Biomarkers For Systemic Lupus Erythematosus Research Articles

ITRAQ-based mass spectrometry screen to identify serum biomarkers in systemic lupus erythematosus

ObjectiveSystemic lupus erythematosus (SLE) is a complex systemic autoimmune disorder with no reliable serum biomarkers currently available other than autoantibodies.MethodsIn the present study, isobaric tags for relative and absolute quantitation-based...

Targeted metabolomics combined with machine learning to identify and validate new biomarkers for early SLE diagnosis and disease activity

BackgroundThe early diagnosis of systemic lupus erythematosus (SLE) and the assessment of disease activity progression remain a great challenge. Targeted metabolomics has great potential to identify new biomarkers of SLE. MethodsSerum from 44 healthy participants and 89 SLE patients were analyzed using HM400 high-throughput targeted metabolomics. Machine learning (ML) with seven learning models and trained the model several times iteratively selected the two best prediction model in a competitive way, which were independent validated by enzyme-linked immunosorbent (ELISA) with 90 SLE patients. ResultsIn this study, 146 differential metabolites, most of them organic acids, amino acids, and bile acids, were detected between patients with initial SLE and healthy participants, and 8 potential biomarkers were found by intersection of ML and statistics (area under the curve [AUC] > 0.95) showing a significant positive correlation with clinical indicators. In addition, we identified and validated 2 potential biomarkers for SLE classification (P < 0.05, AUC > 0.775; N-Methyl-L-glutamic acid, L-2-aminobutyric acid) showing a significant correlation with the SLE Disease Activity Index. These differential metabolites were mainly involved in metabolic pathways, amino acid biosynthesis, 2-oxocarboxylic acid metabolism and other pathways. ConclusionThis study indicated that the tricarboxylic acid cycle might be associated with SLE drug therapy. We identified 8 diagnostic models biomarkers and 2 biomarkers that could be used to identify initial SLE and distinguish different activity degree, which will promote the development of new tools for the diagnosis and evaluation of SLE.

Serum Uric Acid Combined with Homocysteine as a Predictive Biomarker of Lupus Nephritis.

Serum uric acid (UA) and homocysteine (Hcy) are potential biomarkers of systemic lupus erythematosus (SLE). In this study, the expressions of UA and Hcy in SLE patients and the predictive value of these two parameters for lupus nephritis (LN) were studied. A total of 476 SLE patients were recruited to this case-control study, of which 176 SLE patients diagnosed with LN and 300 without LN. Serum UA and Hcy levels were analyzed. Multivariate logistic regression analysis was used to evaluate the relationship between serum UA and Hcy and LN. The receiver operating characteristic (ROC) curves were used to predict the role of combination of serum UA and Hcy in LN. We found that serum UA and Hcy levels in SLE patients with LN were significantly higher than those in controls (p<0.05). Multivariate logistic regressions showed that serum UA (OR+=+1.003, 95+% CI: 1.001-1.006, p+=+0.003), apolipoprotein B (Apo B) (OR+=+21.361, 95+% CI: 2.312-195.373, p+=+0.007) and Hcy (OR+=+1.042, 95+% CI: 1.011-1.080, p+=+0.014) were independent markers of LN. Combined serum UA and Hcy revealed a better result (AUC+=+0.718, 95+% CI: 0.670-0.676, p<0.001) in prediction of LN compared to that of the serum UA (AUC+=+0.710) and Hcy (AUC+=+0.657) independently. In conclusion, serum UA and Hcy could be predictive biomarkers of LN, and joint detection of serum UA and Hcy might be useful in the clinical setting.

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

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

Mapping theme evolution and identifying hotspots in biomarkers of systemic lupus erythematosus based on global research.

Objective: To perform a bibliometric analysis in the field of biomarkers for systemic lupus erythematosus. Methods: Publications were from Web of Science. Microsoft Excel, VOSviewer, Science Mapping Analysis software Tool, CiteSpace and Tableau were used for analysis. Results: A total of 1112 publications were identified; 1503 institutions from 69 countries contributed, with the highest outputs from China and Karolinska University Hospital. Petri had a tremendous impact. Academic collaborations were localized. Lupus and Arthritis & Rheumatology were the top two journals in terms of publications and citations. Lymphocyte, autoantibody, type I interferon, genetic polymorphisms and urinary biomarkers have been high-frequency themes. Conclusion: Global collaboration needs to be further strengthened. Immune cell, cytokine and gene-level research as a whole and noninvasive tests are the future trends.

