Time-Course Transcriptome of Parageobacillus thermoglucosidasius DSM 6285 Grown in the Presence of Carbon Monoxide and Air

Parageobacillus thermoglucosidasius is a metabolically versatile, facultatively anaerobic thermophile belonging to the family Bacillaceae. Previous studies have shown that this bacterium harbours co-localised genes coding for a carbon monoxide (CO) dehydrogenase (CODH) and Ni-Fe hydrogenase (Phc) complex and oxidises CO and produces hydrogen (H2) gas via the water-gas shift (WGS) reaction. To elucidate the genetic events culminating in the WGS reaction, P. thermoglucosidasius DSM 6285 was cultivated under an initial gas atmosphere of 50% CO and 50% air and total RNA was extracted at ~8 (aerobic phase), 20 (anaerobic phase), 27 and 44 (early and late hydrogenogenic phases) hours post inoculation. The rRNA-depleted fraction was sequenced using Illumina NextSeq, v2.5, 1x75bp chemistry. Differential expression revealed that at 8 vs.. 20, 20 vs.. 27 and 27 vs.. 44 h post inoculation, 2190, 2118 and 231 transcripts were differentially (FDR < 0.05) expressed. Cluster analysis revealed 26 distinct gene expression trajectories across the four time points. Of these, two similar clusters, showing overexpression at 20 relative to 8 h and depletion at 27 and 44 h, harboured the CODH and Phc transcripts, suggesting possible regulation by O$_{2}$. The transition between aerobic respiration and anaerobic growth was marked by initial metabolic deterioration, as reflected by up-regulation of transcripts linked to sporulation and down-regulation of transcripts linked to flagellar assembly and metabolism. However, the transcriptome and growth profiles revealed the reversal of this trend during the hydrogenogenic phas

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology

Metabolic modeling of mycobacterium tuberculosis through the integration of large-scale genomics datasets

Thesis (Ph. D.)--Boston UniversityMycobacterium tuberculosis (MTB) is the bacterium that is the causal agent of tuberculosis. MTB is estimated to infect one-third of the world's population. The emergence of multi drug-resistant and extensively drug-resistant strains of the bacterium are becoming a larger threat to global health as they decrease the efficacy of current treatments and make the disease more fatal. These factors combine to make MTB an interesting target for study with novel systems biology approaches. Genome-scale metabolic models have emerged as important platforms for the analysis of datasets that describe highly-interconnected biological processes. We have the first comprehensive profiling of mRNA, proteins, metabolites, and lipids in MTB during an in vitro model of infection that includes a time course of induced hypoxia andre-aeration. Hypoxia and reaeration are important cues during infection of the human host and act to model the environment seen in the host. We use genome-scale metabolic modeling methods to integrate these data with our metabolic model will allow us to generate experimentally testable predictions about metabolic adaptations that occur in response to experimental perturbations that represent an in vitro model of important environmental cues present during infection, dormancy, and re-activation in the human host

Establishment and systematic characterization of Mycobacterium tuberculosis in bioreactors

2016 Fall.Includes bibliographical references.Mycobacterium tuberculosis infection is characterized by active and latent disease states. Granuloma-induced oxygen tension may shift bacteria into bacteriostatic persistence. Current models of hypoxia-induced mycobacteria have limitations, requiring establishment of novel culturing methods. Here, M. tuberculosis was propagated under defined oxygen concentration in bioreactors. Initial analyses confirmed mycobacterial non-replicating persistence. This study will provide insight into core physiological adaptations of M. tuberculosis while reducing bias from the contaminants during adaptation into dormancy. Here we describe a novel method of propagation using defined oxygen concentrations, then enrich the final culture for viability to remove transcriptional bias, and finally interrogate the presence of viable but non-culturable tubercle bacilli in order to obtain a greater sense of true viability. The current study will further contribute to our understanding of the physical adaptation of Mtb during growth and dormancy, by removing bias from the contaminating transcriptome gradient generated by the temporal adaptation of M. tuberculosis into dormancy. This will enhance the accuracy of downstream structural and transcriptomic analyses as well as give rise to a novel high throughput approach to M. tuberculosis propagation for research materials

Identification of Essential Metabolic and Genetic Adaptations to the Quiescent State in Mycobacterium Tuberculosis: A Dissertation

