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<p>Understanding human blood metabolites is essential for deciphering systemic physiology and disease mechanisms, yet remains challenging due to diverse origins and dynamic regulation. In this study, we develop HUBMet (<a href="https://hubmet.app.bio-it.tech/home">https://hubmet.app.bio-it.tech/home</a>), an open-access web server that includes 3,950 metabolites and 129,814 metabolite-protein associations, with four analytical modules: Over-Representation Analysis (ORA) for enrichment analysis; Metabolite Set Enrichment Analysis (MSEA) for quantitative data analysis; Tissue Specificity Analysis (TSA) for assessing metabolite-tissue relevance; Metabolite-Protein Network Analysis (MPNet) for identifying key metabolite-protein associations and functional modules. HUBMet’s utility is demonstrated through a COVID-19 case study revealing metabolic signatures associated with disease severity.</p>

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HUBMet: an integrative database and analytical platform for human blood metabolites and metabolite-protein associations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月27日

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  • Xingyue Wang,
  • Xiangyu Qiao,
  • Alberto Zenere,
  • Swapnali Barde,
  • Jing Wang,
  • Wen Zhong

摘要

Understanding human blood metabolites is essential for deciphering systemic physiology and disease mechanisms, yet remains challenging due to diverse origins and dynamic regulation. In this study, we develop HUBMet (https://hubmet.app.bio-it.tech/home), an open-access web server that includes 3,950 metabolites and 129,814 metabolite-protein associations, with four analytical modules: Over-Representation Analysis (ORA) for enrichment analysis; Metabolite Set Enrichment Analysis (MSEA) for quantitative data analysis; Tissue Specificity Analysis (TSA) for assessing metabolite-tissue relevance; Metabolite-Protein Network Analysis (MPNet) for identifying key metabolite-protein associations and functional modules. HUBMet’s utility is demonstrated through a COVID-19 case study revealing metabolic signatures associated with disease severity.