Modular transcriptional repertoire analyses identify a blood neutrophil signature as a candidate biomarker for lupus nephritis

Exacerbating effects of circadian rhythm disruption on the systemic lupus erythematosus

OBJECTIVE

Circadian rhythm disruption (CRD) has been associated with inflammation and immune disorders, but its role in SLE progression is unclear. We aimed to investigate the impact of circadian rhythms on immune function and inflammation and their contribution to SLE progression to lupus nephritis (LN).

METHODS

This study retrospectively analysed the clinical characteristics and transcriptional profiles of 373 samples using bioinformatics and machine-learning methods. A flare risk score (FRS) was established to predict overall disease progression for patients with lupus. Mendelian randomisation was used to analyse the causal relationship between CRD and SLE progression.

RESULTS

Abnormalities in the circadian pathway were detected in patients with SLE, and lower enrichment levels suggested a disease state (normalised enrichment score=0.6714, p=0.0062). The disruption of circadian rhythms was found to be closely linked to lupus flares, with the FRS showing a strong ability to predict disease progression (area under the curve (AUC) of 5-year prediction: 0.76). The accuracy of disease prediction was improved by using a prognostic nomogram based on FRS (AUC=0.77). Additionally, Mendelian randomisation analysis revealed an inverse causal relationship between CRD and SLE (OR 0.6284 (95% CI 0.3630 to 1.0881), p=0.0485) and a positive causal relationship with glomerular disorders (OR 0.0337 (95% CI 1.634e-3 to 6.934e-1), p=0.0280).

CONCLUSION

Our study reveals that genetic characteristics arising from CRD can serve as biomarkers for predicting the exacerbation of SLE. This highlights the crucial impact of CRD on the progression of lupus.

Discovery of lupus nephritis targeted inhibitors based on De novo molecular design: comprehensive application of vinardo scoring, ADMET analysis, and molecular dynamics simulation

Lupus Nephritis (LN) is an autoimmune disease affecting the kidneys, and conventional drug studies have limitations due to its imprecise and complex pathogenesis. Therefore, the aim of this study was to design a novel Lupus Nephritis-targeted drug with good clinical due potential, high potency and selectivity by computer-assisted approach.NIK belongs to the serine/threonine protein kinase, which is gaining attention as a drug target for Lupus Nephritis. we used bioinformatics, homology modelling and sequence comparison analysis, small molecule ab initio design, ADMET analysis, molecular docking, molecular dynamics simulation, and MM/PBSA analysis to design and explore the selectivity and efficiency of a novel Lupus Nephritis-targeting drug, ClImYnib, and a classical NIK inhibitor, NIK SMI1. We used bioinformatics techniques to determine the correlation between lupus nephritis and the NF-κB signaling pathway. De novo drugs design was used to create a NIK-targeted inhibitor, ClImYnib, with lower toxicity, after which we used molecular dynamics to simulate NIK SMI1 against ClImYnib, and the simulation results showed that ClImYnib had better selectivity and efficiency. Our research delves into the molecular mechanism of protein ligands, and we have designed and validated an excellent NIK inhibitor using multiple computational simulation methods. More importantly, it provides an idea of target designing small molecules.Communicated by Ramaswamy H. Sarma.

Blood immunophenotyping identifies distinct kidney histopathology and outcomes in patients with lupus nephritis

Lupus nephritis (LN) is a frequent manifestation of systemic lupus erythematosus, and fewer than half of patients achieve complete renal response with standard immunosuppressants. Identifying non-invasive, blood-based pathologic immune alterations associated with renal injury could aid therapeutic decisions. Here, we used mass cytometry immunophenotyping of peripheral blood mononuclear cells in 145 patients with biopsy-proven LN and 40 healthy controls to evaluate the heterogeneity of immune activation in patients with LN and to identify correlates of renal parameters and treatment response. Unbiased analysis identified 3 immunologically distinct groups of patients with LN that were associated with different patterns of histopathology, renal cell infiltrates, urine proteomic profiles, and treatment response at one year. Patients with enriched circulating granzyme B+ T cells at baseline showed more severe disease and increased numbers of activated CD8 T cells in the kidney, yet they had the highest likelihood of treatment response. A second group characterized primarily by a high type I interferon signature had a lower likelihood of response to therapy, while a third group appeared immunologically inactive by immunophenotyping at enrollment but with chronic renal injuries. Main immune profiles could be distilled down to 5 simple cytometric parameters that recapitulate several of the associations, highlighting the potential for blood immune profiling to translate to clinically useful non-invasive metrics to assess immune-mediated disease in LN.