LncRNA SNHG1 serves as a biomarker for systemic lupus erythematosus and participates in the disease progression.

LncRNAs play an important role in autoimmune diseases. The purpose of this study was to explore the role of lncRNA SNHG1 in systemic lupus erythematosus (SLE), and laid a theoretical foundation for the study of SLE. The basic clinical information of all subjects was first collected for statistical analysis, and SNHG1 expression in the serum of all subjects was detected by RT-qPCR. The value of SNHG1 in the diagnosis of SLE was assessed by ROC. The correlation between SNHG1 and each blood sample index was analyzed by Pearson correlation analysis. The role of SNHG1 in primary peripheral blood mononuclear cells (PBMCs) apoptosis was explored. SNHG1 expression is relatively upregulated in patients with SLE compared to healthy people. SNHG1 expression was positively correlated with SLEDAI score, IgG, CRP, and ESR, and negatively correlated with C3 and C4. ROC indicated that SNHG1 has the potential to assistin the diagnosis of SLE. PBMCs apoptosis in SLE was higher than that in control group, the knockdown and overexpression of SNHG1 could correspondingly inhibit and promote PBMCs apoptosis. SNHG1 has the potential to be a diagnosis marker for SLE and may be involved in regulating PBMCs apoptosis.

Serum long non-coding Ribonucleic Acid H19 serves as a biomarker for systemic lupus erythematosus and participates in the disease progression.

This study aimed to investigate the expression of H19 and its possible molecular mechanism in systemic lupus erythematosus (SLE). The expression of H19 and miR-19b in serum and peripheral blood mononuclear cells (PBMCs) were detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Receiver operator characteristic (ROC) curve was constructed to evaluate the diagnostic value of serum H19 in SLE. Pearson correlation coefficient was used to analyze the correlation between serum levels of H19 and miR-19b. Flow cytometry and Cell counting kit-8 (CCK-8) assay were performed to detect cell apoptosis and viability. The levels of pro-inflammatory and anti-inflammatory factors were measured by enzyme-linked immunosorbent assay (ELISA). Luciferase reporter gene assay was conducted to verify the interaction between H19 and miR-19b. The expression of H19 and miR-19b in SLE group were up-regulated and down-regulated, respectively. Serum H19 has certain clinical diagnostic value in SLE. In in vitro studies, overexpression of H19 can significantly inhibit the viability of PBMCs and promote apoptosis and inflammatory response of PBMCs by interacting with miR-19b. The expression of H19 is upregulated in patients with SLE and plays a role in cell function and inflammation by targeting miR-19b in PBMCs, which may be one of the pathological mechanisms of SLE.

Biomarkers in Systemic Lupus Erythematosus along with Metabolic Syndrome.

Metabolic syndrome (MetS) is a group of physiological abnormalities characterized by obesity, insulin resistance (IR), and hypertriglyceridemia, which carry the risk of developing cardiovascular disease (CVD) and type 2 diabetes (T2D). Immune and metabolic alterations have been observed in MetS and are associated with autoimmune development. Systemic lupus erythematosus (SLE) is an autoimmune disease caused by a complex interaction of environmental, hormonal, and genetic factors and hyperactivation of immune cells. Patients with SLE have a high prevalence of MetS, in which elevated CVD is observed. Among the efforts of multidisciplinary healthcare teams to make an early diagnosis, a wide variety of factors have been considered and associated with the generation of biomarkers. This review aimed to elucidate some primary biomarkers and propose a set of assessments to improve the projection of the diagnosis and evolution of patients. These biomarkers include metabolic profiles, cytokines, cardiovascular tests, and microRNAs (miRs), which have been observed to be dysregulated in these patients and associated with outcomes.

Comparison of Low-Dose Interleukin 2 Therapy in Conjunction With Standard Therapy in Patients With Systemic Lupus Erythematosus vs Rheumatoid Arthritis: A Systematic Review.

