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Lung gene expression and single cell analyses reveal two subsets of idiopathic pulmonary fibrosis (IPF) patients associated with different pathogenic mechanisms

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  • Jozsef Karman
  • Jing Wang
  • Corneliu Bodea
  • Sherry Cao
  • Marc C Levesque

Abstract

Idiopathic pulmonary fibrosis is a progressive and debilitating lung disease with large unmet medical need and few treatment options. We describe an analysis connecting single cell gene expression with bulk gene expression-based subsetting of patient cohorts to identify IPF patient subsets with different underlying pathogenesis and cellular changes. We reproduced earlier findings indicating the existence of two major subsets in IPF and showed that these subsets display different alterations in cellular composition of the lung. We developed classifiers based on the cellular changes in disease to distinguish subsets. Specifically, we showed that one subset of IPF patients had significant increases in gene signature scores for myeloid cells versus a second subset that had significantly increased gene signature scores for ciliated epithelial cells, suggesting a differential pathogenesis among IPF subsets. Ligand-receptor analyses suggested there was a monocyte-macrophage chemoattractant axis (including potentially CCL2-CCR2 and CCL17-CCR4) among the myeloid-enriched IPF subset and a ciliated epithelium-derived chemokine axis (e.g. CCL15) among the ciliated epithelium-enriched IPF subset. We also found that these IPF subsets had differential expression of pirfenidone-responsive genes suggesting that our findings may provide an approach to identify patients with differential responses to pirfenidone and other drugs. We believe this work is an important step towards targeted therapies and biomarkers of response.

Suggested Citation

  • Jozsef Karman & Jing Wang & Corneliu Bodea & Sherry Cao & Marc C Levesque, 2021. "Lung gene expression and single cell analyses reveal two subsets of idiopathic pulmonary fibrosis (IPF) patients associated with different pathogenic mechanisms," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-28, March.
  • Handle: RePEc:plo:pone00:0248889
    DOI: 10.1371/journal.pone.0248889
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    References listed on IDEAS

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    1. Francesco Vallania & Andrew Tam & Shane Lofgren & Steven Schaffert & Tej D. Azad & Erika Bongen & Winston Haynes & Meia Alsup & Michael Alonso & Mark Davis & Edgar Engleman & Purvesh Khatri, 2018. "Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
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