Serum Metabolomics Signatures Associated With Ankylosing Spondylitis and TNF Inhibitor Therapy

PKM2 promotes proinflammatory macrophage activation in ankylosing spondylitis

Macrophages play a critical role in ankylosing spondylitis by promoting autoimmune tissue inflammation through various effector functions. The inflammatory potential of macrophages is highly influenced by their metabolic environment. Here, we demonstrate that glycolysis is linked to the proinflammatory activation of human blood monocyte-derived macrophages in ankylosing spondylitis. Specifically, ankylosing spondylitis macrophages produced excessive inflammation, including TNFα, IL1β, and IL23, and displayed an overactive status by exhibiting stronger costimulatory signals, such as CD80, CD86, and HLA-DR. Moreover, we found that patient-derived monocyte-derived M1-type macrophages (M1 macrophages) exhibited intensified glycolysis, as evidenced by a higher extracellular acidification rate. Upregulation of PKM2 and GLUT1 was observed in ankylosing spondylitis-derived monocytes and monocyte-derived macrophages, especially in M1 macrophages, indicating glucose metabolic alteration in ankylosing spondylitis macrophages. To investigate the impact of glycolysis on macrophage inflammatory ability, we treated ankylosing spondylitis M1 macrophages with 2 inhibitors: 2-deoxy-D-glucose, a glycolysis inhibitor, and shikonin, a PKM2 inhibitor. Both inhibitors reduced proinflammatory function and reversed the overactive status of ankylosing spondylitis macrophages, suggesting their potential utility in treating the disease. These data place PKM2 at the crosstalk between glucose metabolic changes and the activation of inflammatory macrophages in patients with ankylosing spondylitis.

Comparison of intestinal microbes and metabolites in active VKH versus acute anterior uveitis associated with ankylosing spondylitis

BACKGROUND

It has been reported that the gut microbiome is involved in the pathogenesis of uveitis, but the specific pathogenic microbes and metabolites in different types of uveitis are still unclear.

METHODS

Microbiome and metabolites were detected using 16S ribosomal DNA and LC‒MS/MS (liquid chromatography tandem mass spectrometry) in 45 individuals, including 16 patients with Vogt Koyanagi Harada (VKH), 11 patients with acute anterior uveitis (AAU) and 18 healthy controls.

RESULT

The diversity of intestinal microbes among the VKH, AAU and control groups was not significantly different. Thirteen specific microbes and 38 metabolites were detected in the VKH group, and 7 metabolites (vanillin, erythro-isoleucine, pyrimidine, 1-aminocyclopropanecarboxylic acid, beta-tocopherol, (-)-gallocatechin and N1-methyl-4-pyridone-3-carboxamide) significantly changed only in patients with VKH, which mainly acted on nicotinamide and nicotinamide metabolism and biotin metabolism (p<0.05). Compared with the VKH group, the AAU group had milder intestinal changes. Only 11 specific microbes and 29 metabolites changed in the AAU group, while these metabolites were not specific (p<0.05). These metabolites mainly acted on arachidonic acid metabolism. In addition, three microbes and two metabolites had the same changes in the VKH and AAU groups (p<0.05). Multiple correlations were found between gut microbes and metabolites in the VKH and AAU groups. Six microbes (Pediococcus, Pseudomonas, Rhodococcus, Photobacterium, Gardnerella and Lawsonia) and two metabolites (pyrimidine and gallocatechin) as biomarkers could effectively distinguish patients with VKH from patients with AAU and healthy individuals, with AUC (area under the curve) values greater than 82%. Four microbes (Lentilactobacillus, Lachnospiraceae_UCG-010, Cetobacterium, Liquorilactobacillus) could distinguish patients with AAU from patients with VKH and healthy controls with AUC>76%.

CONCLUSION

Significant differences in intestinal microbes and metabolites suggest their different roles in the pathogenesis of uveitis entities. Changes in the metabolism of certain B vitamins may be involved in the pathogenesis of VKH.

