Uncovering Potential Biomarkers and Metabolic Pathways in Systemic Lupus Erythematosus and Lupus Nephritis through Integrated Microbiome and Metabolome Analysis

Uncovering Potential Biomarkers and Metabolic Pathways in Systemic Lupus Erythematosus and Lupus Nephritis through Integrated Microbiome and Metabolome Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Uncovering Potential Biomarkers and Metabolic Pathways in Systemic Lupus Erythematosus and Lupus Nephritis through Integrated Microbiome and Metabolome Analysis Siyun Cheng, Xiaojie Chu, Zhongyu Wang, Adeel Khan, Yue Tao, Han Shen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5045051/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 May, 2025 Read the published version in BMC Microbiology → Version 1 posted 13 You are reading this latest preprint version Abstract Objective This study aimed to elucidate the relationship between gut microbiota and metabolomic profiles in patients with systemic lupus erythematosus (SLE) and lupus nephritis (LN) to identify potential biomarkers and elucidate their roles in disease progression. Methods Fecal samples from 15 healthy controls (HC) and 36 SLE patients (18 SLE-nonLN and 18 SLE-LN) were analyzed using 16S rRNA gene sequencing and untargeted metabolomics. Differential microbial taxa and metabolites were identified using Linear Discriminant Analysis Effect Size (LEfSe) analysis and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Receiver Operating Characteristic (ROC) curve analysis were employed to evaluate the clinical relevance of identified metabolites. Results Beta diversity analysis demonstrated significant clustering among groups ( p < 0.05). SLE-LN exhibited increased Proteobacteria (28.02% vs. 12.93% in SLE-nonLN) and decreased Firmicutes (39.50% vs. 59.08%). Metabolomic profiling identified 94 differentially abundant metabolites in SLE-LN vs. SLE-nonLN, enriched in primary bile acid biosynthesis (e.g., Glycocholic acid, AUC = 0.951). SLE-nonLN displayed 159 differential metabolites compared to HC, including increased Glycoursodeoxycholic acid (AUC = 0.922) in taurine and hypotaurine metabolism. Microbial-metabolite correlation analysis highlighted Escherichia-Shigella as negatively associated with bile acids ( p < 0.01). Conclusion This study reveals distinct gut microbiota and metabolomic signatures associated with SLE and LN. The identified microbial taxa and metabolites may serve as potential diagnostic biomarkers and therapeutic targets for disease management. SLE LN Gut Microbiota Metabolomics Biomarkers Fecal Samples 16S rRNA Sequencing Metabolic Pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder characterized by the immune system's aberrant attack on healthy tissues. Such dysregulation leads to systemic inflammation and multiorgan damage [ 1 ]. SLE manifests with heterogeneous clinical presentations and variable disease severity, affecting multiple organs. The global prevalence of SLE ranges from 30 to 50 cases per 100,000 individuals [ 2 ]. In China, the reported prevalence is 6.17 per 100,000 males and 67.78 per 100,000 females [ 3 ]. Lupus nephritis (LN), a severe renal complication of SLE, is characterized by immune-mediated kidney damage with diverse pathological types and significant clinical manifestations, making it one of the most serious forms of SLE [ 4 , 5 ]. Despite extensive research, the underlying pathogenesis of LN remains incompletely understood. A primary goal in SLE management is preventing irreversible organ damage, which requires identifying key molecular contributors to disease progression [ 6 ]. Accurate diagnosis, timely intervention, and early relapse management are crucial for effective LN treatment. Although renal biopsy is the gold standard for LN diagnosis, its invasiveness limits its utility for continuous disease monitoring, underscoring the need for reliable non-invasive biomarkers [ 7 , 8 ]. Current routine biomarkers, such as serum creatinine and complement component C3b, have limited utility in assessing LN disease activity or facilitating real-time diagnosis [ 9 ]. Emerging evidence implicates the gut microbiota as a critical modulator of autoimmune processes, including SLE [ 10 , 11 ]. Gut microbiota metabolize dietary components into bioactive metabolites that modulate systemic immune responses [ 12 ]. Microbial metabolites, such as short-chain fatty acids and bile acids, are key mediators of host-microbiota interactions [ 13 , 14 ]. For example, Zhang et al. reported that fecal samples from SLE patients exhibited significantly elevated metabolic activities, including enhanced amino acid biosynthesis, vitamin B1 metabolism, nitrogen cycling, tryptophan degradation, and cyanoamino acid metabolism, compared to healthy controls [ 15 ]. However, single-omics approaches (e.g., metagenomics or metabolomics alone) fail to capture the complexity of disease mechanisms. Integrative analysis of gut microbiota and host metabolome dynamics provides a holistic understanding of microbial and metabolic interactions in pathogenesis [ 16 ]. Despite progress in SLE metabolomic profiling, studies exploring microbiota-metabolome interplay, particularly in differentiating SLE with nephritis (SLE-LN) from SLE without nephritis (SLE-nonLN), remain limited [ 17 ]. In this study, we applied 16S rRNA gene sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics to analyze fecal samples from SLE patients (SLE-LN and SLE-nonLN) and healthy controls (HC). Our objective was to identify potential biomarkers and explore gut microbiota-metabolome interactions to provide novel insights into the mechanisms underlying SLE pathogenesis and progression. Materials and methods Sample Collection We recruited 36 patients diagnosed with SLE from Nanjing Drum Tower Hospital, affiliated with Nanjing University Medical School. All patients were newly admitted and were not receiving immunosuppressive therapy at the time of sample collection. Diagnosis was based on the 1997 revised classification criteria of the American College of Rheumatology (ACR) and further confirmed through clinical evaluation, including serological markers and renal biopsy where applicable. The cohort consisted of 18 SLE-LN and 18 SLE-nonLN. Clinical characteristics, including laboratory test results and medical history, were obtained from the hospital’s electronic medical records system. Additionally, 15 age-, gender-, and BMI-matched HC were recruited from the hospital’s health examination center. These individuals had no prior history of autoimmune diseases, infections, metabolic disorders, or malignancies. All participants provided written informed consent, and the study was approved by the Ethics Committee of Nanjing Drum Tower Hospital. Fecal samples were collected using sterile containers upon admission and immediately processed to maintain sample integrity. Each sample was divided into two aliquots and immediately stored at -80°C for subsequent 16S rRNA sequencing and untargeted metabolomics analysis. The overall study design is illustrated in Fig. 1 . DNA Extraction and 16S rRNA Sequencing Total genomic DNA was extracted using the QIAamp DNA Stool Mini Kit (QIAGEN) with modifications, including an extended 10-minute bead-beating step to enhance the lysis of Gram-positive bacteria. DNA purity (A260/A280 ratio 1.8-2.0) and integrity were verified via NanoDrop 2000 spectrophotometry and 1% agarose gel electrophoresis. The V3-V4 region of the 16S rRNA gene was amplified using primers 341F/806R under the following conditions: 98°C for 1 min; 30 cycles of 98°C for 10 s, 55°C for 30 s, 72°C for 30 s; final extension at 72°C for 5 min. PCR products were purified (AxyPrep DNA Gel Extraction Kit) and quantified (Qubit dsDNA Assay Kit). Equimolar pooled libraries were sequenced on an Illumina MiSeq PE300 platform (2×300 bp) in a single run to minimize batch effects. Sequencing Data Processing and Microbial Diversity Analysis Raw sequencing reads were preprocessed using Cutadapt (v4.0) to trim adapters and remove low-quality bases. Quality filtering was performed with FASTP (v0.23.4), retaining reads with Phred scores ≥ 20 and lengths ≥ 200 bp. Paired-end reads were merged using FLASH (v1.2.11; min overlap = 20 bp, max mismatch = 0.1). Further filtering steps were conducted to remove ambiguous bases (N), sequences with homopolymer runs (> 8 bp), and chimeric reads using USEARCH (v11.0). Operational taxonomic units (OTUs) were de novo clustered at 97% similarity using VSEARCH (v2.21.1) and taxonomically assigned via the QIIME2 Naïve Bayesian Classifier (v2023.2) against the SILVA 142 database (confidence threshold = 80%). Alpha diversity (Chao, Shannon, Simpson) was calculated after rarefaction to 10,000 reads/sample. Beta diversity was assessed using weighted/unweighted UniFrac distances and visualized via principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS). Linear discriminant analysis Effect Size (LEfSe) analysis was performed using Python (v3.9.7) to identify differentially abundant taxa, applying significance thresholds of 0.05 for both the Kruskal-Wallis and Wilcoxon tests, and an LDA score threshold of 4. Metabolite Extraction and LC-MS/MS Analysis Fecal metabolites were extracted by homogenizing 20 mg of sample with 400 µL methanol: water (7:3, v/v) on ice. After sonication (10 min), vortexing (1 min), and centrifugation (12,000 × g, 10 min, 4°C), 200 µL of supernatant was analyzed via LC-MS/MS (Waters ACQUITY UPLC HSS T3 C18 column, 1.8 µm, 2.1 × 100 mm). The mobile phase consisted of 0.1% formic acid in water (solvent A) and acetonitrile (solvent B), with a gradient elution (0.4 mL/min): 0–11 min, 5–90% B; 12–14 min, 5% B. Raw data were converted to mzML format using ProteoWizard version 3.0.23136. Peak detection and retention time alignment were performed using XCMS (v4.7). Metabolites were identified through our in-house database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) online database. Statistical Analysis Constrained Principal Coordinate Analysis (CPCoA) was performed using the prcomp function in R (v4.2.3). Hierarchical clustering analysis was conducted with the ComplexHeatmap package, and results were visualized as heatmaps with dendrograms. Normalized signal intensities of metabolites were displayed as color spectra following unit variance scaling. Differential metabolites were defined by VIP ≥ 1.5, |Log2FC|≥1.0, and p < 0.05. Data were log2-transformed and mean-centered prior to orthogonal partial least squares discriminant analysis (OPLS-DA). Functional and pathway analysis was conducted using the KEGG database, with pathways considered significantly enriched at p < 0.05. Results Altered Microbiota Composition Among Groups The DNA from fecal samples of 15 HC and 36 SLE patients (18 with LN and 18 without LN) was examined using 16S rRNA gene