Object movement detection during observers’ self-motion is critical in navigation. Given ample optical available variables, which of them would be used would help us reveal the strategies being employed. In this work, using functional magnetic resonance imaging (fMRI) methods, we investigated the neural substrate underlying specific visual motion related tasks, such as time to passage (TTP), depth parallax, and collision. Using a visual search paradigm implemented with MATLAB, we developed a psychophysical task to investigate how the target characteristics (initial depth, initial eccentricity, and independent velocity), spatial attention, and heading estimation would affect visual search, for better understanding the mechanisms involved in object movement detection during self-motion. The fMRI analysis shows that: 1. Bilateral precentral sulcus (PreCS), postcentral sulcus (PostCS) and bilateral hMT are strongly activated during the TTP task. 2. Cortical regions along the dorsal visual processing pathway, including bilateral hMT, superior parietal gyrus (SPG), PostCS, PreCS and superior frontal gyrus (SFG), play important roles in our depth perception test. 3. In the collision test, similar activation pattern has been found in normal controls and stroke patients with visual deficits, intraparietal sulcus (IPS), SPG, supplementary area (SMA) and premotor regions are highly activated. The psychophysical results in visual search tasks indicate targets located in central visual field and target placed closer to the observer are easier to detect, looming distractor demands attention, the detrimental effect increases with the increasing of the target eccentricity level, no preference has been found in visual search among different heading directions in this test. In summary, cortical regions along visual motion processing pathway are highly involved in object movement detection during self-motion, the observers will take flexible strategies when different optical cues are provided.

Clinically tractable diagnostics must be low-cost, rapid, sensitive, easy to use, and adaptable to new targets. With its rational design, synthetic biology holds promise for developing diagnostic technologies that can address these needs. In particular, progress in synthetic biology has led to improved circuit-building abilities and a large collection of biomolecular sensors. However, these technologies fundamentally require transcription and translation, limiting their applicability to cellular contexts In vitro cell-free expression systems that contain transcription and translation machinery provide the environment necessary for biologically-based technologies to function independently of living cells. Our lab recently developed a paper-based system for cell-free gene expression, which utilizes cell-free extracts that are freeze-dried on to paper and other porous substrates to allow for long-term preservation of synthetic circuits at room temperature. Our platform represents a scalable, cost-effective technology that is easy to use and is compatible with synthetic biology tools. In this dissertation, I present several advancements to this diagnostic platform that are geared towards improving the system’s clinical tractability. In the context of developing a diagnostic for Zika virus that could be deployed in low-resource settings, I demonstrate improvements to diagnostic sensitivity and rapid sample processing that allow for detection of low femtomolar quantities of active virus directly from blood plasma samples. I also describe preliminary results towards a streamlined one-pot amplification-sensing reaction, and propose the development of a paper-based diagnostic for antibiotic susceptibility testing.

Modern cancer immunotherapy involves the conditioning of endogenous T cells to fight cancerous bodies that have managed to resist or avoid detection. Recently approved antibody drugs target the immune checkpoint pathway in T cells to prevent their tolerance to cancer antigens. There exists a compelling need, especially in the drug discovery world, to develop better assays for screening and to study the underlying mechanisms of these new antibody drugs. The core motivation of my work is to develop a primary cell assay for the immune checkpoint pathway using 1-D imaging cytometry. The assay is focused on high throughput and high content screening. It takes advantage of our novel 1-D imaging cytometer platform. The assay is designed to artificially induce anergy in primary human T cells and systemically study their drug response. An automated statistical method quantifies the functional phenotypes of both healthy and anergic T cells into a single descriptive readout. Reducing localization of biomarkers into a single ‘activity score’ readout has many advantages for drug screening and characterization. Additional assays were developed to study T cell activation dynamics and other signaling events during the immune checkpoint pathway. Our 1-D instrument leverages both the high throughput aspects of traditional flow cytometry and the high spatial content of 2-D imaging cytometers. The PMC data analysis emphasizes an unbiased approach to analyze flow cytometry data, which eliminates the subjective manual gating of current cytometric methods. This is crucial to developing more accurate and reliable assays with minimal supervision and need for expert operators. The high-throughput and high-content capabilities presented enable new types of assays previously not possible with human primary T cells. Adoption of physiological relevant primary cell assays has potential to revolutionize large-scale drug screening and future applications in personalized medicine.

