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

Metabolic alterations in vitamin D deficient systemic lupus erythematosus patients

Vitamin D deficiency is increasingly common in systemic lupus erythematosus (SLE) patients and is associated with the disease activity and proteinuria. Recently, alterations in metabolism have been recognized as key regulators of SLE pathogenesis. Our objective was to identify differential metabolites in the serum metabolome of SLE with vitamin D deficiency. In this study, serum samples from 31 SLE patients were collected. Levels of 25(OH)D3 were assayed by ELISA. Patients were divided into two groups according to their vitamin D level (20 ng/ml). Untargeted metabolomics were used to study the metabolite profiles in serum by high-performance liquid chromatography-tandem mass spectrometry. Subsequently, we performed metabolomics profiling analysis to identify 52 significantly altered metabolites in vitamin D deficient SLE patients. The area under the curve (AUC) from ROC analyses was calculated to assess the diagnostic potential of each candidate metabolite biomarker. Lipids accounted for 66.67% of the differential metabolites in the serum, highlighted the disruption of lipid metabolism. The 52 differential metabolites were mapped to 27 metabolic pathways, with fat digestion and absorption, as well as lipid metabolism, emerging as the most significant pathways. The AUC of (S)-Oleuropeic acid and 2-Hydroxylinolenic acid during ROC analysis were 0.867 and 0.833, respectively, indicating their promising diagnostic potential. In conclusion, our results revealed vitamin D deficiency alters SLE metabolome, impacting lipid metabolism, and thrown insights into the pathogenesis and diagnosis of SLE.

Exploring genetic associations in systemic lupus erythematosus through Mendelian randomization: implications for novel biomarkers and therapeutic targets

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by a significant health burden. There is an essential need for novel biomarkers and therapeutic targets to improve diagnosis and management. Mendelian randomization (MR) was applied to explore causal links between SLE and various biomarkers like immune cells, metabolites, and inflammatory cytokines using multiple databases. Initially, biomarkers significantly associated with SLE were identified. Bidirectional MR helped clarify these relationships, and a two-step mediation MR examined their effects on SLE risk. Intersection analysis was used to identify biomarkers with consistent effects across datasets. Four biomarkers were identified as having significant associations with SLE risk: 1-palmitoyl-2-arachidonoyl-GPI levels [odds ratio (OR), 1.379; 95% confidence interval (CI), 1.180 to 1.613; FDR, 0.046], IL-17A levels (OR, 2.197; 95% CI, 1.412 to 3.418; FDR, 0.044), N-acetyl-aspartyl-glutamate (NAAG) levels (OR, 0.882; 95% CI, 0.831 to 0.936; FDR, 0.030), and ribitol levels (OR, 0.743; 95% CI, 0.644 to 0.857; FDR, 0.012). Bidirectional MR showed an inverse effect of NAAG on IL-17A levels (OR, 0.978; 95% CI, 0.962 to 0.994; p = 0.006). Mediation analysis indicated that NAAG influenced SLE risk both directly (beta =  - 0.108) and indirectly through IL-17A (beta =  - 0.018), highlighting the potential mediating role of IL-17A. After expanding the significance criteria to p < 0.05, intersection analysis across multiple datasets revealed 29 biomarkers with consistent beta directions, including 19 potential risk factors (beta > 0) and 10 protective factors (beta < 0) for SLE. This research has revealed significant genetic associations with SLE and demonstrated that IL-17A mediates the relationship between NAAG levels and SLE risk, highlighting potential new targets for personalized therapeutic interventions. Key Points • This study employs MR to identify significant genetic associations between various biomarkers and SLE, providing novel insights into potential biomarkers and therapeutic targets. • Four key biomarkers were identified as significantly associated with SLE risk: 1-palmitoyl-2-arachidonoyl-GPI, IL-17A, N-acetyl-aspartyl-glutamate (NAAG), and ribitol. • The findings suggest that NAAG levels have a protective effect against SLE, partly mediated through IL-17A, indicating a complex interplay between these biomarkers in the pathogenesis of SLE. • Intersectional analysis across multiple datasets revealed 29 biomarkers with consistent effects on SLE risk, highlighting new directions for future research and potential personalized therapeutic 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.