sequencing. The species accumulation curve (Supplementary Fig. S1A) and rarefaction curve (Supplementary Fig. S1B) approached saturation, indicating that the sequencing depth and coverage adequately captured the diversity within the samples. To assess bacterial diversity differences across the three groups, sequence data was aligned to calculate both within-sample (alpha) and between-sample (beta) diversity. No significant differences in ACE, Chao1, Shannon, and Simpson indices were observed between the HC, SLE, and LN groups (Fig. 1A), indicating no significant differences in microbial richness or diversity among the three groups. Constrained Principal Coordinate Analysis (CPCoA) depicted distinct differences between the SLE (with and without LN) and HC groups. Non-metric multidimensional scaling (NMDS) was used to assess spatial positions within and between groups (Supplementary Fig. S2). These findings suggest that SLE may influence the diversity of gut microbiota. Altered Microbial Composition Associated with SLE The relative abundance of the dominant taxa was assessed across different taxonomic levels among the three groups. At the phylum level, Firmicutes were predominant, representing 49.35%, 59.08%, and 39.50% of the HC, SLE, and LN groups, respectively. The abundance of Proteobacteria was significantly lower in the SLE group than in the HC group (12.93% vs. 23.18%) and markedly higher in the LN group (28.02%). The phylum Verrucomicrobiota was significantly elevated in both the SLE and LN groups compared to that in the HC group. At the genus level, the abundance of Faecalibacterium was higher in the HC group (10.31%) and progressively decreased in the SLE and LN groups (4.06% and 1.90%, respectively). Conversely, the abundance of Bacteroides was lower in the HC group (9.57%) and higher in the SLE and LN groups (11.26% and 14.73%, respectively). Compared to the HC group, the genera Blautia decreased, while Streptococcus , Enterococcus , Akkermansia , and Lactobacillus increased in both the SLE and LN groups (Fig. 2B). LEfSe Analysis Among Groups The LEfSe method was employed to compare microbial compositions among SLE patients, LN patients, and healthy controls to identify specific dominant bacteria associated with SLE. The histogram of LDA scores (Fig. 3A) identified 76 differentially abundant taxa at various taxonomic levels, with 23 from the SLE patients, 19 from the LN group, and 34 from the HC group. Bacilli had the highest LDA score (>5), with Lactobacillales being the most prominent in the SLE group. In contrast, Enterobacteriaceae , Enterobacterales , Proteobacteria , Gammaproteobacteria, Escherichia_Shigella , and Escherichia_coli were the most abundant in the LN group (LDA score >3.7) (Fig. 3A), whereas in HC group Clostridia were more abundant. At the genus level, Kruskal-Wallis and pairwise t-test analyses using STAMP revealed that Enterobacter was most statistically distinct among the three groups (Fig. 3C) (p < 0.001). Pairwise comparisons further indicated significant differences in Faecalibacterium and Streptococcus between the HC and SLE groups with and without LN (p < 0.05). Additionally, Escherichia-Shigella showed a pronounced statistical difference between the LN and SLE groups (p = 0.0208) (Supplementary Fig. S3). Changes in Metabolome and Key Metabolites Gut microbial metabolites has pronounced influence on the physiological functions of the host. Metabolite abundance in the fecal samples was detected using LC-MS technology. OPLS-DA score plots were utilized to evaluate group distinctions. Scatter plots (Fig. 4A) revealed significant clustering within groups and clear separation between groups, indicating distinct metabolic patterns. Permutation tests validated the PLS-DA model (Fig. 4B), with the Y-intercept of the regression line for blue Q2 points below zero, indicating no overfitting and supporting the model's reliability. We identified 177, 159, and 94 differential metabolites in HC vs. LN, HC vs. SLE, and SLE vs. LN groups, respectively. We examined fold changes (FC) of metabolites among groups using the OPLS-DA model and visualized differential metabolites in volcano plots (Fig. 4C). Metabolites with a fold change (FC) ≥ 2 or ≤ 0.5, p < 0.05, and VIP ≥ 1 were considered significantly different. Compared to HC, SLE patients had higher levels of Hydroxychloroquine, [2,2-Bis(2-methylpropoxy)ethyl]benzene, and Cys-Phe, while LN patients had higher levels of 3-Methoxybenzyl, Hydroxychloroquine, and Nomilin. Between LN and SLE groups, 94 differential metabolites were identified (Supplementary Table S1), with Atorvastatin, Nomilin, and Diethyl 1,4-dihydro-2,4,6-trimethyl-3,5-pyridinedicarboxylate upregulated, and Arg-Tyr-Gln-Lys downregulated. Three metabolites—Glycochenodeoxycholic acid, Diethyl 1,4-dihydro-2,4,6-trimethyl-3,5-pyridinedicarboxylate, and Nomilin—showed consistent trends among HC, SLE, and LN groups. Metabolic Pathways Analysis Correlation analysis was used to evaluate the relationships among significantly different metabolites. We conducted a correlation analysis on the top 50 differential metabolites, ranked by VIP scores, using Pearson's correlation method (Supplementary Fig. S4). KEGG pathway enrichment analysis was performed to identify the main metabolic and signaling pathways associated with differential metabolites between SLE patients and HC, and between SLE and LN patients. The KEGG pathway analysis results were visualized using bubble plots (Fig. 5). Taurine and hypotaurine metabolism were the most significantly enriched pathways in SLE patients compared to HC, followed by primary bile acid biosynthesis and histidine metabolism. In contrast, differential metabolites between the SLE and LN groups were involved in primary bile acid biosynthesis, thiamine metabolism, taurine and hypotaurine metabolism, and sulfur metabolism. A total of nine metabolites were found to differ significantly between groups, excluding myristoleic acid, polygodial, and furosemide, which originated from food or drugs. Table 1 lists the identified metabolites, visualized in heatmaps, to show the relationships and expression differences between samples (Supplementary Fig. S5). Among these, three metabolites—Glycocholic acid, Glycochenodeoxycholic acid, and 5,8,11-Eicosatrienoic acid—were common to both the HC vs. SLE and SLE vs. LN comparisons. Identification of metabolite biomarkers to distinguish LN and SLE ROC curve analysis was performed to assess the ability of these metabolites to differentiate between SLE patients and those with LN. Seven differential metabolites involved in key metabolic pathways were examined for their predictive performance between the SLE and LN groups. The analysis indicated that Glycocholic acid (AUC = 0.951), Glycochenodeoxycholic acid (AUC = 0.827), Oxypurinol (AUC = 0.713), Palmitoylcarnitine (AUC = 0.827), Methylsuccinic acid (AUC = 0.685), 5,8,11-Eicosatrienoic acid (AUC = 0.769), and Ursocholic acid (AUC = 0.716) demonstrated strong discriminatory capabilities (Fig. 6A-G). The results suggest that Glycocholic acid and Glycochenodeoxycholic acidhave the potential to distinguish individuals with SLE from healthy individuals. Additionally, ROC curve analysis was conducted on differential metabolites between the SLE and HC groups (Supplementary Fig. S6), indicating that all differential metabolites exhibited high diagnostic performance, with AUC values exceeding 0.533. Correlation Between Microbiota and Metabolites To explore the relationship between gut microbiota and metabolites in SLE, LN, and HC, Spearman’s correlation analysis was conducted on the top 16 fecal microbiomes and metabolic biomarkers. At the genus level, pronounced statistical associations exhibited between microbial genera and metabolites. The correlation heatmap (Fig. 7A) highlighted key relationships: the abundances of Escherichia-Shigella , and Enterobacter were significantly reduced in SLE and LN groups and negatively correlated with elevated levels of bile acid metabolites such as glycochenodeoxycholic acid and glycocholic acid. Conversely, these microbial genera were positively correlated with 5,8,11-eicosatrienoic acid and ursocholic acid. Additionally, we found a significant positive correlation between Bacteroides , Faecalibacterium , Parabacteroides , and methylsuccinic acid. In the comparative analysis of differences in fecal microbiomes and metabolic biomarkers between HC and LN, we found significant negative correlations between Escherichia-Shigella and glycochenodeoxycholic acid, glycocholic acid, and stachydrine. Soyasaponin I showed a significant negative correlation with Subdoligranulum , Agathobacter , and Faecalibacterium , while exhibiting a significant positive correlation with Erysipelatoclostridium (p<0.01). A similar trend was observed for glycocholic acid, with Escherichia-Shigella , Collinsella , Blautia , Faecalibacterium , and Clostridia_UCG-014 all showing negative correlations, whereas Streptococcus exhibited a positive correlation. These results demonstrate that changes in the abundance of certain microbes significantly affect the levels of host metabolites, particularly glycochenodeoxycholic acid and glycocholic acid. Key microbes such as Escherichia-Shigella and Enterobacter make significant contributions to bile acid synthesis. Discussion Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder primarily affecting young women. It causes inflammation in various body tissues. Lupus nephritis (LN) is a serious kidney complication associated with SLE.