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Purpose <p>High-resolution liquid chromatography–mass spectrometry (HR-LC-MS/MS) enables comprehensive metabolite profiling of medicinal plants. This study aimed to characterize the metabolomes of four stress-relieving and cognitive-enhancing plants, <i>Centella asiatica</i>,<i> Lavandula angustifolia</i>,<i> Herpestis monnieri</i>, and <i>Canscora decussata</i>, and explore whether their similar therapeutic effects arise from shared or distinct chemical architectures.</p> Methods <p>A dual acquisition approach combining Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) was applied in both positive and negative ionization modes. Metabolites were annotated using targeted and untargeted workflows. Selected prominent metabolites were further analyzed using in-silico network pharmacology, linking metabolites to protein targets, pathways, and disease associations via SuperPred, UniProt, and DisGeNET databases.</p> Results <p>The integrated DDA–DIA strategy identified 100 metabolites, including species-specific bioactive compounds. Minimal overlap was observed among plants, with only four metabolites shared between pairs. Heatmaps, PCA, and Venn diagrams confirmed distinct metabolomic fingerprints. Network pharmacology analysis revealed that prominent metabolites modulate multiple targets involved in neuronal signaling, DNA repair, immune regulation, and metabolic pathways, highlighting multi-target pharmacological convergence. Notably, neuroactive genes such as MAOA, ACHE, GRIA2, SLC6A5, and NTRK3 were commonly targeted, supporting the traditional use of these plants in cognitive enhancement, stress relief, and neuroprotection.</p> Conclusions <p>Despite distinct metabolomic profiles, the four plants converge functionally at the pathway level, demonstrating that similar therapeutic effects can emerge via multi-target, chemically diverse mechanisms. This study provides a robust framework for integrated metabolomic and network pharmacology analyses, offering mechanistic insight, quality control guidance, and a foundation for future pharmacological validation of medicinal plants.</p>

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Understanding the metabolome of four stress-relieving and cognitive-enhancing medicinal plants: a comprehensive HR-LC-ESI-MS/MS analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Muhammad Noman Khan,
  • Syed Muhammad Zaki Shah,
  • Saeedur Rahman,
  • Syed Ghulam Musharraf

摘要

Purpose

High-resolution liquid chromatography–mass spectrometry (HR-LC-MS/MS) enables comprehensive metabolite profiling of medicinal plants. This study aimed to characterize the metabolomes of four stress-relieving and cognitive-enhancing plants, Centella asiatica, Lavandula angustifolia, Herpestis monnieri, and Canscora decussata, and explore whether their similar therapeutic effects arise from shared or distinct chemical architectures.

Methods

A dual acquisition approach combining Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) was applied in both positive and negative ionization modes. Metabolites were annotated using targeted and untargeted workflows. Selected prominent metabolites were further analyzed using in-silico network pharmacology, linking metabolites to protein targets, pathways, and disease associations via SuperPred, UniProt, and DisGeNET databases.

Results

The integrated DDA–DIA strategy identified 100 metabolites, including species-specific bioactive compounds. Minimal overlap was observed among plants, with only four metabolites shared between pairs. Heatmaps, PCA, and Venn diagrams confirmed distinct metabolomic fingerprints. Network pharmacology analysis revealed that prominent metabolites modulate multiple targets involved in neuronal signaling, DNA repair, immune regulation, and metabolic pathways, highlighting multi-target pharmacological convergence. Notably, neuroactive genes such as MAOA, ACHE, GRIA2, SLC6A5, and NTRK3 were commonly targeted, supporting the traditional use of these plants in cognitive enhancement, stress relief, and neuroprotection.

Conclusions

Despite distinct metabolomic profiles, the four plants converge functionally at the pathway level, demonstrating that similar therapeutic effects can emerge via multi-target, chemically diverse mechanisms. This study provides a robust framework for integrated metabolomic and network pharmacology analyses, offering mechanistic insight, quality control guidance, and a foundation for future pharmacological validation of medicinal plants.