This systematic review aims to compare the efficacy and safety of a novel immunotherapy with low-dose interleukin 2 (IL2) across two of the most prevalent autoimmune diseases i.e. systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA). Contemporary therapeutic practices have not been able to achieve complete remission from these autoimmune disorders. In contrast, low-dose IL2has shown promise in achieving this therapeutic goal via inducing self-tolerance in patients with autoimmune diseases; however, due to variable irregularities among autoimmune processes of variable diseases, the benefit of low-dose IL2 could not be determined among different autoimmune diseases. Therefore, we conducted a study to compare low-dose IL2 therapy effects on SLE and RA. We systematically screened four databases: PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed Central (PMC), and Google Scholar. Inclusion and exclusion criteria were implemented. Quality appraisal of studies chosen for the review was done using the Cochrane Risk-of-Bias (RoB) assessment tool for randomized controlled trials, and the Newcastle-Ottawa Scale (NOS) and JBI critical appraisal tool for non-randomized clinical trials. Information was gathered from seven articles: three randomized controlled trials and four non-randomized clinical trials. Our review concluded that low-dose IL2 therapy in conjunction with respective standard therapies for SLE and RA has a higher efficacy and safety profile as compared to standard therapy alone and the therapeutic effects were comparable in both SLE and RA patients treated with low-dose IL2; however, this novel intervention does not seem to have a significant corrective effect on the biomarkers of RA as it does for SLE biomarkers.

Identification of EPSTI1 as a new potential biomarker for SLE based on GEO database.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with highly heterogeneous. The aim of this study is to find the key genes in peripheral blood mononuclear cells (PBMCs) of SLE patients and to provide a new direction for the diagnosis and treatment of lupus. GSE121239, GSE50772, GSE81622, and GSE144390 mRNA expression profiles were obtained from the website of Gene Expression Omnibus (GEO), and differential expressed genes (DEGs) analysis was performed by R. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to elucidate signaling pathways for the DEGs. Real-time qPCR (RT-qPCR) was used to verify the key gene EPSTI1 in PBMCs of SLE patients. Finally, the correlation analysis and ROC curve analysis of EPSTI1 for SLE were performed. A total of 12 upregulated DEGs were identified, including MMP8, MX1, IFI44, EPSTI1, OAS1, OAS3, HERC5, IFIT1, RSAD2, USP18, IFI44L, and IFI27. GO and KEGG pathway enrichment analysis showed that those DEGs were mainly concentrated in the response to virus and IFN signaling pathways. Real-time qPCR (RT-qPCR) revealed that EPSTI1 was increased in PBMCs of SLE. EPSTI1 was positively correlated with SLEDAI score in SLE patients. Besides, EPSTI1 was positively correlated with T cell activation- or differentiation-associated genes (BCL6 and RORC). Furthermore, ROC analyses proved EPSTI1 may have diagnostic value for SLE. Together, EPSTI1 was found to be a potential biomarker for SLE, closely related to T cell immune imbalance. Key Points • EPSTI1 expression was significantly increased in PBMCs of SLE patients. • EPSTI1 was positively correlated with disease activity and T cell activation- or differentiation-associated genes in SLE patients. • EPSTI1 might have a good diagnostic value for SLE.

Serum Advanced Glycation End Products and Their Soluble Receptor as New Biomarkers in Systemic Lupus Erythematosus.

It has been postulated that advanced glycation end products (AGEs) and their soluble receptor (sRAGE) may play a relevant role as inducers in the chronic inflammatory pathway in various conditions, among them, in immune-mediated diseases such as systemic lupus erythematosus (SLE). However, previous studies show conflicting results about their association with SLE characteristics and their usefulness as disease biomarkers. We aimed to study the association of specific serum AGEs (pentosidine, Nξ-(carboxymethyl)lysine (CML), Nξ-(carboxyethyl)lysine (CEL)), sRAGE levels and AGEs (specific serum AGEs and skin AGEs) to sRAGE ratios with various disease parameters, in order to clarify their potential as new biomarkers in SLE and to study their relationship with cardiovascular disease (CVD). To this aim, serum pentosidine, CML, CEL and sRAGE were measured via ELISA, and skin AGEs levels were measured by skin autofluorescence. Correlations of pentosidine levels with demographic and clinical data, indexes of activity, accrual damage and patient-reported outcomes were analyzed through multiple linear regression models, while correlations of the rest of the AGEs, sRAGE and AGE to sRAGE ratios (non-normal) were analyzed using both an OLS regression model and a GML. All of the analyses were adjusted for confounders. A total of 119 SLE patients were recruited. Serum AGEs and sRAGEs were significantly associated with SLE activity indexes and/or demographic or disease characteristics: pentosidine with pulmonary manifestations; CML with anti-dsDNA antibodies, IL-6, disease duration and non-Caucasian ethnicities; CEL with anti-dsDNA antibodies, IL-6 and accumulated number of manifestations; and sRAGE with male gender, photosensitivity and being on specific immunosuppressants. These results suggest that the AGE-sRAGE axis may serve as a novel biomarker for managing and prognosticating this disease. Its correlation with certain antibodies, demographics and disease presentations may indicate a distinct clinical phenotype associated with varying levels of AGEs and/or sRAGE. The significance of specific AGE/sRAGE ratios, introduced in this study for the first time, warrants additional investigation in forthcoming research. Our study did not confirm the link between serum AGEs and CVD, which merits further exploration through studies designed for this specific purpose.

