The need for new antibiotics has been highlighted recently with the increasing pace of emergence of drug resistant pathogens (MRSA, XDR-TB, etc.). Modification of existing antibiotics with the additions of side chains or other chemical groups and genomics based drug targeting have been the preferred method of drug development at the corporate level in recent years. These approaches have yielded few viable antibiotics and natural products are once again becoming an area of interest for drug discovery. We examined the antimicrobial "Red Soils" of the Hashemite Kingdom of Jordan that have historically been used to prevent infection and cure rashes by the native peoples. Antimicrobial producing bacteria were present in these soils and found to be the reason for their antibiotic activity. After isolation, these bacteria were found to excrete their antimicrobials into the liquid culture media which we could then attempt to isolate for further study. Adsorbent resins were employed to capture the antimicrobial compounds and then elute them in a more concentrated solution. As part of a drug discovery program, we sought a way to quickly characterize other soils for potential antibiotic producing bacteria. The community level physiologic profile was examined to determine if this approach would allow for a rapid categorizing of soils based on their probability of containing antimicrobial producing microorganisms. This method proved to have a high level of variability that could not be overcome even after mixing using a commercial blender. The role of these antimicrobial producing bacteria within their natural microbial community has largely been confined to microbe-plant interactions. The role of antimicrobial-producing microorganisms in driving the diversity of their community has not been a focus of considerable study. The potential of an antimicrobial-producing bacterium to act as a driver of diversity was examined using an artificial microbial community based in a sand microcosm. The changes in the microbial assemblage indicate that antimicrobial-producing bacteria may act in an allelopathic manner rather than in a predatory role. / Ph. D.

<p dir="ltr"><i>Pseudomonas aeruginosa</i> (<i>P. aeruginosa</i>) is a Gram-negative bacterium responsible for many infections in immunocompromised humans. This multi-drug resistance human pathogen can form biofilms, which help protect it from not only clinical treatment but also from main homeostasis and metabolism. Understanding biofilm structures is critical to help combat biofilm formation and develop better ways to treat <i>P. aeruginosa</i> infections. A molecule that helps biofilm formation and virulence infections for <i>P. aeruginosa</i> is pyocyanin, which is believed to be correlated with the invasiveness of the bacteria and the stabilization of biofilms. To better understand the role of pyocyanin in assisting <i>P. aeruginosa</i> with survival, we applied optical imaging to study pyocyanin in biofilms and under blue light treatment. Using nonlinear optical imaging methods, we could successfully detect the aggregation of pyocyanin in biofilms. Furthermore, we discovered that pyocyanin protects <i>P. aeruginosa</i> from blue light inactivation. In addition, we found that blue light treatment alters the structure of pyocyanin, leading to irreversible changes that produce distinct spectra in UV-Vis and fluorescence signals. <i>These results provide new insights into how pyocyanin protects </i><i>P. aeruginosa</i> in blue light treatment. Further investigation would lead to better treatment strategies for more effective treatment of <i>P. aeruginosa</i> and biofilms for various infections.</p>

Recent advances in high-throughput technique enables the generation of a large amount of omic data such as genomics, transcriptomics, proteomics, metabolomics, glycomics etc. Typically, differential expression analysis (e.g., student's t-test, ANOVA) is performed to identify biomolecules (e.g., genes, proteins, metabolites, glycans) with significant changes on individual level between biologically disparate groups (disease cases vs. healthy controls) for cancer biomarker discovery. However, differential expression analysis on independent studies for the same clinical types of patients often led to different sets of significant biomolecules and had only few in common. This may be attributed to the fact that biomolecules are members of strongly intertwined biological pathways and highly interactive with each other. Without considering these interactions, differential expression analysis could lead to biased results. Network-based methods provide a natural framework to study the interactions between biomolecules. Commonly used data-driven network models include relevance network, Bayesian network and Gaussian graphical models. In addition to data-driven network models, there are many publicly available databases such as STRING, KEGG, Reactome, and ConsensusPathDB, where one can extract various types of interactions to build knowledge-driven networks. While both data- and knowledge-driven networks have their pros and cons, an appropriate approach to incorporate the prior biological knowledge from publicly available databases into data-driven network model is desirable for more robust and biologically relevant network reconstruction. Recently, there has been a growing interest in differential network analysis, where the connection in the network represents a statistically significant change in the pairwise interaction between two biomolecules in different groups. From the rewiring interactions shown in differential networks, biomolecules that have strongly altered connectivity between distinct biological groups can be identified. These biomolecules might play an important role in the disease under study. In fact, differential expression and differential network analyses investigate omic data from two complementary perspectives: the former focuses on the change in individual biomolecule level between different groups while the latter concentrates on the change in pairwise biomolecules level. Therefore, an approach that can integrate differential expression and differential network analyses is likely to discover more reliable and powerful biomarkers. To achieve these goals, we start by proposing a novel data-driven network model (i.e., LOPC) to reconstruct sparse biological networks. The sparse networks only contains direct interactions between biomolecules which can help researchers to focus on the more informative connections. Then we propose a novel method (i.e., dwgLASSO) to incorporate prior biological knowledge into data-driven network model to build biologically relevant networks. Differential network analysis is applied based on the networks constructed for biologically disparate groups to identify cancer biomarker candidates. Finally, we propose a novel network-based approach (i.e., INDEED) to integrate differential expression and differential network analyses to identify more reliable and powerful cancer biomarker candidates. INDEED is further expanded as INDEED-M to utilize omic data at different levels of human biological system (e.g., transcriptomics, proteomics, metabolomics), which we believe is promising to increase our understanding of cancer. Matlab and R packages for the proposed methods are developed and available at Github (https://github.com/Hurricaner1989) to share with the research community. / Ph. D.