Changes in birefringence (or dynamic birefringence) provide an arguably cleaner method of measuring IOS as compared to scattering methods. Other imaging methods have substantial limitations. Nerves inherently exhibit a static (rest condition) birefringence that is associated with the structural anisotropies of axonal protein filaments, membrane phospholipids and proteins, as well as surrounding tissues, which include Schwann cells and axon sheaths. The dynamic birefringence, or “crossed-polarized signal” (XPS), in neurons arises from activity in axons and occurs with a rapid momentary change, typically a decrease, in the birefringence when action potentials (APs) propagate along them. We improved the signal-to-noise to make detecting this signal an easier task, and present the XPS as a viable candidate for detecting AP activity in anisotropic nervous tissue. Our data collectively serves as a strong indication that there is a capacitive-charging-like effect directly inducing a gradual recovery (long tail) of the XPS to baseline, and also causing a smoothing of the XPS trace. A setup was constructed that successfully demonstrated the feasibility of tracking propagating compound APs in a peripheral nerve using the XPS. We made significant progress in the attempt to investigate birefringence of myelination. For the first time, the XPS in a myelinated tissue was detected, and it appears to be bipolar in nature. Further work in investigating the nature of this signal is needed, and is currently underway. Since changes in birefringence in neurons are associated instantaneously with electrophysiological phenomena, they are well-suited for fast imaging of propagating action potentials in neuronal tissue. In summary, imaging based on polarization sensing of changes in birefringence offers promise for an improved noninvasive method of detecting and tracking AP activity in myelinated and unmyelinated nerves and could be designed for pre-clinical and surgical applications.

Treatments using a patient’s own T cells to target cancer have applied advances in genetic engineering and cancer immunotherapy as the basis for a powerful, targeted cell-based therapy. Tumor-infiltrating lymphocytes and T cells that have been genetically modified to express cancer antigen-specific receptors—such as T cell receptors (TCRs) and chimeric antigen receptors (CARs)—leverage the innate targeted cytotoxicity of T cells against cancer. Despite promising results in clinical trials, these therapies have elicited serious and sometimes fatal responses. These toxicities include killing of healthy tissue expressing lower levels of antigen, as well as an overstimulation of the immune response called cytokine release syndrome. These outcomes reflect the double-edged nature of T cell-based therapies: the same powerful capability that makes them strong therapeutic candidates can become fatal if modified cells are left to their own devices. T cell therapies would greatly benefit from the development of tools that enable doctors to have bedside control over a cell’s behavior and truly respond to each patient’s needs. The work of this thesis aims to develop genetic circuits that control T cell activity. This platform has been adapted to control when CARs are expressed and at what level. In contrast to the current approach where patients are treated to one therapeutic “state,” these genetic circuits will allow doctors to decide between multiple states defined by CAR expression through the addition of a drug. These circuits contain memory such that long-term administration of the drug is not required to maintain a change. I have designed an ON switch and an OFF switch to control when a CAR is expressed, and an EXPRESSION switch to increase CAR expression. I characterized the performance of these circuits to demonstrate their dynamics over time, as well as their ability to control T cell behavior. I also demonstrate that these circuits contain memory, and that they are tunable. / 2020-07-02T00:00:00Z

