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Description
Circular RNAs (circRNAs) are a class of endogenous, non-coding RNAs that are formed when exons back-splice to each other and represent a new area of transcriptomics research. Numerous RNA sequencing (RNAseq) studies since 2012 have revealed that circRNAs are pervasively expressed in eukaryotes, especially in the mammalian brain. While their functional role and impact remains to be clarified, circRNAs have been found to regulate micro-RNAs (miRNAs) as well as parental gene transcription and may thus have key roles in transcriptional regulation. Although circRNAs have continued to gain attention, our understanding of their expression in a cell-, tissue- , and brain region-specific context remains limited. Further, computational algorithms produce varied results in terms of what circRNAs are detected. This thesis aims to advance current knowledge of circRNA expression in a region specific context focusing on the human brain, as well as address computational challenges.
The overarching goal of my research unfolds over three aims: (i) evaluating circRNAs and their predicted impact on transcriptional regulatory networks in cell-specific RNAseq data; (ii) developing a novel solution for de novo detection of full length circRNAs as well as in silico validation of selected circRNA junctions using assembly; and (iii) application of these assembly based detection and validation workflows, and integrating existing tools, to systematically identify and characterize circRNAs in functionally distinct human brain regions. To this end, I have developed novel bioinformatics workflows that are applicable to non-polyA selected RNAseq datasets and can be used to characterize circRNA expression across various sample types and diseases. Further, I establish a reference dataset of circRNA expression profiles and regulatory networks in a brain region-specific manner. This resource along with existing databases such as circBase will be invaluable in advancing circRNA research as well as improving our understanding of their role in transcriptional regulation and various neurological conditions.
The overarching goal of my research unfolds over three aims: (i) evaluating circRNAs and their predicted impact on transcriptional regulatory networks in cell-specific RNAseq data; (ii) developing a novel solution for de novo detection of full length circRNAs as well as in silico validation of selected circRNA junctions using assembly; and (iii) application of these assembly based detection and validation workflows, and integrating existing tools, to systematically identify and characterize circRNAs in functionally distinct human brain regions. To this end, I have developed novel bioinformatics workflows that are applicable to non-polyA selected RNAseq datasets and can be used to characterize circRNA expression across various sample types and diseases. Further, I establish a reference dataset of circRNA expression profiles and regulatory networks in a brain region-specific manner. This resource along with existing databases such as circBase will be invaluable in advancing circRNA research as well as improving our understanding of their role in transcriptional regulation and various neurological conditions.
ContributorsSekar, Shobana (Author) / Liang, Winnie S (Thesis advisor) / Dinu, Valentin (Thesis advisor) / Craig, David (Committee member) / Liu, Li (Committee member) / Arizona State University (Publisher)
Created2018
Description
Clinical Decision Support (CDS) is primarily associated with alerts, reminders, order entry, rule-based invocation, diagnostic aids, and on-demand information retrieval. While valuable, these foci have been in production use for decades, and do not provide a broader, interoperable means of plugging structured clinical knowledge into live electronic health record (EHR) ecosystems for purposes of orchestrating the user experiences of patients and clinicians. To date, the gap between knowledge representation and user-facing EHR integration has been considered an “implementation concern” requiring unscalable manual human efforts and governance coordination. Drafting a questionnaire engineered to meet the specifications of the HL7 CDS Knowledge Artifact specification, for example, carries no reasonable expectation that it may be imported and deployed into a live system without significant burdens. Dramatic reduction of the time and effort gap in the research and application cycle could be revolutionary. Doing so, however, requires both a floor-to-ceiling precoordination of functional boundaries in the knowledge management lifecycle, as well as formalization of the human processes by which this occurs.
This research introduces ARTAKA: Architecture for Real-Time Application of Knowledge Artifacts, as a concrete floor-to-ceiling technological blueprint for both provider heath IT (HIT) and vendor organizations to incrementally introduce value into existing systems dynamically. This is made possible by service-ization of curated knowledge artifacts, then injected into a highly scalable backend infrastructure by automated orchestration through public marketplaces. Supplementary examples of client app integration are also provided. Compilation of knowledge into platform-specific form has been left flexible, in so far as implementations comply with ARTAKA’s Context Event Service (CES) communication and Health Services Platform (HSP) Marketplace service packaging standards.
