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.
|Qualification||Doctor of Philosophy|
|Award date||22 May 2019|
|Place of Publication||Wageningen|
|Publication status||Published - 2019|