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

Identification of amino acids metabolomic profiling in human plasma distinguishes lupus nephritis from systemic lupus erythematosus

Lupus nephritis (LN) is an immunoinflammatory glomerulonephritis associated with renal involvement in systemic lupus erythematosus (SLE). Given the close relationship between plasma amino acids (AAs) and renal function, this study aimed to elucidate the plasma AA profiles in LN patients and identify key AAs and diagnostic patterns that distinguish LN patients from those with SLE and healthy controls. Participants were categorized into three groups: normal controls (NC), SLE, and LN. Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to quantify AA levels in human plasma. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were utilized to identify key AAs. The diagnostic capacity of the models was assessed using receiver operating characteristic (ROC) curve analysis and area under the ROC curve (AUC) values. Significant alterations in plasma AA profiles were observed in LN patients compared to the SLE and NC groups. The OPLS-DA model effectively separated LN patients from the SLE and NC groups. A joint model using histidine (His), lysine (Lys), and tryptophan (Trp) demonstrated exceptional diagnostic performance, achieving an AUC of 1.0 with 100% sensitivity, specificity, and accuracy in predicting LN. Another joint model comprising arginine (Arg), valine (Val), and Trp also exhibited robust predictive performance, with an AUC of 0.998, sensitivity of 93.80%, specificity of 100%, and accuracy of 95.78% in distinguishing between SLE and LN. The joint forecasting models showed excellent predictive capabilities in identifying LN and categorizing lupus disease status. This approach provides a novel perspective for the early identification, prevention, treatment, and management of LN based on variations in plasma AA levels.

Severe Allergy as a Chronic Inflammatory Condition From a Systems Biology Perspective

Persistent and unresolved inflammation is a common underlying factor observed in several and seemingly unrelated human diseases, including cardiovascular and neurodegenerative diseases. Particularly, in atopic conditions, acute inflammatory responses such as those triggered by insect venom, food or drug allergies possess also a life-threatening potential. However, respiratory allergies predominantly exhibit late immune responses associated with chronic inflammation, that can eventually progress into a severe phenotype displaying similar features as those observed in other chronic inflammatory diseases, as is the case of uncontrolled severe asthma. This review aims to explore the different facets and systems involved in chronic allergic inflammation, including processes such as tissue remodelling and immune cell dysregulation, as well as genetic, metabolic and microbiota alterations, which are common to other inflammatory conditions. Our goal here was to deepen on the understanding of an entangled disease as is chronic allergic inflammation and expose potential avenues for the development of better diagnostic and intervention strategies.

Targeted metabolomics combined with machine learning to identify and validate new biomarkers for early SLE diagnosis and disease activity

BACKGROUND

The early diagnosis of systemic lupus erythematosus (SLE) and the assessment of disease activity progression remain a great challenge. Targeted metabolomics has great potential to identify new biomarkers of SLE.

METHODS

Serum from 44 healthy participants and 89 SLE patients were analyzed using HM400 high-throughput targeted metabolomics. Machine learning (ML) with seven learning models and trained the model several times iteratively selected the two best prediction model in a competitive way, which were independent validated by enzyme-linked immunosorbent (ELISA) with 90 SLE patients.

RESULTS

In this study, 146 differential metabolites, most of them organic acids, amino acids, and bile acids, were detected between patients with initial SLE and healthy participants, and 8 potential biomarkers were found by intersection of ML and statistics (area under the curve [AUC] > 0.95) showing a significant positive correlation with clinical indicators. In addition, we identified and validated 2 potential biomarkers for SLE classification (P < 0.05, AUC > 0.775; N-Methyl-L-glutamic acid, L-2-aminobutyric acid) showing a significant correlation with the SLE Disease Activity Index. These differential metabolites were mainly involved in metabolic pathways, amino acid biosynthesis, 2-oxocarboxylic acid metabolism and other pathways.

CONCLUSION

This study indicated that the tricarboxylic acid cycle might be associated with SLE drug therapy. We identified 8 diagnostic models biomarkers and 2 biomarkers that could be used to identify initial SLE and distinguish different activity degree, which will promote the development of new tools for the diagnosis and evaluation of SLE.

GC/MS and LC/MS serum metabolomic analysis of Chinese LN patients

China, being a densely populated nation, faces a substantial economic burden due to a high incidence of lupus nephritis (LN) cases. The concealed onset of LN has resulted in many individuals have missed the optimal timing for treatment. The aim of the research is to study the serum metabolomics of Chinese LN patients using gas chromatography (GC)/mass spectrometry (MS) and liquid chromatography (LC)/MS to identify potential diagnostic markers. Fifty LN patients and fifty normal controls, matched for Body Mass Index (BMI) and age, were selected. Serum analysis was conducted using GC/MS and LC/MS, followed by multivariate statistical analysis. Various multidimensional analyses, including principal component analysis, partial least squares discrimination analysis, and orthogonal partial least squares discrimination analysis, along with one-dimensional analyses such as t-tests, were performed. Metabolites with variable importance in projection value > 1 and a p-value < 0.05 were considered critical biomarkers for LN. Furthermore, identified biomarkers delineated relevant metabolic pathways, and a metabolic pathway map was obtained from the database. Forty-one metabolites were identified as potential LN biomarkers, primarily associated with immune regulation, energy metabolism, intestinal microbial metabolism, renal damage, and oxidative stress. The potential for diagnosing LN and other diseases through metabolomics is demonstrated. Future research should explore larger sample sizes, metabolomic comparisons across different diseases and health states, and integration of metabolomics with clinical diagnostics. Such studies will enhance the understanding of metabolomics in medical diagnosis and provide robust support for its practical application.

Serum Metabolic Fingerprints Characterize Systemic Lupus Erythematosus

Metabolic fingerprints in serum characterize diverse diseases for diagnostics and biomarker discovery. The identification of systemic lupus erythematosus (SLE) by serum metabolic fingerprints (SMFs) will facilitate precision medicine in SLE in an early and designed manner. Here, a discovery cohort of 731 individuals including 357 SLE patients and 374 healthy controls (HCs), and a validation cohort of 184 individuals (SLE/HC, 91/93) are constructed. Each SMF is directly recorded by nano-assisted laser desorption/ionization mass spectrometry (LDI MS) within 1 minute using 1 µL of native serum, which contains 908 mass to charge features. Sparse learning of SMFs achieves the SLE identification with sensitivity/specificity and area-under-the-curve (AUC) up to 86.0%/92.0% and 0.950 for the discovery cohort. For the independent validation cohort, it exhibits no performance loss by affording the sensitivity/specificity and AUC of 89.0%/100.0% and 0.992. Notably, a metabolic biomarker panel is screened out from the SMFs, demonstrating the unique metabolic pattern of SLE patients different from both HCs and rheumatoid arthritis patients. In conclusion, SMFs characterize SLE by revealing its unique metabolic pattern. Different regulation of small molecule metabolites contributes to the precise diagnosis of autoimmune disease and further exploration of the pathogenic mechanisms.

