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This dissertation encompasses the interaction of antimicrobial chemicals and emerging contaminants with multi-drug resistant (MDR) bacteria and their implications in engineered systems. The aim is to investigate the effect of combination antimicrobials on MDR bacteria E. coli, evaluate the extent of synergism and antagonism of utilizing two distinct biocidal chemicals, and evaluate the influence of endocrine-disrupting chemicals (EDCs) on protein production in response to stressors. Resistance mechanisms of bacteria such as E. coli include the use of protein systems that efflux excess nutrients or toxic compounds. These efflux proteins activate in response to environmental stressors such as contaminants and antimicrobials to varying degrees and are major contributors to antibiotic resistance in pathogenic bacteria. As is the case with engineered microbial environments, large quantities of emerging contaminants interact with bacteria, influencing antibiotic resistance and attenuation of these chemicals to an unknown degree. Interactions of antimicrobials on MDR bacteria such as E. coli have been extensively studied for pathogens, including synergistic combinations. Despite these studies in this field, a fundamental understanding of how chemicals influence antibiotic resistance in biological processes typical of engineered microbial environments is still ongoing. The impacts of EDCs on antibiotic resistance in E. coli were investigated by the characterization of synergism for antimicrobial therapies and the extrapolation of these metrics to the cycling of EDCs in engineered systems to observe the extent of antibiotic resistance proteins to the EDCs. The impact of this work provides insight into the delicate biochemistry and ongoing resistance phenomena regarding engineered systems.
ContributorsNovoa, Diego Erick (Author) / Conroy-Ben, Otakuye (Thesis advisor) / Abbazadegan, Morteza (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work presents a thorough analysis of reconstruction of global wave fields (governed by the inhomogeneous wave equation and the Maxwell vector wave equation) from sensor time series data of the wave field. Three major problems are considered. First, an analysis of circumstances under which wave fields can be fully reconstructed from a network of fixed-location sensors is presented. It is proven that, in many cases, wave fields can be fully reconstructed from a single sensor, but that such reconstructions can be sensitive to small perturbations in sensor placement. Generally, multiple sensors are necessary. The next problem considered is how to obtain a global approximation of an electromagnetic wave field in the presence of an amplifying noisy current density from sensor time series data. This type of noise, described in terms of a cylindrical Wiener process, creates a nonequilibrium system, derived from Maxwell’s equations, where variance increases with time. In this noisy system, longer observation times do not generally provide more accurate estimates of the field coefficients. The mean squared error of the estimates can be decomposed into a sum of the squared bias and the variance. As the observation time $\tau$ increases, the bias decreases as $\mathcal{O}(1/\tau)$ but the variance increases as $\mathcal{O}(\tau)$. The contrasting time scales imply the existence of an ``optimal'' observing time (the bias-variance tradeoff). An iterative algorithm is developed to construct global approximations of the electric field using the optimal observing times. Lastly, the effect of sensor acceleration is considered. When the sensor location is fixed, measurements of wave fields composed of plane waves are almost periodic and so can be written in terms of a standard Fourier basis. When the sensor is accelerating, the resulting time series is no longer almost periodic. This phenomenon is related to the Doppler effect, where a time transformation must be performed to obtain the frequency and amplitude information from the time series data. To obtain frequency and amplitude information from accelerating sensor time series data in a general inhomogeneous medium, a randomized algorithm is presented. The algorithm is analyzed and example wave fields are reconstructed.
