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.

Immune and molecular landscape behind non-response to Mycophenolate Mofetil and Azathioprine in lupus nephritis therapy.

Lupus nephritis (LN) represents one of the most severe complications of systemic lupus erythematosus, leading to end-stage kidney disease in worst cases. Current first-line therapies for LN, including mycophenolate mofetil (MMF) and azathioprine (AZA), fail to induce long-term remission in 60-70% of the patients, evidencing the urgent need to delve into the molecular knowledge-gap behind the non-response to these therapies. A longitudinal cohort of treated LN patients including clinical, cellular and transcriptomic data, was analyzed. Gene-expression signatures behind non-response to different drugs were revealed by differential expression analysis. Drug-specific non-response mechanisms and cell proportion differences were identified. Blood cell subsets mediating non-response were described using single-cell RNASeq data. We show that AZA and MMF non-response implicates different cells and regulatory functions. Mechanistic models were used to suggest add-on therapies to improve their current performance. Our results provide new insights into the molecular mechanisms associated with treatment failures in LN.

Gut epithelial barrier dysfunction in lupus triggers a differential humoral response against gut commensals.

Introduction: Systemic lupus erythematosus is an autoimmune disease with multisystemic involvement including intestinal inflammation. Lupus-associated intestinal inflammation may alter the mucosal barrier where millions of commensals have a dynamic and selective interaction with the host immune system. Here, we investigated the consequences of the intestinal inflammation in a TLR7-mediated lupus model. Methods: IgA humoral and cellular response in the gut was measured. The barrier function of the gut epithelial layer was characterised. Also, microbiota composition in the fecal matter was analysed as well as the systemic humoral response to differential commensals. Results: The lupus-associated intestinal inflammation modifies the IgA+ B cell response in the gut-associated lymphoid tissue in association with dysbiosis. Intestinal inflammation alters the tight junction protein distribution in the epithelial barrier, which correlated with increased permeability of the intestinal barrier and changes in the microbiota composition. This permeability resulted in a differential humoral response against intestinal commensals. Discussion: Lupus development can cause alterations in microbiota composition, allowing specific species to colonize only the lupus gut. Eventually, these alterations and the changes in gut permeability induced by intestinal inflammation could lead to bacterial translocation.

Distinct gene dysregulation patterns herald precision medicine potentiality in systemic lupus erythematosus.

OBJECTIVES: We aimed at investigating the whole-blood transcriptome, expression quantitative trait loci (eQTLs), and levels of selected serological markers in patients with SLE versus healthy controls (HC) to gain insight into pathogenesis and identify drug targets. METHODS: We analyzed differentially expressed genes (DEGs) and dysregulated gene modules in a cohort of 350 SLE patients and 497 HC from the European PRECISESADS project (NTC02890121), split into a discovery (60%) and a replication (40%) set. Replicated DEGs qualified for eQTL, pathway enrichment, regulatory network, and druggability analysis. For validation purposes, a separate gene module analysis was performed in an independent cohort (GSE88887). RESULTS: Analysis of 521 replicated DEGs identified multiple enriched interferon signaling pathways through Reactome. Gene module analysis yielded 18 replicated gene modules in SLE patients, including 11 gene modules that were validated in GSE88887. Three distinct gene module clusters were defined i.e., "interferon/plasma cells", "inflammation", and "lymphocyte signaling". Predominant downregulation of the lymphocyte signaling cluster denoted renal activity. By contrast, upregulation of interferon-related genes indicated hematological activity and vasculitis. Druggability analysis revealed several potential drugs interfering with dysregulated genes within the "interferon" and "PLK1 signaling events" modules. STAT1 was identified as the chief regulator in the most enriched signaling molecule network. Drugs annotated to 15 DEGs associated with cis-eQTLs included bortezomib for its ability to modulate CTSL activity. Belimumab was annotated to TNFSF13B (BAFF) and daratumumab was annotated to CD38 among the remaining replicated DEGs. CONCLUSIONS: Modulation of interferon, STAT1, PLK1, B and plasma cell signatures showed promise as viable approaches to treat SLE, pointing to their importance in SLE pathogenesis.

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.

A comprehensive database for integrated analysis of omics data in autoimmune diseases.