Towards the goal of interoperable human processes, ARTAKA’s treatment of knowledge artifacts as a specialized form of software allows knowledge engineers to operate as a type of software engineering practice. Thus, nearly a century of software development processes, tools, policies, and lessons offer immediate benefit: in some cases, with remarkable parity. Analyses of experimentation is provided with guidelines in how choice aspects of software development life cycles (SDLCs) apply to knowledge artifact development in an ARTAKA environment.
Portions of this culminating document have been further initiated with Standards Developing Organizations (SDOs) intended to ultimately produce normative standards, as have active relationships with other bodies.
This research introduces ARTAKA: Architecture for Real-Time Application of Knowledge Artifacts, as a concrete floor-to-ceiling technological blueprint for both provider heath IT (HIT) and vendor organizations to incrementally introduce value into existing systems dynamically. This is made possible by service-ization of curated knowledge artifacts, then injected into a highly scalable backend infrastructure by automated orchestration through public marketplaces. Supplementary examples of client app integration are also provided. Compilation of knowledge into platform-specific form has been left flexible, in so far as implementations comply with ARTAKA’s Context Event Service (CES) communication and Health Services Platform (HSP) Marketplace service packaging standards.
Towards the goal of interoperable human processes, ARTAKA’s treatment of knowledge artifacts as a specialized form of software allows knowledge engineers to operate as a type of software engineering practice. Thus, nearly a century of software development processes, tools, policies, and lessons offer immediate benefit: in some cases, with remarkable parity. Analyses of experimentation is provided with guidelines in how choice aspects of software development life cycles (SDLCs) apply to knowledge artifact development in an ARTAKA environment.
Portions of this culminating document have been further initiated with Standards Developing Organizations (SDOs) intended to ultimately produce normative standards, as have active relationships with other bodies.
ContributorsLee, Preston Victor (Author) / Dinu, Valentin (Thesis advisor) / Sottara, Davide (Committee member) / Greenes, Robert (Committee member) / Arizona State University (Publisher)
Created2018
Description
Study of canine cancer’s molecular underpinnings holds great potential for informing veterinary and human oncology. Sporadic canine cancers are highly abundant (~4 million diagnoses/year in the United States) and the dog’s unique genomic architecture due to selective inbreeding, alongside the high similarity between dog and human genomes both confer power for improving understanding of cancer genes. However, characterization of canine cancer genome landscapes has been limited. It is hindered by lack of canine-specific tools and resources. To enable robust and reproducible comparative genomic analysis of canine cancers, I have developed a workflow for somatic and germline variant calling in canine cancer genomic data. I have first adapted a human cancer genomics pipeline to create a semi-automated canine pipeline used to map genomic landscapes of canine melanoma, lung adenocarcinoma, osteosarcoma and lymphoma. This pipeline also forms the backbone of my novel comparative genomics workflow.
Practical impediments to comparative genomic analysis of dog and human include challenges identifying similarities in mutation type and function across species. For example, canine genes could have evolved different functions and their human orthologs may perform different functions. Hence, I undertook a systematic statistical evaluation of dog and human cancer genes and assessed functional similarities and differences between orthologs to improve understanding of the roles of these genes in cancer across species. I tested this pipeline canine and human Diffuse Large B-Cell Lymphoma (DLBCL), given that canine DLBCL is the most comprehensively genomically characterized canine cancer. Logistic regression with genes bearing somatic coding mutations in each cancer was used to determine if conservation metrics (sequence identity, network placement, etc.) could explain co-mutation of genes in both species. Using this model, I identified 25 co-mutated and evolutionarily similar genes that may be compelling cross-species cancer genes. For example, PCLO was identified as a co-mutated conserved gene with PCLO having been previously identified as recurrently mutated in human DLBCL, but with an unclear role in oncogenesis. Further investigation of these genes might shed new light on the biology of lymphoma in dogs and human and this approach may more broadly serve to prioritize new genes for comparative cancer biology studies.
Practical impediments to comparative genomic analysis of dog and human include challenges identifying similarities in mutation type and function across species. For example, canine genes could have evolved different functions and their human orthologs may perform different functions. Hence, I undertook a systematic statistical evaluation of dog and human cancer genes and assessed functional similarities and differences between orthologs to improve understanding of the roles of these genes in cancer across species. I tested this pipeline canine and human Diffuse Large B-Cell Lymphoma (DLBCL), given that canine DLBCL is the most comprehensively genomically characterized canine cancer. Logistic regression with genes bearing somatic coding mutations in each cancer was used to determine if conservation metrics (sequence identity, network placement, etc.) could explain co-mutation of genes in both species. Using this model, I identified 25 co-mutated and evolutionarily similar genes that may be compelling cross-species cancer genes. For example, PCLO was identified as a co-mutated conserved gene with PCLO having been previously identified as recurrently mutated in human DLBCL, but with an unclear role in oncogenesis. Further investigation of these genes might shed new light on the biology of lymphoma in dogs and human and this approach may more broadly serve to prioritize new genes for comparative cancer biology studies.
