The antibiotic colistin is regarded as the final line of defense for treating infections caused by Gram-negative bacteria. The combination of Raman spectroscopy (RS) with diverse machine learning methods has helped unravel the complexity of various microbiology problems. This approach offers a culture-free, rapid, and objective tool for identifying antimicrobial resistance (AMR). In this study, we employed the combinatorial approach of machine learning and RS to identify a novel spectral marker associated with phosphoethanolamine modification in the lipid A moiety of colistin-resistant Gram-negative Escherichia coli. The visible spectral fingerprints of this marker have been validated using partial least squares regression and discriminant analysis. The origin of the spectral feature was confirmed through hyperspectral imaging and K-means clustering of a single bacterial cell. The chemical structure of the modified lipid A moiety was verified by employing gold standard MALDI-TOF mass spectrometry. Our findings support the futuristic applicability of this spectroscopic marker in objectively identifying colistin-sensitive and -resistant strains.

中文翻译：

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验证磷酸乙醇胺修饰作为粘菌素耐药性的潜在光谱标志物


抗生素粘菌素被认为是治疗革兰氏阴性菌引起的感染的最后一道防线。拉曼光谱 （RS） 与各种机器学习方法的结合有助于解开各种微生物学问题的复杂性。这种方法为识别抗菌素耐药性 （AMR） 提供了一种无需培养的快速和客观工具。在这项研究中，我们采用机器学习和 RS 的组合方法来确定一种与粘菌素耐药革兰氏阴性大肠杆菌脂质 A 部分中磷酸乙醇胺修饰相关的新型光谱标志物。该标记物的可见光谱指纹已使用偏最小二乘回归和判别分析进行了验证。通过高光谱成像和单个细菌细胞的 K-means 聚类证实了光谱特征的来源。采用金标准 MALDI-TOF 质谱法验证改性脂质 A 部分的化学结构。我们的研究结果支持这种光谱标记物在客观识别粘菌素敏感和耐药菌株方面的未来适用性。