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- Creators: Arizona State University
Description
The greatest barrier to understanding how life interacts with its environment is the complexity in which biology operates. In this work, I present experimental designs, analysis methods, and visualization techniques to overcome the challenges of deciphering complex biological datasets. First, I examine an iron limitation transcriptome of Synechocystis sp. PCC 6803 using a new methodology. Until now, iron limitation in experiments of Synechocystis sp. PCC 6803 gene expression has been achieved through media chelation. Notably, chelation also reduces the bioavailability of other metals, whereas naturally occurring low iron settings likely result from a lack of iron influx and not as a result of chelation. The overall metabolic trends of previous studies are well-characterized but within those trends is significant variability in single gene expression responses. I compare previous transcriptomics analyses with our protocol that limits the addition of bioavailable iron to growth media to identify consistent gene expression signals resulting from iron limitation. Second, I describe a novel method of improving the reliability of centroid-linkage clustering results. The size and complexity of modern sequencing datasets often prohibit constructing distance matrices, which prevents the use of many common clustering algorithms. Centroid-linkage circumvents the need for a distance matrix, but has the adverse effect of producing input-order dependent results. In this chapter, I describe a method of cluster edge counting across iterated centroid-linkage results and reconstructing aggregate clusters from a ranked edge list without a distance matrix and input-order dependence. Finally, I introduce dendritic heat maps, a new figure type that visualizes heat map responses through expanding and contracting sequence clustering specificities. Heat maps are useful for comparing data across a range of possible states. However, data binning is sensitive to clustering cutoffs which are often arbitrarily introduced by researchers and can substantially change the heat map response of any single data point. With an understanding of how the architectural elements of dendrograms and heat maps affect data visualization, I have integrated their salient features to create a figure type aimed at viewing multiple levels of clustering cutoffs, allowing researchers to better understand the effects of environment on metabolism or phylogenetic lineages.
ContributorsKellom, Matthew (Author) / Raymond, Jason (Thesis advisor) / Anbar, Ariel (Committee member) / Elser, James (Committee member) / Shock, Everett (Committee member) / Walker, Sarah (Committee member) / Arizona State University (Publisher)
Created2017
Description
Invasive salmonellosis caused by Salmonella enterica serovar Typhimurium ST313 is a major health crisis in sub-Saharan Africa, with multidrug resistance and atypical clinical presentation challenging current treatment regimens and resulting in high mortality. Moreover, the increased risk of spreading ST313 pathovars worldwide is of major concern, given global public transportation networks and increased populations of immunocompromised individuals (as a result of HIV infection, drug use, cancer therapy, aging, etc). While it is unclear as to how Salmonella ST313 strains cause invasive disease in humans, it is intriguing that the genomic profile of some of these pathovars indicates key differences between classic Typhimurium (broad host range), but similarities to human-specific typhoidal Salmonella Typhi and Paratyphi. In an effort to advance fundamental understanding of the pathogenesis mechanisms of ST313 in humans, I report characterization of the molecular genetic, phenotypic and virulence profiles of D23580 (a representative ST313 strain). Preliminary studies to characterize D23580 virulence, baseline stress responses, and biochemical profiles, and in vitro infection profiles in human surrogate 3-D tissue culture models were done using conventional bacterial culture conditions; while subsequent studies integrated a range of incrementally increasing fluid shear levels relevant to those naturally encountered by D23580 in the infected host to understand the impact of biomechanical forces in altering these characteristics. In response to culture of D23580 under these conditions, distinct differences in transcriptional biosignatures, pathogenesis-related stress responses, in vitro infection profiles and in vivo virulence in mice were observed as compared to those of classic Salmonella pathovars tested.
Collectively, this work represents the first characterization of in vivo virulence and in vitro pathogenesis properties of D23580, the latter using advanced human surrogate models that mimic key aspects of the parental tissue. Results from these studies highlight the importance of studying infectious diseases using an integrated approach that combines actions of biological and physical networks that mimic the host-pathogen microenvironment and regulate pathogen responses.
Collectively, this work represents the first characterization of in vivo virulence and in vitro pathogenesis properties of D23580, the latter using advanced human surrogate models that mimic key aspects of the parental tissue. Results from these studies highlight the importance of studying infectious diseases using an integrated approach that combines actions of biological and physical networks that mimic the host-pathogen microenvironment and regulate pathogen responses.
