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Gpnmb and Spp1 mark a conserved macrophage injury response masking fibrosis-specific programming in the lung
Emily M. King, Yifan Zhao, Camille M. Moore, Benjamin Steinhart, Kelsey C. Anderson, Brian Vestal, Peter K. Moore, Shannon A. McManus, Christopher M. Evans, Kara J. Mould, Elizabeth F. Redente, Alexandra L. McCubbrey, William J. Janssen
Emily M. King, Yifan Zhao, Camille M. Moore, Benjamin Steinhart, Kelsey C. Anderson, Brian Vestal, Peter K. Moore, Shannon A. McManus, Christopher M. Evans, Kara J. Mould, Elizabeth F. Redente, Alexandra L. McCubbrey, William J. Janssen
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Research Article Immunology Inflammation

Gpnmb and Spp1 mark a conserved macrophage injury response masking fibrosis-specific programming in the lung

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Abstract

Macrophages are required for healthy repair of the lungs following injury, but they are also implicated in driving dysregulated repair with fibrosis. How these 2 distinct outcomes of lung injury are mediated by different macrophage subsets is unknown. To assess this, single-cell RNA-Seq was performed on lung macrophages isolated from mice treated with LPS or bleomycin. Macrophages were categorized based on anatomic location (airspace versus interstitium), developmental origin (embryonic versus recruited monocyte derived), time after inflammatory challenge, and injury model. Analysis of the integrated dataset revealed that macrophage subset clustering was driven by macrophage origin and tissue compartment rather than injury model. Gpnmb-expressing recruited macrophages that were enriched for genes typically associated with fibrosis were present in both injury models. Analogous GPNMB-expressing macrophages were identified in datasets from both fibrotic and nonfibrotic lung disease in humans. We conclude that this subset represents a conserved response to tissue injury and is not sufficient to drive fibrosis. Beyond this conserved response, we identified that recruited macrophages failed to gain resident-like programming during fibrotic repair. Overall, fibrotic versus nonfibrotic tissue repair is dictated by dynamic shifts in macrophage subset programming and persistence of recruited macrophages.

Authors

Emily M. King, Yifan Zhao, Camille M. Moore, Benjamin Steinhart, Kelsey C. Anderson, Brian Vestal, Peter K. Moore, Shannon A. McManus, Christopher M. Evans, Kara J. Mould, Elizabeth F. Redente, Alexandra L. McCubbrey, William J. Janssen

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Figure 5

Differential gene expression in Gpnmb RecAM from LPS versus bleomycin.

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Differential gene expression in Gpnmb RecAM from LPS versus bleomycin.
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(A) Number of DEGs between LPS and bleomycin within each cluster at corresponding time points (FDR < 0.05). (B) Sankey plot of DEGs from Gpnmb RecAM at each comparison time point. Significance is based on logFC > 0.5 and FDR < 0.05. (C) Volcano plot of differentially expressed genes (DEGs) for Gpnmb RecAM LPS day 6 versus bleomycin day 7. (D) Cytoscape network visualization of Gpnmb RecAM pathway analysis performed on DEGS up in bleomycin day 7 versus up in LPS day 6. Circles represent enriched pathways and are clustered by similarity. Lines connecting the circles represent genes that overlap between pathways. Circle halves are shaded from grey (no pathway enrichment) to red (high enrichment) with left sides representing LPS and right sides representing bleomycin. Line colors mark the primary group showing significant pathway enrichment, either bleomycin (green) or LPS (purple). (E) Heatmap of transcription factor activity inference scores that were significantly different between Gpnmb RecAM from bleomycin day 7 versus LPS day 6. Only the top 8 transcription factors with the lowest adjusted P values for each injury model are shown. (F) Top 10 GO pathways enriched in the 230 DEGs upregulated in Gpnmb RecAM from bleomycin at both day 7 and day 14 compared with LPS day 6 and 15. Adjusted P < 0.05.

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