Genetically predicted 486 blood metabolites concerning risk of systemic lupus erythematosus: a Mendelian randomization study

Metabolic abnormalities constitute a significant characteristic of systemic lupus erythematosus (SLE). We utilised a two-sample Mendelian randomisation (MR) study to evaluate the potential causal association between 486 blood metabolites and SLE. Exposure data at the metabolite level were extracted from 7824 European Genome-wide association studies (GWAS). Preliminary analysis utilised SLE GWAS data from FinnGen. The primary method for causal analysis relied on random inverse variance weighting (IVW). To ensure robustness, sensitivity analyses included the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis. Steiger testing and linkage disequilibrium score regression were employed to validate the identified metabolites. This study identified 12 metabolites, comprising six known chemical structures: 1,5-anhydroglucitol(1,5-AG) [odds ratio (OR) = 0.100, 95% confidence interval (CI): 0.015-0.773, P = 0.027), gamma-glutamylthreonine (OR = 0.077, 95% CI: 0.010-0.574, P = 0.012), 5-dodecenoate(12:1n7) (OR = 0.205, 95% CI: 0.061-0.685, P = 0.010), linoleoylglycerophosphoethanolamine * (OR = 0.159, 95% CI: 0.027-0.933, P = 0.044), erythrose (OR = 88.331,95% CI:1.098-63.214, P = 0.040) and 1-, adrenate (22:4n6) (OR = 9.876, 95% CI: 1.753-55.639, P = 0.001)]. Additionally, we found associations between SLE and six unknown chemical structures: X-06351 (OR = 0.071, 95% CI: 0.006-0.817, P = 0.034), X-10810 (OR = 4.268 95% CI: 1.260-14.459, P = 0.020), X-11412 (OR = 5.418 95% CI: 1.068-27.487, P = 0.041), X-11905 (OR = 0.551, 95%CI: 0.304-0.997, P = 0.049), X-12038 (OR = 0.178 95%CI: 0.032-0.988, P = 0.045), X-12217 (OR = 0.174 95%CI: 0.044-0.680, P = 0.014). This study offers evidence supporting a causal relationship between SLE and 12 circulating metabolites, six of which have known chemical structures and six that remain unidentified. These findings introduce a new perspective for further exploration of SLE mechanisms.

Possible Role of Dysbiosis of the Gut Microbiome in SLE

PURPOSE OF REVIEW

The resident gut microbiota serves as a double-edged sword that aids the host in multiple ways to preserve a healthy equilibrium and serve as early companions and boosters for the gradual evolution of our immune defensive layers; nevertheless, the perturbation of the symbiotic resident intestinal communities has a profound impact on autoimmunity induction, particularly in systemic lupus erythematosus (SLE). Herein, we seek to critically evaluate the microbiome research in SLE with a focus on intestinal dysbiosis.

RECENT FINDINGS

SLE is a complex and heterogeneous disorder with self-attack due to loss of tolerance, and there is aberrant excessive immune system activation. There is mounting evidence suggesting that intestinal flora disturbances may accelerate the formation and progression of SLE, presumably through a variety of mechanisms, including intestinal barrier dysfunction and leaky gut, molecular mimicry, bystander activation, epitope spreading, gender bias, and biofilms. Gut microbiome plays a critical role in SLE pathogenesis, and additional studies are warranted to properly define the impact of gut microbiome in SLE, which can eventually lead to new and potentially safer management approaches for this debilitating disease.

Metabolic profiling of urinary exosomes for systemic lupus erythematosus discrimination based on HPL-SEC/MALDI-TOF MS

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease which leads to the formation of immune complex deposits in multiple organs and has heterogeneous clinical manifestations. Currently, exosomes for liquid biopsy have been applied in diagnosis and monitoring of diseases, whereas SLE discrimination based on exosomes at the metabolic level is rarely reported. Herein, we constructed a protocol for metabolomic study of urinary exosomes from SLE patients and healthy controls (HCs) with high efficiency and throughput. Exosomes were first obtained by high-performance liquid size-exclusion chromatography (HPL-SEC), and then metabolic fingerprints of urinary exosomes were extracted by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with high throughput and high efficency. With the statistical analysis by orthogonal partial least-squares discriminant analysis (OPLS-DA) model, SLE patients were efficiently distinguished from HCs, the area under the curve (AUC) of the receiver characteristic curve (ROC) was 1.00, and the accuracy of the unsupervised clustering heatmap was 90.32%. In addition, potential biomarkers and related metabolic pathways were analyzed. This method, with the characteristics of high throughput, high efficiency, and high accuracy, will provide the broad prospect of exosome-driven precision medicine and large-scale screening in clinical applications.

Dysbiotic Gut Microbiota-Derived Metabolites and Their Role in Non-Communicable Diseases

Dysbiosis, generally defined as the disruption to gut microbiota composition or function, is observed in most diseases, including allergies, cancer, metabolic diseases, neurological disorders and diseases associated with autoimmunity. Dysbiosis is commonly associated with reduced levels of beneficial gut microbiota-derived metabolites such as short-chain fatty acids (SCFA) and indoles. Supplementation with these beneficial metabolites, or interventions to increase their microbial production, has been shown to ameliorate a variety of inflammatory diseases. Conversely, the production of gut 'dysbiotic' metabolites or by-products by the gut microbiota may contribute to disease development. This review summarizes the various 'dysbiotic' gut-derived products observed in cardiovascular diseases, cancer, inflammatory bowel disease, metabolic diseases including non-alcoholic steatohepatitis and autoimmune disorders such as multiple sclerosis. The increased production of dysbiotic gut microbial products, including trimethylamine, hydrogen sulphide, products of amino acid metabolism such as p-Cresyl sulphate and phenylacetic acid, and secondary bile acids such as deoxycholic acid, is commonly observed across multiple diseases. The simultaneous increased production of dysbiotic metabolites with the impaired production of beneficial metabolites, commonly associated with a modern lifestyle, may partially explain the high prevalence of inflammatory diseases in western countries.

Impaired metabolism predicts coronary artery calcification in women with systemic lupus erythematosus

BACKGROUND

Patients with systemic lupus erythematosus (SLE) exhibit a high risk for cardiovascular diseases (CVD) which is not fully explained by the classical Framingham risk factors. SLE is characterized by major metabolic alterations which can contribute to the elevated prevalence of CVD.

METHODS

A comprehensive analysis of the circulating metabolome and lipidome was conducted in a large cohort of 211 women with SLE who underwent a multi-detector computed tomography scan for quantification of coronary artery calcium (CAC), a robust predictor of coronary heart disease (CHD).

FINDINGS

Beyond traditional risk factors, including age and hypertension, disease activity and duration were independent risk factors for developing CAC in women with SLE. The presence of coronary calcium was associated with major alterations of circulating lipidome dominated by an elevated abundance of ceramides with very long chain fatty acids. Alterations in multiple metabolic pathways, including purine, arginine and proline metabolism, and microbiota-derived metabolites, were also associated with CAC in women with SLE. Logistic regression with bootstrapping of lipidomic and metabolomic variables were used to develop prognostic scores. Strikingly, combining metabolic and lipidomic variables with clinical and biological parameters markedly improved the prediction (area under the curve: 0.887, p < 0.001) of the presence of coronary calcium in women with SLE.

