Supplementary datasets for: Integrated metabolomics and network pharmacology to decipher habitat-driven metabolic diversity and α-glucosidase inhibitory mechanisms in guava leaves

Published: 28 July 2026| Version 1 | DOI: 10.17632/dnnvkddzdg.1
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Description

This dataset contains all supplementary data files (Supplementary Data S1–S6) supporting our study on the chemical profiling, bioactive marker prioritization, and network pharmacology analysis of Psidium guajava. The contents of the individual files are as follows: - Supplementary Data S1: Raw quantitative data for total phenolic content (TPC), total flavonoid content (TFC), antioxidant capacity (ABTS), and α-glucosidase inhibition, along with Pearson correlation analysis across different origins. - Supplementary Data S2: Comprehensive phenolic metabolomics dataset, including the full list of 1,230 phenolic features, 753 differentially accumulated metabolites (DAMs), and 147 prioritized candidate bioactives. - Supplementary Data S3: Disease-related target set retrieved from GeneCards and OMIM databases, and the 226 overlapping intersection targets between bioactive compounds and disease targets. - Supplementary Data S4: Predicted target profiles for the candidate bioactive compounds obtained via SwissTargetPrediction. - Supplementary Data S5: Protein-protein interaction (PPI) network topological parameters (degree, betweenness centrality, and closeness centrality) and screened core target nodes. - Supplementary Data S6: Full output tables for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses.

Files

Steps to reproduce

The dataset was generated and analyzed following the experimental protocols detailed in the accompanying manuscript: 1. Quantitative Assays (Data S1): Spectrophotometric determination of TPC, TFC, ABTS radical scavenging capacity, and α-glucosidase inhibition assays. 2. Metabolomics (Data S2): LC-MS/MS profiling of Psidium guajava extracts, KNN imputation using MetaboAnalyst 6.0, OPLS-DA modeling, and tiered statistical prioritization. 3. Network Pharmacology (Data S3–S5): Bioactive compound targets retrieved via SwissTargetPrediction (Homo sapiens); disease targets acquired from GeneCards (relevance score ≥ 5) and OMIM; PPI network constructed using STRING (confidence score ≥ 0.400) and visualized/analyzed in Cytoscape 3.10.2. 4. Functional Enrichment (Data S6): Gene Ontology (GO) and KEGG pathway enrichment analyses executed using DAVID with Benjamini-Hochberg adjusted p < 0.05.

Institutions

Categories

Food Analysis, Metabolomics, Network Pharmacology

Funders

  • Beijing Natural Science Foundation
    Grant ID: IS24049
  • Department of Science and Technology of Yunnan Province
    Grant ID: 202402AA310032
  • China Biodiversity Conservation and Green Development Foundation
    Grant ID: 2025

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