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- All Subjects: Biology
- Creators: Kuang, Yang
- Member of: Theses and Dissertations
Description
Random peptide microarrays are a powerful tool for both the treatment and diagnostics of infectious diseases. On the treatment side, selected random peptides on the microarray have either binding or lytic potency against certain pathogens cells, thus they can be synthesized into new antimicrobial agents, denoted as synbodies (synthetic antibodies). On the diagnostic side, serum containing specific infection-related antibodies create unique and distinct "pathogen-immunosignatures" on the random peptide microarray distinct from the healthy control serum, and this different mode of binding can be used as a more precise measurement than traditional ELISA tests. My thesis project is separated into these two parts: the first part falls into the treatment side and the second one focuses on the diagnostic side. My first chapter shows that a substitution amino acid peptide library helps to improve the activity of a recently reported synthetic antimicrobial peptide selected by the random peptide microarray. By substituting one or two amino acids of the original lead peptide, the new substitutes show changed hemolytic effects against mouse red blood cells and changed potency against two pathogens: Staphylococcus aureus and Pseudomonas aeruginosa. Two new substitutes are then combined together to form the synbody, which shows a significantly antimicrobial potency against Staphylococcus aureus (<0.5uM). In the second chapter, I explore the possibility of using the 10K Ver.2 random peptide microarray to monitor the humoral immune response of dengue. Over 2.5 billion people (40% of the world's population) live in dengue transmitting areas. However, currently there is no efficient dengue treatment or vaccine. Here, with limited dengue patient serum samples, we show that the immunosignature has the potential to not only distinguish the dengue infection from non-infected people, but also the primary dengue infection from the secondary dengue infections, dengue infection from West Nile Virus (WNV) infection, and even between different dengue serotypes. By further bioinformatic analysis, we demonstrate that the significant peptides selected to distinguish dengue infected and normal samples may indicate the epitopes responsible for the immune response.
ContributorsWang, Xiao (Author) / Johnston, Stephen Albert (Thesis advisor) / Blattman, Joseph (Committee member) / Arntzen, Charles (Committee member) / Arizona State University (Publisher)
Created2013
Description
There has been important progress in understanding ecological dynamics through the development of the theory of ecological stoichiometry. This fast growing theory provides new constraints and mechanisms that can be formulated into mathematical models. Stoichiometric models incorporate the effects of both food quantity and food quality into a single framework that produce rich dynamics. While the effects of nutrient deficiency on consumer growth are well understood, recent discoveries in ecological stoichiometry suggest that consumer dynamics are not only affected by insufficient food nutrient content (low phosphorus (P): carbon (C) ratio) but also by excess food nutrient content (high P:C). This phenomenon, known as the stoichiometric knife edge, in which animal growth is reduced not only by food with low P content but also by food with high P content, needs to be incorporated into mathematical models. Here we present Lotka-Volterra type models to investigate the growth response of Daphnia to algae of varying P:C ratios. Using a nonsmooth system of two ordinary differential equations (ODEs), we formulate the first model to incorporate the phenomenon of the stoichiometric knife edge. We then extend this stoichiometric model by mechanistically deriving and tracking free P in the environment. This resulting full knife edge model is a nonsmooth system of three ODEs. Bifurcation analysis and numerical simulations of the full model, that explicitly tracks phosphorus, leads to quantitatively different predictions than previous models that neglect to track free nutrients. The full model shows that the grazer population is sensitive to excess nutrient concentrations as a dynamical free nutrient pool induces extreme grazer population density changes. These modeling efforts provide insight on the effects of excess nutrient content on grazer dynamics and deepen our understanding of the effects of stoichiometry on the mechanisms governing population dynamics and the interactions between trophic levels.
