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This thesis presents the development and optimization of a high-throughput fluorescence microbead based approach for multiplexed, large scale medical diagnostics of biological fluids. Specifically, different sizes of semiconductor nanocrystals, called quantum dots, are infused into polystyrene microspheres, yielding a set of spectrally unique optical barcodes. The surface of these barcodes are then used for sandwich assays with target molecules and fluorophore-conjugated detection antibodies, changing the optical spectra of beads that have associated with (or captured) biomolecular targets. These assayed microbeads are analyzed at a single bead level in a high-throughput manner using an electrokinetic microfluidic system and laser induced fluorescence. Optical signals collected by solid state photodetectors are then processed using novel signal processing algorithms. This document will discuss developments made in each area of the platform as well as optimization of the platform for improved future performance.

This thesis presents the development and optimization of a high-throughput fluorescence microbead based approach for multiplexed, large scale medical diagnostics of biological fluids. Specifically, different sizes of semiconductor nanocrystals, called quantum dots, are infused into polystyrene microspheres, yielding a set of spectrally unique optical barcodes. The surface of these barcodes are then used for sandwich assays with target molecules and fluorophore-conjugated detection antibodies, changing the optical spectra of beads that have associated with (or captured) biomolecular targets. These assayed microbeads are analyzed at a single bead level in a high-throughput manner using an electrokinetic microfluidic system and laser induced fluorescence. Optical signals collected by solid state photodetectors are then processed using novel signal processing algorithms. This document will discuss developments made in each area of the platform as well as optimization of the platform for improved future performance.

Yes / The majority of Mycobacterium tuberculosis isolates resistant to isoniazid harbour a mutation in katG. Since these mutations cause a wide range of minimum inhibitory concentrations (MICs), largely below the serum level reached with higher dosing (15 mg/L upon 15–20 mg/kg), the drug might still remain partly active in presence of a katG mutation. We therefore investigated which genetic mutations predict the level of phenotypic isoniazid resistance in clinical M. tuberculosis isolates. To this end, the association between known and unknown isoniazid resistance-conferring mutations in whole genome sequences, and the isoniazid MICs of 176 isolates was examined. We found mostly moderate-level resistance characterized by a mode of 6.4 mg/L for the very common katG Ser315Thr mutation, and always very high MICs (≥19.2 mg/L) for the combination of katG Ser315Thr and inhA c-15t. Contrary to common belief, isolates harbouring inhA c-15t alone, partly also showed moderate-level resistance, particularly when combined with inhA Ser94Ala. No overt association between low-confidence or unknown mutations, except in katG, and isoniazid resistance (level) was found. Except for the rare katG deletion, line probe assay is thus not sufficiently accurate to predict the level of isoniazid resistance for a single mutation in katG or inhA. / European Research Council (Starting Grant INTERRUPTB 311725 to CM, LR and BdJ), The Damien Foundation

Effectively developing robust predictive models for forecasting infectious disease dynamics over space and time relies on successful surveillance strategies to accurately assess host infection status. We are constantly refining these models to improve our understanding of transmission and persistence dynamics in host populations but are continuously challenged with difficulties in accurately diagnosing host infection status. These challenges are especially persistent for pathogens of the Mycobacterium tuberculosis Complex (MTBC), which cause tuberculosis (TB) disease in a wide array of mammalian hosts. These challenges are further exacerbated when working with MTBC pathogens in free-ranging wildlife hosts. Although TB disease in humans is a primary concern, TB in free-ranging wildlife hosts poses a large threat to human and animal health. One recently described and novel MTBC pathogen is Mycobacterium mungi, which infects the highly social, group-living banded mongoose (Mungos mungo). M. mungi poses a large threat to human and animal health as banded mongoose hosts thrive in urbanized areas and live in close proximity to humans, but despite this threat, accurately diagnosing M. mungi infection status remains a primary challenge. Here, I develop a host response-based assay for differentiating banded mongoose with clinical M. mungi disease from individuals that are putatively healthy using transcriptional biomarkers in whole blood. To our knowledge, this is the first evaluation of host response-based transcriptional signatures to detect TB infection in unstimulated whole blood collected from a free-ranging wildlife species. I found that the expression of two genes, GBP5 and DUSP3, are significantly upregulated (GBP5, p < .05; DUSP3, p < .005) in banded mongoose with clinical M. mungi disease when compared to that of putatively healthy individuals. These results are consistent with studies of active M. tuberculosis disease in humans and support the use of host response-based assays using blood transcriptional biomarkers for diagnosing TB in free-ranging wildlife hosts. These findings are important for improving surveillance strategies for diagnosing M. mungi infection status in banded mongoose and will be essential in refining predictive models for forecasting transmission and persistence dynamics over space and time. / Creating models to predict how diseases circulate and persist within a population is dependent on our ability to accurately diagnose if a host is infected. Diagnosing infection is difficult for some diseases, including tuberculosis (TB) pathogens, which infect humans and many other mammalian species. While vast improvements have been made in diagnosing TB infection in humans, diagnosing TB in free-ranging wildlife species is a constant challenge. These challenges are further exacerbated across the different pathogen species of TB. Although TB disease in humans is a primary concern, TB in free-ranging wildlife hosts poses a large threat to human and animal health. One recently discovered TB pathogen is Mycobacterium mungi, which infects free-ranging banded mongoose (Mungos mungo). This pathogen poses a large threat to human and animals health since banded mongoose thrive in urbanized areas and live in close proximity to humans. Despite this threat, accurately diagnosing M. mungi infection in banded mongoose remains a challenge. Here, I develop a diagnostic molecular tool that uses banded mongoose blood to measure the expression of specific genes and differentiate diseased individuals from seemingly healthy individuals. To our knowledge, this is the first study that has used this specific approach for diagnosing TB in a free-ranging wildlife species. I found that the expression of two genes are significantly increased in banded mongoose with clinical M. mungi disease when compared to that of seemingly healthy individuals. These results are consistent with studies human TB disease in humans and support the use of this approach for diagnosing TB in free-ranging wildlife hosts. These findings are important for improving diagnostics for M. mungi infection in banded mongoose and will be essential in refining models for predicting how this disease circulates and persists over space and time.

