Stratification of Systemic Lupus Erythematosus Patients Into Three Groups of Disease Activity Progression According to Longitudinal Gene Expression

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

Association of autoantibodies with the IFN signature and NETosis in patients with systemic lupus erythematosus

Objective

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a variety of disease symptoms and an unpredictable clinical course. To improve treatment outcome, stratification based on immunological manifestations commonly seen in patients with SLE such as autoantibodies, type I interferon (IFN) signature and neutrophil extracellular trap (NET) release may help. It is assumed that there is an association between these immunological phenomena, since NET release induces IFN production and IFN induces autoantibody formation via B-cell activation. Here we studied the association between autoantibodies, the IFN signature, NET release, and clinical manifestations in patients with SLE.

Methods

We performed principal component analysis (PCA) and hierarchical clustering of 57 SLE-related autoantibodies in 25 patients with SLE. We correlated each autoantibody to the IFN signature and NET inducing capacity.

Results

We observed two distinct clusters: one cluster contained mostly patients with a high IFN signature. Patients in this cluster often present with cutaneous lupus, and have higher anti-dsDNA concentrations. Another cluster contained a mix of patients with a high and low IFN signature. Patients with high and low NET inducing capacity were equally distributed between the clusters. Variance between the clusters is mainly driven by antibodies against histones, RibP2, RibP0, EphB2, RibP1, PCNA, dsDNA, and nucleosome. In addition, we found a trend towards increased concentrations of autoantibodies against EphB2, RibP1, and RNP70 in patients with an IFN signature. We found a negative correlation of NET inducing capacity with anti-FcER (r = -0.530; p = 0.007) and anti-PmScl100 (r = -0.445; p = 0.03).

Conclusion

We identified a subgroup of patients with an IFN signature that express increased concentrations of antibodies against DNA and RNA-binding proteins, which can be useful for further patient stratification and a more targeted therapy. We did not find positive associations between autoantibodies and NET inducing capacity. Our study further strengthens the evidence of a correlation between RNA-binding autoantibodies and the IFN signature.

Characterization of systemic lupus erythematosus subtypes using cluster analysis: insights from lymphocyte subpopulations

BACKGROUND

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, in which lymphocyte subsets were dysregulated. Incorporating lymphocyte subpopulations in cluster analysis offers a pathway for personalized and precise treatment, targeting specific abnormalities for more effective management.

METHODS

We conducted Gaussian clustering analysis on clinical data, serological data, urine test results, and lymphocyte subpopulations for SLE patients hospitalized from September 2008 to December 2019.

RESULTS

A total of 1863 SLE patients from Xi'Jing Hospital were included. After excluding those without complete assessments, 1281 patients underwent flow cytometry for lymphocyte subsets. Five SLE clusters emerged: Cluster 1 with severe kidney involvement, high SLEDAI scores, and infection rates, often accompanied by rashes and edema; cluster 2 with high urinary protein but better renal function; cluster 3 with normal lymphocyte count and low positive antibodies; cluster 4 with frequent psychiatric symptoms and pulmonary arterial hypertension (PAH); and cluster 5 with fever, arthritis, hematologic involvement, and high IgG levels despite decreased B cells.

CONCLUSION

All enrolled SLE patients were ultimately categorized into five distinct clinical phenotype groups, with lymphocyte testing being meaningful for patient stratification. This finding shed light on the intricate heterogeneity of SLE, emphasizing the need for a personalized medicine approach. Targeting specific abnormalities in lymphocyte subsets holds promise for more effective and precise management of SLE. Key Points • A comprehensive analysis of SLE patients, including lymphocyte subpopulations, revealed five distinct clusters with varying clinical characteristics, emphasizing the heterogeneity of the disease. • This heterogeneity underscores the need for a personalized medicine approach in SLE management, targeting specific lymphocyte subset abnormalities for more effective and precise treatment.

SPATS2L is a positive feedback regulator of the type I interferon signaling pathway and plays a vital role in lupus

Through genome-wide association studies (GWAS) and integrated expression quantitative trait locus (eQTL) analyses, numerous susceptibility genes ("eGenes", whose expressions are significantly associated with common variants) associated with systemic lupus erythematosus (SLE) have been identified. Notably, a subset of these eGenes is correlated with disease activity. However, the precise mechanisms through which these genes contribute to the initiation and progression of the disease remain to be fully elucidated. In this investigation, we initially identify SPATS2L as an SLE eGene correlated with disease activity. eSignaling and transcriptomic analyses suggest its involvement in the type I interferon (IFN) pathway. We observe a significant increase in SPATS2L expression following type I IFN stimulation, and the expression levels are dependent on both the concentration and duration of stimulation. Furthermore, through dual-luciferase reporter assays, western blot analysis, and imaging flow cytometry, we confirm that SPATS2L positively modulates the type I IFN pathway, acting as a positive feedback regulator. Notably, siRNA-mediated intervention targeting SPATS2L, an interferon-inducible gene, in peripheral blood mononuclear cells (PBMCs) from patients with SLE reverses the activation of the interferon pathway. In conclusion, our research highlights the pivotal role of SPATS2L as a positive-feedback regulatory molecule within the type I IFN pathway. Our findings suggest that SPATS2L plays a critical role in the onset and progression of SLE and may serve as a promising target for disease activity assessment and intervention strategies.

Prognostic value and predictors of the alteration of the diffusing capacity of the lungs for carbon monoxide in systemic lupus erythematosus

OBJECTIVES

SLE is a systemic autoimmune disease characterized by heterogeneous manifestations and severity, with frequent lung involvement. Among pulmonary function tests, the measure of the diffusing capacity of the lungs for carbon monoxide (DLCO) is a noninvasive and sensitive tool assessing pulmonary microcirculation. Asymptomatic and isolated DLCO alteration has frequently been reported in SLE, but its clinical relevance has not been established.

METHODS

This retrospective study focused on 232 SLE patients fulfilling the 2019 EULAR/ACR classification criteria for SLE. Data were collected from the patient's medical record, including demographic, clinical and immunological characteristics, while DLCO was measured when performing pulmonary function tests as part of routine patient follow-up.

RESULTS

At the end of follow-up, DLCO alteration (<70% of predicted value) was measured at least once in 154 patients (66.4%), and was associated with a history of smoking as well as interstitial lung disease, but was also associated with renal and neurological involvement. History of smoking, detection of anti-nucleosome autoantibodies and clinical lymphadenopathy at diagnosis were independent predictors of DLCO alteration, while early cutaneous involvement with photosensitivity was a protective factor. DLCO alteration, at baseline or any time during follow-up, was predictive of admission in intensive care unit and/or of all-cause death, both mainly due to severe disease flares and premature cardiovascular complications.

