23 research outputs found
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Ultrasensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of HIV negative patients with tuberculosis.
BACKGROUND:Timely diagnosis of tuberculosis disease is critical for positive patient outcomes, yet potentially millions go undiagnosed or unreported each year. Sputum is widely used as the testing input, but limited by its complexity, heterogeneity, and sourcing problems. Finding methods to interrogate noninvasive, non-sputum clinical specimens is indispensable to improving access to tuberculosis diagnosis and care. In this work, economical plasmonic gratings were used to analyze tuberculosis biomarker lipoarabinomannan (LAM) from clinical urine samples by single molecule fluorescence assay (FLISA) and compared with gold standard sputum GeneXpert MTB/ RIF, culture, and reference ELISA testing results. METHODS AND FINDINGS:In this study, twenty sputum and urine sample sets were selected retrospectively from a repository of HIV-negative patient samples collected before initiation of anti-tuberculosis therapy. GeneXpert MTB/RIF and culture testing of patient sputum confirmed the presence or absence of pulmonary tuberculosis while all patient urines were reference ELISA LAM-negative. Plasmonic gratings produced by low-cost soft lithography were bound with anti-LAM capture antibody, incubated with patient urine samples, and biotinylated detection antibody. Fluorescently labeled streptavidin revealed single molecule emission by epifluorescence microscope. Using a 1 fg/mL baseline for limit of detection, single molecule FLISA demonstrated good qualitative agreement with gold standard tests on 19 of 20 patients, including accurately predicting the gold-standard-negative patients, while one gold-standard-positive patient produced no observable LAM in urine. CONCLUSIONS:Single molecule FLISA by plasmonic grating demonstrated the ability to quantify tuberculosis LAM from complex urine samples of patients from a high endemic setting with negligible interference from the complex media itself. Moreover, agreement with patient diagnoses by gold standard testing suggests that single molecule FLISA could be used as a highly sensitive test to diagnose tuberculosis noninvasively
Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network
Mycobacterium tuberculosis Growth following Aerobic Expression of the DosR Regulon
The Mycobacterium tuberculosis regulator DosR is induced by multiple stimuli including hypoxia, nitric oxide and redox stress. Overlap of these stimuli with conditions thought to promote latency in infected patients fuels a model in which DosR regulon expression is correlated with bacteriostasis in vitro and a proxy for latency in vivo. Here, we find that inducing the DosR regulon to wildtype levels in aerobic, replicating M. tuberculosis does not alter bacterial growth kinetics. We conclude that DosR regulon expression alone is insufficient for bacterial latency, but rather is expressed during a range of growth states in a dynamic environment
Experimental Characterization of the <italic>Mycobacterium tuberculosis</italic> Gene Regulatory Network
Thesis (Ph.D.)--University of Washington, 2012Tuberculosis is a massive public health problem on a global scale and the success of Mycobacterium tuberculosis is linked to its ability to persist within humans for long periods without causing overt disease symptoms. Hypoxia is predicted to be a key host-induced stress limiting growth of the pathogen in vivo. However, studies indicate that M. tuberculosis coordinates a complex transcriptional program in response to long-term changes in oxygen tension in vitro, with the differential regulation of >20% of all transcriptional regulatory proteins. The results presented in this dissertation describe our efforts to create an experimental framework to define the control logic underpinning transcript regulation in M. tuberculosis. Creating a multi-platform experimental foundation for gene regulatory network construction is a critical step in generating predictive models of complex responses in prokaryotes. We describe a workflow that couples a defined perturbation to a regulatory response using chromatin immunoprecipitation followed by high throughput sequencing and transcriptional profiling by tiling microarray. We implement this experimental platform to reconstruct the transcriptional regulatory network of M. tuberculosis with particular attention to oxygen-responsive DNA binding proteins. This network allows us to generate predictive models of gene expression during an in vitro time course of hypoxia and reaeration. In this context, we describe the physiological consequences of aerobic induction of the early hypoxia-responsive regulator DosR. We find that M. tuberculosis growth is unaffected upon the upregulation of dosR and the DosR regulon - in support of the hypothesis that growth inhibition as a result of oxygen limitation is mediated by a complex regulatory response. We also report studies that define the degradation rate of the mRNA pool in M. tuberculosis under different experimental conditions. We find that M. tuberculosis contains an unusually stable pool of mRNA and that this pool can be further stabilized by physiologically relevant alterations to the bacterial environment. There are obvious and pressing needs for greater understanding of the basic biology of M. tuberculosis, and this dissertation describes advancements we have made toward achieving this goal
Ectopic expression of DosR induces the DosR regulon.
