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Description
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.
ContributorsRehling, Thomas Evan (Author) / Buetow, Kenneth (Thesis director) / Wilson, Melissa (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Parabasalia is a phylum of flagellated protists with a large range of cell sizes, spanning from as little as 7 µm in length (e.g. Pentatrichomonas hominis) to well over 300 µm (e.g. Pseudotrichonympha grassii). Many Parabasalia are associated with animals in mutualistic, parasitic, or commensal relationships. The largest Parabasalia species are obligate mutualists of termites, which help to digest lignocellulose. While the specific digestive roles of different protist species are mostly unknown, Parabasalia with different cell sizes are known to inhabit different regions of the termite hindgut. It is currently unclear whether these size differences are driven by selection or drift, but it is well known that cell size correlates with genome size in eukaryotes. Therefore, in order to gain insight into possible selection pressures or mechanisms for cell size increase, genome sizes were estimated for the five Parabasalia species that inhabit the hindgut of Coptotermes formosanus Shiraki. The cell volumes and C-values for the five protist species are 89,190 µm3 and 147 pg in Pseudotrichonympha grassii, 26,679 µm3 and 56 pg in Holomastigotoides hartmanni, 8,985 µm3 and 29 pg in Holomastigotoides minor, 1,996 µm3 and 12 pg in Cononympha leidyi , and 386 µm3 and 6 pg in Cononympha koidzumii. The positive correlation between genome size and cell size was maintained in this group (R2 = 0.76). These genome sizes are much larger than the previously estimated genome sizes of non-termite associated Parabasalia, which spanned 2-fold ranging from 0.088 pg (in Tetratrichomonas gallinarum) to 0.181 pg (in Trichomonas foetus). With these new estimates, the range now spans over 1,500-fold from 0.088 pg to 147 pg in P. grassii, implying potential differences in the level of selective pressures for genome size in termite-associated Parabasalia compared to other protists.
ContributorsMontoya, Samantha (Author) / Gile, Gillian (Thesis advisor) / Wideman, Jeremy (Committee member) / Chouvenc, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Predatory bacteria are a guild of heterotrophs that feed directly on other living bacteria. They belong to several bacterial lineages that evolved this mode of life independently and occur in many microbiomes and environments. Current knowledge of predatory bacteria is based on culture studies and simple detection in natural systems. The ecological consequences of their activity, unlike those of other populational loss factors like viral infection or grazing by protists, are yet to be assessed. During large-scale cultivation of biological soil crusts intended for arid soil rehabilitation, episodes of catastrophic failure were observed in cyanobacterial growth that could be ascribed to the action of an unknown predatory bacterium using bioassays. This predatory bacterium was also present in natural biocrust communities, where it formed clearings (plaques) up to 9 cm in diameter that were visible to the naked eye. Enrichment cultivation and purification by cell-sorting were used to obtain co-cultures of the predator with its cyanobacterial prey, as well as to identify and characterize it genomically, physiologically and ultrastructurally. A Bacteroidetes bacterium, unrelated to any known isolate at the family level, it is endobiotic, non-motile, obligately predatory, displays a complex life cycle and very unusual ultrastructure. Extracellular propagules are small (0.8-1.0 µm) Gram-negative cocci with internal two-membrane-bound compartmentalization. These gain entry to the prey likely using a suite of hydrolytic enzymes, localizing to the cyanobacterial cytoplasm, where growth begins into non-compartmentalized pseudofilaments that undergo secretion of vesicles and simultaneous multiple division to yield new propagules. I formally describe it as Candidatus Cyanoraptor togatus, hereafter Cyanoraptor. Its prey range is restricted to biocrust-forming, filamentous, non-heterocystous, gliding, bundle-making cyanobacteria. Molecular meta-analyses showed its worldwide distribution in biocrusts. Biogeochemical analyses of Cyanoraptor plaques revealed that it causes a complete loss of primary productivity, and significant decreases in other biocrusts properties such as water-retention and dust-trapping capacity. Extensive field surveys in the US Southwest revealed its ubiquity and its dispersal-limited, aggregated spatial distribution and incidence. Overall, its activity reduces biocrust productivity by 10% at the ecosystem scale. My research points to predatory bacteria as a significant, but overlooked, ecological force in shaping soil microbiomes.
