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基于气相色谱-离子迁移谱的初步研究：挥发性代谢谱的分析与应用

Analysis and application of volatile metabolic profiles of : a preliminary GC-IMS-based study.

作者信息

Zheng Yunwei, Li Fuxing, Zhao Chuwen, Zhu Junqi, Fang Youling, Hang Yaping, Hu Longhua

机构信息

Department of Clinical Laboratory, Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University Minde Road No. 1 Nanchang 330006 Jiangxi China

School of Public Health, Nanchang University Nanchang Jiangxi China.

出版信息

RSC Adv. 2024 Aug 13;14(35):25316-25328. doi: 10.1039/d4ra03601h. eCollection 2024 Aug 12.

DOI:10.1039/d4ra03601h
PMID:39139224
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11320052/
Abstract

Nosocomial infections caused by () may pose serious risks to patients, and early identification of pathogenic bacteria and drug sensitivity results can improve patient prognosis. In this study, we clarified the composition and relative content of volatile organic compounds (VOCs) generated by in tryptic soy broth (TSB) using gas chromatography-ion mobility spectrometry (GC-IMS). We explored whether imipenem (IPM) could be utilized to differentiate between carbapenem-sensitive (CSEC) and carbapenem-resistant (CREC). The results revealed that 36 VOCs (alcohols, aldehydes, acids, esters, ketones, pyrazines, heterocyclic compounds, and unknown compounds) were detected using GC-IMS. Besides, the results indicated that changes in the relative content of VOCs as well as changes in the signal intensity of fingerprints were able to assess the growth state of bacteria during bacterial growth and help identify . Lastly, under selective pressure of IPM, volatile fingerprints of could be employed as a model to distinguish CSEC from CREC strains.

摘要

由（）引起的医院感染可能会给患者带来严重风险，早期识别病原菌和药敏结果可改善患者预后。在本研究中，我们使用气相色谱-离子迁移谱（GC-IMS）阐明了在胰蛋白胨大豆肉汤（TSB）中由（）产生的挥发性有机化合物（VOCs）的组成和相对含量。我们探讨了亚胺培南（IPM）是否可用于区分碳青霉烯敏感（CSEC）和碳青霉烯耐药（CREC）（）。结果显示，使用GC-IMS检测到36种VOCs（醇类、醛类、酸类、酯类、酮类、吡嗪类、杂环化合物和未知化合物）。此外，结果表明VOCs相对含量的变化以及指纹图谱信号强度的变化能够在细菌生长过程中评估细菌的生长状态，并有助于识别（）。最后，在IPM的选择压力下，（）的挥发性指纹图谱可作为区分CSEC和CREC菌株的模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/f1bf8aa2c819/d4ra03601h-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/8e31f27bd18a/d4ra03601h-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/f120cf3a81f7/d4ra03601h-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/147ec28c0e2f/d4ra03601h-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/635d00dd0b8b/d4ra03601h-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/f1bf8aa2c819/d4ra03601h-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/8e31f27bd18a/d4ra03601h-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/f120cf3a81f7/d4ra03601h-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/147ec28c0e2f/d4ra03601h-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/635d00dd0b8b/d4ra03601h-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7816/11320052/f1bf8aa2c819/d4ra03601h-f5.jpg

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