Role of Advanced Glycation End Products as New Biomarkers in Systemic Lupus Erythematosus.

Advanced glycation end-products (AGEs) may play a relevant role as inducers in the chronic inflammatory pathway present in immune-mediated diseases, such as systemic lupus erythematosus (SLE). AGEs concentrations have been associated, with discrepant results to date, with some parameters such as disease activity or accrual damage, suggesting their potential usefulness as biomarkers of the disease. Our objectives are to confirm differences in AGEs levels measured by cutaneous autofluorescence between SLE patients and healthy controls (HC) and to study their correlation with various disease parameters. Cross-sectional study, where AGEs levels were measured by skin autofluorescence, and SLE patients' data were compared with those of sex- and age-matched HC in a 1:3 proportion through a multiple linear regression model. Associations of AGEs levels with demographic and clinical data were analyzed through ANOVA tests. Both analyses were adjusted for confounders. AGEs levels in SLE patients were significantly higher than in HC (p < 0.001). We found statistically significant positive associations with SLE disease activity index (SLEDAI) and damage index (SDI), physician and patient global assessment, C-reactive protein, leukocyturia, complement C4, IL-6 and oral ulcers. We also found a negative statistically significant association with current positivity of anti-nuclear and anti-Ro60 antibodies. AGEs seem to have a contribution in LES pathophysiology, being associated with activity and damage and having a role as a new management and prognosis biomarker in this disease. The association with specific antibodies and disease manifestations may indicate a specific clinical phenotype related to higher or lower AGEs levels.

Investigating the causal relationship and potential shared diagnostic genes between primary biliary cholangitis and systemic lupus erythematosus using bidirectional Mendelian randomization and transcriptomic analyses.

The co-occurrence of primary biliary cholangitis (PBC) and systemic lupus erythematosus (SLE) has been consistently reported in observational studies. Nevertheless, the underlying causal correlation between these two conditions still needs to be established. We performed a bidirectional two-sample Mendelian randomization (MR) study to assess their causal association. Five MR analysis methods were utilized for causal inference, with inverse-variance weighted (IVW) selected as the primary method. The Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and the IVW Radial method were applied to exclude outlying SNPs. To assess the robustness of the MR results, five sensitivity analyses were carried out. Multivariable MR (MVMR) analysis was also employed to evaluate the effect of possible confounders. In addition, we integrated transcriptomic data from PBC and SLE, employing Weighted Gene Co-expression Network Analysis (WGCNA) to explore shared genes between the two diseases. Then, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment methods to perform on the shared genes. The Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm was utilized to identify potential shared diagnostic genes. Finally, we verified the potential shared diagnostic genes in peripheral blood mononuclear cells (PBMCs)-specific cell populations of SLE patients by single-cell analysis. Our MR study provided evidence that PBC had a causal relationship with SLE (IVW, OR: 1.347, 95% CI: 1.276 - 1.422, P < 0.001) after removing outliers (MR-PRESSO, rs35464393, rs3771317; IVW Radial, rs11065987, rs12924729, rs3745516). Conversely, SLE also had a causal association with PBC (IVW, OR: 1.225, 95% CI: 1.141 - 1.315, P < 0.001) after outlier correction (MR-PRESSO, rs11065987, rs3763295, rs7774434; IVW Radial, rs2297067). Sensitivity analyses confirmed the robustness of the MR findings. MVMR analysis indicated that body mass index (BMI), smoking and drinking were not confounding factors. Moreover, bioinformatic analysis identified PARP9, ABCA1, CEACAM1, and DDX60L as promising diagnostic biomarkers for PBC and SLE. These four genes are highly expressed in CD14+ monocytes in PBMCs of SLE patients and potentially associated with innate immune responses and immune activation. Our study confirmed the bidirectional causal relationship between PBC and SLE and identified PARP9, ABCA1, CEACAM1, and DDX60L genes as the most potentially shared diagnostic genes between the two diseases, providing insights for the exploration of the underlying mechanisms of these disorders.

Complement C4d as a biomarker for systemic lupus erythematosus and lupus nephritis.