Mycobacterium tuberculosis stably adapts to respiratory limited environments by entering into a nongrowing but metabolically active state termed quiescence. This state is inherently tolerant to antibiotics due to a reduction in growth and activity of associated biosynthetic pathways. Understanding the physiology of the quiescent state, therefore, may be useful in developing new strategies to improve drug efficiency. Here, we used an established in vitro model of respiratory stress, hypoxia, to induce quiescence. We utilized metabolomic and genetic approaches to identify essential and active pathways associated with nongrowth. Our metabolomic profile of hypoxic M. tuberculosis revealed an increase in several free fatty acids, metabolite intermediates in the oxidative pathway of the tricarboxylic acid (TCA) cycle, as well as, the important chemical messenger, cAMP. In tandem, a high-throughput transposon mutant library screen (TnSeq) revealed that a cAMP-regulated protein acetyltransferase, MtPat, was conditionally essential for survival in the hypoxic state. Via 13C-carbon flux tracing we show an MtPat mutant is deficient in re-routing hypoxic metabolism away from the oxidative TCA cycle and that MtPat is involved in inhibiting fatty-acid catabolism in hypoxia. Additionally, we show that reductive TCA metabolism is required for survival of hypoxia by depletion of an essential TCA enzyme, malate dehydrogenase (Mdh) both in in vitro hypoxia and in vivo mouse infection. Inhibition of Mdh with a novel compound resulted in a significantly greater killing efficiency than the first-line anti-M. tuberculosis drug isoniazid (INH). In conclusion, we show that understanding the physiology of the quiescent state can lead to new drug targets for M. tuberculosis

Differences in the susceptibility of mycobacterium tuberculosis to the 1st and 2nd line antituberculosis drugs under aerobic and anaerobic conditions.

Master of Medical Science in Medical Microbiology. University of KwaZulu-Natal, Medical School 2015.Although Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), is now considered a facultative anaerobe, and bacilli isolated from sputum specimen possess morphologies identified from bacilli growing aerobically and under oxygen deprived conditions, most of the targets for the antituberculosis drugs are readily found on bacilli that are thriving aerobically. This raises questions on the efficiency of antituberculosis drugs on eradicating the pathogen from the host during treatment. In this study to determine whether the antituberculosis drugs that are used currently for the treatment of TB have similar effect of these different populations of this mycobacterium, we grew this organism under aerobic and oxygen deprived environments and then subjected them to the antimicrobial agents. The minimum inhibitory concentration (MICs) of these isolates against nine antituberculosis drugs were determined under aerobic conditions for the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay and under both aerobic and anaerobic conditions using the Microscopic Observation Drug Susceptibility (MODS) assays. In addition the bactericidal activities of isoniazid, rifampicin, kanamycin and ofloxacin were tested and compared amongst MDR isolates that were growing aerobically and anaerobically. There were some differences in the MICs determined by the MTT assay and the MODS assay for some isolates. For the susceptible isolates the MICs from the MTT assay were higher than the MICs from the MODS assay. The reverse was true for the drug resistant isolates. The reference strain H37Rv was resistant to some of the antimicrobial agents that were tested in this study. This was under both methods. However, MICs measured under anaerobic conditions with anaerobic bacilli did not yield viable results due to absence of growth as the bacilli are known to replicate at a negligible rate under anaerobic conditions. The bacilli in the inoculum were viable as following 40 days of anaerobic incubation but upon aerobic incubation of these cultures, growth was observed. And again with the bactericidal assays that were conducted on the multidrug resistant (MDR) isolates proved this. Rifampicin was the most potent antimicrobial agent against the anaerobic M. tuberculosis as susceptibility to this antimicrobial agent increased under anaerobic conditions

Análisis de la distribución de flujo metabólico para la síntesis de ácido micólico en Mycobacterium tuberculosis, aislado clínico UT205