ContributorsBarclay, Bryce Matthew (Author) / Mahalov, Alex (Thesis advisor) / Kostelich, Eric J (Thesis advisor) / Moustaoui, Mohamed (Committee member) / Motsch, Sebastien (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2023
Characterization and Manipulation of Microbiomes From Arid Landfills for Improved Methane Production
Description
Environmentally harmful byproducts from solid waste’s decomposition, including methane (CH4) emissions, are managed through standardized landfill engineering and gas-capture mechanisms. Yet only a limited number of studies have analyzed the development and composition of Bacteria and Archaea involved in CH4 production from landfills. The objectives of this research were to compare microbiomes and bioactivity from CH4-producing communities in contrasting spatial areas of arid landfills and to tests a new technology to biostimulate CH4 production (methanogenesis) from solid waste under dynamic environmental conditions controlled in the laboratory. My hypothesis was that the diversity and abundance of methanogenic Archaea in municipal solid waste (MSW), or its leachate, play an important role on CH4 production partially attributed to the group’s wide hydrogen (H2) consumption capabilities. I tested this hypothesis by conducting complementary field observations and laboratory experiments. I describe niches of methanogenic Archaea in MSW leachate across defined areas within a single landfill, while demonstrating functional H2-dependent activity. To alleviate limited H2 bioavailability encountered in-situ, I present biostimulant feasibility and proof-of-concepts studies through the amendment of zero valent metals (ZVMs). My results demonstrate that older-aged MSW was minimally biostimulated for greater CH4 production relative to a control when exposed to iron (Fe0) or manganese (Mn0), due to highly discernable traits of soluble carbon, nitrogen, and unidentified fluorophores found in water extracts between young and old aged, starting MSW. Acetate and inhibitory H2 partial pressures accumulated in microcosms containing old-aged MSW. In a final experiment, repeated amendments of ZVMs to MSW in a 600 day mesocosm experiment mediated significantly higher CH4 concentrations and yields during the first of three ZVM injections. Fe0 and Mn0 experimental treatments at mesocosm-scale also highlighted accelerated development of seemingly important, but elusive Archaea including Methanobacteriaceae, a methane-producing family that is found in diverse environments. Also, prokaryotic classes including Candidatus Bathyarchaeota, an uncultured group commonly found in carbon-rich ecosystems, and Clostridia; All three taxa I identified as highly predictive in the time-dependent progression of MSW decomposition. Altogether, my experiments demonstrate the importance of H2 bioavailability on CH4 production and the consistent development of Methanobacteriaceae in productive MSW microbiomes.
ContributorsReynolds, Mark Christian (Author) / Cadillo-Quiroz, Hinsby (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Wang, Xuan (Committee member) / Kavazanjian, Edward (Committee member) / Arizona State University (Publisher)
Created2022
Description
This study investigated the difference in biofilm growth on pristine and aged polypropylene microplastics exposed to Tempe Town Lake water for 8 weeks. The research question here is, does the aging of microplastic (MPs) change the biofilm formation rate and composition of the biofilm in comparison with the pristine MPs. To answer this question, the biofilm formation was quantified using different methods over time for both pristine polypropylene and aged polypropylene using agar plate counts and crystal violet staining. Colony counts based on agar plating showed an increase in microbial growth over the 8 weeks of treatment, with the aged MPs accumulating higher microbial counts than the pristine MPs. The diversity of the biofilm decreased over time for both MPs and the aged MPs had overall less diversity in biofilm, based on phenotype enumeration, in comparison with the pristine MPs. Higher biofilm growth on aged MPs was confirmed using crystal violet staining, which stains the negatively charged biological compounds such as proteins and the extracellular polymeric substance matrix of the biofilm. Using this complementary approach to colony counting, the same trend of higher biofilm growth on aged MPs was found. Further studies will focus on confirming the phenotype findings using microbiome analysis following DNA extraction. This project created a methodology to quantify biofilm formation on MPs, which was used to show that MPs may accumulate more biofilms in the environment as they age under sunlight.
ContributorsMushro, Noelle (Author) / Perreault, Francois (Thesis advisor) / Hamilton, Kerry (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Arizona State University (Publisher)
Created2022
Description
Sulfate deficiency is seen in children with autism through increased urinary excretion of sulfate and low plasma sulfate levels. Potential factors impacting reduced sulfation include phenosulfotransferase activity, sulfate availability, and the presence of the gut toxin p-cresol. Epsom salt baths, vitamin supplementation, and fecal microbiota transplant therapy are all potential treatments with promising results. Sulfate levels have potential for use as a diagnostic biomarker, allowing for earlier diagnosis and intervention.
