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Updated: Sep 3, 2025
Analyses of Proteinuria, Renal Infiltration of Leukocytes, and Renal Deposition of Proteins in Lupus-prone MRL/lpr Mice
Published on: June 8, 2022
Cross-species transcriptome analysis for early detection and specific therapeutic targeting of human lupus nephritis
Eleni Frangou1,2, Panagiotis Garantziotis1,3, Maria Grigoriou1
1Laboratory of Autoimmunity and Inflammation, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
Objectives:
Patients with lupus nephritis (LN) are in urgent need for early diagnosis and therapeutic interventions targeting aberrant molecular pathways enriched in affected kidneys.
Methods:
We used mRNA-sequencing in effector (spleen) and target (kidneys, brain) tissues from lupus and control mice at sequential time points, and in the blood from 367 individuals (261 systemic lupus erythematosus (SLE) patients and 106 healthy individuals). Comparative cross-tissue and cross-species analyses were performed. The human dataset was split into training and validation sets and machine learning was applied to build LN predictive models.
Results:
In murine SLE, we defined a kidney-specific molecular signature, as well as a molecular signature that underlies transition from preclinical to overt disease and encompasses pathways linked to metabolism, innate immune system and neutrophil degranulation. The murine kidney transcriptome partially mirrors the blood transcriptome of patients with LN with 11 key transcription factors regulating the cross-species active LN molecular signature. Integrated protein-to-protein interaction and drug prediction analyses identified the kinases TRRAP, AKT2, CDK16 and SCYL1 as putative targets of these factors and capable of reversing the LN signature. Using murine kidney-specific genes as disease predictors and machine-learning training of the human RNA-sequencing dataset, we developed and validated a peripheral blood-based algorithm that discriminates LN patients from normal individuals (based on 18 genes) and non-LN SLE patients (based on 20 genes) with excellent sensitivity and specificity (area under the curve range from 0.80 to 0.99).
Conclusions:
Machine-learning analysis of a large whole blood RNA-sequencing dataset of SLE patients using human orthologs of mouse kidney-specific genes can be used for early, non-invasive diagnosis and therapeutic targeting of LN. The kidney-specific gene predictors may facilitate prevention and early intervention trials.
Insights
Researchers developed a blood test using machine learning to detect lupus nephritis (LN) early. This non-invasive method identifies kidney-specific gene signatures for better diagnosis and targeted therapies in systemic lupus erythematosus (SLE) patients.
Area of Science:
- Genomics and Molecular Biology
- Immunology
- Computational Biology
Background:
- Lupus nephritis (LN) requires early diagnosis and targeted therapies for aberrant kidney pathways.
- Current diagnostic methods for LN can be invasive and lack specificity.
Purpose of the Study:
- To identify kidney-specific molecular signatures for lupus nephritis (LN).
- To develop a non-invasive, blood-based diagnostic algorithm for LN using machine learning.
- To identify potential therapeutic targets for LN.
Main Methods:
- mRNA-sequencing of kidney and spleen tissues in lupus and control mice, and blood from systemic lupus erythematosus (SLE) patients and healthy individuals.
- Cross-tissue and cross-species comparative analyses.
- Machine learning model development and validation using human RNA-sequencing data for LN prediction.
Main Results:
- A kidney-specific molecular signature for murine SLE was defined, mirroring aspects of LN patient blood transcriptomes.
- Eleven key transcription factors regulate a cross-species LN molecular signature.
- A validated blood-based algorithm using 18-20 genes accurately discriminates LN patients from healthy individuals and non-LN SLE patients (AUC 0.80-0.99).
- TRRAP, AKT2, CDK16, and SCYL1 were identified as potential therapeutic targets.
Conclusions:
- Machine learning analysis of whole blood RNA-sequencing data, utilizing mouse kidney-specific genes, enables early, non-invasive diagnosis of LN.
- The identified kidney-specific gene predictors can aid in prevention and early intervention trials for LN.
- This approach offers a promising strategy for both diagnosis and therapeutic targeting in LN patients.
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