CONCLUSION

This study suggests a link between DLCO alteration and disease damage, potentially related to SLE vasculopathy, and a prognostic value of DLCO on death or intensive care unit admission in SLE.

Uncoupling interferons and the interferon signature explains clinical and transcriptional subsets in SLE

Systemic lupus erythematosus (SLE) displays a hallmark interferon (IFN) signature. Yet, clinical trials targeting type I IFN (IFN-I) have shown variable efficacy, and blocking IFN-II failed to treat SLE. Here, we show that IFN type levels in SLE vary significantly across clinical and transcriptional endotypes. Whereas skin involvement correlated with IFN-I alone, systemic features like nephritis associated with co-elevation of IFN-I, IFN-II, and IFN-III, indicating additive IFN effects in severe SLE. Notably, while high IFN-II/-III levels without IFN-I had a limited effect on disease activity, IFN-II was linked to IFN-I-independent transcriptional profiles (e.g., OXPHOS and CD8+GZMH+ cells), and IFN-III enhanced IFN-induced gene expression when co-elevated with IFN-I. Moreover, dysregulated IFNs do not explain the IFN signature in 64% of patients or clinical manifestations including cytopenia, serositis, and anti-phospholipid syndrome, implying IFN-independent endotypes in SLE. This study sheds light on mechanisms underlying SLE heterogeneity and the variable response to IFN-targeted therapies in clinical trials.

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.

Identification of EPSTI1 as a new potential biomarker for SLE based on GEO database

OBJECTIVE

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with highly heterogeneous. The aim of this study is to find the key genes in peripheral blood mononuclear cells (PBMCs) of SLE patients and to provide a new direction for the diagnosis and treatment of lupus.

METHODS

GSE121239, GSE50772, GSE81622, and GSE144390 mRNA expression profiles were obtained from the website of Gene Expression Omnibus (GEO), and differential expressed genes (DEGs) analysis was performed by R. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to elucidate signaling pathways for the DEGs. Real-time qPCR (RT-qPCR) was used to verify the key gene EPSTI1 in PBMCs of SLE patients. Finally, the correlation analysis and ROC curve analysis of EPSTI1 for SLE were performed.

RESULTS

A total of 12 upregulated DEGs were identified, including MMP8, MX1, IFI44, EPSTI1, OAS1, OAS3, HERC5, IFIT1, RSAD2, USP18, IFI44L, and IFI27. GO and KEGG pathway enrichment analysis showed that those DEGs were mainly concentrated in the response to virus and IFN signaling pathways. Real-time qPCR (RT-qPCR) revealed that EPSTI1 was increased in PBMCs of SLE. EPSTI1 was positively correlated with SLEDAI score in SLE patients. Besides, EPSTI1 was positively correlated with T cell activation- or differentiation-associated genes (BCL6 and RORC). Furthermore, ROC analyses proved EPSTI1 may have diagnostic value for SLE.

CONCLUSION

Together, EPSTI1 was found to be a potential biomarker for SLE, closely related to T cell immune imbalance. Key Points • EPSTI1 expression was significantly increased in PBMCs of SLE patients. • EPSTI1 was positively correlated with disease activity and T cell activation- or differentiation-associated genes in SLE patients. • EPSTI1 might have a good diagnostic value for SLE.

Platelet TLR7 is essential for the formation of platelet-neutrophil complexes and low-density neutrophils in lupus nephritis

OBJECTIVES

Platelets and low-density neutrophils (LDNs) are major players in the immunopathogenesis of SLE. Despite evidence showing the importance of platelet-neutrophil complexes (PNCs) in inflammation, little is known about the relationship between LDNs and platelets in SLE. We sought to characterize the role of LDNs and Toll-like receptor 7 (TLR7) in clinical disease.

METHODS

Flow cytometry was used to immunophenotype LDNs from SLE patients and controls. The association of LDNs with organ damage was investigated in a cohort of 290 SLE patients. TLR7 mRNA expression was assessed in LDNs and high-density neutrophils (HDNs) using publicly available mRNA sequencing datasets and our own cohort using RT-PCR. The role of TLR7 in platelet binding was evaluated in platelet-HDN mixing studies using TLR7-deficient mice and Klinefelter syndrome patients.

RESULTS

SLE patients with active disease have more LDNs, which are heterogeneous and more immature in patients with evidence of kidney dysfunction. LDNs are platelet bound, in contrast to HDNs. LDNs settle in the peripheral blood mononuclear cell (PBMC) layer due to the increased buoyancy and neutrophil degranulation from platelet binding. Mixing studies demonstrated that this PNC formation was dependent on platelet-TLR7 and that the association results in increased NETosis. The neutrophil:platelet ratio is a useful clinical correlate for LDNs, and a higher NPR is associated with past and current flares of LN.

CONCLUSIONS

LDNs sediment in the upper PBMC fraction due to PNC formation, which is dependent on the expression of TLR7 in platelets. Collectively, our results reveal a novel TLR7-dependent crosstalk between platelets and neutrophils that may be an important therapeutic opportunity for LN.

Urine proteomic signatures of histological class, activity, chronicity, and treatment response in lupus nephritis

Lupus nephritis (LN) is a pathologically heterogenous autoimmune disease linked to end-stage kidney disease and mortality. Better therapeutic strategies are needed as only 30%-40% of patients completely respond to treatment. Noninvasive biomarkers of intrarenal inflammation may guide more precise approaches. Because urine collects the byproducts of kidney inflammation, we studied the urine proteomic profiles of 225 patients with LN (573 samples) in the longitudinal Accelerating Medicines Partnership in RA/SLE cohort. Urinary biomarkers of monocyte/neutrophil degranulation (i.e., PR3, S100A8, azurocidin, catalase, cathepsins, MMP8), macrophage activation (i.e., CD163, CD206, galectin-1), wound healing/matrix degradation (i.e., nidogen-1, decorin), and IL-16 characterized the aggressive proliferative LN classes and significantly correlated with histological activity. A decline of these biomarkers after 3 months of treatment predicted the 1-year response more robustly than proteinuria, the standard of care (AUC: CD206 0.91, EGFR 0.9, CD163 0.89, proteinuria 0.8). Candidate biomarkers were validated and provide potentially treatable targets. We propose these biomarkers of intrarenal immunological activity as noninvasive tools to diagnose LN and guide treatment and as surrogate endpoints for clinical trials. These findings provide insights into the processes involved in LN activity. This data set is a public resource to generate and test hypotheses and validate biomarkers.

