Integrated network pharmacology and bioinformatics analysis reveals MME as key target of Notoginsenoside R1 in diabetic nephropathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41622156", "kind": "science-record", "revision": 3, "content_hash": "sha256:0f012d50f133147a1bb5a4a9fb5aad40c441d2d3e3fb5e932ea12f60c8253c9e", "created_at": "2026-11-01T20:31:44Z", "updated_at": "2026-11-01T22:15:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated network pharmacology and bioinformatics analysis reveals MME as key target of Notoginsenoside R1 in diabetic nephropathy.", "doi": "10.1186/s12906-026-05272-y", "pmid": "41622156", "authors": ["Xinyu Gan", "Mingzhu Liang", "Halinuer Shadekejiang", "Huan Hong", "Jinyu Lu", "Xu Li", "Xueqin Zhang", "Chen Lu"], "journal": "BMC complementary medicine and therapies", "year": 2026, "abstract": "BACKGROUND: Diabetic nephropathy (DN) is a major diabetes complication and a primary cause of end-stage renal failure. Notoginsenoside R1 (NGR1) is known to reduce proteinuria, exert hypoglycemic effects, and enhance renal function in DN patients. However, the exact mechanisms by which NGR1 affects DN are not well understood.\nMETHODS: An integrative approach combining multi-omics bioinformatics and experimental validation was employed. We analyzed GEO datasets (GSE30122, GSE96804) to identify differentially expressed genes and performed WGCNA to define key modules. Network pharmacology predicted NGR1 targets, which were refined via machine learning (LASSO and Random Forest). Immune infiltration was assessed by CIBERSORT. Molecular docking and dynamics simulations evaluated binding interactions. In vitro functional validation used high glucose (HG)-injured MPC5 podocytes, with MME-specific inhibitor (Thiorphan), confirming target necessity.\nRESULTS: Bioinformatic analysis identified three core targets, MME, PTGS2, and S100A9, within the DN pathological network. Functional enrichment revealed their involvement in oxidative stress detoxification, TGF-β-mediated fibrosis, and AGE-RAGE signaling pathways. Immune infiltration analysis indicated an aberrant increase in M0/M2 macrophages and activated mast cells, alongside a reduction in protective γδ T cells, with core targets showing strong correlations with specific immune subsets. Subsequent molecular docking and dynamics simulations indicated that NGR1 exhibited the strongest binding affinity for MME, among the three targets. Corroborating these findings, in vitro experiments confirmed that NGR1 specifically upregulates MME expression transcriptionally and translationally. Crucially, NGR1 counteracted HG-induced oxidative stress, fibrosis, and inflammation. These protective effects were largely abolished upon MME inhibition, demonstrating MME's essential role in this process.\nCONCLUSION: This study suggests that NGR1 may exert renoprotective effects in DN potentially through MME modulation, which appears to mitigate oxidative stress, inflammation, and fibrosis. The findings indicate MME could serve as a promising therapeutic target, providing preliminary mechanistic insights for developing targeted DN therapies.", "claims": [{"id": "claim-method", "statement": "Intersecting genes were analyzed using machine learning techniques, specifically the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) algorithms, to distinguish DN from non-DN healthy controls.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41622156", "locator": "sections[8] \"Identification of hub genes by machine learning\" paragraph 2", "quote": "Intersecting genes were analyzed using machine learning techniques, specifically the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) algorithms, to distinguish DN from non-DN healthy controls."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data analyzed in this study are accessible via the aforementioned websites.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41622156", "locator": "sections[38] \"Data availability\" paragraph 2", "quote": "All data analyzed in this study are accessible via the aforementioned websites."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Bioinformatic analysis identified three core targets, MME, PTGS2, and S100A9, within the DN pathological network.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41622156", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Bioinformatic analysis identified three core targets, MME, PTGS2, and S100A9, within the DN pathological network."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Bioinformatic analysis identified three core targets, MME, PTGS2, and S100A9, within the DN pathological network.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41622156"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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Cell culture Mouse Podocyte Clone-5 (MPC5) cells were obtained"], "order_basis": "paper_text_order:3078", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["ence & Technology Co., Ltd. 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