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Description
Recently, we have demonstrated that a novel RNA origami (RNA-OG) nanostructure functions as a TLR3 agonist both in vitro and in vivo. This RNA nanostructure could induce effective antitumor immunity in a CT26-OVA-iRFP tumor model that expresses both ovalbumin (OVA) and near infrared protein (iRFP), rendering a significant delay in tumor growth or complete tumor-regression. However, in a similar tumor line that expresses iRFP but not OVA, i.e. a CT26-Neo-iRFP model, RNA-OG induced responses that were consistently inferior to those observed in CT26-OVA-iRFP. Interestingly, the antitumor immunity initially generated against CT26-OVA-iRFP was found to render the mice immune to a challenge with the more malignant CT26-Neo-iRFP line. In addition to OVA expression, the two cell lines also showed different levels of MHC-I. Ongoing research has been focused on deciphering the molecular nature of the different responses. Then, we can search for strategies that increase the tumor immunogenicity, and therefore improve the therapeutic efficacy of RNA-OG for inducing long-term tumor-regression.
ContributorsMatiski, Lawrence Theodore Mazzei (Author) / Chang, Yung (Thesis director) / Yan, Hao (Committee member) / School of Molecular Sciences (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The Heliobacterial Reaction Center (HbRC) is the simplest Type I Reaction Center (RC) known today. However, upon illumination it has been found to produce menaquinol, and this has led to experiments investigating the function of this reduction scheme. The goal of the experiment was to investigate the mechanisms of menaquinol production through the use of Photosystem II (PSII) herbicides that are known to inhibit the QB quinone site in Type II RCs. Seven herbicides were chosen, and out of all of them terbuthylazine showed the greatest effect on the RC in isolated membranes when Transient Absorption Spectroscopy was used. In addition, terbuthylazine decreased menaquinone reduction to menaquinol by ~72%, slightly more than the reported effect of teburtryn (68%)1. In addition, terbuthylazine significantly impacted growth of whole cells under high light more than terbutryn.
ContributorsOdeh, Ahmad Osameh (Author) / Redding, Kevin (Thesis director) / Woodbury, Neal (Committee member) / Allen, James (Committee member) / School of Molecular Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Predicting the binding sites of proteins has historically relied on the determination of protein structural data. However, the ability to utilize binding data obtained from a simple assay and computationally make the same predictions using only sequence information would be more efficient, both in time and resources. The purpose of this study was to evaluate the effectiveness of an algorithm developed to predict regions of high-binding on proteins as it applies to determining the regions of interaction between binding partners. This approach was applied to tumor necrosis factor alpha (TNFα), its receptor TNFR2, programmed cell death protein-1 (PD-1), and one of its ligand PD-L1. The algorithms applied accurately predicted the binding region between TNFα and TNFR2 in which the interacting residues are sequential on TNFα, however failed to predict discontinuous regions of binding as accurately. The interface of PD-1 and PD-L1 contained continuous residues interacting with each other, however this region was predicted to bind weaker than the regions on the external portions of the molecules. Limitations of this approach include use of a linear search window (resulting in inability to predict discontinuous binding residues), and the use of proteins with unnaturally exposed regions, in the case of PD-1 and PD-L1 (resulting in observed interactions which would not occur normally). However, this method was overall very effective in utilizing the available information to make accurate predictions. The use of the microarray to obtain binding information and a computer algorithm to analyze is a versatile tool capable of being adapted to refine accuracy.
ContributorsBrooks, Meilia Catherine (Author) / Woodbury, Neal (Thesis director) / Diehnelt, Chris (Committee member) / Ghirlanda, Giovanna (Committee member) / Department of Psychology (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The free-base tetra-tolyl-porphyrin and the corresponding cobalt and iron porphyrin complexes were synthesized and characterized to show that this class of compound can be promising, tunable catalysts for carbon dioxide reduction. During cyclic voltammetry experiments, the iron porphyrin showed an on-set of ‘catalytic current’ at an earlier potential than the cobalt porphyrin’s in organic solutions gassed with carbon dioxide. The cobalt porphyrin yielded larger catalytic currents, but at the same potential as the electrode. This difference, along with the significant changes in the porphyrin’s electronic, optical and redox properties, showed that its capabilities for carbon dioxide reduction can be controlled by metal ions, allotting it unique opportunities for applications in solar fuels catalysis and photochemical reactions.
ContributorsSkibo, Edward Kim (Author) / Moore, Gary (Thesis director) / Woodbury, Neal (Committee member) / School of Molecular Sciences (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Obesity and related health disparities including type 2 diabetes disproportionately impact Latino youth. These health disparities may be the result of gene-environment interactions, but limited research has examined these interactions in the pediatric age group. Lifestyle intervention is the cornerstone for preventing diabetes among high-risk populations and epigenetic and genetic factors may help explain the biological mechanisms underlying diabetes risk reduction following lifestyle changes. MicroRNAs (miRNAs) are small, non-coding RNA’s that regulate gene expression and have emerged as potential biomarkers for predicting type 2 diabetes risk in adults but have yet to be applied to youth. Therefore, the purpose of this study was to identify changes in miRNA expression among Latino youth with prediabetes (4 female/2 male, ages 14-16, BMI percentile 99 ±.2) who participated in a 12-week lifestyle intervention focused on increasing physical activity and improving nutrition-related behaviors.
