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Advancing lupus nephritis research through multi-omics and predictive modeling.

Lisha Mou1,2, Ying Lu1,2, Zijing Wu1,2

  • 1Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China.

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Summary

This study reveals key immune cell drivers in lupus nephritis (LN), identifying specific macrophage and natural killer cell roles. Machine learning models accurately predict LN, offering new diagnostic and therapeutic strategies.

Keywords:
Lupus nephritisNF-κB signalingapoptosiscircadian rhythmsdiagnostic modelinnate immunitysystemic lupus erythematosus

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Area of Science:

  • Immunology
  • Genomics
  • Computational Biology

Background:

  • Lupus nephritis (LN) exhibits complex heterogeneity and pathophysiology challenging traditional analysis.
  • Advanced multi-omics are crucial for understanding LN's cellular and molecular drivers.

Purpose of the Study:

  • To integrate single-cell and bulk RNA sequencing for a comprehensive understanding of LN.
  • To identify key immune cell populations, communication networks, and predictive biomarkers for LN.
  • To develop machine learning models for accurate LN diagnosis and identify therapeutic targets.

Main Methods:

  • Integrative analysis of single-cell RNA sequencing (scRNA-seq) from LN biopsies and bulk RNA-seq cohorts.
  • Non-negative matrix factorization (NMF) for immune meta-programs and CellChat for cell-cell communication.
  • Development and validation of 399 machine learning models using bulk transcriptomics.

Main Results:

  • Identified a rare plasmacytoid dendritic cell (pDC) population and expanded CD56dimCD16+ natural killer (NK) cells with high IFN-γ/perforin.
  • Highlighted CM2 macrophages as a pro-inflammatory hub and observed reduced Treg-B cell interactions.
  • Achieved high diagnostic accuracy (AUC=0.929) with models based on innate immunity, circadian rhythms, apoptosis, and NF-κB signaling; validated hub genes (CYBB, CSF2RB, IRF8).

Conclusions:

  • CM2 macrophages and dysregulated pDC-NK cell interactions are key drivers of lupus nephritis.
  • The study provides a framework for precision diagnostics and identifies potential therapeutic targets in LN.
  • Integrative multi-omics and machine learning offer a robust roadmap for understanding and managing LN.