Patient ancestry significantly contributes to molecular heterogeneity of systemic lupus erythematosus

The role of interferon signaling in neurodegeneration and neuropsychiatric disorders

Recent advances in transcriptomics research have uncovered heightened interferon (IFN) responses in neurodegenerative diseases including Alzheimer's disease, primary tauopathy, Parkinson's disease, TDP-43 proteinopathy, and related mouse models. Augmented IFN signaling is now relatively well established for microglia in these contexts, but emerging work has highlighted a novel role for IFN-responsive T cells in the brain and peripheral blood in some types of neurodegeneration. These findings complement a body of literature implicating dysregulated IFN signaling in neuropsychiatric disorders including major depression and post-traumatic stress disorder. In this review, we will characterize and integrate advances in our understanding of IFN responses in neurodegenerative and neuropsychiatric disease, discuss how sex and ancestry modulate the IFN response, and examine potential mechanistic explanations for the upregulation of antiviral-like IFN signaling pathways in these seemingly non-viral neurological and psychiatric disorders.

Early human migration determines the risk of being attacked by wolves: ethnic gene diversity on the development of systemic lupus erythematosus

The prevalence of systemic lupus erythematosus (SLE) varies significantly based on ethnicity rather than geographic distribution; thus, the prevalence is higher in Asian, Hispanic, and Black African populations than in European populations. The risk of developing lupus nephritis (LN) is the highest among Asian populations. Therefore, we hypothesize that human genetic diversity between races has occurred through the early human migration and human genetic adaptation to various environments, with a particular focus on pathogens. Additionally, we compile the currently available evidence on the ethnic gene diversity of SLE and how it relates to disease severity. The human leukocyte antigen (HLA) locus is well established as associated with susceptibility to SLE; specific allele distributions have been observed across diverse populations. Notably, specific amino acid residues within these HLA loci demonstrate significant associations with SLE risk. The non-HLA genetic loci associated with SLE risk also varies across diverse ancestries, implicating distinct immunological pathways, such as the type-I interferon and janus kinase-signal transducers and activators of transcription (JAK-STAT) pathways in Asians, the type-II interferon signaling pathway in Europeans, and B cell activation pathway in Africans. Furthermore, assessing individual genetic susceptibility using genetic risk scores (GRS) for SLE helps to reveal the diverse prevalence, age of onset, and clinical phenotypes across different ethnicities. A higher GRS increases the risk of LN and the severity of SLE. Therefore, understanding ethnic gene diversity is crucial for elucidating disease mechanisms and SLE severity, which could enable the development of novel drugs specific to each race.

Blood RNA-sequencing across the continuum of ANA-positive autoimmunity reveals insights into initiating immunopathology

OBJECTIVE

Mechanisms underpinning clinical evolution to systemic lupus erythematosus (SLE) from preceding antinuclear antibodies (ANA) positivity are poorly understood. This study aimed to understand blood immune cell transcriptional signatures associated with subclinical ANA positivity, and progression or non-progression to SLE.

METHODS

Bulk RNA-sequencing of peripheral blood mononuclear cells isolated at baseline from 35 ANA positive (ANA+) subjects with non-diagnostic symptoms was analysed using differential gene expression, weighted gene co-expression network analysis, deconvolution of cell subsets and functional enrichment analyses. ANA+ subjects, including those progressing to classifiable SLE at 12 months (n=15) and those with stable subclinical ANA positivity (n=20), were compared with 15 healthy subjects and 18 patients with SLE.

RESULTS

ANA+ subjects demonstrated extensive transcriptomic dysregulation compared with healthy controls with reduced CD4+naïve T-cells and resting NK cells, but higher activated dendritic cells. B-cell lymphopenia was evident in SLE but not ANA+ subjects. Two-thirds of dysregulated genes were common to ANA+ progressors and non-progressors. ANA+ progressors showed elevated modular interferon signature in which constituent genes were inducible by both type I interferon (IFN-I) and type II interferon (IFN-II) in vitro. Baseline downregulation of mitochondrial oxidative phosphorylation complex I components significantly associated with progression to SLE but did not directly correlate with IFN modular activity. Non-progressors demonstrated more diverse cytokine profiles.

CONCLUSIONS

ANA positivity, irrespective of clinical trajectory, is profoundly dysregulated and transcriptomically closer to SLE than to healthy immune function. Metabolic derangements and IFN-I activation occur early in the ANA+ preclinical phase and associated with diverging transcriptomic profiles which distinguish subsequent clinical evolution.

EBNA-1 antibody and autoimmune rheumatic diseases: A Mendelian Randomization Study

Background

Numerous studies have investigated a possible correlation between Epstein-Barr virus (EBV) and autoimmune rheumatic diseases (ARDs). However, establishing a cause-and-effect relationship remains a challenging endeavor. This study employs Mendelian randomization to examine the impact of EBV nuclear antigen-1 antibody (EBNA-1) antibody levels on the susceptibility to nine distinct ARDs, including rheumatoid arthritis (RA), primary Sjogren's syndrome (PSS), systemic lupus erythematosus (SLE), undifferentiated reactive arthritis (UA), systemic sclerosis (SSc), adult-onset Still's disease (AOSD), psoriatic arthritis (PsA), dermatomyositis (DM), and ankylosing spondylitis (AS).

Methods

The researchers applied a two-sample Mendelian randomization approach, utilizing online data from separate cohorts of European descent. We drew upon data from GWAS related to EBNA-1 antibody levels and the nine autoimmune-related disorders. Our primary analyses predominantly relied on the Inverse Variance Weighted methodology, complemented by a range of sensitivity assessments.

Results

Our analysis revealed significant direct associations between EBNA-1 antibody levels and the risk of developing PSS (95 % CI: 0.44 to 0.85, p = 0.003), PsA (95 % CI: 0.36 to 0.99, p = 0.044), AS (95 % CI: 0.07 to 0.88, p = 0.031), and UA (95 % CI: 0.56 to 0.96, p = 0.025). These results remained consistent through comprehensive sensitivity analyses. However, no clear associations were found for the other specified conditions.