INTERPRETATION

The present study uncovers the contribution of disturbed metabolism to the presence of coronary artery calcium and the associated risk of CHD in SLE. Identification of novel lipid and metabolite biomarkers may help stratifying patients for reducing CVD morbidity and mortality in SLE.

FUNDING

INSERM and Sorbonne Université.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Fecal microbiota from MRL/lpr mice exacerbates pristane-induced lupus

BACKGROUND

The roles of gut microbiota in the pathogenesis of SLE have been receiving much attention during recent years. However, it remains unknown how fecal microbiota transplantation (FMT) and microbial metabolites affect immune responses and lupus progression.

METHODS

We transferred fecal microbiota from MRL/lpr (Lpr) mice and MRL/Mpj (Mpj) mice or PBS to pristane-induced lupus mice and observed disease development. We also screened gut microbiota and metabolite spectrums of pristane-induced lupus mice with FMT via 16S rRNA sequencing, metagenomic sequencing, and metabolomics, followed by correlation analysis.

RESULTS

FMT from MRL/lpr mice promoted the pathogenesis of pristane-induced lupus and affected immune cell profiles in the intestine, particularly the plasma cells. The structure and composition of microbial communities in the gut of the FMT-Lpr mice were different from those of the FMT-Mpj mice and FMT-PBS mice. The abundances of specific microbes such as prevotella taxa were predominantly elevated in the gut microbiome of the FMT-Lpr mice, which were positively associated with functional pathways such as cyanoamino acid metabolism. Differential metabolites such as valine and L-isoleucine were identified with varied abundances among the three groups. The abundance alterations of the prevotella taxa may affect the phenotypic changes such as proteinuria levels in the pristane-induced lupus mice.

CONCLUSION

These findings further confirm that gut microbiota play an important role in the pathogenesis of lupus. Thus, altering the gut microbiome may provide a novel way to treat lupus.

Immunometabolomics provides a new perspective for studying systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic multi-organ autoimmune disease characterized by clinical heterogeneity, unpredictable progression, and flare ups. Due to the heterogeneous nature of lupus, it has been challenging to identify sensitive and specific biomarkers for its diagnosis and monitoring. Despite the fact that the mechanism of SLE remains unknown, impressive progress has been made over the last decade towards understanding how different immune cells contribute to its pathogenesis. Research suggests that cellular metabolic programs could affect the immune response by regulating the activation, proliferation, and differentiation of innate and adaptive immune cells. Many studies have shown that the dysregulation of the immune system is associated with changes to metabolite profiles. The study of metabolite profiling may provide a means for mechanism exploration and novel biomarker discovery for disease diagnostic, classification, and monitoring. Here we review the latest advancements in understanding the role of immunometabolism in SLE, as well as the systemic metabolite profiling of this disease along with possible clinical application.

Combined plasma metabolomic and transcriptomic analysis identify histidine as a biomarker and potential contributor in SLE pathogenesis

OBJECTIVES

To investigate metabolite alterations in the plasma of SLE patients to identify novel biomarkers and provide insight into SLE pathogenesis.

METHODS

Patients with SLE (n = 41, discovery cohort and n = 37, replication cohort), healthy controls (n = 30 and n = 29) and patients with RA (n = 19, disease control) were recruited. Metabolic profiles of the plasma samples were analysed using liquid chromatography-time-of-flight mass spectrometry and capillary electrophoresis-time-of-flight mass spectrometry. Transcriptome data was analysed using RNA-sequencing for 18 immune cell subsets. The importance of histidine (His) in plasmablast differentiation was investigated by using mouse splenic B cells.

RESULTS

We demonstrate that a specific amino acid combination including His can effectively distinguish between SLE patients and healthy controls. Random forest and partial least squares-discriminant analysis identified His as an effective classifier for SLE patients. A decrease in His plasma levels correlated with damage accrual independent of prednisolone dosage and type I IFN signature. The oxidative phosphorylation signature in plasmablasts negatively correlated with His levels. We also showed that plasmablast differentiation induced by innate immune signals was dependent on His.

CONCLUSIONS

Plasma His levels are a potential biomarker for SLE patients and are associated with damage accrual. Our data suggest the importance of His as a pathogenic metabolite in SLE pathogenesis.

Bidirectional crosstalk between dysbiotic gut microbiota and systemic lupus erythematosus: What is new in therapeutic approaches?

Systemic lupus erythematosus is an autoimmune disease characterized by chronic inflammation and multiple organs damage. Its pathogenesis is complex and involves multiple factors including gut microbiota. Accumulating evidence indicates the interaction of microbial communities with the host immune system to maintain a state of homeostasis. Imbalances within the gut microbial composition and function may contribute to the development of many autoimmune diseases including SLE. In this review, we aim to highlight the dysregulation of commensal bacteria and their metabolites in the gastrointestinal tract and the resulting autoimmune responses in lupus and to decrypt the cross-link between the altered gut microbiota and the immune system in the SLE condition. We also provide new insights into targeting gut microbiota as a promising therapeutic approach to treat and manage SLE.

Loss of Gut Barrier Integrity In Lupus

Systemic Lupus Erythematosus is a complex autoimmune disease and its etiology remains unknown. Increased gut permeability has been reported in lupus patients, yet whether it promotes or results from lupus progression is unclear. Recent studies indicate that an impaired intestinal barrier allows the translocation of bacteria and bacterial components into systemic organs, increasing immune cell activation and autoantibody generation. Indeed, induced gut leakage in a mouse model of lupus enhanced disease characteristics, including the production of anti-dsDNA antibody, serum IL-6 as well as cell apoptosis. Gut microbiota dysbiosis has been suggested to be one of the factors that decreases gut barrier integrity by outgrowing harmful bacteria and their products, or by perturbation of gut immune homeostasis, which in turn affects gut barrier integrity. The restoration of microbial balance eliminates gut leakage in mice, further confirming the role of microbiota in maintaining gut barrier integrity. In this review, we discuss recent advances on the association between microbiota dysbiosis and leaky gut, as well as their influences on the progression of lupus. The modifications on host microbiota and gut integrity may offer insights into the development of new lupus treatment.

Integration of metabolomics and lipidomics reveals serum biomarkers for systemic lupus erythematosus with different organs involvement

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects various organs or systems. We performed metabolomic and lipidomic profiles analyses of 133 SLE patients and 30 HCs. Differential metabolites and lipids were integrated, and then the biomarker panel was identified using binary logistic regression. We found that a combination of four metabolites or lipids could distinguish SLE from HC with an AUC of 0.998. Three lipids were combined to differentiate inactive SLE and active SLE. The AUC was 0.767. In addition, we also identified the biomarkers for different organ phenotypes of SLE. The AUCs for diagnosing SLE patients with only kidney involvement, skin involvement, blood system involvement, and multisystem involvement were 0.766, 0.718, 0.951, and 0.909, respectively. Our study succeeded in identifying biomarkers associated with different clinical phenotypes in SLE patients, which could facilitate a more precise diagnosis and assessment of disease progression in SLE.