In an increasingly data-driven society, sensors and actuators are the bridge between the physical world and the world of "data." Electroacoustic transducers convert acoustic energy into electrical energy (or vice versa), so it can be interpreted as data. Piezoelectric materials are often used for transducer manufacturing, and recent advancements in additive manufacturing have enabled this material to take on complex geometric forms with micro-scale features. This work advances the additive manufacturing of piezoelectric materials by developing a model for predictive success of complex 3D printed geometries in Mask Image Projection-Stereolithography (MIP-SL) by accounting for mechanical wear on Polydimethylsiloxane (PDMS). This work proposes a framework for the rapid manufacture of 3D printed transducers, adaptable to a multitude of transducer element forms. Using the print model and transducer framework, latticed hydrophone elements are designed and tested, showing evidence of selectively tunable sensitivity, resonance, and directivity pattern. These technology advancements are extended to enable a workflow for users to input polar coordinates and receive an acoustic element of a continuously tuned directivity pattern. Investigation into customer problem spaces via tech-push methods are adapted from the NSF's Lean Launchpad to reveal insight to the problems faced in hydrophone applications and other neighboring problem spaces. / Master of Science / In an increasingly data-driven world, sensors are the bridge between the physical world and the world of "data." The better the sensor; the better the data. Electroacoustic transducers are sensors that convert acoustic sound energy into electrical energy or vice versa. These are observed in the world around us as microphones, speakers, ultrasound devices, and more. In the early 1900's, piezoelectric materials became one of the dominant methods for transducer creation, and recent advancements in additive manufacturing have enabled this material to take on highly complex geometric forms with micro-scale feature sizes. Further advancements to additive manufacturing of piezoelectric materials are contributed through development of a model for predicting the success of complex 3D printed geometries in an Mask Image Projection-Stereolithography (MIP-SL) by accounting for mechanical wear on the Polydimethylsiloxane (PDMS) print window. This work proposes a framework for the rapid manufacture of 3D printed transducers, adaptable to a multitude of element forms. Using the developed print model and transducer framework, latticed hydrophone elements are designed and tested, showing evidence of selectively tunable sensitivity, resonance and beampattern. The advancements in technology are extended to enable a workflow for users to input polar coordinates and receive an acoustic element of continuously tuned beampattern. Investigation into customer problem spaces via tech-push methods are adapted from NSF's Lean Launchpad and reveals great insight to the problems faced in hydrophone applications and other neighboring industry spaces.