Background: Increasing studies in the last decade have led to the widespread understanding that C4d, a split product of complement component 4 (C4), is a potential biomarker for systemic lupus erythematosus (SLE) and lupus nephritis (LN).Purpose: The aim of this review is to summarize the highlights of studies investigating the use of C4d as a biomarker for diagnosing and monitoring SLE and LN patients.Data collection: we searched PubMed/Medline and Wanfang databases using the terms "C4d and systemic lupus erythematosus", "C4d and lupus nephritis", and "Complement C4d".Results: The deposition of C4d on circulating blood cells has been shown in several clinical studies to be a potential diagnostic marker that can be used to monitor patients with SLE. In addition, C4d deposits on circulating blood cells may be a helpful diagnostic marker for LN, one of the most severe complications of SLE. Meanwhile, studies utilizing renal biopsy specimens have indicated that C4d deposition in the renal peritubular capillaries of LN patients may predict more severe LN or a worse patient prognosis. Generally, a high plasma C4d level and a high plasma C4d/C4 ratio may also be promising indicators that can be used to monitor patients with SLE and LN.Conclusions: C4d detection may be a novel strategy for further clinical prediction and therapy.

M6A-Associated Long Non-Coding RNA as a Potential Diagnostic Biomarker for Systemic Lupus Erythematosus

Objective: To identify novel diagnostic biomarkers for Systemic Lupus Erythematosus (SLE) by selecting m6A-associated long non-coding RNAs (lncRNAs) through bioinformatics analysis.Methods: Multiple SLE datasets were obtained from the Gene Expression Omnibus (GEO). The datasets were analyzed for m6A-related genes and lncRNAs. Differentially expressed m6A-associated lncRNAs were further investigated, and mRNAs co-expressed with these lncRNAs were subjected to KEGG pathway analysis, followed by ROC curve plotting. Results: A correlation was observed between six lncRNAs (ANKRD10-IT1, C2orf27A, EGOT, HCG18, POLR2J4, TCL6) and six m6A methyltransferases (RBM15, HNRNPA2B1, ZC3H13, YTHDF2, RBM15B, LRPPRC). The differential analysis indicated that lncRNA POLR2J4 and lncRNA ANKRD10-IT1 were significantly downregulated in SLE patients. ROC curve showed that lncRNA POLR2J4 had an AUC of 0.799 and lncRNA ANKRD10-IT1 had an AUC of 0.516. KEGG analysis revealed that proteins co-expressed with lncRNA POLR2J4 were enriched in T-cell receptor signaling pathways, Toll-like receptor signaling pathways, mTOR signaling pathways, and other SLE-related pathways. Those co-expressed with lncRNA ANKRD10-IT1 were involved in PI3K-Akt, mTOR, and cell apoptosis pathways.Conclusion: lncRNA POLR2J4 may serve as a potential diagnostic biomarker for SLE.

علاقة الانترلوكينات 37 و40 مع داء الذئبة الحمامية الجهازية في نساء مرضى عراقيات

The primary cause of systemic lupus erythematosus (SLE) is autoantibodies to self-antigens. Interleukin 37 (IL-37) and Interleukin 40 (IL-40) are important cytokines which may play a role in regulating inflammation and involved in pathogenesis of autoimmune diseases like SLE. This study was aimed the interleukin levels (IL-37 and IL-40) during systemic lupus erythematous (SLE) development in Iraqi female patients. The present study was conducted on 100 female SLE patients and 100 healthy female controls, with mean ages of patients and controls )32.85 ± 0.99 and 32.05 ± 0.91)years, respectively. Erythrocytes Sedimentation Rate (ESR) and C-reactive protein (CRP) were investigated in this study. Enzyme-linked immune sorbent assay(ELISA) kits examined levels of interleukins in the serum of patients and healthy controls. The results found that IL-37 levels in SLE patients were lower than in controls(23.39 ±0.92ng/mL vs48.02 ±0.46ng/mL), respectively, with a statistically significant. While serum IL-40 levels appeared to have a higher significant in patients compared with the healthy control (9.98 ±0.32ng/mL vs4.16 ±0.05ng/mL) respectively. Also, ESR and CRP was high significant in patients compared with healthy controls. In conclusion, these findings suggest that IL-40 and IL-37 could be used as a biomarker for SLE.

Helios as a Potential Biomarker in Systemic Lupus Erythematosus and New Therapies Based on Immunosuppressive Cells.