Conclusions

Our findings provide compelling evidence that EBNA-1 antibody levels play a role in developing ARDs. These findings enhance our understanding of ARD pathogenesis and hold substantial promise for developing potential treatment strategies.

Unique Interplay Between Antinuclear Antibodies and Nuclear Molecules in the Pathogenesis of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a prototypic autoimmune disease that primarily affects young women and causes a wide range of inflammatory manifestations. The hallmark of SLE is the production of antibodies to components of the cell nucleus (antinuclear antibodies [ANAs]). These antibodies can bind to DNA, RNA, and protein complexes with nucleic acids. Among ANAs, antibodies to DNA (anti-DNA) are markers for classification and disease activity, waxing and waning disease activity in many patients. In the blood, anti-DNA antibodies can bind to DNA to form immune complexes with two distinct roles in pathogenesis: (1) renal deposition to provoke nephritis and (2) stimulation of cytokine production following uptake into innate immune cells and interaction with internal nucleic acid sensors. These sensors are part of an internal host defense system in the cell cytoplasm that can respond to DNA from infecting organisms; during cell stress, DNA from nuclear and mitochondrial sources can also trigger these sensors. The formation of immune complexes requires a source of extracellular DNA in an immunologically accessible form. As shown in in vivo and in vitro systems, extracellular DNA can emerge from dead and dying cells in both a free and a particulate form. Neutrophils undergoing the process of NETosis can release DNA in mesh-like structures called neutrophil extracellular traps. In SLE, therefore, the combination of ANAs and immunologically active DNA can create new structures that can promote inflammation throughout the body as well as drive organ inflammation and damage.

KLF2 enhancer variant rs4808485 increases lupus risk by modulating inflammasome machinery and cellular homoeostasis

OBJECTIVE

A recent genome-wide association study linked KLF2 as a novel Asian-specific locus for systemic lupus erythematosus (SLE) susceptibility. However, the underlying causal functional variant(s), cognate target gene(s) and genetic mechanisms associated with SLE risk are unknown.

METHODS

We used bioinformatics to prioritise likely functional variants and validated the best candidate with diverse experimental techniques, including genome editing. Gene expression was compared between healthy controls (HCs) and patients with SLE with or without lupus nephritis (LN+, LN-).

RESULTS

Through bioinformatics and expression quantitative trait locus analyses, we prioritised rs4808485 in active chromatin, predicted to modulate KLF2 expression. Luciferase reporter assays and chromatin immunoprecipitation-qPCR demonstrated differential allele-specific enhancer activity and binding of active histone marks (H3K27ac, H3K4me3 and H3K4me1), Pol II, CTCF, P300 and the transcription factor PARP1. Chromosome conformation capture-qPCR revealed long-range chromatin interactions between rs4808485 and the KLF2 promoter. These were directly validated by CRISPR-based genetic and epigenetic editing in Jurkat and lymphoblastoid cells. Deleting the rs4808485 enhancer in Jurkat (KO) cells disrupted NLRP3 inflammasome machinery by reducing KLF2 and increasing CASPASE1, IL-1β and GSDMD levels. Knockout cells also exhibited higher proliferation and cell-cycle progression than wild type. RNA-seq validated interplay between KLF2 and inflammasome machinery in HC, LN+ and LN-.

CONCLUSIONS

We demonstrate how rs4808485 modulates the inflammasome and cellular homoeostasis through regulating KLF2 expression. This establishes mechanistic connections between rs4808485 and SLE susceptibility.

Targeting Interferon Signalling in Systemic Lupus Erythematosus: Lessons Learned

The development of new medicines for systemic lupus erythematosus (SLE) has not addressed unmet clinical need, with only three drugs receiving regulatory approval for SLE in the last 60 years, one of which was specifically licensed for lupus nephritis. In the last 20 years it has become clear that activation of type 1 interferons (IFN) is reproducibly detected in the majority of SLE patients, and the actions of IFN in the immune system and on target tissues is consistent with a pathogenic role in SLE. These findings led to considerable drug discovery activity, first with agents directly targeting IFN family cytokines, with results that were encouraging but underwhelming. In contrast, targeting the type I IFN receptor with the monoclonal antibody anifrolumab, thereby blocking all IFN family members, was effective in a phase II clinical trial. This led to a pair of phase III trials, one of which was negative and the other positive, reflecting the difficulty of obtaining outcomes from trials in this complex disease. Nonetheless, the balance of evidence resulted in approval of anifrolumab in multiple jurisdictions from 2021 onwards. Multiple approaches to targeting the type 1 IFN pathway have subsequently had positive phase II clinical trials, including antibodies targeting cells that produce IFN, and small molecules targeting the receptor kinase TYK2, required for IFN signalling. Despite multiple hurdles, it is clear that IFN targeting in SLE is here to stay. The story of IFN-targeting therapy in SLE has lessons for drug development overall in this disease.

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

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.

Different Immunologic Profiles Are Associated With Distinct Clinical Phenotypes in Longitudinally Observed Patients With Systemic Lupus Erythematosus

OBJECTIVE

The aim of this study was to determine the immunologic profile associated with disease flares in patients with systemic lupus erythematosus (SLE) and to investigate the clinical significance of any differences observed between patients during and following a flare.

METHODS

Multiparameter flow cytometry was used to examine 47 immune populations within the peripheral blood of 16 healthy controls, 25 patients with clinically quiescent SLE, and 46 patients with SLE experiencing a flare at baseline and at 6- and 12-month follow-up visits. Unsupervised clustering was used to identify patients with similar immune profiles and to track changes over time. Parametric or nonparametric statistics were used when appropriate to assess the association of cellular phenotypes with clinical and laboratory parameters.

