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

Metabolomic profile of secondary hyperparathyroidism in patients with chronic kidney disease stages 3-5 not receiving dialysis

Introduction

Secondary hyperparathyroidism (SHPT) is a common and serious complication of chronic kidney disease (CKD). Elucidating the metabolic characteristics of SHPT may provide a new theoretical basis for its prevention and treatment. This study aimed to perform a metabolomic analysis of SHPT in patients with CKD stages 3-5 not receiving dialysis.

Methods

A total of 76 patients with CKD, 85 patients with CKD-SHPT, and 67 healthy controls were enrolled in this study. CKD was diagnosed according to the criteria specified in the Kidney Disease Improving Global Outcomes 2012 guidelines. SHPT was diagnosed by experienced clinicians according to the Renal Disease Outcomes Quality Initiative Clinical Practice Guidelines. Serum renal function markers and the lipid profile were analyzed. Untargeted ultra performance liquid chromatography-tandem mass spectrometry was used to analyze the serum metabolites of patients with CKD and SHPT. Multivariate analysis of the data was performed using principal component analysis and partial least square discriminant analysis. Serum differential metabolites were identified and further characterized using databases. Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes database. Correlations between differential metabolites and clinical parameters were determined using the Spearman correlation.

Results

The serum metabolomic profiles of patients with CKD with and without SHPT differed significantly. Differential metabolites were mainly enriched in the top four Kyoto Encyclopedia of Genes and Genomes pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; sphingolipid metabolism; glycerophospholipid metabolism; and phenylalanine metabolism. In total, 31 differential metabolites were identified; of these, L-tryptophan and (R)-(+)-1-phenylethylamine were decreased, while other amino acids and their derivatives, uremia toxins, carnitine, and lipids, were increased significantly in patients with SHPT compared to those without. The 14 lipid metabolites were positively correlated with levels of Urea, serum creatinine, cystatin C, and triglycerides and negatively correlated with the estimated glomerular filtration rate and levels of total and high- and low-density lipoprotein cholesterol.

Discussion

Disturbed amino acid and lipid metabolism were more apparent in patients with SHPT than in those without. This metabolomic profile of SHPT may provide a therapeutic foundation for its future clinical management.

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.

Integrative analysis of COL6A3 in lupus nephritis: insights from single-cell transcriptomics and proteomics

Introduction

Lupus nephritis (LN), a severe complication of systemic lupus erythematosus (SLE), presents significant challenges in patient management and treatment outcomes. The identification of novel LN-related biomarkers and therapeutic targets is critical to enhancing treatment outcomes and prognosis for patients.

Methods

In this study, we analyzed single-cell expression data from LN (n=21) and healthy controls (n=3). A total of 143 differentially expressed genes were identified between the LN and control groups. Then, proteomics analysis of LN patients (n=9) and control (SLE patients without LN, n=11) revealed 55 differentially expressed genes among patients with LN and control group. We further utilizes protein-protein interaction network and functional enrichment analyses to elucidate the pivotal role of COL6A3 in key signaling pathways. Its diagnostic value is evaluate through its correlation with disease progression and renal function metrics, as well as Receiver Operating Characteristic Curve (ROC) analysis. Additionally, immunohistochemistry and qPCR experiments were performed to validate the expression of COL6A3 in LN.

Results

By comparison of single-cell and proteomics data, we discovered that COL6A3 is significantly upregulated, highlighting it as a critical biomarker of LN. Our findings emphasize the substantial involvement of COL6A3 in the pathogenesis of LN, particularly noting its expression in mesangial cells. Through comprehensive protein-protein interaction network and functional enrichment analyses, we uncovered the pivotal role of COL6A3 in key signaling pathways including integrin-mediated signaling pathways, collagen-activated signaling pathways, and ECM-receptor interaction, suggesting potential therapeutic targets. The diagnostic utility is confirmed by its correlation with disease progression and renal function metrics of the glomerular filtration rate. ROC analysis further validates the diagnostic value of COL6A3, with the area under the ROC values of 0.879 in the in-house cohort, and 0.802 and 0.915 in tubular and glomerular external cohort samples, respectively. Furthermore, immunohistochemistry and qPCR experiments were consistent with those obtained from the single-cell RNA sequencing and proteomics studies.

Discussion

These results proved that COL6A3 is a promising biomarker and therapeutic target, advancing personalized medicine strategies for LN.

Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.

Urinary Biomarkers for Lupus Nephritis: A Systems Biology Approach

Systemic lupus erythematosus (SLE) is the prototypical systemic autoimmune disorder. Kidney involvement, termed lupus nephritis (LN), is seen in 40-60% of patients with systemic lupus erythematosus (SLE). After the diagnosis, serial measurement of proteinuria is the most common method of monitoring treatment response and progression. However, present treatments for LN-corticosteroids and immunosuppressants-target inflammation, not proteinuria. Furthermore, subclinical renal inflammation can persist despite improving proteinuria. Serial kidney biopsies-the gold standard for disease monitoring-are also not feasible due to their inherent risk of complications. Biomarkers that reflect the underlying renal inflammatory process and better predict LN progression and treatment response are urgently needed. Urinary biomarkers are particularly relevant as they can be measured non-invasively and may better reflect the compartmentalized renal response in LN, unlike serum studies that are non-specific to the kidney. The past decade has overseen a boom in applying cutting-edge technologies to dissect the pathogenesis of diseases at the molecular and cellular levels. Using these technologies in LN is beginning to reveal novel disease biomarkers and therapeutic targets for LN, potentially improving patient outcomes if successfully translated to clinical practice.

