scholarly journals Cell-type-specific transcriptome architecture underlying the establishment and exacerbation of systemic lupus erythematosus

2022 ◽  
Author(s):  
Masahiro Nakano ◽  
Mineto Ota ◽  
Yusuke Takeshima ◽  
Yukiko Iwasaki ◽  
Hiroaki Hatano ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Disease Activity ◽  
Lupus Erythematosus ◽  
Large Scale ◽  
Immune Cell ◽  
Disease State ◽  
Cell Type ◽  
Sequencing Data ◽  
Systemic Lupus ◽  
Cell Type Specific

Systemic lupus erythematosus (SLE) is a complex and heterogeneous autoimmune disease involving multiple immune cells. A major hurdle to the elucidation of SLE pathogenesis is our limited understanding of dysregulated gene expression linked to various clinical statuses with a high cellular resolution. Here, we conducted a large-scale transcriptome study with 6,386 RNA sequencing data covering 27 immune cell types from 159 SLE and 89 healthy donors. We first profiled two distinct cell-type-specific transcriptomic signatures: disease-state and disease-activity signatures, reflecting disease establishment and exacerbation, respectively. We next identified candidate biological processes unique to each signature. This study suggested the clinical value of disease-activity signatures, which were associated with organ involvement and responses to therapeutic agents such as belimumab. However, disease-activity signatures were less enriched around SLE risk variants than disease-state signatures, suggesting that the genetic studies to date may not well capture clinically vital biology in SLE. Together, we identified comprehensive gene signatures of SLE, which will provide essential foundations for future genomic, genetic, and clinical studies.

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

Lupus ◽  
2021 ◽  
pp. 096120332199010
Author(s):  
Vineeta Shobha ◽  
Anu Mohan ◽  
AV Malini ◽  
Puneet Chopra ◽  
Preethi Karunanithi ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Disease Activity ◽  
Lupus Erythematosus ◽  
Time Course ◽  
Cell Activation ◽  
Immune Cell ◽  
Gene Signature ◽  
Systemic Lupus ◽  
Auto Antibody ◽  
Activation Status

Objective Despite the significant advancement in the understanding of the pathophysiology of systemic lupus erythematosus (SLE) variable clinical response to newer therapies remain a major concern, especially for patients with lupus nephritis and neuropsychiatric systemic lupus erythematosus (NPSLE). We performed this study with an objective to comprehensively characterize Indian SLE patients with renal and neuropsychiatric manifestation with respect to their gene signature, cytokine profile and immune cell phenotypes. Methods We characterized 68 Indian SLE subjects with diverse clinical profiles and disease activity and tried to identify differentially expressed genes and enriched pathways. To understand the temporal profile, same patients were followed at 6 and 12-months intervals. Additionally, auto-antibody profile, levels of various chemokines, cytokines and the proportion of different immune cells and their activation status were captured in these subjects. Results Multiple IFN-related pathways were enriched with significant increase in IFN-I gene signature in SLE patients as compared to normal healthy volunteers (NHV). We identified two transcriptionally distinct clusters within the same cohort of SLE patients with differential immune cell activation status, auto-antibody as well as plasma chemokines and cytokines profile. Conclusions Identification of two distinct clusters of patients based on IFN-I signature provided new insights into the heterogeneity of underlying disease pathogenesis of Indian SLE cohort. Importantly, patient within those clusters retain their distinct expression dynamics of IFN-I signature over the time course of one year despite change in disease activity. This study will guide clinicians and researchers while designing future clinical trials on Indian SLE cohort.