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.

Classification of systemic lupus erythematosus: From the development of classification criteria to a new taxonomy?

SLE is a highly variable systemic autoimmune disease. Its immunopathological effector phase is partly understood. However, the background of its variability is not. SLE classification criteria have been relying on the clinical manifestations and standard autoimmune serology. This still holds true for the 2019 EULAR/ACR classification criteria. On one hand, this has led to significant precision in defining patients with SLE. On the other hand, the information in the criteria neither helps understanding the individual patient's pathophysiology, nor does it predict the efficacy of the available immunomodulatory therapies. Chances of further improvement of clinical criteria are most likely limited. This is where new multi-omic approaches have started to make an impact. While not yet able to differentiate diseases with the same precision as the classification criteria, the results of these studies go far beyond the scope of the criteria with regard to immune dysregulation. Looking at both sides in detail, we here try to synthesize the available data, aiming at a better understanding of SLE and its immune pathophysiology.

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.

Uncoupling interferons and the interferon signature explain clinical and transcriptional subsets in SLE

Interferons (IFN) are thought to be key players in systemic lupus erythematosus (SLE). The unique and interactive roles of the different IFN families in SLE pathogenesis, however, remain poorly understood. Using reporter cells engineered to precisely quantify IFN-I, IFN-II and IFN-III activity levels in serum/plasma, we found that while IFNs play essential role in SLE pathogenesis and disease activity, they are only significant in specific subsets of patients. Interestingly, whereas IFN-I is the main IFN that governs disease activity in SLE, clinical subsets are defined by the co-elevation of IFN-II and IFN-III. Thus, increased IFN-I alone was only associated with cutaneous lupus. In contrast, systemic features, such as nephritis, were linked to co-elevation of IFN-I plus IFN-II and IFN-III, implying a synergistic effect of IFNs in severe SLE. Intriguingly, while increased IFN-I levels were strongly associated with IFN-induced gene expression (93.5%), in up to 64% of cases, the IFN signature was not associated with IFN-I. Importantly, neither IFN-II nor IFN-III explained IFN-induced gene expression in patients with normal IFN-I levels, and not every feature in SLE was associated with elevated IFNs, suggesting IFN-independent subsets in SLE. Together, the data suggest that, unlike the IFN signature, direct quantification of bioactive IFNs can identify pathogenic and clinically relevant SLE subsets amenable for precise anti-IFN therapies. Since IFN-I is only elevated in a subset of SLE patients expressing the IFN signature, this study explains the heterogeneous response in clinical trials targeting IFN-I, where patients were selected based on IFN-induced gene expression rather than IFN-I levels.

Deep stratification by transcriptome molecular characters for precision treatment of patients with systemic lupus erythematosus

OBJECTIVES

To leverage the high clinical heterogeneity of systemic lupus erythematosus (SLE), we developed and validated a new stratification scheme by integrating genome-scale transcriptomic profiles to identify patient subtypes sharing similar transcriptomic markers and drug targets.

METHODS

A normalized compendium of transcription profiles was created from peripheral blood mononuclear cells (PBMCs) of 1046 SLE patients and 86 healthy controls (HCs), covering an intersection of 13 689 genes from six microarray datasets. Upregulated differentially expressed genes were subjected to functional and network analysis in which samples were grouped using unsupervised clustering to identify patient subtypes. Then, clustering stability was evaluated by the stratification of six integrated RNA-sequencing datasets using the same method. Finally, the Xgboost classifier was applied to the independent datasets to identify factors associated with treatment outcomes.

RESULTS

Based on 278 upregulated DEGs of the transcript profiles, SLE patients were classified into three subtypes (subtype A-C) each with distinct molecular and cellular signatures. Neutrophil activation-related pathways were markedly activated in subtype A (named NE-driving), whereas lymphocyte and IFN-related pathways were more enriched in subtype B (IFN-driving). As the most severe subtype, subtype C [NE-IFN-dual-driving (Dual-driving)] shared functional mechanisms with both NE-driving and IFN-driving, which was closely associated with clinical features and could be used to predict the responses of treatment.

CONCLUSION

We developed the largest cohesive SLE transcriptomic compendium for deep stratification using the most comprehensive microarray and RNA sequencing datasets to date. This result could guide future design of molecular diagnosis and the development of stratified therapy for SLE patients.

Artificial intelligence-driven drug development against autoimmune diseases

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders

Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body's natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.

Cytotoxic CD161-CD8+ TEMRA cells contribute to the pathogenesis of systemic lupus erythematosus

BACKGROUND

Systemic lupus erythematosus (SLE) is a prototypical autoimmune disease affecting multiple organs and tissues with high cellular heterogeneity. CD8⁺ T cell activity is involved in the SLE pathogenesis. However, the cellular heterogeneity and the underlying mechanisms of CD8⁺ T cells in SLE remain to be identified.

METHODS

Single-cell RNA sequencing (scRNA-seq) of PBMCs from a SLE family pedigree (including 3 HCs and 2 SLE patients) was performed to identify the SLE-associated CD8⁺ T cell subsets. Flow cytometry analysis of a SLE cohort (including 23 HCs and 33 SLE patients), qPCR analysis of another SLE cohort (including 30 HCs and 25 SLE patients) and public scRNA-seq datasets of autoimmune diseases were employed to validate the finding. Whole-exome sequencing (WES) of this SLE family pedigree was used to investigate the genetic basis in dysregulation of CD8⁺ T cell subsets identified in this study. Co-culture experiments were performed to analyze the activity of CD8⁺ T cells.

FINDINGS

We elucidated the cellular heterogeneity of SLE and identified a new highly cytotoxic CD8⁺ T cell subset, CD161^-CD8⁺ T_EMRA cell subpopulation, which was remarkably increased in SLE patients. Meanwhile, we discovered a close correlation between mutation of DTHD1 and the abnormal accumulation of CD161^-CD8⁺ T_EMRA cells in SLE. DTHD1 interacted with MYD88 to suppress its activity in T cells and DTHD1 mutation promoted MYD88-dependent pathway and subsequently increased the proliferation and cytotoxicity of CD161^-CD8⁺ T_EMRA cells. Furthermore, the differentially expressed genes in CD161^-CD8⁺ T_EMRA cells displayed a strong out-of-sample prediction for case-control status of SLE.

INTERPRETATION

This study identified DTHD1-associated expansion of CD161^-CD8⁺ T_EMRA cell subpopulation is critical for SLE. Our study highlights genetic association and cellular heterogeneity of SLE pathogenesis and provides a mechanistical insight into the diagnosis and treatment of SLE.