Diagnostic serum biomarkers associated with ankylosing spondylitis

Ankylosing spondylitis (AS) is an autoimmune rheumatic disease that mostly affects the axial skeleton. This study aimed to investigate reliable diagnostic serum biomarkers for AS. Serum samples were collected from 20 AS patients and 20 healthy controls (HCs) and analyzed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differential metabolites between the AS patients and HCs were profiled using univariate and multivariate statistical analyses. Pathway analysis and a heat map were also conducted. Random forest (RF) analysis and the least absolute shrinkage and selection operator (LASSO) were used to establish predictive and diagnostic models. After controlling the variable importance in the projection (VIP) value > 1 and false discovery rate (FDR) < 0.05, a total of 61 differential metabolites were identified from 995 metabolites, which exhibited significant differences in the pathway analysis and heat map between the AS patients and HCs. RF as a predictive model also identified differential metabolites with 95% predictive accuracy and a high area under the curve (AUC) of 1. A diagnostic model comprising nine metabolites (cysteinylglycine disulfide, choline, N6, N6, N6-trimethyllysine, histidine, sphingosine, fibrinopeptide A, glycerol 3-phosphate, 1-linoleoyl-GPA (18:2), and fibrinopeptide A (3-16)) was generated using LASSO regression, capable of distinguishing HCs from AS with a high AUC of 1. Our results indicated that the UPLC-MS/MS analysis method is a powerful tool for identifying AS metabolite profiles. We developed a nine-metabolites-based model serving as a diagnostic tool to separate AS patients from HCs, and the identified diagnostic biomarkers appeared to have a diagnostic value for AS.

ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI

BACKGROUND

Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients' quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI).

MATERIALS AND METHODS

In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model.

RESULTS

We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained.

CONCLUSIONS

Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model's general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms.

Exploring hub pyroptosis-related genes, molecular subtypes, and potential drugs in ankylosing spondylitis by comprehensive bioinformatics analysis and molecular docking

BACKGROUND

Ankylosing spondylitis (AS) is a chronic inflammatory autoimmune disease, and the diagnosis and treatment of AS have been limited because its pathogenesis is still unclear. Pyroptosis is a proinflammatory type of cell death that plays an important role in the immune system. However, the relationship between pyroptosis genes and AS has never been elucidated.

METHODS

GSE73754, GSE25101, and GSE221786 datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis-related genes (DE-PRGs) were identified by R software. Machine learning and PPI networks were used to screen key genes to construct a diagnostic model of AS. AS patients were clustered into different pyroptosis subtypes according to DE-PRGs using consensus cluster analysis and validated using principal component analysis (PCA). WGCNA was used for screening hub gene modules between two subtypes. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for enrichment analysis to elucidate underlying mechanisms. The ESTIMATE and CIBERSORT algorithms were used to reveal immune signatures. The connectivity map (CMAP) database was used to predict potential drugs for the treatment of AS. Molecular docking was used to calculate the binding affinity between potential drugs and the hub gene.

RESULTS

Sixteen DE-PRGs were detected in AS compared to healthy controls, and some of these genes showed a significant correlation with immune cells such as neutrophils, CD8 + T cells, and resting NK cells. Enrichment analysis showed that DE-PRGs were mainly related to pyroptosis, IL-1β, and TNF signaling pathways. The key genes (TNF, NLRC4, and GZMB) screened by machine learning and the protein-protein interaction (PPI) network were used to establish the diagnostic model of AS. ROC analysis showed that the diagnostic model had good diagnostic properties in GSE73754 (AUC: 0.881), GSE25101 (AUC: 0.797), and GSE221786 (AUC: 0.713). Using 16 DE-PRGs, AS patients were divided into C1 and C2 subtypes, and these two subtypes showed significant differences in immune infiltration. A key gene module was identified from the two subtypes using WGCNA, and enrichment analysis suggested that the module was mainly related to immune function. Three potential drugs, including ascorbic acid, RO 90-7501, and celastrol, were selected based on CMAP analysis. Cytoscape showed GZMB as the highest-scoring hub gene. Finally, molecular docking results showed that GZMB and ascorbic acid formed three hydrogen bonds, including ARG-41, LYS-40, and HIS-57 (affinity: -5.3 kcal/mol). GZMB and RO-90-7501 formed one hydrogen bond, including CYS-136 (affinity: -8.8 kcal/mol). GZMB and celastrol formed three hydrogen bonds, including TYR-94, HIS-57, and LYS-40 (affinity: -9.4 kcal/mol).

