A Bioinformatics-Assisted Review on Iron Metabolism and Immune System to Identify Potential Biomarkers of Exercise Stress-Induced Immunosuppression

Abstract

The immune function is closely related to iron (Fe) homeostasis and allostasis. The aim of this bioinformatics-assisted review was twofold; (i) to update the current knowledge of Fe metabolism and its relationship to the immune system, and (ii) to perform a prediction analysis of regulatory network hubs that might serve as potential biomarkers during stress-induced immunosuppression. Several literature and bioinformatics databases/repositories were utilized to review Fe metabolism and complement the molecular description of prioritized proteins. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to build a protein-protein interactions network for subsequent network topology analysis. Importantly, Fe is a sensitive double-edged sword where two extremes of its nutritional status may have harmful effects on innate and adaptive immunity. We identified clearly connected important hubs that belong to two clusters: (i) presentation of peptide antigens to the immune system with the involvement of redox reactions of Fe, heme, and Fe trafficking/transport; and (ii) ubiquitination, endocytosis, and degradation processes of proteins related to Fe metabolism in immune cells (e.g., macrophages). The identified potential biomarkers were in agreement with the current experimental evidence, are included in several immunological/biomarkers databases, and/or are emerging genetic markers for different stressful conditions. Although further validation is warranted, this hybrid method (human-machine collaboration) to extract meaningful biological applications using available data in literature and bioinformatics tools should be highlighted.

Keywords: allostasis; exercise; ferritins; hemeproteins; immune system; metabolic networks and pathways; physiological stress response; transferrin receptor.

Figure 1

Representation of the changes in the immune function in response to stress. The figure shows the response pattern of immune activity to distinguish between allostatic load in the normal life cycle and allostatic overload that exceeds the capacity of the biological system to cope. See the previous paragraphs of the manuscript for further rationale. Source: designed by the authors (D.A.B.) based on published materials [29,50,51,52].

Figure 2

Overview of the bioinformatics-assisted review workflow to identify potential biomarkers.

Figure 3

Iron absorption in the duodenum. The protein structures were taken from UniProtKB and PDB repositories. Structure prediction by homology modeling was carried out using SWISS-MODEL via the ExPASy web server if the protein structure was not available at UniProtKB or PKB. ATP1A3, basolateral sodium/potassium-transporting ATPase; CD163, scavenger receptor cysteine-rich type 1 protein M130; CYBRD1, cytochrome b reductase 1; CP, ceruloplasmin; Hb, hemoglobin; HEPH, hephaestin; HMOX1/2, heme oxygenases 1/2; IREBs, iron-responsive element-binding proteins; IREs, iron-responsive elements; mRNA, messenger RNA; NRAM2, natural resistance-associated macrophage protein 2; PCBP, poly(rC)-binding protein; SCARA5, scavenger receptor class A member 5; SLC9A1, apical sodium/hydrogen exchanger 1; SLC40A1, solute carrier family 40 member 1; SLC46A1, proton-coupled folate transporter; TF, transferrin. Source: designed by the authors (D.A.B.).

Figure 4

Integration of iron metabolism to hemoglobin production and immune cell function. (A) The figure represents the integration of Fe to hemoglobin through bone marrow macrophages. Blue ovals represent the SLC40A1 (also known as ferroportin). The green six-pointed star represents ceruloplasmin (CP), which needs Cu²⁺. Bright yellow ovals are pointed with arrows as apotransferrin (TF) while the orange bar on the membrane of erythroid progenitors (purple cells) represents the TFRC. (B) Fe bioavailability orchestrates complex metabolic programs in immune cell function and inflammation (for comprehensive reviews on this topic, please refer to [83,102,103,104]). Fe, iron; HEPH, hephaestin; SLC40A1, solute carrier family 40 members 1; TFRC, transferrin receptor protein 1. Source: designed by the authors (D.A.B.) using figure templates developed by Servier Medical Art (Les Laboratoires Servier, Suresnes, France), licensed under a Creative Common Attribution 3.0 Generic License. http://smart.servier.com/ (accessed on 20 February 2021).

Figure 5

Protein-protein interactions network of iron metabolism and the immune system. The colored nodes represent the results of the Markov cluster algorithm to group proteins in two main biological functions: presentation of peptide antigens to the immune system (red) and ubiquitination, endocytosis, and degradation processes of proteins related to Fe metabolism in immune cells (e.g., macrophages) (green). The colors of interactions correspond to: known from curated databases (cyan), experimentally determined (purple); predicted interactions based on gene neighborhood (green), gene fusions (red), and gene co-occurrence (dark blue); and others, such as text-mining (yellow), co-expression (black), and protein homology (light blue). The input proteins were: CD163, scavenger receptor cysteine-rich type 1 protein M130; CP, ceruloplasmin; CYBRD1, cytochrome b reductase 1; FTH1, ferritin heavy chain; FTL, ferritin light chain; HAMP, hepcidin; HEPH, hephaestin; HMOX1, heme oxygenase 1; IREB2, iron-responsive element-binding protein 2; MB, myoglobin; SCARA5, scavenger receptor class A member 5; SLC11A2, natural resistance-associated macrophage protein 2; SLC40A1, solute carrier family 40 member 1; SLC46A1, proton-coupled folate transporter; TF, transferrin; TFRC, transferrin receptor protein 1. The network is available at https://version-11-0b.string-db.org/cgi/network?networkId=b1HF4feAW2Nr (accessed on 17 June 2021).

Figure 6

Matrix-like plot showing pairwise correlations of the centrality scores. The upper-right part shows the numerical correlation between the given topological features, whereas the lower-right part of the matrix is the scatterplot of one feature against another. These high correlations between centrality metrics provide useful insights into the potential of different nodes within a network [147]; particularly, the presence of highly connected nodes is likely to be rated as central by other metrics, representing a putative core that for the aims of this study might result in potential biomarkers. Figures were obtained from the Network Analysis Profiler v2.0 [61]. *** Statistically significant correlation (p < 0.001).