Weaning Maintenance Therapy in Lupus Nephritis: For Whom, When, and How?

Lupus nephritis (LN) is one of the main determinants of the severity of systemic lupus erythematosus (SLE). LN flares can lead to organ damage with chronic kidney disease (CKD) or even end-stage kidney disease (ESKD) and impair patients' survival. The "treat-to-target" strategy, which aims at obtaining and maintaining remission or low disease activity of SLE to alleviate symptoms and prevent organ damage, also refers to the control of residual activity in the kidney. But damage in SLE can also come from treatments, and toxicities related to long-term use of treatments should be prevented. This may contribute to the frequent nonadherence in patients with SLE. The de-escalation or even weaning of treatments whenever possible, or "think-to-untreat" (T2U) strategy, is to be considered in patients with LN. This possibility of treatment weaning in LN was explored in retrospective cohorts, on the basis of long-term clinical remission. It was also proposed prospectively with a kidney-biopsy-based approach, combining clinical and pathologic remission to secure treatment weaning. The WIN-Lupus trial was the first randomized controlled trial comparing the continuation to the discontinuation of maintenance immunosuppressive therapy (IST) after 2 to 3 years in patients with LN in remission. It showed a higher risk of severe SLE flares in patients who discontinued treatment, but also a possibility of weaning without flare in some patients, who need to be better identified. We propose here a narrative review of the available literature on the weaning of treatment in LN and discuss how to secure a T2U strategy.

Categorization of patients with systemic lupus erythematosus using disease activity, patient-reported outcomes, and transcriptomic signatures

OBJECTIVE

Patients with systemic lupus erythematosus (SLE) display symptoms that are not always related to disease activity and may distort clinical trial results. Recently, a clinical categorization based on the presence of type 1 (inflammatory manifestations) and/or type 2 (widespread pain, fatigue, depression) symptoms has been proposed in SLE. Our aim was to develop a type 2 score derived from the Short-Form health survey (SF-36) to categorize SLE patients and to compare immunological and transcriptomic profiles between groups.

METHOD

Seventeen items from the SF-36 were selected to build a type 2 score for 50 SLE patients (100 visits; LUPUCE cohort), and the SLEDAI was used to define type 1 symptoms. Patients were categorized into four groups: minimal (no symptoms), type 1, type 2, and mixed (both type 1 and type 2 symptoms). Clinical, immunological, and transcriptomic profiles were compared between the groups.

RESULTS

Type 2 scores ranged from 0 to 31, with a cutoff value of 14 (75th percentile). The sample categorization was minimal in 39%, type 1 in 37%, and type 2 in 9%, and mixed in 15%. Type 2 patients were older than minimal patients and had a longer disease duration than type 1 and mixed patients. Immunological data and modular interferon signatures did not differ between the groups.

CONCLUSION

Patients with SLE can be categorized into four clinical groups using the SLEDAI score and our SF-36-derived type 2 score. This categorization is non-redundant with immunological or transcriptomic profiles and could prove useful to stratify patients in clinical trials. Key Points • A score derived from selected items of the SF-36 can be used to identify SLE patients with type 2 symptoms according to the Duke University categorization. • Using the SLEDAI and this type 2 score, SLE patients can be categorized into four clinical groups. • This categorization is not related to immunological activity or blood transcriptome profiles (and not to the interferon signature in particular). • This categorization could be useful in the daily care of patients as well as in clinical trials, for upstream patient stratification or for the interpretation of results.

Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis

Objective

To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.

Methods

Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in the area under the ROC curve observed in the validation dataset. CIBERSORT was used to analyze 22 immune cell fractions from LN patients and to analyze their correlation with diagnostic markers.

