Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization

Abstract

Background: Clostridium difficile is a bacterium which can infect various animal species, including humans. Infection with this bacterium is a leading healthcare-associated illness. A better understanding of this organism and the relationship between its genotype and phenotype is essential to the search for an effective treatment. Genome-scale metabolic models contain all known biochemical reactions of a microorganism and can be used to investigate this relationship.

Results: We present icdf834, an updated metabolic network of C. difficile that builds on iMLTC806cdf and features 1227 reactions, 834 genes, and 807 metabolites. We used this metabolic network to reconstruct the metabolic landscape of this bacterium. The standard metabolic model cannot account for changes in the bacterial metabolism in response to different environmental conditions. To account for this limitation, we also integrated transcriptomic data, which details the gene expression of the bacterium in a wide array of environments. Importantly, to bridge the gap between gene expression levels and protein abundance, we accounted for the synonymous codon usage bias of the bacterium in the model. To our knowledge, this is the first time codon usage has been quantified and integrated into a metabolic model. The metabolic fluxes were defined as a function of protein abundance. To determine potential therapeutic targets using the model, we conducted gene essentiality and metabolic pathway sensitivity analyses and calculated flux control coefficients. We obtained 92.3% accuracy in predicting gene essentiality when compared to experimental data for C. difficile R20291 (ribotype 027) homologs. We validated our context-specific metabolic models using sensitivity and robustness analyses and compared model predictions with literature on C. difficile. The model predicts interesting facets of the bacterium's metabolism, such as changes in the bacterium's growth in response to different environmental conditions.

Conclusions: After an extensive validation process, we used icdf834 to obtain state-of-the-art predictions of therapeutic targets for C. difficile. We show how context-specific metabolic models augmented with codon usage information can be a beneficial resource for better understanding C. difficile and for identifying novel therapeutic targets. We remark that our approach can be applied to investigate and treat against other pathogens.

Keywords: Antibiotic resistance; Clostridium difficile; Flux balance analysis; Genome scale modeling; Metabolic modeling; Metabolic networks; Metabolic pathways; Sensitivity analysis.

Fig. 1

Framework for modeling the metabolism of C.difficile. The updated metabolic network of the bacterium was used to create a metabolic model that was assessed using sensitivity and robustness analyses. Integrating gene expression and codon usage data yielded context-specific metabolic models that were evaluated against biological rationale and found fit for clinical applications. The augmented metabolic models were then used to identify potential therapeutic targets using gene essentiality analysis, PoSA, and flux control coefficient calculations

Fig. 2

Genetic analysis using multi-objective optimization. Regions of objective space explored by the optimization algorithm for the objectives of maximization of biomass and minimization of total intracellular flux. Solutions are represented by progressively warmer colors depending on the time step of the algorithm in which they had been adaptively generated from the initial point. The Pareto front is shown in black in the inset

Fig. 3

PoSA was used to compare the most sensitive pathways of iMLTC806cdf and icdf834. The iMLTC806cdf model is composed of 48 metabolic pathways and the icdf834 model is composed of 50 metabolic pathways. Biomass production is most sensitive to pathways with higher calculated μ

Fig. 4

Genes encoding the enzymes with the largest flux control coefficients for biomass production in different conditions (top). Table of metabolic pathway(s) hosting the genes and of gene descriptions [64] (bottom). A flux control coefficient of 1 implies full control of the metabolite flux by the associated enzyme