(18). Our previous research identified tRNA-derived small noncoding RNAs (tsRNAs) within urinary exosomes as novel biomarkers for the diagnosis of LN(19, 20). To elucidate the pathophysiological connections between SLE and LN, we analyzed clinical indicators, gut microbiota composition, and fecal metabolites. By integrating 16S rRNA sequencing with untargeted metabolomics, we aimed to identify non-invasive biomarkers associated with the progression of both SLE and LN. Gut microbiota, characterized by its rich diversity, functions as a quasi-organ and plays crucial roles in immune regulation, pathogen defense, vitamin synthesis, hormone secretion, and nutrient absorption(21). Our findings demonstrated that the gut flora of SLE patients exhibited a relatively stable alpha diversity, with a notable divergence between the beta diversity and HC groups, suggesting that SLE may influence the composition of the human gut microbiota. Furthermore, we observed distinct differences in the gut microbiome composition among SLE patients, LN patients, and healthy controls. Specifically, the relative abundances of Firmicutes and Proteobacteria differed significantly between the groups. Firmicutes were significantly enriched in SLE patients, while Proteobacteria were markedly depleted. These findings contrast with those of a Spanish study, which may be attributed to variations in dietary habits across different geographical regions(22). Interestingly, our study found that this trend reverses with the progression of SLE to LN, where there is a significant depletion of Firmicutes , falling below the levels observed in healthy controls, and a marked increase in Proteobacteria , which reached the highest proportions among the three groups. Previous studies have demonstrated that increased Proteobacteria and decreased Firmicutes are linked to gastrointestinal damage in lupus nephritis(23). Firmicutes and Proteobacteri a are crucial in maintaining intestinal homeostasis, and these bacterial phyla emerge as primary differentiators for SLE and potential diagnostic markers for the disease, a pattern also observed in other chronic autoimmune diseases such as systemic sclerosis and rheumatoid arthritis(24-26). LEfSe analysis identified key bacterial taxa that were significantly associated with SLE and LN, such as Bacilli and Lactobacillales , which were differentially abundant in the SLE group. These findings align with studies identifying that Bacilli as prevalent in the bloodstream of Asian SLE patients and that Lactobacillus abundance is increased in the fecal microbiota of SLE patients(27, 28). Research on lupus-prone mouse models has demonstrated that Lactobacillus levels increase during active SLE and decrease in lupus models(29). The reduction of specific Lactobacilli , such as Lactobacillus reuteri , in nephritic mice contributes to lupus-related inflammation, suggesting that Lactobacilli may play a pathogenic role in human lupus through mechanisms involving barrier integrity and immune modulation(30). SLE alters the gut microbiota, with dysbiosis becoming more pronounced in LN and particularly marked by an increase in Enterobacteriaceae . Previous studies have documented significant changes in the gut microbiota of LN patients(31). Our results show increased relative abundances of Enterobacteriaceae , Enterobacterales , Proteobacteria , Gammaproteobacteria , Escherichia_Shigella , and Escherichia_coli in the LN group.These findings align with previous research and underscore the importance of gut microbial imbalance in the development of autoimmune conditions. The increased presence of pro-inflammatory bacteria, such as Enterobacteriaceae , in LN patients indicates a potential association between the gut microbiota and renal inflammation in SLE(32). Metabolomics, which is known for its high resolution and sensitivity, has attracted considerable attention in scientific research(33). This study revealed notabledifferences in untargeted fecal metabolomics among SLE patients with and without LN and HCs, with notable alterations primarily observed in lipids and amino acids. Dysregulation of lipid metabolism is a well-established feature of the progression from SLE to LN, although detailed metabolic changes in many lipid species remain underexplored(34, 35). Mead acid (5,8,11-Eicosatrienoic acid), a polyunsaturated fatty acid synthesized de novo in the human body, serves as a marker of essential fatty acid deficiency(36). Elevated Mead acid levels, typically seen in dietary essential fatty acid deficiency, particularly arachidonic acid, may exacerbate autoimmune disease progression(37). Consistent with findings reported by Zhang et al., our data indicate that Mead acid levels are significantly higher in LN patients compared to SLE patients, reinforcing its role in the transition from SLE to LN(38). Additionally, a statistically valid reduction in palmitoylcarnitine levels was observed in LN patients. Our findings suggest that palmitoylcarnitine plays a role in facilitating the transfer of long-chain fatty acids from the cytoplasm to the mitochondria during fatty acid oxidation(39), which may contribute to inflammation in LN patients. Changes in lipid metabolism are intricately linked to lipid-induced nephrotoxicity and play a significant role in LN pathophysiology. Bile acids, which are crucial for lipid metabolism, regulate this process through TGR5 modulation. Specific bile acids, including deoxycholic acid, hypodeoxycholic acid, ursodeoxycholic acid, and arachidonic acid, were significantly correlated with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score, indicating their potential as biomarkers for disease activity(40). Taurine and hypotaurine metabolism and primary bile acid biosynthesis were key pathways in the KEGG pathway of SLE and LN. Metabolites in these pathways, such as glycocholic acid and glycochenodeoxycholic acid, exhibited high sensitivity and specificity for both SLE and LN, with AUC of 0.951 and 0.827, respectively. Biosynthesis of primary bile acids is associated with characteristic lipid changes in SLE patients(41). High cholesterol and sphingolipid levels in T cell membranes alter the signaling platform, promoting pro-inflammatory signaling, as suggested by our findings. However, as SLE progresses to LN, we observed a decrease in glycocholic acid and glycochenodeoxycholic acid levels and a concurrent increase in ursodeoxycholic acid levels. Recent studies have reported decreased plasma levels of glycochenodeoxycholic acid and glycocholic acid in LN patients, likely due to reduced glomerular filtration mediated by the apical sodium-dependent bile acid transporter MRP2 and organic solute transporters α and β(42). This reduction may lead to mesangial and endothelial cell proliferation, glomerulosclerosis, interstitial fibrosis, and vascular sclerosis, impairing glomerular filtration and indicating decreased bile acid filtration function in LN patients. Additionally, bile acids function as signaling molecules through bile acid receptors. In lupus mouse models, the FXR agonist phenobarbital has been shown to suppress the expression of inflammatory cytokines, such as TNF-α, IFN-γ, and IL-6(34). Our findings strongly suggest that the fecal levels of glycochenodeoxycholic acid and glycocholic acid could be critical biomarkers for differentiating between SLE and LN. We observed statistically distinct elevation in the levels of glucogenic amino acid metabolites, including glycine, proline, L-cysteine, phenylalanine, and isoleucine, in fecal samples from SLE patients. This suggests a potential imbalance in glucose and energy metabolism, possibly indicating a shift towards alternative energy sources. It is noteworthy that while the levels of glycine and proline were found to be increased in patients with SLE, their concentrations were decreased in those with LN. This decrease was observed to be associated with alterations in the gut microbiome. These changes may Our findings indicate that the genus Escherichia-Shigella and Enterobacter play a crucial role, exhibiting a pronounced statistical variation among the three groups, particularly with a pronounced increase in the LN group (p < 0.001). Escherichia-Shigella exhibited a negative correlation with elevated levels of bile acid metabolites, including glycochenodeoxycholic acid and glycocholic acid, across all three groups. Similarly, Enterobacter was negatively correlated with nearly all differential metabolites, such as pterine, L-cysteine, and glycoursodeoxycholic acid, which are strongly associated with bile acid biosynthesis and thiamine metabolism pathways. We speculate that the overgrowth of Enterobacter and Escherichia-Shigella may contribute significantly to LN pathogenesis, possibly through interactions between gut microbiota and metabolites. However, the study's limitations include the small sample size, which hinders subgroup analysis based on clinical data. Future research should involve additional control groups and larger samples from patients with other autoimmune or inflammatory diseases. Longitudinal studies will be essential to determine the exact sequence of changes in the microbiome and metabolome, and how these alterations correlate with disease progression over time. The close association between gut microbiome and metabolome in SLE and LN patients highlights new insights into disease mechanisms. Compared to serum or urine samples, fecal samples offer the advantages of easy collection and non-invasive nature, reflecting direct dietary interactions with the gut microbiome. Conclusion Our study highlights significant alterations in the gut microbiota and metabolite profiles in patients with Systemic Lupus Erythematosus (SLE) and Lupus Nephritis (LN) compared to healthy controls. SLE patients showed an increase in Firmicutes and a decrease in Proteobacteria, while LN was marked by elevated Enterobacteriaceae. Metabolomic analysis revealed higher Mead acid and lower levels of glycocholic and glycochenodeoxycholic acids in LN. The correlations between gut microbiota and metabolites suggest potential biomarkers and therapeutic targets, underscoring the need for integrating microbiome and metabolome analyses to advance the understanding and management of SLE and LN. Declarations Acknowledgements We thank the participants for joining our study and reviewers for their valuable suggestion. Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Nanjing Drum Tower Hospital (approval number: 2022-461-02) and in accordance with national law and the Helsinki Declaration of 1975 (in its current, revised form). All participants provided written informed consent to participate before enrolment. Consent for publication Not applicable. Availability of data and materials The amplicon sequencing data are available in the NCBI Sequence Read Archive (SRA) database (BioProject: PRJNA1163353). The detail data and materials available please see https://www.ncbi.nlm.nih.gov/bioproject/ PRJNA1163353. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 82202600), the Nanjing Drum Tower Hospital Clinical Research Special Fund Project (No. 2024-LCYJ-MS-11), and the New Technology Development Fund of Nanjing Drum Tower Hospital (grant number: XJSFZJJ201905). Authors ’ contributions PY initiated and supervised the study. PY, HS and YT designed the study and were major contributors in writing and revising the manuscript. SYC collected the samples. SYC, XJC and ZYW were major contributors in conducting statistical analysis and interpretation of data. AK provided technical assistance. All authors have read and approved the manuscript. 