Results

Thirty and twenty-one DEGs were screened in kidney tissue and peripheral blood, respectively. Both of which covered macrophages and interferons. The disease enrichment analysis of DEGs in kidney tissues showed that they were mainly involved in immune and renal diseases, and in peripheral blood it was mainly enriched in cardiovascular system, bone marrow, and oral cavity. The machine learning algorithm combined with external dataset validation revealed that C1QA(AUC = 0.741), C1QB(AUC = 0.758), MX1(AUC = 0.865), RORC(AUC = 0.911), CD177(AUC = 0.855), DEFA4(AUC= 0.843)and HERC5(AUC = 0.880) had high diagnostic value and could be used as diagnostic biomarkers of LN. Compared to controls, pathways such as cell adhesion molecule cam, and systemic lupus erythematosus were activated in kidney tissues; cell cycle, cytoplasmic DNA sensing pathways, NOD-like receptor signaling pathways, proteasome, and RIG-1-like receptors were activated in peripheral blood. Immune cell infiltration analysis showed that diagnostic markers in kidney tissue were associated with T cells CD8 and Dendritic cells resting, and in blood were associated with T cells CD4 memory resting, suggesting that CD4 T cells, CD8 T cells and dendritic cells are closely related to the development and progression of LN.

Conclusion

C1QA, C1QB, MX1, RORC, CD177, DEFA4 and HERC5 could be used as new candidate molecular markers for LN. It may provide new insights into the diagnosis and molecular treatment of LN in the future.

Immunosuppressant exposure confounds gene expression analysis in systemic lupus erythematosus

Objectives

The analysis of gene module expression in SLE is emerging as a tool to identify active biological pathways, with the aim of developing targeted therapies for subsets of patients. Detailed information on the effect of immunosuppressants on gene module expression is lacking. We aimed to examine the impact of medication exposure on gene module expression.

Methods

A set of commercially available disease-relevant gene modules were measured in 730 whole blood samples from a dedicated lupus clinic on whom prospectively collected, contemporaneous clinical data including medication exposure were available.

Results

Compared to heathy controls, SLE patients showed over-expression of IFN and under-expression of B cell, T cell and pDC modules. Neutrophil module over-expression and under-expression of B and T cell modules were observed in patients with active lupus nephritis or highly active disease (SLEDAI-2K > 8), while Lupus Low Disease Activity State (LLDAS) had inverse associations. Disease activity in other organ domains was not associated with specific gene modules. In contrast, medications were associated with multiple effects. Glucocorticoid use was associated with under-expression of T cell, B cell and plasmablast modules, and over-expression of neutrophil modules. Mycophenolate and azathioprine exposure were associated with plasmablast module and B cell module under-expression respectively. Disease activity associations with neutrophil over-expression and lymphocyte module under-expression were attenuated by multivariable adjustment for medication exposure.

Conclusion

Medications have significant effect on gene module expression in SLE patients. These findings emphasize the need to control for medications in studies of gene expression in SLE.

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.

REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD.

Clinical and Immunological Biomarkers for Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is characterized by immune system dysfunction and is clinically heterogeneous, exhibiting renal, dermatological, neuropsychiatric, and cardiovascular symptoms. Clinical and physiological assessment is usually inadequate for diagnosing and assessing pathophysiological processes in SLE. Clinical and immunological biomarkers could play a critical role in improving diagnosis, assessment, and ultimately, control of SLE. This article reviews clinical and immunological biomarkers that could diagnose and monitor disease activity in SLE, with and without organ-specific injury. In addition, novel SLE biomarkers that have been discovered through “omics” research are also reviewed.

Granulocyte colony-stimulating factor is not pathogenic in lupus nephritis

Systemic lupus erythematosus (lupus) is an autoimmune disease characterized by autoantibodies that form immune complexes with self‐antigens, which deposit in various tissues, leading to inflammation and disease. The etiology of disease is complex and still not completely elucidated. Dysregulated inflammation is an important disease feature, and the mainstay of lupus treatment still utilizes nonspecific anti‐inflammatory drugs. Granulocyte colony‐stimulating factor (G‐CSF) is a growth, survival, and activation factor for neutrophils and a mobilizer of hematopoietic stem cells, both of which underlie inflammatory responses in lupus. To determine whether G‐CSF has a causal role in lupus, we genetically deleted G‐CSF from Lyn‐deficient mice, an experimental model of lupus nephritis. Lyn^−/−G‐CSF^−/− mice displayed many of the inflammatory features of Lyn‐deficient mice; however, they had reduced bone marrow and tissue neutrophils, consistent with G‐CSF's role in neutrophil development. Unexpectedly, in comparison to aged Lyn‐deficient mice, matched Lyn^−/−G‐CSF^−/− mice maintained neutrophil hyperactivation and exhibited exacerbated numbers of effector memory T cells, augmented autoantibody titers, and worsened lupus nephritis. In humans, serum G‐CSF levels were not elevated in patients with lupus or with active renal disease. Thus, these studies suggest that G‐CSF is not pathogenic in lupus, and therefore G‐CSF blockade is an unsuitable therapeutic avenue.