The roles of adenosine signaling in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with multiple etiological factors. Immune disorder contributes to SLE development and is an important clinical manifestation of SLE patients. Immune dysfunction is characterized by abnormal of B cells, T cells, monocyte-macrophages and dendritic cells (DCs), in both quantity and quality. Adenosine is a critical factor for human immune homeostasis, which acts as an immunosuppressive signal and can prevent the hyperactivity of human immune system. Adenosine levels are significant decreased in serum from SLE patients. Adenosine level is regulated by the CD39, CD73 and adenosine deaminase (ADA). CD39/CD73/ADA catalyzed the cascade enzymatic reaction, which contained the adenosine generation and degradation. Adenosine affects the function of various immune cells via bind to the adenosine receptors, which are expressed on the cell surface. This review aims to export the changes of immune cells and adenosine signal pathway in SLE, as well as the effect of adenosine signal pathway in SLE development.

Metabolomics profiling reveals low blood tyrosine levels as a metabolic feature of newborns from systemic lupus erythematosus pregnancies

Introduction

Pregnancy outcomes of patients with systemic lupus erythematosus (SLE) have improved over the past four decades, leading to an increased desire for pregnancy among this cohort. However, the offspring of patients with SLE still face the risks of preterm birth, low birth weight, learning disabilities, and neurological disorders, while the causes underlying these risks remain unclear.

Methods

In this study, we analyzed the blood metabolic features of neonates born to 30 SLE patients and 52 healthy control mothers by employing tandem mass spectrometry with the dual aims of identifying the etiology of metabolic features specific to infants born from mothers with SLE and providing new insights into the clinical management of such infants.

Results

We found significant differences in serum metabolite levels between infants born from mothers with SLE and those born from mothers without SLE, including 15 metabolites with reduced serum levels. Further analysis revealed a disrupted tyrosine metabolism pathway in the offspring of mothers with SLE.

Discussion

By constructing a composite model incorporating various factors, such as serum tyrosine levels, gestational age, and birth weight, we were able to accurately differentiate between newborns of SLE and non-SLE pregnancies. Our data reveal significant differences in serum concentrations of amino acids and acylcarnitines in newborns born to mothers with SLE. We conclude that the reduction of blood L-tyrosine levels is a feature that is characteristic of adverse neurological outcomes in infants born from mothers with SLE.

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.

Mass Spectrometry Analysis for Clinical Applications: A Review

Mass spectrometry (MS) has become an attractive analytical method in clinical analysis due to its comprehensive advantages of high sensitivity, high specificity and high throughput. Separation techniques coupled MS detection (e.g., LC-MS/MS) have shown unique advantages over immunoassay and have developed as golden criterion for many clinical applications. This review summarizes the characteristics and applications of MS, and emphasizes the high efficiency of MS in clinical research. In addition, this review also put forward further prospects for the future of mass spectrometry technology, including the introduction of miniature MS instruments, point-of-care detection and high-throughput analysis, to achieve better development of MS technology in various fields of clinical application. Moreover, as ambient ionization mass spectrometry (AIMS) requires little or no sample pretreatment and improves the flux of MS, this review also summarizes its potential applications in clinic.

Discovery and validation of metabolite markers in bloodstains for bloodstain age estimation

Bloodstain age estimation involves measuring time-dependent changes in the levels of biomolecules in bloodstains. Although several studies have identified bloodstain metabolites as markers for estimating bloodstain age, none have considered sex, age-related metabolomic differences, or long-time bloodstain age. Therefore, we aimed to identify metabolite markers for estimating the age of bloodstains at weekly intervals within 28 days and validate them through multiple reaction monitoring. Adenosine 5'-monophosphate, choline, and pyroglutamic acid were selected as markers. Seven metabolites were validated, including five previously reported metabolites, ergothioneine, hypoxanthine, L-isoleucine, L-tryptophan, and pyroglutamic acid. Choline and hypoxanthine can be used to differentiate bloodstains between days 0 and 14 after deposition at weekly intervals, whereas L-isoleucine and L-tryptophan can help distinguish bloodstains between 7 days before and 14 days after deposition. Evaluation of the changes in metabolite levels according to sex and age revealed that the average levels of all seven metabolites were higher in women on day 0. Moreover, the level of ergothioneine was significantly higher in elderly individuals than in young individuals at all time points. In this study, we confirmed the potential effectiveness of metabolites in bloodstains as forensic markers and provided a new perspective on metabolomic approaches linked to forensic science.