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Table Table1: Metabolites with intergroup differences in fecal samples Compounds Class HC vs SLE SLE vs LN VIP FC P Trend VIP FC P Trend Glycocholic acid Bile acids 2.99 5.06 0.00 ↑ 3.10 0.37 0.00 ↓ 5,8,11-Eicosatrienoic acid Fatty Acyls 1.66 0.31 0.01 ↓ 1.80 3.11 0.00 ↑ Stachydrine Amino acid and Its metabolites 1.23 4.31 0.03 ↑ — — — — Glycine deoxycholic acid Organic acid And Its derivatives 1.83 11.02 0.04 ↑ — — — — Glycochenodeoxycholic acid Bile acids 2.52 8.88 0.00 ↑ 2.56 0.28 0.01 ↓ Methylsuccinic acid Organic acid And Its derivatives — — — — 1.06 0.35 0.02 ↓ Palmitoylcarnitine Alkaloids — — — — 1.28 2.78 0.03 ↑ Ursocholic acid Bile acids — — — — 1.41 6.95 0.02 ↑ Oxypurinol Heterocyclic compounds — — — — 1.54 0.15 0.04 ↓ Notes: ↑ Represents upregulation of metabolite; ↓ represents downregulation of metabolite. Abbreviations: VIP, variable importance in the projection; FC, fold change Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1. Species accumulation curve(A) and rarefaction curve of samples(B). FigureS2.tif Figure S2. Non-metric Multidimensional Scaling (NMDS) analysis of β-diversity. FigureS3.tif Figure S3. STAMP analysis of microbial communities at the genus level between the groups. (A) HC vs. SLE, (B) HC vs. LN, and (C) SLE vs. LN. FigureS4.tif Figure S4. Correlation analysis of differential metabolites. (A) HC vs. SLE, (B) HC vs. LN, (C) SLE vs. LN. The axes represent the names of differential metabolites. Different colors indicate the strength of the Pearson correlation coefficients, with the color gradient detailed in the right-side legend. Red indicates stronger positive correlations, green indicates stronger negative correlations, and darker colors represent larger absolute values of correlation coefficients. FigureS5.tif Figure S5. Heatmap of differential metabolites. (A) Differential metabolites between HC and SLE patients. (B) Differential metabolites between SLE and LN. The x-axis shows sample names, while the y-axis displays differential metabolites. The color gradient, ranging from blue to red, indicates metabolite expression levels, with blue representing a lower abundance and red representing a higher abundance. FigureS6.tif Figure S6. ROC analysis of potential biomarkers distinguishing patients with SLE from HC. (A) Glycine deoxycholic acid, (B) Glycochenodeoxycholic acid, (C) Stachydrine, (D) Soyasaponin I, and (E) 5,8,11-Eicosatrienoic acid. SupplementaryTable1.xlsx Cite Share Download PDF Status: Published Journal Publication published 07 May, 2025 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 18 Mar, 2025 Reviews received at journal 11 Mar, 2025 Reviewers agreed at journal 07 Mar, 2025 Reviewers agreed at journal 06 Mar, 2025 Reviewers agreed at journal 10 Feb, 2025 Reviews received at journal 03 Nov, 2024 Reviewers agreed at journal 06 Oct, 2024 Reviewers agreed at journal 02 Oct, 2024 Reviewers invited by journal 02 Oct, 2024 Editor assigned by journal 02 Oct, 2024 Editor invited by journal 26 Sep, 2024 Submission checks completed at journal 23 Sep, 2024 First submitted to journal 23 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5045051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430689511,"identity":"5e3de89c-52e0-4d74-b2bf-b045e2430c95","order_by":0,"name":"Siyun Cheng","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siyun","middleName":"","lastName":"Cheng","suffix":""},{"id":430689512,"identity":"d8c6fad6-bcac-4401-99ba-9b7825846e03","order_by":1,"name":"Xiaojie Chu","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojie","middleName":"","lastName":"Chu","suffix":""},{"id":430689513,"identity":"fc345e07-982e-4f6b-a817-9ff0c5c0ddd8","order_by":2,"name":"Zhongyu Wang","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhongyu","middleName":"","lastName":"Wang","suffix":""},{"id":430689514,"identity":"7bb44543-b23b-4e37-990e-6cf4be46253e","order_by":3,"name":"Adeel Khan","email":"","orcid":"","institution":"University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Adeel","middleName":"","lastName":"Khan","suffix":""},{"id":430689515,"identity":"d102f72f-76c8-4cd3-a11d-bb6179034d94","order_by":4,"name":"Yue Tao","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Tao","suffix":""},{"id":430689516,"identity":"acb81f7e-7508-4417-bf9f-1f964aa6798e","order_by":5,"name":"Han Shen","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Shen","suffix":""},{"id":430689517,"identity":"be85a94c-ea43-452a-8246-bdbd4379e2b4","order_by":6,"name":"Ping Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACZijNz8CQwPCAJC2SDUAtCSTZZnCAgYE4LebsvIdf89Tcsdt8u+Hxi8S2wwz87d34dVo286VZ8xx7lrztzoE0C5AWiTNnN+B3z2EeM2MetsPJZjcS0gxAWgwkconR8u9wsvEMErQYP+ZtO2xnIJGQ/IAoLZbNPGaMc/sOJ0gAHcaQcC6dh6BfzPnPGH948+2wPf+MnOQPH8qs5fjbewk4jIGBTYqHgSGxgYEnTYKRrZkHr3KoFuaPPxgY7BkY2A9/YPhTR1DHKBgFo2AUjDwAAM7oTd9Mn+noAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Xuzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ping","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-09-06 15:01:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5045051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5045051/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-025-03995-5","type":"published","date":"2025-05-07T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80143447,"identity":"eefa2208-c00c-4860-937f-9d0baebf0fa5","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrouping Design and Analysis Flowchart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/605da3e9d782e073c36f949e.png"},{"id":80143448,"identity":"d43a7efd-7dd9-4113-b71d-46651156b21d","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of microbial diversity and relative composition.\u003c/strong\u003e (A) Measures of α-diversity observed among HC, SLE, and LN groups, including ACE, Chao, Shannon, and Simpson indices. (B) Constrained Principal Coordinates Analysis (CPCoA) showing differentiation between sample groups. The relative abundances of gut microbiota at the (C) phylum level and (D) genus level were compared.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/0e556acdaaeca59855e52403.png"},{"id":80144573,"identity":"65be2975-25f3-41a7-b1b1-769969160f3a","added_by":"auto","created_at":"2025-04-08 12:14:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":346032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial community analysis using LEfSe and STAMP.\u003c/strong\u003e (A) Histogram of LDA scores indicating the effect size of differentially abundant microbial taxa (LDA score \u0026gt; 3). (B) Cladogram of specific differential taxa. (C) STAMP analysis of the microbial communities in the HC, SLE, and LN groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/fc588308a02cb537447e311f.png"},{"id":80143451,"identity":"c3e4447b-8496-48fb-ba5e-da57477464d5","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":177007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic profile analysis.\u003c/strong\u003e (A) OPLS-DA score plot exhibiting statistically distinct \u0026nbsp;differences in metabolic profiles between the groups. The x-axis represents between-group variability, and the y-axis represents within-group variability. (B) Validation of the model. The x-axis indicates between-group variability, and the y-axis represents within-group variability. (C) Volcano plot of differential metabolites. The x-axis denotes group comparisons, and the y-axis represents fold changes in metabolites (log2FoldChange). Metabolites upregulated in each group are shown above the x-axis, whereas downregulated metabolites are shown below.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/c9c42c57d4628392b86ff098.png"},{"id":80143787,"identity":"c1b5429d-de4b-412f-a5c5-7290f522c3a3","added_by":"auto","created_at":"2025-04-08 12:06:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":139660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG pathway enrichment analysis.\u003c/strong\u003e (A) HC vs. SLE, (B) SLE vs. LN. The y-axis lists the pathway names, while the x-axis represents enrichment factors (calculated as the number of significant metabolites divided by the total number of metabolites in the pathway). Bubble size corresponds to the number of enriched metabolites within each pathway, and the color gradient reflects the significance level of the enrichment.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/3476b06931b446ef601ff8ed.png"},{"id":80143790,"identity":"57c40099-b60a-490d-b693-19adab7dea68","added_by":"auto","created_at":"2025-04-08 12:06:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":233630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis of potential biomarkers distinguishing SLE patients with or without LN.\u003c/strong\u003e (A) Glycocholic acid, (B) Methylsuccinic acid, (C) Oxypurinol, (D) Palmitoylcarnitine, (E) 5,8,11-Eicosatrienoic acid, (F) Ursocholic acid, (G) Glycochenodeoxycholic acid.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/bd588e155f1c56ae3278412a.png"},{"id":80144574,"identity":"3619f8d1-150a-4f47-8832-bd3a100969d4","added_by":"auto","created_at":"2025-04-08 12:14:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":326443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation analysis between 16 gut microbiota and discriminative metabolites in (A) SLE and LN, and (B) HC and SLE.\u003c/strong\u003e The x-axis represents metabolites with varying abundances, while the y-axis represents bacterial genera with varying abundances at the 16S gene level. Red indicates a positive correlation, blue indicates a negative correlation. (*) denotes P \u0026lt; 0.05, and (**) denotes P \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/505a534bb0c50b558b8ab20f.png"},{"id":82537637,"identity":"944c7bbe-7140-40cf-b2f5-f89242df2022","added_by":"auto","created_at":"2025-05-12 16:09:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2428161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/face11df-a1e5-465f-b15e-f671fb186ccf.pdf"},{"id":80143450,"identity":"704b5664-6c2b-4068-b23f-0ffa2ccebdf2","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1763104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. Species accumulation curve(A) and rarefaction curve of samples(B).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/2e534198bb368647b1d8a83b.tif"},{"id":80143454,"identity":"28b7e849-19ca-45fa-be34-cf3a3535774d","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2215166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. Non-metric Multidimensional Scaling (NMDS) analysis of β-diversity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/02e2d544b36176fccd44c49d.tif"},{"id":80143459,"identity":"25c7da4e-5d0c-4dfb-8744-6ac846368ec5","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7134500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3\u003c/strong\u003e. \u003cstrong\u003eSTAMP analysis of microbial communities at the genus level between the groups.\u003c/strong\u003e (A) HC vs. SLE, (B) HC vs. LN, and (C) SLE vs. LN.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/cde67851cc350ba8fa6cefbc.tif"},{"id":80143791,"identity":"7011046c-d5dd-4cdf-9866-c80c99c0edcb","added_by":"auto","created_at":"2025-04-08 12:06:45","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3217728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4. Correlation analysis of differential metabolites.