Protein interactions regulate gene expression, cell signaling, catalysis, and many other functions across all of molecular biology. We must understand them quantitatively, and experimental methods have provided the data that form the basis of our current understanding. They remain our most accurate tools. However, their low efficiency and high cost leave room for predictive, computational approaches that can provide faster and more detailed answers to biological problems. A rigid-body simulation can quickly and effectively calculate the predicted interaction energy between two molecular structures in proximity. The fast Fourier-transform-based mapping algorithm FTMap predicts small molecule binding 'hot spots' on a protein's surface and can provide likely orientations of specific ligands of interest that may occupy those hot spots. This process now allows unique ligands to be used by this algorithm while permitting additional small molecular cofactors to remain in their bound conformation. By keeping the cofactors bound, FTMap can reduce false positives where the algorithm identifies a true, but incorrect, ligand pocket where the known cofactor already binds. A related algorithm, ClusPro, can evaluate interaction energies for billions of docked conformations of macromolecular structures. The work reported in this thesis can predict protein-polysaccharide interactions and the software now contains a publicly available feature for predicting protein-heparin interactions. In addition, a new approach for determining regions of predicted activity on a protein's surface allows prediction of a protein-protein interface. This new tool can also identify the interface in encounter complexes formed by the process of protein association—more closely resembling the biological nature of the interaction than the former, calculated, binary, bound and unbound states.

An abnormal multicellular architecture and a stiffened extracellular matrix (ECM) are defining characteristics of breast cancer, and yet, most in vitro tumor models fail to recapitulate the aberrant tumor microenvironment or accurately predict in vivo cellular responses to therapeutics. This dissertation aims to fill this gap in knowledge by developing and applying a suite of novel in vitro tools to investigate how the physical properties of the tumor microenvironment drive cancer progression. Our approach to develop and apply in vitro tools rests on three independent, but synergistic pillars. First, we established a 3D in vitro tumor model that mimics critical cell-cell and cell-ECM interactions by embedding multicellular spheroids within 3D collagen matrices. We assessed the in vivo relevance of our 3D collagen embedded spheroid model by quantifying the presence of highly malignant cancer stem cells (CSCs) before and after chemotherapeutic treatment with either paclitaxel or cisplatin. By characterizing the CSC response within two other commonly used in vitro models—a 2D monolayer and a 3D collagen model in which single cells are diffusely embedded—we found the CSC response to be model-dependent. Our results therefore highlight the need to screen potential CSC-specific chemotherapy drugs within in vitro models that recapitulate the in vivo 3D multicellular tumor architecture. Second, through integrating computational and experimental approaches, we developed a mathematical model of the transcriptional regulators—Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ)—which are often overactive in late-stage cancers despite mutations within their upstream signaling pathway being rare. Here, dysregulated cytoskeletal tension and disrupted apical-basal polarity, two defining characteristics of breast cancer, have been suggested to promote overactive YAP/TAZ signaling. Therefore, we developed a computational model to study how overactive cytoskeletal tension, due to increased ECM stiffness, leads to aberrant YAP/TAZ signaling in cancer. The model revealed that simultaneous alterations in cell mechanics and cell-cell adhesion signaling synergistically converge on YAP/TAZ activity and lead to its overactivation, a process poorly understood in cancer progression. Finally, in an effort to decouple the effects of collagen fiber density and network mechanics on cancer cell behavior, we developed a highly tunable in vitro 3D interpenetrating network (IPN) platform consisting of a primary collagen network reinforced by a secondary visible-light-mediated thiol-ene PEG network. The IPN platform is cytocompatible, inherently bioactive, and mechanically tunable, which makes it a useful tool for studying mechanotransductive signaling pathways. Moreover, while this thesis work focused on in vitro applications, our approach raises the interesting possibility of altering the physical properties of the tumor microenvironment as a potential therapeutic. In summary, this work addresses the question of how the physical properties of the tumor microenvironment affect cancer progression by deploying three distinct, but complementary approaches, and suggests that addressing the physical aspects of cancer progression may improve clinical outcomes. / 2020-02-20T00:00:00Z

<p> The overall goal of this thesis work is to study the effects of biomodulations on Alzheimer's disease (AD) related cellular pathways, using biophysical and photobiological methods, including secretory phospholipase A2, various fatty acids treatments and low energy light irradiation. By increasing membrane fluidity in neuronal cells, secretory phospholipase A2 and unsaturated fatty acids with 4 or more double bonds are able to increase the secretion of neuroprotective and neurotrophic &alpha;-secretase-cleaved soluble APP (sAPP_&alpha;). Low energy laser at 632.8 nm is able to suppress amyloid-&beta; peptide (A&beta;)-induced oxidative and inflammatory responses in primary astrocytes, suggesting it has neuroprotective effects against oxidative stress and inflammation in AD. This thesis work provides insights into potential therapeutic treatments and prevention of AD.</p><p>

