Abstract
Genome-scale metabolic models of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, have been envisioned as a platform for drug discovery. By systematically probing the networks that underpin such models, 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. Nevertheless, this is complicated by the limited knowledge on the environment that Mtb encounters during 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 the metabolic state requires a precise biomass reaction for the described condition. In recent years, additional insights in the in-host environment occupied by Mtb have been gained as transcript abundance data of interacting host and pathogen have become available. Therefore, we used transcript abundance data and developed 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 to predict nutrient uptake rates and gene essentiality. Predictions were subsequently compared to available experimental data. Our results show that nutrient uptake can accurately be predicted. Gene essentiality can also be predicted but accurate predictions remain difficult to obtain. In conclusion, a viable strategy to model Mtb metabolism in hard-to-access environments that is virtually free of pre-set assumptions is provided.
Highlights
Constraint-based genome-scale metabolic models (GEMs) enable prediction of metabolic states
The term “biomass reaction” is perhaps not the most suitable term as these reactions cover synthesis of metabolites used for biomass production, and synthesis of excreted enzymes and small molecules, repair of damaged lipid membranes and other metabolites involved in host-pathogen interaction
The sMtb model was used as a platform to integrate the expression data and to calculate two condition-specific biomass reactions of Mycobacterium tuberculosis (Mtb), condition specific biomass reaction of infection (CSI) and CSM, for both aforementioned conditions, respectively
Summary
Constraint-based genome-scale metabolic models (GEMs) enable prediction of metabolic states. GEMs comprise linear equations describing conversions among metabolites, uptake or secretion processes, and transport processes over different compartments. These equations are referred to as flux balance constraints and are founded on an underlying metabolic network wherein all metabolites are interconnected by conversion and transport reactions. The whole of all possible fluxes that satisfy all constraints of a GEM is referred to as the solution space (Bordbar et al, 2014). Additional constraints present an opportunity to further limit the size of the solution space, which results in a more accurate calculation of the metabolic state. A suitable way to increase the amount of constraints is to measure uptake and/or secretion rates of metabolites/nutrients. Knowledge of a few of these rates can considerably shrink the solution space (Reed, 2012)
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