Matching Items (91)
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Description
The emergence of invasive non-Typhoidal Salmonella (iNTS) infections belonging to sequence type (ST) 313 are associated with severe bacteremia and high mortality in sub-Saharan Africa. Distinct features of ST313 strains include resistance to multiple antibiotics, extensive genomic degradation, and atypical clinical diagnosis including bloodstream infections, respiratory symptoms, and fever. Herein,

The emergence of invasive non-Typhoidal Salmonella (iNTS) infections belonging to sequence type (ST) 313 are associated with severe bacteremia and high mortality in sub-Saharan Africa. Distinct features of ST313 strains include resistance to multiple antibiotics, extensive genomic degradation, and atypical clinical diagnosis including bloodstream infections, respiratory symptoms, and fever. Herein, I report the use of dynamic bioreactor technology to profile the impact of physiological fluid shear levels on the pathogenesis-related responses of ST313 pathovar, 5579. I show that culture of 5579 under these conditions induces profoundly different pathogenesis-related phenotypes than those normally observed when cultures are grown conventionally. Surprisingly, in response to physiological fluid shear, 5579 exhibited positive swimming motility, which was unexpected, since this strain was initially thought to be non-motile. Moreover, fluid shear altered the resistance of 5579 to acid, oxidative and bile stress, as well as its ability to colonize human colonic epithelial cells. This work leverages from and advances studies over the past 16 years in the Nickerson lab, which are at the forefront of bacterial mechanosensation and further demonstrates that bacterial pathogens are “hardwired” to respond to the force of fluid shear in ways that are not observed during conventional culture, and stresses the importance of mimicking the dynamic physical force microenvironment when studying host-pathogen interactions. The results from this study lay the foundation for future work to determine the underlying mechanisms operative in 5579 that are responsible for these phenotypic observations.
ContributorsCastro, Christian (Author) / Nickerson, Cheryl A. (Thesis advisor) / Ott, C. Mark (Committee member) / Roland, Kenneth (Committee member) / Barrila, Jennifer (Committee member) / Arizona State University (Publisher)
Created2016
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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.

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
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Description
Glycans are monosaccharide-based heteropolymers that are found covalently attached to many different proteins and lipids and are ubiquitously displayed on the exterior surfaces of cells. Serum glycan composition and structure are well known to be altered in many different types of cancer. In fact, glycans represent a promising but only

Glycans are monosaccharide-based heteropolymers that are found covalently attached to many different proteins and lipids and are ubiquitously displayed on the exterior surfaces of cells. Serum glycan composition and structure are well known to be altered in many different types of cancer. In fact, glycans represent a promising but only marginally accessed source of cancer markers. The approach used in this dissertation, which is referred to as “glycan node analysis”, is a molecularly bottom-up approach to plasma/serum (P/S) glycomics based on glycan linkage analysis that captures features such as α2-6 sialylation, β1-6 branching, and core fucosylation as single analytical signals.

The diagnostic utility of this approach as applied to lung cancer patients across all stages as well as prostate, serous ovarian, and pancreatic cancer patients compared to certifiably healthy individuals, nominally healthy individuals and/or risk-matched controls is reported. Markers for terminal fucosylation, α2-6 sialylation, β1-4 branching, β1-6 branching and outer-arm fucosylation were most able to differentiate cases from controls. These markers behaved in a stage-dependent manner in lung cancer as well as other types of cancer. Using a Cox proportional hazards regression model, the ability of these markers to predict progression and survival in lung cancer patients was assessed. In addition, the potential mechanistic role of aberrant P/S glycans in cancer progression is discussed.