ContributorsBethany Rakes, Julie Ann (Author) / Garcia-Pichel, Ferran (Thesis advisor) / Gile, Gillian (Committee member) / Cao, Huansheng (Committee member) / Jacobs, Bertram (Committee member) / Arizona State University (Publisher)
Created2022
Description
While only the sixth most common cancer globally, liver cancer is the third most deadly. Despite the importance of accurate diagnosis and effective treatment, standard diagnostic tests for most solid organ neoplasms are not required for the most common type of liver cancer, Hepatocellular Carcinoma (HCC). In addition, major discrepancies in the practices currently in place limits the ability to develop more precise oncological treatment and prognosis. This study aimed to identify biomarkers, with potential to more accurately diagnose how far cancer has advanced within a patient and determine prognosis. It is the hope that pathways provided by this study form the basis for future research into more standardized practices and potential treatment based on specific affected biological processes. The PathOlogist tool was utilized to calculate activity metrics for 1,324 biological pathways in 374 The Cancer Genome Atlas (TCGA) hepatocellular carcinoma donors. Further statistical analysis was done on two datasets, formed to identify grade or stage at time of diagnosis for the activity levels calculated by PathOlogist. The datasets were evaluated individually. Based on the variance and normality of each pathway’s activity levels in the respective data sets analysis of variance, Tukey-Kramer, Kruskal-Wallis, and Mann-Whitney-Wilcox tests were performed, when appropriate, to determine any statistically significant differences in pathway activity levels. Pathways were identified in both stage and grade data analyses that show significant differences in activity levels across designation. While some overlap is seen, there was a significant number of pathways unique to either stage or grade. These pathways are known to affect the cell cycle, cellular transport, disease, immune system, and metabolism regulation.
The biological pathways named by this research depict prospective biomarkers for progression of hepatocellular carcinoma per subdivision within both stage and grade. These findings may be instrumental to new methods of early and more accurate diagnosis. The distinct differences in identified pathways in grade and stage illustrate the need for these new methods to not only look at stage but also grade when determining prognosis. Furthermore, the pathways identified herein have potential to aid in the development of targeted treatment based on the affected biological processes.
ContributorsGarrison, Alyssa Cameron (Author) / Buetow, Kenneth (Thesis advisor) / Hinde, Katie (Committee member) / Wilson, Melissa (Committee member) / Arizona State University (Publisher)
Created2022
Description
To combat the global antimalarial resistance crisis effective resistance management strategies are needed. To do so, I need to gain a better understanding of the ecological interactions occurring within malaria infections. Despite the importance of the complex interplay among co-infecting strains, our current knowledge and empirical data of within-host diversity and malaria disease dynamics is limited. In this thesis, I explore the multifaceted dynamics of malaria infections through an ecological lens. My overall research question is: "How do ecological interactions, including niche complementarity, competition dynamics, and the cost of resistance, shape the outcomes of malaria infections, and what implications does this have on understanding and improving resistance management strategies?” In Chapter II, titled “Niche Complementarity in Malaria Infections” I demonstrate that ecological principles are observed in malarial infections by experimentally manipulating the biodiversity of rodent malaria P. chabaudi infections. I observed that some parasites experienced competitive suppression, others experienced competitive facilitation, while others were not impacted. Next, in Chapter III, titled “Determining the Differential Impact of Competition Between Genetically Distinct Plasmodium falciparum Strains” I investigate the differential effect of competition among six genetically distinct strains. The impact of competition varied between strain combinations, and both suppression and facilitation were observed, but most pairings had no competitive interactions. Lastly, in Chapter IV, titled “Assessing Fitness Costs in Malaria Parasites: A Comprehensive Review and Implications for Drug Resistance Management”, I summarize where the field currently stands and what evidence there is for the presence of a fitness cost, or lack thereof, and I highlight the current gaps in knowledge. I found that evidence from field, in vitro, and animal models are overall suggestive of the presence of a fitness cost, however, these costs were not always found. Amid the current focus on malaria eradication, it is crucial to understand the impact of biodiversity on disease severity. By incorporating an ecological approach to infectious disease systems, I can gain insights on within-host interactions and how they impact parasite fitness and transmissibility.
