Metabolic profiling reveals new serum biomarkers of lupus nephritis

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.

Regulation of macrophage polarization by targeted metabolic reprogramming for the treatment of lupus nephritis

Lupus nephritis (LN) is a severe and common manifestation of systemic lupus erythematosus (SLE) that is frequently identified with a poor prognosis. Macrophages play an important role in its pathogenesis. Different macrophage subtypes have different effects on lupus-affected kidneys. Based on their origin, macrophages can be divided into monocyte-derived macrophages (MoMacs) and tissue-resident macrophages (TrMacs). During nephritis, TrMacs develop a hybrid pro-inflammatory and anti-inflammatory functional phenotype, as they do not secrete arginase or nitric oxide (NO) when stimulated by cytokines. The infiltration of these mixed-phenotype macrophages is related to the continuous damage caused by immune complexes and exposure to circulating inflammatory mediators, which is an indication of the failure to resolve inflammation. On the other hand, MoMacs differentiate into M1 or M2 cells under cytokine stimulation. M1 macrophages are pro-inflammatory and secrete pro-inflammatory cytokines, while the M2 main phenotype is essentially anti-inflammatory and promotes tissue repair. Conversely, MoMacs undergo differentiation into M1 or M2 cells in response to cytokine stimulation. M1 macrophages are considered pro-inflammatory cells and secrete pro-inflammatory mediators, whereas the M2 main phenotype is primarily anti-inflammatory and promotes tissue repair. Moreover, based on cytokine expression, M2 macrophages can be further divided into M2a, M2b, and M2c phenotypes. M2a and M2c have anti-inflammatory effects and participate in tissue repair, while M2b cells have immunoregulatory and pro-inflammatory properties. Further, memory macrophages also have a role in the advancement of LN. Studies have demonstrated that the polarization of macrophages is controlled by multiple metabolic pathways, such as glycolysis, the pentose phosphate pathway, fatty acid oxidation, sphingolipid metabolism, the tricarboxylic acid cycle, and arginine metabolism. The changes in these metabolic pathways can be regulated by substances such as fish oil, polyenylphosphatidylcholine, taurine, fumaric acid, metformin, and salbutamol, which inhibit M1 polarization of macrophages and promote M2 polarization, thereby alleviating LN.

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.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

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.

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.

Untargeted serum metabolomic profiling for early detection of Schistosoma mekongi infection in mouse model

Mekong schistosomiasis is a parasitic disease caused by blood flukes in the Lao People’s Democratic Republic and in Cambodia. The standard method for diagnosis of schistosomiasis is detection of parasite eggs from patient samples. However, this method is not sufficient to detect asymptomatic patients, low egg numbers, or early infection. Therefore, diagnostic methods with higher sensitivity at the early stage of the disease are needed to fill this gap. The aim of this study was to identify potential biomarkers of early schistosomiasis using an untargeted metabolomics approach. Serum of uninfected and S. mekongi-infected mice was collected at 2, 4, and 8 weeks post-infection. Samples were extracted for metabolites and analyzed with a liquid chromatography-tandem mass spectrometer. Metabolites were annotated with the MS-DIAL platform and analyzed with Metaboanalyst bioinformatic tools. Multivariate analysis distinguished between metabolites from the different experimental conditions. Biomarker screening was performed using three methods: correlation coefficient analysis; feature important detection with a random forest algorithm; and receiver operating characteristic (ROC) curve analysis. Three compounds were identified as potential biomarkers at the early stage of the disease: heptadecanoyl ethanolamide; picrotin; and theophylline. The levels of these three compounds changed significantly during early-stage infection, and therefore these molecules may be promising schistosomiasis markers. These findings may help to improve early diagnosis of schistosomiasis, thus reducing the burden on patients and limiting spread of the disease in endemic areas.