CONCLUSIONS

Our research systematically analyzed the relationship between pyroptosis and AS. Pyroptosis may play an essential role in the immune microenvironment of AS. Our findings will contribute to a further understanding of the pathogenesis of AS.

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.

Colonization of intervertebral discs by Cutibacterium acnes in patients with low back pain: Protocol for an analytical study with microbiological, phenotypic, genotypic, and multiomic techniques

Lumbar disc degeneration (LDD) and low back pain (LBP) are two conditions that are closely related. Several studies have shown Cutibacterium acnes colonization of degenerated discs, but whether and how these finding correlates with LBP is unknown. A prospective study was planned to identify molecules present in lumbar intervertebral discs (LLIVD) colonized by C. acnes in patients with LDD and LBP and correlate them with their clinical, radiological, and demographic profiles. The clinical manifestations, risk factors, and demographic characteristics of participants undergoing surgical microdiscectomy will be tracked. Samples will be isolated and pathogens found in LLIVD will be characterized phenotypically and genotypically. Whole genome sequencing (WGS) of isolated species will be used to phylotype and detect genes associated with virulence, resistance, and oxidative stress. Multiomic analyses of LLIVD colonized and non-colonized will be carried out to explain not only the pathogen's role in LDD, but also its involvement in the pathophysiology of LBP. This study was approved by the Institutional Review Board (CAAE 50077521.0.0000.5258). All patients who agree to participate in the study will sign an informed consent form. Regardless of the study's findings, the results will be published in a peer-reviewed medical journal. Trials registration number NCT05090553; pre-results.

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts

Introduction

The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS.

Methods

In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients.

Results

The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care.

Discussion

In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

Dysregulation of Bile Acids, Lipids, and Nucleotides in Psoriatic Arthritis Revealed by Unbiased Profiling of Serum Metabolites

OBJECTIVE

The transition from psoriasis to psoriatic arthritis (PsA) occurs in 20-30% of patients; however, the mechanisms underlying the emergence of musculoskeletal disease are not well understood. Metabolic disease is prevalent in psoriasis patients, but whether metabolic factors, other than obesity, increase arthritis risk in psoriasis patients is not known. This study was undertaken to investigate the link between metabolic changes and disease progression in psoriasis patients.

METHODS

To characterize the metabolic alterations during the progression of arthritis in psoriasis patients, we analyzed cross-sectional healthy controls and PsA samples and longitudinal psoriasis serum samples, before and after PsA onset. Nontargeted metabolomic profiling was performed using liquid chromatography mass spectrometry.

RESULTS

We identified several serum metabolites that differed between PsA patients, psoriasis patients, and healthy controls. Differentially abundant bile acids, purines, pyrimidines, glutathione, lipids, and amino acid metabolites were noted in these 3 groups. We also noted differences between psoriasis patients who progressed and those who did not progress to PsA. Bile acid and butyrate levels were depressed in those who progressed to PsA compared to those who did not, and the level of inflammatory lipid mediators increased following PsA diagnosis. In particular, the combination of leukotriene B4 and glycoursodeoxycholic acid sulfate were sensitive and specific predictors of PsA progression.

CONCLUSION

We observed notable differences in bile acid, purine, lipid, and amino acid-derived metabolites, among the healthy controls, psoriasis patients, and PsA patients and identified changes during the transition from psoriasis to PsA. The decreased bile acid and butyrate levels and elevated guanine levels in psoriasis patients at risk for PsA were particularly striking and may reflect gut microbial dysbiosis and dysregulated hepatic metabolism, leading to altered proliferation of immune cells and enhanced cytokine expression.

