Matching Items (107)
Description
With needs for carbon sequestration and sustainable chemical feedstocks increasing formate stands out as a real possibility in addressing these growing problems. One of the principal issues with positioning formate as the central compound of a bioeconomy is establishing a sustainable and reliable method for producing it. The goal of this project was to take the first steps towards engineering a formate production cell factory in Chlamydomonas reinhardtii by introducing the biosynthetic pathway necessary for the creation of molybdenum cofactor which would later be used as an integral part of the function of a formate dehydrogenase enzyme capable of reducing carbon dioxide to make formate. I was able to get some seemingly successful transformants but unable to confidently confirm whether or not these transformants hardboard the molybdenum cofactor synthesis genes.
ContributorsNikkel, Zachary (Author) / Redding, Kevin (Thesis director) / Ghirlanda, Giovanna (Committee member) / Borges, Chad (Committee member) / Barrett, The Honors College (Contributor) / School of Molecular Sciences (Contributor)
Created2022-05
Description
Most drugs work by binding to receptors on the cell surface. These receptors can then carry the message into the cell and have a wide array of results. However, studying how fast the binding is can be difficult. Current methods involve extracting the receptor and labeling them, but both these steps have issues. Previous works found that binding on the cell surface is accompanied with a small change in cell size, generally an increase. They have also developed an algorithm that can track these small changes without a label using a simple bright field microscope. Here, this relationship is further explored by comparing edge tracking results to a more widely used method, surface plasmon resonance. The kinetic constants found from the two methods are in agreement. No corrections or manipulations were needed to create agreement. The Bland-Altman plots shows that the error between the two methods is about 0.009 s-1. This is about the same error between cells, making it a non-dominant source of error.
ContributorsHunt, Ashley (Author) / Tao, Nongjian (Thesis advisor) / Ros, Alexandra (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Measuring molecular interaction with membrane proteins is critical for understanding cellular functions, validating biomarkers and screening drugs. Despite the importance, developing such a capability has been a difficult challenge, especially for small molecules binding to membrane proteins in their native cellular environment. The current mainstream practice is to isolate membrane proteins from the cell membranes, which is difficult and often lead to the loss of their native structures and functions. In this thesis, novel detection methods for in situ quantification of molecular interactions with membrane proteins are described.
First, a label-free surface plasmon resonance imaging (SPRi) platform is developed for the in situ detection of the molecular interactions between membrane protein drug target and its specific antibody drug molecule on cell surface. With this method, the binding kinetics of the drug-target interaction is quantified for drug evaluation and the receptor density on the cell surface is also determined.
Second, a label-free mechanically amplification detection method coupled with a microfluidic device is developed for the detection of both large and small molecules on single cells. Using this method, four major types of transmembrane proteins, including glycoproteins, ion channels, G-protein coupled receptors (GPCRs) and tyrosine kinase receptors on single whole cells are studied with their specific drug molecules. The basic principle of this method is established by developing a thermodynamic model to express the binding-induced nanometer-scale cellular deformation in terms of membrane protein density and cellular mechanical properties. Experiments are carried out to validate the model.
Last, by tracking the cell membrane edge deformation, molecular binding induced downstream event – granule exocytosis is measured with a dual-optical imaging system. Using this method, the single granule exocytosis events in single cells are monitored and the temporal-spatial distribution of the granule fusion-induced cell membrane deformation are mapped. Different patterns of granule release are resolved, including multiple release events occurring close in time and position. The label-free cell membrane deformation tracking method was validated with the simultaneous fluorescence recording. And the simultaneous cell membrane deformation detection and fluorescence recording allow the study of the propagation of the granule release-induced membrane deformation along cell surfaces.
First, a label-free surface plasmon resonance imaging (SPRi) platform is developed for the in situ detection of the molecular interactions between membrane protein drug target and its specific antibody drug molecule on cell surface. With this method, the binding kinetics of the drug-target interaction is quantified for drug evaluation and the receptor density on the cell surface is also determined.
Second, a label-free mechanically amplification detection method coupled with a microfluidic device is developed for the detection of both large and small molecules on single cells. Using this method, four major types of transmembrane proteins, including glycoproteins, ion channels, G-protein coupled receptors (GPCRs) and tyrosine kinase receptors on single whole cells are studied with their specific drug molecules. The basic principle of this method is established by developing a thermodynamic model to express the binding-induced nanometer-scale cellular deformation in terms of membrane protein density and cellular mechanical properties. Experiments are carried out to validate the model.