Structural characterization of a fucoidan from Ascophyllum nodosum and comparison of its protective effect against cellular oxidative stress with its analogues

In the present study, a fucoidan fraction (ANP-3) was isolated from Ascophyllum nodosum, and the combined application of desulfation, methylation, HPGPC, HPLC-MSn, FT-IR, GC-MS, NMR, and Congo red test elucidated ANP-3 (124.5 kDa) as a triple-helical sulfated polysaccharide constituted by →2)-α-Fucp3S-(1→, →3)-α-Fucp2S4S-(1→, →3,6)-β-Galp4S-(1→, →3,6)-β-Manp4S-(1→, →3,6)-β-Galp4S-(1→，→6)-β-Manp-(1→, →3)-β-Galp-(1→, α-Fucp-(1→, and α-GlcAp-(1→ residues. To better understand the relationship between the fucoidan structure of A. nodosum and protective effects against oxidative stress, two fractions ANP-6 and ANP-7 were used as contrast. ANP-6 (63.2 kDa) exhibited no protective effect against H2O2-induced oxidative stress. However, ANP-3 and ANP-7 with the same molecular weight of 124.5 kDa could protect against oxidative stress by down-regulating reactive oxygen species (ROS) and malondialdehyde (MDA) levels and up-regulating total antioxidant capability (T-AOC), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPX) activities. Then metabolites analysis indicated that arginine biosynthesis and phenylalanine, tyrosine, and tryptophan biosynthesis metabolic pathways and metabolic biomarkers such as betaine were involved in the effects of ANP-3 and ANP-7. The better protective effect of ANP-7 compared to that of ANP-3 could be attributed to its relatively higher molecular weight, sulfate substitution and →6)-β-Galp-(1→ content, and lower uronic acid content.

Integration of metabolomics and transcriptomics to reveal ferroptosis is involved in Tripterygium wilfordii polyglycoside tablet-induced testicular injury

ETHNOPHARMACOLOGICAL RELEVANCE

Tripterygium wilfordii polyglycoside tablet (TWP), a traditional Chinese medicine preparation, has multiple pharmacological properties, including anti-inflammatory, immune-modulatory and anti-proliferative activities. However, the reproductive toxicity of TWP greatly limits its clinical application and the mechanism of TWP-induced reproductive toxicity is not fully understood yet.

AIM OF THE STUDY

This study was designed to explore the mechanism of TWP-induced testis injury in male rats.

MATERIALS AND METHODS

The mechanism underlying TWP-induced rat testicular injury was firstly investigated by integration of metabolomics and transcriptomics. Meanwhile, histopathological analysis, Western blot and RT-qPCR were performed to confirm the damaging effects and mechanisms of TWP on rat testis.

RESULTS

Histopathological analysis revealed that TWP had significant testicular damage, which severely reduced the testis's tubular diameter and epithelium height. Further, TWP caused the protein level of ZO-1, CLDN11, PLZF, and OCT4 significantly downregulate, suggesting the blood-testis barrier function and spermatogenesis were damaged. Differentially expressed genes (DEGs), including 4952 upregulated and 2626 downregulated, were found in TWP-exposed testis compared to the normal group. Moreover, 77 changed metabolites were identified from testis samples. With integrated analysis of DEGs and changed metabolites, we found that glutathione metabolism and ferroptosis played an essential role in testicular injury. Additionally, the levels of ferroptosis-related protein GPX4, SLC7A11, and NRF2 were significantly downregulated, and the protein level of 4-HNE, a leading product of lipid peroxidation and oxidative stress, was upregulated. The changes in ferroptosis-related genes indicated that TWP might promote ferroptosis in rat testis.