ContributorsSegovia, Xyonane (Author) / Huijben, Silvie (Thesis advisor) / Bean, Heather (Committee member) / Gile, Gillian (Committee member) / Hogue, Ian (Committee member) / Lake, Douglas (Committee member) / Arizona State University (Publisher)
Created2024
Description
Evolutionary theory provides a rich framework for understanding cancer dynamics across scales of biological organization. The field of cancer evolution has largely been divided into two domains, comparative oncology - the study of cancer across the tree of life, and tumor evolution. This work provides a theoretical framework to unify these subfields with the intent that an understanding of the evolutionary dynamics driving cancer risk at one scale can inform the understanding of the dynamics on another scale. The evolution of multicellular life and the unique vulnerabilities in the cellular mechanisms that underpin it explain the ubiquity of cancer prevalence across the tree of life. The breakdown in cellular cooperation and communication that were required for multicellular life define the hallmarks of cancer. As divergent life histories drove speciation events, it similarly drove divergences in fundamental cancer risk across species. An understanding of the impact that species’ life history theory has on the underlying network of multicellular cooperation and somatic evolution allows for robust predictions on cross-species cancer risk. A large-scale veterinary cancer database is utilized to validate many of the predictions on cancer risk made from life history evolution. Changing scales to the cellular level, it lays predictions on the fate of somatic mutations and the fitness benefits they confer to neoplastic cells compared to their healthy counterparts. The cancer hallmarks, far more than just a way to unify the many seemingly unique pathologies defined as cancer, is a powerful toolset to understand how specific mutations may change the fitness of somatic cells throughout carcinogenesis and tumor progression. Alongside highlighting the significant advances in evolutionary approaches to cancer across scales, this work provides a lucid confirmation that an understanding of both scales provides the most complete portrait of evolutionary cancer dynamics.
ContributorsCompton, Zachary Taylor (Author) / Maley, Carlo C. (Thesis advisor) / Aktipis, Athena (Committee member) / Buetow, Kenneth (Committee member) / Nedelcu, Aurora (Committee member) / Compton, Carolyn (Committee member) / Arizona State University (Publisher)
Created2023
Description
Understanding changes and trends in biomedical knowledge is crucial for individuals, groups, and institutions as biomedicine improves people’s lives, supports national economies, and facilitates innovation. However, as knowledge changes what evidence illustrates knowledge changes? In the case of microbiome, a multi-dimensional concept from biomedicine, there are significant increases in publications, citations, funding, collaborations, and other explanatory variables or contextual factors. What is observed in the microbiome, or any historical evolution of a scientific field or scientific knowledge, is that these changes are related to changes in knowledge, but what is not understood is how to measure and track changes in knowledge. This investigation highlights how contextual factors from the language and social context of the microbiome are related to changes in the usage, meaning, and scientific knowledge on the microbiome. Two interconnected studies integrating qualitative and quantitative evidence examine the variation and change of the microbiome evidence are presented. First, the concepts microbiome, metagenome, and metabolome are compared to determine the boundaries of the microbiome concept in relation to other concepts where the conceptual boundaries have been cited as overlapping. A collection of publications for each concept or corpus is presented, with a focus on how to create, collect, curate, and analyze large data collections. This study concludes with suggestions on how to analyze biomedical concepts using a hybrid approach that combines results from the larger language context and individual words. Second, the results of a systematic review that describes the variation and change of microbiome research, funding, and knowledge are examined. A corpus of approximately 28,000 articles on the microbiome are characterized, and a spectrum of microbiome interpretations are suggested based on differences related to context. The collective results suggest the microbiome is a separate concept from the metagenome and metabolome, and the variation and change to the microbiome concept was influenced by contextual factors. These results provide insight into how concepts with extensive resources behave within biomedicine and suggest the microbiome is possibly representative of conceptual change or a preview of new dynamics within science that are expected in the future.
