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.

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

OBJECTIVES: Type I interferon (IFN) plays a role in the pathogenesis of systemic lupus erythematosus (SLE), but insufficient attention has been directed to the differences in IFN responses between ancestral populations. Here, we explored the expression of the interferon gene signatures (IGSs) in SLE patients of European ancestry (EA) and Asian ancestry (AsA). METHODS: We used gene set variation analysis with multiple IGS encompassing the response to both type 1 and type 2 IFN in isolated CD14+ monocytes, CD19+B cells, CD4+T cells and Natural Killer (NK) cells from patients with SLE stratified by self-identified ancestry. The expression of genes upstream of the IGS and influenced by lupus-associated risk alleles was also examined. Lastly, we employed machine learning (ML) models to assess the most important features classifying patients by disease activity. RESULTS: AsA patients with SLE exhibited greater enrichment in the IFN core and IFNA2 IGS compared with EA patients in all cell types examined and, in the presence and absence of autoantibodies. Overall, AsA patients with SLE demonstrated higher expression of genes upstream of the IGS than EA counterparts. ML with feature importance analysis indicated that IGS expression in NK cells, anti-dsDNA, complement levels and AsA status contributed to disease activity. CONCLUSIONS: AsA patients with SLE exhibited higher IGS than EA patients in all cell types regardless of autoantibody status, with enhanced expression of genetically associated genes upstream of the IGS potentially contributing. AsA, along with the IGS in NK cells, anti-dsDNA and complement, independently influenced SLE disease activity.

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

Objective: Rheumatoid arthritis is a chronic inflammatory autoimmune disease that can lead to synovial damage, persistent joint pain, and functional disability. Our objective was to evaluate baseline synovial transcriptome from early inflammatory arthritis patients (EIA) and identify pretreatment biomarkers that could potentially provide insights into long-term functional outcomes of rheumatoid arthritis (RA). Methods: Synovial biopsies from clinically inflamed knee joints were procured from either 17 EIA patients before initiation of disease modifying anti-rheumatic drug (DMARD) therapy (DMARD-naïve EIA) using the minimally invasive closed needle biopsy technique or advanced RA patients undergoing arthroplasty. Affymetrix Human Genome U133 Plus 2 microarray platform was used to profile the synovial transcriptome. The cohort was followed clinically for a median of 12.3 years, and patient data was collected at each visit. Short-term and long-term clinical outcomes were determined by assessing RA-associated clinical parameters Statistical adjustments were made to account for asynchronous clinical visits and duration of follow up. Results: Based on the transcriptomic analysis, we identified 5 differentially expressed genes (DEGs), including matrix metalloproteinase (MMP)-1 (fibroblast collagenase) and MMP-3 (stromelysin-1) in DMARD-naïve EIA patients, relative to advanced RA patients (q < 0.05). Dichotomous expression of MMP-1 and MMP-3 mRNA and protein was confirmed by qPCR and immunohistochemistry respectively, based on which DMARD-naïve EIA subjects were classified as MMP-high or MMP-low. Hierarchical clustering of transcriptomic data identified 947 DEGs between MMP-high and MMP-low cohorts. Co-expression and IPA analysis of DEGs in the MMP-high cohort showed an enrichment of genes that participated in metabolic or biochemical functions and intracellular immune signaling were regulated through NF-κB and ß-catenin complexes and correlated with markers of systemic inflammation. Analysis of short-term clinical outcomes in MMP-high cohort showed a significant reduction in the DAS-CRP scores relative to baseline (P <0.001), whereas area under the curve analyses of modified HAQ (mHAQ) scores correlated negatively with baseline MMP-1 (R = -0.59, P = 0.03). Further, longitudinal mHAQ scores, number of swollen joints, number of DMARDs and median follow-up duration appeared to be higher in MMP-low cohort. Conclusion: Overall, our results indicate that the gene expression profiling of synovial biopsies obtained at the DMARD-naive stage in patients with EIA categorizes them into subsets with varying degrees of inflammation and can predict the future of long-term clinical outcome.

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

OBJECTIVES: The goals of these studies were to elucidate the inter-relationships of specific anti-nuclear antibody (ANA), complement, and the interferon gene signature (IGS) in the pathogenesis of systemic lupus erythematosus (SLE). METHODS: Data from the Illuminate trials were analysed for antibodies to dsDNA as well as RNA-binding proteins (RBP), levels of C3, C4 and various IGS. Statistical hypothesis testing, linear regression analyses and classification and regression trees analysis were employed to assess relationships between the laboratory features of SLE. RESULTS: Inter-relationships of ANAs, complement and the IGS differed between patients of African Ancestry (AA) and European Ancestry (EA); anti-RNP and multiple autoantibodies were more common in AA patients and, although both related to the presence of the IGS, relationships between autoantibodies and complement differed. Whereas, anti-dsDNA had an inverse relationship to C3 and C4, levels of anti-RNP were not related to these markers. The IGS was only correlated with anti-dsDNA in EA SLE and complement was more correlated to the IGS in AA SLE. Finally, autoantibodies occurred in the presence and absence of the IGS, whereas the IGS was infrequent in anti-dsDNA/anti-RBP-negative SLE patients. CONCLUSION: There is a complex relationship between autoantibodies and the IGS, with anti-RNP associated in AA and both anti-dsDNA and RNP associated in EA. Moreover, there was a difference in the relationship between anti-dsDNA, but not anti-RBP, with complement levels. The lack of a relationship of anti-RNP with C3 and C4 suggests that anti-RNP immune complexes (ICs) may drive the IGS without complement fixation, whereas anti-dsDNA ICs involve complement consumption.

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.

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

SARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients. Our results indicate that COVID-19 pathogenesis is driven by populations of myeloid-lineage cells with highly inflammatory but distinct transcriptional signatures in each compartment. The relative absence of cytotoxic cells in the lung suggests a model in which delayed clearance of the virus may permit exaggerated myeloid cell activation that contributes to disease pathogenesis by the production of inflammatory mediators. The gene expression profiles also identify potential therapeutic targets that could be modified with available drugs. The data suggest that transcriptomic profiling can provide an understanding of the pathogenesis of COVID-19 in individual patients.

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

Arthritis is a common manifestation of systemic lupus erythematosus (SLE) yet understanding of the underlying pathogenic mechanisms remains incomplete. We, therefore, interrogated gene expression profiles of SLE synovium to gain insight into the nature of lupus arthritis (LA), using osteoarthritis (OA) and rheumatoid arthritis (RA) as comparators. Knee synovia from SLE, OA, and RA patients were analyzed for differentially expressed genes (DEGs) and also by Weighted Gene Co-expression Network Analysis (WGCNA) to identify modules of highly co-expressed genes. Genes upregulated and/or co-expressed in LA revealed numerous immune/inflammatory cells dominated by a myeloid phenotype, in which pathogenic macrophages, myeloid-lineage cells, and their secreted products perpetuate inflammation, whereas OA was characterized by fibroblasts and RA of lymphocytes. Genes governing trafficking of immune cells into the synovium by chemokines were identified, but not in situ generation of germinal centers (GCs). Gene Set Variation Analysis (GSVA) confirmed activation of specific immune cell types in LA. Numerous therapies were predicted to target LA, including TNF, NFκB, MAPK, and CDK inhibitors. Detailed gene expression analysis identified a unique pattern of cellular components and physiologic pathways operative in LA, as well as drugs potentially able to target this common manifestation of SLE.