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OP0332 The genomic architecture of systemic lupus erythematosus (SLE) by RNA-SEQ: distinct disease susceptibility, activity and severity signatures and extensive genetic effects on whole blood gene expression
  1. G Bertsias1,1,2,2,
  2. N Panousis3,
  3. I Gergianaki1,2,
  4. M Tektonidou4,
  5. M Trachana5,
  6. C Pamfil6,
  7. A Fanouriakis7,
  8. E Dermitzakis3,
  9. D Boumpas2,7,8,9,10
  1. 1Rheumatology, Clinical Immunology and Allergy, University of Crete School of Medicine
  2. 2Laboratory of Autoimmunity and Inflammation, Institute of Molecular Biology-Biotechnology, FORTH, Iraklio, Greece
  3. 3Institute for Genetics and Genomics in Geneva, University of Geneva Medical School, Geneva, Switzerland
  4. 4Medical School, National and Kapodistrian University of Athens, Athens
  5. 51st Department of Pediatrics, Aristotle University, Thessaloniki, Greece
  6. 6Iuliu Haţieganu University of Medicine and Pharmacy, Cluj, Romania
  7. 74th Department of Medicine
  8. 8Joint Rheumatology Program, Attikon University Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
  9. 9Medical School, University of Cyprus, Nicosia, Cyprus
  10. 10Laboratory of Autoimmunity and Inflammation, Biomedical Research Foundation of the Academy of Athens, Athens, Greece


Background SLE displays significant immunological and clinical heterogeneity. Understanding the molecular basis of this variability may facilitate early diagnosis, risk stratification and personalized therapy.

Objectives To perform full transcriptome analysis in SLE patients in order to identify molecular sub-phenotypes and explore the genomic basis for the disease susceptibility and severity.

Methods Whole blood mRNA and genomic DNA were extracted from 142 SLE patients with varying levels of disease activity/severity and 48 matched healthy volunteers. Paired-end RNA sequencing was performed using the Illumina HiSeq 2000 platform and genotyping with the Infinium CoreExome followed by imputation from the 1000 Genomes. To integrate blood transcriptome with genotype data we used the enrichment analysis of expression-quantitative trail loci (eQTLs). The CIBERSORT tool was used to provide an estimation of the abundancies of different circulating immune cell types.

Results We found a large number (6730, 5% False Detection Rate [FDR]) of differentially expressed genes (DEGs) between SLE patients and controls. Interferon signaling was significantly upregulated in SLE with most of the DEGs (146 out of 281) being regulated by both type I and type II interferon. Analysis of the blood composition in different immune cell types revealed global upregulation of type I interferon and antiviral response genes as well as immune cell-specific alterations in gene expression in SLE patients. Comparison of the transcriptome in active/inactive SLE and healthy individuals identified distinct “disease susceptibility” and “disease activity” gene signatures encompassing 2738 and 377 DEGs, respectively. Analysis according to individual organ involvement revealed more widespread aberrancies in gene expression in SLE patients with active nephritis as compared to activity from other organs, corresponding to oxidative phosphorylation, granulocyte activation and antimicrobial humoral response pathways. By integration of genotyping data, we mapped a total 3142 (5% FDR) cis-eQTLs in SLE patients suggesting extensive genetic effects on whole blood gene expression. Importantly, linear discriminant analysis enabled the definition of a set of DEGs which discriminated SLE versus healthy state with median sensitivity 83% and specificity 100%. Design of gene expression panels and expression profile/clinical trait correlation matrices for improved diagnostics, stratification and personalized therapy is in progress.

Conclusions Specific gene networks confer susceptibility to SLE as well as to severe forms of the disease. These results may facilitate the early diagnosis, monitoring and prognosis, and the molecular taxonomy of SLE patients into pathophysiologically and prognostically distinct subsets for personalized therapy.

Disclosure of Interest None declared

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