Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer

Abstract

Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment (TME), which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the TME. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.

Keywords: Cancer; Health informatics; Human genetics; Quantitative genetics.

Graphical abstract

Figure 1

Size of context-specific genome-scale models (A) Size of models generated by integrating patient-derived transcriptomics data from M1 and M2 macrophages using five distinct approaches. (B) The number of components (genes, reactions, and metabolites) and stoichiometric matrix rank for each model generated for M1 macrophages. (C) The number of components (genes, reactions, and metabolites) and stoichiometric matrix rank for each model generated for M2 macrophages.

Figure 2

Comparison of model ensembles (A) Number of genes (top), reactions (middle), and metabolites (bottom) that are found in the GEMs for various ensemble thresholds. (B) Venn diagram showing the number of model components shared between models generated by each integration algorithm: genes (top), reactions (middle), and metabolites (bottom) for M1 (left) and M2 (right).

Figure 3

Characteristics of the consensus models (A) Sizes of M1- and M2-specific models, including shared (purple) genes, reactions, and metabolites, and components only found in the M1 (red) or M2 (blue) models. (B) Comparison of the pathway composition for M1 and M2 models, relative to the pathway size in Recon3D. The pathways significantly enriched in M1 and M2 are marked with a red or blue asterisk, respectively. (C) Results from FEA for the presence or absence of M1 and M2 metabolic pathways. Red and blue dotted lines represent significant values of p = 0.05 for M1 and M2 models, respectively; the diagonal line represents the case where M1 and M2 subsystems are equally enriched.

Figure 4

Model flux predictions (A) KL divergence values from pairwise comparison of flux distributions for reactions present in the M1 and M2 consensus models. (B) Comparison of the weighted PageRank scores for M1 and M2 consensus models for all shared metabolic reactions (circles). Red, reactions that were highly important in M1 but not M2; blue, reactions that were highly important in M2 but not M1. (C) Comparison of the metabolite rankings between the M1 and M2 consensus models. Subtype-specific metabolites: red, M1 and blue, M2.

Figure 5

Reaction knockout analysis (A) KL divergence for M2 fluxes in the baseline model and knockout model for all highly divergent reactions identified by the KL metric shown in Figure 4A. (B) Visualization of metabolic flux samples in low-dimensional space for the baseline M2 model (blue), knockout M2 model (orange), and baseline M1 model (red).