Proportions of B-cell subsets are altered in incomplete systemic lupus erythematosus and correlate with interferon score and IgG levels

A comprehensive exploration of the heterogeneity of immune cells in Han and Zang systemic lupus erythematosus patients via single-cell RNA sequencing

Systemic Lupus Erythematosus (SLE) is an autoimmune sickness with unclear pathogenesis. The goal of this research was to reveal the heterogeneity of immune cells in SLE patients of Han and Zang nationality by single-cell RNA sequencing (scRNA-seq) and bioinformatics profiling.

METHODS

A total of 94,102 peripheral blood mononuclear cells (PBMCs) from six volunteers with SLE (3 Zang, 3 Han) and six healthy controls were first conducted through scRNA-seq analysis. The immune cell subsets in the pathogenesis of SLE were analyzed as well. Real-time quantitative PCR (RT-qPCR) was applied to confirm the results of sc-RNA seq analysis.

RESULTS

For the Tibetan samples, the ratios of Naïve CD4 RPS4Y1 cells, Naïve CD4 cells, Memory BC CD24 and Memory BC differed significantly between the SLE and control samples, while that of CD8 CTL MAL cells was significantly different between the two groups in Han nationality samples. Variable differentiation states of CD8 CTL MAL cells, CD8 CTL GZMK cells, and Naïve CD4 cells were detected through pseudotime analysis. Moreover, T-cell receptor (TCR) abundance was notably higher in Tibetan SLE specimens than that in controls, while B-cell receptor (BCR) abundance in Tibetan and Han samples was higher than in control groups.

CONCLUSIONS

In summary, the immune cellular heterogeneity of SLE patients both Han and Zang nationality was explored based on various bioinformatics approaches, providing new perspectives for immunological characteristics of SLE among different ethnic groups.

Screening Biomarkers for Systemic Lupus Erythematosus Based on Machine Learning and Exploring Their Expression Correlations With the Ratios of Various Immune Cells

Background

Systemic lupus erythematosus (SLE) is an autoimmune illness caused by a malfunctioning immunomodulatory system. China has the second highest prevalence of SLE in the world, from 0.03% to 0.07%. SLE is diagnosed using a combination of immunological markers, clinical symptoms, and even invasive biopsy. As a result, genetic diagnostic biomarkers for SLE diagnosis are desperately needed.

Method

From the Gene Expression Omnibus (GEO) database, we downloaded three array data sets of SLE patients' and healthy people's peripheral blood mononuclear cells (PBMC) (GSE65391, GSE121239 and GSE61635) as the discovery metadata (n_SLE = 1315, n_normal = 122), and pooled four data sets (GSE4588, GSE50772, GSE99967, and GSE24706) as the validate data set (n_SLE = 146, n_normal = 76). We screened the differentially expressed genes (DEGs) between the SLE and control samples, and employed the least absolute shrinkage and selection operator (LASSO) regression, and support vector machine recursive feature elimination (SVM-RFE) analyze to discover possible diagnostic biomarkers. The candidate markers' diagnostic efficacy was assessed using the receiver operating characteristic (ROC) curve. The reverse transcription quantitative polymerase chain reaction (RT-qPCR) was utilized to confirm the expression of the putative biomarkers using our own Chinese cohort (n_SLE = 13, n_normal = 10). Finally, the proportion of 22 immune cells in SLE patients was determined using the CIBERSORT algorithm, and the correlations between the biomarkers' expression and immune cell ratios were also investigated.

Results

We obtained a total of 284 DEGs and uncovered that they were largely involved in several immune relevant pathways, such as type І interferon signaling pathway, defense response to virus, and inflammatory response. Following that, six candidate diagnostic biomarkers for SLE were selected, namely ABCB1, EIF2AK2, HERC6, ID3, IFI27, and PLSCR1, whose expression levels were validated by the discovery and validation cohort data sets. As a signature, the area under curve (AUC) values of these six genes reached to 0.96 and 0.913, respectively, in the discovery and validation data sets. After that, we checked to see if the expression of ABCB1, IFI27, and PLSCR1 in our own Chinese cohort matched that of the discovery and validation sets. Subsequently, we revealed the potentially disturbed immune cell types in SLE patients using the CIBERSORT analysis, and uncovered the most relevant immune cells with the expression of ABCB1, IFI27, and PLSCR1.

Conclusion

Our study identified ABCB1, IFI27, and PLSCR1 as potential diagnostic genes for Chinese SLE patients, and uncovered their most relevant immune cells. The findings in this paper provide possible biomarkers for diagnosing Chinese SLE patients.

The Role of Clinical Features and Serum Biomarkers in Identifying Patients with Incomplete Lupus Erythematosus at Higher Risk of Transitioning to Systemic Lupus Erythematosus: Current Perspectives

Discovery of antinuclear antibodies (ANA) enabled earlier diagnosis of systemic lupus erythematosus (SLE) and other ANA⁺ connective tissue diseases (CTD). Rheumatologists increasingly encounter high referral volume of ANA⁺ patients. It has been estimated that only a small percentage of these patients will eventually transition to either SLE or other specified CTD. Incomplete lupus erythematosus (ILE) has been defined as a subset of patients who have some SLE-specific clinical manifestations but do not meet currently accepted classification criteria for SLE. Several studies have been performed with the goal of identifying clinical features, serum and tissue biomarkers that can distinguish those patients with ILE at risk of transitioning to SLE from those who will not. Increased autoantibody diversity, presence of anti-double-stranded DNA (dsDNA) antibodies, high expression of type I and type II interferon (IFN)-gene products, increased serum levels of B-cell-activating factor of the TNF family (BAFF), and certain serum cytokines and complement products have been identified as markers with positive predictive value, particularly when combined together. Once this patient population is better characterized biochemically, clinical trials should be considered with the primary objective to completely halt or slow down the transition from ILE to SLE. Hydroxychloroquine (HCQ) appears to be a promising agent due to its good tolerability and low toxicity profile and open-label studies in ILE patients have already shown its ability to delay the onset of SLE. Other therapeutics, like those targeting abnormal type I and type II IFN-signatures, B-cell specific signaling pathways, complement activation pathways and high BAFF levels should also be evaluated, but the risk to benefit ratio must be carefully determined before they can be considered.