Unraveling transcriptomic signatures and dysregulated pathways in systemic lupus erythematosus across disease states

Abstract

Objectives: This study aims to elucidate the transcriptomic signatures and dysregulated pathways in patients with Systemic Lupus Erythematosus (SLE), with a particular focus on those persisting during disease remission.

Methods: We conducted bulk RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from a well-defined cohort comprising 26 remission patients meeting the Low Lupus Disease Activity State (LLDAS) criteria, 76 patients experiencing disease flares, and 15 healthy controls. To elucidate immune signature changes associated with varying disease states, we performed extensive analyses, including the identification of differentially expressed genes and pathways, as well as the construction of protein-protein interaction networks.

Results: Several transcriptomic features recovered during remission compared to the active disease state, including down-regulation of plasma and cell cycle signatures, as well as up-regulation of lymphocytes. However, specific innate immune response signatures, such as the interferon (IFN) signature, and gene modules involved in chromatin structure modification, persisted across different disease states. Drug repurposing analysis revealed certain drug classes that can target these persistent signatures, potentially preventing disease relapse.

Conclusion: Our comprehensive transcriptomic study revealed gene expression signatures for SLE in both active and remission states. The discovery of gene expression modules persisting in the remission stage may shed light on the underlying mechanisms of vulnerability to relapse in these patients, providing valuable insights for their treatment.

Keywords: Gene signatures; PBMC; Remission; Systemic lupus erythematosus.

Fig. 1

Transcriptomic profiling of SLE patients. A Study design and workflow of the study. B Principal component analysis. C Volcano plot of the differentially expressed genes in Active Patients vs Healthy Controls (left), Remission Patients vs Healthy Controls (middle), Remission Patients vs Active Patients (right). (AP: Active Patient, RP: Remission Patient, HC: Healthy Control)

Fig. 2

Molecular signatures in the SLE patients in different disease states. A GO enrichment analysis of the DEGs among the three groups. B Heatmap of the differentially expressed immune-related pathways identified from GSVA analysis. The top panel contains the pathways that are down-regulated in active patients and recovered in remission patients. The middle panel contains the pathways that are up-regulated in active patients and down-regulated in remission patients. The bottom panel contains the pathways that are up-regulated in active patients and remained up-regulated in remission patients (AP: Active Patient, RP: Remission Patient, HC: Healthy Control). C Boxplot of the clinical records compared between active patients and remission patients

Fig. 3

Correlation between transcriptomic signatures and clinical features with Pearson coefficient and p-value shown. A Positive correlation of plasma cell signature with SLEDAI score and IgG level. B No significant correlation observed between IFN/Neutrophil signatures and SLEDAI score. C Negative correlation of plasma cell signature with C3 and C4 level. D Negative correlation of Th2/Th17 signatures with SLEDAI score. E-F Positive correlation of fatty acid beta oxidation, amino acid metabolism, mitochondrial translation, glycolysis signatures with SLEDAI score

Fig. 4

Stratification of the genes dysregulated in active SLE patients. A-B Overview and examples of genes altered in active SLE patients. There are 1721 genes up-regulated in the active patients, of which 696 significantly decreased in remission patients and 1025 remained unchanged. There were 928 genes down-regulated in the active patients, of which 372 significantly increased in remission patients and 556 remained unchanged (C) Protein-Protein interaction network and the identification of core modules of the genes persistently up-regulated in the remission patients compared to the healthy control. D Protein-Protein interaction network and the core modules of the genes down-regulated in the remission patients comparing to the active patients

Fig. 5

Expression level of the functional modules identified from PPI analysis throughout the disease course. A Violin plot shows the expression level of persistent IFN module, persistent transcriptional regulation module, recovered-down cell cycle module, recovered-down immunoglobulin module among healthy, active patients and remission patients. B Boxplot illustrates the expression changes of the functional modules in paired samples among different disease stages such as active disease, after intensive hospital treatment, and during remission

Fig. 6

Connectivity Map Drug Repurposing Analysis. A Heatmap of the enrichment score of the top 10 perturbagens in 9 cell lines. B Sankey diagram of the top 10 molecules predicted to reverse the expression of the genes in the persistent up core modules with their mechanisms of actions