FUNDINGS

Stated in the Acknowledgements section of the manuscript.

Biomarker-driven development of new therapies for autoimmune diseases: current status and future promises

INTRODUCTION

Auto-immune diseases are complex and heterogeneous. Various types of biomarkers can be used to support precision medicine approaches to autoimmune diseases, ensuring that the right patient receives the most appropriate therapy to improve treatment outcomes.

AREAS COVERED

We review the recent progress made in modeling several autoimmune diseases such as Systemic Lupus Erythematosus, primary Sjogren Syndrome, and Rheumatoid Arthritis following extensive molecular profiling of large cohorts of patients. From this knowledge, BMKs are being identified which support diagnostic as well as patient stratification and prediction of response to treatment. The identification of biomarkers should be initiated early in drug development and properly validated during subsequent clinical trials. To ensure the robustness and reproducibility of biomarkers, the PERMIT Consortium recently established recommendations highlighting the importance of relevant study design, sample size, and appropriate validation of analytical methods.

EXPERT OPINION

The integration by AI-powered analytics of massive data provided by multi-omics technologies, high-resolution medical imaging and sensors borne by patients will eventually allow the identification of clinically relevant BMKs, likely in the form of combinatorial predictive algorithms, to support future drug development for autoimmune diseases.

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.R27 strain as a means to define mechanisms associated with resistance to chronic nephritis. Gene expression profiles in lupus mice were compared with those in human LN.

Results

NZM2328 mice exhibited progress from acute to transitional and then to chronic glomerulonephritis (GN). Each stage manifested a unique molecular profile. Neither male mice nor R27 mice progressed past the acute GN stage, with the former exhibiting minimal immune infiltration and the latter enrichment of immunoregulatory gene signatures in conjunction with robust kidney tubule cell profiles indicative of resistance to cellular damage. The gene expression profiles of human LN were similar to those noted in the NZM2328 mouse suggesting comparable stages of LN progression.

Conclusions

Overall, this work provides a comprehensive examination of the immune processes involved in progression of murine LN and thus contributes to our understanding of the risk factors for end-stage renal disease. In addition, this work presents a foundation for improved classification of LN and illustrates the applicability of murine models to identify the stages of human disease.

Artificial intelligence and high-dimensional technologies in the theragnosis of systemic lupus erythematosus

Systemic lupus erythematosus is a complex, systemic autoimmune disease characterised by immune dysregulation. Pathogenesis is multifactorial, contributing to clinical heterogeneity and posing challenges for diagnosis and treatment. Although strides in treatment options have been made in the past 15 years, with the US Food and Drug Administration approval of belimumab in 2011, there are still many patients who have inadequate responses to therapy. A better understanding of underlying disease mechanisms with a holistic and multiparametric approach is required to improve clinical assessment and treatment. This Review discusses the evolution of genomics, epigenomics, transcriptomics, and proteomics in the study of systemic lupus erythematosus and ways to amalgamate these silos of data with a systems-based approach while also discussing ways to strengthen the overall process. These mechanistic insights will facilitate the discovery of functionally relevant biomarkers to guide rational therapeutic selection to improve patient outcomes.

Application of Machine Learning Models in Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario.

Current state and completeness of reporting clinical prediction models using machine learning in systemic lupus erythematosus: A systematic review

OBJECTIVE

We carried out a systematic review (SR) of adherence in diagnostic and prognostic applications of ML in SLE using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

METHODS

A SR employing five databases was conducted from its inception until December 2021. We identified articles that evaluated the utilization of ML for prognostic and/or diagnostic purposes. This SR was reported based on the PRISMA guidelines. The TRIPOD statement assessed adherence to reporting standards. Assessment for risk of bias was done using PROBAST tool.

RESULTS

We included 45 studies: 29 (64.4%) diagnostic and 16 (35.5%) prognostic prediction- model studies. Overall, articles adhered by between 17% and 67% (median 43%, IQR 37-49%) to TRIPOD items. Only few articles reported the model's predictive performance (2.3%, 95% CI 0.06-12.0), testing of interaction terms (2.3%, 95% CI 0.06-12.0), flow of participants (50%, 95% CI; 34.6-65.4), blinding of predictors (2.3%, 95% CI 0.06-12.0), handling of missing data (36.4%, 95% CI 22.4-52.2), and appropriate title (20.5%, 95% CI 9.8-35.3). Some items were almost completely reported: the source of data (88.6%, 95% CI 75.4-96.2), eligibility criteria (86.4%, 95% CI 76.2-96.5), and interpretation of findings (88.6%, 95% CI 75.4-96.2). In addition, most of model studies had high risk of bias.

CONCLUSIONS

The reporting adherence of ML-based model developed for SLE, is currently inadequate. Several items deemed crucial for transparent reporting were not fully reported in studies on ML-based prediction models.

REVIEW REGISTRATION

PROSPERO ID# CRD42021284881. (Amended to limit the scope).

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study

OBJECTIVES

Clinical phenotyping and predicting treatment responses in SLE patients is challenging. Extensive blood transcriptional profiling has identified various gene modules that are promising for stratification of SLE patients. We aimed to translate existing transcriptomic data into simpler gene signatures suitable for daily clinical practice.

METHODS

Real-time PCR of multiple genes from the IFN M1.2, IFN M5.12, neutrophil (NPh) and plasma cell (PLC) modules, followed by a principle component analysis, was used to identify indicator genes per gene signature. Gene signatures were measured in longitudinal samples from two childhood-onset SLE cohorts (n = 101 and n = 34, respectively), and associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (SELENA)-SLEDAI. Cluster analysis subdivided patients into three mutually exclusive fingerprint-groups termed (1) all-signatures-low, (2) only IFN high (M1.2 and/or M5.12) and (3) high NPh and/or PLC.

RESULTS

All gene signatures were significantly associated with disease activity in cross-sectionally collected samples. The PLC-signature showed the highest association with disease activity. Interestingly, in longitudinally collected samples, the PLC-signature was associated with disease activity and showed a decrease over time. When patients were divided into fingerprints, the highest disease activity was observed in the high NPh and/or PLC group. The lowest disease activity was observed in the all-signatures-low group. The same distribution was reproduced in samples from an independent SLE cohort.

CONCLUSIONS

The identified gene signatures were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices.

Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression

OBJECTIVES

Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions.

METHODS

Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores.

RESULTS

MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes.

