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Description
Nucleic acid polymers have numerous applications in both therapeutics and research to control gene expression and bind biologically relevant targets. However, due to poor biological stability their clinical applications are limited. Chemical modifications can improve both intracellular and extracellular stability and enhance resistance to nuclease degradation. To identify a potential candidate for a highly stable synthetic nucleic acid, the biostability of α-L-threofuranosyl nucleic acid (TNA) was evaluated under simulated biological conditions. TNA contains a four-carbon sugar and is linked by 2’, 3’ phosphodiester bonds. We hypothesized that this distinct chemical structure would yield greater nuclease resistance in human serum and human liver microsomes, which were selected as biologically relevant nuclease conditions. We found that TNA oligonucleotides remained undigested for 7 days in these conditions. In addition, TNA/DNA heteropolymers and TNA/RNA oligonucleotide duplexes displayed nuclease resistance, suggesting that TNA has a protective effect over DNA and RNA. In conclusion TNA demonstrates potential as a viable synthetic nucleic acid for use in numerous clinical and therapeutic applications.
ContributorsCulbertson, Michelle Catherine (Author) / Maley, Carlo (Thesis director) / Mangone, Marco (Committee member) / Larsen, Andrew (Committee member) / School of Molecular Sciences (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Disturbances in the protein interactome often play a large role in cancer progression. Investigation of protein-protein interactions (PPI) can increase our understanding of cancer pathways and will disclose unknown targets involved in cancer disease biology. Although numerous methods are available to study protein interactions, most platforms suffer from drawbacks including high false positive rates, low throughput, and lack of quantification. Moreover, most methods are not compatible for use in a clinical setting. To address these limitations, we have developed a multiplexed, in-solution protein microarray (MISPA) platform with broad applications in proteomics. MISPA can be used to quantitatively profile PPIs and as a robust technology for early detection of cancers. This method utilizes unique DNA barcoding of individual proteins coupled with next generation sequencing to quantitatively assess interactions via barcode enrichment. We have tested the feasibility of this technology in the detection of patient immune responses to oropharyngeal carcinomas and in the discovery of novel PPIs in the B-cell receptor (BCR) pathway. To achieve this goal, 96 human papillomavirus (HPV) antigen genes were cloned into pJFT7-cHalo (99% success) and pJFT7-n3xFlag-Halo (100% success) expression vectors. These libraries were expressed via a cell-free in vitro transcription-translation system with 93% and 96% success, respectively. A small-scale study of patient serum interactions with barcoded HPV16 antigens was performed and a HPV proteome-wide study will follow using additional patient samples. In addition, 15 query proteins were cloned into pJFT7_nGST expression vectors, expressed, and purified with 93% success to probe a library of 100 BCR pathway proteins and detect novel PPIs.
ContributorsRinaldi, Capria Lakshmi (Author) / LaBaer, Joshua (Thesis director) / Mangone, Marco (Committee member) / Borges, Chad (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The splicing of precursor messenger RNAs (pre-mRNAs) plays an essential role in dictating the mature mRNA profiles of eukaryotic cells. Mis-regulation of splicing, due to mutations in pre-mRNAs or in components of the splicing machinery, is associated with many diseases. Therefore, knowledge of pre-mRNA splicing mechanisms is required to understand gene expression regulation during states of homeostasis and disease, and for the development of therapeutic interventions.Splicing is catalyzed by the spliceosome, a dynamic and protein-rich ribozyme composed of five small nuclear ribonucleoproteins (snRNPs) and ~170 auxiliary factors. Early interactions that occur in prespliceosomal complexes formed by the 5′- and 3′-splice-site bound U1 and U2 snRNPs are responsible for committing introns for removal. However, the mechanisms underlying these early interactions remain to be fully characterized for understanding the influence of alternative splicing factors and the impact of recurrent disease-associated mutations in prespliceosomal proteins. The goal of my dissertation research was to delineate the role of the U1 small nuclear RNA (snRNA) during prespliceosome assembly. By applying a cellular minigene reporter assay and a variety of in vitro techniques including cell-free protein expression, UV-crosslinking, electrophoretic mobility shift assays, surface plasmon resonance, and RNA affinity purification, my work establishes critical roles for the U1 snRNA stem-loops 3 (SL3) and 4 (SL4) in formation of intron definition interactions during prespliceosome assembly. Previously, the SL4 of the U1 snRNA was shown to form a molecular bridge across introns by contacting the U2-specific splicing factor 3A1 (SF3A1). I identified the Ubiquitin-like domain of SF3A1 as a non-canonical RNA binding domain responsible for U1-SL4 binding. I also determined a role for the SL3 region of the U1 snRNA in splicing and characterized the spliceosomal RNA helicase UAP56 as an SL3 interacting protein. By knocking-down the SL3- and SL4-interacting proteins, I confirmed that U1 splicing activity in vivo relies on UAP56 and SF3A1 and that their functions are interdependent. These findings, in addition to the observations made using in vitro splicing assays, support a model whereby UAP56, through its interaction with U1-SL3, enhances the cross-intron interaction between U1-SL4 and SF3A1 to promote prespliceosome formation.
