UPLC-MS/MS-based plasma lipidomics reveal a distinctive signature in systemic lupus erythematosus patients

Exploring genetic associations in systemic lupus erythematosus through Mendelian randomization: implications for novel biomarkers and therapeutic targets

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by a significant health burden. There is an essential need for novel biomarkers and therapeutic targets to improve diagnosis and management. Mendelian randomization (MR) was applied to explore causal links between SLE and various biomarkers like immune cells, metabolites, and inflammatory cytokines using multiple databases. Initially, biomarkers significantly associated with SLE were identified. Bidirectional MR helped clarify these relationships, and a two-step mediation MR examined their effects on SLE risk. Intersection analysis was used to identify biomarkers with consistent effects across datasets. Four biomarkers were identified as having significant associations with SLE risk: 1-palmitoyl-2-arachidonoyl-GPI levels [odds ratio (OR), 1.379; 95% confidence interval (CI), 1.180 to 1.613; FDR, 0.046], IL-17A levels (OR, 2.197; 95% CI, 1.412 to 3.418; FDR, 0.044), N-acetyl-aspartyl-glutamate (NAAG) levels (OR, 0.882; 95% CI, 0.831 to 0.936; FDR, 0.030), and ribitol levels (OR, 0.743; 95% CI, 0.644 to 0.857; FDR, 0.012). Bidirectional MR showed an inverse effect of NAAG on IL-17A levels (OR, 0.978; 95% CI, 0.962 to 0.994; p = 0.006). Mediation analysis indicated that NAAG influenced SLE risk both directly (beta =  - 0.108) and indirectly through IL-17A (beta =  - 0.018), highlighting the potential mediating role of IL-17A. After expanding the significance criteria to p < 0.05, intersection analysis across multiple datasets revealed 29 biomarkers with consistent beta directions, including 19 potential risk factors (beta > 0) and 10 protective factors (beta < 0) for SLE. This research has revealed significant genetic associations with SLE and demonstrated that IL-17A mediates the relationship between NAAG levels and SLE risk, highlighting potential new targets for personalized therapeutic interventions. Key Points • This study employs MR to identify significant genetic associations between various biomarkers and SLE, providing novel insights into potential biomarkers and therapeutic targets. • Four key biomarkers were identified as significantly associated with SLE risk: 1-palmitoyl-2-arachidonoyl-GPI, IL-17A, N-acetyl-aspartyl-glutamate (NAAG), and ribitol. • The findings suggest that NAAG levels have a protective effect against SLE, partly mediated through IL-17A, indicating a complex interplay between these biomarkers in the pathogenesis of SLE. • Intersectional analysis across multiple datasets revealed 29 biomarkers with consistent effects on SLE risk, highlighting new directions for future research and potential personalized therapeutic strategies.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

Genetically predicted 486 blood metabolites concerning risk of systemic lupus erythematosus: a Mendelian randomization study

Metabolic abnormalities constitute a significant characteristic of systemic lupus erythematosus (SLE). We utilised a two-sample Mendelian randomisation (MR) study to evaluate the potential causal association between 486 blood metabolites and SLE. Exposure data at the metabolite level were extracted from 7824 European Genome-wide association studies (GWAS). Preliminary analysis utilised SLE GWAS data from FinnGen. The primary method for causal analysis relied on random inverse variance weighting (IVW). To ensure robustness, sensitivity analyses included the Cochran Q test, MR-Egger intercept test, MR-PRESSO, and leave-one-out analysis. Steiger testing and linkage disequilibrium score regression were employed to validate the identified metabolites. This study identified 12 metabolites, comprising six known chemical structures: 1,5-anhydroglucitol(1,5-AG) [odds ratio (OR) = 0.100, 95% confidence interval (CI): 0.015-0.773, P = 0.027), gamma-glutamylthreonine (OR = 0.077, 95% CI: 0.010-0.574, P = 0.012), 5-dodecenoate(12:1n7) (OR = 0.205, 95% CI: 0.061-0.685, P = 0.010), linoleoylglycerophosphoethanolamine * (OR = 0.159, 95% CI: 0.027-0.933, P = 0.044), erythrose (OR = 88.331,95% CI:1.098-63.214, P = 0.040) and 1-, adrenate (22:4n6) (OR = 9.876, 95% CI: 1.753-55.639, P = 0.001)]. Additionally, we found associations between SLE and six unknown chemical structures: X-06351 (OR = 0.071, 95% CI: 0.006-0.817, P = 0.034), X-10810 (OR = 4.268 95% CI: 1.260-14.459, P = 0.020), X-11412 (OR = 5.418 95% CI: 1.068-27.487, P = 0.041), X-11905 (OR = 0.551, 95%CI: 0.304-0.997, P = 0.049), X-12038 (OR = 0.178 95%CI: 0.032-0.988, P = 0.045), X-12217 (OR = 0.174 95%CI: 0.044-0.680, P = 0.014). This study offers evidence supporting a causal relationship between SLE and 12 circulating metabolites, six of which have known chemical structures and six that remain unidentified. These findings introduce a new perspective for further exploration of SLE mechanisms.