CONCLUSIONS

MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

Involvement of MST1/mTORC1/STAT1 activity in the regulation of B-cell receptor signalling by chemokine receptor 2

BACKGROUND

CCR2 is involved in maintaining immune homeostasis and regulating immune function. This study aims to elucidate the mechanism by which CCR2 regulates B-cell signalling.

METHODS

In Ccr2-knockout mice, the development and differentiation of B cells, BCR proximal signals, actin movement and B-cell immune response were determined. Besides, the level of CCR2 in PBMC of SLE patients was analysed by bioinformatics.

RESULTS

CCR2 deficiency reduces the proportion and number of follicular B cells, upregulates BCR proximal signalling and enhances the oxidative phosphorylation of B cells. Meanwhile, increased actin filaments aggregation and its associated early-activation events of B cells are also induced by CCR2 deficiency. The MST1/mTORC1/STAT1 axis in B cells is responsible for the regulation of actin remodelling, metabolic activities and transcriptional signalling, specific MST1, mTORC1 or STAT1 inhibitor can rescue the upregulated BCR signalling. Glomerular IgG deposition is obvious in CCR2-deficient mice, accompanied by increased anti-dsDNA IgG level. Additionally, the CCR2 expression in peripheral B cells of SLE patients is decreased than that of healthy controls.

CONCLUSIONS

CCR2 can utilise MST1/mTORC1/STAT1 axis to regulate BCR signalling. The interaction between CCR2 and BCR may contribute to exploring the mechanism of autoimmune diseases.

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.

Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data

Transcriptomic analyses are commonly used to identify differentially expressed genes between patients and controls, or within individuals across disease courses. These methods, whilst effective, cannot encompass the combinatorial effects of genes driving disease. We applied rule-based machine learning (RBML) models and rule networks (RN) to an existing paediatric Systemic Lupus Erythematosus (SLE) blood expression dataset, with the goal of developing gene networks to separate low and high disease activity (DA1 and DA3). The resultant model had an 81% accuracy to distinguish between DA1 and DA3, with unsupervised hierarchical clustering revealing additional subgroups indicative of the immune axis involved or state of disease flare. These subgroups correlated with clinical variables, suggesting that the gene sets identified may further the understanding of gene networks that act in concert to drive disease progression. This included roles for genes (i) induced by interferons (IFI35 and OTOF), (ii) key to SLE cell types (KLRB1 encoding CD161), or (iii) with roles in autophagy and NF-κB pathway responses (CKAP4). As demonstrated here, RBML approaches have the potential to reveal novel gene patterns from within a heterogeneous disease, facilitating patient clinical and therapeutic stratification.

Transcriptome Studies in Lupus Nephritis

The present review is aimed at describing the main works that have used gene expression to analyze tissue kidney samples of lupus nephritis patients. Most studies used the gene expression arrays, which enormously advanced our knowledge on the possible mechanisms behind lupus nephritis. However, using bulk gene expression platforms, either as arrays, or as sequencing of RNA is not enough to go into detail of the cells and their molecular patterns and single cell mechanisms of disease. More recently, the first single cell RNA Sequencing study was published and this will also be discussed in the context of lupus nephritis. Single cell RNA sequencing allows to retrieve the genes expressed in each cell in the tissue of interest or in blood. In this context, the results of such studies give us a first glimpse of how a lupus nephritis kidney looks like, but much is still to be done to understand the changes that occur with treatment or with the different pathological subtypes of lupus nephritis and their cellular content.

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.

NETs decorated with bioactive IL-33 infiltrate inflamed tissues and induce IFN-α production in patients with SLE

IL-33, a nuclear alarmin released during cell death, exerts context-specific effects on adaptive and innate immune cells, eliciting potent inflammatory responses. We screened blood, skin, and kidney tissues from patients with systemic lupus erythematosus (SLE), a systemic autoimmune disease driven by unabated type I IFN production, and found increased amounts of extracellular IL-33 complexed with neutrophil extracellular traps (NETs), correlating with severe, active disease. Using a combination of molecular, imaging, and proteomic approaches, we show that SLE neutrophils, activated by disease immunocomplexes, release IL-33–decorated NETs that stimulate robust IFN-α synthesis by plasmacytoid DCs in a manner dependent on the IL-33 receptor ST2L. IL33-silenced neutrophil-like cells cultured under lupus-inducing conditions generated NETs with diminished interferogenic effect. Importantly, NETs derived from patients with SLE are enriched in mature bioactive isoforms of IL-33 processed by the neutrophil proteases elastase and cathepsin G. Pharmacological inhibition of these proteases neutralized IL-33–dependent IFN-α production elicited by NETs. We believe these data demonstrate a novel role for cleaved IL-33 alarmin decorating NETs in human SLE, linking neutrophil activation, type I IFN production, and end-organ inflammation, with skin pathology mirroring that observed in the kidneys.

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.

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.

The Pathogenesis, Molecular Mechanisms, and Therapeutic Potential of the Interferon Pathway in Systemic Lupus Erythematosus and Other Autoimmune Diseases

Therapeutic success in treating patients with systemic lupus erythematosus (SLE) is limited by the multivariate disease etiology, multi-organ presentation, systemic involvement, and complex immunopathogenesis. Agents targeting B-cell differentiation and survival are not efficacious for all patients, indicating a need to target other inflammatory mediators. One such target is the type I interferon pathway. Type I interferons upregulate interferon gene signatures and mediate critical antiviral responses. Dysregulated type I interferon signaling is detectable in many patients with SLE and other autoimmune diseases, and the extent of this dysregulation is associated with disease severity, making type I interferons therapeutically tangible targets. The recent approval of the type I interferon-blocking antibody, anifrolumab, by the US Food and Drug Administration for the treatment of patients with SLE demonstrates the value of targeting this pathway. Nevertheless, the interferon pathway has pleiotropic biology, with multiple cellular targets and signaling components that are incompletely understood. Deconvoluting the complexity of the type I interferon pathway and its intersection with lupus disease pathology will be valuable for further development of targeted SLE therapeutics. This review summarizes the immune mediators of the interferon pathway, its association with disease pathogenesis, and therapeutic modalities targeting the dysregulated interferon pathway.