ContributorsMartelly, William (Author) / Sharma, Shalini (Thesis advisor) / Mangone, Marco (Thesis advisor) / Gustin, Kurt (Committee member) / Chen, Julian (Committee member) / Arizona State University (Publisher)
Created2021
Description
This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data
ContributorsTa, Duyan Nguyen (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Hansford, Dianne (Committee member) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Cardiovascular disease (CVD) remains the leading cause of mortality, resulting in 1 out of 4 deaths in the United States at the alarming rate of 1 death every 36 seconds, despite great efforts in ongoing research. In vitro research to study CVDs has had limited success, due to lack of biomimicry and structural complexity of 2D models. As such, there is a critical need to develop a 3D, biomimetic human cardiac tissue within precisely engineered in vitro platforms. This PhD dissertation involved development of an innovative anisotropic 3D human stem cell-derived cardiac tissue on-a-chip model (i.e., heart on-a-chip), with an enhanced maturation tissue state, as demonstrated through extensive biological assessments. To demonstrate the potential of the platform to study cardiac-specific diseases, the developed heart on-a-chip was used to model myocardial infarction (MI) due to exposure to hypoxia. The successful induction of MI on-a-chip (heart attack-on-a-chip) was evidenced through fibrotic tissue response, contractile dysregulation, and transcriptomic regulation of key pathways.This dissertation also described incorporation of CRISPR/Cas9 gene-editing to create a human induced pluripotent stem cell line (hiPSC) with a mutation in KCNH2, the gene implicated in Long QT Syndrome Type 2 (LQTS2). This novel stem cell line, combined with the developed heart on-a-chip technology, led to creation of a 3D human cardiac on-chip tissue model of LQTS2 disease.. Extensive mechanistic biological and electrophysiological characterizations were performed to elucidate the mechanism of R531W mutation in KCNH2, significantly adding to existing knowledge about LQTS2. In summary, this thesis described creation of a LQTS2 cardiac on-a-chip model, incorporated with gene-edited hiPSC-cardiomyocytes and hiPSC-cardiac fibroblasts, to study mechanisms of LQTS2.
Overall, this dissertation provides broad impact for fundamental studies toward cardiac biological studies as well as drug screening applications. Specifically, the developed heart on-a-chip from this dissertation provides a unique alternative platform to animal testing and 2D studies that recapitulates the human myocardium, with capabilities to model critical CVDs to study disease mechanisms, and/or ultimately lead to development of future therapeutic strategies.
ContributorsVeldhuizen, Jaimeson (Author) / Nikkhah, Mehdi (Thesis advisor) / Brafman, David (Committee member) / Ebrahimkhani, Mo (Committee member) / Migrino, Raymond Q (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2021
Description
Retinotopic map, the map between visual inputs on the retina and neuronal activation in brain visual areas, is one of the central topics in visual neuroscience. For human observers, the map is typically obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Biological evidences show the retinotopic mapping is topology-preserving/topological (i.e. keep the neighboring relationship after human brain process) within each visual region.
Unfortunately, due to limited spatial resolution and the signal-noise ratio of fMRI, state of art retinotopic map is not topological.
The topic was to model the topology-preserving condition mathematically, fix non-topological retinotopic map with numerical methods, and improve the quality of retinotopic maps.
The impose of topological condition, benefits several applications.
With the topological retinotopic maps, one may have a better insight on human retinotopic maps, including better cortical magnification factor quantification, more precise description of retinotopic maps, and potentially better exam ways of in Ophthalmology clinic.