Repeated administration of dapirolizumab pegol in a randomised phase I study is well tolerated and accompanied by improvements in several composite measures of systemic lupus erythematosus disease activity and changes in whole blood transcriptomic profiles

2017 ◽  
Vol 76 (11) ◽  
pp. 1837-1844 ◽  
Author(s):  
Chris Chamberlain ◽  
Peter J Colman ◽  
Ann M Ranger ◽  
Linda C Burkly ◽  
Geoffrey I Johnston ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Adverse Events ◽  
Disease Activity ◽  
Lupus Erythematosus ◽  
Immune Cell ◽  
Repeated Administration ◽  
Clinical Activity ◽  
Systemic Lupus ◽  
Fab Fragment ◽  
Double Blind

ObjectivesSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease associated with diffuse immune cell dysfunction. CD40–CD40 ligand (CD40L) interaction activates B cells, antigen-presenting cells and platelets. CD40L blockade might provide an innovative treatment for systemic autoimmune disorders. We investigated the safety and clinical activity of dapirolizumab pegol, a polyethylene glycol conjugated anti-CD40L Fab' fragment, in patients with SLE.MethodsThis 32-week randomised, double-blind, multicentre study (NCT01764594) evaluated repeated intravenous administration of dapirolizumab pegol in patients with SLE who were positive for/had history of antidouble stranded DNA/antinuclear antibodies and were on stable doses of immunomodulatory therapies (if applicable). Sixteen patients were randomised to 30 mg/kg dapirolizumab pegol followed by 15 mg/kg every 2 weeks for 10 weeks; eight patients received a matched placebo regimen. Randomisation was stratified by evidence of antiphospholipid antibodies. Patients were followed for 18 weeks after the final dose.ResultsNo serious treatment-emergent adverse events, thromboembolic events or deaths occurred. Adverse events were mild or moderate, transient and resolved without intervention. One patient withdrew due to infection.Efficacy assessments were conducted only in patients with high disease activity at baseline. Five of 11 (46%) dapirolizumab pegol-treated patients achieved British Isles Lupus Assessment Group-based Composite Lupus Assessment response (vs 1/7; 14% placebo) and 5/12 (42%) evaluable for SLE Responder Index-4 responded by week 12 (vs 1/7; 14% placebo). Mechanism-related gene expression changes were observed in blood RNA samples.ConclusionsDapirolizumab pegol could be an effective biological treatment for SLE. Further studies are required to address efficacy and safety.Trial registration numberNCT01764594.

scholarly journals Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

2020 ◽  
Author(s):  
Gaia Andreoletti ◽  
Cristina M. Lanata ◽  
Ishan Paranjpe ◽  
Tia S. Jain ◽  
Joanne Nititham ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
B Cells ◽  
Disease Activity ◽  
Nk Cells ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Differentially Expressed ◽  
Cell Type ◽  
Systemic Lupus

AbstractSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease in which outcomes vary among different racial groups. The aim of this study is to leverage large-scale transcriptomic data from diverse populations to better sub-classify SLE patients into more clinically actionable groups. We leverage cell sorted RNA-seq data (CD14+ monocytes, B cells, CD4+T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four tier analytical approach to identify SLE subgroups within this multiethnic cohort: unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning. K-means clustering on the individual cell type data resulted in three clusters for CD4 and CD14, and two clusters for B cells and NK cells. Correlation analysis revealed significant positive associations between the transcriptomic clusters of each immune cell and clinical parameters including disease activity and ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across the four cell types were involved in SLE or other autoimmune related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity defined by SLEDAI score >=6) and White-low, Asians-low (SLEDAI < 6). Random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White. The results from these analyses will help stratify patients based on their gene expression signatures to enable precision medicine for SLE.

scholarly journals Ethnicity-specific transcriptomic variation in immune cells and correlation with disease activity in systemic lupus erythematosus

2020 ◽  
Author(s):  
Gaia Andreoletti ◽  
Cristina Lanata ◽  
Ishan Paranjpe ◽  
Tia Jain ◽  
Joanne Nititham ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
Disease Activity ◽  
Nk Cells ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Cd4 T Cells ◽  
Differentially Expressed ◽  
Cell Type ◽  
Systemic Lupus

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

scholarly journals POS0174 IMMUNOPHENOTYPE OF SJÖGREN´S SYNDROME AND SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS IDENTIFIED TWO ENDOTYPES WITH POTENTIAL THERAPEUTIC IMPLICATIONS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 300.1-300
Author(s):  
L. Martin-Gutierrez ◽  
J. Peng ◽  
G. Robinson ◽  
M. Naja ◽  
H. Peckham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Disease Activity ◽  
Lupus Erythematosus ◽  
Immune Cell ◽  
T Cell Subsets ◽  
Systemic Lupus ◽  
Immune Cell Subsets ◽  
Group 2 ◽  
Group 1