ContributorsKarch, Jamie (Co-author) / Day, Samantha (Co-author) / Shaibi, Gabriel (Thesis director) / Coletta, Dawn (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.
ContributorsKumar, Pallav (Author) / Woodbury, Neal (Thesis advisor) / Allen, James (Committee member) / Johnston, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Membrane proteins act as sensors, gatekeepers and information carriers in the cell membranes. Functional engineering of these proteins is important for the development of molecular tools for biosensing, therapeutics and as components of artificial cells. However, using protein engineering to modify existing protein structures is challenging due to the limitations of structural changes and difficulty in folding polypeptides into defined protein structures. Recent studies have shown that nanoscale architectures created by DNA nanotechnology can be used to mimic various protein functions, including some membrane proteins. However, mimicking the highly sophisticated structural dynamics of membrane proteins by DNA nanostructures is still in its infancy, mainly due to lack of transmembrane DNA nanostructures that can mimic the dynamic behavior, ubiquitous to membrane proteins. Here, I demonstrate design of dynamic DNA nanostructures to mimic two important class of membrane proteins. First, I describe a DNA nanostructure that inserts through lipid membrane and dynamically reconfigures upon sensing a membrane-enclosed DNA or RNA target, thereby transducing biomolecular information across the lipid membrane similar to G-protein coupled receptors (GPCR’s). I use the non-destructive sensing property of our GPCR-mimetic nanodevice to sense cancer associated micro-RNA biomarkers inside exosomes without the need of RNA extraction and amplification. Second, I demonstrate a fully reversibly gated DNA nanopore that mimics the ligand mediated gating of ion channel proteins. The 20.4 X 20.4 nm-wide channel of the DNA nanopore allows timed delivery of folded proteins across synthetic and biological membranes. These studies represent early examples of dynamic DNA nanostructures in mimicking membrane protein functions. I envision that they will be used in synthetic biology to create artificial cells containing GPCR-like and ion channel-like receptors, in site-specific drug or vaccine delivery and highly sensitive biosensing applications.
ContributorsDey, Swarup (Author) / Yan, Hao (Thesis advisor) / Hariadi, Rizal F (Thesis advisor) / Liu, Yan (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.
ContributorsWilson, Eric Andrew (Author) / Anderson, Karen (Thesis advisor) / Singharoy, Abhishek (Thesis advisor) / Woodbury, Neal (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2022
Description
Originally conceived as a way to scaffold molecules of interest into three-dimensional (3D) crystalline lattices for X ray crystallography, the field of deoxyribonucleic acid (DNA) nanotechnology has dramatically evolved since its inception. The unique properties of DNA nanostructures have promoted their use not only for X ray crystallography, but for a suite of biomedical applications as well. The work presented in this dissertation focuses on both of these exciting applications in the field: 1) Nucleic acid nanostructures as multifunctional drug and vaccine delivery platforms, and 2) 3D DNA crystals for structure elucidation of scaffolded guest molecules.Chapter 1 illustrates how a wide variety of DNA nanostructures have been developed for the delivery of drugs and vaccine components. However, their applications are limited under physiological conditions due to their lack of stability in low salt environments, susceptibility to enzymatic degradation, and tendency for endosomal entrapment. To address these issues, Chapter 2 describes a PEGylated peptide coating molecule was designed to electrostatically adhere to and protect DNA origami nanostructures and to facilitate their cytosolic delivery by peptide-mediated endosomal escape. The development of this molecule will aid in the use of nucleic acid nanostructures for biomedical purposes, such as the delivery of messenger ribonucleic acid (mRNA) vaccine constructs. To this end, Chapter 3 discusses the fabrication of a structured mRNA nanoparticle for more cost-efficient mRNA vaccine manufacture and proposes a multi-epitope mRNA nanostructure vaccine design for targeting human papillomavirus (HPV) type 16-induced head and neck cancers.
DNA nanotechnology was originally envisioned to serve as three-dimensional scaffolds capable of positioning proteins in a rigid array for their structure elucidation by X ray crystallography. Accordingly, Chapter 4 explores design parameters, such as sequence and Holliday junction isomeric forms, for efficient crystallization of 3D DNA lattices. Furthermore, previously published DNA crystal motifs are used to site-specifically position and structurally evaluate minor groove binding molecules with defined occupancies. The results of this study provide significant advancement towards the ultimate goal of the field.
ContributorsHenry, Skylar J.W. (Author) / Stephanopoulos, Nicholas (Thesis advisor) / Anderson, Karen (Thesis advisor) / Blattman, Joseph (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2023