CONCLUSION

These results suggested that ferroptosis was involved in the testicular damage caused by TWP, which might provide a new strategy to alleviate TWP- induced testicular injury.

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.

Lipidomics Changes in a Murine Model of Neuropsychiatric Lupus

Purpose

Neuropsychiatric lupus (NPSLE) is one of the important manifestations of systemic lupus erythematosus. Previous studies mainly focused on the disruption of the blood-brain barrier and the production of brain-reactive autoantibodies, However, there is no comprehensive lipidomic analysis in NPSLE. Therefore, this research evaluated the lipidomic analysis in the hippocampus and liver of NPSLE mice with mood disorders, to explore the influence of the liver-brain axis on this disease.

Methods

MRL/lpr mice and MRL/mpj mice were respectively used as NPSLE and control groups. Behavioral tests and systemic disease characteristics of mice were assessed at the age of 18 weeks. Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) was used for lipid metabolite determination. Multivariate statistical analysis was used to identify lipid metabolites that were differentially expressed in two groups.

Results

Our results showed that 355 and 405 lipid metabolites were differentially expressed between the NPSLE and control groups in the hippocampus and liver. According to the pathway enrichment analysis, several pathways were affected, and the glycerophospholipid metabolism pathway was most relevant to the mouse's depressive behavior.

Conclusion

Based on UPLC-MS/MS, the results provide evidence for how the liver-brain axis affects NPSLE and improve the understanding of NPSLE pathogenesis.

Discovery and Validation of Potential Serum Biomarkers with Pro-Inflammatory and DNA Damage Activities in Ulcerative Colitis: A Comprehensive Untargeted Metabolomic Study

Ulcerative colitis is a type of non-specific inflammatory bowel disease with unclear etiology. It is considered a progressive disease with risks of bowel motility disorders, anorectal dysfunction, and even colorectal cancer. Commonly used diagnostic markers have poor specificity and cannot predict the development of ulcerative colitis. In this study, 77 serum samples (31 patients, 46 healthy controls) were analyzed using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry and 31 metabolites with significant level changes were found, revealing the relationship of ulcerative colitis to disturbed glutathione metabolism and caffeine metabolism. In addition, pyroglutamic acid, a biomarker of cervical cancer and gastric cancer, was identified with elevated levels in the serum of ulcerative colitis patients. The role of pyroglutamic acid was further analyzed, and the results indicated its positive correlation with the upregulation of inflammatory factors and increased levels of phosphorylated histone H2AX (γH2AX) in IEC-6 cells, which are related to DNA damage. All these results suggest that pyroglutamic acid is not only a biomarker for distinguishing ulcerative colitis status, but that it is also a potential effective metabolite that promotes the transformation of ulcerative colitis to colorectal cancer.

Absolute quantification and characterization of oxylipins in lupus nephritis and systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with multi-organ inflammation and defect, which is linked to many molecule mediators. Oxylipins as a class of lipid mediator have not been broadly investigated in SLE. Here, we applied targeted mass spectrometry analysis to screen the alteration of oxylipins in serum of 98 SLE patients and 106 healthy controls. The correlation of oxylipins to lupus nephritis (LN) and SLE disease activity, and the biomarkers for SLE classification, were analyzed. Among 128 oxylipins analyzed, 92 were absolutely quantified and 26 were significantly changed. They were mainly generated from the metabolism of several polyunsaturated fatty acids, including arachidonic acid (AA), linoleic acid (LA), docosahexanoic acid (DHA), eicosapentanoic acid (EPA) and dihomo-γ-linolenic acid (DGLA). Several oxylipins, especially those produced from AA, showed different abundance between patients with and without lupus nephritis (LN). The DGLA metabolic activity and DGLA generated PGE1, were significantly associated with SLE disease activity. Random forest-based machine learning identified a 5-oxylipin combination as potential biomarker for SLE classification with high accuracy. Seven individual oxylipin biomarkers were also identified with good performance in distinguishing SLE patients from healthy controls (individual AUC > 0.7). Interestingly, the biomarkers for differentiating SLE patients from healthy controls are distinct from the oxylipins differentially expressed in LN patients vs. non-LN patients. This study provides possibilities for the understanding of SLE characteristics and the development of new tools for SLE classification.