ContributorsPeace, Angela (Author) / Kuang, Yang (Thesis advisor) / Elser, James J (Committee member) / Baer, Steven (Committee member) / Tang, Wenbo (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
The majority of non-small cell lung cancer (NSCLC) patients (70%) are diagnosed with adenocarcinoma versus other histological subtypes. These patients often present with advanced, metastatic disease and frequently relapse after treatment. The tumor suppressor, Liver Kinase B1, is frequently inactivated in adenocarcinomas and loss of function is associated with a highly aggressive, metastatic tumor (1). Identification of the mechanisms deregulated with LKB1 inactivation could yield targeted therapeutic options for adenocarcinoma patients. Re-purposing the immune system to support tumor growth and aid in metastasis has been shown to be a feature in cancer progression (2). Tumor associated macrophages (TAMs) differentiate from monocytes, which are recruited to the tumor microenvironment via secretion of chemotaxic factors by cancer cells. We find that NSCLC cells deficient in LKB1 display increased secretion of C-C motif ligand 2 (CCL2), a chemokine involved in monocyte recruitment. To elucidate the molecular pathway regulating CCL2 up-regulation, we investigated inhibitors of substrates downstream of LKB1 signaling in A549, H23, H2030 and H838 cell lines. Noticeably, BAY-11-7082 (NF-κB inhibitor) reduced CCL2 secretion by an average 92%. We further demonstrate that a CCR2 antagonist and neutralizing CCL2 antibody substantially reduce monocyte migration to NSCLC (H23) cell line conditioned media. Using an in vivo model of NSCLC, we find that LKB1 deleted tumors demonstrate a discernible increase in CCL2 levels compared to normal lung. Moreover, tumors display an increase in the M2:M1 macrophage ratio and increase in tumor associated neutrophil (TAN) infiltrate compared to normal lung. This M2 shift was significantly reduced in mice treated with anti-CCL2 or a CCR2 antagonist and the TAN infiltrate was significantly reduced with the CCR2 antagonist. These data suggest that deregulation of the CCL2/CCR2 signaling axis could play a role in cancer progression in LKB1 deficient tumors.
ContributorsFriel, Jacqueline (Author) / Inge, Landon (Thesis advisor) / Lake, Douglas (Thesis advisor) / Blattman, Joseph (Committee member) / Arizona State University (Publisher)
Created2015
Description
The phycologist, M. R. Droop, studied vitamin B12 limitation in the flagellate Monochrysis lutheri and concluded that its specific growth rate depended on the concentration of the vitamin within the cell; i.e. the cell quota of the vitamin B12. The Droop model provides a mathematical expression to link growth rate to the intracellular concentration of a limiting nutrient. Although the Droop model has been an important modeling tool in ecology, it has only recently been applied to study cancer biology. Cancer cells live in an ecological setting, interacting and competing with normal and other cancerous cells for nutrients and space, and evolving and adapting to their environment. Here, the Droop equation is used to model three cancers.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
First, prostate cancer is modeled, where androgen is considered the limiting nutrient since most tumors depend on androgen for proliferation and survival. The model's accuracy for predicting the biomarker for patients on intermittent androgen deprivation therapy is tested by comparing the simulation results to clinical data as well as to an existing simpler model. The results suggest that a simpler model may be more beneficial for a predictive use, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting.
Next, two chronic myeloid leukemia models are compared that consider Imatinib treatment, a drug that inhibits the constitutively active tyrosine kinase BCR-ABL. Both models describe the competition of leukemic and normal cells, however the first model also describes intracellular dynamics by considering BCR-ABL as the limiting nutrient. Using clinical data, the differences in estimated parameters between the models and the capacity for each model to predict drug resistance are analyzed.
Last, a simple model is presented that considers ovarian tumor growth and tumor induced angiogenesis, subject to on and off anti-angiogenesis treatment. In this environment, the cell quota represents the intracellular concentration of necessary nutrients provided through blood supply. Mathematical analysis of the model is presented and model simulation results are compared to pre-clinical data. This simple model is able to fit both on- and off-treatment data using the same biologically relevant parameters.