Osteometabolism: Metabolic Alterations in Bone Pathologies

Renewing interest in the study of intermediate metabolism and cellular bioenergetics is brought on by the global increase in the prevalence of metabolic illnesses. Understanding of the mechanisms that integrate energy metabolism in the entire organism has significantly improved with the application of contemporary biochemical tools for quantifying the fuel substrate metabolism with cutting-edge mouse genetic procedures. Several unexpected findings in genetically altered mice have prompted research into the direction of intermediate metabolism of skeletal cells. These findings point to the possibility of novel endocrine connections through which bone cells can convey their energy status to other metabolic control centers. Understanding the expanded function of skeleton system has in turn inspired new lines of research aimed at characterizing the energy needs and bioenergetic characteristics of these bone cells. Bone-forming osteoblast and bone-resorbing osteoclast cells require a constant and large supply of energy substrates such as glucose, fatty acids, glutamine, etc., for their differentiation and functional activity. According to latest research, important developmental signaling pathways in bone cells are connected to bioenergetic programs, which may accommodate variations in energy requirements during their life cycle. The present review article provides a unique perspective of the past and present research in the metabolic characteristics of bone cells along with mechanisms governing energy substrate utilization and bioenergetics. In addition, we discussed the therapeutic inventions which are currently being utilized for the treatment and management of bone-related diseases such as osteoporosis, rheumatoid arthritis (RA), osteogenesis imperfecta (OIM), etc., by modulating the energetics of bone cells. We further emphasized on the role of GUT-associated metabolites (GAMs) such as short-chain fatty acids (SCFAs), medium-chain fatty acids (MCFAs), indole derivates, bile acids, etc., in regulating the energetics of bone cells and their plausible role in maintaining bone health. Emphasis is importantly placed on highlighting knowledge gaps in this novel field of skeletal biology, i.e., "Osteometabolism" (proposed by our group) that need to be further explored to characterize the physiological importance of skeletal cell bioenergetics in the context of human health and bone related metabolic diseases.

Metabolomic analysis in spondyloarthritis: A systematic review

Spondyloarthritis (SpA) is a group of rheumatic diseases that cause joint inflammation. Accumulating studies have focused on the metabolomic profiling of SpA in recent years. We conducted a systematic review to provide a collective summary of previous findings on metabolomic profiling associated with SpA. We systematically searched PubMed, Medline, Embase and Web of Science for studies on comparisons of the metabolomic analysis of SpA patients and non-SpA controls. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included articles. From 482 records identified, 31 studies were included in the analysis. A number of metabolites were differentially distributed between SpA and non-SpA cases. SpA patients showed higher levels of glucose, succinic acid, malic acid and lactate in carbohydrate metabolism, higher glycerol levels and lower fatty acid (especially unsaturated fatty acid) levels in lipid metabolism, and lower levels of tryptophan and glutamine in amino acid metabolism than healthy controls. Both conventional and biological therapy of SpA can insufficiently reverse the aberrant metabolism state toward that of the controls. However, the differences in the results of metabolic profiling between patients with SpA and other inflammatory diseases as well as among patients with several subtypes of SpA are inconsistent across studies. Studies on metabolomics have provided insights into etiological factors and biomarkers for SpA. Supplementation with the metabolites that exhibit decreased levels, such as short-chain fatty acids (SCFAs), has good treatment prospects for modulating immunity. Further studies are needed to elucidate the role of disordered metabolic molecules in the pathogenesis of SpA.

Metabolomic profiling in ankylosing spondylitis using time-of-flight mass spectrometry

BACKGROUND & AIMS

Ankylosing spondylitis (AS) is an inflammatory disease associated with destructive changes in the skeleton and joints. The exact molecular mechanism of the disease has not been fully elucidated. This study aimed to determine metabolic differences between active AS patients and healthy controls to understand the molecular mechanism of AS.

PATIENTS AND METHODS

The study included 38 subjects, comprising 18 patients with active AS and 20 healthy controls. Metabolic profiling of the plasma was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC Q-TOF/MS). Data acquisition, classification, and identification were achieved with the METLIN (https://metlin.scripps.edu/) database and XCMS (https://xcmsonline.scripps.edu).