Last, by tracking the cell membrane edge deformation, molecular binding induced downstream event – granule exocytosis is measured with a dual-optical imaging system. Using this method, the single granule exocytosis events in single cells are monitored and the temporal-spatial distribution of the granule fusion-induced cell membrane deformation are mapped. Different patterns of granule release are resolved, including multiple release events occurring close in time and position. The label-free cell membrane deformation tracking method was validated with the simultaneous fluorescence recording. And the simultaneous cell membrane deformation detection and fluorescence recording allow the study of the propagation of the granule release-induced membrane deformation along cell surfaces.
ContributorsZhang, Fenni (Author) / Tao, Nongjian (Thesis advisor) / Chae, Junseok (Committee member) / Borges, Chad (Committee member) / Jing, Tianwei (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2018
Description
Measurements of different molecular species from single cells have the potential to reveal cell-to-cell variations, which are precluded by population-based measurements. An increasing percentage of researches have been focused on proteins, for its central roles in biological processes. Immunofluorescence (IF) has been a well-established protein analysis platform. To gain comprehensive insights into cell biology and diagnostic pathology, a crucial direction would be to increase the multiplexity of current single cell protein analysis technologies.
An azide-based chemical cleavable linker has been introduced to design and synthesis novel fluorescent probes. These probes allow cyclic immunofluorescence staining which leads to the feasibility of highly multiplexed single cell in situ protein profiling. These highly multiplexed imaging-based platforms have the potential to quantify more than 100 protein targets in cultured cells and more than 50 protein targets in single cells in tissues.
This approach has been successfully applied in formalin-fixed paraffin-embedded (FFPE) brain tissues. Multiplexed protein expression level results reveal neuronal heterogeneity in the human hippocampus.
An azide-based chemical cleavable linker has been introduced to design and synthesis novel fluorescent probes. These probes allow cyclic immunofluorescence staining which leads to the feasibility of highly multiplexed single cell in situ protein profiling. These highly multiplexed imaging-based platforms have the potential to quantify more than 100 protein targets in cultured cells and more than 50 protein targets in single cells in tissues.
This approach has been successfully applied in formalin-fixed paraffin-embedded (FFPE) brain tissues. Multiplexed protein expression level results reveal neuronal heterogeneity in the human hippocampus.
ContributorsLiao, Renjie (Author) / Guo, Jia (Thesis advisor) / Borges, Chad (Committee member) / Liu, Yan (Committee member) / Arizona State University (Publisher)
Created2019
Description
DNA and RNA are generally regarded as one of the central molecules in molecular biology. Recent advancements in the field of DNA/RNA nanotechnology witnessed the success of usage of DNA/RNA as programmable molecules to construct nano-objects with predefined shapes and dynamic molecular machines for various functions. From the perspective of structural design with nucleic acid, there are basically two types of assembly method, DNA tile based assembly and DNA origami based assembly, used to construct infinite-sized crystal structures and finite-sized molecular structures. The assembled structure can be used for arrangement of other molecules or nanoparticles with the resolution of nanometers to create new type of materials. The dynamic nucleic acid machine is based on the DNA strand displacement, which allows two nucleic acid strands to hybridize with each other to displace one or more prehybridized strands in the process. Strand displacement reaction has been implemented to construct a variety of dynamic molecular systems, such as molecular computer, oscillators, in vivo devices for gene expression control.
This thesis will focus on the computational design of structural and dynamic nucleic acid systems, particularly for new type of DNA structure design and high precision control of gene expression in vivo. Firstly, a new type of fundamental DNA structural motif, the layered-crossover motif, will be introduced. The layered-crossover allow non-parallel alignment of DNA helices with precisely controlled angle. By using the layered-crossover motif, the scaffold can go through the 3D framework DNA origami structures. The properties of precise angle control of the layered-crossover tiles can also be used to assemble 2D and 3D crystals. One the dynamic control part, a de-novo-designed riboregulator is developed that can recognize single nucleotide variation. The riboregulators can also be used to develop paper-based diagnostic devices.
