Computational Modeling of Macrophage Iron Sequestration during Host Defense against Aspergillus

Abstract

Iron is essential to the virulence of Aspergillus species, and restricting iron availability is a critical mechanism of antimicrobial host defense. Macrophages recruited to the site of infection are at the crux of this process, employing multiple intersecting mechanisms to orchestrate iron sequestration from pathogens. To gain an integrated understanding of how this is achieved in aspergillosis, we generated a transcriptomic time series of the response of human monocyte-derived macrophages to Aspergillus and used this and the available literature to construct a mechanistic computational model of iron handling of macrophages during this infection. We found an overwhelming macrophage response beginning 2 to 4 h after exposure to the fungus, which included upregulated transcription of iron import proteins transferrin receptor-1, divalent metal transporter-1, and ZIP family transporters, and downregulated transcription of the iron exporter ferroportin. The computational model, based on a discrete dynamical systems framework, consisted of 21 3-state nodes, and was validated with additional experimental data that were not used in model generation. The model accurately captures the steady state and the trajectories of most of the quantitatively measured nodes. In the experimental data, we surprisingly found that transferrin receptor-1 upregulation preceded the induction of inflammatory cytokines, a feature that deviated from model predictions. Model simulations suggested that direct induction of transferrin receptor-1 (TfR1) after fungal recognition, independent of the iron regulatory protein-labile iron pool (IRP-LIP) system, explains this finding. We anticipate that this model will contribute to a quantitative understanding of iron regulation as a fundamental host defense mechanism during aspergillosis. IMPORTANCE Invasive pulmonary aspergillosis is a major cause of death among immunosuppressed individuals despite the best available therapy. Depriving the pathogen of iron is an essential component of host defense in this infection, but the mechanisms by which the host achieves this are complex. To understand how recruited macrophages mediate iron deprivation during the infection, we developed and validated a mechanistic computational model that integrates the available information in the field. The insights provided by this approach can help in designing iron modulation therapies as anti-fungal treatments.

Keywords: Aspergillus fumigatus; iron regulation; macrophage; mathematical model.

FIG 1

Differential expression analysis of macrophages infected with Aspergillus. (A and B) Volcano plots and Euler diagram of genes with |Log₂ fold change| ≥ 0.5 differential expression in infected compared to uninfected macrophages with adjusted P value < 0.001. The number of differentially regulated genes is indicated in each panel. (C) Principal-component analysis plots of read counts of differentially expressed genes at each time point, after variance stabilizing transformation. Open and filled symbols indicate uninfected and infected cells, respectively, and the color of symbols denotes the donor.

FIG 2

Enrichment analysis of differentially expressed genes in macrophages infected with Aspergillus. GO terms (A–C) and Reactome pathways (D–F) at 4, 6, and 8 h after infection, respectively. Enrichment analysis was performed with differentially expressed genes (adjusted P value <0.001 and |Log₂ fold change| ≥ 0.5). Enriched terms for Gene ontology (top 20 for biological processes, and top 5 for cellular components and molecular functions) and Reactome pathways (top 20) are reported. Cell. comp., cellular component; GeneRatio, the ratio of the number of enriched genes in a given pathway to the total number of genes in that pathway; Molec. funct., molecular function.

FIG 3

Heatmap of differentially regulated iron-associated genes after unsupervised clustering. Heatmap showing treatment groups on the x axis and differentially regulated iron-associated genes with a |Log₂ fold change| ≥ 1 on the y axis. Each cell represents the median expression value of 5 biological replicates after variance stabilizing transformation on size factor normalized count data.

FIG 4

Computational model created with BioRender.com. (A) Diagrammatic representation of key processes in iron regulation in macrophages during invasive pulmonary aspergillosis. (B) Wiring diagram of macrophage iron regulation during invasive pulmonary aspergillosis (see Table 2 for references). Pointed arrows represent activation and blunt arrows represent inhibition. Some arrows are colored for better visualization. Extracellular, membrane, cytoplasm, and intracellular molecules are indicated by ex-, mem-, cyt-, and in- prefixes. BDH2, 3-hydroxybutyrate dehydrogenase-2; DMT1, divalent metal transporter-1; Fe²⁺, ferrous iron forms; Fe³⁺, ferric iron forms; FPN, ferroportin; FTH1, ferritin heavy-chain-1; HAMP, hepcidin; HO1, heme oxygenase-1; IL-6, interleukin-6; LIP, labile iron pool; IRP1, iron-regulatory protein-1; PAMP, pathogen-associated molecular pattern; TfR1, transferrin receptor-1; TNF, tumor necrosis factor; Zip14, zinc transporter-14.

FIG 5

Different states of the computation model. Steady state simulations for the model under the conditions defined in Table 1 – uninfected macrophages in normal extracellular iron condition, and infected macrophages in normal, low, or high extracellular iron conditions. 0, low; 1, medium/normal; 2, high.

FIG 6

Validation of the computational model. (A) Simulated steady states for infected macrophages under normal extracellular iron level and the RNA-seq data at 8 h. Top row shows model output under conditions of exposure to the fungus and normal extracellular iron. Bottom row shows RNA-seq data discretized based on differential expression. (B) Simulated time-series of the model output under conditions of exposure to the fungus and absent extracellular iron. (C) RNA-seq experimental data was obtained from macrophage-Aspergillus cocultures without an external iron source. Read counts were normalized by the library size and a value of 0.5 was added to the normalized counts to generate pseudo counts, which were then transformed with a Log₂ scale. Log-scaled reads are plotted against time and actual raw read counts, and the line was fitted with loess regression. Counts were plotted using DESeq2 function plotCounts method. *, P < 0.05, and the line was fitted to the data with loess regression. (D) Mean and SEM of qRT-PCR measurements from macrophages infected with Aspergillus. (E) Concentration of cytokines in supernatants of infected and uninfected macrophages after 8 h. Each line represents one donor. 0, downregulated; 1, no change; 2, upregulated. *, P < 0.05.