RESULTS

Five clusters of patients were identified that variably contained patients with active and quiescent SLE, and that had distinct clinical phenotypes. Patients characterized by increased T peripheral helper, activated B, and age-associated B cells were the most likely to be flaring at baseline, as well as the most likely to remain active or flare over the subsequent year if they acquired or retained this phenotype at follow-up. In contrast, patients who had increased T helper (Th) cells in the absence of B cell changes, or who had increased Th1 cells and innate immune populations, mostly developed quiescent SLE on follow-up. A significant proportion of patients with SLE had depletion of many immune populations at flare and only showed increases in these populations post-flare.

CONCLUSION

Cellular phenotyping of patients with SLE reveals several distinct immunologic profiles that may help to stratify patients with regard to prognosis and treatment.

Local Ancestry at the Major Histocompatibility Complex Region is Not a Major Contributor to Disease Heterogeneity in a Multiethnic Lupus Cohort

OBJECTIVE

Systemic lupus erythematosus (SLE) is an autoimmune disease resulting in debilitating clinical manifestations that vary in severity by race and ethnicity with a disproportionate burden in African American, Mestizo, and Asian populations compared with populations of European descent. Differences in global and local genetic ancestry may shed light on the underlying mechanisms contributing to these disparities, including increased prevalence of lupus nephritis, younger age of symptom onset, and presence of autoantibodies.

METHODS

A total of 1,139 European, African American, and Mestizos patients with SLE were genotyped using the Affymetrix LAT1 World array. Global ancestry proportions were estimated using ADMIXTURE, and local ancestry was estimated using RFMIXv2.0. We investigated associations between lupus nephritis, age at onset, and autoantibody status with both global and local ancestry proportions within the Major Histocompatibility Complex region.

RESULTS

Our results showed small effect sizes that did not meet the threshold for statistical significance for global or local ancestry proportions in either African American or Mestizo patients with SLE who presented with the clinical manifestations of interest compared with those who did not.

CONCLUSION

These findings suggest that local genetic ancestry within the Major Histocompatibility Complex region is not a major contributor to these SLE manifestations among patients with SLE from admixed populations.

Prevalence of concomitant rheumatologic diseases and autoantibody specificities among racial and ethnic groups in SLE patients

Objective

Leveraging the Manhattan Lupus Surveillance Program (MLSP), a population-based registry of cases of systemic lupus erythematosus (SLE) and related diseases, we investigated the proportion of SLE with concomitant rheumatic diseases, including Sjögren's disease (SjD), antiphospholipid syndrome (APLS), and fibromyalgia (FM), as well as the prevalence of autoantibodies in SLE by sex and race/ethnicity.

Methods

Prevalent SLE cases fulfilled one of three sets of classification criteria. Additional rheumatic diseases were defined using modified criteria based on data available in the MLSP: SjD (anti-SSA/Ro positive and evidence of keratoconjunctivitis sicca and/or xerostomia), APLS (antiphospholipid antibody positive and evidence of a blood clot), and FM (diagnosis in the chart).

Results

1,342 patients fulfilled SLE classification criteria. Of these, SjD was identified in 147 (11.0%, 95% CI 9.2-12.7%) patients with women and non-Latino Asian patients being the most highly represented. APLS was diagnosed in 119 (8.9%, 95% CI 7.3-10.5%) patients with the highest frequency in Latino patients. FM was present in 120 (8.9%, 95% CI 7.3-10.5) patients with non-Latino White and Latino patients having the highest frequency. Anti-dsDNA antibodies were most prevalent in non-Latino Asian, Black, and Latino patients while anti-Sm antibodies showed the highest proportion in non-Latino Black and Asian patients. Anti-SSA/Ro and anti-SSB/La antibodies were most prevalent in non-Latino Asian patients and least prevalent in non-Latino White patients. Men were more likely to be anti-Sm positive.

Conclusion

Data from the MLSP revealed differences among patients classified as SLE in the prevalence of concomitant rheumatic diseases and autoantibody profiles by sex and race/ethnicity underscoring comorbidities associated with SLE.

Systemic lupus in the era of machine learning medicine

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

Translational implications of newly characterized pathogenic pathways in systemic lupus erythematosus

Improved characterization of relevant pathogenic pathways in systemic lupus erythematosus (SLE) has been further delineated over the last decades. This led to the development of targeted treatments including belimumab and anifrolumab, which recently became available in clinics. Therapeutic targets in SLE encompass interferon (IFN) signaling, B-T costimulation including immune checkpoints, and increasing modalities of B lineage targeting, such as chimeric antigen receptor (CAR) T cells directed against CD19 or sequential anti-B cell targeting. Patient profiling based on characterization of underlying molecular abnormalities, often performed through comprehensive omics analyses, has recently been shown to better predict patients' treatment responses and also holds promise to unravel key molecular mechanisms driving SLE. SLE carries two key signatures, namely the IFN and B lineage/plasma cell signatures. Recent advances in SLE treatments clearly indicate that targeting innate and adaptive immunity is successful in such a complex autoimmune disease. Although those signatures may interact at the molecular level and provide the basis for the first selective treatments in SLE, it remains to be clarified whether these distinct treatments show different treatment responses among certain patient subsets. In fact, notwithstanding the remarkable amount of novel clues for innovative SLE treatment, harmonization of big data within tailored treatment strategies will be instrumental to better understand and treat this challenging autoimmune disorder. This review will provide an overview of recent improvements in SLE pathogenesis, related insights by analyses of big data and machine learning as well as technical improvements in conducting clinical trials with the ultimate goal that translational research results in improved patient outcomes.

