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606 Cell-specific human endogenous retrovirus expression, host gene expression and SLE phenotypes
  1. Zachary Cutts1,
  2. Sarah Patterson2,
  3. Lenka Maliskova3,
  4. Chun Jimmie Ye2,
  5. Maria Dall’Era2,
  6. Jinoos Yazdany2,
  7. Lindsey A Criswell4,
  8. Chaz Langelier5,
  9. Marina Sirota1 and
  10. Cristina Lanata4
  1. 1Bakar Computational Health Science Institute, University of California, San Francisco
  2. 2Russell/Engleman Rheumatology Research Center, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA4
  3. 3Institute for Human Genetics, University of California, San Francisco
  4. 4National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
  5. 5Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA


Background/purpose Human endogenous retroviruses (HERVs) and long interspersed nuclear elements (LINEs) make up 5-8% and 21% of the human genome. Their expression may contribute to production of type I interferon and the generation of autoantibodies. The objective of this study was to detect

HERVs and LINEs in 4 cell-types in SLE patients and characterize their relationship to host gene expression and SLE phenotypes.

Methods Peripheral blood mononuclear cells were isolated from 120 deeply-phenotyped SLE participants. Cells were sorted utilizing magnetic beads (CD14+ monocytes, B cells, CD4+ T cells, and NK cells) and STEM cell technologies for a total of 480 samples. Libraries were sequenced on a HiSeq4000 PE150. Trimmed fastq files were aligned to GRCh38 release 104 using default settings with STAR to generate alignment files. Alignment files were converted to gene counts using featureCounts. Raw counts from Telescope were normalized using DESeq2 and summed per patient; patients were then separated into tertiles based on the summed counts for HERVs and LINEs. DESeq2 was used to perform differential gene expression analysis using gene counts from featureCounts, comparing the third to the first tertile. Gene set enrichment analysis was performed using genes with adjusted p values < 0.05, ranking genes by log2FoldChange, and running WebGestalt. For clinical outcomes, outliers were identified and dropped per cell type and differential expression analysis was run using raw counts from Telescope with DESeq2 per cell type, adjusting for race, lane, sex, and immunosuppressant use at the time of blood draw.

Outcomes studied included disease activity (SLEDAI score), autoantibody production (dsDNA, RNP, Sm), ACR renal criteria and disease severity as defined by clinical clusters previously described in the same SLE participants, (Lanata et al, Nat Commun. Aug 29 2019;10(1):3902).

Results A total of 26,768 HERVs/LINEs were detected across the 480 samples. These were mostly cell-specific (figure 1). High HERVs/LINEs expression correlated with host gene transcription in a cell specific manner. Significant associations with retroviral load include differentially expressed genes in pathways of: olfactory signaling pathway, regulation of IFNA signaling, and interferon alpha/beta signaling in CD14 cells; DNA repair and host response of HIV factors in CD4 cells; activation of HOX genes and antimicrobial peptides in CD19 cells; and regulation of complement cascade, neutrophil degranulation and several metabolic pathways in NK cells (figure 2). Significant associations between HERVs/LINEs expression and clinical outcomes are summarized in table 1. We found that CD19 cells had the most robust associations with disease severity, SLEDAI score, history of renal disease, and autoantibody production (FDR p<0.05). Other findings included high HERVs/LINEs expression in NK cells in patient with severe disease, and in CD4 cells in patients with dsDNA production (FDR p<0.05).

Conclusion HERVs/LINEs expression is associated with gene expression in a cell specific manner. Further, we demonstrated a strong association between HERVs/LINEs expression and clinical outcomes, particularly in CD19 cells, in SLE patients.

Abstract 606 Figure 1

PCA plot of HERVs and LINEs expression in 4 cell types of 120 SLE individuals.

Abstract 606 Figure 2

Pathways of differentially expressed genes associated with high HERVs/LINEs expression per cell type in 120 SLE patients.

Abstract 606 Table 1

HERVs/LINEs expression associated with clinical outcomes, adjusted for sex, race and immunosuppressive medication use in 120 SLE participants. (FDR p<0.05)

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