Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications.

Background: Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients.

Differential regulation of the interferon response in systemic lupus erythematosus distinguishes patients of Asian ancestry.

Objectives: Type I interferon (IFN) plays a role in the pathogenesis of systemic lupus erythematosus (SLE), but insufficient attention has been directed to the differences in IFN responses between ancestral populations. Here, we explored the expression of the interferon gene signatures (IGSs) in SLE patients of European ancestry (EA) and Asian ancestry (AsA).

Methods: We used gene set variation analysis with multiple IGS encompassing the response to both type 1 and type 2 IFN in isolated CD14+ monocytes, CD19+B cells, CD4+T cells and Natural Killer (NK) cells from patients with SLE stratified by self-identified ancestry.

Molecular pathways identified from single nucleotide polymorphisms demonstrate mechanistic differences in systemic lupus erythematosus patients of Asian and European ancestry.

Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disorder with a prominent genetic component. Individuals of Asian-Ancestry (AsA) disproportionately experience more severe SLE compared to individuals of European-Ancestry (EA), including increased renal involvement and tissue damage. However, the mechanisms underlying elevated severity in the AsA population remain unclear.

Abnormalities in intron retention characterize patients with systemic lupus erythematosus.

Regulation of intron retention (IR), a form of alternative splicing, is a newly recognized checkpoint in gene expression. Since there are numerous abnormalities in gene expression in the prototypic autoimmune disease systemic lupus erythematosus (SLE), we sought to determine whether IR was intact in patients with this disease. We, therefore, studied global gene expression and IR patterns of lymphocytes in SLE patients.

Molecular mechanisms governing the progression of nephritis in lupus prone mice and human lupus patients.

Introduction: Pathologic inflammation is a major driver of kidney damage in lupus nephritis (LN), but the immune mechanisms of disease progression and risk factors for end organ damage are poorly understood.

Methods: To characterize molecular profiles through the development of LN, we carried out gene expression analysis of microdissected kidneys from lupus-prone NZM2328 mice. We examined male mice and the congenic NZM2328.

Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features.

The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients.

Molecular endotypes of type 1 and type 2 SLE.

Objective: To character the molecular landscape of patients with type 1 and type 2 SLE by analysing gene expression profiles from peripheral blood.

Methods: Full transcriptomic RNA sequencing was carried out on whole blood samples from 18 subjects with SLE selected by the presence of manifestations typical of type 1 and type 2 SLE. The top 5000 row variance genes were analysed by Multiscale Embedded Gene Co-expression Network Analysis to generate gene co-expression modules that were functionally annotated and correlated with various demographic traits, clinical features and laboratory measures.

COVID-19 patients exhibit unique transcriptional signatures indicative of disease severity.

COVID-19 manifests a spectrum of respiratory symptoms, with the more severe often requiring hospitalization. To identify markers for disease progression, we analyzed longitudinal gene expression data from patients with confirmed SARS-CoV-2 infection admitted to the intensive care unit (ICU) for acute hypoxic respiratory failure (AHRF) as well as other ICU patients with or without AHRF and correlated results of gene set enrichment analysis with clinical features. The results were then compared with a second dataset of COVID-19 patients separated by disease stage and severity.

Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases.

Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML.

Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting.

Systemic lupus erythematosus (SLE) is a chronic, multisystem, autoimmune inflammatory disease with genomic and non-genomic contributions to risk. We hypothesize that epigenetic factors are a significant contributor to SLE risk and may be informative for identifying pathogenic mechanisms and therapeutic targets. To test this hypothesis while controlling for genetic background, we performed an epigenome-wide analysis of DNA methylation in genomic DNA from whole blood in three pairs of female monozygotic (MZ) twins of European ancestry, discordant for SLE.

Altered expression of genes controlling metabolism characterizes the tissue response to immune injury in lupus.

To compare lupus pathogenesis in disparate tissues, we analyzed gene expression profiles of human discoid lupus erythematosus (DLE) and lupus nephritis (LN). We found common increases in myeloid cell-defining gene sets and decreases in genes controlling glucose and lipid metabolism in lupus-affected skin and kidney. Regression models in DLE indicated increased glycolysis was correlated with keratinocyte, endothelial, and inflammatory cell transcripts, and decreased tricarboxylic (TCA) cycle genes were correlated with the keratinocyte signature.

Expression of Human Endogenous Retroviruses in Systemic Lupus Erythematosus: Multiomic Integration With Gene Expression.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies predominantly to nuclear material. Many aspects of disease pathology are mediated by the deposition of nucleic acid containing immune complexes, which also induce the type 1interferon response, a characteristic feature of SLE. Notably, SLE is remarkably heterogeneous, with a variety of organs involved in different individuals, who also show variation in disease severity related to their ancestries.

Transcriptome and IgH Repertoire Analyses Show That CD11c B Cells Are a Distinct Population With Similarity to B Cells Arising in Autoimmunity and Infection.

