Surface Enhanced Raman Spectroscopy and Machine Learning for Identification of Beta-Lactam Antibiotics Resistance Gene Fragment in Bacterial Plasmid

Background: The appearance of antibiotic-resistant bacteria represents a critical medical problem with high risk to patient health. Therefore, simple, express, and reliable methods of antibiotic resistance detection should be developed. Results: In this work, we propose a combination of highly sensitive surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) for the detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and the created fragments were captured by functional SERS substrates without preliminary (bio)samples separation or purification. Based on subsequent SERS measurements, a database was created for the training and validation of ML. Significance: The reliability of the proposed method was tested on control samples and we showed the possibility of express SEPS-ML detection of bacterial plasmids containing a characteristic gene up to the 10-7 concentration of the initial plasmid, despite the complex composition of the biological sample (i.e. the presence of the excess of alternative plasmids or various biomolecules). The proposed approach provides a good alternative to modern methods for monitoring antibiotic-resistant bacteria and is favored by its simplicity, low detection limit, and the possibility of express and unpretentious analysis.背景：耐抗生素细菌的出现是威胁患者健康的重大医学难题，因此亟待开发简便、快速且可靠的抗生素耐药性检测方法。 结果：本研究提出将高灵敏度表面增强拉曼光谱（surface-enhanced Raman spectroscopy, SERS）与机器学习（machine learning, ML）相结合，用于检测介导抗生素耐药性产生与传播的特征基因片段。为使检测流程更贴近实际应用场景，本研究以携带或不携带编码β-内酰胺类抗生素耐药性特征基因片段的细菌质粒作为初始生物样本，二者比例最高可达1:10。对上述质粒进行酶切处理后，所得片段可直接被功能化SERS基底捕获，无需预先对生物样本进行分离或纯化操作。基于后续的SERS检测数据，构建了用于机器学习模型训练与验证的数据集。 意义：本研究通过对照样本验证了所提方法的可靠性，结果表明，即便生物样本成分复杂（如存在过量的其他质粒或各类生物分子），仍可实现对携带特征基因的细菌质粒的快速SERS-ML检测，其检测下限可达初始质粒浓度的10^-7量级。本研究提出的方法为当前耐抗生素细菌监测手段提供了优质替代方案，其优势在于操作简便、检测限低，且可实现快速且无需复杂前处理的分析检测。