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.

Type I interferons affect the metabolic fitness of CD8+ T cells from patients with systemic lupus erythematosus

The majority of patients with systemic lupus erythematosus (SLE) have high expression of type I IFN-stimulated genes. Mitochondrial abnormalities have also been reported, but the contribution of type I IFN exposure to these changes is unknown. Here, we show downregulation of mitochondria-derived genes and mitochondria-associated metabolic pathways in IFN-High patients from transcriptomic analysis of CD4+ and CD8+ T cells. CD8+ T cells from these patients have enlarged mitochondria and lower spare respiratory capacity associated with increased cell death upon rechallenge with TCR stimulation. These mitochondrial abnormalities can be phenocopied by exposing CD8+ T cells from healthy volunteers to type I IFN and TCR stimulation. Mechanistically these 'SLE-like' conditions increase CD8+ T cell NAD+ consumption resulting in impaired mitochondrial respiration and reduced cell viability, both of which can be rectified by NAD+ supplementation. Our data suggest that type I IFN exposure contributes to SLE pathogenesis by promoting CD8+ T cell death via metabolic rewiring.

Modular gene analysis reveals distinct molecular signatures for subsets of patients with cutaneous lupus erythematosus

BACKGROUND

Cutaneous lupus erythematosus (CLE) is a heterogeneous autoimmune disease with clinical sequelae such as itching, dyspigmentation and scarring.

OBJECTIVES

We applied a previously described modular analysis approach to assess the molecular heterogeneity of patients with CLE.

METHODS

Whole-blood transcriptomes of RNA sequencing data from a racially and ethnically diverse group of patients with CLE (n = 62) were used to calculate gene co-expression module scores. An unsupervised cluster analysis and k-means clustering based on these module scores were then performed. We used Fisher's exact tests and Kruskal-Wallis tests to compare characteristics between patient clusters.

RESULTS

Six unique clusters of patients with CLE were identified from the cluster analysis. We observed that seven inflammation modules were elevated in two clusters of patients with CLE. Additionally, these clusters were characterized by interferon, neutrophil and cell-death signatures, suggesting that interferon-related proteins, neutrophils and cell-death processes could be driving the inflammatory response in these subgroups. Three different clusters had a predominant T-cell signature, which were supported by lymphocyte counts.

CONCLUSIONS

Our data support a diverse molecular profile in CLE that further adds to the clinical variations of this skin disease, and may affect disease course and treatment selection. Future studies with a larger and diverse cohort of patients with CLE are warranted to confirm these findings.

Low-Density Neutrophils in Systemic Lupus Erythematosus

Patients with systemic lupus erythematosus (SLE) display increased numbers of immature neutrophils in the blood, but the exact role of these immature neutrophils is unclear. Neutrophils that sediment within the peripheral blood mononuclear cell fraction after density centrifugation of blood are generally defined as low-density neutrophils (LDNs). Far beyond antimicrobial functions, LDNs are emerging as decision-shapers during innate and adaptive immune responses. Traditionally, neutrophils have been viewed as a homogeneous population. However, the various LDN populations identified in SLE to date are heterogeneously composed of mixed populations of activated mature neutrophils and immature neutrophils at various stages of differentiation. Controversy also surrounds the role of LDNs in SLE in terms of whether they are proinflammatory or polymorphonuclear myeloid-derived suppressor cells. It is clear that LDNs in SLE can secrete increased levels of type I interferon (IFN) and that they contribute to the cycle of inflammation and tissue damage. They readily form neutrophil extracellular traps, exposing modified autoantigens and oxidized mitochondrial DNA, which contribute to autoantibody production and type I IFN signaling, respectively. Importantly, the ability of LDNs in SLE to perform canonical neutrophil functions is polarized, based on mature CD10+ and immature CD10- neutrophils. Although this field is still relatively new, multiomic approaches have advanced our understanding of the diverse origins, phenotype, and function of LDNs in SLE. This review updates the literature on the origin and nature of LDNs, their distinctive features, and their biologic roles in the immunopathogenesis and end-organ damage in SLE.

Unique Sjögren's syndrome patient subsets defined by molecular features

OBJECTIVE

To address heterogeneity complicating primary SS (pSS) clinical trials, research and care by characterizing and clustering patients by their molecular phenotypes.