<p>Scatterplot displaying transcript levels of all <i>M. tuberculosis</i> genes after 24 hours of treatment with either 10 ng/mL Atc (induced) or an equivalent volume of sterile DMSO (uninduced). Three biological replicates were RMA-normalized and the median pixel intensity data are plotted on a log<sub>2</sub> scale. Genes of the DosR regulon are represented as dark gray circles. Significantly induced genes (moderated t-test with Benjamini-Hochberg FDR correction, p<0.05) not part of the DosR regulon are presented as black diamonds, and the <i>dosR</i> transcript is indicated with a star.</p
The Helicobacter pylori Urease B Subunit Binds to CD74 on Gastric Epithelial Cells and Induces NF-κB Activation and Interleukin-8 Production
The pathogenesis associated with Helicobacter pylori infection is the result of both bacterial factors and the host response. We have previously shown that H. pylori binds to CD74 on gastric epithelial cells. In this study, we sought to identify the bacterial protein responsible for this interaction. H. pylori urease from a pool of bacterial surface proteins was found to coprecipitate with CD74. To determine how urease binds to CD74, we used recombinant urease A and B subunits. Recombinant urease B was found to bind directly to CD74 in immunoprecipitation and flow cytometry studies. By utilizing both recombinant urease subunits and urease B knockout bacteria, the urease B-CD74 interaction was shown to induce NF-κB activation and interleukin-8 (IL-8) production. This response was decreased by blocking CD74 with monoclonal antibodies. Further confirmation of the interaction of urease B with CD74 was obtained using a fibroblast cell line transfected with CD74 that also responded with NF-κB activation and IL-8 production. The binding of the H. pylori urease B subunit to CD74 expressed on gastric epithelial cells presents a novel insight into a previously unrecognized H. pylori interaction that may contribute to the proinflammatory immune response seen during infection
1198 Cytokine affinity tuning and antibody discovery/optimization using the AlphaSeq and AlphaBind platforms to generate targeted immuno-oncology therapeutics
Integrated Modeling of Gene Regulatory and Metabolic Networks in <i>Mycobacterium tuberculosis</i>
<div><p><i>Mycobacterium tuberculosis</i> (MTB) is the causative bacterium of tuberculosis, a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics. The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations. Therefore, we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism (PROM) framework. Our model, <i>MTB</i>PROM2.0, represents a substantial knowledge base update and extension of simulation capability. We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors (TFs) (representing a 3.5-fold expansion of TF coverage). We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model. We used <i>MTB</i>PROM2.0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model. <i>MTB</i>PROM2.0 improves performance of knockout growth defect predictions compared to the original PROM MTB model, and it can successfully predict growth defects associated with TF overexpression. Moreover, condition-specific models of <i>MTB</i>PROM2.0 successfully predicted synergistic growth consequences of overexpressing the TF <i>whiB4</i> in the presence of two standard anti-TB drugs. <i>MTB</i>PROM2.0 can screen <i>in silico</i> condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts.</p></div
Representative time-course growth and metabolic activity of wild-type and <i>whiB4</i>-overexpression strains of MTB after treatment with drugs ethionamide (ETH) and isoniazid (INH).
<p>(A, C) The growth time-courses measured by OD600 of wild-type (blue) and <i>whiB4-</i>overexpressing MTB strains (red) without drug (pale, dashed lines) and post treatment with 3μM ETH (Panel A) and 2μM INH (Panel C). (B, D) Time-courses of metabolic activity measured by Alamar Blue reduction. Data represent mean ± standard deviation of three biological replicates.</p