ContributorsErickson, Payton (Author) / Adams, James (Thesis director) / Krajmalnik-Brown, Rosa (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Human Evolution & Social Change (Contributor)
Created2023-05
Description
Climate is a critical determinant of agricultural productivity, and the ability to accurately predict this productivity is necessary to provide guidance regarding food security and agricultural management. Previous predictions vary in approach due to the myriad of factors influencing agricultural productivity but generally suggest long-term declines in productivity and agricultural land suitability under climate change. In this paper, I relate predicted climate changes to yield for three major United States crops, namely corn, soybeans, and wheat, using a moderate emissions scenario. By adopting data-driven machine learning approaches, I used the following machine learning methods: random forest (RF), extreme gradient boosting (XGB), and artificial neural networks (ANN) to perform comparative analysis and ensemble methodology. I omitted the western US due to the region's susceptibility to water stress and the prevalence of artificial irrigation as a means to compensate for dry conditions. By considering only climate, the model's results suggest an ensemble mean decline in crop yield of 23.4\% for corn, 19.1\% for soybeans, and 7.8\% for wheat between the years of 2017 and 2100. These results emphasize potential negative impacts of climate change on the current agricultural industry as a result of shifting bio-climactic conditions.
ContributorsSwarup, Shray (Author) / Eikenberry, Steffen (Thesis director) / Mahalov, Alex (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
Traditional public health strategies for assessing human behavior, exposure, and activity are considered resource-exhaustive, time-consuming, and expensive, warranting a need for alternative methods to enhance data acquisition and subsequent interventions. This dissertation critically evaluated the use of wastewater-based epidemiology (WBE) as an inclusive and non-invasive tool for conducting near real-time population health assessments. A rigorous literature review was performed to gauge the current landscape of WBE to monitor for biomarkers indicative of diet, as well as exposure to estrogen-mimicking endocrine disrupting (EED) chemicals via route of ingestion. Wastewater-derived measurements of phytoestrogens from August 2017 through July 2019 (n = 156 samples) in a small sewer catchment revealed seasonal patterns, with highest average per capita consumption rates in January through March of each year (2018: 7.0 ± 2.0 mg d-1; 2019: 8.2 ± 2.3 mg d-1) and statistically significant differences (p = 0.01) between fall and winter (3.4 ± 1.2 vs. 6.1 ± 2.9 mg d-1; p ≤ 0.01) and spring and summer (5.6 ± 2.1 vs. 3.4 ± 1.5 mg d-1; p ≤ 0.01). Additional investigations, including a human gut microbial composition analysis of community wastewater, were performed to support a methodological framework for future implementation of WBE to assess population-level dietary behavior. In response to the COVID-19 global pandemic, a high-frequency, high-resolution sample collection approach with public data sharing was implemented throughout the City of Tempe, Arizona, and analyzed for SARS-CoV-2 (E gene) from April 2020 through March 2021 (n = 1,556 samples). Results indicate early warning capability during the first wave (June 2020) compared to newly reported clinical cases (8.5 ± 2.1 days), later transitioning to a slight lagging indicator in December/January 2020-21 (-2.0 ± 1.4 days). A viral hotspot from within a larger catchment area was detected, prompting targeted interventions to successfully mitigate community spread; reinforcing the importance of sample collection within the sewer infrastructure. I conclude that by working in tandem with traditional approaches, WBE can enlighten a comprehensive understanding of population health, with methods and strategies implemented in this work recommended for future expansion to produce timely, actionable data in support of public health.