ContributorsSivaprakasam, Karthigayini (Author) / Dinu, Valentin (Thesis advisor) / Trent, Jeffrey (Thesis advisor) / Hendricks, William (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2018
Description
Biological soil crusts (biocrusts) are topsoil communities of organisms that contribute to soil fertility and erosion resistance in drylands. Anthropogenic disturbances can quickly damage these communities and their natural recovery can take decades. With the development of accelerated restoration strategies in mind, I studied physiological mechanisms controlling the establishment of cyanobacteria in biocrusts, since these photoautotrophs are not just the biocrust pioneer organisms, but also largely responsible for improving key soil attributes such as physical stability, nutrient content, water retention and albedo. I started by determining the cyanobacterial community composition of a variety of biocrust types from deserts in the Southwestern US. I then isolated a large number of cyanobacterial strains from these locations, pedigreed them based on their 16SrRNA gene sequences, and selective representatives that matched the most abundant cyanobacterial field populations. I then developed methodologies for large-scale growth of the selected isolates to produce location-specific and genetically autochthonous inoculum for restoration. I also developed and tested viable methodologies to physiologically harden this inoculum and improve its survival under harsh field conditions. My tests proved that in most cases good viability of the inoculum could be attained under field-like conditions. In parallel, I used molecular ecology approaches to show that the biocrust pioneer, Microcoleus vaginatus, shapes its surrounding heterotrophic microbiome, enriching for a compositionally-differentiated “cyanosphere” that concentrates the nitrogen-fixing function. I proposed that a mutualism based on carbon for nitrogen exchange between M. vaginatus and its cyanosphere creates a consortium that constitutes the true pioneer community enabling the colonization of nitrogen-poor, bare soils. Using the right mixture of photosynthetic and diazotrophic cultures will thus likely help in soil restoration. Additionally, using physiological assays and molecular meta-analyses, I demonstrated that the largest contributors to N2-fixation in late successional biocrusts (three genera of heterocystous cyanobacteria) partition their niche along temperature gradients, and that this can explain their geographic patterns of dominance within biocrusts worldwide. This finding can improve restoration strategies by incorporating climate-matched physiological types in inoculum formulations. In all, this dissertation resulted in the establishment of a comprehensive "cyanobacterial biocrust nursery", that includes a culture collection containing 101 strains, isolation and cultivation methods, inoculum design strategies as well as field conditioning protocols. It constitutes a new interdisciplinary application of microbiology in restoration ecology.
ContributorsGiraldo Silva, Ana Maria (Author) / Garcia-Pichel, Ferran (Thesis advisor) / Barger, Nichole N (Committee member) / Bowker, Mathew A (Committee member) / Sala, Osvaldo (Committee member) / Arizona State University (Publisher)
Created2019
Description
Breast cancer is the most common cancer and currently the second leading cause of death among women in the United States. Patients’ five-year relative survival rate decreases from 99% to 25% when breast cancer is diagnosed late. Immune checkpoint blockage has shown to be a promising therapy to improve patients’ outcome in many other cancers. However, due to the lack of early diagnosis, the treatment is normally given in the later stages. An early diagnosis system for breast cancer could potentially revolutionize current treatment strategies, improve patients’ outcomes and even eradicate the disease. The current breast cancer diagnostic methods cannot meet this demand. A simple, effective, noninvasive and inexpensive early diagnostic technology is needed. Immunosignature technology leverages the power of the immune system to find cancer early. Antibodies targeting tumor antigens in the blood are probed on a high-throughput random peptide array and generate a specific binding pattern called the immunosignature.
In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early.
Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.
In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early.
Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.
