GC–MS metabolomic dataset of Zingiber officinale and Zingiber cassumunar essential oils with antibacterial activity

IF 2.4 Q2 Chemistry
Chemical Data Collections Pub Date : 2026-02-01 Epub Date: 2025-12-01 DOI:10.1016/j.cdc.2025.101217
Hendri Wasito , Amelia Rizqa Fauziyah , Aprilia Nur Tasfiyati , Harris Antonius , Yuli Widyastuti , Abdi Wira Septama
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引用次数: 0

Abstract

Antibiotic resistance poses a global health challenge, driving the search for novel antibacterial agents. Plant-derived essential oils offer promising potential due to their diverse bioactive constituents. This study investigates the essential oils of Zingiber cassumunar and Zingiber officinale, traditionally recognized for their medicinal properties. Metabolite profiling was conducted using a non-targeted gas chromatography–mass spectrometry (GC–MS) metabolomics approach combined with chemometrics, while antibacterial activity against Escherichia coli, Salmonella typhi, Shigella sonnei, and Bacillus cereus was assessed via the broth microdilution method. The synergistic effect with tetracycline was evaluated using the checkerboard assay. A total of 169 metabolites were detected, with 85 putatively annotated. Z. officinale oil demonstrated stronger antibacterial activity (MIC 7.8–15.6 μg/mL) and synergistic interaction with tetracycline (FICI 0.61). These findings highlight the potential of Zingiber essential oils as alternative antibacterial agents and underscore the utility of metabolomics for elucidating their bioactive profiles.
具有抗菌活性的药用生姜和香薷精油的GC-MS代谢组学数据集
抗生素耐药性是一个全球性的健康挑战,促使人们寻找新的抗菌剂。植物源性精油因其多种生物活性成分而具有广阔的应用前景。本研究研究了传统上公认具有药用价值的木香姜和药用姜的精油。采用非靶向气相色谱-质谱(GC-MS)代谢组学方法结合化学计量学进行代谢物分析,同时通过肉汤微量稀释法评估其对大肠杆菌、伤寒沙门氏菌、索尼氏志贺氏菌和蜡样芽孢杆菌的抗菌活性。采用棋盘法评价其与四环素的协同作用。共检测到169种代谢物,其中85种推定注释。山茱萸油具有较强的抗菌活性(MIC值7.8 ~ 15.6 μg/mL),与四环素具有协同作用(FICI值0.61)。这些发现突出了生姜精油作为替代抗菌剂的潜力,并强调了代谢组学在阐明其生物活性谱方面的效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chemical Data Collections
Chemical Data Collections Chemistry-Chemistry (all)
CiteScore
6.10
自引率
0.00%
发文量
169
审稿时长
24 days
期刊介绍: Chemical Data Collections (CDC) provides a publication outlet for the increasing need to make research material and data easy to share and re-use. Publication of research data with CDC will allow scientists to: -Make their data easy to find and access -Benefit from the fast publication process -Contribute to proper data citation and attribution -Publish their intermediate and null/negative results -Receive recognition for the work that does not fit traditional article format. The research data will be published as ''data articles'' that support fast and easy submission and quick peer-review processes. Data articles introduced by CDC are short self-contained publications about research materials and data. They must provide the scientific context of the described work and contain the following elements: a title, list of authors (plus affiliations), abstract, keywords, graphical abstract, metadata table, main text and at least three references. The journal welcomes submissions focusing on (but not limited to) the following categories of research output: spectral data, syntheses, crystallographic data, computational simulations, molecular dynamics and models, physicochemical data, etc.
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