METHODS

pSS patients met American-European Consensus Group classification criteria and had at least one systemic manifestation and stimulated salivary flow of ⩾0.1 ml/min. Correlated transcriptional modules were derived from gene expression microarray data from blood (n = 47 with appropriate samples). Patients were clustered based on this molecular information using an unbiased random forest modelling approach. In addition, multiplex, bead-based assays and ELISAs were used to assess 30 serum cytokines, chemokines and soluble receptors. Eleven autoantibodies, including anti-Ro/SSA and anti-La/SSB, were measured by Bio-Rad Bioplex 2200.

RESULTS

Transcriptional modules distinguished three clusters of pSS patients. Cluster 1 showed no significant elevation of IFN or inflammation modules. Cluster 2 showed strong IFN and inflammation modular network signatures, as well as high plasma protein levels of IP-10/CXCL10, MIG/CXCL9, BLyS (BAFF) and LIGHT. Cluster 3 samples exhibited moderately elevated IFN modules, but with suppressed inflammatory modules, increased IP-10/CXCL10 and B cell-attracting chemokine 1/CXCL13 and trends toward increased MIG/CXCL9, IL-1α, and IL-21. Anti-Ro/SSA and anti-La/SSB were present in all three clusters.

CONCLUSION

Molecular profiles encompassing IFN, inflammation and other signatures can be used to separate patients with pSS into distinct clusters. In the future, such profiles may inform patient selection for clinical trials and guide treatment decisions.

Lupus nephritis: challenges and progress

PURPOSE OF REVIEW

The management of lupus nephritis remains unsatisfactory due to insufficiently effective treatment regimens and the dearth of reliable predictors of disease onset or progression to guide individualized therapeutic decisions. This review summarizes new findings related to lupus nephritis over the last 18 months and discusses clinical needs that should be considered to advance trials of mechanism-based therapeutic strategies.

RECENT FINDINGS

Collaborative teams are addressing how to improve disease definitions and are developing predictive models for disease onset, disease response and risk of flare in individual patients. More attention is being paid to clinical trial design. Advanced technologic approaches are allowing the analysis of small amounts of human tissue and urine in unprecedented detail so as to discover new pathogenic mechanisms and identify disease biomarkers. Novel therapies continue to be tested in disease models and include new strategies to protect renal tissue from cell damage and fibrosis.

SUMMARY

The collaborative efforts of patients, clinical and translational researchers, the pharmaceutical industry and funding sources are needed to advance therapies for lupus nephritis. Specialized clinical centers can then deliver optimal and more personalized patient care that will improve patient outcomes.

Serum GlycA Level is Elevated in Active Systemic Lupus Erythematosus and Correlates to Disease Activity and Lupus Nephritis Severity

Objective: Reliable non-invasive biomarkers are needed to assess disease activity and prognosis in patients with systemic lupus erythematosus (SLE). Glycoprotein acetylation (GlycA), a novel biomarker for chronic inflammation, has been reported to be increased in several inflammatory diseases. We investigated the relevance of serum GlycA in SLE patients exhibiting various levels of activity and severity, especially with regards to renal involvement. Methods: Serum GlycA was measured by nuclear magnetic resonance spectroscopy in samples from well characterized SLE patients and from both healthy controls and patients with other kidney diseases (KD). Disease activity was evaluated using the Systemic Lupus Erythematosus Activity Index 2000 (SLEDAI-2K). Renal severity was assessed by kidney biopsy. Results: Serum GlycA was elevated in active (n = 105) compared to quiescent SLE patients (n = 39, p < 10⁻⁶), healthy controls (n = 20, p = 0.009) and KD controls (n = 21, p = 0.04), despite a more severely altered renal function in the latter. GlycA level was correlated to disease activity (SLEDAI-2K, ρ = 0.37, p < 10⁻⁴), C-reactive protein, neutrophil count, triglyceride levels, proteinuria and inversely to serum albumin. In patients with biopsy-proven lupus nephritis (LN), GlycA levels were higher in proliferative (n = 26) than non-proliferative LN (n = 10) in univariate analysis (p = 0.04), and was shown to predict proliferative LN independently of renal parameters, immunological activity, neutrophil count and daily corticosteroid dosage by multivariate analysis (p < 5 × 10⁻³ for all models). In LN patients with repeated longitudinal GlycA measurement (n = 11), GlycA varied over time and seemed to peak at the time of the flare. Conclusions: GlycA, as a summary measure for different inflammatory processes, could be a valuable biomarker of disease activity in patients with SLE, and a non-invasive biomarker of pathological severity in the context of LN.