An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients

Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To address this, we created an interpretable ML approach based on blood transcriptomics to predict phenotype in systemic lupus erythematosus (SLE), a heterogeneous autoimmune disease. We employed a sequential grouped feature importance algorithm to assess the performance of gene sets, including immune and metabolic pathways and cell types, known to be abnormal in SLE in predicting disease activity and organ involvement. Gene sets related to interferon, tumor necrosis factor, the mitoribosome, and T cell activation were the best predictors of phenotype with excellent performance. These results suggest potential relationships between the molecular pathways identified in each model and manifestations of SLE. This ML approach to phenotype prediction can be applied to other diseases and tissues.

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.

METHODS

We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting.

RESULTS

Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab).

CONCLUSIONS

Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.

Erythema multiforme-like lesions in a patient with systemic lupus erythematosus progressing to toxic epidermal necrolysis-like lupus

A 34-year-old woman of African ancestry had a diagnosis of systemic lupus erythematosus that was antinuclear antibodies (> 1 : 640, speckled), anti-U1-ribonucleoprotein, anti-Smith and anti-C1q positive. She presented to hospital with severely painful annular and polycyclic erosions, some with a targetoid-like appearance. After 6 weeks she developed slowly evolving dusky papules/plaques and flaccid bullae, suggestive of toxic epidermal necrolysis-like lupus, and was successfully treated with intravenous rituximab and intravenous immunoglobulin (2 g kg–1 over 3 days).

Glucose oxidation-dependent survival of activated B cells provides a putative novel therapeutic target for lupus treatment

Aberrant metabolic demand is observed in immune/inflammatory disorders, yet the role in pathogenesis remains unclear. Here, we discover that in lupus, activated B cells, including germinal center B (GCB) cells, have remarkably high glycolytic requirement for survival over T cell populations, as demonstrated by increased metabolic activity in lupus-activated B cells compared to immunization-induced cells. The augmented reliance on glucose oxidation makes GCB cells vulnerable to mitochondrial ROS-induced oxidative stress and apoptosis. Short-term glycolysis inhibition selectively reduces pathogenic activated B in lupus-prone mice, extending their lifespan, without affecting T follicular helper cells. Particularly, BCMA-expressing GCB cells rely heavily on glucose oxidation. Depleting BCMA-expressing activated B cells with APRIL-based CAR-T cells significantly prolongs the lifespan of mice with severe autoimmune disease. These results reveal that glycolysis-dependent activated B and GCB cells, especially those expressing BCMA, are potentially key lupus mediators, and could be targeted to improve disease outcomes.

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. The expression of genes upstream of the IGS and influenced by lupus-associated risk alleles was also examined. Lastly, we employed machine learning (ML) models to assess the most important features classifying patients by disease activity.

RESULTS

AsA patients with SLE exhibited greater enrichment in the IFN core and IFNA2 IGS compared with EA patients in all cell types examined and, in the presence and absence of autoantibodies. Overall, AsA patients with SLE demonstrated higher expression of genes upstream of the IGS than EA counterparts. ML with feature importance analysis indicated that IGS expression in NK cells, anti-dsDNA, complement levels and AsA status contributed to disease activity.

CONCLUSIONS

AsA patients with SLE exhibited higher IGS than EA patients in all cell types regardless of autoantibody status, with enhanced expression of genetically associated genes upstream of the IGS potentially contributing. AsA, along with the IGS in NK cells, anti-dsDNA and complement, independently influenced SLE disease activity.

Ancestry-based differences in the immune phenotype are associated with lupus activity

Systemic lupus erythematosus (SLE) affects 1 in 537 Black women, which is >2-fold more than White women. Black patients develop the disease at a younger age, have more severe symptoms, and have a greater chance of early mortality. We used a multiomics approach to uncover ancestry-associated immune alterations in patients with SLE and healthy controls that may contribute biologically to disease disparities. Cell composition, signaling, epigenetics, and proteomics were evaluated by mass cytometry; droplet-based single-cell transcriptomics and proteomics; and bead-based multiplex soluble mediator levels in plasma. We observed altered whole blood frequencies and enhanced activity in CD8+ T cells, B cells, monocytes, and DCs in Black patients with more active disease. Epigenetic modifications in CD8+ T cells (H3K27ac) could distinguish disease activity level in Black patients and differentiate Black from White patient samples. TLR3/4/7/8/9-related gene expression was elevated in immune cells from Black patients with SLE, and TLR7/8/9 and IFN-α phospho-signaling and cytokine responses were heightened even in immune cells from healthy Black control patients compared with White individuals. TLR stimulation of healthy immune cells recapitulated the ancestry-associated SLE immunophenotypes. This multiomic resource defines ancestry-associated immune phenotypes that differ between Black and White patients with SLE, which may influence the course and severity of SLE and other diseases.

Pathogenesis of systemic lupus erythematosus: risks, mechanisms and therapeutic targets

Research elucidating the pathogenesis of systemic lupus erythematosus (SLE) has defined two critical families of mediators, type I interferon (IFN-I) and autoantibodies targeting nucleic acids and nucleic acid-binding proteins, as fundamental contributors to the disease. On the fertile background of significant genetic risk, a triggering stimulus, perhaps microbial, induces IFN-I, autoantibody production or most likely both. When innate and adaptive immune system cells are engaged and collaborate in the autoimmune response, clinical SLE can develop. This review describes recent data from genetic analyses of patients with SLE, along with current studies of innate and adaptive immune function that contribute to sustained IFN-I pathway activation, immune activation and autoantibody production, generation of inflammatory mediators and tissue damage. The goal of these studies is to understand disease mechanisms, identify therapeutic targets and stimulate development of therapeutics that can achieve improved outcomes for patients.

Artificial Intelligence and laboratory data in rheumatic diseases

Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.