Autophagy receptor CCDC50 tunes the STING-mediated interferon response in viral infections and autoimmune diseases

DNA sensing and timely activation of interferon (IFN)-mediated innate immunity are crucial for the defense against DNA virus infections and the clearance of abnormal cells. However, overactivation of immune responses may lead to tissue damage and autoimmune diseases; therefore, these processes must be intricately regulated. STING is the key adaptor protein, which is activated by cyclic GMP-AMP, the second messenger derived from cGAS-mediated DNA sensing. Here, we report that CCDC50, a newly identified autophagy receptor, tunes STING-directed type I IFN signaling activity by delivering K63-polyubiquitinated STING to autolysosomes for degradation. Knockout of CCDC50 significantly increases herpes simplex virus 1 (HSV-1)- or DNA ligand-induced production of type I IFN and proinflammatory cytokines. Ccdc50-deficient mice show increased production of IFN, decreased viral replication, reduced cell infiltration, and improved survival rates compared with their wild-type littermates when challenged with HSV-1. Remarkably, the expression of CCDC50 is downregulated in systemic lupus erythematosus (SLE), a chronic autoimmune disease. CCDC50 levels are negatively correlated with IFN signaling pathway activation and disease severity in human SLE patients. CCDC50 deficiency potentiates the cGAS-STING-mediated immune response triggered by SLE serum. Thus, our findings reveal the critical role of CCDC50 in the immune regulation of viral infections and autoimmune diseases and provide insights into the therapeutic implications of CCDC50 manipulation.

Stratification of Patients With Sjögren's Syndrome and Patients With Systemic Lupus Erythematosus According to Two Shared Immune Cell Signatures, With Potential Therapeutic Implications

OBJECTIVE

Similarities in the clinical and laboratory features of primary Sjögren's syndrome (SS) and systemic lupus erythematosus (SLE) have led to attempts to treat patients with primary SS or SLE with similar biologic therapeutics. However, the results of many clinical trials are disappointing, and no biologic treatments are licensed for use in primary SS, while only a few biologic agents are available to treat SLE patients whose disease has remained refractory to other treatments. With the aim of improving treatment selections, this study was undertaken to identify distinct immunologic signatures in patients with primary SS and patients with SLE, using a stratification approach based on immune cell endotypes.

METHODS

Immunophentyping of 29 immune cell subsets was performed using flow cytometry in peripheral blood from patients with primary SS (n = 45), patients with SLE (n = 29), and patients with secondary SS associated with SLE (SLE/SS) (n = 14), all of whom were considered to have low disease activity or be in clinical remission, and sex-matched healthy controls (n = 31). Data were analyzed using supervised machine learning (balanced random forest, sparse partial least squares discriminant analysis), logistic regression, and multiple t-tests. Patients were stratified by K-means clustering and clinical trajectory analysis.

RESULTS

Patients with primary SS and patients with SLE had a similar immunologic architecture despite having different clinical presentations and prognoses. Stratification of the combined primary SS, SLE, and SLE/SS patient cohorts by K-means cluster analysis revealed 2 endotypes, characterized by distinct immune cell profiles spanning the diagnoses. A signature of 8 T cell subsets that distinctly differentiated the 2 endotypes with high accuracy (area under the curve 0.9979) was identified in logistic regression and machine learning models. In clinical trajectory analyses, the change in damage scores and disease activity levels from baseline to 5 years differed between the 2 endotypes.

CONCLUSION

These findings identify an immune cell toolkit that may be useful for differentiating, with high accuracy, the immunologic profiles of patients with primary SS and patients with SLE as a way to achieve targeted therapeutic approaches.

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Transcriptomic analysis plays a key role in biomedical research. Linear dimensionality reduction methods, especially principal-component analysis (PCA), are widely used in detecting sample-to-sample heterogeneity, while recently developed non-linear methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), can efficiently cluster heterogeneous samples in single-cell RNA sequencing analysis. Yet, the application of t-SNE and UMAP in bulk transcriptomic analysis and comparison with conventional methods have not been achieved. We compare four major dimensionality reduction methods (PCA, multidimensional scaling [MDS], t-SNE, and UMAP) in analyzing 71 large bulk transcriptomic datasets. UMAP is superior to PCA and MDS but shows some advantages over t-SNE in differentiating batch effects, identifying pre-defined biological groups, and revealing in-depth clusters in two-dimensional space. Importantly, UMAP generates sample clusters uncovering biological features and clinical meaning. We recommend deploying UMAP in visualizing and analyzing sizable bulk transcriptomic datasets to reinforce sample heterogeneity analysis.

Dietary Supplementation With Eicosapentaenoic Acid Inhibits Plasma Cell Differentiation and Attenuates Lupus Autoimmunity

Accumulating evidence suggests that cholesterol accumulation in leukocytes is causally associated with the development of autoimmune diseases. However, the mechanism by which fatty acid composition influences autoimmune responses remains unclear. To determine whether the fatty acid composition of diet modulates leukocyte function and the development of systemic lupus erythematosus, we examined the effect of eicosapentaenoic acid (EPA) on the pathology of lupus in drug-induced and spontaneous mouse models. We found that dietary EPA supplementation ameliorated representative lupus manifestations, including autoantibody production and immunocomplex deposition in the kidneys. A combination of lipidomic and membrane dynamics analyses revealed that EPA remodels the lipid composition and fluidity of B cell membranes, thereby preventing B cell differentiation into autoantibody-producing plasma cells. These results highlight a previously unrecognized mechanism by which fatty acid composition affects B cell differentiation into autoantibody-producing plasma cells during autoimmunity, and imply that EPA supplementation may be beneficial for therapy of lupus.

"Precision Medicine in Autoimmune Diseases: Fact or Fiction"

Much is said about precision medicine, but its real significance and the possibility of making it a real possibility is far from certain. Several studies in each of the autoimmune diseases have provided important insight into molecular pathways but the use of molecular studies, particularly those looking into transcriptome pathways, have seldom approached the possibility of using the data for disease stratification and then for prediction, or diagnosis. Only the type I interferon signature has been considered in the use of this signature for therapeutic purposes, particularly in the case of systemic lupus erythematosus. Here, the authors provide an update on precision medicine, what can be translated into clinical practice, and what do single-cell molecular studies provide to our knowledge in autoimmune diseases, focusing on a few examples. The main message being that we should try to move from precision medicine of established disease to preventive medicine in order to predict the development of disease.

Neutrophil and lymphocyte counts are associated with different immunopathological mechanisms in systemic lupus erythematosus

Objective

Neutrophils contribute to the SLE pathogenesis. Neutrophil to lymphocyte ratio (NLR) is reported to correlate with disease activity in SLE. The aim of the study was to evaluate whether NLR reflects underlying immunopathogenic activity in SLE, as well as to determine the contribution of each component of NLR, neutrophil and lymphocyte count.

Methods

Data were obtained from a cohort of patients with SLE (n=141) recruited at Lund University, Sweden. NLR levels were compared between patients with SLE and healthy controls (n=79). The relationship between NLR and clinical and immunological markers was examined using Mann-Whitney U test and logistic regression analysis. High NLR was defined as above the 90th percentile of healthy individuals.