Plasma samples from former bladder cancer patients with currently no evidence of disease (NED), non-muscle invasive bladder cancer (NMIBC), and muscle invasive bladder cancer (MIBC) along with certifiably healthy controls were analyzed. Markers for α2-6 sialylation, β1-4 branching, β1-6 branching, and outer-arm fucosylation were able to separate current and former (NED) cases from controls; but NED, NMIBC, and MIBC were not distinguished from one another. Markers for α2-6 sialylation and β1-6 branching were able to predict recurrence from the NED state using a Cox proportional hazards regression model adjusted for age, gender, and time from cancer. These two glycan features were found to be correlated to the concentration of C-reactive protein, a known prognostic marker for bladder cancer, further strengthening the link between inflammation and abnormal plasma protein glycosylation.
ContributorsRoshdiferdosi, Shadi (Author) / Borges, Chad R (Thesis advisor) / Woodbury, Neal (Committee member) / Hayes, Mark (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Though DNA nanostructures (DNs) have become interesting subjects of drug delivery, in vivo imaging and biosensor research, however, for real biological applications, they should be ‘long circulating’ in blood. One of the crucial requirements for DN stability is high salt concentration (like ~5–20 mM Mg2+) that is unavailable in a

Though DNA nanostructures (DNs) have become interesting subjects of drug delivery, in vivo imaging and biosensor research, however, for real biological applications, they should be ‘long circulating’ in blood. One of the crucial requirements for DN stability is high salt concentration (like ~5–20 mM Mg2+) that is unavailable in a cell culture medium or in blood. Hence DNs denature promptly when injected into living systems. Another important factor is the presence of nucleases that cause fast degradation of unprotected DNs. The third factor is ‘opsonization’ which is the immune process by which phagocytes target foreign particles introduced into the bloodstream. The primary aim of this thesis is to design strategies that can improve the in vivo stability of DNs, thus improving their pharmacodynamics and biodistribution.

Several strategies were investigated to address the three previously mentioned limitations. The first attempt was to study the effect length and conformation of polyethylene glycol (PEG) on DN stability. DNs were also coated with PEG-lipid and human serum albumin (HSA) and their stealth efficiencies were compared. The findings reveal that both PEGylation and albumin coating enhance low salt stability, increase resistance towards nuclease action and reduce uptake of DNs by macrophages. Any protective coating around a DN increases its hydrodynamic radius, which is a crucial parameter influencing their clearance. Keeping this in mind, intrinsically stable DNs that can survive low salt concentration without any polymer coating were built. Several DNA compaction agents and DNA binders were screened to stabilize DNs in low magnesium conditions. Among them arginine, lysine, bis-lysine and hexamine cobalt showed the potential to enhance DN stability.

This thesis also presents a sensitive assay, the Proximity Ligation Assay (PLA), for the estimation of DN stability with time. It requires very simple modifications on the DNs and it can yield precise results from a very small amount of sample. The applicability of PLA was successfully tested on several DNs ranging from a simple wireframe tetrahedron to a 3D origami and the protocol to collect in vivo samples, isolate the DNs and measure their stability was developed.
ContributorsBanerjee, Saswata (Author) / Yan, Hao (Thesis advisor) / Angell, Austen (Committee member) / Woodbury, Neal (Committee member) / Liu, Yan (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Exerting bias on a diverse pool of random short single stranded oligonucleotides (ODNs) by favoring binding to a specific target has led to the identification of countless high affinity aptamers with specificity to a single target. By exerting this same bias without prior knowledge of targets generates libraries to

Exerting bias on a diverse pool of random short single stranded oligonucleotides (ODNs) by favoring binding to a specific target has led to the identification of countless high affinity aptamers with specificity to a single target. By exerting this same bias without prior knowledge of targets generates libraries to capture the complex network of molecular interactions presented in various biological states such as disease or cancer. Aptamers and enriched libraries have vast applications in bio-sensing, therapeutics, targeted drug delivery, biomarker discovery, and assay development. Here I describe a novel method of computational biophysical characterization of molecular interactions between a single aptamer and its cognate target as well as an alternative to next generation sequencing (NGS) as a readout for a SELEX-based assay. I demonstrate the capability of an artificial neural network (ANN) trained on the results of screening an aptamer against a random sampling of a combinatorial library of short synthetic 11mer peptides to accurately predict the binding intensities of that aptamer to the remainder of the combinatorial space originally sampled. This machine learned comprehensive non-linear relationship between amino acid sequence and aptamer binding to synthetic peptides can also make biologically relevant predictions for probable molecular interactions between the aptamer and its cognate target. Results of SELEX-based assays are determined by quantifying the presence and frequency of informative species after probing patient specimen. Here I show the potential of DNA microarrays to simultaneously monitor a pool of informative sequences within a diverse library with similar variability and reproducibility as NGS.
ContributorsLevenberg, Symon (Author) / Woodbury, Neal (Thesis advisor) / Borges, Chad (Committee member) / Ghirlanda, Giovanna (Committee member) / Redding, Kevin (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Urban areas across the Unites States are facing a housing affordability crisis. One approach some cities and states have taken is to reduce or eliminate single-family zoning. Single-family zoning prevents the construction of more-affordable apartments in vast swaths of the American urban landscape. This policy shift has already occurred in