Protecting the kidney in systemic lupus erythematosus: from diagnosis to therapy

Lupus nephritis (LN) is a common manifestation of systemic lupus erythematosus that can lead to irreversible renal impairment. Although the prognosis of LN has improved substantially over the past 50 years, outcomes have plateaued in the USA in the past 20 years as immunosuppressive therapies have failed to reverse disease in more than half of treated patients. This failure might reflect disease complexity and heterogeneity, as well as social and economic barriers to health-care access that can delay intervention until after damage has already occurred. LN progression is still poorly understood and involves multiple cell types and both immune and non-immune mechanisms. Single-cell analysis of intrinsic renal cells and infiltrating cells from patients with LN is a new approach that will help to define the pathways of renal injury at a cellular level. Although many new immune-modulating therapies are being tested in the clinic, the development of therapies to improve regeneration of the injured kidney and to prevent fibrosis requires a better understanding of the mechanisms of LN progression. This mechanistic understanding, together with the development of clinical measures to evaluate risk and detect early disease and better access to expert health-care providers, should improve outcomes for patients with LN.

Adults with systemic lupus exhibit distinct molecular phenotypes in a cross-sectional study

BACKGROUND

The clinical and pathologic diversity of systemic lupus erythematosus (SLE) hinders diagnosis, management, and treatment development. This study addresses heterogeneity in SLE through comprehensive molecular phenotyping and machine learning clustering.

METHODS

Adult SLE patients (n = 198) provided plasma, serum, and RNA. Disease activity was scored by modified SELENA-SLEDAI. Twenty-nine co-expression module scores were calculated from microarray gene-expression data. Plasma soluble mediators (n = 23) and autoantibodies (n = 13) were assessed by multiplex bead-based assays and ELISAs. Patient clusters were identified by machine learning combining K-means clustering and random forest analysis of co-expression module scores and soluble mediators.

FINDINGS

SLEDAI scores correlated with interferon, plasma cell, and select cell cycle modules, and with circulating IFN-α, IP10, and IL-1α levels. Co-expression modules and soluble mediators differentiated seven clusters of SLE patients with unique molecular phenotypes. Inflammation and interferon modules were elevated in Clusters 1 (moderately) and 4 (strongly), with decreased T cell modules in Cluster 4. Monocyte, neutrophil, plasmablast, B cell, and T cell modules distinguished the remaining clusters. Active clinical features were similar across clusters. Clinical SLEDAI trended highest in Clusters 3 and 4, though Cluster 3 lacked strong interferon and inflammation signatures. Renal activity was more frequent in Cluster 4, and rare in Clusters 2, 5, and 7. Serology findings were lowest in Clusters 2 and 5. Musculoskeletal and mucocutaneous activity were common in all clusters.

INTERPRETATION

Molecular profiles distinguish SLE subsets that are not apparent from clinical information. Prospective longitudinal studies of these profiles may help improve prognostic evaluation, clinical trial design, and precision medicine approaches.

FUNDING

US National Institutes of Health.

Utilization of Biomarkers in Lupus Nephritis

Lupus nephritis (LN) occurs in up to 60% of SLE patients, and is a leading cause of disability and death. Current treatment of LN consists of a combination of high dose corticosteroids that non-specifically decrease inflammation and cytotoxic medications that reduce auto-antibody production. That combination of therapy is associated with significant side effects while remission rates remain inadequate. Since the introduction of biologics into the pharmacological armamentarium, there has been hope for less toxic and more effective therapies for LN. Unfortunately, after multiple clinical trials, no biologic has improved efficacy over standard of care therapies for LN. This is likely, in part, due to disease heterogeneity. The utilization of biomarkers in LN may provide a way to stratify patients and guide therapeutic options. In this review, we summarize traditional and novel LN biomarkers and discuss how they may be used to diagnose, stratify, and guide therapy in patients with LN, bringing precision medicine to the forefront of LN therapy.

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

The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity.

Association between changes in gene signatures expression and disease activity among patients with systemic lupus erythematosus

BACKGROUND

We assessed the stability of BAFF, interferon, plasma cell and LDG neutrophil gene expression signatures over time, and whether changes in expression coincided with changes in SLE disease activity.

