Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus
Winston A. Haynes, … , Paul J. Utz, Purvesh Khatri
Winston A. Haynes, … , Paul J. Utz, Purvesh Khatri
Published January 23, 2020
Citation Information: JCI Insight. 2020;5(4):e122312. https://doi.org/10.1172/jci.insight.122312.
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Research Article

Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus

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 1

Identification and validation of a SLE-specific gene signature using integrated, multicohort analysis.

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Identification and validation of a SLE-specific gene signature using int...
(A) We downloaded 40 publicly available data sets from 17 centers in 5 countries comprising 7,471 samples. We identified data sets that included whole blood or PBMC samples from SLE patients and healthy volunteers to serve as discovery (6 studies) and validation (8 studies) sets. The remaining 26 studies contained samples from other tissue types or lacked healthy volunteer samples, and they were examined as extended validation data sets. We used the MetaIntegrator framework to identify a 93-gene SLE MetaSignature (effect size > 1, FDR < 0.05, measured in ≥ 4 data sets). We examined the classification accuracy of the signature in validation data and the generalizability of the signature in the extended validation data. To prospectively validate the SLE meta-analysis signature using an external cohort, we analyzed individuals who have pSLE (n = 43) or JIA (n = 12) from the Stanford Pediatric Rheumatology Clinic, as well as healthy adult (n = 10) volunteers using Fluidigm qPCR arrays. (B) We leveraged publicly available data to identify non-IFN components of the SLE MetaSignature, examine the role of neutrophils in SLE, and study heavy metal exposure.