Islet amyloid polypeptide (IAPP) is a 37-residue amyloidogenic hormone implicated in the progression of Type II Diabetes (T2D). T2D affects an estimated 422 million people yearly and is a co-morbidity with numerous diseases. IAPP forms toxic oligomers and amyloid fibrils that reduce pancreatic β-cell mass and exacerbate the T2D disease state. Toxic oligomer formation is attributed, in part, to the formation of inter-peptide β-strands comprised of residues 23-27 (FGAIL). Flavonoids, a class of polyphenolic natural products, have been found experimentally to inhibit IAPP aggregate formation. Many of these known IAPP aggregation attenuating small flavonoids differ structurally only slightly; the influence of functional group placement on inhibiting the aggregation of the IAPP(20-29) has yet to be explored. To probe the role of small-molecule structural features that impede IAPP aggregation, molecular dynamics (MD) simulations were performed on a model fragment of IAPP(20-29) in the presence of morin, quercetin, dihydroquercetin, epicatechin, and myricetin. Contacts between Phe23 residues are critical to oligomer formation, and small-molecule contacts with Phe23 are a key predictor of β-strand reduction. Structural properties influencing the ability of compounds to disrupt Phe23-Phe23 contacts include carbonyl and hydroxyl group placement. These structural features influence aromaticity and hydrophobicity, principally affecting ability to disrupt IAPP(20-29) oligomer formation. This work provides key information on design considerations for T2D therapeutics. / Master of Science in Life Sciences / Type II Diabetes (T2D) affects an estimated 422 million people worldwide, with the World Health Organization (WHO) reporting that approximately 1.5 million deaths were directly caused by T2D in 2019. The progression of T2D has been attributed to a protein, called islet amyloid polypeptide (IAPP, or amylin) that is co-secreted with insulin after individuals eat or consumes calories. IAPP has been discovered to form toxic aggregates or clumps of protein material that worsen the disease state and cause a loss of mass of pancreatic cells. There is a large market for therapeutics of T2D and more small molecule drugs are needed to slow progression and severity of T2D. Flavonoids, a class of natural molecules, have been found to inhibit the processes by which IAPP promotes T2D disease progression by stopping the aggregation of IAPP. The structures of these flavonoid compounds differ slightly but show difference in ability to slow IAPP aggregation. By understanding how those differences confer more or less protection against T2D and inhibit IAPP aggregation, we can design more potent and specific drugs to target IAPP. To probe the role of molecular structure in preventing IAPP aggregation, molecular dynamics (MD) simulations — a powerful computational technique — were performed on a model fragment of IAPP in the presence of molecules morin, quercetin, dihydroquercetin, epicatechin, and myricetin. MD simulations provide extremely detailed information about potential drug interactions with a given target, serving as an important tool in the development of new drugs. This work has identified key features and predictors of effective IAPP drugs, providing a framework for the further development of therapeutics against T2D and similar diseases.

Malaria continues to be a global health burden that affects millions of people worldwide each year. Increasing demand for malaria control and eradication has led research to focus on sexual development of the malaria parasite. Sexual development is initiated when pre-destined intraerythrocytic ring stage parasites leave asexual reproduction and develop into gametocytes. A mosquito vector will ingest mature gametocytes during a blood meal. Sexual reproduction will occur in the midgut, leading to the production of sporozoites that will migrate to the salivary gland. The sporozoites will be injected to another human host during the next blood meal consequently, transmitting malaria. Due to decreased drug susceptibility of mature gametocytes, more investigation of the biology and metabolic requirements of malaria parasites during gametocytogenesis, as well as during the mosquito stages, are urgently needed to reveal novel targets for development of transmission-blocking agents. Furthermore, increasing drug resistance of the parasites to current antimalarials, including slowed clearance rates to artemisinin, requires the discovery of innovative drugs against asexual intraerythrocytic stages with novel mechanisms of action. Here, we have investigated the role of the apicoplast during Plasmodium falciparum gametocytogenesis. In addition, we describe drug-screening studies that have elucidated a novel mode of action of one compound from the Malaria Box, as well as identified new natural product compounds that may be serve as starting molecules for antimalarial development. / Ph. D.

