Glucocorticoid-induced leucine zipper (GILZ) inhibits B cell activation in systemic lupus erythematosus

Jones, Sarah A.; Toh, Andrew E. J.; Odobasic, Dragana; Oudin, Marie-Anne Virginie; Cheng, Qiang; Lee, Jacinta P. W.; White, Stefan J.; Russ, Brendan E.; Infantino, Simona; Light, Amanda; Tarlinton, David M.; Harris, James; Morand, Eric Francis

doi:10.1136/annrheumdis-2015-207744

Cited by 36 publications

(48 citation statements)

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“…TSC22D3 (also known as GILZ ) was identified as a negative regulator of B cells and lack of GILZ can drive autoimmune disease. 9 GILZ expression was markedly diminished in C2, suggesting a loss of B cell regulation. GILZ was upregulated in C3 and C4, possibly as an effect of glucocorticoid induction (figure 4E).…”

Section: Resultsmentioning

confidence: 97%

“…Two examples are mice overexpressing B cell activating factor of the TNF family (BAFF, also known as TNFSF13B) i.e. BAFF-transgenic mice, in which low-affinity self-reactive B cells aberrantly survive, 7, 8 and glucocorticoid-induced leucine zipper (GILZ)-deficient mice 9 with impaired regulation of activated B cells. These and various other mouse models of SLE replicate some aspects of disease relevant to some patients with SLE, but most likely do not individually account for all the disease symptoms and pathogenesis mechanisms in humans.…”

Section: Introductionmentioning

confidence: 99%

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

Figgett

Monaghan

et al. 2019

Preprint

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16Objective. Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that 17 is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus 18 responses to available treatments are unpredictable. Here, we developed a new stratification 19 scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE. 20Methods. We applied machine learning approaches to RNA-sequencing (RNA-seq) datasets 21 to stratify patients with SLE into four distinct clusters based on their gene expression profiles. 22A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and 23 an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in 24 this research; 141 patients with SLE and 51 healthy donors were analysed in total. 25 Results. Examination of SLE clusters, as opposed to unstratified SLE patients, revealed 26 underappreciated differences in the pattern of expression of disease-related genes relative to 27 clinical presentation. Moreover, gene signatures correlated to flare activity were successfully 28 identified. 29 Conclusion. Given that disease heterogeneity has confounded research studies and clinical 30 trials, our approach addresses current unmet medical needs and provides a greater 31 understanding of SLE heterogeneity in humans. Stratification of patients based on gene 32 expression signatures may be a valuable strategy to harness disease heterogeneity and identify 33 patient populations that may be at an increased risk of disease symptoms. Further, this approach 34 can be used to understand the variability in responsiveness to therapeutics, thereby improving 35 the design of clinical trials and advancing personalised therapy. 36 37 Abstract word count: 242 38 39 Keywords 40 SLE, autoimmunity, RNA-seq, transcriptomics, stratification. 41 3 Abbreviations 42 ACR, American College of Rheumatology; ANA, anti-nuclear autoantibodies; BAFF, B cell 43 activating factor of the TNF family; cpm, counts per million; ECOC, error-correcting output 44 codes; ENA, extractible nuclear antigens; FPKM, fragments per kilobase of transcript per 45 million mapped reads; GILZ, glucocorticoid-induced leucine zipper; GO, gene ontology; HPC, 46 high performance computing; ISM, interferon signature metric; KEGG, Kyoto Encyclopedia 47 MATERIALS AND METHODS 85 Human subjects 86Human subjects in Datasets 1 and 3 are previously described (table 1). 12, 13 Patients with SLE 87 and healthy donors in Dataset 2 were recruited from the Monash Medical Centre. 14 Patients 88 with SLE fulfilled the American College of Rheumatology (ACR) classification criteria. 15 The 89 SLE disease activity index 2000 (SLEDAI-2k) 16 and the Physician Global Assessment (PGA; 90 range 0-3) 17 scores were recorded. Blood was collected into PAXgene Blood RNA tubes (BD), 91 which were frozen at -20 °C for later RNA extraction. Patients did not participate in the 92 analysis. 93 94 RNA extraction and RNA-sequencing 95

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Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

Figgett

Monaghan

et al. 2019

Preprint

Self Cite

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“…TSC22D3 (also known as GILZ) was identified as a negative regulator of B cells, and lack of GILZ drives autoimmune disease (Figure 4e). 10 GILZ expression was markedly diminished in C2, suggesting possible loss of B-cell regulation. GILZ was upregulated in C3 and C4, possibly as an effect of glucocorticoid induction (Figure 4e).…”

Section: Resultsmentioning

confidence: 98%

“…In our stratification, PELI1 was not significantly underexpressed in any SLE clusters, but was upregulated in C3 and C4, possibly induced for NF‐κB regulation (Figure e). TSC22D3 (also known as GILZ ) was identified as a negative regulator of B cells, and lack of GILZ drives autoimmune disease (Figure e) . GILZ expression was markedly diminished in C2, suggesting possible loss of B‐cell regulation.…”

Section: Resultsmentioning

confidence: 99%

“…[3][4][5][6][7] SLE has been studied using numerous useful mouse models, each of which manifests SLE-like symptoms underpinned by different molecular mechanisms. Two examples are mice overexpressing B-cell-activating factor of the TNF family (BAFF, also known as TNFSF13B), that is BAFF-transgenic mice, in which low-affinity selfreactive B cells aberrantly survive, 8,9 and glucocorticoid-induced leucine zipper (GILZ)deficient mice 10 with impaired regulation of activated B cells. These and various other mouse models of SLE replicate some aspects of disease relevant to some patients with SLE, but most likely do not individually account for all the disease symptoms and pathogenesis mechanisms in humans.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Figgett

Monaghan

et al. 2019

Clin & Trans Imm

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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.

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Balancing Strategies: GC and GILZ Axis

Nataraja

Zhu

Dankers

et al. 2021

Pathogenesis of Systemic Lupus Erythematosus

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Glucocorticoid-induced leucine zipper (GILZ) inhibits B cell activation in systemic lupus erythematosus

Abstract: Our findings demonstrated that GILZ is a non-redundant regulator of B cell activity, with important potential clinical implications in SLE.

Cited by 36 publications

References 67 publications

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

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

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

Balancing Strategies: GC and GILZ Axis

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