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Description
African Swine Fever (ASF), endemic in many African countries, is now spreading to other continents. Though ASF is capable of incurring serious economic losses in affected countries, no vaccine exists to provide immunity to animals. Disease control relies largely on rapid diagnosis and the implementation of movement restrictions and strict eradication programs. Developing a scalable, accurate and low cost diagnostic for ASF will be of great help for the current situation. CIM's 10K random peptide microarray is a new high-throughput platform that allows systematic investigations of immune responses associated with disease and shows promise as a diagnostic tool. In this study, this new technology was applied to characterize the immune responses of ASF virus (ASFV) infections and immunizations. Six sets of sera from ASFV antigen immunized pigs, 6 sera from infected pigs and 20 sera samples from unexposed pigs were tested and analyzed statistically. Results show that both ASFV antigen immunized pigs and ASFV viral infected pigs can be distinguished from unexposed pigs. Since it appears that immune responses to other viral infections are also distinguishable on this platform, it holds the potential of being useful in developing a new ASF diagnostic. The ability of this platform to identify specific ASFV antibody epitopes was also explored. A subtle motif was found to be shared among a set of peptides displaying the highest reactivity for an antigen specific antibody. However, this motif does not seem to match with any antibody epitopes predicted by a linear antibody epitope prediction.
ContributorsXiao, Liang (Author) / Sykes, Kathryn (Thesis advisor) / Zhao, Zhan-Gong (Committee member) / Stafford, Phillip (Committee member) / Arizona State University (Publisher)
Created2011
Description
Phytoplankton comprise the base of the marine food web, and, along with heterotrophic protists, they are key players in the biological pump that transports carbon from the surface to the deep ocean. In the world's subtropical oligotrophic gyres, plankton communities exhibit strong seasonality. Winter storms vent deep water into the euphotic zone, triggering a surge in primary productivity in the form of a spring phytoplankton bloom. Although the hydrographic trends of this "boom and bust" cycle have been well studied for decades, community composition and its seasonal and annual variability remains an integral subject of research. It is hypothesized here that proportions of different phytoplankton and protistan taxa vary dramatically between seasons and years, and that picoplankton represent an important component of this community and contributor to carbon in the surface ocean. Monthly samples from the Bermuda Atlantic Time-series Study (BATS) site were analyzed by epifluorescence microscopy, which permits classification by morphology, size, and trophic type. Epifluorescence counts were supplemented with flow cytometric quantification of Synechococcus, Prochlorococcus, and autotrophic pico- and nanoeukaryotes. Results from this study indicate Synechococcus and Prochlorococcus, prymnesiophytes, and hetero- and mixotrophic nano- and dinoflagellates were the major players in the BATS region plankton community. Ciliates, cryptophytes, diatoms, unidentified phototrophs, and other taxa represented rarer groups. Both flow cytometry and epifluorescence microscopy revealed Synechococcus to be most prevalent during the spring bloom. Prymnesiophytes likewise displayed distinct seasonality, with the highest concentrations again being noted during the bloom. Heterotrophic nano- and dinoflagellates, however, were most common in fall and winter. Mixotrophic dinoflagellates, while less abundant than their heterotrophic counterparts, displayed similar seasonality. A key finding of this study was the interannual variability revealed between the two years. While most taxa were more abundant in the first year, prymnesiophytes experienced much greater abundance in the second year bloom. Analyses of integrated carbon revealed further stark contrasts between the two years, both in terms of total carbon and the contributions of different groups. Total integrated carbon varied widely in the first study year but displayed less fluctuation after June 2009, and values were noticeably reduced in the second year.
ContributorsHansen, Amy (Author) / Neuer, Susanne (Thesis advisor) / Krajmalnik-Brown, Rosa (Committee member) / Sommerfeld, Milton (Committee member) / Arizona State University (Publisher)
Created2010
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
Photosynthesis converts sunlight to biomass at a global scale. Among the photosynthetic organisms, cyanobacteria provide an excellent model to study how photosynthesis can become a practical platform of large-scale biotechnology. One novel approach involves metabolically engineering the cyanobacterium Synechocystis sp. PCC 6803 to excrete laurate, which is harvested directly.
This work begins by defining a working window of light intensity (LI). Wild-type and laurate-excreting Synechocystis required an LI of at least 5 µE/m2-s to sustain themselves, but are photo-inhibited by LI of 346 to 598 µE/m2-s.