Implications of Sphingolipid Metabolites in Kidney Diseases

Sphingolipids, which act as a bioactive signaling molecules, are involved in several cellular processes such as cell survival, proliferation, migration and apoptosis. An imbalance in the levels of sphingolipids can be lethal to cells. Abnormalities in the levels of sphingolipids are associated with several human diseases including kidney diseases. Several studies demonstrate that sphingolipids play an important role in maintaining proper renal function. Sphingolipids can alter the glomerular filtration barrier by affecting the functioning of podocytes, which are key cellular components of the glomerular filtration barrier. This review summarizes the studies in our understanding of the regulation of sphingolipid signaling in kidney diseases, especially in glomerular and tubulointerstitial diseases, and the potential to target sphingolipid pathways in developing therapeutics for the treatment of renal diseases.

Serum Metabolomic Profiling in Rheumatoid Arthritis Patients With Interstitial Lung Disease: A Case-Control Study

Objectives: Interstitial lung disease (ILD) is an extra-articular manifestation in rheumatoid arthritis (RA), detected in 10.7% of patients, and causing a poor prognosis. Hence, biomarkers for ILD are urgently required in RA. Low molecular weight metabolites can be assessed by metabolomic analyses, and although these have been conducted in RA and in idiopathic pulmonary fibrosis, few have been carried out for ILD in the context of RA. Therefore, we analyzed serum metabolomic profiles of ILD in RA to identify novel biomarkers.

Methods: Serum samples from 100 RA patients with ILD and 100 matched RA patients without chronic lung disease (CLD) were collected. These samples were subjected to metabolomic analyses using capillary electrophoresis time-of-flight mass spectrometry.

Results: A total of 299 metabolites were detected in the metabolomic analysis. By univariate analysis, serum levels of decanoic acid and morpholine were lower in RA with ILD (false discovery rate Q = 1.87 × 10⁻¹¹ and 7.09 × 10⁻⁶, respectively), and glycerol was higher (Q = 1.20 × 10⁻⁶), relative to RA without CLD. Serum levels of these metabolites in RA with usual interstitial pneumonia or RA with non-specific interstitial pneumonia were also altered. The partial least squares-discriminant analysis model generated from these three metabolites could successfully discriminate ILD in RA (area under the curve: 0.919, 95% confidence interval: 0.867–0.968, sensitivity 0.880, specificity 0.780).

Conclusions: Serum levels of some metabolites were significantly different in RA with ILD compared with RA without CLD. It is concluded that metabolomic profiling will be useful for discovering candidate screening biomarkers for ILD in RA.

Sphingolipids and Diagnosis, Prognosis, and Organ Damage in Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that involves multiple organs and disproportionality affects females, especially African Americans from 15 to 44 years of age. SLE can lead to end organ damage including kidneys, lungs, cardiovascular and neuropsychiatric systems, with cardiovascular complications being the primary cause of death. Usually, SLE is diagnosed and its activity is assessed using the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), Systemic Lupus International Collaborating Clinics Damage Index (SLICC/ACR), and British Isles Lupus Assessment Group (BILAG) Scales, which unfortunately often occurs after a certain degree of systemic involvements, disease activity or organ damage already exists. There is certainly a need for the identification of early biomarkers to diagnose and assess disease activity as well as to evaluate disease prognosis and response to treatment earlier in the course of the disease. Here we review advancements made in the area of sphingolipidomics as a diagnostic/prognostic tool for SLE and its co-morbidities. We also discuss recent reports on differential sphingolipid metabolism and blood sphingolipid profiles in SLE-prone animal models as well as in diverse cohorts of SLE patients. In addition, we address targeting sphingolipids and their metabolism as a method of treating SLE and some of its complications. Although such treatments have already shown promise in preventing organ-specific pathology caused by SLE, further investigational studies and clinical trials are warranted.

