ArticleJournal of cellular and molecular medicine2020
CMap analysis identifies Atractyloside as a potential drug candidate for type 2 diabetes based on integration of metabolomics and transcriptomics.
Article in Journal of cellular and molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Comprehensive bioinformatic analysis identifies potential therapeutic drugs for CryAB (R120G)-related cardiomyopathy.BMC cardiovascular disorders · 2025Article
- HRMS Characterization and Antioxidant Evaluation of Costa Rican Spent Coffee Grounds as a Source of Bioactive Polyphenolic Extracts.Foods (Basel, Switzerland) · 2025Article
- Atractyloside inhibits gefitinib‑resistant non‑small‑cell lung cancer cell proliferation.Oncology letters · 2024Article
- NADPH Dynamics: Linking Insulin Resistance and β-Cells Ferroptosis in Diabetes Mellitus.International journal of molecular sciences · 2023Review
- Small molecule metabolites: discovery of biomarkers and therapeutic targets.Signal transduction and targeted therapy · 2023Review
- Construction of a Prognostic Model for Predicting Colorectal Cancer Prognosis and Response to Immunotherapy Based on Cuproptosis-Associated lncRNAs.Journal of oncology · 2023Article
- Significance of traditional herbal medicine for dyslipidemia.American journal of translational research · 2023Review
- Pyroptosis-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with UCEC.Molecular therapy. Nucleic acids · 2022Article
- Tandem mass tag-based proteomic profiling revealed potential therapeutic targets and mechanisms of liraglutide for the treatment of impaired glucose tolerance.Frontiers in endocrinology · 2022Article
- Screening for Potential Therapeutic Agents for Non-Small Cell Lung Cancer by Targeting Ferroptosis.Frontiers in molecular biosciences · 2022Article
- Connectivity Mapping Using a NovelFrontiers in molecular neuroscience · 2022Article
- CMap analysis identifies Atractyloside as a potential drug candidate for type 2 diabetes based on integration of metabolomics and transcriptomics.Journal of cellular and molecular medicine · 2020Article
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Authors and funding
10 authors.
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Abstract
backgroundThis research aimed at exploring the mechanisms of alterations of metabolites and pathways in T2D from the perspective of metabolomics and transcriptomics, as well as uncovering novel drug candidate for T2D treatment.
methodsMetabolites in human plasma from 42 T2D patients and 45 non-diabetic volunteers were detected by liquid chromatography-mass spectrometer (LC-MS). Microarray dataset of the transcriptome was obtained from Gene Expression Omnibus (GEO) database. Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to conduct pathway enrichment analysis. Connectivity Map (CMap) was employed to select potential drugs for T2D therapy. In vivo assay was performed to verify above findings. The protein expression levels of ME1, ME2 and MDH1 were detected by Western blot to determine the status of NAD/NADH cofactor system.
resultsIn our study, differentially expressed metabolites were selected out between healthy samples and T2D samples with selection criteria P value < .05, |Fold Change| > 2, including N-acetylglutamate and Malate. Genes set enrichment analysis (GSEA) revealed that 34 pathways were significantly enriched in T2D. Based on CMap analysis and animal experiments, Atractyloside was identified as a potential novel drug for T2D treatment via targeting ME1, ME2 and MDH1 and regulating the NAD/NADH cofactor system.
conclusionThe present research revealed differentially expressed metabolites and genes, as well as significantly altered pathways in T2D via an integration of metabolomics, transcriptomics and CMap analysis. It was also demonstrated that comprehensive analysis based on metabolomics and transcriptomics was an effective approach for identification and verification of metabolic biomarkers and alternated pathways.
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