METHODS

Two hundred forty-three patients with SLE were evaluated for disease activity, serological parameters and peripheral blood gene signatures in clinic visits (2 or more per patient) that occurred between 2009 and 2012. Levels of the BAFF gene transcript, plasma cell signature, Interferon (IFN) signature and the low density granulocytes (LDG)-associated neutrophil gene signature were assessed in PAX-gene-preserved peripheral blood by global microarray. The stability of repeated measures of gene expression was quantified using intra-class correlation coefficients. SLE disease activity was measured using the Physicians Global Assessment and the SELENA-SLEDAI index and its components. Using a mixed effects regression model we assessed: 1) the association between a patient's average gene signature expression over time and disease activity, and 2) the association between a patient's changes in gene expression over time and changes in disease activity.

RESULTS

Gene expression signatures showed more within-person stability than systolic blood pressure. The IFN signature exhibited the most stability. Patients with high levels of BAFF and IFN transcripts tended to have significantly higher levels of musculoskeletal disease, skin disease, anti-dsDNA, and erythrocyte sedimentation rate, and lower levels of complement. However, changes in BAFF or IFN gene signatures were not associated with changes in disease activity. Similar associations were seen between the LDG gene signature and disease activity. However, when LDG increased, complement tended to increase. Patients with high levels of plasma cell gene signature tended to have higher levels of anti-dsDNA and lower levels of complement. However, unlike the other gene signatures, changes in plasma cell gene signature significantly coincided with changes in anti-dsDNA and complement.

CONCLUSIONS

The gene expression signatures were relatively stable within patients over time. BAFF and interferon gene expression were markers of patients with generally higher disease activity, but changes in these gene signatures did not coincide with changes in disease activity. Plasma Cell gene signature expression tracked with the traditional SLE serologic markers of anti-dsDNA and complement.

Lupus-Associated Immune Complexes Activate Human Neutrophils in an FcγRIIA-Dependent but TLR-Independent Response

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the presence of autoantibodies against nucleic acids and nucleoproteins. Anti-dsDNA Abs are considered a hallmark of SLE, and previous studies have indicated that nucleic acid-containing immune complexes (ICs) induce B cell and dendritic cell activation in a TLR-dependent process. How ICs containing nucleic acids affect neutrophil function has not been well investigated. In this study, we report that nucleic acid-containing ICs derived from the sera of SLE patients induce human and mouse neutrophil activation through TLR-independent mechanisms. Soluble ICs containing Sm/RNP, an RNA Ag, activate human neutrophils to produce reactive oxygen species (ROS) and IL-8. In contrast, ICs containing DNA have to be immobilized to efficiently activate neutrophils. We found that deleting TLR7 or TLR9, the receptors for RNA and DNA, had no effect on mouse neutrophil activation induced by RNA-containing and immobilized DNA-containing ICs. Binding of ICs are mediated through FcγRIIA and FcγRIIIB. However, neutrophil activation induced by RNA- and DNA-containing ICs requires FcγRIIA, as blocking FcγRIIA inhibited ROS release from neutrophils. RNA-containing ICs induce calcium flux, whereas TLR7/8 ligand R848 do not. Surprisingly, chloroquine inhibits calcium flux induced by RNA-containing ICs, suggesting that this lesser known function of chloroquine is involved in the neutrophil activation induced by ICs. These data indicate the SLE-derived ICs activate neutrophils to release ROS and chemokines in an FcγRIIA-dependent and TLR7- and TLR9-independent manner that likely contributes to local tissue inflammation and damage.