Predictive biomarkers of IgA vasculitis with nephritis by metabolomic analysis

BACKGROUND

IgA vasculitis (IgAV) is the most common vasculitis of childhood. Renal involvement defines late morbidity of the disease. A better understanding of the pathophysiology of the progression to kidney disease and predictive biomarkers are required for better management of IgAV and its nephritis (IgAVN).

OBJECTIVES

An untargeted metabolomics approach was performed to reveal the underlying molecular mechanism of disease pathogenesis and to define potential biomarkers from plasma samples from IgAV and IgAVN patients.

METHODS

Forty-five active IgAV patients (H) and six healthy controls (C) were enrolled in the study. Plasma samples were collected on the same day of diagnosis and before any immunosuppressive treatment was initiated. At the time of diagnosis and sample collection, none of the patients had renal involvement. We used Quadrupole Time of Flight Mass Spectrometry (Q-TOF LC/MS) to investigate the alterations in plasma metabolomic profiles. Three separate pools were created: healthy controls (group C), active IgAV patients who did not develop renal involvement (group H), and patients who developed IgAVN at follow up (group N). Peak picking, grouping, and comparison parts were performed via XCMS (https://xcmsonline.scripps.edu/) software.

RESULTS

At follow-up, IgAVN developed in 6 out of 45 IgAV patients. The median time of renal involvement development is 23 days (range 5-45 days). Of these, 3 had nephritic proteinuria, one had nephrotic proteinuria, and 2 had microscopic hematuria. There were no significant differences in gender, age, clinical manifestations, and laboratory findings between the six patients who developed renal involvement and those who did not. In multivariate analysis, there was no significant association between any of the defined demographic and clinical characteristics (male sex, gastrointestinal system involvement, joint involvement, CRP, WBC, PLT) and the occurrence of renal involvement. Totally 2618 peaks were detected for group H, N, and C. Among them, 355 peaks were found to be statistically significant and reliable (p<0.05), and 155 of these peaks were found to be changed (fold change >1.5) between the groups C and H, and 66 peaks were found to be changed (fold change >1.5) between the groups H and N. The number of the peaks on the intersection of the peaks found to be different between the groups (C and H) and (H and N) was 39. Based on putative identification results, 15 putatively identified metabolites matched with 11 peaks were presented as biomarker candidates after careful evaluation with a clinical perspective.

CONCLUSION

We suggest that DHAP (18:0), prostaglandin D2/I2, porphobilinogen, 5-methyltetrahydrofolic acid, and N-Acetyl-4-O-acetylneuraminic acid/N-Acetyl-7-O-acetylneuraminic acid may serve as biomarkers for predicting kidney disease. Future studies with larger groups of IgAV patients are needed to validate the identified metabolic profile.

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.

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.

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.

Metabolic Biomarkers In Midtrimester Maternal Plasma Can Accurately Predict Adverse Pregnancy Outcome in Patients with SLE

Patients with systemic lupus erythematosus (SLE) are at increased risk for adverse pregnancy outcome (APO). Accurate prediction of APO is critical to identify, counsel, and manage these high-risk patients. We undertook this study to identify novel biomarkers in mid-trimester maternal plasma to identify pregnant patients with SLE at increased risk of APOs. The study population consisted of pregnant women whose plasma was taken in mid-trimester and available for metabolic signature: (1) SLE and normal pregnancy outcome (Group 1, n = 21); (2) SLE with APO (Group 2, n = 12); and (3) healthy pregnant controls (Group 3, n = 10). Mid-trimester maternal plasma was analyzed for integrative profiles of primary metabolite and phospholipid using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and liquid chromatography Orbitrap mass spectrometry (LC-Orbitrap MS). For performance comparison and validation, plasma samples were analyzed for sFlt-1/PlGF ratio. In the study population, APO developed in 12 of 33 women with SLE (36%). Metabolite profiling of mid-trimester maternal plasma samples identified a total of 327 metabolites using GC-TOF MS and LC-Orbitrap MS. Partial least squares discriminant analysis (PLS-DA) showed clear discrimination among the profiles of SLE groups and healthy pregnant controls (Groups 1/2 vs. 3). Moreover, direct comparison between Groups 1 and 2 demonstrated that 4 primary metabolites and 13 lipid molecules were significantly different. Binary logistic regression analysis suggested a potential metabolic biomarker model that could discriminate Groups 1 and 2. Receiver operating characteristic (ROC) analysis revealed the best predictability for APO with the combination model of two metabolites (LysoPC C22:5 and tryptophan) with AUC of 0.944, comparable to the AUC of sFlt-1/PlGF (AUC 0.857). In conclusion, metabolic biomarkers in mid-trimester maternal plasma can accurately predict APO in patients with SLE.