Urban areas across the Unites States are facing a housing affordability crisis. One approach some cities and states have taken is to reduce or eliminate single-family zoning. Single-family zoning prevents the construction of more-affordable apartments in vast swaths of the American urban landscape. This policy shift has already occurred in Minneapolis, Sacramento, and Oregon, and is under discussion in California, Massachusetts, and North Carolina, among others. Independent of any effects on housing affordability, changes to land use will have effects on transport. I evaluate these effects using a microsimulation framework. In order for land use policies to have an effect on transport, they need to first have an effect on land use, so I first build an economic model to simulate where development will occur given a loosening of single-family zoning. Transport outcomes will vary depending on which households live in which parts of the region, so I use an equilibrium sorting model to forecast how residents will re-sort across the region in response to the land use changes induced by new land-use policies. This model also jointly forecasts how many vehicles each household will choose to own. Finally, I apply an activity-based travel demand microsimulation model to forecast the changes in transport associated with the forecast changes from the previous models. I find that while there is opportunity for economically-feasible redevelopment of single-family homes into multifamily structures, the amount of redevelopment that will occur varies greatly depending on the exact expectations of developers about future market conditions. Redevelopment is focused in higher-income neighborhoods. The transport effects of the redevelopment are minimal. Average car ownership across the region does not change hardly at all, although residents of new housing units do have somewhat lower car ownership. Vehicles kilometers traveled, mode choice, and congestion change very little as well. This does not mean that upzoning does not affect transport in general, but that more nuanced proposals may be necessary to promote desirable transport outcomes. Alternatively, the results suggest that upzoning will not worsen transport outcomes, promising for those who support upzoning on affordability grounds.
ContributorsConway, Matthew Wigginton (Author) / Salon, Deborah (Thesis advisor) / Pfeiffer, Deirdre (Committee member) / Fotheringham, A Stewart (Committee member) / van Eggermond, Michael AB (Committee member) / Arizona State University (Publisher)
Created2021
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Description
RNA aptamers adopt tertiary structures that enable them to bind to specific ligands. This capability has enabled aptamers to be used for a variety of diagnostic, therapeutic, and regulatory applications. This dissertation focuses on the use RNA aptamers in two biological applications: (1) nucleic acid diagnostic assays and (2) scaffolding