A distinct B cell population marked by elevated CD11c expression is found in patients with systemic lupus erythematosus (SLE). Cells with a similar phenotype have been described during chronic infection, but variable gating strategies and nomenclature have led to uncertainty of their relationship to each other. We isolated CD11c cells from peripheral blood and characterized them using transcriptome and IgH repertoire analyses.

Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway.

SARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients.

Gut Microbiota and Bacterial DNA Suppress Autoimmunity by Stimulating Regulatory B Cells in a Murine Model of Lupus.

Autoimmune diseases, such as systemic lupus erythematosus, are characterized by excessive inflammation in response to self-antigens. Loss of appropriate immunoregulatory mechanisms contribute to disease exacerbation. We previously showed the suppressive effect of vancomycin treatment during the "active-disease" stage of lupus.

Analysis of gene expression from systemic lupus erythematosus synovium reveals myeloid cell-driven pathogenesis of lupus arthritis.

Arthritis is a common manifestation of systemic lupus erythematosus (SLE) yet understanding of the underlying pathogenic mechanisms remains incomplete. We, therefore, interrogated gene expression profiles of SLE synovium to gain insight into the nature of lupus arthritis (LA), using osteoarthritis (OA) and rheumatoid arthritis (RA) as comparators. Knee synovia from SLE, OA, and RA patients were analyzed for differentially expressed genes (DEGs) and also by Weighted Gene Co-expression Network Analysis (WGCNA) to identify modules of highly co-expressed genes.

Patient ancestry significantly contributes to molecular heterogeneity of systemic lupus erythematosus.

Gene expression signatures can stratify patients with heterogeneous diseases, such as systemic lupus erythematosus (SLE), yet understanding the contributions of ancestral background to this heterogeneity is not well understood. We hypothesized that ancestry would significantly influence gene expression signatures and measured 34 gene modules in 1566 SLE patients of African ancestry (AA), European ancestry (EA), or Native American ancestry (NAA). Healthy subject ancestry-specific gene expression provided the transcriptomic background upon which the SLE patient signatures were built.

Selective Histone Deacetylase 6 Inhibition Normalizes B Cell Activation and Germinal Center Formation in a Model of Systemic Lupus Erythematosus.

Autoantibody production by plasma cells (PCs) plays a pivotal role in the pathogenesis of systemic lupus erythematosus (SLE). The molecular pathways by which B cells become pathogenic PC secreting autoantibodies in SLE are incompletely characterized. Histone deactylase 6 (HDAC6) is a unique cytoplasmic HDAC that modifies the interaction of a number of tubulin- associated proteins; inhibition of HDAC6 has been shown to be beneficial in murine models of SLE, but the downstream pathways accounting for the therapeutic benefit have not been clearly delineated.

Identification and Characterization of Post-activated B Cells in Systemic Autoimmune Diseases.

Autoimmune diseases (AID) such as systemic lupus erythematosus (SLE), primary Sjögren's syndrome (pSS), and rheumatoid arthritis (RA) are chronic inflammatory diseases in which abnormalities of B cell function play a central role. Although it is widely accepted that autoimmune B cells are hyperactive , a full understanding of their functional status in AID has not been delineated. Here, we present a detailed analysis of the functional capabilities of AID B cells and dissect the mechanisms underlying altered B cell function.

Machine learning approaches to predict lupus disease activity from gene expression data.

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms.

Gene expression analysis delineates the potential roles of multiple interferons in systemic lupus erythematosus.

A role for interferon (IFN) in systemic lupus erythematosus (SLE) pathogenesis is inferred from the prominent IFN gene signature (IGS), but the major IFN species and its relationship to disease activity are unknown. A bioinformatic approach employing individual IFN species gene signatures to interrogate SLE microarray datasets demonstrates a putative role for numerous IFN species, with prominent expression of IFNB1 and IFNW signatures. In contrast with other SLE-affected organs, the IGS is less prominent in lupus nephritis.

Genomic Identification of Low-Density Granulocytes and Analysis of Their Role in the Pathogenesis of Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the presence of low-density granulocytes (LDGs) with a heightened capacity for spontaneous NETosis, but the contribution of LDGs to SLE pathogenesis remains unclear. To characterize LDGs in human SLE, gene expression profiles derived from isolated LDGs were characterized by weighted gene coexpression network analysis, and a 92-gene module was identified. The LDG gene signature was enriched in genes related to neutrophil degranulation and cell cycle regulation.

Identification of alterations in macrophage activation associated with disease activity in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is characterized by abnormalities in B cell and T cell function, but the role of disturbances in the activation status of macrophages (Mϕ) has not been well described in human patients. To address this, gene expression profiles from isolated lymphoid and myeloid populations were analyzed to identify differentially expressed (DE) genes between healthy controls and patients with either inactive or active SLE. While hundreds of DE genes were identified in B and T cells of active SLE patients, there were no DE genes found in B or T cells from patients with inactive SLE compared to healthy controls.