Cervical cancer is a major problem in the developing world and low- resource settings where standard screening techniques are not accessible. Cervical cancer is one of the few cancers that can be successfully treated when detected early. Therefore, there exists a high clinical need to screen for cervical cancer early. The etiological agent of cervical cancer is the human papillomavirus (HPV), with 70% of cases related to HPV genotypes 16 and 18. I sought to increase access to screening by developing a fully integrated and multiplexed molecular diagnostic assay to extract, amplify, detect, and distinguish HPV16 and HPV18 DNA on a low-cost paperfluidic platform for point- of-care (POC) applications. Isothermal (one temperature) loop-mediated amplification (LAMP) was used to amplify HPV DNA instead of the traditional polymerase chain reaction (PCR) that requires multiple temperatures. The amplified HPV16 and HPV18 DNA were differentially detected on a simple lateral flow strip – similar to that used in a pregnancy test – generating a visible colorimetric readout for each specific genotype. LAMP amplification is difficult to characterize and current methods were insufficient in providing specificity at the level needed for a multiplexed assay. Therefore, a novel characterization strategy was developed based on fluorescence to distinguish positive LAMP amplification products. This workflow used differential fluorescent tags to identify whether HPV16 DNA or HPV18 DNA was present and simplified complex nonspecific LAMP smears to a specific band pattern. After singleplex HPV16 and HPV18 LAMP assays were optimized with the new workflow, the two singleplex assays were successfully combined into one multiplex reaction with 12 primers, a nontrivial feat. Each assay step – DNA extraction, amplification, and detection – was optimized and integrated into a single chip that can control the timing of each step. Several chip configurations were tested to determine the optimal chip form factor, and a small subset of clinical samples were tested to demonstrate feasibility in low-resource settings. With this diagnostic platform, asymptomatic patients positive for HPV16 DNA and HPV18 DNA can be screened more closely, allocating precious resources to those most at risk, a beneficial use in both low-resource settings and the USA.

Trypanosoma brucei is a medically important protozoan parasite that causes African sleeping sickness in humans. Although disease treatment is possible, it is hindered by the limited availability of effective drugs and the rapid emergence of drug resistance. It is, therefore, important to identify novel drug targets for the development of new, more effective drugs. Mitochondrial carrier family (MCF) proteins transport a wide range of key metabolites across the mitochondrial inner membrane. They are important for the maintenance of key metabolic pathways in all eukaryotic cells. In particular, the MCF proteins that have been implicated in mitochondrial adenosine triphosphate (ATP) import appear to be essential for cell function and survival due to their roles in defence against oxidative stress and in the provision of the ATP required for mitochondrial electron transport and associated ATP production. Based on their important physiological roles, mitochondrial nucleotide transporters are potential novel drug targets. The main aim of this thesis was to identify and functionally characterise the putative mitochondrial nucleotide transporters in T. brucei. Sequence analysis revealed that the T. brucei genome contains 5 MCF proteins: TbMCP1, TbMCP15, TbMCP16, TbMCP20 and TbMCP23. The proteins were predicted to have roles in mitochondrial nucleotide transport based on their homology to functionally characterised MCF proteins that transport nucleotides. Of these TbMCPs, TbMCP1 has the highest sequence similarity to functionally characterised mitochondrial flavin carriers and peroxisomal ATP/adenosine monophosphate carriers in other eukaryotes, such as Flx1 in Saccharomyces cerevisiae. Sequence analysis further predicted that TbMCP15 and TbMCP16 were closely related to mitochondrial adenosine diphosphate (ADP/ATP) transporters, such as AAC2 in S. cerevisiae, whereas TbMCP20 appeared to be closely related to mitochondrial S-adenosylmethionine transporters, such as SAM5 in S. cerevisiae, and TbMCP23 appeared to be closely related to mitochondrial pyrimidine transporters, such as S. cerevisiae RIM2.