ContributorsEverett, Rebecca Anne (Author) / Kuang, Yang (Thesis advisor) / Nagy, John (Committee member) / Milner, Fabio (Committee member) / Crook, Sharon (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Arizona State University (Publisher)
Created2015
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
Description
The most advanced social insects, the eusocial insects, form often large societies in which there is reproductive division of labor, queens and workers, have overlapping generations, and cooperative brood care where daughter workers remain in the nest with their queen mother and care for their siblings. The eusocial insects are composed of representative species of bees and wasps, and all species of ants and termites. Much is known about their organizational structure, but remains to be discovered.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
ContributorsRodríguez Messan, Marisabel (Author) / Kang, Yun (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Kuang, Yang (Committee member) / Page Jr., Robert E (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2018
Description
Measles is a contagious, vaccine-preventable disease that continues to be the leading
cause of death in children younger than the age of 5 years. While the introduction of the Measles, Mumps, and Rubella vaccine (MMR) has significantly decreased morbidity and mortality rates worldwide, vaccine coverage is highly variable across global regions. Current diagnostic methods rely on enzyme immunoassays (EIA) to detect IgM or IgG Abs in serum. Commercially available Diamedix Immunosimplicity® Measles IgG test kit has been shown to have 91.1% sensitivity and 93.8% specificity, with a positive predictive value of 88.7% and a negative predictive value of 90.9% on the basis of a PRN titer of 120. There is an increasing need for rapid screening for measles specific immunity in outbreak settings. This study aims to develop a rapid molecular diagnostic assay to detect IgG reactive to three individual measles virus (MeV) proteins.
Measles virus (MeV) genes were subcloned into the pJFT7_nGST vector to generate N- terminal GST fusion proteins. Single MeV cistrons were expressed using in vitro transcription/translation (IVTT) with human cell lysate. Expression of GST-tagged proteins was measured with mouse anti-GST mAb and sheep anti-mouse IgG. Relative light units (RLUs) as luminescence was measured. Antibodies to MeV antigens were measured in 40 serum samples from healthy subjects.
Protein expression of three MeV genes of interest was measured in comparison with vector control and statistical significance was determined using the Student’s t-test (p<0.05). N expressed at the highest level with an average RLU value of 3.01 x 109 (p<0.001) and all proteins were expressed at least 50% greater than vector control (4.56 x 106 RLU). 36/40 serum samples had IgG to N (Ag:GST ratio>1.21), F (Ag:GST ratio>1.92), or H (Ag:GST ratio> 1.23).
These data indicate that the in vitro expression of MeV antigens, N, F, and H, were markedly improved by subcloning into pJFT7_nGST vector to generate N-terminal GST fusion proteins. The expression of single MeV genes N, F and H, are suitable antigens for serologic capture analysis of measles-specific antibodies. These preliminary data can be used to design a more intensive study to explore the possibilities of using these MeV antigens as a diagnostic marker.
cause of death in children younger than the age of 5 years. While the introduction of the Measles, Mumps, and Rubella vaccine (MMR) has significantly decreased morbidity and mortality rates worldwide, vaccine coverage is highly variable across global regions. Current diagnostic methods rely on enzyme immunoassays (EIA) to detect IgM or IgG Abs in serum. Commercially available Diamedix Immunosimplicity® Measles IgG test kit has been shown to have 91.1% sensitivity and 93.8% specificity, with a positive predictive value of 88.7% and a negative predictive value of 90.9% on the basis of a PRN titer of 120. There is an increasing need for rapid screening for measles specific immunity in outbreak settings. This study aims to develop a rapid molecular diagnostic assay to detect IgG reactive to three individual measles virus (MeV) proteins.
Measles virus (MeV) genes were subcloned into the pJFT7_nGST vector to generate N- terminal GST fusion proteins. Single MeV cistrons were expressed using in vitro transcription/translation (IVTT) with human cell lysate. Expression of GST-tagged proteins was measured with mouse anti-GST mAb and sheep anti-mouse IgG. Relative light units (RLUs) as luminescence was measured. Antibodies to MeV antigens were measured in 40 serum samples from healthy subjects.