Network analysis of membranous glomerulonephritis based on metabolomics data

Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component‑reaction‑enzyme‑gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography‑mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold‑change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein‑protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold‑changes: Dopamine, carnosine, fumarate, nicotinamide D‑ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L‑citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine‑4‑hydroxlylase and cystathionine γ‑lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: 'Pyrimidine‑containing compound biosynthetic process', 'purine ribonucleoside metabolic process', 'nucleoside catabolic process', 'ribonucleoside metabolic process' and 'aromatic amino acid family metabolic process'. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.

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.

NMR-Based Serum Metabolomics Reveals Reprogramming of Lipid Dysregulation Following Cyclophosphamide-Based Induction Therapy in Lupus Nephritis

Lupus nephritis (LN) is a major cause of morbidity and mortality in lupus. Renal biopsy is the gold standard for classification of nephritis, but because of its impracticality, new approaches for improving patient prognostication and monitoring treatment efficacy are needed. We aimed to evaluate the potential of metabolic profiling in identifying biomarkers to distinguish disease and monitor treatment efficacy in patients with LN. Serum samples from patients with LN ( n = 18) were profiled on NMR-based metabolomics platforms at diagnosis and after 6 months of treatment. LN patients had a different metabolomic fingerprint as compared with healthy controls, with increased lipoproteins and lipids and reduced acetate and amino acids. Using multivariate statistical analysis, we found that the metabolic changes observed in naïve LN patients at diagnosis displayed a variation in the opposite direction upon responding to treatment. Increased levels of lipid metabolites including low- and very-low-density lipoproteins (LDL/VLDL) in LN patients significantly decreased after 6 months of treatment, whereas the serum levels of acetate increased. These levels correlated significantly with SLE Disease Activity Index (SLEDAI 2K), renal SLEDAI, and serum C3 and C4 levels. The result presented in this pilot longitudinal study revealed the reprogramming of metabolome in LN patients on immunosuppressive therapy using NMR-based metabolomics, and thus this approach may be used to monitor the response to treatment.

Polyamine patterns in plasma of patients with systemic lupus erythematosus and fever

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease with various clinical manifestations and serologic markers. In this study, we analyzed nine polyamine (PA) profiles of plasma from patients with SLE and healthy controls (HCs), and the relationship between the PA profiles and disease activity. PA alterations in plasma of 44 patients with SLE and fever were investigated using gas chromatography mass spectrometry (GC-MS) in selected ion monitoring mode using N-ethoxycarbonyl/ N-pentafluoropropionyl derivatives, and compared with those of 43 HCs. Patients with SLE and HCs showed differences in five of nine PA profiles. Among five changed PA levels, four PAs, namely N¹-acetylcadaverine, spermidine, N¹-acetylspermidine, and spermine, were dramatically decreased. However, the level of cadaverine was increased in patients with SLE. In the partial correlation with PA profiles and disease activity markers of SLE, several disease activity markers and nutritional markers were correlated with cadaverine, spermidine, and N ⁸-acetylspermidine. Thus, our results provide a comprehensive understanding of the relationship between PA metabolomics and disease activity markers in patients with SLE.

Diagnostic and prognostic tests in systemic lupus erythematosus

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