ContributorsDuan, Hu (Author) / Johnston, Stephen Albert (Thesis advisor) / Hartwell, Leland Harrison (Committee member) / Dinu, Valentin (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015
Description
Euendolithic cyanobacteria have the remarkable ability to actively excavate and grow within certain minerals. Their activity leads to increased erosion of marine and terrestrial carbonates, negatively affecting coral reef and bivalve ecology. Despite their environmental relevance, the boring mechanism has remained elusive and paradoxical, in that cyanobacteria alkalinize their surroundings, typically leading to carbonate precipitation, not dissolution. Thus, euendoliths must rely on unique adaptations to bore. Recent work using the filamentous model euendolith Mastigocoleus testarum strain BC008 indicated that excavation relied on transcellular calcium transport mediated by P-type ATPases, but the phenomenon remained unclear. Here I present evidence that excavation in M. testarum involves an unprecedented set of adaptations. Long-range calcium transport is achieved through the coordinated pumping of multiple cells, orchestrated by the localization of calcium ATPases in a repeating annular pattern, positioned at a single cell pole, adjacent to each cell septum along the filament. Additionally, specialized chlorotic cells that I named calcicytes, differentiate and accumulate calcium at concentrations more than 500 fold those of canonical cells, likely allowing for fast calcium flow at non-toxic concentrations through undifferentiated cells. I also show, using 13C stable isotope tracers and NanoSIMS imaging, that endolithic M. testarum derives most of its carbon from the mineral carbonates it dissolves, the first autotroph ever shown to fix mineral carbon, confirming the existence of a direct link between oxidized solid carbon pools and reduced organic pools in the biosphere. Finally, using genomic and transcriptomic approaches, I analyze gene expression searching for additional adaptations related to the endolithic lifestyle. A large and diverse set of genes (24% of 6917 genes) were significantly differentially regulated while boring, including several master regulators and genes expectedly needed under this condition (such as transport, nutrient scavenging, oxidative stress, and calcium-binding protein genes). However, I also discovered the up-regulation of several puzzling gene sets involved in alternative carbon fixation pathways, anaerobic metabolism, and some related to photosynthesis and respiration. This transcriptomic data provides us with several new, readily testable hypotheses regarding adaptations to the endolithic lifestyle. In all, my data clearly show that boring organisms show extraordinarily interesting adaptations.
ContributorsGuida, Brandon Scott (Author) / Garcia-Pichel, Ferran (Thesis advisor) / Chandler, Douglas (Committee member) / Bingham, Scott (Committee member) / Roberson, Robert (Committee member) / Arizona State University (Publisher)
Created2016
Description
Biological soil crusts (BSCs) dominate the soil surface of drylands in the western United States and possess properties thought to influence local hydrology. Little agreement exists, however, on the effects of BSCs on runoff, infiltration, and evaporative rates. This study aims to improve the predictive capability of an ecohydrology model in order to understand how BSCs affect the storage, retention, and infiltration of water into soils characteristic of the Colorado Plateau. A set of soil moisture measurements obtained at a climate manipulation experiment near Moab, Utah, are used for model development and testing. Over five years, different rainfall treatments over experimental plots resulted in the development of BSC cover with different properties that influence soil moisture differently. This study used numerical simulations to isolate the relative roles of different BSC properties on the hydrologic response at the plot-scale. On-site meteorological, soil texture and vegetation property datasets are utilized as inputs into a ecohydrology model, modified to include local processes: (1) temperature-dependent precipitation partitioning, snow accumulation and melt, (2) seasonally-variable potential evapotranspiration, (3) plant species-specific transpiration factors, and (4) a new module to account for the water balance of the BSC. Soil, BSC and vegetation parameters were determined from field measurements or through model calibration to the soil moisture observations using the Shuffled Complex Evolution algorithm. Model performance is assessed against five years of soil moisture measurements at each experimental site, representing a wide range of crust cover properties. Simulation experiments were then carried out using the calibrated ecohydrology model in which BSC parameters were varied according to the level of development of the BSC, as represented by the BSC roughness. These results indicate that BSCs act to both buffer against evaporative soil moisture losses by enhancing BSC moisture evaporation and significantly alter the rates of soil water infiltration by reducing moisture storage and increasing conductivity in the BSC. The simulation results for soil water infiltration, storage and retention across a wide range of meteorological events help explain the conflicting hydrologic outcomes present in the literature on BSCs. In addition, identifying how BSCs mediate infiltration and evaporation processes has implications for dryland ecosystem function in the western United States.
ContributorsWhitney, Kristen M (Author) / Vivoni, Enrique R (Thesis advisor) / Farmer, Jack D (Committee member) / Garcia-Pichel, Ferran (Committee member) / Arizona State University (Publisher)
Created2015
Description
No two cancers are alike. Cancer is a dynamic and heterogeneous disease, such heterogeneity arise among patients with the same cancer type, among cancer cells within the same individual’s tumor and even among cells within the same sub-clone over time. The recent application of next-generation sequencing and precision medicine techniques is the driving force to uncover the complexity of cancer and the best clinical practice. The core concept of precision medicine is to move away from crowd-based, best-for-most treatment and take individual variability into account when optimizing the prevention and treatment strategies. Next-generation sequencing is the method to sift through the entire 3 billion letters of each patient’s DNA genetic code in a massively parallel fashion.
