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Abstract: PO1772

Comparative Cross-Tissue and Cross-Species Transcriptome Analyses Predict Lupus Nephritis in Human Systemic Lupus Erythematosus and Guide Therapy in a Tissue-Specific Manner

Session Information

Category: Glomerular Diseases

  • 1202 Glomerular Diseases: Immunology and Inflammation

Authors

  • Frangou, Eleni A., Geniko Nosokomeio Lemesou, Lemesos, Lemesos, Cyprus
  • Garantziotis, Panayiotis, Idryma Iatrobiologikon Ereunon tes Akademias Athenon, Athens, Attica, Greece
  • Grigoriou, Maria, Idryma Iatrobiologikon Ereunon tes Akademias Athenon, Athens, Attica, Greece
  • Banos, Aggelos, Idryma Iatrobiologikon Ereunon tes Akademias Athenon, Athens, Attica, Greece
  • Bertsias, George, Panepistemio Kretes Iatrike Schole, Heraklion, Crete, Greece
  • Filia, Anastasia, Idryma Iatrobiologikon Ereunon tes Akademias Athenon, Athens, Attica, Greece
  • Boumpas, Dimitrios, Idryma Iatrobiologikon Ereunon tes Akademias Athenon, Athens, Attica, Greece
Background

Despite advances, morbidity and mortality in systemic lupus erythematosus (SLE) and lupus nephritis (LN) remain increased. Most clinical trials on novel therapies failed to meet their primary end-points, highlighting the need for therapeutic interventions targeting pathways enriched within individual tissues.

Methods

We applied RNA-sequencing to spleen, kidneys and brain from NZB/W-F1 lupus-prone mice at three stages: the pre-puberty, pre-autoimmunity and nephritic stage. Differentially expressed genes (DEGs) were analyzed with DESeq and functionally annotated with gProfiler. ChEA and Genes2Network were used to infer transcription factors and identify proteins that physically interact with them, respectively. KEA was used to link kinases predicted to regulate DEGs. Implications for human disease were explored in our whole-blood RNA-sequencing dataset of 120 SLE patients [55 LN and 65 non-LN SLE patients and 58 healthy individuals (HI)]. The L1000CDS2engine was used to identify drugs/small molecules predicted to reverse DEGs. Human orthologs of DEGs were compared to human DEGs. Using machine learning, orthologs from the mouse dataset were used to predict LN in the human dataset, which was split in training and validation sets.

Results

We define lupus-susceptibility and lupus-progression signatures that reveal pathways and gene hubs, and a common cross-tissue signature that depicts transcription factors as putative upstream regulators and kinases as potential targets. Tissue-specific signatures uncovers distinct tissue response and repair mechanisms in end-organ injury and distinct targets. 7 small molecules/drugs are predicted to reverse gene signatures in both murine and human SLE.193 orthologs accurately predict LN patients from HI (accuracy=0.86, sensitivity=0.82, specificity=0.91 in the validation set) and 30 orthologs with age and gender best predict LN from non-LN SLE patients (accuracy=0.71, sensitivity=0.73, specificity=0.69 in the validation set).

Conclusion

A murine cross-tissue transcriptome analysis uncovers gene signatures, pathways and tissue-specific targets. The cross-species transcriptome analysis predicts LN in human SLE and guides therapy in a tissue-specific manner.