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. 2015 Aug 25:5:13351.
doi: 10.1038/srep13351.

Integrated Transcriptomics Establish Macrophage Polarization Signatures and have Potential Applications for Clinical Health and Disease

Matheus Becker  1   2 , Marco A De Bastiani  1   2 , Mariana M Parisi  3 , Fátima T C R Guma  4 , Melissa M Markoski  5 , Mauro A A Castro  6 , Mark H Kaplan  7 , Florencia M Barbé-Tuana  3   8 , Fábio Klamt  1   2
Affiliations

Integrated Transcriptomics Establish Macrophage Polarization Signatures and have Potential Applications for Clinical Health and Disease

Matheus Becker et al. Sci Rep. .

Abstract

Growing evidence defines macrophages (Mφ) as plastic cells with wide-ranging states of activation and expression of different markers that are time and location dependent. Distinct from the simple M1/M2 dichotomy initially proposed, extensive diversity of macrophage phenotypes have been extensively demonstrated as characteristic features of monocyte-macrophage differentiation, highlighting the difficulty of defining complex profiles by a limited number of genes. Since the description of macrophage activation is currently contentious and confusing, the generation of a simple and reliable framework to categorize major Mφ phenotypes in the context of complex clinical conditions would be extremely relevant to unravel different roles played by these cells in pathophysiological scenarios. In the current study, we integrated transcriptome data using bioinformatics tools to generate two macrophage molecular signatures. We validated our signatures in in vitro experiments and in clinical samples. More importantly, we were able to attribute prognostic and predictive values to components of our signatures. Our study provides a framework to guide the interrogation of macrophage phenotypes in the context of health and disease. The approach described here could be used to propose new biomarkers for diagnosis in diverse clinical settings including dengue infections, asthma and sepsis resolution.

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Figures

Figure 1
Figure 1. Macrophage phenotypes signatures construction and gene network representation.
(A) Protocol design for M(IFNγ + LPS, TNFα) and M(IL-4, IL-13) gene signatures. (B) Volcano plots representation of differential expression analyses. Red dots are genes present in all three datasets with adjusted P value ≤0.0001. (C) M(IFNγ + LPS, TNFα) and M(IL-4, IL-13) gene networks (left) and their illustrative topological representation (landscape analysis) showing changes in relative gene expression after IFNγ + LPS or IL-4 stimuli (right) (see Supplementary Table S2 & S3 for the complete list of retrieved genes).
Figure 2
Figure 2. In vitro validation of selected genes from M(IFNγ + LPS, TNFα) and M(IL-4, IL-13) signatures.
(A) RT-qPCR from human MDM activated with 50 ng/mL of M-CSF for 7 days and stimulated with IFNγ (20 ng/mL) + LPS (100 ng/mL) or IL-4 (20 ng/mL) for additional 18 h. (B) RT-qPCR from THP-1 (human acute monocyte leukemia cell line) differentiated with 20 ng/mL PMA for 3 days and stimulated with IFNγ (20 ng/mL) + LPS (100 ng/mL) or IL-4 (20 ng/mL) for additional 24 h. Data represent median and IQR (interquartile range) of five independent experiments normalized to TATA binding box protein (TBP). Data was considered statistically significant for *(P ≤ 0.05) and ** (P ≤ 0.01) (Mann-Whitney U test).
Figure 3
Figure 3. Validation of M(IFNγ + LPS, TNFα) and M(IL-4, IL-13) signatures under controlled clinical settings.
(A) M(IFNγ + LPS, TNFα) signature response of alveolar macrophages after LPS instillation in the lung based on Gene Set Enrichment Analysis (GSEA) (left) and topological representation (landscape analysis) (right). (B) M(IL-4, IL-13) signature response of bronchial biopsy from asthmatic patients based on GSEA (left) and landscape analysis (right).
Figure 4
Figure 4. Prognostic and predictive values of selected components derived from M(IFNγ + LPS, TNFα) and M(IL-4, IL-13) signatures in complex clinical settings.
Protocol design to select consensus responsive genes in infectious (A) and non-infections (B) conditions to interrogate different clinical datasets. The input lists for the consensus analysis comprised of 106 M(IFNγ + LPS, TNFα) and 58 M(IL-4, IL-13) genes. M(IFNγ + LPS, TNFα) list was interrogated for association with viral and bacterial (6 independent gene expression signatures each) infections, retrieving 12 and 35 consensus gene markers, respectively. M(IL-4, IL-13) list was interrogated for association with non-infectious conditions (2 independent gene expression signatures), retrieving 7 consensus gene markers (see Supplementary Table S5 & S6 for the complete description of datasets). Prognostic or predictive values of these markers were assessed by logistic regression analysis using selected clinical cohorts. Data were expressed as Odds Ratio (OR). (Drawings made by F. M. B-T).
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