Metabolic Profiling in Rheumatoid Arthritis, Psoriatic Arthritis, and Psoriasis: Elucidating Pathogenesis, Improving Diagnosis, and Monitoring Disease Activity

Immune-mediated inflammatory diseases (IMIDs), such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), and psoriasis (Ps), represent autoinflammatory and autoimmune disorders, as well as conditions that have an overlap of both categories. Understanding the underlying pathogeneses, making diagnoses, and choosing individualized treatments remain challenging due to heterogeneous disease phenotypes and the lack of reliable biomarkers that drive the treatment choice. In this review, we provide an overview of the low-molecular-weight metabolites that might be employed as biomarkers for various applications, e.g., early diagnosis, disease activity monitoring, and treatment-response prediction, in RA, PsA, and Ps. The literature was evaluated, and putative biomarkers in different matrices were identified, categorized, and summarized. While some of these candidate biomarkers appeared to be disease-specific, others were shared across multiple IMIDs, indicating common underlying disease mechanisms. However, there is still a long way to go for their application in a routine clinical setting. We propose that studies integrating omics analyses of large patient cohorts from different IMIDs should be performed to further elucidate their pathomechanisms and treatment options. This could lead to the identification and validation of biomarkers that might be applied in the context of precision medicine to improve the clinical outcomes of these IMID patients.

Identification of Serum Biomarkers and Pathways of Systemic Lupus Erythematosus with Skin Involvement Through GC/MS-Based Metabolomics Analysis

Purpose

Skin involvement is the second most common symptom of systemic lupus erythematosus (SLE), and the prevention of skin lesion development might benefit to lessen the system inflammation burden in SLE. However, the mechanisms of skin lesion in SLE remain unclear.

Patients and Methods

Metabolome based on gas chromatography-mass spectrometry (GC-MS) was used for comparison of serum metabolism among 11 SLE patients with skin lesion (SL), 10 SLE patients without skin lesion (SNL), and 16 healthy controls (HC). The analysis of metabolism profiles was through LUG database, Human Metabolome Database (HMDB) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG).

Results

A total of 14 most meaningful metabolites were found in SL patients compared to SNL patients, and 19 metabolic pathways were enriched. Meanwhile, L-alpha-aminobutyric acid, dehydroascorbic acid, glycine, and L-tyrosine achieved an area under receiver-operating characteristic (ROC) curve of 0.8636, 0.8091, 0.7727, and 0.7636, respectively, indicating their diagnostic potential for SL patients. In addition, the combined model of L-alpha-aminobutyric acid and dehydroascorbic acid provided better diagnostic accuracy.

Conclusion

The metabolomic features of SLE patients with skin lesion could be detected by GC/MS assay. Our study tried to provide new insights into the mechanism of SLE skin injury. Further validation of these findings through larger sample size studies may contribute to the use of metabolic profile analysis.

Metabolomics in Autoimmune Diseases: Focus on Rheumatoid Arthritis, Systemic Lupus Erythematous, and Multiple Sclerosis

The metabolomics approach represents the last downstream phenotype and is widely used in clinical studies and drug discovery. In this paper, we outline recent advances in the metabolomics research of autoimmune diseases (ADs) such as rheumatoid arthritis (RA), multiple sclerosis (MuS), and systemic lupus erythematosus (SLE). The newly discovered biomarkers and the metabolic mechanism studies for these ADs are described here. In addition, studies elucidating the metabolic mechanisms underlying these ADs are presented. Metabolomics has the potential to contribute to pharmacotherapy personalization; thus, we summarize the biomarker studies performed to predict the personalization of medicine and drug response.

Lupus gut microbiota transplants cause autoimmunity and inflammation

BACKGROUND

The etiology of systemic lupus erythematosus (SLE) is multifactorial. Recently, growing evidence suggests that the microbiota plays a role in SLE, yet whether gut microbiota participates in the development of SLE remains largely unknown. To investigate this issue, we carried out 16 s rDNA sequencing analyses in a cohort of 18 female un-treated active SLE patients and 7 female healthy controls, and performed fecal microbiota transplantation from patients and healthy controls to germ-free (GF) mice.

RESULTS

Compared to the healthy controls, we found no significant different microbial diversity but some significantly different species in SLE patients including Turicibacter genus and other 5 species. Fecal transfer from SLE patients to GF mice caused GF mice to develop a series of lupus-like phenotypic features, including increased serum autoimmune antibodies, imbalanced cytokines, altered distribution of immune cells in mucosal and peripheral immune response, and upregulated expression of genes related to SLE in recipient mice that received SLE fecal microbiota transplantation (FMT). Moreover, the metabolism of histidine was significantly altered in GF mice treated with SLE patient feces, as compared to those which received healthy fecal transplants.

CONCLUSIONS

Overall, our results describe a causal role of aberrant gut microbiota in contributing to the pathogenesis of SLE. The interplay of gut microbial and histidine metabolism may be one of the mechanisms intertwined with autoimmune activation in SLE.

Correlation Analysis between Gut Microbiota and Metabolites in Children with Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune-mediated diffuse connective tissue disease characterized by immune inflammation with an unclear aetiology and pathogenesis. This work profiled the intestinal flora and faecal metabolome of patients with SLE using 16S RNA sequencing and gas chromatography-mass spectrometry (GC-MS). We identified unchanged alpha diversity and partially altered beta diversity of the intestinal flora. Another important finding was the increase in Proteobacteria and Enterobacteriales and the decrease in Ruminococcaceae among SLE patients. For metabolites, amino acids and short-chain fatty acids were enriched when long-chain fatty acids were downregulated in SLE faecal samples. KEGG analysis showed the significance of the protein digestion and absorption pathway, and association analysis revealed the key role of 3-phenylpropanoic acid and Sphingomonas. Sphingomonas were reported to be less abundant in healthy periodontal sites of SLE patients than in those of HCs, indicating transmission of oral species to the gut. This study contributes to the understanding of the pathogenesis of SLE disease from the perspective of intestinal microorganisms, explains the pathogenesis of SLE, and serves as a basis for exploring potential treatments for the disease.

Gut Microbiome and Metabolites in Systemic Lupus Erythematosus: Link, Mechanisms and Intervention

Systemic lupus erythematosus (SLE), often considered the prototype of autoimmune diseases, is characterized by over-activation of the autoimmune system with abnormal functions of innate and adaptive immune cells and the production of a large number of autoantibodies against nuclear components. Given the highly complex and heterogeneous nature of SLE, the pathogenesis of this disease remains incompletely understood and is presumed to involve both genetic and environmental factors. Currently, disturbance of the gut microbiota has emerged as a novel player involved in the pathogenesis of SLE. With in-depth research, the understanding of the intestinal bacteria-host interaction in SLE is much more comprehensive. Recent years have also seen an increase in metabolomics studies in SLE with the attempt to identify potential biomarkers for diagnosis or disease activity monitoring. An intricate relationship between gut microbiome changes and metabolic alterations could help explain the mechanisms by which gut bacteria play roles in the pathogenesis of SLE. Here, we review the role of microbiota dysbiosis in the aetiology of SLE and how intestinal microbiota interact with the host metabolism axis. A proposed treatment strategy for SLE based on gut microbiome (GM) regulation is also discussed in this review. Increasing our understanding of gut microbiota and their function in lupus will provide us with novel opportunities to develop effective and precise diagnostic strategies and to explore potential microbiota-based treatments for patients with lupus.