Metabolic profiling reveals new serum signatures to discriminate lupus nephritis from systemic lupus erythematosus

Background

Lupus nephritis (LN) occurs in 50% of patients with systemic lupus erythematosus (SLE), causing considerable morbidity and even mortality. Previous studies had shown the potential of metabolic profiling in the diagnosis of SLE or LN. However, few metabonomics studies have attempted to distinguish SLE from LN based on metabolic changes. The current study was designed to find new candidate serum signatures that could differentiate LN from SLE patients using a non-targeted metabonomics method based on ultra high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS).

Method

Metabolic profiling of sera obtained from 21 healthy controls, 52 SLE patients and 43 LN patients. We used SPSS 25.0 for statistical analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and metabolic pathway analysis were used to analyze the metabolic data.

Results

Upon comparison of SLE and LN groups, 28 differential metabolites were detected, the majority of which were lipids and amino acids. Glycerolphospholipid metabolism, pentose and glucuronate interconversions and porphyrin and chlorophyll metabolism were obviously enriched in LN patients versus those with SLE. Among the 28 characteristic metabolites, five key serum metabolites including SM d34:2, DG (18:3(9Z,12Z,15Z)/20:5(5Z,8Z,11Z,14Z,17Z)/0:0), nervonic acid, Cer-NS d27:4, and PC (18:3(6Z,9Z,12Z)/18:3(6Z,9Z,12Z) performed higher diagnostic performance in discriminating LN from SLE (all AUC &gt; 0.75). Moreover, combined analysis of neuritic acid, C1q, and CysC (AUC = 0.916) produced the best combined diagnosis.

Conclusion

This study identified five serum metabolites that are potential indicators for the differential diagnosis of SLE and LN. Glycerolphospholipid metabolism may play an important role in the development of SLE to LN. The metabolites we screened can provide more references for the diagnosis of LN and more support for the pathophysiological study of SLE progressed to LN.

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.

Altered Fecal Metabolomics and Potential Biomarkers of Psoriatic Arthritis Differing From Rheumatoid Arthritis

Psoriatic arthritis (PsA) is a chronic inflammatory joint disease, and the diagnosis is quite difficult due to the unavailability of reliable clinical markers. This study aimed to investigate the fecal metabolites in PsA by comparison with rheumatoid arthritis (RA), and to identify potential diagnostic biomarkers for PsA. The metabolic profiles of the fecal samples from 27 PsA and 29 RA patients and also 36 healthy controls (HCs) were performed on ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). And differentially altered metabolites were screened and assessed using multivariate analysis for exploring the potential biomarkers of PsA. The results showed that 154 fecal metabolites were significantly altered in PsA patients when compared with HCs, and 45 metabolites were different when compared with RA patients. A total of 14 common differential metabolites could be defined as candidate biomarkers. Furthermore, a support vector machines (SVM) model was performed to distinguish PsA from RA patients and HCs, and 5 fecal metabolites, namely, α/β-turmerone, glycerol 1-hexadecanoate, dihydrosphingosine, pantothenic acid and glutamine, were determined as biomarkers for PsA. Through the metabolic pathways analysis, we found that the abnormality of amino acid metabolism, bile acid metabolism and lipid metabolism might contribute to the occurrence and development of PsA. In summary, our research provided ideas for the early diagnosis and treatment of PsA by identifying fecal biomarkers and analyzing metabolic pathways.

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.

