Project description:The gold standard for diagnosing lupus nephritis, one of the common symptoms of systemic lupus erythematosus, is an invasive procedure, a Renal biopsy. To overcome the risk of invasive methods, we constructed a spectral library using biological samples that can be obtained non-invasively and analyzed it using the SWATH-MS method, a quantitative proteomic analysis.
2023-10-24 | PXD040099 | Pride
Project description:Biopanning of phage-displayed peptides
Project description:Transcriptional modules were used as a basis for the selection of biomarkers and the development of a multivariate transcriptional indicator of disease progression in patients with systemic lupus erythematosus.
Project description:Gene expression profile studies have identified an interferon signature in whole blood or mononuclear cell samples from patients with systemic lupus erythematosus. This study was designed to determine whether specific lymphocyte and myeloid subsets freshly isolated from the blood of systemic lupus erythematosus patients demonstrated unique gene expression profiles compared to subsets isolated from healthy controls. Experiment Overall Design: The entire study included 67 samples. One CEL file was not available for SLE13_CD4
Project description:Transcriptional profiling of peripheral blood CD8+ T cells from juvenile systemic lupus erythematosus (JSLE) patients and healthy controls.
Project description:<p><strong>Background:</strong> Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE), underscoring an urgent need for non-invasive diagnostic biomarkers. </p><p><strong>Objective: </strong>This study aimed to define the metabolomic signature of urinary extracellular vesicles (uEVs) in LN and to identify novel biomarkers for precision diagnosis.</p><p><strong>Methods:</strong> We isolated uEVs from 29 patients with LN, 22 SLE patients without nephritis, and 20 healthy controls. uEVs were characterized according to MISEV guidelines, and their metabolomes were profiled using untargeted liquid chromatography–tandem mass spectrometry (LC–MS/MS). Differential metabolites were analyzed through bioinformatics and a random forest machine-learning algorithm to construct a diagnostic model. </p><p><strong>Results:</strong> Among 897 identified metabolites, 284 were significantly dysregulated in LN. A random forest model prioritized a ten-metabolite panel. Three metabolites—Glucosylsphingosine, PE-NMe, and PC(20:5/TXB2)—exhibited outstanding diagnostic performance, with area under the curve (AUC) values of 0.912, 0.906, and 0.897, respectively, for distinguishing LN from non-renal SLE. </p><p><strong>Conclusion:</strong> We identified a distinct uEV metabolic signature in LN and developed a robust, non-invasive biomarker panel. This strategy holds significant promise for the early detection and personalized management of LN, offering a compelling alternative to invasive renal biopsy.</p>