRS lead to misestimates of hereditary disease risks when they are applied to individuals with different ancestries. To remedy this problem, my research illustrates that PRS can be generated that correct for existing biases by incorporating allele dosage, SNP effect sizes, and ancestral or derived state of the risk alleles. These corrections can partially alleviate challenges that arise when PRS are applied to individuals of African descent. Overall, my work implies that caution must be taken when extrapolating GWAS results from one population to predict disease risks in another population.

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Probing pseudomonas aeruginosa physiology during infection using –omics techniques, phenotypic assays and mouse models

2020-07-22 , Michie, Kelly Leorah

The opportunistic pathogen Pseudomonas aeruginosa causes severe disease in people with compromised immune systems or co-morbidities such as diabetes or cystic fibrosis. Since even intense antibiotic regimens are often ineffective, there is a great need to better understand P. aeruginosa infection biology. Our first research goal was to elucidate the role of glutathione (GSH) biosynthesis for P. aeruginosa during infection. GSH is a major cellular antioxidant that is important for protection from oxidative stress. We found that GSH biosynthesis provides protection against some antimicrobials, such as bleach and ciprofloxacin. We also discovered that GSH biosynthesis provides a modest fitness benefit to P. aeruginosa in a mouse model of acute pneumonia, but not in chronic wound, abscess, and burn wound mouse models. Our second research goal was to characterize the transcriptomic and proteomic signatures of growth rate in P. aeruginosa. Growth rate has significant impacts on cellular physiology, from cell size to stress tolerance. We cultured P. aeruginosa at four different growth rates using a chemostat, and quantified mRNA and protein abundances using RNA-seq and proteomics mass spectrometry, respectively. We observed modest correlations between mRNA and protein expression. We also discovered that there was greater variation in mRNA expression compared to protein expression, and that mRNA expression was more strongly affected by changes in growth rate. We calculated protein-to-mRNA ratios, or conversion factors, which could be used to more accurately predict protein abundance from RNA-seq data. The information presented in this work may be useful for better understanding, and ultimately treating, P. aeruginosa infections.

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Climate change & the physiology, ecology, and behavior of coral reef organisms

2020-03-16 , Johnston, Nicole K.

The magnitude of ocean acidification (OA) and warming predicted to occur within the next century could have significant negative effects for organisms that inhabit coral reefs. Our understanding of how these stressors will impact coral reef organisms is complicated by the diverse behavioral and ecological interactions that exist on these reefs. In a series of experiments, I explored interactions between coral reef organisms, evaluated how some of these interactions may be affected by OA and warming, and then studied how environment may shape an organism’s response to a changing climate. First, through a sensory manipulated tank and a twochamber choice flume, I demonstrated that anemonefish respond to both chemical and visual conspecific cues, but they require a combination of these two cues to correctly identify conspecifics. Given that previous research indicates that fish behavioral responses to chemical cues are altered under conditions of future OA, this inability to compensate for the loss of one cue through a second cue could affect their ability to acclimate as climate changes. Second, I found that the common Caribbean mounding coral Porites astreoides, is unaffected by competition with Montastraea cavernosa and Orbicella faveolata under ambient environmental conditions, but exhibits significant reductions in photosynthetic efficiency in areas of direct contact with M. cavernosa and O. faveolata under conditions of elevated CO2 and temperature that are anticipated to occur by the year 2100. These results demonstrated that climate change can interact with competition to alter the rate and severity of coral-coral interactions on reefs of the future. Next, I compared the effects of OA and warming on the physiology of two congeneric coral species (Oculina arbuscula and Oculina diffusa) representing temperate (O. arbuscula) and tropical (O. diffusa) environments and found that, although both corals were negatively impacted by ocean acidification and warming, the temperate coral was slightly more resistant to these stressors. This suggests that temperate species may not be as disadvantaged by climate change as one might expect and may not be easily displaced by more tropical species moving poleward as global oceans warm. Finally, I evaluated the effect of elevated temperature on the well-being of the temperate coral, O. arbuscula when collected from deeper more physically stable environments versus shallower more physically variable environments. I found that corals from both deep and shallow sites were negatively impacted by elevated temperature, but that corals from deeper sites were more strongly impacted. These findings suggest that the physiologies, biotic interactions, and behaviors of reef organisms may all be affected by climate change and that outcomes of these interactions may not be simple to predict as global oceans warm and acidify and as tropical organisms shift poleward and intermix with temperate species to form novel communities.

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