\u003c/strong\u003e (A) HC vs. SLE, (B) HC vs. LN, (C) SLE vs. LN. The axes represent the names of differential metabolites. Different colors indicate the strength of the Pearson correlation coefficients, with the color gradient detailed in the right-side legend. Red indicates stronger positive correlations, green indicates stronger negative correlations, and darker colors represent larger absolute values of correlation coefficients.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/149b2924e98280f76762ebe6.tif"},{"id":80143458,"identity":"e03687aa-b7a7-4f82-8769-2bf94990bc6f","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3545488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5. Heatmap of differential metabolites. \u003c/strong\u003e(A) Differential metabolites between HC and SLE patients. (B) Differential metabolites between SLE and LN. The x-axis shows sample names, while the y-axis displays differential metabolites. The color gradient, ranging from blue to red, indicates metabolite expression levels, with blue representing a lower abundance and red representing a higher abundance.\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/0d7260f92ce805e479ee3e46.tif"},{"id":80143460,"identity":"2a7ef199-558c-4cd7-8505-f107f31e6f33","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6786328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S6. ROC analysis of potential biomarkers distinguishing patients with SLE from HC.\u003c/strong\u003e (A) Glycine deoxycholic acid, (B) Glycochenodeoxycholic acid, (C) Stachydrine, (D) Soyasaponin I, and (E) 5,8,11-Eicosatrienoic acid.\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/1b5f525d5deff234c07b45a9.tif"},{"id":80143457,"identity":"c9b8f781-96e0-4a04-b770-36cce87a34d3","added_by":"auto","created_at":"2025-04-08 11:58:45","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":20852,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5045051/v1/b59f688af576f103a00972c9.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering Potential Biomarkers and Metabolic Pathways in Systemic Lupus Erythematosus and Lupus Nephritis through Integrated Microbiome and Metabolome Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSystemic lupus erythematosus (SLE) is a chronic autoimmune disorder characterized by the immune system's aberrant attack on healthy tissues. Such dysregulation leads to systemic inflammation and multiorgan damage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. SLE manifests with heterogeneous clinical presentations and variable disease severity, affecting multiple organs. The global prevalence of SLE ranges from 30 to 50 cases per 100,000 individuals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In China, the reported prevalence is 6.17 per 100,000 males and 67.78 per 100,000 females [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Lupus nephritis (LN), a severe renal complication of SLE, is characterized by immune-mediated kidney damage with diverse pathological types and significant clinical manifestations, making it one of the most serious forms of SLE [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite extensive research, the underlying pathogenesis of LN remains incompletely understood. A primary goal in SLE management is preventing irreversible organ damage, which requires identifying key molecular contributors to disease progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Accurate diagnosis, timely intervention, and early relapse management are crucial for effective LN treatment. Although renal biopsy is the gold standard for LN diagnosis, its invasiveness limits its utility for continuous disease monitoring, underscoring the need for reliable non-invasive biomarkers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Current routine biomarkers, such as serum creatinine and complement component C3b, have limited utility in assessing LN disease activity or facilitating real-time diagnosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging evidence implicates the gut microbiota as a critical modulator of autoimmune processes, including SLE [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Gut microbiota metabolize dietary components into bioactive metabolites that modulate systemic immune responses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Microbial metabolites, such as short-chain fatty acids and bile acids, are key mediators of host-microbiota interactions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For example, Zhang et al. reported that fecal samples from SLE patients exhibited significantly elevated metabolic activities, including enhanced amino acid biosynthesis, vitamin B1 metabolism, nitrogen cycling, tryptophan degradation, and cyanoamino acid metabolism, compared to healthy controls [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, single-omics approaches (e.g., metagenomics or metabolomics alone) fail to capture the complexity of disease mechanisms. Integrative analysis of gut microbiota and host metabolome dynamics provides a holistic understanding of microbial and metabolic interactions in pathogenesis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite progress in SLE metabolomic profiling, studies exploring microbiota-metabolome interplay, particularly in differentiating SLE with nephritis (SLE-LN) from SLE without nephritis (SLE-nonLN), remain limited [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we applied 16S rRNA gene sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based untargeted metabolomics to analyze fecal samples from SLE patients (SLE-LN and SLE-nonLN) and healthy controls (HC). Our objective was to identify potential biomarkers and explore gut microbiota-metabolome interactions to provide novel insights into the mechanisms underlying SLE pathogenesis and progression.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection\u003c/h2\u003e \u003cp\u003eWe recruited 36 patients diagnosed with SLE from Nanjing Drum Tower Hospital, affiliated with Nanjing University Medical School. All patients were newly admitted and were not receiving immunosuppressive therapy at the time of sample collection. Diagnosis was based on the 1997 revised classification criteria of the American College of Rheumatology (ACR) and further confirmed through clinical evaluation, including serological markers and renal biopsy where applicable. The cohort consisted of 18 SLE-LN and 18 SLE-nonLN. Clinical characteristics, including laboratory test results and medical history, were obtained from the hospital\u0026rsquo;s electronic medical records system. Additionally, 15 age-, gender-, and BMI-matched HC were recruited from the hospital\u0026rsquo;s health examination center. These individuals had no prior history of autoimmune diseases, infections, metabolic disorders, or malignancies. All participants provided written informed consent, and the study was approved by the Ethics Committee of Nanjing Drum Tower Hospital. Fecal samples were collected using sterile containers upon admission and immediately processed to maintain sample integrity. Each sample was divided into two aliquots and immediately stored at -80\u0026deg;C for subsequent 16S rRNA sequencing and untargeted metabolomics analysis. The overall study design is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA Extraction and 16S rRNA Sequencing\u003c/h3\u003e\n\u003cp\u003eTotal genomic DNA was extracted using the QIAamp DNA Stool Mini Kit (QIAGEN) with modifications, including an extended 10-minute bead-beating step to enhance the lysis of Gram-positive bacteria. DNA purity (A260/A280 ratio 1.8-2.0) and integrity were verified via NanoDrop 2000 spectrophotometry and 1% agarose gel electrophoresis. The V3-V4 region of the 16S rRNA gene was amplified using primers 341F/806R under the following conditions: 98\u0026deg;C for 1 min; 30 cycles of 98\u0026deg;C for 10 s, 55\u0026deg;C for 30 s, 72\u0026deg;C for 30 s; final extension at 72\u0026deg;C for 5 min. PCR products were purified (AxyPrep DNA Gel Extraction Kit) and quantified (Qubit dsDNA Assay Kit). Equimolar pooled libraries were sequenced on an Illumina MiSeq PE300 platform (2\u0026times;300 bp) in a single run to minimize batch effects.\u003c/p\u003e\n\u003ch3\u003eSequencing Data Processing and Microbial Diversity Analysis\u003c/h3\u003e\n\u003cp\u003eRaw sequencing reads were preprocessed using Cutadapt (v4.0) to trim adapters and remove low-quality bases. Quality filtering was performed with FASTP (v0.23.4), retaining reads with Phred scores\u0026thinsp;\u0026ge;\u0026thinsp;20 and lengths\u0026thinsp;\u0026ge;\u0026thinsp;200 bp. Paired-end reads were merged using FLASH (v1.2.11; min overlap\u0026thinsp;=\u0026thinsp;20 bp, max mismatch\u0026thinsp;=\u0026thinsp;0.1). Further filtering steps were conducted to remove ambiguous bases (N), sequences with homopolymer runs (\u0026gt;\u0026thinsp;8 bp), and chimeric reads using USEARCH (v11.0). Operational taxonomic units (OTUs) were de novo clustered at 97% similarity using VSEARCH (v2.21.1) and taxonomically assigned via the QIIME2 Na\u0026iuml;ve Bayesian Classifier (v2023.2) against the SILVA 142 database (confidence threshold\u0026thinsp;=\u0026thinsp;80%). Alpha diversity (Chao, Shannon, Simpson) was calculated after rarefaction to 10,000 reads/sample. Beta diversity was assessed using weighted/unweighted UniFrac distances and visualized via principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS). Linear discriminant analysis Effect Size (LEfSe) analysis was performed using Python (v3.9.7) to identify differentially abundant taxa, applying significance thresholds of 0.05 for both the Kruskal-Wallis and Wilcoxon tests, and an LDA score threshold of 4.\u003c/p\u003e\n\u003ch3\u003eMetabolite Extraction and LC-MS/MS Analysis\u003c/h3\u003e\n\u003cp\u003eFecal metabolites were extracted by homogenizing 20 mg of sample with 400 \u0026micro;L methanol: water (7:3, v/v) on ice. After sonication (10 min), vortexing (1 min), and centrifugation (12,000 \u0026times; g, 10 min, 4\u0026deg;C), 200 \u0026micro;L of supernatant was analyzed via LC-MS/MS (Waters ACQUITY UPLC HSS T3 C18 column, 1.8 \u0026micro;m, 2.1 \u0026times; 100 mm). The mobile phase consisted of 0.1% formic acid in water (solvent A) and acetonitrile (solvent B), with a gradient elution (0.4 mL/min): 0\u0026ndash;11 min, 5\u0026ndash;90% B; 12\u0026ndash;14 min, 5% B. Raw data were converted to mzML format using ProteoWizard version 3.0.23136. Peak detection and retention time alignment were performed using XCMS (v4.7). Metabolites were identified through our in-house database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) online database.