bioRxivPathologyPreprint
A multi-omics map of diabetic nephropathy links c-Jun activation to tubular injury and metabolic stress
Qiwen DengiD, Yu LiuiD, Nathan A. BraceyiD, Yi-Chuan WangiD, Paul A. WadsworthiD, Marjia AfriniD, Matteo Santoro, Shih-Yu CheniD, Joseph C. WuiD, Vivek CharuiD, Gerlinde WernigiD
Abstract
Diabetic nephropathy (DN) is a major cause of end-stage renal disease, yet the molecular mechanisms driving tubular injury and fibrosis remain poorly defined. Here, we integrated single-cell multiplexed protein imaging, spatial transcriptomics, single-nucleus and single-cell RNA sequencing and chromatin accessibility profiling to comprehensively characterize human DN pathology. Our multi-modal analysis precisely maps kidney cell types and their spatial distributions, immune-fibrotic interactions, and key transcriptional regulators. We identified eight distinct cellular neighborhoods defining the immune-fibrotic microenvironment and uncovered molecular networks driving tubular injury and fibrosis. JUN (encoding c-Jun) emerged as a central regulator of transcriptional reprogramming during tubular injury and fibrogenic remodeling. In a diabetic mouse model, c-Jun is activated in injured proximal tubules. Using an inducible c-Jun mouse model, we demonstrated that tubular-specific c-Jun activation alone is sufficient to induce tubular injury, chronic inflammation, progressive fibrosis, and systemic metabolic alterations, including impaired glucose homeostasis. We also observed reduced expression of SLC4A4, a bicarbonate transporter essential for proximal tubular function, in injured tubules. Together, our findings establish a spatially resolved framework for understanding DN pathogenesis and identify c-Jun as a key mediator of tubular injury and fibrosis in diabetic kidney disease.
- organism: human
- organism: mouse
- assay: spatial transcriptomics
- assay: scRNA-seq
- assay: scATAC-seq
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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