RNA aptamers adopt tertiary structures that enable them to bind to specific ligands. This capability has enabled aptamers to be used for a variety of diagnostic, therapeutic, and regulatory applications. This dissertation focuses on the use RNA aptamers in two biological applications: (1) nucleic acid diagnostic assays and (2) scaffolding of enzymatic pathways. First, sensors for detecting arbitrary target RNAs based the fluorogenic RNA aptamer Broccoli are designed and validated. Studies of three different sensor designs reveal that toehold-initiated Broccoli-based aptasensors provide the lowest signal leakage and highest signal intensity in absence and in presence of the target RNA, respectively. This toehold-initiated design is used for developing aptasensors targeting pathogens. Diagnostic assays for detecting pathogen nucleic acids are implemented by integrating Broccoli-based aptasensors with isothermal amplification methods. When coupling with recombinase polymerase amplification (RPA), aptasensors enable detection of synthetic valley fever DNA down to concentrations of 2 fM. Integration of Broccoli-based aptasensors with nucleic acid sequence-based amplification (NASBA) enables as few as 120 copies/mL of synthetic dengue RNA to be detected in reactions taking less than three hours. Moreover, the aptasensor-NASBA assay successfully detects dengue RNA in clinical samples. Second, RNA scaffolds containing peptide-binding RNA aptamers are employed for programming the synthesis of nonribosomal peptides (NRPs). Using the NRP enterobactin pathway as a model, RNA scaffolds are developed to direct the assembly of the enzymes entE, entB, and entF from E. coli, along with the aryl-carrier protein dhbB from B. subtilis. These scaffolds employ X-shaped RNA motifs from bacteriophage packaging motors, kissing loop interactions from HIV, and peptide-binding RNA aptamers to position peptide-modified NRP enzymes. The resulting RNA scaffolds functionalized with different aptamers are designed and evaluated for in vitro production of enterobactin. The best RNA scaffold provides a 418% increase in enterobactin production compared with the system in absence of the RNA scaffold. Moreover, the chimeric scaffold, with E. coli and B. subtilis enzymes, reaches approximately 56% of the activity of the wild-type enzyme assembly. The studies presented in this dissertation will be helpful for future development of nucleic acid-based assays and for controlling protein interaction for NRPs biosynthesis.
ContributorsTang, Anli (Author) / Green, Alexander (Thesis advisor) / Yan, Hao (Committee member) / Woodbury, Neal (Committee member) / Arizona State University (Publisher)
Created2020
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Description
Elucidation of Antigen-Antibody (Ag-Ab) interactions is critical to the understanding of humoral immune responses to pathogenic infection. B cells are crucial components of the immune system that generate highly specific antibodies, such as IgG, towards epitopes on antigens. Serum IgG molecules carry specific molecular recognition information concerning the antigens that

Elucidation of Antigen-Antibody (Ag-Ab) interactions is critical to the understanding of humoral immune responses to pathogenic infection. B cells are crucial components of the immune system that generate highly specific antibodies, such as IgG, towards epitopes on antigens. Serum IgG molecules carry specific molecular recognition information concerning the antigens that initiated their production. If one could read it, this information can be used to predict B cell epitopes on target antigens in order to design effective epitope driven vaccines, therapies and serological assays. Immunosignature technology captures the specific information content of serum IgG from infected and uninfected individuals on high density microarrays containing ~105 nearly random peptide sequences. Although the sequences of the peptides are chosen to evenly cover amino acid sequence space, the pattern of serum IgG binding to the array contains a consistent signature associated with each specific disease (e.g., Valley fever, influenza) among many individuals. Here, the disease specific but agnostic behavior of the technology has been explored by profiling molecular recognition information for five pathogens causing life threatening infectious diseases (e.g. DENV, WNV, HCV, HBV, and T.cruzi). This was done by models developed using a machine learning algorithm to model the sequence dependence of the humoral immune responses as measured by the peptide arrays. It was shown that the disease specific binding information could be accurately related to the peptide sequences used on the array by the machine learning (ML) models. Importantly, it was demonstrated that the ML models could identify or predict known linear epitopes on antigens of the four viruses. Moreover, the models identified potential novel linear epitopes on antigens of the four viruses (each has 4-10 proteins in the proteome) and of T.cruzi (a eukaryotic parasite which has over 12,000 proteins in its proteome). Finally, the predicted epitopes were tested in serum IgG binding assays such as ELISAs. Unfortunately, the assay results were inconsistent due to problems with peptide/surface interactions. In a separate study for the development of antibody recruiting molecules (ARMs) to combat microbial infections, 10 peptides from the high density peptide arrays were tested in IgG binding assays using sera of healthy individuals to find a set of antibody binding termini (ABT, a ligand that binds to a variable region of the IgG). It was concluded that one peptide (peptide 7) may be used as a potential ABT. Overall, these findings demonstrate the applications of the immunosignature technology ranging from developing tools to predict linear epitopes on pathogens of small to large proteomes to the identification of an ABT for ARMs.
ContributorsCHOWDHURY, ROBAYET (Author) / Woodbury, Neal (Thesis advisor) / LaBaer, Joshua (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2020
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Description
Interactions between proteins form the basis of almost all biological mechanisms. The majority of proteins perform their functions as a part of an assembled complex, rather than as an isolated species. Understanding the functional pathways of these protein complexes helps in uncovering the molecular mechanisms involved in the interactions. In