[Title: Metabolic flux analysis of mycolic acid pathway, Mycobacterium tuberculosis clinical isolate UT205] Every year in Colombia, there are more than eleven thousand new cases of tuberculosis, indicating that this disease remains as a serious health problem. The bacterium that causes tuberculosis, Mycobacterium tuberculosis (M. tuberculosis), has a broad genetic diversity with significant phenotypic differences between clinical isolates; this diversity and the metabolic changes that occurs during infection are some of the reasons that hinders a full eradication of the disease. This work seeks to apply the metabolic flux analysis (MFA) strategy to study the biochemical pathways involved in the synthesis of mycolic acid in M. tuberculosis. This approach provided a better understanding of the cell performance during the infectious process and allowed to compare the metabolic capabilities of the clinical isolate M. tuberculosis UT205 with regard to the reference strain M. tuberculosis H37Rv. For this, it was constructed a representative model of the metabolic pathways of the pathogenic bacteria M. tuberculosis H37Rv involving 82 metabolites in 101 reactions; the proposed metabolic map was extended to consider the metabolism of the clinical isolate UT205. Flux balance analysis was performed so as to generate various hypotheses regarding how M. tuberculosis UT205 changes its flux distribution in order to overcome some limitations caused by the presence of genetic deletions. The proposed model shows an acceptable robustness and can be used as a preliminary tool for representing the metabolism of M. tuberculosis.En Colombia cada año se reportan más de once mil nuevos casos de tuberculosis, indicando que esta enfermedad aún continúa siendo un serio problema para la salud. La bacteria causante de la tuberculosis, Mycobacterium tuberculosis (M. tuberculosis), presenta una diversidad genética amplia con diferencias fenotípicas significativas entre aislados clínicos; esta diversidad y el cambio que se da en el metabolismo durante las fases de infección son, entre otras, las razones que han dificultado la completa erradicación de la enfermedad. En el presente estudio se aplicaron técnicas de evaluación de flujo metabólico a lo largo de las diferentes rutas bioquímicas involucradas en la síntesis de ácido micólico, con el fin de contribuir a obtener un mejor entendimiento de la actividad bioquímica de la bacteria durante el proceso infeccioso. La metodología usada involucró el desarrollo de un modelo matemático a partir de la identificación de las principales rutas metabólicas que el organismo usa para defenderse de los efectos adversos del medio; el modelo fue inicialmente desarrollado para considerar el metabolismo de Mycobacterium tuberculosis H37Rv y posteriormente fue extendido para considerar el metabolismo de Mycobacterium tuberculosis aislado clínico UT205. El modelo metabólico consideró 82 metabolitos en 100 reacciones y fue usado para realizar análisis de balance de flujo a partir de los cuales se generaron algunas hipótesis respecto de cómo M. tuberculosis UT205 cambia la distribución de sus flujos metabólicos para suplir algunas deficiencias ocasionadas por la presencia de deleciones genéticas. El modelo generado en este trabajo presenta grado de robustez aceptable que de manera preliminar representa la actividad metabólica de Mycobacterium tuberculosis. Los resultados encontrados en este trabajo permiten tener un primer acercamiento al conocimiento de las características metabólicas de la cepa UT205; concretamente el comportamiento de las reacciones afectadas por la ausencia del gen Rv1997 y que diferencian a la cepa UT205 de la cepa de referencia. Sin embargo, es necesario enriquecer el modelo con mayor información sobre genes, enzimas y variaciones en rutas metabólicas presentes en la cepa UT205, de manera que se obtenga un modelo más representativo del comportamiento real del aislado clínico.Magister en Ciencias-BiotecnologíaMaestrí

Modeling metabolism of Mycobacterium tuberculosis

Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode enzymes directly involved in its metabolism. These enzymes represent potential drug targets that can be systematically probed with constraint based (CB) models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. A first step in creating such a model is a thoroughly curated and extended genome-scale CB metabolic model of Mtb metabolism. The history of genome-scale CB models of Mtb metabolism up to model sMtb are discussed and sMtb is quantitatively validated using 13C measurements. The human pathogen Mtb has the capacity to escape eradication by professional phagocytes. During infection, Mtb resists the harsh environment of phagosomes and actively manipulates macrophages and dendritic cells to ensure prolonged intracellular survival. In contrast to many other intracellular pathogens, it has remained difficult to capture the transcriptome of mycobacteria during infection due to an unfavorable host-to-pathogen ratio. The human macrophage-like cell line THP-1 was infected with the attenuated Mtb surrogate Mycobacterium bovis Bacillus Calmette–Guérin (M. bovis BCG). Mycobacterial RNA was up to 1000-fold underrepresented in total RNA preparations of infected host cells. By combining microbial enrichment with specific ribosomal RNA depletion the transcriptional responses of host and pathogen during infection were simultaneously analyzed using dual RNA sequencing. Mycobacterial pathways for cholesterol degradation and iron acquisition are upregulated during infection. In addition, genes involved in the methylcitrate cycle, aspartate metabolism and recycling of mycolic acids are induced. In response to M. bovis BCG infection, host cells upregulate de novo cholesterol biosynthesis presumably to compensate for the loss of this metabolite by bacterial catabolism. By systematically probing the metabolic network underpinning sMtb, the reactions that are essential for Mtb are identified. A majority of these reactions are catalyzed by enzymes and thus represent candidate drug targets to fight an Mtb infection. Modeling the behavior of the bacteria during infection requires knowledge of the so-called biomass reaction that represents bacterial biomass composition. This composition varies in different environments or bacterial growth phases. Accurate modeling of all fluxes through metabolism under a given condition at a moment in time, the so called metabolic state, requires a precise description of the biomass reaction for the described condition. The transcript abundance data obtained by dual RNA sequencing was used to develop a straightforward and systematic method to obtain a condition-specific biomass reaction for Mtb during in vitro growth and during infection of its host. The method described herein is virtually free of any pre-set assumptions on uptake rates of nutrients, making it suitable for exploring environments with limited accessibility. The condition-specific biomass reaction represents the 'metabolic objective' of Mtb in a given environment (in-host growth and growth on defined medium) at a specific time point, and as such allows modeling the bacterial metabolic state in these environments. Five different biomass reactions were used predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Nutrient uptake can accurately be predicted, but accurate gene essentiality predictions remain difficult to obtain. By combining sMtb and a model of human metabolism, model sMtb-RECON was developed and used to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently the effect of increasing dosages of drugs, targeting metabolism, on the metabolic state of the pathogen was assessed and resulting metabolic adaptations and flux rerouting through various pathways is predicted. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine and threonine metabolism become less important for survival. Notably, an effect of eight out of eleven metabolically active drugs could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.</p