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

Systemic lupus erythematosus (SLE) is characterized by abnormalities in B cell and T cell function, but the role of disturbances in the activation status of macrophages (Mϕ) has not been well described in human patients. To address this, gene expression profiles from isolated lymphoid and myeloid populations were analyzed to identify differentially expressed (DE) genes between healthy controls and patients with either inactive or active SLE. While hundreds of DE genes were identified in B and T cells of active SLE patients, there were no DE genes found in B or T cells from patients with inactive SLE compared to healthy controls. In contrast, large numbers of DE genes were found in myeloid cells (MC) from both active and inactive SLE patients. Among the DE genes were several known to play roles in Mϕ activation and polarization, including the M1 genes STAT1 and SOCS3 and the M2 genes STAT3, STAT6, and CD163. M1-associated genes were far more frequent in data sets from active versus inactive SLE patients. To characterize the relationship between Mϕ activation and disease activity in greater detail, weighted gene co-expression network analysis (WGCNA) was used to identify modules of genes associated with clinical activity in SLE patients. Among these were disease activity-correlated modules containing activation signatures of predominantly M1-associated genes. No disease activity-correlated modules were enriched in M2-associated genes. Pathway and upstream regulator analysis of DE genes from both active and inactive SLE MC were cross-referenced with high-scoring hits from the drug discovery Library of Integrated Network-based Cellular Signatures (LINCS) to identify new strategies to treat both stages of SLE. A machine learning approach employing MC gene modules and a generalized linear model was able to predict the disease activity status in unrelated gene expression data sets. In summary, altered MC gene expression is characteristic of both active and inactive SLE. However, disease activity is associated with an alteration in the activation of MC, with a bias toward the M1 proinflammatory phenotype. These data suggest that while hyperactivity of B cells and T cells is associated with active SLE, MC potentially direct flare-ups and remission by altering their activation status toward the M1 state.

Ectonucleotidase-Mediated Suppression of Lupus Autoimmunity and Vascular Dysfunction

Objectives

CD39 and CD73 are surface enzymes that jut into the extracellular space where they mediate the step-wise phosphohydrolysis of the autocrine and paracrine danger signals ATP and ADP into anti-inflammatory adenosine. Given the role of vascular and immune cells' "purinergic halo" in maintaining homeostasis, we hypothesized that the ectonucleotidases CD39 and CD73 might play a protective role in lupus.

Methods

Lupus was modeled by intraperitoneal administration of pristane to three groups of mice: wild-type (WT), CD39^-/-, and CD73^-/-. After 36 weeks, autoantibodies, endothelial function, kidney disease, splenocyte activation/polarization, and neutrophil activation were characterized.

Results

As compared with WT mice, CD39^-/- mice developed exaggerated splenomegaly in response to pristane, while both groups of ectonucleotidase-deficient mice demonstrated heightened anti-ribonucleoprotein production. The administration of pristane to WT mice triggered only subtle dysfunction of the arterial endothelium; however, both CD39^-/- and CD73^-/- mice demonstrated striking endothelial dysfunction following induction of lupus, which could be reversed by superoxide dismutase. Activated B cells and plasma cells were expanded in CD73^-/- mice, while deficiency of either ectonucleotidase led to expansion of T_H17 cells. CD39^-/- and CD73^-/- mice demonstrated exaggerated neutrophil extracellular trap release, while CD73^-/- mice additionally had higher levels of plasma cell-free DNA.

Conclusion

These data are the first to link ectonucleotidases with lupus autoimmunity and vascular disease. New therapeutic strategies may harness purinergic nucleotide dissipation or signaling to limit the damage inflicted upon organs and blood vessels by lupus.

Identification of a neutrophil-related gene expression signature that is enriched in adult systemic lupus erythematosus patients with active nephritis: Clinical/pathologic associations and etiologic mechanisms

Both a lack of biomarkers and relatively ineffective treatments constitute impediments to management of lupus nephritis (LN). Here we used gene expression microarrays to contrast the transcriptomic profiles of active SLE patients with and without LN to identify potential biomarkers for this condition. RNA isolated from whole peripheral blood of active SLE patients was used for transcriptomic profiling and the data analyzed by linear modeling, with corrections for multiple testing. Results were validated in a second cohort of SLE patients, using NanoString technology. The majority of genes demonstrating altered transcript abundance between patients with and without LN were neutrophil-related. Findings in the validation cohort confirmed this observation and showed that levels of RNA abundance in renal remission were similar to active patients without LN. In secondary analyses, RNA abundance correlated with disease activity, hematuria and proteinuria, but not renal biopsy changes. As abundance levels of the individual transcripts correlated strongly with each other, a composite neutrophil score was generated by summing all levels before examining additional correlations. There was a modest correlation between the neutrophil score and the blood neutrophil count, which was largely driven by the dose of glucocorticosteroids and not the proportion of low density and/or activated neutrophils. Analysis of longitudinal data revealed no correlation between baseline neutrophil score or changes over the first year of follow-up with subsequent renal flare or treatment outcomes, respectively. The findings argue that although the neutrophil score is associated with LN, its clinical utility as a biomarker may be limited.