Interactions between proteins form the basis of almost all biological mechanisms. The majority of proteins perform their functions as a part of an assembled complex, rather than as an isolated species. Understanding the functional pathways of these protein complexes helps in uncovering the molecular mechanisms involved in the interactions. In this thesis, this has been explored in two fundamental ways. First, a biohybrid complex was assembled using the photosystem I (PSI) protein complex to translate the biochemical pathways into a non-cellular environment. This involved incorporating PSI on a porous antimony-doped tin oxide electrode using cytochrome c. Photocurrent was generated upon illumination of the PSI/electrode system alone at microamp/cm2 levels, with reduced oxygen apparently as the primary carrier. When the PSI/electrode system was coupled with ferredoxin, ferredoxin-NADP+ reductase (FNR), and NADP+, the resulting light-powered NADPH production was coupled to a dehydrogenase system for enzymatic carbon reduction. The results demonstrated that light-dependent reduction readily takes place. However, the pathways do not always match the biological pathways of PSI in nature. To create a complex self-assembled system such as the one involving PSI that is structurally well defined, there is a need to develop ways to guide the molecular interactions. In the second part of the thesis, this problem was approached by studying a well-defined system involving monoclonal antibodies (mAbs) binding their cognate epitope sequences to understand the molecular recognition properties associated with protein-protein interactions. This approach used a neural network model to derive a comprehensive and quantitative relationship between an amino acid sequence and its function by using sparse measurements of mAb binding to peptides on a high density peptide microarray. The resulting model can be used to predict the function of any peptide in the possible combinatorial sequence space. The results demonstrated that by training the model on just ~105 peptides out of the total combinatorial space of ~1010, the target sequences of the mAbs (cognate epitopes) can be predicted with high statistical accuracy. Furthermore, the biological relevance of the algorithm’s predictive ability has also been demonstrated.
ContributorsSingh, Akanksha (Author) / Woodbury, Neal (Thesis advisor) / Liu, Yan (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2021
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Description

Swinging arms are a key functional component of multistep catalytic transformations in many naturally occurring multi-enzyme complexes. This arm is typically a prosthetic chemical group that is covalently attached to the enzyme complex via a flexible linker, allowing the direct transfer of substrate molecules between multiple active sites within the

Swinging arms are a key functional component of multistep catalytic transformations in many naturally occurring multi-enzyme complexes. This arm is typically a prosthetic chemical group that is covalently attached to the enzyme complex via a flexible linker, allowing the direct transfer of substrate molecules between multiple active sites within the complex. Mimicking this method of substrate channelling outside the cellular environment requires precise control over the spatial parameters of the individual components within the assembled complex. DNA nanostructures can be used to organize functional molecules with nanoscale precision and can also provide nanomechanical control. Until now, protein–DNA assemblies have been used to organize cascades of enzymatic reactions by controlling the relative distance and orientation of enzymatic components or by facilitating the interface between enzymes/cofactors and electrode surfaces. Here, we show that a DNA nanostructure can be used to create a multi-enzyme complex in which an artificial swinging arm facilitates hydride transfer between two coupled dehydrogenases. By exploiting the programmability of DNA nanostructures, key parameters including position, stoichiometry and inter-enzyme distance can be manipulated for optimal activity.

ContributorsFu, Jinglin (Author) / Yang, Yuhe (Author) / Johnson-Buck, Alexander (Author) / Liu, Minghui (Author) / Liu, Yan (Author) / Walter, Nils G. (Author) / Woodbury, Neal (Author) / Yan, Hao (Author) / Biodesign Institute (Contributor)
Created2014-07-01