RESULTS

Significant alterations were identified in the unsaturated fatty acids (FA), linoleic acid, alpha-linolenic acid, FA degradation, and FA biosynthesis pathways. Down -regulations were observed in phosphatidylcholine (PC) (16:0/0:0), beta-d-Fructose, stearic acid, trimipramine N-Oxide and muconic acid, and up-regulation were detected in PC (18:2/0:0), 3-Methylindole, palmitic acid (PA), alpha-Tocotrienol, and beta-d-glucopyranoside in active AS patients compared to the healthy control subjects.

CONCLUSION

Pathway analysis revealed that dysregulation in FA metabolism is associated with AS, and therefore, modulation of diet according to PA and PC may be potential therapeutic targets.

New Insights into the Regulatory Role of Ferroptosis in Ankylosing Spondylitis via Consensus Clustering of Ferroptosis-Related Genes and Weighted Gene Co-Expression Network Analysis

Background: The pathogenesis of ankylosing spondylitis (AS) remains undetermined. Ferroptosis is a newly discovered form of regulated cell death involved in multiple autoimmune diseases. Currently, there are no reports on the connection between ferroptosis and AS. Methods: AS samples from the Gene Expression Omnibus were divided into two subgroups using consensus clustering of ferroptosis-related genes (FRGs). Weighted gene co-expression network analysis (WGCNA) of the intergroup differentially expressed genes (DEGs) and protein–protein interaction (PPI) analysis of the key module were used to screen out hub genes. A multifactor regulatory network was then constructed based on hub genes. Results: The 52 AS patients in dataset GSE73754 were divided into cluster 1 (n = 24) and cluster 2 (n = 28). DEGs were mainly enriched in pathways related to mitochondria, ubiquitin, and neurodegeneration. Candidate hub genes, screened by PPI and WGCNA, were intersected. Subsequently, 12 overlapping genes were identified as definitive hub genes. A multifactor interaction network with 45 nodes and 150 edges was generated, comprising the 12 hub genes and 32 non-coding RNAs. Conclusions: AS can be divided into two subtypes according to FRG expression. Ferroptosis might play a regulatory role in AS. Tailoring treatment according to the ferroptosis status of AS patients can be a promising direction.

Glycolysis in Innate Immune Cells Contributes to Autoimmunity

Autoimmune diseases (AIDs) refer to connective tissue inflammation caused by aberrant autoantibodies resulting from dysfunctional immune surveillance. Most of the current treatments for AIDs use non-selective immunosuppressive agents. Although these therapies successfully control the disease process, patients experience significant side effects, particularly an increased risk of infection. There is a great need to study the pathogenesis of AIDs to facilitate the development of selective inhibitors for inflammatory signaling to overcome the limitations of traditional therapies. Immune cells alter their predominant metabolic profile from mitochondrial respiration to glycolysis in AIDs. This metabolic reprogramming, known to occur in adaptive immune cells, i.e., B and T lymphocytes, is critical to the pathogenesis of connective tissue inflammation. At the cellular level, this metabolic switch involves multiple signaling molecules, including serine–threonine protein kinase, mammalian target of rapamycin, and phosphoinositide 3-kinase. Although glycolysis is less efficient than mitochondrial respiration in terms of ATP production, immune cells can promote disease progression by enhancing glycolysis to satisfy cellular functions. Recent studies have shown that active glycolytic metabolism may also account for the cellular physiology of innate immune cells in AIDs. However, the mechanism by which glycolysis affects innate immunity and participates in the pathogenesis of AIDs remains to be elucidated. Therefore, we reviewed the molecular mechanisms, including key enzymes, signaling pathways, and inflammatory factors, that could explain the relationship between glycolysis and the pro-inflammatory phenotype of innate immune cells such as neutrophils, macrophages, and dendritic cells. Additionally, we summarize the impact of glycolysis on the pathophysiological processes of AIDs, including systemic lupus erythematosus, rheumatoid arthritis, vasculitis, and ankylosing spondylitis, and discuss potential therapeutic targets. The discovery that immune cell metabolism characterized by glycolysis may regulate inflammation broadens the avenues for treating AIDs by modulating immune cell metabolism.