ContributorsBowes, Devin Ashley (Author) / Halden, Rolf U (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Conroy-Ben, Otakuye (Committee member) / Varsani, Arvind (Committee member) / Whisner, Corrie (Committee member) / Arizona State University (Publisher)
Created2022
Description
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that not only affects communication and behavior with often co-occurring gastrointestinal (GI) issues such as constipation and diarrhea. Recent studies have shown that many GI and behavioral symptoms in individuals with ASD are linked to dysregulated immune systems and altered gut microbiomes (bacteria and fungi). In fungal microbiota, a common GI commensal and opportunistic pathogen, Candida, has been found in higher abundance in children with ASD. Few studies have investigated total IgA and IgG levels in both blood and feces of ASD individuals with relatively mixed findings, showing either significantly higher or lower IgG and IgA abundance in ASD vs. TD (typically developing) individuals. Mixed results are likely due to a lack of a standardized method of immunoglobulin (Ig) quantification. In this study, we attempt to standardize an enzyme-linked immunoassay (ELISA) procedure to measure total IgA, total IgG, and anti-Candida albicans IgA and IgG levels in fecal samples of adults with ASD. Measuring Ig levels can reflect altered gut microbiota, GI tract, and immune status in ASD and potentially characterize Ig as a biomarker for ASD. Although we were unable to successfully standardize an Ig ELISA quantification method, SDS-PAGE confirmed the presence of IgA in fecal Ig extracts. Based on our ELISA results, we suspect that dilution factors of fecal Ig extracts need to be modified further to detect the IgA within the detection range. The experimental methodology in this study can be used as a reference to develop and improve a full-proof method of quantifying immunoglobulin from ASD fecal samples, which will help to reveal immune status in ASD.
ContributorsMarwah, Mira (Author) / Campos, Nicole (Co-author) / Krajmalnik-Brown, Rosa (Thesis director) / Nirmalkar, Khemlal (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Department of Psychology (Contributor)
Created2022-05
Description
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that not only affects communication and behavior with often co-occurring gastrointestinal (GI) issues such as constipation and diarrhea. Recent studies have shown that many GI and behavioral symptoms in individuals with ASD are linked to dysregulated immune systems and altered gut microbiomes (bacteria and fungi). In fungal microbiota, a common GI commensal and opportunistic pathogen, Candida, has been found in higher abundance in children with ASD. Few studies have investigated total IgA and IgG levels in both blood and feces of ASD individuals with relatively mixed findings, showing either significantly higher or lower IgG and IgA abundance in ASD vs. TD (typically developing) individuals. Mixed results are likely due to a lack of a standardized method of immunoglobulin (Ig) quantification. In this study, we attempt to standardize an enzyme-linked immunoassay (ELISA) procedure to measure total IgA, total IgG, and anti-Candida albicans IgA and IgG levels in fecal samples of adults with ASD. Measuring Ig levels can reflect altered gut microbiota, GI tract, and immune status in ASD and potentially characterize Ig as a biomarker for ASD. Although we were unable to successfully standardize an Ig ELISA quantification method, SDS-PAGE confirmed the presence of IgA in fecal Ig extracts. Based on our ELISA results, we suspect that dilution factors of fecal Ig extracts need to be modified further to detect the IgA within the detection range. The experimental methodology in this study can be used as a reference to develop and improve a full-proof method of quantifying immunoglobulin from ASD fecal samples, which will help to reveal immune status in ASD.
ContributorsCampos, Nicole (Author) / Marwah, Mira (Co-author) / Krajmalnik-Brown, Rosa (Thesis director) / Nirmalkar, Khemlal (Committee member) / Barrett, The Honors College (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / School of Life Sciences (Contributor)
Created2022-05
Description
For my thesis I investigated an abnormal gut-derived metabolite of interest identified as 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA) that may serve as a potential biomarker for autism, and help us get a better understanding of the underlying mechanisms of this disorder. Currently a laboratory test for autism does not exist, posing severe consequences on individuals with autism. In order to gather research on my metabolite of interest and its connection to autism as well as disorders correlated with autism, I analyzed different pieces of scientific literature investigating HPHPA and compiled this data into a literature review.
ContributorsNawaz, Umar (Author) / Adams, James (Thesis director) / Krajmalnik-Brown, Rosa (Committee member) / Flynn, Christina (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2024-05