Results

Patients with SLE had elevated neutrophil count (p=0.04) and reduced lymphocyte count (p<0.0001), resulting in elevated NLR as compared with healthy controls (p<0.0001). Patients with high NLR had more active disease, and were more frequently on prednisone use and immunosuppressive medicines. High NLR was associated with immune complex (IC)-driven disease with presence of antidouble-stranded DNA antibodies (p=0.006), circulating ICs (p=0.02) and type I interferon (IFN) activity (p=0.009). Further, high NLR was associated with neutrophil abnormalities, including enrichment for low-density granulocytes (LDGs) (p=0.001), and increased levels of the serum neutrophil activation marker, calprotectin (p=0.02). Assessing the individual components within NLR, that is, neutrophil and lymphocyte count, high neutrophil count was associated with neutrophil activation markers (p<0.0001), whereas low lymphocyte count was associated with type I IFN activity and elevated numbers of LDGs (p=0.006 and p=0.001, respectively).

Conclusions

NLR is elevated in patients with SLE as compared with healthy individuals, and is associated with key immunopathological events, including type I IFN activity and neutrophil activation. Neutrophil and lymphocyte count reflected different aspects of the pathogenesis of SLE. Further studies are needed to determine the causality of the associations.

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

OBJECTIVE

Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis.

METHODS

Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time.

RESULTS

Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient.

CONCLUSION

Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases.

Transcriptomic analysis of immune cells in a multi-ethnic cohort of systemic lupus erythematosus patients identifies ethnicity- and disease-specific expression signatures

Systemic lupus erythematosus (SLE) is an autoimmune disease in which outcomes vary among different racial groups. We leverage cell-sorted RNA-seq data (CD14+ monocytes, B cells, CD4+ T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four-tier approach including unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning to identify SLE subgroups within this multiethnic cohort. K-means clustering on each cell-type resulted in three clusters for CD4 and CD14, and two for B and NK cells. To understand the identified clusters, correlation analysis revealed significant positive associations between the clusters and clinical parameters including disease activity as well as ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across cells were involved in SLE or other autoimmune-related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Finally, random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White individuals. The results from these analyses will help stratify patients based on their gene expression signatures to enable SLE precision medicine.

Type I Interferons in Systemic Autoimmune Diseases: Distinguishing Between Afferent and Efferent Functions for Precision Medicine and Individualized Treatment

A sustained increase in type I interferon (IFN-I) may accompany clinical manifestations and disease activity in systemic autoimmune diseases (SADs). Despite the very frequent presence of IFN-I in SADs, clinical manifestations are extremely varied between and within SADs. The present short review will address the following key questions associated with high IFN-I in SADs in the perspective of precision medicine. 1) What are the mechanisms leading to high IFN-I? 2) What are the predisposing conditions favoring high IFN-I production? 3) What is the role of IFN-I in the development of distinct clinical manifestations within SADs? 4) Would therapeutic strategies targeting IFN-I be helpful in controlling or even preventing SADs? In answering these questions, we will underlie areas of incertitude and the intertwined role of autoantibodies, immune complexes, and neutrophils.

Transcription Factor Activity Inference in Systemic Lupus Erythematosus

Background: Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease with diverse clinical manifestations. Although most of the SLE-associated loci are located in regulatory regions, there is a lack of global information about transcription factor (TFs) activities, the mode of regulation of the TFs, or the cell or sample-specific regulatory circuits. The aim of this work is to decipher TFs implicated in SLE. Methods: In order to decipher regulatory mechanisms in SLE, we have inferred TF activities from transcriptomic data for almost all human TFs, defined clusters of SLE patients based on the estimated TF activities and analyzed the differential activity patterns among SLE and healthy samples in two different cohorts. The Transcription Factor activity matrix was used to stratify SLE patients and define sets of TFs with statistically significant differential activity among the disease and control samples. Results: TF activities were able to identify two main subgroups of patients characterized by distinct neutrophil-to-lymphocyte ratio (NLR), with consistent patterns in two independent datasets—one from pediatric patients and other from adults. Furthermore, after contrasting all subgroups of patients and controls, we obtained a significant and robust list of 14 TFs implicated in the dysregulation of SLE by different mechanisms and pathways. Among them, well-known regulators of SLE, such as STAT or IRF, were found, but others suggest new pathways that might have important roles in SLE. Conclusions: These results provide a foundation to comprehend the regulatory mechanism underlying SLE and the established regulatory factors behind SLE heterogeneity that could be potential therapeutic targets.

Identification of highly active systemic lupus erythematosus by combined type I interferon and neutrophil gene scores vs classical serologic markers

OBJECTIVES

In SLE, heterogeneous clinical expression and activity may reflect diverse pathogenic and/or effector mechanisms. We investigated SLE heterogeneity by assessing the expression of three gene sets representative of type I IFN (IFN-I), polymorphonuclear neutrophil (PMN) and plasmablast (PB) signatures in a well-characterized, multidisciplinary cohort of SLE patients. We further assessed whether individual gene products could be representative of these three signatures.

METHODS

Whole blood, serum and clinical data were obtained from 140 SLE individuals. Gene expression was assessed by NanoString technology, using a panel of 37 probes to compute six IFN-I, one PMN and one PB scores. Protein levels were measured by ELISA.

RESULTS

Depending on the score, 45-50% of SLE individuals showed high IFN-I gene expression. All six IFN-I scores were significantly associated with active skin involvement, and two of six were associated with arthritis. IFN-induced Mx1 protein (MX1) level was correlated with IFN-I score (P < 0.0001) and associated with a similar clinical phenotype. In all, 25% of SLE individuals showed high PMN gene expression, associated with SLE fever, serositis, leukopoenia and glucocorticoid use. PB gene expression was highly affected by immunosuppressant agents, with no association with SLE features. Combined IFN-I and PMN gene scores were significantly associated with high disease activity and outperformed anti-dsDNA and anti-C1q autoantibody and complement levels for predicting SLE activity.

CONCLUSION

IFN-I and PMN gene scores segregate with distinct SLE clinical features, and their combination may identify high disease activity. MX1 protein level performed similar to IFN-I gene expression.