BACKGROUND: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. RESULTS: Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. CONCLUSIONS: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

Precision medicine in autoimmune diseases: fact or fiction.

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

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.

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.

A survey of gene expression meta-analysis: methods and applications.

The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.

Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification.

Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.

ImaGEO: integrative gene expression meta-analysis from GEO database.

SUMMARY: The Gene Expression Omnibus (GEO) database provides an invaluable resource of publicly available gene expression data that can be integrated and analyzed to derive new hypothesis and knowledge. In this context, gene expression meta-analysis (geMAs) is increasingly used in several fields to improve study reproducibility and discovering robust biomarkers. Nevertheless, integrating data is not straightforward without bioinformatics expertise. Here, we present ImaGEO, a web tool for geMAs that implements a complete and comprehensive meta-analysis workflow starting from GEO dataset identifiers. The application integrates GEO datasets, applies different meta-analysis techniques and provides functional analysis results in an easy-to-use environment. ImaGEO is a powerful and useful resource that allows researchers to integrate and perform meta-analysis of GEO datasets to lead robust findings for biomarker discovery studies. AVAILABILITY AND IMPLEMENTATION: ImaGEO is accessible at http://bioinfo.genyo.es/imageo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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

OBJECTIVE: The highly heterogeneous clinical presentation of systemic lupus erythematosus (SLE) is characterized by the unpredictable occurrence of disease flares and organ damage. Attempts to stratify lupus patients have been limited to classification based on clinical information, leading to unsuccessful clinical trials and controversial research results. This study was undertaken to develop and validate a robust method to stratify patients with lupus according to longitudinal disease activity and whole-genome gene expression data in order to establish subgroups of patients who share disease progression mechanisms. METHODS: We used a cluster-based approach to stratify SLE patients based on the correlation between disease activity scores and longitudinal gene expression information. Clustering robustness was evaluated by the bootstrap method, and the clusters were characterized in terms of clinical and functional features. RESULTS: We observed a clear partition into 3 different disease clusters in 2 independent sets of patients, one pediatric and one adult, which was not influenced by treatment, race, or other source of bias. Two of the clusters differentiated into a group showing a correlation between the percentage of neutrophils and disease activity progression and a group showing a correlation between the percentage of lymphocytes and disease activity progression. The third cluster, in which the percentage of neutrophils correlated to a lesser degree with disease activity, was functionally more heterogeneous. Patients in the neutrophil-driven clusters had an increased risk of developing proliferative nephritis. CONCLUSION: Our findings indicate that SLE patients can be stratified into 3 subgroups of patients who show different mechanisms of disease progression and are clinically differentiated. Our results have important implications for treatment options, the design of clinical trials, our understanding of the etiology of the disease, and the prediction of severe glomerulonephritis.

MetaGenyo: a web tool for meta-analysis of genetic association studies.

BACKGROUND: Genetic association studies (GAS) aims to evaluate the association between genetic variants and phenotypes. In the last few years, the number of this type of study has increased exponentially, but the results are not always reproducible due to experimental designs, low sample sizes and other methodological errors. In this field, meta-analysis techniques are becoming very popular tools to combine results across studies to increase statistical power and to resolve discrepancies in genetic association studies. A meta-analysis summarizes research findings, increases statistical power and enables the identification of genuine associations between genotypes and phenotypes. Meta-analysis techniques are increasingly used in GAS, but it is also increasing the amount of published meta-analysis containing different errors. Although there are several software packages that implement meta-analysis, none of them are specifically designed for genetic association studies and in most cases their use requires advanced programming or scripting expertise. RESULTS: We have developed MetaGenyo, a web tool for meta-analysis in GAS. MetaGenyo implements a complete and comprehensive workflow that can be executed in an easy-to-use environment without programming knowledge. MetaGenyo has been developed to guide users through the main steps of a GAS meta-analysis, covering Hardy-Weinberg test, statistical association for different genetic models, analysis of heterogeneity, testing for publication bias, subgroup analysis and robustness testing of the results. CONCLUSIONS: MetaGenyo is a useful tool to conduct comprehensive genetic association meta-analysis. The application is freely available at http://bioinfo.genyo.es/metagenyo/ .