Mechanistic insight into premature atherosclerosis and cardiovascular complications in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is associated with a significant risk of cardiovascular disease (CVD), which substantially increases disease mortality and morbidity. The overall mechanisms associated with the development of premature atherosclerosis and CVD in SLE remain unclear, but has been considered as a result of an intricate interplay between the profound immune dysregulation and traditional CVD risk factors. Aberrant systemic inflammation in SLE may lead to an abnormal lipid profile and dysfunction, which can further fuel the pro-atherosclerotic environment. The existence of a strong imbalance between endothelial damage and vascular repair/angiogenesis promotes vascular injury, which is the early step in the progression of atherosclerotic CVD. Profound innate and adaptive immune dysregulation, characterized by excessive type I interferon burden, aberrant macrophage, platelet and complements activation, neutrophil dysregulation and neutrophil extracellular traps formation, uncontrolled T cell activation, and excessive autoantibody production and immune complex formation, have been proposed to promote accelerated CVD in SLE. While designing targeted therapies to correct the dysregulated immune activation may be beneficial in the treatment of SLE-related CVD, much additional work is needed to determine how to translate these findings into clinical practice. Additionally, a number of biomarkers display diagnostic potentials in improving CVD risk stratification in SLE, further prospective studies will help understand which biomarker(s) will be the most impactful one(s) in assessing SLE-linked CVD. Continued efforts to identify novel mechanisms and to establish criteria for assessing CVD risk as well as predicting CVD progression are in great need to improve CVD outcomes in SLE.

Glycolysis in Innate Immune Cells Contributes to Autoimmunity

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

UPLC-MS/MS-based plasma lipidomics reveal a distinctive signature in systemic lupus erythematosus patients

Global lipidomics is of considerable utility for exploring altered lipid profiles and unique diagnostic biomarkers in diseases. We aim to apply ultra-performance liquid chromatography-tandem mass spectrometry to characterize the lipidomics profile in systemic lupus erythematosus (SLE) patients and explore the underlying pathogenic pathways using the lipidomics approach. Plasma samples from 18 SLE patients, 20 rheumatoid arthritis (RA) patients, and 20 healthy controls (HC) were collected. A total of 467 lipids molecular features were annotated from each sample. Orthogonal partial least square-discriminant analysis, K-mean clustering analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated disrupted lipid metabolism in SLE patients, especially in phospholipid, glycerol, and sphingolipid metabolism. The area under curve (AUC) of lipid metabolism biomarkers was better than SLE inflammation markers that ordinarily used in the clinic. Proposed model of monoglyceride (MG) (16:0), MG (18:0), phosphatidylethanolamine (PE) (18:3-16:0), PE (16:0-20:4), and phosphatidylcholine (PC) (O-16:2-18:3) yielded AUC 1.000 (95% CI, 1.000-1.000), specificity 100% and sensitivity 100% in the diagnosis of SLE from HC. A panel of three lipids molecular PC (18:3-18:1), PE (20:3-18:0), PE (16:0-20:4) permitted to accurately diagnosis of SLE from RA, with AUC 0.921 (95% CI, 0.828-1.000), 70% specificity, and 100% sensitivity. The plasma lipidomics signatures could serve as an efficient and accurate tool for early diagnosis and provide unprecedented insight into the pathogenesis of SLE.