The Helios protein (encoded by the IKZF2 gene) is a member of the Ikaros transcription family and it has recently been proposed as a promising biomarker for systemic lupus erythematosus (SLE) disease progression in both mouse models and patients. Helios is beginning to be studied extensively for its influence on the T regulatory (Treg) compartment, both CD4+ Tregs and KIR+/Ly49+ CD8+ Tregs, with alterations to the number and function of these cells correlated to the autoimmune phenomenon. This review analyzes the most recent research on Helios expression in relation to the main immune cell populations and its role in SLE immune homeostasis, specifically focusing on the interaction between T cells and tolerogenic dendritic cells (tolDCs). This information could be potentially useful in the design of new therapies, with a particular focus on transfer therapies using immunosuppressive cells. Finally, we will discuss the possibility of using nanotechnology for magnetic targeting to overcome some of the obstacles related to these therapeutic approaches.

Altered expression of miR-125a and dysregulated cytokines in systemic lupus erythematosus: Unveiling diagnostic and prognostic markers.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder impacting multiple organs, influenced by genetic factors, especially those related to the immune system. However, there is a need for new biomarkers in SLE. MicroRNA-125a (miR-125a) levels are decreased in T cells, B cells, and dendritic cells of SLE patients. MiR-125a plays a regulatory role in controlling the levels of tumor necrosis factor-alpha (TNF-α) and interleukin 12 (IL-12), which are crucial pro-inflammatory cytokines in SLE pathogenesis. To assess the levels of miR-125a, IL-12, and TNF-α in SLE patients' plasma, evaluating their diagnostic and prognostic value. The study included 100 healthy individuals, 50 newly diagnosed (ND), and 50 SLE patients undergoing treatment. The patients were monitored for a duration of 24 wk to observe and record instances of relapses. MiR-125a expression was measured using real-time reverse transcription polymerase chain reaction, while ELISA kits were used to assess IL-12 and TNF-α production. The results showed significantly reduced miR-125a expression in SLE patients compared to healthy individuals, with the lowest levels in ND patients. TNF-α and IL-12 expression levels were significantly elevated in SLE patients, especially in the early stages of the disease. Receiver operating characteristic curve analyses, and Cox-Mantel Log-rank tests indicated miR-125a, TNF-α, and IL-12 as proper diagnostic biomarkers for SLE. A negative correlation was found between plasma miR-125a expression and IL-12/TNF-α levels in SLE patients. Decreased miR-125a levels may be involved in the development of SLE, while elevated levels of IL-12 and TNF-α contribute to immune dysregulation. These findings offer new diagnostic and prognostic markers for SLE. Moreover, the negative correlation observed suggests an interaction between miR-125a, TNF-α, and IL-12. Further research is necessary to uncover the underlying mechanisms that govern these relationships.

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning.

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women's health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females. Stool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females. Both alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value. This study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

Novel insights into molecular signatures and pathogenic cell populations shared by systemic lupus erythematosus and vascular dementia.

Although vascular dementia (VD) and systemic lupus erythematosus (SLE) may share immune-mediated pathophysiologic processes, the underlying mechanisms are unclear. This study investigated shared gene signatures in SLE versus VD, as well as their potential molecular mechanisms. Bulk RNA sequencing (RNAseq) and single-cell or single-nucleus RNAseq (sc/snRNAseq) datasets from SLE blood samples and VD brain samples were obtained from Gene Expression Omnibus. The identification of genes associated with both SLE and VD was performed using the weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. For the sc/snRNAseq data, an unbiased clustering pipeline based on Seurat and CellChat was used to determine the cellular landscape profile and examine intracellular communication, respectively. The results were subsequently validated using a mice model of SLE with cognitive dysfunction (female MRL/lpr mice). WGCNA and machine learning identified C1QA, LY96, CD163, and MS4A4A as key genes for SLE and VD. sc/snRNAseq analyses revealed that CD163 and MS4A4A were upregulated in mononuclear phagocytes (MPs) from SLE and VD samples and were associated with monocyte-macrophage differentiation. Intriguingly, LGALS9-associated molecular pathway, as the only signaling pathway common between SLE and VD via CellChat analysis, exhibited significant upregulation in cortical microglia of MRL/lpr mice. Our analyses identified C1QA, LY96, CD163, and MS4A4A as potential biomarkers for SLE and VD. Moreover, the upregulation of CD163/MS4A4A and activation of LGALS9 signaling in MPs may contribute to the pathogenesis of VD with SLE. These findings offer novel insight into the mechanisms underlying VD in SLE patients.