<p>Idiopathic membranous nephropathy (IMN) is the major cause of autoimmune-related nephrotic syndrome. The role immune cells play in the risk and prognosis of IMN remains elusive. We employ multi-omics data and a variety of approaches to evaluate the causal link between 731 immune-cell phenotypes and IMN. In light of the findings emanating from Mendelian randomization analyses, only the regulatory T cell (Treg) subtype (CD39<sup>+</sup> Tregs) survived from Bonferroni correction and is causally related to IMN. These cells are significantly enriched in the IMN microenvironment and are negatively correlated with treatment response and prognosis. We validate our findings through multiple immunofluorescence staining and explore the characteristics of CD39<sup>+</sup> Tregs using Single-cell transcriptome analysis and flow cytometry. Based on the signature genes of CD39<sup>+</sup> Tregs, we construct 107 composited machine-learning models to identify MN. We show the substantial contribution of CD39<sup>+</sup> Tregs in both the risk factor determination and prognosis of IMN.</p>

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Multi-omics insights of immune cells in the risk and prognosis of idiopathic membranous nephropathy

  • Xiaoyi Song,
  • Wen Zhu,
  • Yang Li,
  • Zhanyu Li,
  • Wanwei Cao,
  • Jieyu Lu,
  • Wanping Pan,
  • Jingyue Wei,
  • Man Li

摘要

Idiopathic membranous nephropathy (IMN) is the major cause of autoimmune-related nephrotic syndrome. The role immune cells play in the risk and prognosis of IMN remains elusive. We employ multi-omics data and a variety of approaches to evaluate the causal link between 731 immune-cell phenotypes and IMN. In light of the findings emanating from Mendelian randomization analyses, only the regulatory T cell (Treg) subtype (CD39+ Tregs) survived from Bonferroni correction and is causally related to IMN. These cells are significantly enriched in the IMN microenvironment and are negatively correlated with treatment response and prognosis. We validate our findings through multiple immunofluorescence staining and explore the characteristics of CD39+ Tregs using Single-cell transcriptome analysis and flow cytometry. Based on the signature genes of CD39+ Tregs, we construct 107 composited machine-learning models to identify MN. We show the substantial contribution of CD39+ Tregs in both the risk factor determination and prognosis of IMN.