This thesis will focus on the computational design of structural and dynamic nucleic acid systems, particularly for new type of DNA structure design and high precision control of gene expression in vivo. Firstly, a new type of fundamental DNA structural motif, the layered-crossover motif, will be introduced. The layered-crossover allow non-parallel alignment of DNA helices with precisely controlled angle. By using the layered-crossover motif, the scaffold can go through the 3D framework DNA origami structures. The properties of precise angle control of the layered-crossover tiles can also be used to assemble 2D and 3D crystals. One the dynamic control part, a de-novo-designed riboregulator is developed that can recognize single nucleotide variation. The riboregulators can also be used to develop paper-based diagnostic devices.
ContributorsHong, Fan, Ph. D (Author) / Yan, Hao (Thesis advisor) / Liu, Yan (Thesis advisor) / Green, Alexander A. (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2019
Description
Longitudinal recursive partitioning (LRP) is a tree-based method for longitudinal data. It takes a sample of individuals that were each measured repeatedly across time, and it splits them based on a set of covariates such that individuals with similar trajectories become grouped together into nodes. LRP does this by fitting a mixed-effects model to each node every time that it becomes partitioned and extracting the deviance, which is the measure of node purity. LRP is implemented using the classification and regression tree algorithm, which suffers from a variable selection bias and does not guarantee reaching a global optimum. Additionally, fitting mixed-effects models to each potential split only to extract the deviance and discard the rest of the information is a computationally intensive procedure. Therefore, in this dissertation, I address the high computational demand, variable selection bias, and local optimum solution. I propose three approximation methods that reduce the computational demand of LRP, and at the same time, allow for a straightforward extension to recursive partitioning algorithms that do not have a variable selection bias and can reach the global optimum solution. In the three proposed approximations, a mixed-effects model is fit to the full data, and the growth curve coefficients for each individual are extracted. Then, (1) a principal component analysis is fit to the set of coefficients and the principal component score is extracted for each individual, (2) a one-factor model is fit to the coefficients and the factor score is extracted, or (3) the coefficients are summed. The three methods result in each individual having a single score that represents the growth curve trajectory. Therefore, now that the outcome is a single score for each individual, any tree-based method may be used for partitioning the data and group the individuals together. Once the individuals are assigned to their final nodes, a mixed-effects model is fit to each terminal node with the individuals belonging to it.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
I conduct a simulation study, where I show that the approximation methods achieve the goals proposed while maintaining a similar level of out-of-sample prediction accuracy as LRP. I then illustrate and compare the methods using an applied data.
ContributorsStegmann, Gabriela (Author) / Grimm, Kevin (Thesis advisor) / Edwards, Michael (Committee member) / MacKinnon, David (Committee member) / McNeish, Daniel (Committee member) / Arizona State University (Publisher)
Created2019
Description
There is a need to reinvent evidence-based interventions (EBIs) for pediatric anxiety problems to better address the demands of real-word service delivery settings and achieve public health impact. The time- and resource-intensive nature of most EBIs for youth anxiety has frequently been noted as a barrier to the utilization of EBIs in community settings, leading to increased attention towards exploring the viability of briefer, more accessible protocols. Principally, this research reports between-group effect sizes from brief-interventions targeting pediatric anxiety and classifies each as well-established, probably efficacious, possibly efficacious, experimental, or questionable. brief interventions yielded an overall mean effect size of 0.19 on pediatric anxiety outcomes from pre to post. Effect sizes varied significantly by level of intervention: Pre to post-intervention effects were strongest for brief-treatments (0.35), followed by brief-targeted prevention (0.22), and weakest for brief-universal prevention (0.09). No participant or other intervention characteristic emerged as significant moderators of effect sizes. In terms of standard of evidence, one brief intervention is well-established, and five are probably efficacious, with most drawing on cognitive and behavioral change procedures and/or family systems models. At this juncture, the minimal intervention needed for clinical change in pediatric anxiety points to in-vivo exposures for specific phobias (~3 hours), cognitive-behavioral therapy (CBT) with social skills training (~3 hours), and CBT based parent training (~6 hours, eight digital modules with clinician support). This research concludes with a discussion on limitations to available brief EBIs, practice guidelines, and future research needed to capitalize on the viability of briefer protocols in enhancing access to, and impact of, evidence-based care in the real-world.