Serum levels of n-3 and n-6 polyunsaturated fatty acids in patients with systemic lupus erythematosus and their association with disease activity: a pilot study

Objective: Polyunsaturated fatty acids (PUFAs) may modulate the inflammatory process in systemic autoimmune diseases, including systemic lupus erythematosus (SLE). The aim of this study was to assess the serum concentrations of essential 18-carbon PUFAs and their long-chain derivatives in patients with SLE and healthy controls, and to analyse their associations with laboratory and clinical features of the disease.Method: n-6 and n-3 PUFA composition was assessed in the sera of 30 SLE patients and 20 healthy controls using gas chromatography-mass spectrometry. We investigated the associations between PUFAs and disease activity measured with Systemic Lupus Erythematosus Activity Index (SLEDAI) scores, erythrocyte sedimentation rate, C-reactive protein, complement C3 and C4 concentrations, anti-nuclear antibody (ANA) titre, anti-double-stranded DNA (anti-dsDNA) antibody concentration, and medications.Results: Serum linoleic acid (LA) and α-linolenic acid concentrations were significantly higher in SLE patients compared with healthy controls. LA concentration correlated positively with the ANA titre and corticosteroid doses; eicosapentaenoic acid (EPA) and docosahexaenoic acid correlated inversely with anti-dsDNA antibody concentration. Patients treated with immunosuppressants had significantly lower concentrations of LA, arachidonic acid, and EPA.Conclusion: Both n-6 and n-3 PUFA precursors can participate in the inflammatory process in SLE patients. The mechanism of the PUFA metabolism disturbance needs further exploration.

Time Course of Metabolic Alterations Associated with the Progression of Systemic Lupus Erythematosus in MRL/lpr Mice Based on GC/MS

Exploring the dynamic changes of metabolites and metabolic pathways during the development of the disease can help to further understand the etiology and pathogenesis of systemic lupus erythematosus (SLE). In this study, serum metabolomics based on gas chromatography/mass spectrometry (GC/MS) was employed to investigate the metabolic alterations at different stages of SLE using lupus-prone mice (MRL/lpr) of 9, 11, and 13 weeks of age. Multivariate statistical analysis was performed to view the alterations of metabolic profiles between MRL/lpr mice and age-matched C57BL/6 mice, and t-test and fold change criteria were used to identify differential metabolites at each stage. 11 changed metabolites were found in MRL/lpr mice at 9 weeks of age, which were mainly involved in the tricarboxylic acid (TCA) cycle, glycolysis, and butanoate metabolism; with the increase of week age, the TCA cycle was still disturbed, and the biosynthesis of fatty acids was significantly upregulated since 11 weeks of age; in addition, urea, urate, and indole-3-lactate were increased at 13 weeks of age. We found a time course of metabolic alterations in MRL/lpr mice, which may be related to the progression of SLE. These findings could provide a reference for studying the mechanism of SLE and judging the pathological stage and severity of the disease. The MS data have been deposited in Mendeley (https://www.mendeley.com/).

Fecal Metabolites Were Altered, Identified as Biomarkers and Correlated With Disease Activity in Patients With Systemic Lupus Erythematosus in a GC-MS-Based Metabolomics Study

Gut metabolites are products of the crosstalk between microbes and their host and play an important role in the occurrence, development, diagnosis, and treatment of autoimmune diseases. This work profiled the fecal metabolome of patients with systemic lupus erythematosus (SLE) using gas chromatography-mass spectrometry (GC-MS) and analyzed the potential roles of metabolites in the diagnosis and development of SLE. Fecal sample from 29 SLE patients without any other diseases and 30 healthy controls (HCs) were analyzed by metabolomics profiling. All participants took no antibiotics in the month before sampling and clinical data collecting. The metabolome profiles of patients with SLE and HCs were significantly different. Thirty fecal metabolites, such as deoxycholic acid, erucamide, L-tryptophan and putrescine, were significantly enriched, while nine metabolites, such as glyceric acid, γ-tocopherol, (Z)-13-octadecenoic acid and 2,4-di-tert-butylphenol, were depleted in SLE patients vs. HCs. The areas under the curve (AUCs) of L-valine, pyrimidine, erucamide, and L-leucine during ROC analysis were 0.886, 0.833, 0.829, and 0.803, indicating their good diagnostic potential. Moreover, the combination of L-valine, erucamide and 2,4-di-tert-butylphenol gave an AUC of 0.959. SLE-altered metabolites were significantly located in 28 pathways, such as ABC transporters (p = 3.40E-13) and aminoacyl-tRNA biosynthesis (p = 2.11E-12). Furthermore, SLE-altered fecal metabolites were closely correlated with SLE indicators, e.g., L-tryptophan was positively correlated with the SLEDAI-2K (p = 0.007). Our results suggest that the SLE fecal metabolome is closely associated with the occurrence and development of SLE and is of great diagnostic value.

Microbiome and Metabolome Analyses Reveal the Disruption of Lipid Metabolism in Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease that affects thousands of people worldwide. Recently, alterations in metabolism and gut microbiome have emerged as key regulators of SLE pathogenesis. However, it is not clear about the coordination of gut commensal bacteria and SLE metabolism. Here, by integrating 16S sequencing and metabolomics data, we characterized the gut microbiome and fecal and serum metabolome alterations in patients with SLE. Microbial diversity sequencing revealed gut microflora dysbiosis in SLE patients with significantly increased beta diversity. The metabolomics profiling identified 43 and 55 significantly changed metabolites in serum and feces samples in SLE patients. Notably, lipids accounted for about 65% altered metabolites in serum, highlighted the disruption of lipid metabolism. Integrated correlation analysis provided a link between the gut microbiome and lipid metabolism in patients with SLE, particularly according to regulate the conversion of primary bile acids to secondary bile acids. Overall, our results illustrate the perturbation of the gut microbiome and metabolome in SLE patients which may facilitate the development of new SLE interventions.

Caution in studying and interpreting the lupus metabolome

Several metabolomics studies have shed substantial light on the pathophysiological pathways underlying multiple diseases including systemic lupus erythematosus (SLE). This review takes stock of our current understanding of this field. We compare, collate, and investigate the metabolites in SLE patients and healthy volunteers, as gleaned from published metabolomics studies on SLE. In the surveyed primary reports, serum or plasma samples from SLE patients and healthy controls were assayed using mass spectrometry or nuclear magnetic resonance spectroscopy, and metabolites differentiating SLE from controls were identified. Collectively, the circulating metabolome in SLE is characterized by reduced energy substrates from glycolysis, Krebs cycle, fatty acid β oxidation, and glucogenic and ketogenic amino acid metabolism; enhanced activity of the urea cycle; decreased long-chain fatty acids; increased medium-chain and free fatty acids; and augmented peroxidation and inflammation. However, these findings should be interpreted with caution because several of the same metabolic pathways are also significantly influenced by the medications commonly used in SLE patients, common co-morbidities, and other factors including smoking and diet. In particular, whereas the metabolic alterations relating to inflammation, oxidative stress, lipid peroxidation, and glutathione generation do not appear to be steroid-dependent, the other metabolic changes may in part be influenced by steroids. To conclude, metabolomics studies of SLE and other rheumatic diseases ought to factor in the potential contributions of confounders such as medications, co-morbidities, smoking, and diet.