ContributorsAiello, Kenneth (Author) / Laubichler, Manfred D (Thesis advisor) / Simeone, Michael (Committee member) / Buetow, Kenneth (Committee member) / Walker, Sara I (Committee member) / Arizona State University (Publisher)
Created2018
Description
In most diploid cells, autosomal genes are equally expressed from the paternal and maternal alleles resulting in biallelic expression. However, as an exception, there exists a small number of genes that show a pattern of monoallelic or biased-allele expression based on the allele’s parent-of-origin. This phenomenon is termed genomic imprinting and is an evolutionary paradox. The best explanation for imprinting is David Haig's kinship theory, which hypothesizes that monoallelic gene expression is largely the result of evolutionary conflict between males and females over maternal involvement in their offspring. One previous RNAseq study has investigated the presence of parent-of-origin effects, or imprinting, in the parasitic jewel wasp Nasonia vitripennis (N. vitripennis) and its sister species Nasonia giraulti (N. giraulti) to test the predictions of kinship theory in a non-eusocial species for comparison to a eusocial one. In order to continue to tease apart the connection between social and eusocial Hymenoptera, this study proposed a similar RNAseq study that attempted to reproduce these results in unique samples of reciprocal F1 Nasonia hybrids. Building a pseudo N. giraulti reference genome, differences were observed when aligning RNAseq reads to a N. vitripennis reference genome compared to aligning reads to a pseudo N. giraulti reference. As well, no evidence for parent-of-origin or imprinting patterns in adult Nasonia were found. These results demonstrated a species-of-origin effect. Importantly, the study continued to build a repository of support with the aim to elucidate the mechanisms behind imprinting in an excellent epigenetic model species, as it can also help with understanding the phenomenon of imprinting in complex human diseases.
ContributorsUnderwood, Avery Elizabeth (Author) / Wilson, Melissa (Thesis advisor) / Buetow, Kenneth (Committee member) / Gile, Gillian (Committee member) / Arizona State University (Publisher)
Created2019
Description
In 1968, phycologist M.R. Droop published his famous discovery on the functional relationship between growth rate and internal nutrient status of algae in chemostat culture. The simple notion that growth is directly dependent on intracellular nutrient concentration is useful for understanding the dynamics in many ecological systems. The cell quota in particular lends itself to ecological stoichiometry, which is a powerful framework for mathematical ecology. Three models are developed based on the cell quota principal in order to demonstrate its applications beyond chemostat culture.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
First, a data-driven model is derived for neutral lipid synthesis in green microalgae with respect to nitrogen limitation. This model synthesizes several established frameworks in phycology and ecological stoichiometry. The model demonstrates how the cell quota is a useful abstraction for understanding the metabolic shift to neutral lipid production that is observed in certain oleaginous species.
Next a producer-grazer model is developed based on the cell quota model and nutrient recycling. The model incorporates a novel feedback loop to account for animal toxicity due to accumulation of nitrogen waste. The model exhibits rich, complex dynamics which leave several open mathematical questions.
Lastly, disease dynamics in vivo are in many ways analogous to those of an ecosystem, giving natural extensions of the cell quota concept to disease modeling. Prostate cancer can be modeled within this framework, with androgen the limiting nutrient and the prostate and cancer cells as competing species. Here the cell quota model provides a useful abstraction for the dependence of cellular proliferation and apoptosis on androgen and the androgen receptor. Androgen ablation therapy is often used for patients in biochemical recurrence or late-stage disease progression and is in general initially effective. However, for many patients the cancer eventually develops resistance months to years after treatment begins. Understanding how and predicting when hormone therapy facilitates evolution of resistant phenotypes has immediate implications for treatment. Cell quota models for prostate cancer can be useful tools for this purpose and motivate applications to other diseases.
ContributorsPacker, Aaron (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Smith, Hal (Committee member) / Kostelich, Eric (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Predicting resistant prostate cancer is critical for lowering medical costs and improving the quality of life of advanced prostate cancer patients. I formulate, compare, and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). I accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). I demonstrate that the inverse problem of parameter estimation might be too complicated and simply relying on data fitting can give incorrect conclusions, since there is a large error in parameter values estimated and parameters might be unidentifiable. I provide confidence intervals to give estimate forecasts using data assimilation via an ensemble Kalman Filter. Using the ensemble Kalman Filter, I perform dual estimation of parameters and state variables to test the prediction accuracy of the models. Finally, I present a novel model with time delay and a delay-dependent parameter. I provide a geometric stability result to study the behavior of this model and show that the inclusion of time delay may improve the accuracy of predictions. Also, I demonstrate with clinical data that the inclusion of the delay-dependent parameter facilitates the identification and estimation of parameters.
ContributorsBaez, Javier (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Nagy, John (Committee member) / Arizona State University (Publisher)
Created2017