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Abstract Number: 0973

Modeling of Clinical Phenotypes in SLE Based on Platelet Transcriptomic Analysis and FCGR2a Biallelic Variants

MacIntosh Cornwell1, Hanane EL Bannoudi2, Elliot Luttrell-Williams1, Khrystyna Myndzar1, Alexis Engel3, Peter Izmirly4, H. Michael Belmont5, Robert Clancy6, Jeffrey Berger1, Kelly Ruggles1 and Jill Buyon6, 1New York University, New York, NY, 2NYU Langone Health, New York, NY, 3NYU Langone Health, New Yok, NY, 4New York University School of Medicine, New York, NY, 5NYU School of Medicine, New York, NY, 6NYU Grossman School of Medicine, New York, NY

Meeting: ACR Convergence 2021

Keywords: Bioinformatics, Fc receptors, genomics, Lupus nephritis, Systemic lupus erythematosus (SLE)

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Session Information

Date: Sunday, November 7, 2021

Title: Abstracts: SLE – Etiology & Pathogenesis (0970–0973)

Session Type: Abstract Session

Session Time: 2:45PM-3:00PM

Background/Purpose: The clinical heterogeneity of SLE with its complex pathogenesis remains challenging as we strive to provide optimal management. The contribution of platelets to endovascular homeostasis, inflammation and immune regulation highlights their potential importance in SLE. Prior work from our group showed that the Fcγ receptor type IIa (FcγRIIa)–R/H131 biallelic polymorphism is associated with increased platelet activity and cardiovascular risk in SLE. The study was initiated to investigate the platelet transcriptome in patients with SLE and evaluate its association across FcγRIIa genotypes and distinct clinical features.

Methods: RNA-sequencing was done on platelets isolated from 51 patients fulfilling criteria for the classification of SLE based on recent EULAR/ACR definitions, and 18 healthy controls matched on age, sex, and race. Unsupervised clustering, differential gene expression, and gene set enrichment analysis (GSEA) were used to analyze differences between SLE patients and controls, and SLE subpopulations, based on SELENA SLEDAI Hybrid disease activity, specific organ manifestations, and FcγRIIa genotype. Weighted Gene Correlation Network Analysis (WGCNA) was performed to create a modular transcriptomic framework.

Results: Our cross-sectional SLE cohort (N=51, age = 41.1±12.3, 100% female, 45% Hispanic, 24% black, 22% Asian, 51% white, SLEDAI = 4.4±4.2) was comprised of patients consecutively enrolled excluding those on aspirin or anticoagulants. Compared to the 18 controls, there were 2290 (p.adj < 0.05) differentially expressed genes. (Figure 1 A,B) GSEA revealed positive enrichment for pathways related to interferon response, TNFa signaling, and coagulation in SLE. (Figure 1C) WGCNA was used to create a modular transcriptomic framework. (Figure 2A) Modules enriched for platelet activity, immune response, and WNT signaling were significantly increased in SLE versus controls. Moreover, modules enriched for interferon response and WNT signaling paralleled increases in disease activity. (Figure 2B) When analyzing patients with proteinuria, modules associated with oxidative phosphorylation and platelet activity were unexpectedly decreased. (Figure 2C) Analyzing the ratio of fold changes between SLE/Control vs SLE Proteinuria/SLE No Proteinuria, genes increased in SLE and those with proteinuria were enriched for immune effector processes, while genes increased in SLE but decreased in proteinuria were enriched for coagulation and cell adhesion. (Figure 2D) The module enriched for FCR activation was decreased in SLE and was affected by the FcγRIIa genotype. (Figure 3A) FcγRIIa R131 and H131 patients showed significantly different platelet transcriptomes. (Figure 3B) The combination of SLE with an FcγRIIa R131 variant leads to a significant increase in the platelet activity module not seen in controls. (Figure 3C)

Conclusion: These analyses reveal that SLE patients have a significantly different platelet transcriptome from controls, different phenotypic presentations of SLE patients associate with distinct platelet transcriptomic signatures, and FCGR2a variants may differentially influence the role of platelets in the contribution to SLE disease activity.

Figure 1: Transcriptomic analyses reveal differences between SLE and Control platelet transcriptomes. (A) Unsupervised clustering of out cohort based on the top 10% of varied genes. (B) Volcano plot depicting differential expression between the SLE and Control cohorts. (C) Gene Set Enrichment Analysis (GSEA) normalized enrichment scores and p values for pathways differentially enriched between our cohorts.

Figure 2: Modular analyses of the lupus cohort demonstrate differences between SLE patients. (A) Weighted Gene Correlation Network Analysis (WGCNA) derived modules and their average values between our control and SLE cohort, and between control and our SLE cohort when divided by SLEDAI value. (B) Boxplots for the individual patient values of the Interferon Response module and WNT & RNA Splicing modules seen in our SLE cohort. (C) Boxplots for the Oxidative Phosphorylation, Kinase Activity, and Platelet Activity & Coagulation modules seen in our cohort when separated into control, proteinuria, and no proteinuria. (D) Scatterplot of log2fc values for SLE vs control, and proteinuria vs no proteinuria, divided into quadrants associated with the directionality of the two log2fc ratios. GSEA analysis was performed on the UR (upper right) quadrant and the LR (lower right) quadrant.

Figure 3: FCGR2a variants significantly affect the platelet transcriptome. (A) The WGCNA module enriched for FCR activation represented as boxplots when our cohort is split by (1) SLE vs control, (2) control vs SLEDAI groups, and (2) SLE and Control cohorts split by their FCGR2a genotype. (B) Heatmap of differentially expressed genes derived from the pairwise comparisons of our three genotype groups: Homozygous R131, Heterozygous R131, and Homozygous H131. The table shows number of genes up in the respective group for each pairwise comparison (e.g. Homozygous R131 / Homozygous H131 had 472 genes upregulated in Homo R131, and 663 genes upregulated in Homo H131) (C) WGCNA Modules for Platelet Activation and Leukocyte Mediated Response when split by diagnosis and FCGR2a genotype.


Disclosures: M. Cornwell, None; H. EL Bannoudi, None; E. Luttrell-Williams, None; K. Myndzar, None; A. Engel, None; P. Izmirly, Momenta/Janssen, 1; H. Belmont, Alexion, 6; R. Clancy, None; J. Berger, None; K. Ruggles, None; J. Buyon, Bristol Myers Squibb, 1, GlaxoSmithKline, 2, Janssen, 2, Ventus, 2, Equillium, 2, Dagamma, 12, In-kind donation of fetal heart rate monitors for clinical trial.

To cite this abstract in AMA style:

Cornwell M, EL Bannoudi H, Luttrell-Williams E, Myndzar K, Engel A, Izmirly P, Belmont H, Clancy R, Berger J, Ruggles K, Buyon J. Modeling of Clinical Phenotypes in SLE Based on Platelet Transcriptomic Analysis and FCGR2a Biallelic Variants [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 9). https://acrabstracts.org/abstract/modeling-of-clinical-phenotypes-in-sle-based-on-platelet-transcriptomic-analysis-and-fcgr2a-biallelic-variants/. Accessed .
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