Bacterial antibiotic resistance is a globally recognized problem that has prompted extensive research into novel antimicrobial compounds. This dissertation describes research focusing on two types of potential antimicrobial molecules, organometallic compounds (OMC) and peptide nucleic acids (PNA). Organometallic compounds show promise as antimicrobial drugs because of their structural difference from conventional antibiotics and antimicrobials, and because of the ability to "tune" their chemical and biological properties by varying ligand attachments. Peptide nucleic acids, when linked to a cell-penetrating peptide (CPP), can suppress bacterial gene expression by an antisense mechanism and are attractive candidates for antimicrobial drugs because they bind strongly to target nucleic acids and are resistant to nucleases. Chapters 1 and 2 of the dissertation provide an introduction and broad literature review to frame the experimental questions addressed. Chapter 3 describes work to test the cytotoxicity and cellular penetration capabilities of novel OMCs by evaluating their effects on J774A.1 murine macrophage-like cells that were either uninfected or were infected with Mycobacterium bovis BCG. Results indicate that OMCs with an iridium (Ir) metal center and an amino acid ligand show minimal cytotoxicity against eukaryotic cells but likely do not penetrate the intracellular compartment in significant amounts. Chapter 4 presents research into in vitro effects of CPP-PNAs targeting the tetA and tetR antibiotic resistance genes (CPP-anti-tetA PNA and CPP-anti-tetR PNA, respectively) in tetracycline-resistant Salmonella enterica ssp. enterica serovar Typhimurium DT104 (DT104). Through the use of modified minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays it was shown that both the CPP-anti-tetA PNA and CPP-anti-tetR PNA increase tetracycline susceptibility in DT104. Chapter 5 explores the molecular mechanism of the CPP-anti-tetA PNA and CPP-anti-tetR PNA through the use of reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Results indicate good specificity of the CPP-anti-tetA PNA for its nucleic acid target as evidenced by suppression of tetA mRNA expression in DT104 cultures treated with a combination of tetracycline and the PNA. Chapter 6 describes the development of a mouse model of DT104 infection using BALB/c mice, followed by implementation of that model to test in vivo antimicrobial effects of the CPP-anti-tetA PNA and the CPP-Sal-tsf PNA, which targets expression of the essential tsf gene. An optimal dose of DT104 was identified that causes clinical illness within 2-4 days. At the doses tested, concurrent treatment of infected mice with tetracycline and the CPP-anti-tetA PNA or with the CPP-Sal-tsf PNA alone did not have a protective effect. Final conclusions are 1) that further research with the OMCs should focus on compounds with an Ir center and an amino acid ligand, and should explore ways to enhance intracellular penetration, 2) that the in vitro results of the PNA studies suggest that PNAs targeting expression of antibiotic resistance genes could allow for repurposing of antibiotics to which bacteria are resistant, and 3) additional study of the behavior of PNAs in vivo is advised. / Doctor of Philosophy / Antibiotic-resistant bacteria are increasingly recognized as a threat to global health, and new antibacterial drugs are urgently needed. Before a chemical compound can advance far in the journey to becoming a new drug it must be tested for toxicity against mammalian cells. A portion of this dissertation research involved testing the toxicity of several organometallic compounds (OMCs) previously shown to have antibacterial potential. Mouse-derived mammalian cells were treated with several of the OMCs, and initial results indicated that one of the OMCs is non-toxic and is likely a safe option for additional analysis. This OMC was further tested to see if it could inhibit mycobacterial growth inside of the mammalian cells. It did not effectively clear bacteria from inside of the mammalian cells, likely because of poor penetration of the cell membrane. Further research with this compound should focus on ways to effectively transport the OMC inside infected mammalian cells so that it can reach the bacteria it is meant to target. A second portion of this research involved using a peptide nucleic acid (PNA) to try and reverse tetracycline antibiotic resistance in the bacterial strain Salmonella enterica ssp. enterica serovar Typhimurium DT104 (DT104). Peptide nucleic acids are short linear molecules that can bind strongly to complementary DNA and RNA sequences and thus be used to interfere with expression of specific genes. A PNA was designed to inhibit expression of the bacterial tetA gene that codes for a protein called the TetA tetracycline efflux pump, which imparts resistances to tetracycline. Treating the bacteria with the PNA resulted in a lower dose of tetracycline needed to inhibit bacterial growth, indicating a successful increase in tetracycline susceptibility. By using a molecular analysis technique called reversetranscriptase quantitative polymerase chain reaction (RT-qPCR), it was possible to measure the amount of tetA messenger RNA (mRNA) in cultures of DT104 treated only with tetracycline or with a combination of tetracycline and the PNA. As expected, bacteria treated with both the antibiotic and the PNA had less tetA mRNA than the cultures treated only with tetracycline, supporting the hypothesis that the PNA prevents the bacteria from effectively expressing the tetA gene. The PNA was next used in conjunction with tetracycline as an experimental treatment for mice infected with DT104. The PNA did not provide the expected protective effect under these circumstances. The overall conclusion for this part of the research is that PNAs offer an exciting potential avenue for counteracting antibiotic resistance, but additional experimentation is needed. Future research should focus on investigating more effective ways to get the PNAs inside the bacteria and on understanding more about how the PNAs behave in live animals. Several other PNAs targeting different genes involved in antibiotic resistance or essential bacterial functions were also tested against DT104 with variable success.