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eConstrained Principal Coordinate Analysis (CPCoA) was performed using the prcomp function in R (v4.2.3). Hierarchical clustering analysis was conducted with the ComplexHeatmap package, and results were visualized as heatmaps with dendrograms. Normalized signal intensities of metabolites were displayed as color spectra following unit variance scaling. Differential metabolites were defined by VIP\u0026thinsp;\u0026ge;\u0026thinsp;1.5, |Log2FC|\u0026ge;1.0, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data were log2-transformed and mean-centered prior to orthogonal partial least squares discriminant analysis (OPLS-DA). Functional and pathway analysis was conducted using the KEGG database, with pathways considered significantly enriched at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eAltered Microbiota Composition Among Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DNA from fecal samples of 15 HC and 36 SLE patients (18 with LN and 18 without LN) was examined using 16S rRNA gene sequencing. The species accumulation curve (Supplementary Fig. S1A) and rarefaction curve (Supplementary Fig. S1B) approached saturation, indicating that the sequencing depth and coverage adequately captured the diversity within the samples. To assess bacterial diversity differences across the three groups, sequence data was aligned to calculate both within-sample (alpha) and between-sample (beta) diversity. No significant differences in ACE, Chao1, Shannon, and Simpson indices were observed between the HC, SLE, and LN groups (Fig. 1A), indicating no significant differences in microbial richness or diversity among the three groups. Constrained Principal Coordinate Analysis (CPCoA) depicted distinct differences between the SLE (with and without LN) and HC groups. Non-metric multidimensional scaling (NMDS) was used to assess spatial positions within and between groups (Supplementary Fig. S2). These findings suggest that SLE may influence the diversity of gut microbiota.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAltered Microbial Composition Associated with SLE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relative abundance of the dominant taxa was assessed across different taxonomic levels among the three groups. At the phylum level, \u003cem\u003eFirmicutes\u003c/em\u003e were predominant, representing 49.35%, 59.08%, and 39.50% of the HC, SLE, and LN groups, respectively. The abundance of\u003cem\u003e\u0026nbsp;Proteobacteria\u0026nbsp;\u003c/em\u003ewas significantly lower in the SLE group than in the HC group (12.93% vs. 23.18%) and markedly higher in the LN group (28.02%). The phylum \u003cem\u003eVerrucomicrobiota\u003c/em\u003e was significantly elevated in both the SLE and LN groups compared to that in the HC group. At the genus level, \u003cem\u003ethe abundance of Faecalibacterium\u0026nbsp;\u003c/em\u003ewas higher in the HC group (10.31%) and progressively decreased in the SLE and LN groups (4.06% and 1.90%, respectively). Conversely, the abundance of \u003cem\u003eBacteroides\u003c/em\u003e was lower in the HC group (9.57%) and higher in the SLE and LN groups (11.26% and 14.73%, respectively). Compared to the HC group, the genera \u003cem\u003eBlautia\u003c/em\u003e decreased, while \u003cem\u003eStreptococcus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Enterococcus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Akkermansia\u003c/em\u003e, and \u003cem\u003eLactobacillus\u003c/em\u003e increased in both the SLE and LN groups (Fig. 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLEfSe Analysis Among Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LEfSe method was employed to compare microbial compositions among SLE patients, LN patients, and healthy controls to identify specific dominant bacteria associated with SLE. The histogram of LDA scores (Fig. 3A) identified 76 differentially abundant taxa at various taxonomic levels, with 23 from the SLE patients, 19 from the LN group, and 34 from the HC group. \u003cem\u003eBacilli\u003c/em\u003e had the highest LDA score (\u0026gt;5), with \u003cem\u003eLactobacillales\u003c/em\u003e being the most prominent in the SLE group. In contrast, \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, \u003cem\u003eEnterobacterales\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eGammaproteobacteria,\u003c/em\u003e \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, and \u003cem\u003eEscherichia_coli\u003c/em\u003e were the most abundant in the LN group (LDA score \u0026gt;3.7) (Fig. 3A), whereas in HC group Clostridia were more abundant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the genus level, Kruskal-Wallis and pairwise t-test analyses using STAMP revealed that \u003cem\u003eEnterobacter\u0026nbsp;\u003c/em\u003ewas most statistically distinct among the three groups\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Fig. 3C) (p \u0026lt; 0.001). \u003cem\u003ePairwise\u003c/em\u003e comparisons further indicated significant differences in \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e between the HC and SLE groups with and without LN (p \u0026lt; 0.05). Additionally, \u003cem\u003eEscherichia-Shigella\u003c/em\u003e showed a pronounced statistical difference between the LN and SLE groups (p = 0.0208) (Supplementary Fig. S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in Metabolome and Key Metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGut microbial metabolites has pronounced influence on the physiological functions of the host. Metabolite abundance in the fecal samples was detected using LC-MS technology. OPLS-DA score plots were utilized to evaluate group distinctions. Scatter plots (Fig. 4A) revealed significant clustering within groups and clear separation between groups, indicating distinct metabolic patterns. Permutation tests validated the PLS-DA model (Fig. 4B), with the Y-intercept of the regression line for blue Q2 points below zero, indicating no overfitting and supporting the model\u0026apos;s reliability.\u003c/p\u003e\n\u003cp\u003eWe identified 177, 159, and 94 differential metabolites in HC vs. LN, HC vs. SLE, and SLE vs. LN groups, respectively. We examined fold changes (FC) of metabolites among groups using the OPLS-DA model and visualized differential metabolites in volcano plots (Fig. 4C). Metabolites with a fold change (FC) \u0026ge; 2 or \u0026le; 0.5, p \u0026lt; 0.05, and VIP \u0026ge; 1 were considered significantly different. Compared to HC, SLE patients had higher levels of Hydroxychloroquine, [2,2-Bis(2-methylpropoxy)ethyl]benzene, and Cys-Phe, while LN patients had higher levels of 3-Methoxybenzyl, Hydroxychloroquine, and Nomilin. Between LN and SLE groups, 94 differential metabolites were identified (Supplementary Table S1), with Atorvastatin, Nomilin, and Diethyl 1,4-dihydro-2,4,6-trimethyl-3,5-pyridinedicarboxylate upregulated, and Arg-Tyr-Gln-Lys downregulated. Three metabolites\u0026mdash;Glycochenodeoxycholic acid, Diethyl 1,4-dihydro-2,4,6-trimethyl-3,5-pyridinedicarboxylate, and Nomilin\u0026mdash;showed consistent trends among HC, SLE, and LN groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic Pathways Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis was used to evaluate the relationships among significantly different metabolites. We conducted a correlation analysis on the top 50 differential metabolites, ranked by VIP scores, using Pearson\u0026apos;s correlation method (Supplementary Fig. S4). KEGG pathway enrichment analysis was performed to identify the main metabolic and signaling pathways associated with differential metabolites between SLE patients and HC, and between SLE and LN patients. The KEGG pathway analysis results were visualized using bubble plots (Fig. 5). Taurine and hypotaurine metabolism were the most significantly enriched pathways in SLE patients compared to HC, followed by primary bile acid biosynthesis and histidine metabolism. In contrast, differential metabolites between the SLE and LN groups were involved in primary bile acid biosynthesis, thiamine metabolism, taurine and hypotaurine metabolism, and sulfur metabolism.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A total of nine metabolites were found to differ significantly between groups, excluding myristoleic acid, polygodial, and furosemide, which originated from food or drugs. Table 1 lists the identified metabolites, visualized in heatmaps, to show the relationships and expression differences between samples (Supplementary Fig. S5). Among these, three metabolites\u0026mdash;Glycocholic acid, Glycochenodeoxycholic acid, and 5,8,11-Eicosatrienoic acid\u0026mdash;were common to both the HC vs. SLE and SLE vs. LN comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of metabolite biomarkers to distinguish LN and SLE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis was performed to assess the ability of these metabolites to differentiate between SLE patients and those with LN. Seven differential metabolites involved in key metabolic pathways were examined for their predictive performance between the SLE and LN groups. The analysis indicated that Glycocholic acid (AUC = 0.951), Glycochenodeoxycholic acid (AUC = 0.827), Oxypurinol (AUC = 0.713), Palmitoylcarnitine (AUC = 0.827), Methylsuccinic acid (AUC = 0.685), 5,8,11-Eicosatrienoic acid (AUC = 0.769), and Ursocholic acid (AUC = 0.716) demonstrated strong discriminatory capabilities (Fig. 6A-G). The results suggest that Glycocholic acid and Glycochenodeoxycholic acidhave the potential to distinguish individuals with SLE from healthy individuals. Additionally, ROC curve analysis was conducted on differential metabolites between the SLE and HC groups (Supplementary Fig. S6), indicating that all differential metabolites exhibited high diagnostic performance, with AUC values exceeding 0.533.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Between Microbiota and Metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the relationship between gut microbiota and metabolites in SLE, LN, and HC, Spearman\u0026rsquo;s correlation analysis was conducted on the top 16 fecal microbiomes and metabolic biomarkers. At the genus level, pronounced statistical associations exhibited between microbial genera and metabolites. The correlation heatmap (Fig. 7A) highlighted key relationships: the abundances of \u003cem\u003eEscherichia-Shigella\u003c/em\u003e, and \u003cem\u003eEnterobacter\u003c/em\u003e were significantly reduced in SLE and LN groups and negatively correlated with elevated levels of bile acid metabolites such as glycochenodeoxycholic acid and glycocholic acid. Conversely, these microbial genera were positively correlated with 5,8,11-eicosatrienoic acid and ursocholic acid. Additionally, we found a significant positive correlation between \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, and methylsuccinic acid.