Disturbance of Gut Bacteria and Metabolites Are Associated with Disease Severity and Predict Outcome of NMDAR Encephalitis: A Prospective Case-Control Study

Objective

We aimed to investigate the associations between the intestinal microbiota, metabolites, cytokines, and clinical severity in anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis and to further determine the predictive value of the intestinal microbiota or metabolites in clinical prognosis.

Methods

In this prospective observational cohort study of 58 NMDAR encephalitis patients and 49 healthy controls, fecal microbiota, metabolites, and cytokines were quantified and characterized by16S rRNA gene sequencing, liquid chromatography–mass spectrometry, and the Luminex assay, respectively.

Results

There were marked variations in the gut microbiota composition and metabolites in critically ill patients. We identified 8 metabolite modules (mainly characterized by fatty acid, glycerophosphoethanolamines, and glycerophosphocholines) that were distinctly classified as negatively or positively associated with bacterial co-abundance groups (CAGs). These CAGs were mainly composed of Bacteroides, Eubacterium_hallii_group, Anaerostipes, Ruminococcus, Butyricicoccus, and Faecalibacterium, which were substantially altered in patients. In addition, these fecal and serum metabolic modules were further correlated with the serum cytokines. Additionally, the combination of clinical features, microbial marker (Granulicatella), and a panel of metabolic markers could further enhance the performance of prognosis discrimination significantly, which yielded an area under the receiver operating characteristic curve of (AUC) of 0.94 (95%CI = 0.7–0.9). Patients with low bacterial diversity are more likely to develop relapse than those with higher bacterial diversity (log-rank p = 0.04, HR = 2.7, 95%CI = 1.0–7.0).

Interpretation

The associations between the multi-omics data suggested that certain bacteria might affect the pathogenesis of NMDAR encephalitis by modulating the metabolic pathways of the host and affecting the production of pro-inflammatory cytokines. Furthermore, the disturbance of fecal bacteria may predict the long-term outcome and relapse in NMDAR encephalitis.

Metabolomics: An Emerging Approach to Understand Pathogenesis and to Assess Diagnosis and Response to Treatment in Spondyloarthritis

Spondyloarthritis (SpA) is a group of rheumatic diseases whose pathogenesis relies on a complex interplay between genetic and environmental factors. Over the last several years, the importance of the alteration of the gut microbiota, known as dysbiosis, and the interaction of bacterial products with host immunity have been highlighted as intriguing key players in SpA development. The recent advent of the so called “-omics” sciences, that include metabolomics, opened the way to a new approach to SpA through a deeper characterisation of the pathogenetic mechanisms behind the disease. In addition, metabolomics can reveal potential new biomarkers to diagnose and monitor SpA patients. The aim of this review is to highlight the most recent advances concerning the application of metabolomics to SpA, in particular focusing attention on Ankylosing Spondylitis and Psoriatic Arthritis.

Disease Differentiation and Monitoring of Anti-TNF Treatment in Rheumatoid Arthritis and Spondyloarthropathies

Rheumatoid arthritis (RA), ankylosing spondylitis (AS), and psoriatic arthritis (PsA) are comprehensive immunological disorders. The treatment of these disorders is limited to ameliorating the symptoms and improving the quality of life of patients. In this study, serum samples from RA, AS, and PsA patients were analyzed with metabolomic tools employing the 1H NMR method in combination with univariate and multivariate analyses. The results obtained in this study showed that the changes in metabolites were the highest for AS > RA > PsA. The study demonstrated that the time until remission or until low disease activity is achieved is shortest (approximately three months) for AS, longer for RA and longest for PsA. The statistically common metabolite that was found to be negatively correlated with the healing processes of these disorders is ethanol, which may indicate the involvement of the gut microflora and/or the breakdown of malondialdehyde as a cell membrane lipid peroxide product.