ContributorsStoll, Ryan (Author) / Pina, Armando A. (Thesis advisor) / Gonzales, Nancy (Committee member) / MacKinnon, David (Committee member) / Perez, Marisol (Committee member) / Arizona State University (Publisher)
Created2019
Description
Dielectrophoresis (DEP) is a technique that influences the motion of polarizable particles in an electric field gradient. DEP can be combined with other effects that influence the motion of a particle in a microchannel, such as electrophoresis and electroosmosis. Together, these three can be used to probe properties of an analyte, including charge, conductivity, and zeta potential. DEP shows promise as a high-resolution differentiation and separation method, with the ability to distinguish between subtly-different populations. This, combined with the fast (on the order of minutes) analysis times offered by the technique, lend it many of the features necessary to be used in rapid diagnostics and point-of-care devices.
Here, a mathematical model of dielectrophoretic data is presented to connect analyte properties with data features, including the intercept and slope, enabling DEP to be used in applications which require this information. The promise of DEP to distinguish between analytes with small differences is illustrated with antibiotic resistant bacteria. The DEP system is shown to differentiate between methicillin-resistant and susceptible Staphylococcus aureus. This differentiation was achieved both label free and with bacteria that had been fluorescently-labeled. Klebsiella pneumoniae carbapenemase-positive and negative Klebsiella pneumoniae were also distinguished, demonstrating the differentiation for a different mechanism of antibiotic resistance. Differences in dielectrophoretic behavior as displayed by S. aureus and K. pneumoniae were also shown by Staphylococcus epidermidis. These differences were exploited for a separation in space of gentamicin-resistant and -susceptible S. epidermidis. Besides establishing the ability of DEP to distinguish between populations with small biophysical differences, these studies illustrate the possibility for the use of DEP in applications such as rapid diagnostics.
Here, a mathematical model of dielectrophoretic data is presented to connect analyte properties with data features, including the intercept and slope, enabling DEP to be used in applications which require this information. The promise of DEP to distinguish between analytes with small differences is illustrated with antibiotic resistant bacteria. The DEP system is shown to differentiate between methicillin-resistant and susceptible Staphylococcus aureus. This differentiation was achieved both label free and with bacteria that had been fluorescently-labeled. Klebsiella pneumoniae carbapenemase-positive and negative Klebsiella pneumoniae were also distinguished, demonstrating the differentiation for a different mechanism of antibiotic resistance. Differences in dielectrophoretic behavior as displayed by S. aureus and K. pneumoniae were also shown by Staphylococcus epidermidis. These differences were exploited for a separation in space of gentamicin-resistant and -susceptible S. epidermidis. Besides establishing the ability of DEP to distinguish between populations with small biophysical differences, these studies illustrate the possibility for the use of DEP in applications such as rapid diagnostics.
ContributorsHilton, Shannon (Author) / Hayes, Mark A. (Thesis advisor) / Borges, Chad (Committee member) / Herckes, Pierre (Committee member) / Arizona State University (Publisher)
Created2019
Description
Antibodies are naturally occurring proteins that protect a host during infection through direct neutralization and/or recruitment of the innate immune system. Unfortunately, in some infections, antibodies present unique hurdles that must be overcome for a safer and more efficacious antibody-based therapeutic (e.g., antibody dependent viral enhancement (ADE) and inflammatory pathology). This dissertation describes the utilization of plant expression systems to produce N-glycan specific antibody-based therapeutics for Dengue Virus (DENV) and Chikungunya Virus (CHIKV). The Fc region of an antibody interacts with Fcγ Receptors (FcγRs) on immune cells and components of the innate immune system. Each class of immune cells has a distinct action of neutralization (e.g., antibody dependent cell-mediated cytotoxicity (ADCC) and antibody dependent cell-mediated phagocytosis (ADCP)). Therefore, structural alteration of the Fc region results in novel immune pathways of protection. One approach is to modulate the N-glycosylation in the Fc region of the antibody. Of scientific significance, is the plant’s capacity to express human antibodies with homogenous plant and humanized N-glycosylation (WT and GnGn, respectively). This allows to study how specific glycovariants interact with other components of the immune system to clear an infection, producing a tailor-made antibody for distinct diseases. In the first section, plant-produced glycovariants were explored for reduced interactions with specific FcγRs for the overall reduction in ADE for DENV infections. The results demonstrate a reduction in ADE of our plant-produced monoclonal antibodies in in vitro experiments, which led to a greater survival in vivo of immunodeficient mice challenged with lethal doses of DENV and a sub-lethal dose of DENV in ADE conditions. In the second section, plant-produced glycovariants were explored for increased interaction with specific FcγRs to improve ADCC in the treatment of the highly inflammatory CHIKV. The results demonstrate an increase ADCC activity in in vitro experiments and a reduction in CHIKV-associated inflammation in in vivo mouse models. Overall, the significance of this dissertation is that it can provide a treatment for DENV and CHIKV; but equally importantly, give insight to the role of N-glycosylation in antibody effector functions, which has a broader implication for therapeutic development for other viral infections.