ContributorsTu, Yanshuai (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Crook, Sharon (Committee member) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2022
Description
Glioblastoma (GBM), the most common and aggressive primary brain tumor affecting adults, is characterized by an aberrant yet druggable epigenetic landscape. The Histone Deacetylases (HDACs), a major family of epigenetic regulators, favor transcriptional repression by mediating chromatin compaction and are frequently overexpressed in human cancers, including GBM. Hence, over the last decade there has been considerable interest in using HDAC inhibitors (HDACi) for the treatment of malignant primary brain tumors. However, to date most HDACi tested in clinical trials have failed to provide significant therapeutic benefit to patients with GBM. This is because current HDACi have poor or unknown pharmacokinetic profiles, lack selectivity towards the different HDAC isoforms, and have narrow therapeutic windows. Isoform selectivity for HDACi is important given that broad inhibition of all HDACs results in widespread toxicity across different organs. Moreover, the functional roles of individual HDAC isoforms in GBM are still not well understood. Here, I demonstrate that HDAC1 expression increases with brain tumor grade and is correlated with decreased survival in GBM. I find that HDAC1 is the essential HDAC isoform in glioma stem cells and its loss is not compensated for by its paralogue HDAC2 or other members of the HDAC family. Loss of HDAC1 alone has profound effects on the glioma stem cell phenotype in a p53-dependent manner and leads to significant suppression of tumor growth in vivo. While no HDAC isoform-selective inhibitors are currently available, the second-generation HDACi quisinostat harbors high specificity for HDAC1. I show that quisinostat exhibits potent growth inhibition in multiple patient-derived glioma stem cells. Using a pharmacokinetics- and pharmacodynamics-driven approach, I demonstrate that quisinostat is a brain-penetrant molecule that reduces tumor burden in flank and orthotopic models of GBM and significantly extends survival both alone and in combination with radiotherapy. The work presented in this thesis thereby unveils the non-redundant functions of HDAC1 in therapy- resistant glioma stem cells and identifies a brain-penetrant HDACi with higher selectivity towards HDAC1 as a potent radiosensitizer in preclinical models of GBM. Together, these results provide a rationale for developing quisinostat as a potential adjuvant therapy for the treatment of GBM.
ContributorsLo Cascio, Costanza (Author) / LaBaer, Joshua (Thesis advisor) / Mehta, Shwetal (Committee member) / Mirzadeh, Zaman (Committee member) / Mangone, Marco (Committee member) / Paek, Andrew (Committee member) / Arizona State University (Publisher)
Created2022
Description
Skin wounds can be caused by traumatic lacerations or incisions which disrupt the structural and functional integrity of the skin. Wound closure and primary intention treatment of the wound as soon as possible is crucial to avoid or minimize the risk of infection that can result in a compromised healing rate or advanced functional intricacy. The gold standard treatment for skin wound healing is suturing. Light-activated tissue sealing is an appealing alternative to sutures as it seals the wound edges minimizing the risk of infection and scarring, especially when utilized along with biodegradable polymeric biomaterials in the wound bed. Silk fibroins can be used as a biodegradable biomaterial that possesses properties supporting cell migration and proliferation in the tissue it interacts with. In addition, histamine treatment is shown to have extensive effects on cellular functions promoting wound healing. Here, the evaluation of Laser-activated Sealants (LASE) consisting of silk fibroin films induced with Indocyanine Green dye in a wound sealed with laser in the presence of Histamine receptor agonists H1R, H2R and H4R take place. The results were evaluated using Trans-epidermal Water Loss (TEWL), histological and analytical techniques where immune cell biomarkers Arginase-1, Ly6G, iNOS, Alpha-SMA, Proliferating Cell Nuclear Antigen (PCNA), and E-Cadherin were used to study the activity of specific cells such as macrophages, neutrophils, and myofibroblasts that aid in wound healing. PBS was used as a control for histamine receptor agonists. It was found that TEWL increased when treated with H1 receptor agonists while decreasing significantly in H2R and H4R-treated wounds. Arginase-1 activity improved, while it displayed an inverse relationship compared to iNOS. H4R agonist escalated Alpha-SMA cells, while others did not have any significant difference. Ly6G activity depleted in all histamine agonists significantly, while PCNA and E-Cadherin failed to show a positive or negative effect.
ContributorsPatel, Dirghau Manishbhai (Author) / Rege, Kaushal (Thesis advisor) / Massia, Stephen (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2022