THE METABOLOMICS SIGNATURE ASSOCIATED WITH RESPONSIVENESS TO STEROID THERAPY IN FOCAL SEGMENTAL GLOMERULOSCLEROSIS: A PILOT STUDY.

BACKGROUND: Focal segmental glomerulosclerosis (FSGS) is considered one of the most severe glomerular diseases and around 80% of cases are resistant to steroid treatment. Since a large proportion of steroid-resistant (SR) FSGS patients progress to end-stage renal disease, other therapeutic strategies may benefit this population. However, identification of non-invasive biomarkers to predict this high-risk population is needed. OBJECTIVE: We aimed to identify the biomarker candidates to distinguish SR from steroid-sensitive (SS) patients using metabolomics approach and to identify the possible molecular mechanism of resistance. METHODS: Urine was collected from biopsy-proven FSGS patients eligible for monotherapy with prednisolone. Patients were followed for 6-8 weeks and categorized as SS or SR. Metabolite profile of urine samples was analyzed by one-dimensional 1H-nuclear magnetic resonance (1H-NMR). Predictive biomarker candidates and their diagnostic importance impaired molecular pathways in SR patients, and the common target molecules between biomarker candidates and drug were predicted. RESULTS: Homovanillic acid, 4-methylcatechol, and tyrosine were suggested as the significant predictive biomarker candidates, while L-3,4-dihydroxyphenylalanine, norepinephrine, and gentisic acid had high accuracy as well. Tyrosine metabolism was the most important pathway that is perturbed in SR patients. Common targets of the action of biomarker candidates and prednisolone were molecules that contributed in apoptosis. CONCLUSION: Urine metabolites including homovanillic acid, 4-methylcatechol, and tyrosine may serve as potential non-invasive predictive biomarkers for evaluating the responsiveness of FSGS patients.

The metabolomics signature associated with responsiveness to steroid therapy in focal segmental glomerulosclerosis: a pilot study

Abstract Background Focal segmental glomerulosclerosis (FSGS) is considered one of the most severe glomerular diseases and around 80% of cases are resistant to steroid treatment. Since a large proportion of steroid-resistant (SR) FSGS patients progress to end-stage renal disease, other therapeutic strategies may benefit this population. However, identification of non-invasive biomarkers to predict this high-risk population is needed. Objective We aimed to identify the biomarker candidates to distinguish SR from steroid-sensitive (SS) patients using metabolomics approach and to identify the possible molecular mechanism of resistance. Methods Urine was collected from biopsy-proven FSGS patients eligible for monotherapy with prednisolone. Patients were followed for 6-8 weeks and categorized as SS or SR. Metabolite profile of urine samples was analyzed by one-dimensional 1H-nuclear magnetic resonance (1H-NMR). Predictive biomarker candidates and their diagnostic importance impaired molecular pathways in SR patients, and the common target molecules between biomarker candidates and drug were predicted. Results Homovanillic acid, 4-methylcatechol, and tyrosine were suggested as the significant predictive biomarker candidates, while L-3,4-dihydroxyphenylalanine, norepinephrine, and gentisic acid had high accuracy as well. Tyrosine metabolism was the most important pathway that is perturbed in SR patients. Common targets of the action of biomarker candidates and prednisolone were molecules that contributed in apoptosis. Conclusion Urine metabolites including homovanillic acid, 4-methylcatechol, and tyrosine may serve as potential non-invasive predictive biomarkers for evaluating the responsiveness of FSGS patients.

[Prediction of Molecular Signature, Potential Biomarkers, and Molecular Pathways Associated with Membranous Nephropathy Based on Protein Protein Interactions].

Background: Membranous nephropathy (MN) is one of the causes of nephrotic syndrome in adults that lead to end-stage renal disease with an unknown molecular signature. The current diagnosis is based on renal biopsy, which is an invasive method and has several complications and challenges. Thus, identification of the novel biomarker candidates, as well as impaired pathways, will be helpful for non-invasive molecular-based diagnosis. Objectives: We aimed to study the molecular signature of MN and facilitate the systematic discovery of diagnostic candidate biomarkers, molecular pathway, and potential therapeutic targets using bioinformatics predictions. Methods: The protein-protein interaction (PPI) network of an integrated list of downloaded microarray data, differential proteins from a published proteomic study, and a list of retrieved scientific literature mining was constructed and analyzed in terms of functional modules, enriched biological pathways, hub genes, master regulator, and target genes. Results: These network analyses revealed several functional modules and hub genes including Vitamin D3 receptor, retinoic acid receptor RXR-alpha, interleukin 8, and SH3GL2. TEAD4 and FOXA1 were identified as the regulatory master molecules. LRP1 and ITGA3 were identified as the important target genes. Extracellular matrix organization, cell surface receptor signaling pathway, and defense and inflammatory response were found to be impaired in MN using functional analyses. A specific subnetwork for MN was suggested using PPI approach. Discussion: Omics data integration and systems biology analysis on the level of interaction networks provide a powerful approach for identification of pathway-specific biomarkers for MN.