Due to the increasing resistance demonstrated by insects to conventional insecticides, the need for compounds with novel modes of action is becoming more urgent. Also, the discovery and production of new insecticides is vital as regulations and restrictions on conventional insecticides become increasingly stringent (Casida and Quistad 1998). Research in this area requires screening of many candidate compounds which is costly and time-consuming. The goal of this research was to produce in vitro insect neurons from Sf21 insect ovarian cell lines, which could lead to new high throughput screening methods and a way to mass produce insect material for basic research. This study used a culture of Sf21 cells and a mixture of differentiation agents to produce viable neuron-like cells. In the presence of the molting hormone 20-hydroxyecdysone (20-HE), or insulin, in the growth medium, Sf21 cells began to express neuronal morphology, or the production of elongated, axon-like processes within 2-3 days. Maximal differentiation occurred when in the presence of 42 μM 20-HE or 10 μM insulin. Effects were maximal on day 2 for 20-E and day 3 for insulin. Insulin was more potent at day 2 for inducing differentiation (EC₅₀ = 247 nM) than 20-HE (EC₅₀ = 13 μM). In combination, 20-HE and insulin produced apparent synergistic effects on differentiation. Caffeine, a central nervous system (CNS) stimulant, inhibited induction of elongated processes by 20-HE and/or insulin. Caffeine was a potent inhibitor of 42 μM 20-HE, with an IC50 of 9 nM, and the inhibition was incomplete, resulting in about one quarter of the differentiated cells remaining, even at high concentrations (up to 1 mM). The ability to induce a neural phenotype simplifies studies with of insect cells, compared to either the use of primary nervous tissue or genetic engineering techniques. The presence of ion channels or receptors in the differentiated cells remains to be determined. If they are present, high throughput screening for new insecticides will be accelerated and made more economical by the utility of this method. / Master of Science

A mobile ad hoc network (MANET) is formed by a collection of self-organizing nodes. Such networks are being deployed in a variety of environments, for example to provide mission-critical services in times of crises. Nodes participating in a MANET tend to have limited energy and computing resources and depend on various network-based resources to operate as a cohesive system. The same features such as dynamic and adaptive network topologies that make MANETs powerful also make the discovery and operation of network services a challenge. This thesis presents the design and implementation of an extensible information dissemination scheme that is integrated with the Optimized Link State Routing (OLSR) protocol to address the challenges of service discovery in mobile ad hoc networks. The thesis presents a detailed design of the information dissemination scheme based on the Naval Research Laboratory's (NRL) ProtoLib network protocol programming framework. In the proposed scheme, a solution that separates the routing process from the NRL OLSR routing protocol was designed, thus making OLSR a topology discovery protocol. This can further facilitate the implementation of various routing algorithms based on other metrics, such as the signal-to-noise ratio (SNR) of wireless links or the nodes' level of cooperation, when forwarding messages in the network. Additionally, a reusable event-driven programming interface to the NRL OLSR routing protocol was designed and implemented in this research. Events are triggered based on changes in the network topology. This programming interface can be used by other management and monitoring clients on the network for receiving real-time updates about link and topology changes as seen by OLSR. A priority message delivery scheme was developed that provides different quality of service (QoS) levels for information dissemination in mobile ad hoc networks. To ensure a fair use of the transport media and support various message sizes, a message fragmentation solution was implemented. The proposed information dissemination solution was then deployed in a real wireless ad-hoc environment for further validation and testing. Using experiments with six nodes and various test scenarios, this research verified the functionality and characterized the performance of the proposed system. It was observed that the link-state nature of information dissemination solution helped it to adapt to topology changes. It was also realized that service discovery latency after information convergence in the network was independent of the number of nodes between the service providers and clients. The experiments also confirmed that the immediate message delivery scheme provides superior quality of service to registered users in presence of radio interference and other delays caused by Multipoint Relay Nodes (MPR) message forwarding in OLSR. / Master of Science

No / We examine how information risk and transaction costs influence the initial and subsequent market reaction to earnings news. We find that the initial market reaction is higher per unit of earnings surprise for higher information risk firms (information content effect). Furthermore, it is information risk that induces transaction costs that limit the initial market reaction and lead to higher subsequent drift (transaction costs effect). Information risk does not have an effect on drift beyond that achieved through transaction costs. Our findings highlight the importance of understanding the linkage between information risk and transaction costs in price discovery around public disclosure.