ContributorsHurtado, Jonathan (Author) / Chen, Qiang (Thesis advisor) / Arntzen, Charles (Committee member) / Borges, Chad (Committee member) / Lake, Douglas (Committee member) / Arizona State University (Publisher)
Created2019
Description
DNA, RNA and Protein are three pivotal biomolecules in human and other organisms, playing decisive roles in functionality, appearance, diseases development and other physiological phenomena. Hence, sequencing of these biomolecules acquires the prime interest in the scientific community. Single molecular identification of their building blocks can be done by a technique called Recognition Tunneling (RT) based on Scanning Tunneling Microscope (STM). A single layer of specially designed recognition molecule is attached to the STM electrodes, which trap the targeted molecules (DNA nucleoside monophosphates, RNA nucleoside monophosphates or amino acids) inside the STM nanogap. Depending on their different binding interactions with the recognition molecules, the analyte molecules generate stochastic signal trains accommodating their “electronic fingerprints”. Signal features are used to detect the molecules using a machine learning algorithm and different molecules can be identified with significantly high accuracy. This, in turn, paves the way for rapid, economical nanopore sequencing platform, overcoming the drawbacks of Next Generation Sequencing (NGS) techniques.
To read DNA nucleotides with high accuracy in an STM tunnel junction a series of nitrogen-based heterocycles were designed and examined to check their capabilities to interact with naturally occurring DNA nucleotides by hydrogen bonding in the tunnel junction. These recognition molecules are Benzimidazole, Imidazole, Triazole and Pyrrole. Benzimidazole proved to be best among them showing DNA nucleotide classification accuracy close to 99%. Also, Imidazole reader can read an abasic monophosphate (AP), a product from depurination or depyrimidination that occurs 10,000 times per human cell per day.
In another study, I have investigated a new universal reader, 1-(2-mercaptoethyl)pyrene (Pyrene reader) based on stacking interactions, which should be more specific to the canonical DNA nucleosides. In addition, Pyrene reader showed higher DNA base-calling accuracy compare to Imidazole reader, the workhorse in our previous projects. In my other projects, various amino acids and RNA nucleoside monophosphates were also classified with significantly high accuracy using RT. Twenty naturally occurring amino acids and various RNA nucleosides (four canonical and two modified) were successfully identified. Thus, we envision nanopore sequencing biomolecules using Recognition Tunneling (RT) that should provide comprehensive betterment over current technologies in terms of time, chemical and instrumental cost and capability of de novo sequencing.
To read DNA nucleotides with high accuracy in an STM tunnel junction a series of nitrogen-based heterocycles were designed and examined to check their capabilities to interact with naturally occurring DNA nucleotides by hydrogen bonding in the tunnel junction. These recognition molecules are Benzimidazole, Imidazole, Triazole and Pyrrole. Benzimidazole proved to be best among them showing DNA nucleotide classification accuracy close to 99%. Also, Imidazole reader can read an abasic monophosphate (AP), a product from depurination or depyrimidination that occurs 10,000 times per human cell per day.
In another study, I have investigated a new universal reader, 1-(2-mercaptoethyl)pyrene (Pyrene reader) based on stacking interactions, which should be more specific to the canonical DNA nucleosides. In addition, Pyrene reader showed higher DNA base-calling accuracy compare to Imidazole reader, the workhorse in our previous projects. In my other projects, various amino acids and RNA nucleoside monophosphates were also classified with significantly high accuracy using RT. Twenty naturally occurring amino acids and various RNA nucleosides (four canonical and two modified) were successfully identified. Thus, we envision nanopore sequencing biomolecules using Recognition Tunneling (RT) that should provide comprehensive betterment over current technologies in terms of time, chemical and instrumental cost and capability of de novo sequencing.
ContributorsSen, Suman (Author) / Lindsay, Stuart (Thesis advisor) / Zhang, Peiming (Thesis advisor) / Gould, Ian R. (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2016