Identifying active xenobiotics in humans by use of a suspect screening technique coupled with lipidomic analysis

Lipidomic analysis has been proven to be a powerful technique to explore the underlying associations between xenobiotics and health status of organisms. Here, we established a strategy that combined the lipidomic analysis with high-throughput suspect contaminant screening technique with an aim to efficiently identify active xenobiotics in humans. Firstly, in the light of single liquid phase equilibrium of chloroform-methanol-water (15:14:2, v/v/v), we developed an efficient method that was able to simultaneously extract both polar and nonpolar lipids in serum samples. By use of this method, targeted and non-targeted lipid analyses were conducted for n = 120 serum samples collected from Wuxi city, China. Secondly, we established a suspect database containing 1450 contaminants that have been previously reported in human samples, and contaminants in this database were screened in the same batch of serum samples by use of high-resolution mass spectrometry (HR-MS). Thirdly, the underlying associations between suspect contaminants and lipids were explored and discussed, and we observed that levels of some lipids were statistically correlated with concentrations of numerous contaminants. Among these active contaminants, 23 ones were identified on the basis of HR MS¹ and MS² characteristics, and these contaminants belonged to the classes of phthalates, phenols, parabens, or perfluorinated compounds (PFCs). Three active xenobiotics were fully validated by comparison with authentic standards, and they were perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and diethyl phthalate (DEP). There were statistically significant changes in levels of triglyceride (TG), lysophosphocholine (LPC), and sphingomyelin (SM) as peak areas of xenobiotics increase. We also observed that, among target lipid molecules, 18:0 lysophosphatidylethanolamine (LPE(18:0)) was very sensitive, and this lipid responded to exposure of various contaminants. Our present study provides novel knowledge on potential alteration of lipid metabolism in humans following exposure to xenobiotics, and provides an efficient strategy for efficiently identifying active xenobiotics in humans.

High humidity aggravates the severity of arthritis in collagen-induced arthritis mice by upregulating xylitol and L-pyroglutamic acid

Background

Humidity was an unfavorable factor for patients with rheumatoid arthritis (RA). RA disease activity was severe in high humidity conditions. However, there is no evidence to demonstrate the effects of humidity on arthritis in the animal experiments and explore its relevant mechanism.

Methods

Using the DBA/1 mice, this study addressed the effects of a high humidity (80 ± 5%) on arthritis in collagen-induced arthritis (CIA) mice. Then, this study used the gas chromatography-mass spectrometer (GC-MS) to explore alterations in serum metabolome caused by the high humidity. Furthermore, xylitol and L-pyroglutamic acid, which were both significantly upregulated by the high humidity, were selected to further study their effects on arthritis in the CIA mice.

Results

The high humidity (80 ± 5%) could aggravate arthritis variables including increasing arthritis score and swelling, serum autoantibodies (anti-COII and anti-CCP), and proinflammatory cytokines (IL-6, IL-17A, and G-CSF). In addition, the high humidity could cause significant alterations in serum metabolome in the CIA mice. Xylitol and L-pyroglutamic acid were the representative serum metabolites that were significantly upregulated by the high humidity. Further experiments demonstrated that the supplementation of 0.4 mg/mL xylitol in drinking water after inducing the CIA model and 2.0 mg/mL in drinking water before inducing the CIA model could both aggravate arthritis in the CIA mice.

Conclusions

These data demonstrated that high humidity was not beneficial for arthritis development and its mechanism might be associated with xylitol and L-pyroglutamic acid.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02681-x.

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.

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.

Bioactive lipids, inflammation and chronic diseases

Endogenous bioactive lipids are part of a complex network that modulates a plethora of cellular and molecular processes involved in health and disease, of which inflammation represents one of the most prominent examples. Inflammation serves as a well-conserved defence mechanism, triggered in the event of chemical, mechanical or microbial damage, that is meant to eradicate the source of damage and restore tissue function. However, excessive inflammatory signals, or impairment of pro-resolving/anti-inflammatory pathways leads to chronic inflammation, which is a hallmark of chronic pathologies. All main classes of endogenous bioactive lipids - namely eicosanoids, specialized pro-resolving lipid mediators, lysoglycerophopsholipids and endocannabinoids - have been consistently involved in the chronic inflammation that characterises pathologies such as cancer, diabetes, atherosclerosis, asthma, as well as autoimmune and neurodegenerative disorders and inflammatory bowel diseases. This review gathers the current knowledge concerning the involvement of endogenous bioactive lipids in the pathogenic processes of chronic inflammatory pathologies.