Targeted Biologic Therapy for Systemic Lupus Erythematosus: Emerging Pathways and Drug Pipeline

Following the approval of belimumab, the first drug to be approved for systemic lupus erythematosus (SLE) in over 50 years, advances in our understanding of the pathogenesis of the disease have led to a remarkable number of clinical trials for investigational drugs, each with a unique mechanism of action. These include, but are not limited to, antibodies targeting B or T cells or their interaction, dendritic cells, interferon, and other cytokines. Frustratingly, this boost of studies has not been accompanied by a corresponding success and subsequent approval of novel agents, for reasons only partly attributed to the efficacy of the drugs per se. Successful phase II trials are often followed by failed phase III studies, which typically require many more patients. Nevertheless, recent successes, such as the ustekinumab and baricitinib trials and the positive results from the phase III TULIP-2 study of anifrolumab, provide room for cautious optimism. In this review, we attempt to draw the current landscape of the drug pipeline in SLE, focusing on the rationale behind each drug development, its mechanism of action, and the available preclinical and clinical data. We also highlight lessons learned from failed attempts that have helped to optimize clinical trial design for this challenging disease. We conclude with a look into the future, commenting on the surge of studies in the field of biomarkers and the use of omics technologies in lupus, which aim to pinpoint different disease phenotypes and, ideally, identify subsets of patients with disease that will respond to different biologic drugs.

Identification and stratification of systemic lupus erythematosus patients into two transcriptionally distinct clusters based on IFN-I signature

OBJECTIVE

Despite the significant advancement in the understanding of the pathophysiology of systemic lupus erythematosus (SLE) variable clinical response to newer therapies remain a major concern, especially for patients with lupus nephritis and neuropsychiatric systemic lupus erythematosus (NPSLE). We performed this study with an objective to comprehensively characterize Indian SLE patients with renal and neuropsychiatric manifestation with respect to their gene signature, cytokine profile and immune cell phenotypes.

METHODS

We characterized 68 Indian SLE subjects with diverse clinical profiles and disease activity and tried to identify differentially expressed genes and enriched pathways. To understand the temporal profile, same patients were followed at 6 and 12-months intervals. Additionally, auto-antibody profile, levels of various chemokines, cytokines and the proportion of different immune cells and their activation status were captured in these subjects.

RESULTS

Multiple IFN-related pathways were enriched with significant increase in IFN-I gene signature in SLE patients as compared to normal healthy volunteers (NHV). We identified two transcriptionally distinct clusters within the same cohort of SLE patients with differential immune cell activation status, auto-antibody as well as plasma chemokines and cytokines profile.

CONCLUSIONS

Identification of two distinct clusters of patients based on IFN-I signature provided new insights into the heterogeneity of underlying disease pathogenesis of Indian SLE cohort. Importantly, patient within those clusters retain their distinct expression dynamics of IFN-I signature over the time course of one year despite change in disease activity. This study will guide clinicians and researchers while designing future clinical trials on Indian SLE cohort.

Lymphocyte subset clustering analysis in treatment-naive patients with systemic lupus erythematosus

OBJECTIVES

The aim of the study is to identify clusters of lymphocyte subsets within treatment-naive systemic lupus erythematosus (SLE) patients and evaluate the potential association of these clusters with disease activities.

METHODS

We conducted a cross-sectional study of consecutive 143 treatment-naive SLE patients in the Affiliated Hospital of Nantong University, China. We used hierarchical cluster analysis to classify individuals into clusters based on circulating lymphocyte subset proportions (CD3⁺CD4⁺T cell, CD3⁺CD8⁺T cell, CD19⁺B cell, and CD3-CD16 + CD56 NK cell) via R software. Demographic variables, clinical manifestations, laboratory variables, and disease activities were compared among clusters.

RESULTS

The SLE patients (median age 35 (26-48) years, 90.9% female) were divided into four clusters. The clustering features were as follows: cluster 1 (B high), cluster 2 (CD4 high), cluster 3 (CD8 high), and cluster 4 (NK high). SLE patients in cluster 1 showed the highest incidence of arthritis (70.6%, 34.2%, 48.3%, and 42.9% in clusters 1, 2, 3, and 4, respectively; P = 0.046), and patients in cluster 3 and cluster 4 showed significantly a higher incidence of nephritis (35.3%, 25.0%, 48.3%, and 61.9% in in clusters 1, 2, 3, and 4, respectively; P = 0.008). Patients in cluster 2 suffered from lower SLE Disease Activity Index (SLEDAI) score (12.1 ± 5.0, 10.3 ± 5.6, 12.2 ± 4.6, and 14.4 ± 7.3 in clusters 1, 2, 3, and 4, respectively; P = 0.046). Regression analysis indicated that, compared with patients in cluster 2, patients in cluster 1 exhibited higher rate of arthritis (OR 4.53, 95% CI 1.38-14.86, P = 0.013), while patients in cluster 3 (OR 2.85, 95%CI 1.15-7.08, P = 0.024) and cluster 4 (OR 4.93, 95%CI 1.76-13.85, P = 0.002) exhibited higher rate of nephritis.

CONCLUSION

This study supports the existence of lymphocyte subset clusters with different clinical features in treatment-naive SLE patients, which could help to differentiate between various subsets of SLE. Key Points • Lymphocyte subsets may occur in a pattern of cluster in treatment-naive SLE patients.

Impact of autoimmune cytopenias on severity of childhood-onset systemic lupus erythematosus: A single-center retrospective cohort study

OBJECTIVE

To assess whether children with autoimmune cytopenias prior to or at diagnosis of systemic lupus erythematosus (cSLE), differ phenotypically from other cSLE patients; and have a lower risk and severity of lupus nephritis (LN) as observed in prior adult studies. To assess the effect of prior immune therapy for autoimmune cytopenias on 2-year risk of LN.

METHODS

This was a retrospective cohort study of incident cSLE cases. We included patients aged less than 17 years at diagnosis. We excluded patients with LN at cSLE diagnosis. Our follow-up period was 2 years. We defined autoimmune cytopenias as either autoimmune hemolytic anemia, immune thrombocytopenia or Evan's syndrome.

RESULTS

Forty-three (33%) of the 130 patients had autoimmune cytopenias before or at cSLE diagnosis. Those with autoimmune cytopenias had significantly more neuropsychiatric symptoms and higher mean ESR but less arthritis, malar rash and myositis versus those without autoimmune cytopenias. They had lower 2-year incidence proportion of LN compared to other cSLE patients (7% vs 15%). Of the 16 patients who developed LN, those with autoimmune cytopenias had mostly class V (2 of 3 patients) versus mostly class III and IV in those without autoimmune cytopenias (6 of 12 patients). None of the 13 patients pre-treated for autoimmune cytopenias developed LN.

CONCLUSION

Patients with autoimmune cytopenias before or at cSLE diagnosis have intriguing differences from other cSLE patients. They may represent a unique sub-type of cSLE patients and should be further explored.