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Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus
Winston A. Haynes, D. James Haddon, Vivian K. Diep, Avani Khatri, Erika Bongen, Gloria Yiu, Imelda Balboni, Christopher R. Bolen, Rong Mao, Paul J. Utz, Purvesh Khatri
Winston A. Haynes, D. James Haddon, Vivian K. Diep, Avani Khatri, Erika Bongen, Gloria Yiu, Imelda Balboni, Christopher R. Bolen, Rong Mao, Paul J. Utz, Purvesh Khatri
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Research Article

Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus

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Abstract

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that follows an unpredictable disease course and affects multiple organs and tissues. We performed an integrated, multicohort analysis of 7,471 transcriptomic profiles from 40 independent studies to identify robust gene expression changes associated with SLE. We identified a 93-gene signature (SLE MetaSignature) that is differentially expressed in the blood of patients with SLE compared with healthy volunteers; distinguishes SLE from other autoimmune, inflammatory, and infectious diseases; and persists across diverse tissues and cell types. The SLE MetaSignature correlated significantly with disease activity and other clinical measures of inflammation. We prospectively validated the SLE MetaSignature in an independent cohort of pediatric patients with SLE using a microfluidic quantitative PCR (qPCR) array. We found that 14 of the 93 genes in the SLE MetaSignature were independent of IFN-induced and neutrophil-related transcriptional profiles that have previously been associated with SLE. Pathway analysis revealed dysregulation associated with nucleic acid biosynthesis and immunometabolism in SLE. We further refined a neutropoiesis signature and identified underappreciated transcripts related to immune cells and oxidative stress. In our multicohort, transcriptomic analysis has uncovered underappreciated genes and pathways associated with SLE pathogenesis, with the potential to advance clinical diagnosis, biomarker development, and targeted therapeutics for SLE.

Authors

Winston A. Haynes, D. James Haddon, Vivian K. Diep, Avani Khatri, Erika Bongen, Gloria Yiu, Imelda Balboni, Christopher R. Bolen, Rong Mao, Paul J. Utz, Purvesh Khatri

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Figure 4

Disease activity is significantly associated with the SLE MetaScore.

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Disease activity is significantly associated with the SLE MetaScore.
(A)...
(A) Positive Spearman correlations are observed across all 5 data sets where SLEDAI was available. Box size is proportional to the confidence of the correlation estimate. Summary is the pooled, inverse variance summary correlation value. (B) The SLE MetaScore in an example SLE whole blood data set is positively correlated with SLEDAI. Spearman’s rank correlation = 0.25, P = 2.47 × 10–26 (calculated using AS 89 algorithm; ref. 122). (C) SLE MetaScore is inversely correlated with complement C3 levels. Spearman’s rank correlation = –0.303, P = 1.79 × 10–20 (calculated using AS 89 algorithm; ref. 122). See also Supplemental Figure 5.

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