Metabolomic profiling reveals serum L-pyroglutamic acid as a potential diagnostic biomarker for systemic lupus erythematosus

Objective

The spectrum of clinical manifestations and serological phenomena of SLE is heterogeneous among patients and even changes over time unpredictably in individual patients. For this reason, clinical diagnosis especially in complicated or atypical cases is often difficult or delayed leading to poor prognosis. Despite the medical progress nowadays in the understanding of SLE pathogenesis, disease-specific biomarkers for SLE remain an outstanding challenge. Therefore, we undertook this study to investigate potential biomarkers for SLE diagnosis.

Methods

Serum samples from 32 patients with SLE and 25 gender-matched healthy controls (HCs) were analysed by metabolic profiling based on liquid chromatography–tandem mass spectrometry metabolomics platform. The further validation for the potential biomarker was performed in an independent set consisting of 36 SLE patients and 30 HCs.

Results

The metabolite profiles of serum samples allowed differentiation of SLE patients from HCs. The levels of arachidonic acid, sphingomyelin (SM) 24:1, monoacylglycerol (MG) 17:0, lysophosphatidyl ethanolamine (lysoPE) 18:0, lysoPE 16:0, lysophosphatidyl choline (lysoPC) 20:0, lysoPC 18:0 and adenosine were significantly decreased in SLE patients, and the MG 20:2 and L-pyroglutamic acid were significantly increased in SLE group. In addition, L-pyroglutamic acid achieved an area under the receiver-operating characteristic curve of 0.955 with high sensitivity (97.22%) and specificity (83.33%) at the cut-off of 61.54 μM in the further targeted metabolism, indicating diagnostic potential.

Conclusion

Serum metabolic profiling is differential between SLE patients and HCs and depicts increased L-pyroglutamic acid as a promising bitformatomarker for SLE.

Microbiota and metabolites in rheumatic diseases

As a gigantic community in the human body, the microbiota exerts pleiotropic roles in human health and disease ranging from digestion and absorption of nutrients from food, defense against infection of pathogens, to regulation of immune system development and immune homeostasis. Recent advances in "omics" studies and bioinformatics analyses have broadened our insights of the microbiota composition of the inner and other surfaces of the body and their interactions with the host. Apart from the direct contact of microbes at the mucosal barrier, metabolites produced or metabolized by the gut microbes can serve as important immune regulators or initiators in a wide variety of diseases, including gastrointestinal diseases, metabolic disorders and systemic rheumatic diseases. This review focuses on the most recent understanding of how the microbiota and metabolites shape rheumatic diseases. Studies that explore the mechanistic interplay between microbes, metabolites and the host could thereby provide clues for novel methods in the diagnosis, therapy, and prevention of rheumatic diseases.

Dysbiosis of oral microbiota is associated with systemic lupus erythematosus

OBJECTIVE

The important role of intestinal microbiota in systemic lupus erythematosus (SLE) has been recognized. Oral-gut microbiome axis is a crucial link in human health and disease, but few researches indicated the relationship between oral microorganisms and SLE. This study mainly explored the composition and changes of oral microorganisms in SLE patients with different stages, clinical manifestations and biomarkers.

DESIGN

Oral microbiota was detected by 16S ribosomal RNA gene sequencing from 20 SLE patients and 19 healthy controls (HCs). The evenness, diversity and composition of oral microbiota were analyzed. Moreover, receiver-operating characteristic analysis was conducted. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to investigate microbiota functions.

RESULTS

The oral microbiota of SLE patients was imbalanced and the diversity was decreased, but no difference was found between new-onset and treated SLE patients. Families Lactobacillaceae, Veillonellaceae and Moraxellaceae were enriched in SLE patients. Families like Corynebacteriaceae, Micrococcaceae, Defluviitaleaceae, Caulobacteraceae, Phyllobacteriaceae, Methylobacteriaceae, Hyphomicrobiaceae, Sphingomonadaceae, Halomonadaceae, Pseudomonadaceae, Xanthomonadaceae, etc. were decreased in SLE patients. After multiple testing adjustment, families Sphingomonadaceae, Halomonadaceae, and Xanthomonadaceae were significantly decreased in SLE patients. And area under the curve was 0.953 (95% confidence intervals 0.890-1.000) to distinguish SLE patients from HCs. There were differences in metabolic pathways between SLE and HCs (P = 0.025).

CONCLUSIONS

These findings collectively support that oral microbiota dysbiosis and aberrant metabolic pathways were observed in patients with SLE. Our findings may provide suggestive evidences for the diagnosis and treatment of SLE.

Metabolic determinants of lupus pathogenesis

The metabolism of healthy murine and more recently human immune cells has been investigated with an increasing amount of details. These studies have revealed the challenges presented by immune cells to respond rapidly to a wide variety of triggers by adjusting the amount, type, and utilization of the nutrients they import. A concept has emerged that cellular metabolic programs regulate the size of the immune response and the plasticity of its effector functions. This has generated a lot of enthusiasm with the prediction that cellular metabolism could be manipulated to either enhance or limit an immune response. In support of this hypothesis, studies in animal models as well as human subjects have shown that the dysregulation of the immune system in autoimmune diseases is associated with a skewing of the immunometabolic programs. These studies have been mostly conducted on autoimmune CD4⁺ T cells, with the metabolism of other immune cells in autoimmune settings still being understudied. Here we discuss systemic metabolism as well as cellular immunometabolism as novel tools to decipher fundamental mechanisms of autoimmunity. We review the contribution of each major metabolic pathway to autoimmune diseases, with a focus on systemic lupus erythematosus (SLE), with the relevant translational opportunities, existing or predicted from results obtained with healthy immune cells. Finally, we review how targeting metabolic programs may present novel therapeutic venues.

Metabolic Profiling Reveals an Abnormal Pattern of Serum Fatty Acids in MRL/lpr Mice Under Treatment With Prednisone

Glucocorticoids (GCs) are commonly used to treat systemic lupus erythematosus (SLE). Unfortunately, excessive GCs can induce many side effects associated with disordered fatty acid (FA) metabolism. Although an increased level of total FA has been found after GCs treatment, it is not clear whether all FA species increased or only certain FA species were altered. A gas chromatography–mass spectrometry-based FA profiling approach was performed to reveal the alterations of FA species in SLE model mice (MRL/lpr) after treatment with 5 mg/kg of prednisone. The study showed a distinct FA profile in MRL/lpr mice compared to the controls, mainly manifested by elevated polyunsaturated FAs (arachidonate, docosahexaenoate, etc.), which are related to the inflammatory state; and altered (product FA/precursor FA) ratios representing the estimated activities of FA desaturase and elongase (higher activities of multiple elongases, △4 desaturase, △5 desaturase, △6 desaturase, and lower activity of △8 desaturase). Treatment with 5 mg/kg of prednisone decreased the total level of n-6 polyunsaturated FA in MRL/lpr mice; in particular, the level of arachidonate and estimated activity of △5 desaturase were reduced to the control level. Moreover, prednisone induced additional perturbations in FAs, including not only saturated FAs, but also monounsaturated FAs and n-3 polyunsaturated FAs, indicating that there was a strong effect of prednisone on FA metabolism. These results may be valuable for further studies of the side effects of GCs treatment.