\u003c/p\u003e\n\u003cp\u003eIn the comparative analysis of differences in fecal microbiomes and metabolic biomarkers between HC and LN, we found significant negative correlations between \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and glycochenodeoxycholic acid, glycocholic acid, and stachydrine. Soyasaponin I showed a significant negative correlation with \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eAgathobacter\u003c/em\u003e, and \u003cem\u003eFaecalibacterium\u003c/em\u003e, while exhibiting a significant positive correlation with \u003cem\u003eErysipelatoclostridium\u003c/em\u003e (p\u0026lt;0.01). A similar trend was observed for glycocholic acid, with \u003cem\u003eEscherichia-Shigella\u003c/em\u003e, \u003cem\u003eCollinsella\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, and \u003cem\u003eClostridia_UCG-014\u003c/em\u003e all showing negative correlations, whereas \u003cem\u003eStreptococcus\u003c/em\u003e exhibited a positive correlation.\u0026nbsp;These results demonstrate that changes in the abundance of certain microbes significantly affect the levels of host metabolites, particularly glycochenodeoxycholic acid and glycocholic acid. Key microbes such as \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eEnterobacter\u003c/em\u003e make significant contributions to bile acid synthesis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSystemic lupus erythematosus (SLE) is a chronic autoimmune disorder primarily affecting young women. It causes inflammation in various body tissues. Lupus nephritis (LN) is a serious kidney complication associated with SLE.(18). Our previous research identified tRNA-derived small noncoding RNAs (tsRNAs) within urinary exosomes as novel biomarkers for the diagnosis of LN(19, 20). To elucidate the pathophysiological connections between SLE and LN, we analyzed clinical indicators, gut microbiota composition, and fecal metabolites. By integrating 16S rRNA sequencing with untargeted metabolomics, we aimed to identify non-invasive biomarkers associated with the progression of both SLE and LN.\u003c/p\u003e\n\u003cp\u003eGut microbiota, characterized by its rich diversity, functions as a quasi-organ and plays crucial roles in immune regulation, pathogen defense, vitamin synthesis, hormone secretion, and nutrient absorption(21). Our findings demonstrated that the gut flora of SLE patients exhibited a relatively stable alpha diversity, with a notable divergence between the beta diversity and HC groups, suggesting that SLE may influence the composition of the human gut microbiota. Furthermore, we observed distinct differences in the gut microbiome composition among SLE patients, LN patients, and healthy controls. Specifically, the relative abundances of \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eProteobacteria\u003c/em\u003e differed significantly between the groups. \u003cem\u003eFirmicutes\u003c/em\u003e were significantly enriched in SLE patients, while \u003cem\u003eProteobacteria\u003c/em\u003e were markedly depleted. These findings contrast with those of a Spanish study, which may be attributed to variations in dietary habits across different geographical regions(22). Interestingly, our study found that this trend reverses with the progression of SLE to LN, where there is a significant depletion of \u003cem\u003eFirmicutes\u003c/em\u003e, falling below the levels observed in healthy controls, and a marked increase in \u003cem\u003eProteobacteria\u003c/em\u003e, which reached the highest proportions among the three groups. Previous studies have demonstrated that increased \u003cem\u003eProteobacteria\u003c/em\u003e and decreased \u003cem\u003eFirmicutes\u0026nbsp;\u003c/em\u003eare linked to gastrointestinal damage in lupus nephritis(23). \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eProteobacteri\u003c/em\u003ea are crucial in maintaining intestinal homeostasis, and these bacterial phyla emerge as primary differentiators for SLE and potential diagnostic markers for the disease, a pattern also observed in other chronic autoimmune diseases such as systemic sclerosis and rheumatoid arthritis(24-26). LEfSe analysis identified key bacterial taxa that were significantly associated with SLE and LN, such as \u003cem\u003eBacilli\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Lactobacillales\u003c/em\u003e, which were differentially abundant in the SLE group. These findings align with studies identifying that \u003cem\u003eBacilli\u003c/em\u003e as prevalent in the bloodstream of Asian SLE patients and that \u003cem\u003eLactobacillus\u003c/em\u003e abundance is increased in the fecal microbiota of SLE patients(27, 28). Research on lupus-prone mouse models has demonstrated that \u003cem\u003eLactobacillus\u003c/em\u003e levels increase during active SLE and decrease in lupus models(29). The reduction of specific \u003cem\u003eLactobacilli\u003c/em\u003e, such as \u003cem\u003eLactobacillus reuteri\u003c/em\u003e, in nephritic mice contributes to lupus-related inflammation, suggesting that \u003cem\u003eLactobacilli\u0026nbsp;\u003c/em\u003emay play a pathogenic role in human lupus through mechanisms involving barrier integrity and immune modulation(30).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSLE alters the gut microbiota, with dysbiosis becoming more pronounced in LN and particularly marked by an increase in \u003cem\u003eEnterobacteriaceae\u003c/em\u003e. Previous studies have documented significant changes in the gut microbiota of LN patients(31). Our results show increased relative abundances of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, \u003cem\u003eEnterobacterales\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, and \u003cem\u003eEscherichia_coli\u003c/em\u003e in the LN group.These findings align with previous research and underscore the importance of gut microbial imbalance in the development of autoimmune conditions. \u0026nbsp;The increased presence of pro-inflammatory bacteria, such as \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, in LN patients indicates a potential association \u0026nbsp;between the gut microbiota and renal inflammation in SLE(32).\u003c/p\u003e\n\u003cp\u003eMetabolomics, which is known for its high resolution and sensitivity, has attracted considerable attention in scientific research(33).\u0026nbsp;This study revealed notabledifferences in untargeted fecal metabolomics among SLE patients with and without LN and HCs, with notable alterations primarily observed in lipids and amino acids. Dysregulation of lipid metabolism is a well-established feature of the progression from SLE to LN, although detailed metabolic changes in many lipid species remain underexplored(34, 35). Mead acid (5,8,11-Eicosatrienoic acid), a polyunsaturated fatty acid synthesized de novo in the human body, serves as a marker of essential fatty acid deficiency(36). Elevated Mead acid levels, typically seen in dietary essential fatty acid deficiency, particularly arachidonic acid, may exacerbate autoimmune disease progression(37). Consistent with findings reported by Zhang et al., our data indicate that Mead acid levels are significantly higher in LN patients compared to SLE patients, reinforcing its role in the transition from SLE to LN(38). Additionally, a statistically valid reduction in palmitoylcarnitine levels was observed in LN patients. Our findings suggest that palmitoylcarnitine plays a role in facilitating the transfer of long-chain fatty acids from the cytoplasm to the mitochondria during fatty acid oxidation(39), which may contribute to inflammation in LN patients.\u003c/p\u003e\n\u003cp\u003eChanges in lipid metabolism are intricately linked to lipid-induced nephrotoxicity and play a significant role in LN pathophysiology. Bile acids, which are crucial for lipid metabolism, regulate this process through TGR5 modulation. Specific bile acids, including deoxycholic acid, hypodeoxycholic acid, ursodeoxycholic acid, and arachidonic acid, were significantly correlated with the Systemic Lupus Erythematosus Disease Activity Index \u0026nbsp; (SLEDAI) score, indicating their potential as biomarkers for disease activity(40). Taurine and hypotaurine metabolism and primary bile acid biosynthesis were key pathways in the KEGG pathway of SLE and LN. Metabolites in these pathways, such as glycocholic acid and glycochenodeoxycholic acid, exhibited high sensitivity and specificity for both SLE and LN, with AUC of 0.951 and 0.827, respectively. Biosynthesis of primary bile acids is associated with characteristic lipid changes in SLE patients(41). High cholesterol and sphingolipid levels in T cell membranes alter the signaling platform, promoting pro-inflammatory signaling, as suggested by our findings. However, as SLE progresses to LN, we observed a decrease in glycocholic acid and glycochenodeoxycholic acid levels and a concurrent increase in ursodeoxycholic acid levels. Recent studies have reported decreased plasma levels of glycochenodeoxycholic acid and glycocholic acid in LN patients, likely due to reduced glomerular filtration mediated by the apical sodium-dependent bile acid transporter MRP2 and organic solute transporters α and β(42). This reduction may lead to mesangial and endothelial cell proliferation, glomerulosclerosis, interstitial fibrosis, and vascular sclerosis, impairing glomerular filtration and indicating decreased bile acid filtration function in LN patients. Additionally, bile acids function as signaling molecules through bile acid receptors. In lupus mouse models, the FXR agonist phenobarbital has been shown to suppress the expression of inflammatory cytokines, such as TNF-α, IFN-γ, and IL-6(34). Our findings strongly suggest that the fecal levels of glycochenodeoxycholic acid and glycocholic acid could be critical biomarkers for differentiating between SLE and LN.\u003c/p\u003e\n\u003cp\u003eWe observed statistically distinct elevation in the levels of glucogenic amino acid metabolites, including glycine, proline, L-cysteine, phenylalanine, and isoleucine, in fecal samples from SLE patients. This suggests a potential imbalance in glucose and energy metabolism, possibly indicating a shift towards alternative energy sources. It is noteworthy that while the levels of glycine and proline were found to be increased in patients with SLE, their concentrations were decreased in those with LN. This decrease was observed to be associated with alterations in the gut microbiome. These changes may\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings indicate that the genus \u003cem\u003eEscherichia-Shigella\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Enterobacter\u003c/em\u003e play a crucial role, exhibiting a pronounced statistical variation among the three groups, particularly with a pronounced increase in the LN group (p \u0026lt; 0.001).