Pathogenesis of autoimmune disease

Autoimmune diseases are a diverse group of conditions characterized by aberrant B cell and T cell reactivity to normal constituents of the host. These diseases occur widely and affect individuals of all ages, especially women. Among these diseases, the most prominent immunological manifestation is the production of autoantibodies, which provide valuable biomarkers for diagnosis, classification and disease activity. Although T cells have a key role in pathogenesis, they are technically more difficult to assay. In general, autoimmune disease results from an interplay between a genetic predisposition and environmental factors. Genetic predisposition to autoimmunity is complex and can involve multiple genes that regulate the function of immune cell populations. Less frequently, autoimmunity can result from single-gene mutations that affect key regulatory pathways. Infection seems to be a common trigger for autoimmune disease, although the microbiota can also influence pathogenesis. As shown in seminal studies, patients may express autoantibodies many years before the appearance of clinical or laboratory signs of disease - a period called pre-clinical autoimmunity. Monitoring autoantibody expression in at-risk populations may therefore enable early detection and the initiation of therapy to prevent or attenuate tissue damage. Autoimmunity may not be static, however, and remission can be achieved by some patients treated with current agents.

Gene Expression and Autoantibody Analysis Revealing Distinct Ancestry-Specific Profiles Associated With Response to Rituximab in Refractory Systemic Lupus Erythematosus

OBJECTIVE

Gene expression profiles are associated with the clinical heterogeneity of systemic lupus erythematosus (SLE) but are not well studied as biomarkers for therapy. We studied gene expression and response to rituximab in a multiethnic UK cohort who were refractory to standard therapy.

METHODS

We evaluated baseline expression levels of transcripts known to associate with clinical features of SLE using a 96-probe TaqMan array and whole blood samples from 213 patients with active SLE who had been prospectively enrolled in the British Isles Lupus Assessment Group (BILAG) Biologics Register. We measured autoantibodies using immunoprecipitation and enzyme-linked immunosorbent assays. We determined responses to first-cycle rituximab at 6 months from treatment start in 110 SLE patients by assessing BILAG 2004 disease activity.

RESULTS

Interferon gene expression scores were lower in patients of European ancestry than in all other ancestry groups. The relationship between blood interferon gene expression scores and scores annotated to plasmablasts, neutrophils, myeloid lineage, inflammation, and erythropoiesis differed between patients of European and non-European ancestries. Hierarchical clustering revealed 3 distinct non-European ancestry patient subsets with stratified responses to rituximab that were not explained by sociodemographic and clinical variables, with responses lowest in an interferon-low, neutrophil-high cluster and highest in a cluster with high expression levels across all signatures (P < 0.001). Clusters in European ancestry patients did not predict response to rituximab but segregated patients by global disease activity and renal involvement. In both ancestral groups, interferon-high clusters were associated with U1 RNP/Sm antibodies.

CONCLUSION

Ancestry appears central to the immunologic and clinical heterogeneity in SLE. These results suggest that ancestry, disease activity, and transcriptional signatures could each assist in predicting the effectiveness of B cell depletion therapies.

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. We analyzed RNA-seq data from peripheral blood T cell samples from 14 patients suffering from systemic lupus erythematosus (SLE) and 4 healthy controls and a second, independent data set of RNA-seq data from B cells from16 SLE patients and 4 healthy controls. We identified intron retention levels from 26,372 well annotated genes as well as differential gene expression and tested for differences between cases and controls using unbiased hierarchical clustering and principal component analysis. We followed with gene-disease enrichment analysis and gene-ontology enrichment analysis. Finally, we then tested for significant differences in intron retention between cases and controls both globally and with respect to specific genes. Overall decreased IR was found in T cells from one cohort and B cells from another cohort of patients with SLE and was associated with increased expression of numerous genes, including those encoding spliceosome components. Different introns within the same gene displayed both up- and down-regulated retention profiles indicating a complex regulatory mechanism. These results indicate that decreased IR in immune cells is characteristic of patients with active SLE and may contribute to the abnormal expression of specific genes in this autoimmune disease.

Autoantibody repertoire characterization provides insight into the pathogenesis of monogenic and polygenic autoimmune diseases

Autoimmune diseases vary in the magnitude and diversity of autoantibody profiles, and these differences may be a consequence of different types of breaks in tolerance. Here, we compared the disparate autoimmune diseases autoimmune polyendocrinopathy-candidiasis-ecto-dermal dystrophy (APECED), systemic lupus erythematosus (SLE), and Sjogren's syndrome (SjS) to gain insight into the etiology of breaks in tolerance triggering autoimmunity. APECED was chosen as a prototypical monogenic disease with organ-specific pathology while SjS and SLE represent polygenic autoimmunity with focal or systemic disease. Using protein microarrays for autoantibody profiling, we found that APECED patients develop a focused but highly reactive set of shared mostly anti-cytokine antibodies, while SLE patients develop broad and less expanded autoantibody repertoires against mostly intracellular autoantigens. SjS patients had few autoantibody specificities with the highest shared reactivities observed against Ro-52 and La. RNA-seq B-cell receptor analysis revealed that APECED samples have fewer, but highly expanded, clonotypes compared with SLE samples containing a diverse, but less clonally expanded, B-cell receptor repertoire. Based on these data, we propose a model whereby the presence of autoreactive T-cells in APECED allows T-dependent B-cell responses against autoantigens, while SLE is driven by breaks in peripheral B-cell tolerance and extrafollicular B-cell activation. These results highlight differences in the autoimmunity observed in several monogenic and polygenic disorders and may be generalizable to other autoimmune diseases.

RBP-RNA interactions in the control of autoimmunity and autoinflammation

Autoimmunity and autoinflammation arise from aberrant immunological and inflammatory responses toward self-components, contributing to various autoimmune diseases and autoinflammatory diseases. RNA-binding proteins (RBPs) are essential for immune cell development and function, mainly via exerting post-transcriptional regulation of RNA metabolism and function. Functional dysregulation of RBPs and abnormities in RNA metabolism are closely associated with multiple autoimmune or autoinflammatory disorders. Distinct RBPs play critical roles in aberrant autoreactive inflammatory responses via orchestrating a complex regulatory network consisting of DNAs, RNAs and proteins within immune cells. In-depth characterizations of RBP-RNA interactomes during autoimmunity and autoinflammation will lead to a better understanding of autoimmune pathogenesis and facilitate the development of effective therapeutic strategies. In this review, we summarize and discuss the functions of RBP-RNA interactions in controlling aberrant autoimmune inflammation and their potential as biomarkers and therapeutic targets.