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.

Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning

Background

Lupus nephritis (LN) is a common and severe glomerulonephritis that often occurs as an organ manifestation of systemic lupus erythematosus (SLE). However, the complex pathological mechanisms associated with LN have hindered the progress of targeted therapies.

Methods

We analyzed glomerular tissues from 133 patients with LN and 51 normal controls using data obtained from the GEO database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key gene modules. The least absolute shrinkage and selection operator (LASSO) and random forest were used to identify hub genes. We also analyzed immune cell infiltration using CIBERSORT. Additionally, we investigated the relationships between hub genes and clinicopathological features, as well as examined the distribution and expression of hub genes in the kidney.

Results

A total of 270 DEGs were identified in LN. Using weighted gene co-expression network analysis (WGCNA), we clustered these DEGs into 14 modules. Among them, the turquoise module displayed a significant correlation with LN (cor=0.88, p<0.0001). Machine learning techniques identified four hub genes, namely CD53 (AUC=0.995), TGFBI (AUC=0.997), MS4A6A (AUC=0.994), and HERC6 (AUC=0.999), which are involved in inflammation response and immune activation. CIBERSORT analysis suggested that these hub genes may contribute to immune cell infiltration. Furthermore, these hub genes exhibited strong correlations with the classification, renal function, and proteinuria of LN. Interestingly, the highest hub gene expression score was observed in macrophages.

Conclusion

CD53, TGFBI, MS4A6A, and HERC6 have emerged as promising candidate driver genes for LN. These hub genes hold the potential to offer valuable insights into the molecular diagnosis and treatment of LN.

Longitudinal gut microbiome analyses and blooms of pathogenic strains during lupus disease flares

OBJECTIVE

Whereas genetic susceptibility for systemic lupus erythematosus (SLE) has been well explored, the triggers for clinical disease flares remain elusive. To investigate relationships between microbiota community resilience and disease activity, we performed the first longitudinal analyses of lupus gut-microbiota communities.

METHODS

In an observational study, taxononomic analyses, including multivariate analysis of ß-diversity, assessed time-dependent alterations in faecal communities from patients and healthy controls. From gut blooms, strains were isolated, with genomes and associated glycans analysed.

RESULTS

Multivariate analyses documented that, unlike healthy controls, significant temporal community-wide ecological microbiota instability was common in SLE patients, and transient intestinal growth spikes of several pathogenic species were documented. Expansions of only the anaerobic commensal, Ruminococcus (blautia) gnavus (RG) occurred at times of high-disease activity, and were detected in almost half of patients during lupus nephritis (LN) disease flares. Whole genome sequence analysis of RG strains isolated during these flares documented 34 genes postulated to aid adaptation and expansion within a host with an inflammatory condition. Yet, the most specific feature of strains found during lupus flares was the common expression of a novel type of cell membrane-associated lipoglycan. These lipoglycans share conserved structural features documented by mass spectroscopy, and highly immunogenic repetitive antigenic-determinants, recognised by high-level serum IgG2 antibodies, that spontaneously arose, concurrent with RG blooms and lupus flares.

CONCLUSIONS

Our findings rationalise how blooms of the RG pathobiont may be common drivers of clinical flares of often remitting-relapsing lupus disease, and highlight the potential pathogenic properties of specific strains isolated from active LN patients.

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

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

Serum Metabolomics Analysis of Skin-Involved Systemic Lupus Erythematosus: Association of Anti-SSA Antibodies with Photosensitivity

Purpose

Systemic lupus erythematosus is a heterogeneous autoimmune disease in which skin involvement is a common manifestation. It is currently thought that the photosensitivity of SLE skin involvement is associated with anti-SSA antibodies. This study aimed to expand the current state of knowledge surrounding the molecular pathophysiology of SLE skin photosensitivity through Serum metabolomics analysis.

Patients and Methods

The serum metabolites of 23 cases of skin-involved SLE (SI) group, 14 cases of no SI (NSI) group, and 30 cases of healthy controls (HC) were analyzed by using UPLC-MS/MS technology, and subgroup analysis was performed according to the expression of anti-SSA antibodies in SI. MetaboAnalyst 5.0 was used for enrichment analysis and ROC curve construction, identifying serum metabolic markers of skin-involved SLE associated with anti-SSA antibodies.

Results

We identified several metabolites and metabolic pathways associated with SLE photosensitivity. Two metabolites, SM (d18:1/24:0) and gamma-CEHC can distinguish between anti-SSA antibody-positive and negative SI, with AUC of 0.829 and 0.806. These two photosensitization-related substances may be potential markers of skin involvement in SLE associated with anti-SSA antibody.

Conclusion

This study provides new insights into the pathogenesis of SI patients, and provides a new molecular biological basis for the association between anti-SSA antibodies and skin photoallergic manifestations of SLE.

Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus

OBJECTIVE

Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients.

METHOD

Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile.

RESULTS

In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617).

CONCLUSIONS

Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.