Protein expression of three MeV genes of interest was measured in comparison with vector control and statistical significance was determined using the Student’s t-test (p<0.05). N expressed at the highest level with an average RLU value of 3.01 x 109 (p<0.001) and all proteins were expressed at least 50% greater than vector control (4.56 x 106 RLU). 36/40 serum samples had IgG to N (Ag:GST ratio>1.21), F (Ag:GST ratio>1.92), or H (Ag:GST ratio> 1.23).
These data indicate that the in vitro expression of MeV antigens, N, F, and H, were markedly improved by subcloning into pJFT7_nGST vector to generate N-terminal GST fusion proteins. The expression of single MeV genes N, F and H, are suitable antigens for serologic capture analysis of measles-specific antibodies. These preliminary data can be used to design a more intensive study to explore the possibilities of using these MeV antigens as a diagnostic marker.
ContributorsMushtaq, Zuena (Author) / Anderson, Karen (Thesis advisor) / Blattman, Joseph (Committee member) / Lake, Douglas (Committee member) / Arizona State University (Publisher)
Created2016
Description
Principle-based ethical frameworks, which commonly make use of codes of ethics, have come to be the popular approach in guiding ethical behavior within scientific research. In this thesis project, I investigate the benefits and shortcomings of this approach, ultimately to argue that codes of ethics are valuable as an exercise in developing a reconciled value profile for a given research community, and also function well as an internal and external proclamation of values and norms. However, this approach results in technical adherence, at best, and given the extent to which scientific research now irreversibly shapes our experience as human beings, I argue for the importance of cultivating ethical virtues in scientific research. In the interest of doing so I explore concepts from Aristotelian virtue ethics, to consider how to ameliorate the shortcomings of principle-based approaches. This project was inspired by a call to research and develop an ethical framework upon which to found a cooperative research network that would be aimed at combating the spread of emerging and re-emerging infectious diseases in resource-restricted countries, specifically throughout Latin America. The desire to found this network on an ethics-based framework is to move beyond technical compliance and cultivate a research community committed to integrity, therefore establishing and maintaining trust and communication that will allow for unprecedented productive collaboration and meaningful outcomes. I demonstrate in this thesis that this requires more than a code of ethics, and use this initiative as a case study to exhibit the merit of integrating concepts from virtue ethics.
ContributorsCraer, Jennifer Ryan (Author) / Ellison, Karin (Thesis advisor) / Sarewitz, Daniel (Committee member) / Blattman, Joseph (Committee member) / Robert, Jason S (Committee member) / Arizona State University (Publisher)
Created2017
Description
Adaptive therapy utilizes competitive interactions between resistant and sensitive cells by keeping some sensitive cells to control tumor burden with the aim of increasing overall survival and time to progression. The use of adaptive therapy to treat breast cancer, ovarian cancer, and pancreatic cancer in preclinical models has shown significant results in controlling tumor growth. The adaptive therapy model comes from the integrated pest management agricultural strategy, predator prey model, and the unique intra- and inter-tumor heterogeneity of tumors. The purpose of this thesis is to analyze and compare gemcitabine dose response on hormone refractory breast cancer cells retrieved from mice using an adaptive therapy strategy with standard therapy treatment. In this study, we compared intermittent (drug holiday) adaptive therapy with maximum tolerated dose therapy. The MCF7 resistant cell lines to both fulvestrant and palbociclib were injected into the mammary fat pads of 8 weeks old NOD/SCID gamma (NSG) mice which were then treated with gemcitabine. Tumor burden graphs were made to track tumor growth/decline during different treatments while Drug Dose Response (DDR) curves were made to test the sensitivity of the cell lines to the drug gemcitabine. The tumor burden graphs showed success in controlling the tumor burden with intermittent treatment. The DDR curves showed a positive result in using the adaptive therapy treatment method to treat mice with gemcitabine. Due to some fluctuating DDR results, the sensitivity of the cell lines to gemcitabine needs to be further studied by repeating the DDR experiment on the other mice cell lines for stronger results.
ContributorsConti, Aviona Christina (Author) / Maley, Carlo (Thesis advisor) / Blattman, Joseph (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2022