Background:Primary Sjögren’s syndrome (pSS) and systemic lupus erythematosus (SLE) are chronic autoimmune rheumatic diseases (ARDs) that share a strong female gender bias, as well as genetic, clinical and serological characteristics.Although significant progress has been made in improving treatment and patient related outcomes in pSS and SLE, there is a need for improved early diagnosis, adequate therapy monitoring, treatment of refractory manifestations and strategies to address co-morbidities.However, the results of many clinical trials are disappointing, and nobiologic treatments are licensedin pSS, while few are available for SLE patients with refractory disease.Objectives:Identifying shared immunological features between patients with pSS and SLE that could lead to better treatment selection using a stratification approach.Methods:Immune-phenotyping of 29 immune-cell subsets in peripheral blood from patients with pSS (n=45), SLE (n=29) and secondary SS associated with SLE (SLE/SS) (n=14) with low disease activity or in clinical remission, and sex-matched healthy controls (n=31), was performed using flow cytometry. Data were analysed using logistic regression and multiple t-tests andsupervised machine learning (balanced random forest-BRF, sparse partial least squares discriminant analysis-sPLS-DA). Patients were stratified by k-means clustering. Clinical trajectories were analysed over 5 year follow-up.Results:Comparing the immune profile of pSS and SLE patients using a variety of statistical and machine learning (ML) approaches, identified very few statistically significant differences between the two cohorts despite patients having a different clinical presentation and diagnosis. Thus, we hypothesised that immune-based subtypes could be shared between pSS, SLE and SLE/SS patients. Unsupervised k-means clustering was applied to the immunological features of the combined patient cohorts and two distinct patient endotypes, were identified: Group-1 (n=49; pSS=24, SLE=19, SLE/SS=6) and Group-2 (n=39; pSS=21, SLE=10, SLE/SS=8). Significant differences in immune-cell phenotypes across B-cell and T-cell subsets were identified by logistic regression, BRF (AUC=0.9942, assessed by 10-fold cross-validation) and sPLS-DA analysis. Comparison of the multiple analysis approaches identified eight common immune-cell subsets, including total and memory CD4+ and CD8+ T-cell subsets but no B-cell subsets. Using this common immune-signature the stratification between the groups was maintained and slightly improved (AUC=0.9979 and accuracy 96.16%). Interestingly, patients in Group-2 had elevated disease activity measures at baseline and over a 5-year trajectory compared to Group-1. Finally, correlation analysis identifed correlations between disease activity markers and the top ranked immune features from the ML models.Conclusion:The identified immune-cell signatures could reflect the underlying disease pathogenesis that spans diagnositc criteria and could be used to select patients for targeted therapeutic approaches.Acknowledgements:LM-G is supported by a project grant from The Dunhill Medical Trust (RPGF1902\117); JP is supported by Versus Arthritis (21226). GAR is supported by Lupus UK, The Rosetrees Trust (M409) and Versus Arthritis (21593). MN is supported by NIHR UCLH Biomedical Research Centre (BRC525/III/CC/191350). HP has a Versus Arthritis PhD studentship (22203). This work was performed within the Centre for Adolescent Rheumatology Versus Arthritis at UCL UCLH and GOSH supported by grants from Versus Arthritis (21593 and 20164), GOSCC, and the NIHR-Biomedical Research Centres at both GOSH and UCLH.We would like to thank Mr Jamie Evans for expert support with flow cytometry analysis and Ms Eve McLoughlin for support with patient recruitment.Disclosure of Interests:None declared

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

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaia Andreoletti ◽  
Cristina M. Lanata ◽  
Laura Trupin ◽  
Ishan Paranjpe ◽  
Tia S. Jain ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
T Cells ◽  
Disease Activity ◽  
Nk Cells ◽  
Differentially Expressed Genes ◽  
Lupus Erythematosus ◽  
Cd4 T Cells ◽  
Differentially Expressed ◽  
Cell Type ◽  
Systemic Lupus

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

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