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.

RESULTS

Expression of specific gene co-expression modules correlated with individual features of type 1 and type 2 SLE and also effectively segregated samples from patients with type 1 SLE from those with type 2 SLE. Unique type 1 SLE enrichment included interferon, monocytes, T cells, cell cycle and neurotransmitter pathways, whereas unique type 2 SLE enrichment included B cells and metabolic and neuromuscular pathways. Gene co-expression modules of patients with type 2 SLE were identified in subsets of previously reported patients with inactive SLE and idiopathic fibromyalgia (FM) and also identified subsets of patients with active SLE with a greater frequency of severe fatigue.

CONCLUSION

Gene co-expression analysis successfully identified unique transcriptional patterns that segregate type 1 SLE from type 2 SLE and further identified type 2 molecular features in patients with inactive SLE or FM and with active SLE with severe fatigue.

Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis

Background

Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support.

Methods

Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated.

Results

Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25.

Conclusion

Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

PURPOSE OF REVIEW

Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care.

RECENT FINDINGS

The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists.

SUMMARY

Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.

The promise of precision medicine in rheumatology

Systemic autoimmune rheumatic diseases (SARDs) exhibit extensive heterogeneity in clinical presentation, disease course, and treatment response. Therefore, precision medicine - whereby treatment is tailored according to the underlying pathogenic mechanisms of an individual patient at a specific time - represents the 'holy grail' in SARD clinical care. Current strategies include treat-to-target therapies and autoantibody testing for patient stratification; however, these are far from optimal. Recent innovations in high-throughput 'omic' technologies are now enabling comprehensive profiling at multiple levels, helping to identify subgroups of patients who may taper off potentially toxic medications or better respond to current molecular targeted therapies. Such advances may help to optimize outcomes and identify new pathways for treatment, but there are many challenges along the path towards clinical translation. In this Review, we discuss recent efforts to dissect cellular and molecular heterogeneity across multiple SARDs and future directions for implementing stratification approaches for SARD treatment in the clinic.

Utility of Baseline Transcriptomic Analysis of Rheumatoid Arthritis Synovium as an Indicator for Long-Term Clinical Outcomes

Objective:

Rheumatoid arthritis is a chronic inflammatory autoimmune disease that can lead to synovial damage, persistent joint pain, and functional disability. Our objective was to evaluate baseline synovial transcriptome from early inflammatory arthritis patients (EIA) and identify pretreatment biomarkers that could potentially provide insights into long-term functional outcomes of rheumatoid arthritis (RA).

Methods

Synovial biopsies from clinically inflamed knee joints were procured from either 17 EIA patients before initiation of disease modifying anti-rheumatic drug (DMARD) therapy (DMARD-naïve EIA) using the minimally invasive closed needle biopsy technique or advanced RA patients undergoing arthroplasty. Affymetrix Human Genome U133 Plus 2 microarray platform was used to profile the synovial transcriptome. The cohort was followed clinically for a median of 12.3 years, and patient data was collected at each visit. Short-term and long-term clinical outcomes were determined by assessing RA-associated clinical parameters Statistical adjustments were made to account for asynchronous clinical visits and duration of follow up.

Results

Based on the transcriptomic analysis, we identified 5 differentially expressed genes (DEGs), including matrix metalloproteinase (MMP)-1 (fibroblast collagenase) and MMP-3 (stromelysin-1) in DMARD-naïve EIA patients, relative to advanced RA patients (q < 0.05). Dichotomous expression of MMP-1 and MMP-3 mRNA and protein was confirmed by qPCR and immunohistochemistry respectively, based on which DMARD-naïve EIA subjects were classified as MMP-high or MMP-low. Hierarchical clustering of transcriptomic data identified 947 DEGs between MMP-high and MMP-low cohorts. Co-expression and IPA analysis of DEGs in the MMP-high cohort showed an enrichment of genes that participated in metabolic or biochemical functions and intracellular immune signaling were regulated through NF-κB and β-catenin complexes and correlated with markers of systemic inflammation. Analysis of short-term clinical outcomes in MMP-high cohort showed a significant reduction in the DAS-CRP scores relative to baseline (P <0.001), whereas area under the curve analyses of modified HAQ (mHAQ) scores correlated negatively with baseline MMP-1 (R = −0.59, P = 0.03). Further, longitudinal mHAQ scores, number of swollen joints, number of DMARDs and median follow-up duration appeared to be higher in MMP-low cohort.

Conclusion

Overall, our results indicate that the gene expression profiling of synovial biopsies obtained at the DMARD-naive stage in patients with EIA categorizes them into subsets with varying degrees of inflammation and can predict the future of long-term clinical outcome.

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. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases have unique profiles of gene expression abnormalities, especially in nonlesional skin, and suggest a model in which disease-specific abnormalities in "prelesional" skin may permit environmental stimuli to trigger inflammatory responses leading to both the unique and shared manifestations of each disease.

Biological impact of iberdomide in patients with active systemic lupus erythematosus

OBJECTIVES

Iberdomide is a high-affinity cereblon ligand that promotes proteasomal degradation of transcription factors Ikaros (IKZF1) and Aiolos (IKZF3). Pharmacodynamics and pharmacokinetics of oral iberdomide were evaluated in a phase 2b study of patients with active systemic lupus erythematosus (SLE).

METHODS

Adults with autoantibody-positive SLE were randomised to placebo (n=83) or once daily iberdomide 0.15 mg (n=42), 0.3 mg (n=82) or 0.45 mg (n=81). Pharmacodynamic changes in whole blood leucocytes were measured by flow cytometry, regulatory T cells (Tregs) by epigenetic assay, plasma cytokines by ultrasensitive cytokine assay and gene expression by Modular Immune Profiling.