Metabolomic profile overlap in prototypical autoimmune humoral disease: a comparison of myasthenia gravis and rheumatoid arthritis

INTRODUCTION

Myasthenia gravis (MG) and rheumatoid arthritis (RA) are examples of antibody-mediated chronic, progressive autoimmune diseases. Phenotypically dissimilar, MG and RA share common immunological features. However, the immunometabolomic features common to humoral autoimmune diseases remain largely unexplored.

OBJECTIVES

The aim of this study was to reveal and illustrate the metabolomic profile overlap found between these two diseases and describe the immunometabolomic significance.

METHODS

Metabolic analyses using acid- and dansyl-labelled was performed on serum from adult patients with seropositive MG (n = 46), RA (n = 23) and healthy controls (n = 49) presenting to the University of Alberta Hospital specialty clinics. Chemical isotope labelling liquid chromatography mass spectrometry (CIL LC-MS) methods were utilized to assess the serum metabolome in patients; 12C/13C-dansyl chloride (DnsCl) was used to label amine/phenol metabolites and 12C/13C-p-dimethylaminophenacyl bromide (DmPA) was used for carboxylic acids. Metabolites matching our criteria for significance were selected if they were present in both groups. Multivariate statistical analysis [including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)] and biochemical pathway analysis was then conducted to gain understanding of the principal pathways involved in antibody-mediated pathogenesis.

RESULTS

We found 20 metabolites dysregulated in both MG and RA when compared to healthy controls. Most prominently, observed changes were related to pathways associated with phenylalanine metabolism, tyrosine metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and pyruvate metabolism.

CONCLUSION

From these results it is evident that many metabolites are common to humoral disease and exhibit significant immunometabolomic properties. This observation may lead to an enhanced understanding of the metabolic underpinnings common to antibody-mediated autoimmune disease. Further, contextualizing these findings within a larger clinical and systems biology context could provide new insights into the pathogenesis and management of these diseases.

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

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.

Korean Traditional Medicine (Jakyakgamcho-tang) Ameliorates Colitis by Regulating Gut Microbiota

The objective of this study was to examine the anti-colitis activity of Jakyakgamcho-tang (JGT) in dextran sulfate sodium (DSS)-induced colitis and explore changes of the gut microbial community using 16S rRNA amplicon sequencing and metabolomics approaches. It was found that treatment with JGT or 5-aminosalicylic acid (5-ASA) alleviated the severity of colitis symptoms by suppressing inflammatory cytokine levels of IL-6, IL-12, and IFN-γ. The non-metric multidimensional scaling analysis of gut microbiome revealed that JGT groups were clearly separated from the DSS group, suggesting that JGT administration altered gut microbiota. The operational taxonomic units (OTUs) that were decreased by DSS but increased by JGT include Akkermansia and Allobaculum. On the other hand, OTUs that were increased by DSS but decreased by 5-ASA or JGT treatments include Bacteroidales S24-7, Ruminococcaceae, and Rikenellaceae, and the genera Bacteroides, Parabacteroides, Oscillospira, and Coprobacillus. After JGT administration, the metabolites, including most amino acids and lactic acid that were altered by colitis induction, became similar to those of the control group. This study demonstrates that JGT might have potential to effectively treat colitis by restoring dysbiosis of gut microbiota and host metabolites.

Serum Metabolomic Profiles of Rheumatoid Arthritis Patients With Acute-Onset Diffuse Interstitial Lung Disease

Objective:

Acute-onset diffuse interstitial lung disease (AoDILD) includes acute exacerbation of interstitial lung disease (ILD), drug-induced ILD, and Pneumocystis pneumonia, and frequently occurs in patients with rheumatoid arthritis (RA). Since AoDILD causes a poor prognosis in RA, biomarkers for AoDILD were eagerly desired. Metabolomic analyses were extensively performed in cancer patients and successfully generated better diagnostic biomarkers. In the present study, serum metabolomic profiles of AoDILD in RA were investigated to generate better potential metabolomic biomarkers.

Methods:

Serum samples of 10 RA patients with AoDILD were collected on admission and in a stable state, more than 3 months before the admission. Serum metabolomic analyses were conducted on the samples from these RA patients with AoDILD.

Results:

Apparently distinct serum metabolomic profiles in AoDILD were not observed in univariate or hierarchical cluster analyses. Partial least squares-discriminant analysis (PLS-DA) was performed to select candidate metabolites based on variable importance in projection (VIP) scores. The PLS-DA model generated from the four metabolites with VIP scores more than 2.25 (mannosamine, alliin, kynurenine, and 2-hydroxybutyric acid) could successfully discriminate AoDILD from the stable condition (area under the curve: 0.962, 95% confidence interval: 0.778–1.000).

Conclusion:

It was demonstrated that metabolomic profiling was useful to generate better biomarkers in AoDILD.

Fecal Metabolomics and Potential Biomarkers for Systemic Lupus Erythematosus

The role of metabolomics in autoimmune diseases has been a rapidly expanding area in researches over the last decade, while its pathophysiologic impact on systemic lupus erythematosus (SLE) remains poorly elucidated. In this study, we analyzed the metabolic profiling of fecal samples from SLE patients and healthy controls based on ultra-high-performance liquid chromatography equipped with mass spectrometry for exploring the potential biomarkers of SLE. The results showed that 23 differential metabolites and 5 perturbed pathways were identified between the two groups, including aminoacyl-tRNA biosynthesis, thiamine metabolism, nitrogen metabolism, tryptophan metabolism, and cyanoamino acid metabolism. In addition, logistic regression and ROC analysis were used to establish a diagnostic model for distinguishing SLE patients from healthy controls. The combined model of fecal PG 27:2 and proline achieved an area under the ROC curve of 0.846, and had a good diagnostic efficacy. In the present study, we analyzed the correlations between fecal metabolic perturbations and SLE pathogenesis. In summary, we firstly illustrate the comprehensive metabolic profiles of feces in SLE patients, suggesting that the fecal metabolites could be used as the potential non-invasive biomarkers for SLE.