Fixing electrons into valuable organic products, e.g., biomass and excreted laurate, is critical to success. Wild-type Synechocystis channeled 75% to 84% of its fixed electrons to biomass; laurate-excreting Synechocystis fixed 64 to 69% as biomass and 6.6% to 10% as laurate. This means that 16 to 30% of the electrons were diverted to non-valuable soluble products, and the trend was accentuated with higher LI.
How the Ci concentration depended on the pH and the nitrogen source was quantified by the proton condition and experimentally validated. Nitrate increased, ammonium decreased, but ammonium nitrate stabilized alkalinity and Ci. This finding provides a mechanistically sound tool to manage Ci and pH independently.
Independent evaluation pH and Ci on the growth kinetics of Synechocystis showed that pH 8.5 supported the fastest maximum specific growth rate (µmax): 2.4/day and 1.7/day, respectively, for the wild type and modified strains with LI of 202 µE/m2-s. Half-maximum-rate concentrations (KCi) were less than 0.1 mM, meaning that Synechocystis should attain its µmax with a modest Ci concentration (≥1.0 mM).
Biomass grown with day-night cycles had a night endogenous decay rate of 0.05-1.0/day, with decay being faster with higher LI and the beginning of dark periods. Supplying light at a fraction of daylight reduced dark decay rate and improved overall biomass productivity.
This dissertation systematically evaluates and synthesizes fundamental growth factors of cyanobacteria: light, inorganic carbon (Ci), and pH. LI remains the most critical growth condition to promote biomass productivity and desired forms of biomass, while Ci and pH now can be managed to support optimal productivity.
This work begins by defining a working window of light intensity (LI). Wild-type and laurate-excreting Synechocystis required an LI of at least 5 µE/m2-s to sustain themselves, but are photo-inhibited by LI of 346 to 598 µE/m2-s.
Fixing electrons into valuable organic products, e.g., biomass and excreted laurate, is critical to success. Wild-type Synechocystis channeled 75% to 84% of its fixed electrons to biomass; laurate-excreting Synechocystis fixed 64 to 69% as biomass and 6.6% to 10% as laurate. This means that 16 to 30% of the electrons were diverted to non-valuable soluble products, and the trend was accentuated with higher LI.
How the Ci concentration depended on the pH and the nitrogen source was quantified by the proton condition and experimentally validated. Nitrate increased, ammonium decreased, but ammonium nitrate stabilized alkalinity and Ci. This finding provides a mechanistically sound tool to manage Ci and pH independently.
Independent evaluation pH and Ci on the growth kinetics of Synechocystis showed that pH 8.5 supported the fastest maximum specific growth rate (µmax): 2.4/day and 1.7/day, respectively, for the wild type and modified strains with LI of 202 µE/m2-s. Half-maximum-rate concentrations (KCi) were less than 0.1 mM, meaning that Synechocystis should attain its µmax with a modest Ci concentration (≥1.0 mM).
Biomass grown with day-night cycles had a night endogenous decay rate of 0.05-1.0/day, with decay being faster with higher LI and the beginning of dark periods. Supplying light at a fraction of daylight reduced dark decay rate and improved overall biomass productivity.
This dissertation systematically evaluates and synthesizes fundamental growth factors of cyanobacteria: light, inorganic carbon (Ci), and pH. LI remains the most critical growth condition to promote biomass productivity and desired forms of biomass, while Ci and pH now can be managed to support optimal productivity.
ContributorsNguyen, Binh Thanh (Author) / Rittmann, Bruce E. (Thesis advisor) / Krajmalnik-Brown, Rosa (Committee member) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2015
Description
The advent of new high throughput technology allows for increasingly detailed characterization of the immune system in healthy, disease, and age states. The immune system is composed of two main branches: the innate and adaptive immune system, though the border between these two states is appearing less distinct. The adaptive immune system is further split into two main categories: humoral and cellular immunity. The humoral immune response produces antibodies against specific targets, and these antibodies can be used to learn about disease and normal states. In this document, I use antibodies to characterize the immune system in two ways: 1. I determine the Antibody Status (AbStat) from the data collected from applying sera to an array of non-natural sequence peptides, and demonstrate that this AbStat measure can distinguish between disease, normal, and aged samples as well as produce a single AbStat number for each sample; 2. I search for antigens for use in a cancer vaccine, and this search results in several candidates as well as a new hypothesis. Antibodies provide us with a powerful tool for characterizing the immune system, and this natural tool combined with emerging technologies allows us to learn more about healthy and disease states.