RESULTS

Iberdomide exhibited linear pharmacokinetics and dose-dependently modulated leucocytes and cytokines. Compared with placebo at week 24, iberdomide 0.45 mg significantly (p<0.001) reduced B cells, including those expressing CD268 (TNFRSF13C) (-58.3%), and plasmacytoid dendritic cells (-73.9%), and increased Tregs (+104.9%) and interleukin 2 (IL-2) (+144.1%). Clinical efficacy was previously reported in patients with high IKZF3 expression and high type I interferon (IFN) signature at baseline and confirmed here in those with an especially high IFN signature. Iberdomide decreased the type I IFN gene signature only in patients with high expression at baseline (-81.5%; p<0.001) but decreased other gene signatures in all patients.

CONCLUSION

Iberdomide significantly reduced activity of type I IFN and B cell pathways, and increased IL-2 and Tregs, suggesting a selective rebalancing of immune abnormalities in SLE. Clinical efficacy corresponded to reduction of the type I IFN gene signature.

TRIAL REGISTRATION NUMBER

NCT03161483.

Anti-RNP antibodies are associated with the interferon gene signature but not decreased complement levels in SLE

OBJECTIVES

The goals of these studies were to elucidate the inter-relationships of specific anti-nuclear antibody (ANA), complement, and the interferon gene signature (IGS) in the pathogenesis of systemic lupus erythematosus (SLE).

METHODS

Data from the Illuminate trials were analysed for antibodies to dsDNA as well as RNA-binding proteins (RBP), levels of C3, C4 and various IGS. Statistical hypothesis testing, linear regression analyses and classification and regression trees analysis were employed to assess relationships between the laboratory features of SLE.

RESULTS

Inter-relationships of ANAs, complement and the IGS differed between patients of African Ancestry (AA) and European Ancestry (EA); anti-RNP and multiple autoantibodies were more common in AA patients and, although both related to the presence of the IGS, relationships between autoantibodies and complement differed. Whereas, anti-dsDNA had an inverse relationship to C3 and C4, levels of anti-RNP were not related to these markers. The IGS was only correlated with anti-dsDNA in EA SLE and complement was more correlated to the IGS in AA SLE. Finally, autoantibodies occurred in the presence and absence of the IGS, whereas the IGS was infrequent in anti-dsDNA/anti-RBP-negative SLE patients.

CONCLUSION

There is a complex relationship between autoantibodies and the IGS, with anti-RNP associated in AA and both anti-dsDNA and RNP associated in EA. Moreover, there was a difference in the relationship between anti-dsDNA, but not anti-RBP, with complement levels. The lack of a relationship of anti-RNP with C3 and C4 suggests that anti-RNP immune complexes (ICs) may drive the IGS without complement fixation, whereas anti-dsDNA ICs involve complement consumption.

Deconvoluting the heterogeneity of SLE: The contribution of ancestry

Systemic lupus erythematosus (SLE) is a multiorgan autoimmune disorder with a prominent genetic component. Evidence has shown that individuals of non-European ancestry experience the disease more severely, exhibiting an increased incidence of cardiovascular disease, renal involvement, and tissue damage compared with European ancestry populations. Furthermore, there seems to be variability in the response of individuals within different ancestral groups to standard medications, including cyclophosphamide, mycophenolate, rituximab, and belimumab. Although the widespread application of candidate gene, Immunochip, and genome-wide association studies has contributed to our understanding of the link between genetic variation (typically single nucleotide polymorphisms) and SLE, despite decades of research it is still unclear why ancestry remains a key determinant of poorer outcome in non-European-ancestry patients with SLE. Here, we will discuss the impact of ancestry on SLE disease burden in patients from diverse backgrounds and highlight how research efforts using novel bioinformatic and pathway-based approaches have begun to disentangle the complex genetic architecture linking ancestry to SLE susceptibility. Finally, we will illustrate how genomic and gene expression analyses can be combined to help identify novel molecular pathways and drug candidates that might uniquely impact SLE among different ancestral populations.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Sub-setting systemic lupus erythematosus by combined molecular phenotypes defines divergent populations in two phase III randomized trials

OBJECTIVES

Heterogeneity of SLE patients in clinical trials remains a challenge for developing new therapies. This study used a combinatorial analysis of four molecular biomarkers to define key sources of heterogeneity.

METHODS

Combinations of IFN (high/low), anti-dsDNA (+/-) and C3 and C4 (low/normal) were used to subset n = 1747 patients from two randomized phase III trials. A dichotomous classification scheme defined SLE (+) as: IFN (high), anti-dsDNA (+), C3 (low) and/or C4 (low). SLE (-) required all of the following: IFN (low), anti-dsDNA (-), C3 (normal) and C4 (normal). Additional analyses subset the data further by IFN, anti-dsDNA and complement.

RESULTS

The trials enrolled n = 2262 patients of which n = 1747 patients had data for IFN, anti-dsDNA, C3 and C4 at baseline. There were n = 247 patients in the SLE (-) population and n = 1500 patients in the SLE (+) population. The SLE (-) population had more mucocutaneous and musculoskeletal disease at baseline, while SLE (+) had more haematological, renal and vascular involvement. There was lower concomitant medication use in the SLE (-) population for corticosteroids and immunosuppressants, except for MTX. Time to severe flare was significantly longer in SLE (-) vs SLE (+) (P < 0.0001) and SRI-4 response rate was significantly lower in SLE (-) vs SLE (+) (P = 0.00016). The USA had more SLE (-) patients (22%) than Mexico/Central America/South America (10%), Europe (7%) and the rest of the world (5%).

