Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets

Abstract

Background: Mycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them.

Results: We completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints.

Conclusion: Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.

Figure 1

The Gene Index for Mycobacterium tuberculosis H37Rv was downloaded from The Institute for Genomic Research (TIGR) [45]. Reconstruction content was defined based on the sequence annotation, legacy data, the Tuberculist database [31], ancillary sources such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), and SEED [47]. Reactions were defined and according to the Tuberculist web and KEGG. Legacy data was also used in the process of building the model from the reconstruction. The manual curation aspects of the reconstruction are outlined above and discussed in further detail in [42]. Debugging was started once the first draft of the reconstruction was completed and functional testing (i.e. flux balance analysis calculations, etc.) were begun.

Figure 2

a. Phase plane diagram of glycerol versus glucose uptake while optimizing for growth on Middlebrook media. iNJ661 can grow with glycerol serving as the sole carbon source, but not glucose. The open dots are the calculated phase points and the solid black lines indicate isoclines. b. Phase plane diagram for phosphate versus sulfate uptake on Middlebrook media. Although the transport fluxes are very small, they are both necessary for the organism to be able to grow.

Figure 3

Biomass optimization on Middlebrook media without glucose. Carbon monoxide uptake can affect the growth at very low glycerol uptake rates via Carbon Monoxide Dehydrogenase, which generate protons through the oxidation of carbon dioxide.

Figure 4

Summary of experimental datasets and their overlap with iNJ661 and the iNJ661 gene deletion studies. OptGro refers to the dataset described in [23], ConstExp refers to the dataset described in [29], and Infect describes the dataset in [30]. ^ denotes the subset of genes from the experimental dataset contained in iNJ661. iNJ661 represents the 661 genes in the reconstruction. iNJ661 optimal growth represents the subset of genes required for optimal growth with the original objective function. iNJ661 optimal growth* represents the subset of genes required for optimal growth with the objective function expanded to include vitamins and cofactors. iNJ661 alternative objective represents the set of genes required for optimal growth using the objective function constructed based on the ConstExp dataset. Panel A: The Venn diagram shows the overlap between the different experimental gene expression datasets. The accompanying chart summarizes the total number of genes in each dataset and how many of those genes are found in the iNJ661 reconstruction. Panel B: Series of Venn diagrams summarizing the results for the gene deletion studies carried out of iNJ661 compared to the experimental gene expression datasets.

Figure 5

The stoichiometric matrix is created from the metabolic network. The HCR (Hard Coupled Reaction) sets are calculated directly from the stoichiometric matrix and mapped back to the gene loci. Gene-Protein-Reaction relationships: top box is the gene, the next box is the peptide, the oval represents the functional protein, and the bottom boxes are the reactions catalyzed by the protein.

Figure 6

A partial metabolic map of iNJ661 with the 25 drug target HCR sets. Each reaction is numbered and color coded according to the HCR which it belongs to. The number of the HCR sets matches those in the Additional files. Only pathways or parts of pathways which included members of the 25 HCRs are depicted.