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
ContributorsPeng, Sen (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Arizona State University (Publisher)
Created2015
Description
The ocean sequesters more than 25% of the carbon released by anthropogenic action every year, and oligotrophic oceans, such as the Sargasso Sea, are responsible for about 50% of the global carbon export. Pico- and nano-phytoplankton (cells < 5 µm), mostly unicellular eukaryotes (protists) and cyanobacteria, dominate the primary production in the Sargasso Sea; however, little is known about their contribution to the export of carbon into the deep ocean via sinking particles. The overall goal of this study is to examine the link between growth and grazing rates of pico- and nano-phytoplankton and the carbon export in the Sargasso Sea. I investigate three aspects: 1) how microzooplankton grazing and physical forcing affect taxon-specific primary productivity in this region, 2) how these microbial trophic dynamics impact their contribution to the export of particulate matter, and 3) how much pico-phytoplankton, specifically the pico-cyanobacteria Synechococcus and Prochlorococcus, contribute to the carbon export. I collected seawater samples within the sunlit (euphotic) zone, and sinking particles at 150 m depth using particle traps in the Sargasso Sea during the winter and summer seasons of 2011 and 2012. I conducted dilution experiments to determine the growth and grazing rates of the pico- and nano-phytoplankton community, and used 454 pyrosequencing and quantitative Polymerase Chain Reaction to measure the relative and absolute contribution of these primary producers to the plankton community within the euphotic zone and in the sinking particles. I found that micrograzing controls taxon-specific primary production, and that microbial trophic dynamics impact directly the taxonomical composition of the sinking particles. For the first time, I was able to quantify clade-specific carbon export of pico-cyanobacteria and found that, despite their small size, these tiny primary producers are capable of sinking from the surface to the deeper oceans. However, their contribution to the carbon flux is often less than one tenth of their biomass contribution in the euphotic zone. Our study provides a comprehensive approach to better understand the role of pico- and nano-phytoplankton in the carbon cycle of oligotrophic oceans, and a baseline to study changes in the carbon export in future warmer oceans.
ContributorsDe Martini, Francesca (Author) / Neuer, Susanne (Thesis advisor) / Garcia-Pichel, Ferran (Committee member) / Hartnett, Hilairy (Committee member) / Lomas, Michael (Committee member) / Arizona State University (Publisher)
Created2016
Description
The aboveground surfaces of plants (i.e. the phyllosphere) comprise the largest biological interface on Earth (over 108 km2). The phyllosphere is a diverse microbial environment where bacterial inhabitants have been shown to sequester and degrade airborne pollutants (i.e. phylloremediation). However, phyllosphere dynamics are not well understood in urban environments, and this environment has never been studied in the City of Phoenix, which maintains roughly 92,000 city trees. The phyllosphere will grow if the City of Phoenix is able to achieve its goal of 25% canopy coverage by 2030, but this begs the question: How and where should the urban canopy expand? I addressed this question from a phyllosphere perspective by sampling city trees of two species, Ulmus parvifolia (Chinese Elm) and Dalbergia sissoo (Indian Rosewood) in parks and on roadsides. I identified characteristics of the bacterial community structure and interpreted the ecosystem service potential of trees in these two settings. I used culture-independent methods to compare the abundance of each unique bacterial lineage (i.e. ontological taxonomic units or OTUs) on the leaves of park trees versus on roadside tree leaves. I found numerous bacteria (81 OTUs) that were significantly more abundant on park trees than on roadside trees. Many of these OTUs are ubiquitous to bacterial phyllosphere communities, are known to promote the health of the host tree, or have been shown to degrade airborne pollutants. Roadside trees had fewer bacteria (10 OTUs) that were significantly more abundant when compared to park trees, but several have been linked to the remediation of petroleum combustion by-products. These findings, that were not available prior to this study, may inform the City of Phoenix as it is designing its future urban forests.
ContributorsMacNeille, Benjamin C (Author) / Childers, Daniel L. (Thesis advisor) / Garcia-Pichel, Ferran (Committee member) / Cease, Arianne J (Committee member) / Arizona State University (Publisher)
Created2016