CONCLUSION

Combinatorial analysis of four molecular biomarkers revealed subsets of SLE patients that discriminated by disease manifestations, concomitant medication use, geography, time to severe flare and SRI-4 response. These data may be useful for designing clinical trials and identifying subsets of patients for analysis. Rheumatology key messages SLE patients from a P3 trial were categorized by IFN, anti-dsDNA, C3 and C4 status. Patients lacking molecular markers of SLE distinguished from biomarker positive patients on multiple clinical parameters. Biomarker negative patients have distinct disease characteristics that may impact clinical trial outcomes.

Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus

PURPOSE OF REVIEW

To summarize recent studies stratifying SLE patients into subgroups based on gene expression profiling and suggest future improvements for employing transcriptomic data to foster precision medicine.

RECENT FINDINGS

Bioinformatic & machine learning pipelines have been employed to dissect the transcriptomic heterogeneity of lupus patients and identify more homogenous subgroups. Some examples include the use of unsupervised random forest and k-means clustering to separate adult SLE patients into seven clusters and hierarchical clustering of single-cell RNA-sequencing (scRNA-seq) of immune cells yielding four clusters in a cohort of adult SLE and pediatric SLE participants. Random forest classification of bulk RNA-seq data from sorted blood cells enabled prediction of high or low disease activity in European and Asian SLE patients. Inferred transcription factor activity stratified adult and pediatric SLE into two subgroups.

SUMMARY

Several different endotypes of SLE patients with differing molecular profiles have been reported but a global consensus of clinically actionable groups has not been reached. Moreover, heterogeneity between datasets, reproducibility of predictions as well as the most effective classification approach have not been resolved. Nevertheless, gene expression-based precision medicine remains an attractive option to subset lupus patients.

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. In LN, regression models demonstrated decreased glycolysis and TCA cycle genes were correlated with increased endothelial or decreased kidney cell transcripts, respectively. Less severe glomerular LN exhibited similar alterations in metabolism and tissue cell transcripts before monocyte/myeloid cell infiltration in some patients. Additionally, changes to mitochondrial and peroxisomal transcripts were associated with specific cells rather than global signal changes. Examination of murine LN gene expression demonstrated metabolic changes were not driven by acute exposure to type I interferon and could be restored after immunosuppression. Finally, expression of HAVCR1, a tubule damage marker, was negatively correlated with the TCA cycle signature in LN models. These results indicate that altered metabolic dysfunction is a common, reversible change in lupus-affected tissues and appears to reflect damage downstream of immunologic processes.

Current Status of the Evaluation and Management of Lupus Patients and Future Prospects

The vastly diverse nature of systemic lupus erythematosus (SLE) poses great challenges to clinicians and patients, as well as to research and drug development efforts. Precise management of lupus patients would be advanced by the ability to identify specific abnormalities operative in individual patients at the time of encounter with the clinician. Advances in new technologies and bioinformatics have greatly improved the understanding of the pathophysiology of SLE. Recent research has focused on the discovery and classification of sensitive and specific markers that could aid early accurate diagnosis, better monitoring of disease and identification of appropriate therapy choices based on specific dysregulated molecular pathways. Here, we summarize some of the advances and discuss the challenges in moving toward precise patient-centric management modalities in SLE.

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. Our results indicate that COVID-19 pathogenesis is driven by populations of myeloid-lineage cells with highly inflammatory but distinct transcriptional signatures in each compartment. The relative absence of cytotoxic cells in the lung suggests a model in which delayed clearance of the virus may permit exaggerated myeloid cell activation that contributes to disease pathogenesis by the production of inflammatory mediators. The gene expression profiles also identify potential therapeutic targets that could be modified with available drugs. The data suggest that transcriptomic profiling can provide an understanding of the pathogenesis of COVID-19 in individual patients.

The Autoantigen Repertoire and the Microbial RNP World

Although autoimmunity and autoimmune disease (AID) are relatively common, the repertoire of autoantigens is paradoxically very limited. Highly enriched in this autoantigen repertoire are nucleic acids and their binding proteins, which together form large macromolecular structures. Most of these complexes are of ancient evolutionary origin, with homologs throughout multiple kingdoms of life. Why and if these nucleic acid-protein particles drive the development of autoimmunity remains unresolved. Recent advances in our understanding of the microbiome may provide clues about the origins of autoimmunity - and the particular puzzle of why the autoantigen repertoire is so particularly enriched in ribonucleoprotein particles (RNPs). We discuss the possibility that autoimmunity to some RNPs may arise from molecular mimicry to microbial orthologs.

Analysis of Trans-Ancestral SLE Risk Loci Identifies Unique Biologic Networks and Drug Targets in African and European Ancestries

Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disorder with a prominent genetic component. Individuals of African ancestry (AA) experience the disease more severely and with an increased co-morbidity burden compared to European ancestry (EA) populations. We hypothesize that the disparities in disease prevalence, activity, and response to standard medications between AA and EA populations is partially conferred by genomic influences on biological pathways. To address this, we applied a comprehensive approach to identify all genes predicted from SNP-associated risk loci detected with the Immunochip. By combining genes predicted via eQTL analysis, as well as those predicted from base-pair changes in intergenic enhancer sites, coding-region variants, and SNP-gene proximity, we were able to identify 1,731 potential ancestry-specific and trans-ancestry genetic drivers of SLE. Gene associations were linked to upstream and downstream regulators using connectivity mapping, and predicted biological pathways were mined for candidate drug targets. Examination of trans-ancestral pathways reflect the well-defined role for interferons in SLE and revealed pathways associated with tissue repair and remodeling. EA-dominant genetic drivers were more often associated with innate immune and myeloid cell function pathways, whereas AA-dominant pathways mirror clinical findings in AA subjects, suggesting disease progression is driven by aberrant B cell activity accompanied by ER stress and metabolic dysfunction. Finally, potential ancestry-specific and non-specific drug candidates were identified. The integration of all SLE SNP-predicted genes into functional pathways revealed critical molecular pathways representative of each population, underscoring the influence of ancestry on disease mechanism and also providing key insight for therapeutic selection.