ContributorsYang, Jiseon (Author) / Nickerson, Cheryl A. (Thesis advisor) / Chang, Yung (Committee member) / Stout, Valerie (Committee member) / Ott, C Mark (Committee member) / Roland, Kenneth (Committee member) / Barrila, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015
Description
Aromatic compounds have traditionally been generated via petroleum feedstocks and have wide ranging applications in a variety of fields such as cosmetics, food, plastics, and pharmaceuticals. Substantial improvements have been made to sustainably produce many aromatic chemicals from renewable sources utilizing microbes as bio-factories. By assembling and optimizing native and non-native pathways to produce natural and non-natural bioproducts, the diversity of biochemical aromatics which can be produced is constantly being improved upon. One such compound, 2-Phenylethanol (2PE), is a key molecule used in the fragrance and food industries, as well as a potential biofuel. Here, a novel, non-natural pathway was engineered in Escherichia coli and subsequently evaluated. Following strain and bioprocess optimization, accumulation of inhibitory acetate byproduct was reduced and 2PE titers approached 2 g/L – a ~2-fold increase over previously implemented pathways in E. coli. Furthermore, a recently developed mechanism to
allow E. coli to consume xylose and glucose, two ubiquitous and industrially relevant microbial feedstocks, simultaneously was implemented and systematically evaluated for its effects on L-phenylalanine (Phe; a precursor to many microbially-derived aromatics such as 2PE) production. Ultimately, by incorporating this mutation into a Phe overproducing strain of E. coli, improvements in overall Phe titers, yields and sugar consumption in glucose-xylose mixed feeds could be obtained. While upstream efforts to improve precursor availability are necessary to ultimately reach economically-viable production, the effect of end-product toxicity on production metrics for many aromatics is severe. By utilizing a transcriptional profiling technique (i.e., RNA sequencing), key insights into the mechanisms behind styrene-induced toxicity in E. coli and the cellular response systems that are activated to maintain cell viability were obtained. By investigating variances in the transcriptional response between styrene-producing cells and cells where styrene was added exogenously, better understanding on how mechanisms such as the phage shock, heat-shock and membrane-altering responses react in different scenarios. Ultimately, these efforts to diversify the collection of microbially-produced aromatics, improve intracellular precursor pools and further the understanding of cellular response to toxic aromatic compounds, give insight into methods for improved future metabolic engineering endeavors.
allow E. coli to consume xylose and glucose, two ubiquitous and industrially relevant microbial feedstocks, simultaneously was implemented and systematically evaluated for its effects on L-phenylalanine (Phe; a precursor to many microbially-derived aromatics such as 2PE) production. Ultimately, by incorporating this mutation into a Phe overproducing strain of E. coli, improvements in overall Phe titers, yields and sugar consumption in glucose-xylose mixed feeds could be obtained. While upstream efforts to improve precursor availability are necessary to ultimately reach economically-viable production, the effect of end-product toxicity on production metrics for many aromatics is severe. By utilizing a transcriptional profiling technique (i.e., RNA sequencing), key insights into the mechanisms behind styrene-induced toxicity in E. coli and the cellular response systems that are activated to maintain cell viability were obtained. By investigating variances in the transcriptional response between styrene-producing cells and cells where styrene was added exogenously, better understanding on how mechanisms such as the phage shock, heat-shock and membrane-altering responses react in different scenarios. Ultimately, these efforts to diversify the collection of microbially-produced aromatics, improve intracellular precursor pools and further the understanding of cellular response to toxic aromatic compounds, give insight into methods for improved future metabolic engineering endeavors.
ContributorsMachas, Michael (Author) / Nielsen, David R (Thesis advisor) / Haynes, Karmella (Committee member) / Wang, Xuan (Committee member) / Nannenga, Brent (Committee member) / Varman, Arul (Committee member) / Arizona State University (Publisher)
Created2019
Description
The human transcriptional regulatory machine utilizes hundreds of transcription factors which bind to specific genic sites resulting in either activation or repression of targeted genes. Networks comprised of nodes and edges can be constructed to model the relationships of regulators and their targets. Within these biological networks small enriched structural patterns containing at least three nodes can be identified as potential building blocks from which a network is organized. A first iteration computational pipeline was designed to generate a disease specific gene regulatory network for motif detection using established computational tools. The first goal was to identify motifs that can express themselves in a state that results in differential patient survival in one of the 32 different cancer types studied. This study identified issues for detecting strongly correlated motifs that also effect patient survival, yielding preliminary results for possible driving cancer etiology. Second, a comparison was performed for the topology of network motifs across multiple different data types to identify possible divergence from a conserved enrichment pattern in network perturbing diseases. The topology of enriched motifs across all the datasets converged upon a single conserved pattern reported in a previous study which did not appear to diverge dependent upon the type of disease. This report highlights possible methods to improve detection of disease driving motifs that can aid in identifying possible treatment targets in cancer. Finally, networks where only minimally perturbed, suggesting that regulatory programs were run from evolved circuits into a cancer context.
ContributorsStriker, Shawn Scott (Author) / Plaisier, Christopher (Thesis advisor) / Brafman, David (Committee member) / Wang, Xiao (Committee member) / Arizona State University (Publisher)
Created2020
Description
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.
ContributorsRehling, Thomas E (Author) / Buetow, Kenneth (Thesis advisor) / Wilson, Melissa (Committee member) / Maley, Carlo (Committee member) / Arizona State University (Publisher)
Created2021
Description
Lophomonas is a genus of flagellated parabasalids that exist as commensal symbionts in the hindguts of a variety of pest cockroaches. The genus contains two species: Lophomonas blattarum and Lophomonas striata. The two species differ by way of bacterial ectosymbionts that attach to the outside of L. striata, giving rise to a striated and spindle-shaped appearance. As the attachment of bacterial symbionts prohibits L. striata from taking up large food particles in the same manner as L. blattarum, it is likely the two species differ in which metabolic genes they possess. Here, a comparison of transcriptomes between the two Lophomonas species show slight differences between the species. Metagenomic analysis of L. striata also presents the possibility of L. striata ectosymbionts as belonging to the genus Parabacteroides.
ContributorsNguyen, Leann (Author) / Gile, Gillian (Thesis advisor) / Dada, Nsa (Committee member) / Wideman, Jeremy (Committee member) / Arizona State University (Publisher)
Created2023