Metabolism as a key regulator in the pathogenesis of systemic lupus erythematosus

In the middle of the 20th century, biologists focused on investigating the mechanism of gene regulation and signal transduction in cells, which led to the concept that metabolites were products of gene expression and signal transduction pathways. In the 1920s, the importance of cellular metabolism was shown in the Warburg effect, in which cancer cells are characterized by a mitochondrial defect that shifts towards aerobic glycolysis. Recently, it is accepted that each organ and cell subset needs specific metabolic conditions and metabolic regulatory systems. Immunometabolism is a relatively new field of metabolism studies. The immune system consists of various cell subsets that have unique requirements and functions. The metabolic reprogramming in each immune cell causes different effects on different cell subsets. For example, resting lymphocytes generate energy through oxidative phosphorylation (OXPHOS) and fatty acid oxidation (FAO), whereas activated lymphocytes rapidly shift to the glycolytic pathway. A detailed understanding of metabolic regulation has progressed rapidly, especially in T cells during their differentiation from naïve to effector T cells. Metabolism is now considered to play a key role in autoimmune diseases. Metabolic changes in autoimmune diseases might be due to inflammation as well as being involved in autoimmune pathogenesis. Systemic lupus erythematosus (SLE) is an autoimmune disease with heterogenous clinical presentations whose precise pathophysiological mechanism is largely unknown. In this report, we review the altered metabolism in SLE and discuss the potential of metabolomics for accelerating the discovery of novel cellular autoimmune therapies and novel disease biomarkers.

Serum Metabolic Profiling Identified a Distinct Metabolic Signature in Bladder Cancer Smokers: A Key Metabolic Enzyme Associated with Patient Survival

BACKGROUND

The current system to predict the outcome of smokers with bladder cancer is insufficient due to complex genomic and transcriptomic heterogeneities. This study aims to identify serum metabolite-associated genes related to survival in this population.

METHODS

We performed LC/MS-based targeted metabolomic analysis for >300 metabolites in serum obtained from two independent cohorts of bladder cancer never smokers, smokers, healthy smokers, and healthy never smokers. A subset of differential metabolites was validated using Biocrates absoluteIDQ p180 Kit. Genes associated with differential metabolites were integrated with a publicly available cohort of The Cancer Genome Atlas (TCGA) to obtain an intersecting signature specific for bladder cancer smokers.

RESULTS

Forty metabolites (FDR < 0.25) were identified to be differential between bladder cancer never smokers and smokers. Increased abundance of amino acids (tyrosine, phenylalanine, proline, serine, valine, isoleucine, glycine, and asparagine) and taurine were observed in bladder cancer smokers. Integration of differential metabolomic gene signature and transcriptomics data from TCGA cohort revealed an intersection of 17 genes that showed significant correlation with patient survival in bladder cancer smokers. Importantly, catechol-O-methyltransferase, iodotyrosine deiodinase, and tubulin tyrosine ligase showed a significant association with patient survival in publicly available bladder cancer smoker datasets and did not have any clinical association in never smokers.

CONCLUSIONS

Serum metabolic profiling of bladder cancer smokers revealed dysregulated amino acid metabolism. It provides a distinct gene signature that shows a prognostic value in predicting bladder cancer smoker survival.

IMPACT

Serum metabolic signature-derived genes act as a predictive tool for studying the bladder cancer progression in smokers.

Emerging role of metabolomics in rheumatology

The pursuit for understanding disease pathogenesis, in this age of rapid laboratory diagnostics and fast-paced research, has led scientists worldwide to take recourse in hypothesis-free approaches for molecular diagnosis. Metabolomics is one such powerful tool that explores comprehensibly the metabolic alternations in human diseases. It involves study of small molecules of less than 1 kD in size by either LSMS or nuclear magnetic resonance. Unlike genomics, which tells us what may have happened, metabolomics reflects what did happen. The NMR technique has an advantage of analyzing metabolites without sample preparation, thereby diminishing artifacts, is less cumbersome and with the latest database on Metabolome; about 30 000 metabolites can be identified. The study of metabolomics for several rheumatic diseases, including rheumatoid arthritis, lupus, osteoarthritis and vasculitis, has revealed distinctive metabolic signatures. Thus, metabolomics is a technique that promises precision medicine with better biomarkers, robust predictors of drug response and of disease outcome, discovery of newer metabolites and pathways in disease pathogenesis, and finally, targeted drug development. This review intends to decipher its relevance in common rheumatic diseases.

Urinary Metabolomics Study of Patients with Gout Using Gas Chromatography-Mass Spectrometry

Objectives

Gout is a common type of inflammatory arthritis. The aim of this study was to detect urinary metabolic changes in gout patients which may contribute to understanding the pathological mechanism of gout and discovering potential metabolite markers.

Methods

Urine samples from 35 gout patients and 29 healthy volunteers were analyzed by gas chromatography-mass spectrometry (GC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed to screen differential metabolites between two groups, and the variable importance for projection (VIP) values and Student's t-test results were combined to define the significant metabolic changes caused by gout. Further, binary logistic regression analysis was performed to establish a model to distinguish gout patients from healthy people, and receiver operating characteristic (ROC) curve was made to evaluate the potential for diagnosis of gout.

Result

A total of 30 characteristic metabolites were significantly different between gout patients and controls, mainly including amino acids, carbohydrates, organic acids, and their derivatives, associated with perturbations in purine nucleotide synthesis, amino acid metabolism, purine metabolism, lipid metabolism, carbohydrate metabolism, and tricarboxylic acid cycle. Binary logistic regression and ROC curve analysis showed the combination of urate and isoxanthopterin can effectively discriminate the gout patients from controls with the area under the curve (AUC) of 0.879.

Conclusion

Thus, the urinary metabolomics study is an efficient tool for a better understanding of the metabolic changes of gout, which may support the clinical diagnosis and pathological mechanism study of gout.

Shotgun Lipidomics Revealed Altered Profiles of Serum Lipids in Systemic Lupus Erythematosus Closely Associated with Disease Activity

The pathogenesis of systemic lupus erythematosus (SLE) remains elusive. It appears that serum lipid metabolism is aberrant in SLE patients. Determination of lipid profiles in the serum of SLE patients may provide insights into the underlying mechanism(s) leading to SLE and may discover potential biomarkers for early diagnosis of SLE. This study aimed to identify and quantify the profile of serum lipids in SLE patients (N = 30) with our powerful multi-dimensional mass spectrometry-based shotgun lipidomics platform. Multivariate analysis in the form of partial least squares-discriminate analysis was performed, and the associations between the changed lipids with cytokines and SLE disease activity index (SLEDAI) were analyzed using a multiple regression method. The results of this study indicated that the composition of lipid species including diacyl phosphatidylethanolamine (dPE) (16:0/18:2, 18:0/18:2, 16:0/22:6, 18:0/20:4, and 18:0/22:6), 18:2 lysoPC (LPC), and ceramide (N22:0 and N24:1) was significantly altered in SLE patients with p < 0.05 and variable importance of the projection (VIP) > 1 in partial least squares-discriminate analysis (PLS-DA). There existed significant associations between IL-10, and both 18:0/18:2 and 16:0/22:6 dPE species with p < 0.0001 and predicting 85.7 and 95.8% of the variability of IL-10 levels, respectively. All the altered lipid species could obviously predict IL-10 levels with F (8, 21) = 3.729, p = 0.007, and R² = 0.766. There was also a significant correlation between the SLEDAI score and 18:0/18:2 dPE (p = 0.031) with explaining 22.6% of the variability of SLEDAI score. Therefore, the panel of changed compositions of dPE and ceramide species may serve as additional biomarkers for early diagnosis and/or prognosis of SLE.