Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions

Abstract

Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host-pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host-pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host-pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host-pathogen network and were able to describe three distinct pathological states. Integrated host-pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.

Figure 1

Workflow of building the cell-specific model iAB-AMØ-1410. Gene expression data for alveolar macrophages and macrophage-specific exchanges were fed into two model building algorithms (GIMME and Shlomi-NBT-08) to build two preliminary context-specific alveolar macrophage networks. Using enzyme databases (BRENDA and HPRD), immunohistological staining databases (Human Protein Atlas), transporter databases (HMTD), primary literature (see Supplementary information), and network features, the preliminary models were reconciled and manually curated into the final iAB-AMØ-1410.

Figure 2

Comparison of reactions and network capabilities of iAB-AMØ-1410 and the global human network (Recon 1). (A) The overall gene and reaction counts of Recon 1 and iAB- AMØ-1410 are provided. The majority of genes and reactions are preserved. (B) Most reactions were included in the final macrophage network. The largest reductions in number of reactions by subsystem were in amino-acid, lipid, and polysaccharide metabolism. Carbohydrate and central metabolism were well preserved as expected. (C) In stark contrast to the number of reactions included, the network capabilities of iAB-AMØ-1410 are much reduced as compared with Recon 1. Large reductions of metabolic functions occurred in polysaccharide, cofactor, amino-acid, and nucleotide metabolism. This is due in part to removing key reactions from Recon 1, but more importantly to the exchange constraints of the model.

Figure 3

Workflow of building the host–pathogen model, iAB-AMØ-1410-Mt-661. Integration involved combining the two stoichiometric matrices, recompartmentalizing the metabolites and reactions, and creating a relevant phagosome environment through transport and sink reactions. In order to characterize the model under in vivo conditions, the biomass objective function was revised. Using gene expression data from infectious states in vitro and in vivo, the objective function was iteratively modified to match gene expression tests completed by sampling the solution space. The new objective function better represents the metabolic activity of the pathogen under in vivo conditions.

Figure 4

Schematic of the integration and results of the alveolar macrophage (iAB-AMØ-1410) and Mycobacterium tuberculosis (iNJ661) reconstructions. (A) Metabolic links between the extracellular space (e), alveolar macrophage (am), phagosome (ph), and Mycobacterium tuberculosis (mt) in iAB-AMØ-1410-Mt-661. The model is compartmentalized using the abbreviations as shown. The major carbon sources of the alveolar macrophage are glucose and glutamine. The macrophage is also aerobic and requires the essential amino acids. Albeit its use of oxygen, the macrophage exhibits anaerobic respiration and produces much lactate. The major carbon sources available for M. tb in the phagosome environment are glycerol and fatty acids. The phagosome environment is also functionally hypoxic. (B) The flux span of iAB-AMØ-1410-Mt-661 is significantly reduced (51%) compared with iAB-AMØ-1410. This shows a stricter definition of the alveolar macrophage solution space without adding additional constraints on the alveolar macrophage portion of the network. (C) Reaction, metabolite, and gene properties of the three reconstructions. Maximum production rates of ATP, nitric oxide, redox potential (NADH), and biomass are shown.

Figure 5

Topological map of the metabolites and reactions in the central metabolism of M. tb. The map shows predicted change of expression states when comparing in vivo (iAB-AMØ-1410-Mt-661) and in vitro (iNJ661) conditions. The expression states were determined from the change in the solution space by randomized sampling. Reactions that were up-regulated in iAB-AMØ-1410-Mt-661 are labeled in green, while down-regulated reactions are labeled in red. Pathways pertaining to glyoxylate metabolism were up-regulated while glycolysis is down-regulated. The sampling results of three key reactions of central metabolism are shown. Isocitrate lyase (ICL) and fructose bisphosphatase (FBP) are up-regulated in iAB-AMØ-1410-Mt-661, while enolase (ENO) is down-regulated. It is important to note that the results are absolute values of the normalized values. The flux state of enolase in iAB-AMØ-1410-Mt-661 is negative suggesting gluconeogenesis.

Figure 6

Reaction comparison of the three infection-specific models of iAB-AMØ-1410-Mt-661 for latent, pulmonary, and meningeal tuberculosis. (A) We used macrophage gene expression data from three types of M. tb infections to build context-specific models of iAB-AMØ-1410-Mt-661 for latent (L), pulmonary (P), and meningeal (M) tuberculosis. The models had significant differences in reactions for the macrophage and surprisingly M. tb. (B) There were many reactions with disparate activity between the three infections. We determined the subsystems of the reactions with differential activity. (C) The total number of disparate reactions was calculated. The left-most panel shows the macrophage reactions shown to be active in each state as calculated from the expression profile. This data was then used with the GIMME algorithm to build three flux-balance models using iAB-AMØ-1410-Mt-661. The middle diagram shows the active M. tb reactions in each model. The right-most diagram shows the active macrophage reactions in each model.