Dataset Information

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus.

ABSTRACT: Objectives:Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients' whole-blood transcriptomes. Methods:We applied machine learning approaches to RNA-sequencing (RNA-seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on three recently published whole-blood RNA-seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed. Results:Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified. Conclusion:Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

SUBMITTER: Figgett WA

PROVIDER: S-EPMC6946916 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus.

Figgett William A WA Monaghan Katherine K Ng Milica M Alhamdoosh Monther M Maraskovsky Eugene E Wilson Nicholas J NJ Hoi Alberta Y AY Morand Eric F EF Mackay Fabienne F

Clinical & translational immunology 20191212 12

<h4>Objectives</h4>Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients' whole-blood transcriptomes.<h4>Methods</h4>We applied machine learning approaches to RNA-sequencing (RNA-seq) data sets to stratify ...[more]

PMID: 31921420

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Project description:BackgroundSystemic lupus erythematosus (SLE) is an autoimmune disease characterized by disturbance of pro-inflammatory and anti-inflammatory lymphocytes. Growing evidence shown that gut microbiota participated in the occurrence and development of SLE by affecting the differentiation and function of intestinal immune cells. The purpose of this study was to investigate the changes of gut microbiota in SLE and judge its associations with peripheral T lymphocytes.MethodsA total of 19 SLE patients and 16 HCs were enrolled in this study. Flow cytometry was used to detect the number of peripheral T lymphocyte subsets, and 16 s rRNA was used to detect the relative abundance of gut microbiota. Analyzed the correlation between gut microbiota with SLEDAI, ESR, ds-DNA and complement. SPSS26.0 software was used to analyze the experimental data. Mann-Whitney U test was applied to compare T lymphocyte subsets. Spearman analysis was used for calculating correlation.ResultsCompared with HCs, the proportions of Tregs (P = 0.001), Tfh cells (P = 0.018) and Naïve CD4 + T cells (P = 0.004) significantly decreased in SLE patients, and proportions of Th17 cells (P = 0.020) and γδT cells (P = 0.018) increased in SLE. The diversity of SLE patients were significantly decreased. Addition, there were 11 species of flora were discovered to be distinctly different in SLE group (P < 0.05). In the correlation analysis of SLE, Tregs were positively correlated with Ruminococcus2 (P = 0.042), Th17 cells were positively correlated with Megamonas (P = 0.009), γδT cells were positively correlated with Megamonas (P = 0.003) and Streptococcus (P = 0.004), Tfh cells were positively correlated with Bacteroides (P = 0.040), and Th1 cells were negatively correlated with Bifidobacterium (P = 0.005). As for clinical indicators, the level of Tregs was negatively correlated with ESR (P = 0.031), but not with C3 and C4, and the remaining cells were not significantly correlated with ESR, C3 and C4.ConclusionGut microbiota and T lymphocyte subsets of SLE changed and related to each other, which may break the immune balance and affect the occurrence and development of SLE. Therefore, it is necessary to pay attention to the changes of gut microbiota and provide new ideas for the treatment of SLE.

Project description:IntroductionMost childhood-onset SLE patients (cSLE) develop lupus nephritis (cLN), but only a small proportion achieve complete response to current therapies. The prognosis of children with LN and end-stage renal disease is particularly dire. Mortality rates within the first five years of renal replacement therapy may reach 22%. Thus, there is urgent need to decipher and target immune mechanisms that drive cLN. Despite the clear role of autoantibody production in SLE, targeted B cell therapies such as rituximab (anti-CD20) and belimumab (anti-BAFF) have shown only modest efficacy in cLN. While many studies have linked dysregulation of germinal center formation to SLE pathogenesis, other work supports a role for extrafollicular B cell activation in generation of pathogenic antibody secreting cells. However, whether extrafollicular B cell subsets and their T cell collaborators play a role in specific organ involvement in cLN and/or track with disease activity remains unknown.MethodsWe analyzed high-dimensional mass cytometry and gene expression data from 24 treatment naïve cSLE patients at the time of diagnosis and longitudinally, applying novel computational tools to identify abnormalities associated with clinical manifestations (cLN) and disease activity (SLEDAI).ResultscSLE patients have an extrafollicular B cell expansion signature, with increased frequency of i) DN2, ii) Bnd2, iii) plasmablasts, and iv) peripheral T helper cells. Most importantly, we discovered that this extrafollicular signature correlates with disease activity in cLN, supporting extrafollicular T/B interactions as a mechanism underlying pediatric renal pathogenesis.DiscussionThis study integrates established and emerging themes of extrafollicular B cell involvement in SLE by providing evidence for extrafollicular B and peripheral T helper cell expansion, along with elevated type 1 IFN activation, in a homogeneous cohort of treatment-naïve cSLE patients, a point at which they should display the most extreme state of their immune dysregulation.

Dataset Information

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus.

Publications

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus.

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