Serum metabolomic profiling in patients with systemic lupus erythematosus by GC/MS

Yan, Bin; Huang, Jia; Zhang, Chunmei; Hu, Xin; Gao, Ming; Ai, Shi; Zha, Weibin; Shi, Luyi; Huang, Cibo; Yang, Liping

doi:10.3109/14397595.2016.1158895

Cited by 71 publications

(73 citation statements)

References 39 publications

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“…Metabolomics analyses of sera from patients with SLE have revealed a variety of considerable alterations in metabolites and metabolic pathways that correlate with disease activity and manifestations 69–71 . Although serum metabolites can be affected by multiple cell types and tissues, several metabolic pathways have been shown to differ between T cells of healthy individuals and patients with SLE, and between healthy and lupus-prone animals.…”

Section: Immunometabolism In Diseasementioning

confidence: 99%

Fine tuning of immunometabolism for the treatment of rheumatic diseases

2017

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All immune cells depend on specific and efficient metabolic pathways to mount an appropriate response. Over the past decade, the field of immunometabolism has expanded our understanding of the various means by which cells modulate metabolism to achieve the effector functions necessary to fight infection or maintain homeostasis. Harnessing these metabolic pathways to manipulate inappropriate immune responses as a therapeutic strategy in cancer and autoimmunity has received increasing scrutiny by the scientific community. Fine tuning immunometabolism to provide the desired response, or prevent a deleterious response, is an attractive alternative to chemotherapy or overt immunosuppression. The various metabolic pathways used by immune cells in rheumatoid arthritis, systemic lupus erythematosus and osteoarthritis offer numerous opportunities for selective targeting of specific immune cell subsets to manipulate cellular metabolism for therapeutic benefit in these rheumatologic diseases.

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Section: Immunometabolism In Diseasementioning

confidence: 99%

Fine tuning of immunometabolism for the treatment of rheumatic diseases

2017

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“…Our study contributes a new stratification scheme derived from the convergence of four RNA‐seq data sets, resolving patients into four main subtypes with machine‐learned gene expression signatures. Additional systems biology approaches (such as microbial metagenomics and metabolomics) are becoming available in SLE, and combining matching data from additional profiling methods may allow for improved sets of clinically useful biomarkers …”

Section: Discussionmentioning

confidence: 99%

“…Additional systems biology approaches (such as microbial metagenomics and metabolomics) are becoming available in SLE, and combining matching data from additional profiling methods may allow for improved sets of clinically useful biomarkers. [40][41][42][43] Transient flare activity in SLE patients causes a significant surge in inflammation requiring increased medical attention, but much remains to be understood about the underlying molecular mechanism triggering flare activity. We identified several genes that were differentially expressed in patients with flare activity, including the RETN gene, encoding the proinflammatory adipokine resistin (Figure 5c).…”

Section: Discussionmentioning

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

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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“…In a recent study, a larger sample size was used to examine the metabolomic profile in the serum of SLE patients by the use of GC-MS, and correlated these to SLEDAI and conventional serologic markers [92]. Compared to HC, patients with SLE had significantly lower levels of most amino acids, carbohydrates, fatty acids, and other endogenous metabolites, with the data indicating that the most likely affected pathway in SLE is acid turnover and protein biosynthesis.…”

Section: Novel Sle Testingmentioning

confidence: 99%

Diagnostic and prognostic tests in systemic lupus erythematosus

Wahezi

Putterman

2017

Best Practice & Research Clinical Rheumatology

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Systemic lupus erythematous (SLE) is a chronic autoimmune inflammatory disease characterized by autoantibodies directed against numerous self-nuclear antigens. Due to the heterogeneous nature of lupus, it has been challenging to identify markers that are sensitive and specific enough for its diagnosis and monitoring. However, with the sequencing of the human genome, rapid development of high throughput approaches has allowed for a better understanding of the etiopathogenesis of complex diseases, including SLE. Here we will present a review of the latest advancements in biomarker discovery during the ‘omics’ era, using these novel technologies, for assisting in the diagnosis and prognosis of patients with SLE.

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Serum metabolomic profiling in patients with systemic lupus erythematosus by GC/MS

Abstract: Our study distinguished serum metabotypes associated with SLE and disease activities. The implementation of this metabolomic strategy may help to develop biochemical insight into the metabolic alterations in SLE.

Cited by 71 publications

References 39 publications

Fine tuning of immunometabolism for the treatment of rheumatic diseases

Fine tuning of immunometabolism for the treatment of rheumatic diseases

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

Diagnostic and prognostic tests in systemic lupus erythematosus

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