ContributorsWhittemore, Kurt (Author) / Sykes, Kathryn (Thesis advisor) / Johnston, Stephen A. (Committee member) / Jacobs, Bertram (Committee member) / Stafford, Phillip (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2014
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
ContributorsRicher, Joshua Amos (Author) / Johnston, Stephen A. (Thesis advisor) / Woodbury, Neal (Committee member) / Stafford, Phillip (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014
Description
Immunotherapy has been revitalized with the advent of immune checkpoint blockade
treatments, and neo-antigens are the targets of immune system in cancer patients who
respond to the treatments. The cancer vaccine field is focused on using neo-antigens from
unique point mutations of genomic sequence in the cancer patient for making
personalized cancer vaccines. However, we choose a different path to find frameshift
neo-antigens at the mRNA level and develop broadly effective cancer vaccines based on
frameshift antigens.
In this dissertation, I have summarized and characterized all the potential frameshift
antigens from microsatellite regions in human, dog and mouse. A list of frameshift
antigens was validated by PCR in tumor samples and the mutation rate was calculated for
one candidate – SEC62. I develop a method to screen the antibody response against
frameshift antigens in human and dog cancer patients by using frameshift peptide arrays.
Frameshift antigens selected by positive antibody response in cancer patients or by MHC
predictions show protection in different mouse tumor models. A dog version of the
cancer vaccine based on frameshift antigens was developed and tested in a small safety
trial. The results demonstrate that the vaccine is safe and it can induce strong B and T cell
immune responses. Further, I built the human exon junction frameshift database which
includes all possible frameshift antigens from mis-splicing events in exon junctions, and I
develop a method to find potential frameshift antigens from large cancer
immunosignature dataset with these databases. In addition, I test the idea of ‘early cancer
diagnosis, early treatment’ in a transgenic mouse cancer model. The results show that
ii
early treatment gives significantly better protection than late treatment and the correct
time point for treatment is crucial to give the best clinical benefit. A model for early
treatment is developed with these results.
Frameshift neo-antigens from microsatellite regions and mis-splicing events are
abundant at mRNA level and they are better antigens than neo-antigens from point
mutations in the genomic sequences of cancer patients in terms of high immunogenicity,
low probability to cause autoimmune diseases and low cost to develop a broadly effective
vaccine. This dissertation demonstrates the feasibility of using frameshift antigens for
cancer vaccine development.
treatments, and neo-antigens are the targets of immune system in cancer patients who
respond to the treatments. The cancer vaccine field is focused on using neo-antigens from
unique point mutations of genomic sequence in the cancer patient for making
personalized cancer vaccines. However, we choose a different path to find frameshift
neo-antigens at the mRNA level and develop broadly effective cancer vaccines based on
frameshift antigens.
In this dissertation, I have summarized and characterized all the potential frameshift
antigens from microsatellite regions in human, dog and mouse. A list of frameshift
antigens was validated by PCR in tumor samples and the mutation rate was calculated for
one candidate – SEC62. I develop a method to screen the antibody response against
frameshift antigens in human and dog cancer patients by using frameshift peptide arrays.
Frameshift antigens selected by positive antibody response in cancer patients or by MHC
predictions show protection in different mouse tumor models. A dog version of the
cancer vaccine based on frameshift antigens was developed and tested in a small safety
trial. The results demonstrate that the vaccine is safe and it can induce strong B and T cell
immune responses. Further, I built the human exon junction frameshift database which
includes all possible frameshift antigens from mis-splicing events in exon junctions, and I
develop a method to find potential frameshift antigens from large cancer
immunosignature dataset with these databases. In addition, I test the idea of ‘early cancer
diagnosis, early treatment’ in a transgenic mouse cancer model. The results show that
ii
early treatment gives significantly better protection than late treatment and the correct
time point for treatment is crucial to give the best clinical benefit. A model for early
treatment is developed with these results.
Frameshift neo-antigens from microsatellite regions and mis-splicing events are
abundant at mRNA level and they are better antigens than neo-antigens from point
mutations in the genomic sequences of cancer patients in terms of high immunogenicity,
low probability to cause autoimmune diseases and low cost to develop a broadly effective
vaccine. This dissertation demonstrates the feasibility of using frameshift antigens for
cancer vaccine development.
ContributorsZhang, Jian (Author) / Johnston, Stephen Albert (Thesis advisor) / Chang, Yung (Committee member) / Stafford, Phillip (Committee member) / Chen, Qiang (Committee member) / Arizona State University (Publisher)
Created2018