The focus of the work has been on computational approaches to solving biological problems. First, microarray analysis was used to study the role of PLDα1 in drought stress in Arabidopsis. Second, a tool for designing and in-silico testing of primers for PCR-based pathogen detection will be discussed. Phospholipase D (PLD) has been implicated in a variety of stresses including osmotic stress and wounding. PLDα 1-derived phosphatidic acid interacts with ABI1 phosphatase 2C and promotes abscisic acid signaling. Plants with abrogated PLDα 1 show insensitivity to ABA and impaired stomatal conductance. My goal is to identify PLDα-mediated downstream events in response to progressive drought stress in Arabidopsis. <i>Arabidopsis thaliana</i> (Col-0) and antisense-PLDα 1 (Anti-PLDα) were drought stressed by withholding water. Anti-PLDα experienced severe water stress at the same time period that Col-0 experienced less water stress. Diurnal leaf water potential (LWP) measurements showed that Anti-PLDα had lower LWP than Col-0 under drought stress conditions. qRT-PCR revealed up to 18-fold lower values for PLDα transcripts in stressed Anti-PLDα plants when compared to stressed Col-0. Microarray expression profiles revealed distinct gene expression patterns in Col-0 and Anti-PLDα. ROP8, PLDδ and lipid transfer proteins were among the differentially expressed genes between the two genotypes. Different microarray analyses methods (TM4 and Expresso) were also compared on two different data sets. The results obtained from Expresso analysis were more accurate when compared with quantitative RT-PCR data. Rapid diagnosis of disease-causing agents is extremely important since delayed diagnosis can result in disease spread and delayed prophylaxis. It is even more important in an era where disease-causing agents are used as bioterrorism agents. Rapid advances in sequencing technology have resulted in the sequencing of thousands of microorganisms in recent years. Availability of genomic sequences has made it possible to identify and characterize microorganisms at the molecular level. PCR-based detection is powerful for pathogen diagnostics since it is rapid and sensitive. We have developed a tool, PathPrime, that can design primers, computationally test them against target genes, and potential contaminant sequences, and identify a minimum set of primers that can unambiguously detect a given list of sequences. / Ph. D.

<p>A novel system has been simulated with accompanying experimental data that is designed to provide spatial information of elemental concentrations at biologically relevant levels. Using a deuterium-deuterium (DD) neutron generator, two large high-purity germanium (HPGe) detectors operating in tandem, and the associated particle imaging (API) technique, elemental iron concentrations as low as 100 ppm have been resolved <i>in vivo</i> in the liver of a simulated reference man with an equivalent dose to the region of interest of < 5 mSv and an estimated whole body dose of 0.82 mSv. Using the Monte Carlo Neutral Particle (MCNP) transport code, achievable spatial resolutions in the projective and depth dimensions of < 1 cm and < 3 cm are achievable, respectively, for iron-containing voxels on the order of 1,000 ppm Fe – with an overall 225 ps system timing resolution, 6.25 mm² imaging plate pixels, and a Gaussian-distributed DD neutron source spot with a diameter of 2 mm. Additionally, as a departure from Monte Carlo simulations, the underlying concepts of fast neutron inelastic scatter analysis as an initial surrogate to true associated particle neutron elemental imaging (APNEI) were demonstrated using a DD neutron generator, iron-made interrogation targets, a sodium iodide detector, and physical neutron/gamma shielding, which yielded an approximate detection limit for iron of 3.45 kg which was simulated to improve to 0.44 kg upon incorporation of the associated particle collimation methodology.</p> The API technique allows concentrations of elements such as iron to be quantified due to time-tagged electronic collimation and corresponding background signal reduction. Inherent to the API process is the collection of spatial and temporal information, which allows the perceived origin of a photon signal to be identified in 3D space. This process was modeled algorithmically in MCNP and employed using relevant equipment and shielding geometries. By leveraging the capabilities of modern-day neutron generator and coincident timing technologies with high throughput signal processing discrimination, the applicability of APNEI to disease diagnostics and etiological research is promising.

No / Whole genome sequencing (WGS) of Mycobacterium tuberculosis has rapidly progressed from a research tool to a clinical application for the diagnosis and management of tuberculosis and in public health surveillance. This development has been facilitated by drastic drops in cost, advances in technology and concerted efforts to translate sequencing data into actionable information. There is, however, a risk that, in the absence of a consensus and international standards, the widespread use of WGS technology may result in data and processes that lack harmonization, comparability and validation. In this Review, we outline the current landscape of WGS pipelines and applications, and set out best practices for M. tuberculosis WGS, including standards for bioinformatics pipelines, curated repositories of resistance-causing variants, phylogenetic analyses, quality control and standardized reporting. / European Research Council grant (INTERRUPTB; no. 311725), European Research Council grant (TB-ACCELERATE; no. 638553), Foundation for Innovative New Diagnostics, German Center for Infection Research (DZIF), Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy (EXC 22167–390884018), FWO Odysseus G0F8316N, US National Institutes of Health BD2K K01 (MRF ES026835), Agence Nationale de la Recherche (ANR-16-CD35-0009)