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蛋白质组学对细菌抗生素反应的见解:一篇综述

Proteomic Insights into Bacterial Responses to Antibiotics: A Narrative Review.

作者信息

Aita Sara Elsa, Ristori Maria Vittoria, Cristiano Antonio, Marfoli Tiziana, De Cesaris Marina, La Vaccara Vincenzo, Cammarata Roberto, Caputo Damiano, Spoto Silvia, Angeletti Silvia

机构信息

Operative Research Unit of General Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy.

Operative Research Unit of Laboratory, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy.

出版信息

Int J Mol Sci. 2025 Jul 27;26(15):7255. doi: 10.3390/ijms26157255.

DOI:10.3390/ijms26157255
PMID:40806388
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12346982/
Abstract

Antimicrobial resistance is an escalating global threat that undermines the efficacy of modern antibiotics and places a substantial economic burden on healthcare systems-costing Europe alone over EUR 11.7 billion each year due to rising medical expenses and productivity losses. While genomics and transcriptomics have significantly advanced our understanding of the genetic foundations of resistance, they often fail to capture the dynamic, real-time adaptations that enable bacterial survival. Proteomics, particularly mass spectrometry-based strategies, bridges this gap by uncovering the functional protein-level changes that drive resistance, persistence, and tolerance under antibiotic pressure. In this review, we examine how proteomic approaches provide new insights into resistance mechanisms across various antibiotic classes, with a particular focus on β-lactams, aminoglycosides, and fluoroquinolones, highlighting clinically relevant pathogens, especially members of the ESKAPE group. Finally, we examine future directions, including the integration of proteomics with other omic technologies and the growing role of artificial intelligence in resistance prediction, paving the way for more predictive, personalized, and effective solutions to combat antimicrobial resistance.

摘要

抗菌药物耐药性是一个不断升级的全球威胁,它削弱了现代抗生素的疗效,并给医疗系统带来了巨大的经济负担——仅欧洲每年就因医疗费用上涨和生产力损失而花费超过117亿欧元。虽然基因组学和转录组学极大地推进了我们对抗药遗传基础的理解,但它们往往无法捕捉到使细菌得以存活的动态实时适应性变化。蛋白质组学,尤其是基于质谱的策略,通过揭示在抗生素压力下驱动耐药性、持续性和耐受性的功能性蛋白质水平变化,填补了这一空白。在本综述中,我们研究蛋白质组学方法如何为各类抗生素的耐药机制提供新见解,特别关注β-内酰胺类、氨基糖苷类和氟喹诺酮类,突出临床相关病原体,尤其是ESKAPE组的成员。最后,我们探讨未来的方向,包括蛋白质组学与其他组学技术的整合以及人工智能在耐药性预测中日益重要的作用,为对抗抗菌药物耐药性提供更具预测性、个性化和有效的解决方案铺平道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/12346982/6541c4375095/ijms-26-07255-g005.jpg
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本文引用的文献

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An integrated proteo-transcriptomics approach reveals novel drug targets against multidrug resistant .一种整合的蛋白质组学-转录组学方法揭示了针对多药耐药性的新型药物靶点。
Front Microbiol. 2025 Feb 25;16:1531739. doi: 10.3389/fmicb.2025.1531739. eCollection 2025.
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Adaptation of Escherichia coli to ciprofloxacin and enrofloxacin: Differential proteomics of the SOS response and RecA-independent mechanisms.
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Int J Antimicrob Agents. 2025 Feb;65(2):107420. doi: 10.1016/j.ijantimicag.2024.107420. Epub 2024 Dec 30.
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Predicting drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra.利用人工智能和临床 MALDI-TOF 质谱预测药物耐药性。
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