Metagene projection characterizes GEN2.2 and CAL-1 as relevant human plasmacytoid dendritic cell models.

MOTIVATION: Plasmacytoid dendritic cells (pDC) play a major role in the regulation of adaptive and innate immunity. Human pDC are difficult to isolate from peripheral blood and do not survive in culture making the study of their biology challenging. Recently, two leukemic counterparts of pDC, CAL-1 and GEN2.2, have been proposed as representative models of human pDC. Nevertheless, their relationship with pDC has been established only by means of particular functional and phenotypic similarities. With the aim of characterizing GEN2.2 and CAL-1 in the context of the main circulating immune cell populations we have performed microarray gene expression profiling of GEN2.2 and carried out an integrated analysis using publicly available gene expression datasets of CAL-1 and the main circulating primary leukocyte lineages. RESULTS: Our results show that GEN2.2 and CAL-1 share common gene expression programs with primary pDC, clustering apart from the rest of circulating hematopoietic lineages. We have also identified common differentially expressed genes that can be relevant in pDC biology. In addition, we have revealed the common and differential pathways activated in primary pDC and cell lines upon CpG stimulatio. AVAILABILITY AND IMPLEMENTATION: R code and data are available in the supplementary material. CONTACT: pedro.carmona@genyo.es or concepcion.maranon@genyo.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Support for phosphoinositol 3 kinase and mTOR inhibitors as treatment for lupus using in-silico drug-repurposing analysis.

BACKGROUND: Systemic lupus erythematosus (SLE) is an autoimmune disease with few treatment options. Current therapies are not fully effective and show highly variable responses. In this regard, large efforts have focused on developing more effective therapeutic strategies. Drug repurposing based on the comparison of gene expression signatures is an effective technique for the identification of new therapeutic approaches. Here we present a drug-repurposing exploratory analysis using gene expression signatures from SLE patients to discover potential new drug candidates and target genes. METHODS: We collected a compendium of gene expression signatures comprising peripheral blood cells and different separate blood cell types from SLE patients. The Lincscloud database was mined to link SLE signatures with drugs, gene knock-down, and knock-in expression signatures. The derived dataset was analyzed in order to identify compounds, genes, and pathways that were significantly correlated with SLE gene expression signatures. RESULTS: We obtained a list of drugs that showed an inverse correlation with SLE gene expression signatures as well as a set of potential target genes and their associated biological pathways. The list includes drugs never or little studied in the context of SLE treatment, as well as recently studied compounds. CONCLUSION: Our exploratory analysis provides evidence that phosphoinositol 3 kinase and mammalian target of rapamycin (mTOR) inhibitors could be potential therapeutic options in SLE worth further future testing.

Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjögren's syndrome uncovered through gene expression meta-analysis.

INTRODUCTION: Systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and Sjögren's syndrome (SjS) are inflammatory systemic autoimmune diseases (SADs) that share several clinical and pathological features. The shared biological mechanisms are not yet fully characterized. The objective of this study was to perform a meta-analysis using publicly available gene expression data about the three diseases to identify shared gene expression signatures and overlapping biological processes. METHODS: Previously reported gene expression datasets were selected and downloaded from the Gene Expression Omnibus database. Normalization and initial preprocessing were performed using the statistical programming language R and random effects model-based meta-analysis was carried out using INMEX software. Functional analysis of over- and underexpressed genes was done using the GeneCodis tool. RESULTS: The gene expression meta-analysis revealed a SAD signature composed of 371 differentially expressed genes in patients and healthy controls, 187 of which were underexpressed and 184 overexpressed. Many of these genes have previously been reported as significant biomarkers for individual diseases, but others provide new clues to the shared pathological state. Functional analysis showed that overexpressed genes were involved mainly in immune and inflammatory responses, mitotic cell cycles, cytokine-mediated signaling pathways, apoptotic processes, type I interferon-mediated signaling pathways and responses to viruses. Underexpressed genes were involved primarily in inhibition of protein synthesis. CONCLUSIONS: We define a common gene expression signature for SLE, RA and SjS. The analysis of this signature revealed relevant biological processes that may play important roles in the shared development of these pathologies.