\u003cem\u003e\u0026nbsp;Escherichia-Shigella\u003c/em\u003e exhibited a negative correlation with elevated levels of bile acid metabolites, including glycochenodeoxycholic acid and glycocholic acid, across all three groups. Similarly, \u003cem\u003eEnterobacter\u003c/em\u003e was negatively correlated with nearly all differential metabolites, such as pterine, L-cysteine, and glycoursodeoxycholic acid, which are strongly associated with bile acid biosynthesis and thiamine metabolism pathways. We speculate that the overgrowth of \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e may contribute significantly to LN pathogenesis, possibly through interactions between gut microbiota and metabolites.\u003c/p\u003e\n\u003cp\u003eHowever, the study's limitations include the small sample size, which hinders subgroup analysis based on clinical data. Future research should involve additional control groups and larger samples from patients with other autoimmune or inflammatory diseases. Longitudinal studies will be essential to determine the exact sequence of changes in the microbiome and metabolome, and how these alterations correlate with disease progression over time. The close association between gut microbiome and metabolome in SLE and LN patients highlights new insights into disease mechanisms. Compared to serum or urine samples, fecal samples offer the advantages of easy collection and non-invasive nature, reflecting direct dietary interactions with the gut microbiome.\u0026nbsp;\u003c/p\u003e\n\n"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlights significant alterations in the gut microbiota and metabolite profiles in patients with Systemic Lupus Erythematosus (SLE) and Lupus Nephritis (LN) compared to healthy controls. SLE patients showed an increase in Firmicutes and a decrease in Proteobacteria, while LN was marked by elevated Enterobacteriaceae. Metabolomic analysis revealed higher Mead acid and lower levels of glycocholic and glycochenodeoxycholic acids in LN. The correlations between gut microbiota and metabolites suggest potential biomarkers and therapeutic targets, underscoring the need for integrating microbiome and metabolome analyses to advance the understanding and management of SLE and LN.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants for joining our study and reviewers for their valuable suggestion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Nanjing Drum Tower Hospital (approval number: 2022-461-02) and in accordance with national law and the Helsinki Declaration of 1975 (in its current, revised form). All participants provided written informed consent to participate before enrolment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe amplicon sequencing data are available in the NCBI Sequence Read Archive (SRA) database (BioProject: PRJNA1163353). The detail data and materials available please see https://www.ncbi.nlm.nih.gov/bioproject/ PRJNA1163353.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No. 82202600), the Nanjing Drum Tower Hospital Clinical Research Special Fund Project (No. 2024-LCYJ-MS-11), and the New Technology Development Fund of Nanjing Drum Tower Hospital (grant number: XJSFZJJ201905).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePY initiated and supervised the study. PY, HS and YT designed the study and were major contributors in writing and revising the manuscript. SYC collected the samples. SYC, XJC and ZYW were major contributors in conducting statistical analysis and interpretation of data. AK provided technical assistance. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCaielli S, Wan Z, Pascual V. Systemic lupus erythematosus pathogenesis: interferon and beyond. Annual review of immunology. 2023;41(1):533-60.\u003c/li\u003e\n\u003cli\u003eBarber MR, Drenkard C, Falasinnu T, Hoi A, Mak A, Kow NY, et al. Global epidemiology of systemic lupus erythematosus. Nature Reviews Rheumatology. 2021;17(9):515-32.\u003c/li\u003e\n\u003cli\u003eZou Y-F, Feng C-C, Zhu J-M, Tao J-H, Chen G-M, Ye Q-L, et al. Prevalence of systemic lupus erythematosus and risk factors in rural areas of Anhui Province. 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Frontiers in Immunology. 2022;13:949033.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable1:\u003c/strong\u003e Metabolites with intergroup differences in fecal samples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"856\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCompounds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 200px;\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 245px;\"\u003e\n \u003cp\u003eHC vs SLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 236px;\"\u003e\n \u003cp\u003eSLE vs LN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eVIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eTrend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eVIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eTrend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eGlycocholic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eBile acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e5,8,11-Eicosatrienoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eFatty Acyls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eStachydrine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eAmino acid and Its metabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eGlycine deoxycholic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eOrganic acid And Its derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e11.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eGlycochenodeoxycholic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eBile acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eMethylsuccinic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eOrganic acid And Its derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003ePalmitoylcarnitine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eAlkaloids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eUrsocholic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eBile acids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eOxypurinol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003eHeterocyclic compounds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: \u0026uarr; Represents upregulation of metabolite; \u0026darr; represents downregulation of metabolite.\u003c/p\u003e\n\u003cp\u003eAbbreviations: VIP, variable importance in the projection; FC, fold change\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SLE, LN, Gut Microbiota, Metabolomics, Biomarkers, Fecal Samples, 16S rRNA Sequencing, Metabolic Pathways","lastPublishedDoi":"10.21203/rs.3.rs-5045051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5045051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to elucidate the relationship between gut microbiota and metabolomic profiles in patients with systemic lupus erythematosus (SLE) and lupus nephritis (LN) to identify potential biomarkers and elucidate their roles in disease progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFecal samples from 15 healthy controls (HC) and 36 SLE patients (18 SLE-nonLN and 18 SLE-LN) were analyzed using 16S rRNA gene sequencing and untargeted metabolomics. Differential microbial taxa and metabolites were identified using Linear Discriminant Analysis Effect Size (LEfSe) analysis and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Receiver Operating Characteristic (ROC) curve analysis were employed to evaluate the clinical relevance of identified metabolites.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBeta diversity analysis demonstrated significant clustering among groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SLE-LN exhibited increased Proteobacteria (28.02% vs. 12.93% in SLE-nonLN) and decreased Firmicutes (39.50% vs. 59.08%). Metabolomic profiling identified 94 differentially abundant metabolites in SLE-LN vs. SLE-nonLN, enriched in primary bile acid biosynthesis (e.g., Glycocholic acid, AUC\u0026thinsp;=\u0026thinsp;0.951). SLE-nonLN displayed 159 differential metabolites compared to HC, including increased Glycoursodeoxycholic acid (AUC\u0026thinsp;=\u0026thinsp;0.922) in taurine and hypotaurine metabolism. Microbial-metabolite correlation analysis highlighted \u003cem\u003eEscherichia-Shigella\u003c/em\u003e as negatively associated with bile acids (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study reveals distinct gut microbiota and metabolomic signatures associated with SLE and LN. The identified microbial taxa and metabolites may serve as potential diagnostic biomarkers and therapeutic targets for disease management.\u003c/p\u003e","manuscriptTitle":"Uncovering Potential Biomarkers and Metabolic Pathways in Systemic Lupus Erythematosus and Lupus Nephritis through Integrated Microbiome and Metabolome Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-08 11:58:40","doi":"10.21203/rs.3.rs-5045051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-18T20:18:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-11T21:24:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199115254202575848433524480440309589656","date":"2025-03-07T06:57:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31881329105113069683402216627087034018","date":"2025-03-06T17:55:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240382077063021736100276291653822063484","date":"2025-02-10T17:40:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-03T09:21:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282818273742266853898461748229873232491","date":"2024-10-06T23:23:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84537672452665899452605882212723850190","date":"2024-10-02T14:41:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-02T06:40:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-02T06:33:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-26T15:16:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-23T12:35:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2024-09-23T12:34:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8c0adcc-e553-428f-ac5f-43f6b270d6b7","owner":[],"postedDate":"April 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T16:05:03+00:00","versionOfRecord":{"articleIdentity":"rs-5045051","link":"https://doi.org/10.1186/s12866-025-03995-5","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-05-07 15:57:21","publishedOnDateReadable":"May 7th, 2025"},"versionCreatedAt":"2025-04-08 11:58:40","video":"","vorDoi":"10.1186/s12866-025-03995-5","vorDoiUrl":"https://doi.org/10.1186/s12866-025-03995-5","workflowStages":[]},"version":"v1","identity":"rs-5045051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5045051","identity":"rs-5045051","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}