P0359CROSS-TISSUE AND MURINE-HUMAN COMPARATIVE TRANSCRIPTOME ANALYSES IDENTIFY TARGETABLE GENES FOR HUMAN SYSTEMIC LUPUS ERYTHEMATOUS AND LUPUS NEPHRITIS
Authors: Frangou Eleni , Garantziotis Panagiotis , Grigoriou Maria , Banos Angelos , Panousis Nikolaos , Dermitzakis Emmanouil , Bertsias George , Boumpas Dimitrios T , Filia Anastasia 
Published: June 2020

Abstract

Background and Aims
Systemic Lupus Erythematosus (SLE) is a complex disease associated with non-synchronous multi-tissue dysfunction of varying severity. Involvement of major organs, such as kidneys, contributes significantly to morbidity and mortality. We sought to unravel new pathways to be used as potential biomarkers and therapeutic targets. To this end, we compared the patterns of gene transcription -as determined by the use of RNA-sequencing- across tissues between healthy and lupus-prone mice at different stages of the disease, and explored their implications for human disease and prediction of kidney involvement, in our whole blood RNA sequencing dataset comprised of 120 SLE [55 lupus nephritis (LN), 65 non-LN] patients and 58 healthy individuals (HI).

Method
NZB/W-F1 lupus-prone mice were sacrificed at the pre-puberty, pre-autoimmunity and nephritic stage. Age-matched C57BL/6 mice were used as controls. An “effector” tissue (spleen) and major end-organ tissues (kidneys, brain) were collected. Total RNA was isolated, and mRNA-sequencing was performed. Differential expression and time-series analyses were performed using DESeq2. Differentially expressed genes (DEGs) were hierarchically clustered and functionally interpreted using gProfiler enrichment analysis. Human orthologous genes of mouse common DEGs in each tissue and across all disease stages, were compared to human DEGs. Using machine learning techniques, human orthologs identified in the common DEGs across all stages in the kidneys of lupus-prone vs healthy mice were used to predict kidney involvement in the human dataset, which was split in training and validation sets.
Results: Gene signatures: the common cross-tissue signature was identified by the comparison of DEGs between tissues of lupus-prone vs healthy mice at each stage of the disease. A total 134 genes (including C4A, LYRM7 and HDDC3) were found, suggesting their involvement in a common pathogenic mechanism across “effector” and end-organ tissues. Tissue-specific signatures showed enrichment of FCERI mediated NF-kB activation pathway in the spleen, steroid hormone biosynthesis pathway in the kidney and phosphatidylcholine metabolic process in the brain, suggesting distinct pathways implicated in end-organ injury.
Comparative murine-human transcriptome analysis: 76 human orthologs (including CCL5, IFIT and HLA genes) identified in the murine spleen signature were also differentially expressed in SLE patients vs HI, suggesting their involvement in systemic autoimmunity. A total of 68 human orthologs (including FCGR2A, C1R and JAK1) identified in the mouse kidney-signature and 25 human orthologs (including APOA2) identified in the mouse brain-signature, were differentially expressed in LN patients vs HI and neuropsychiatric SLE patients vs HI, respectively.
Kidney involvement prediction in human SLE: using a neural network model, 193 human orthologs predicted LN patients vs HI with high accuracy (accuracy=0.86, sensitivity=0.82, specificity=0.91 in the validation set). Using a support vector machine model, 30 human orthologs and age and gender were the best predictors of LN vs non-LN SLE patients (accuracy=0.71, sensitivity=0.73, specificity=0.69 in the validation set).

Conclusion
Murine RNA-sequencing uncovered both shared cross-tissue and tissue-specific gene signatures that could be potentially targeted in SLE. Murine-human comparative transcriptome analysis revealed common gene signatures suggesting that similar biological processes and pathways are disturbed across species, with murine kidney lupus signature predicting kidney involvement in human SLE. Validation in other datasets is ongoing.