Department of Solid State Engineering, University of Chemistry and Technology, 16628, Prague, Czech Republic.
Center of Bioscience and Bioengineering, Siberian State Medical University, 2 Moskovsky Trakt, Tomsk, 634050, Russia; Research School of Chemical and Biomedical Engineering, Tomsk Polytechnic University, Lenin Ave. 30, Tomsk, 634050, Russia.
Anal Chim Acta. 2024 Nov 15;1329:343118. doi: 10.1016/j.aca.2024.343118. Epub 2024 Aug 16.
Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative need for expeditious and precise early detection methods underscores their role in facilitating timely intervention, curbing the propagation of antibiotic resistance, and enhancing patient outcomes.
This study introduces the utilization of surface-enhanced Raman spectroscopy (SERS) in tandem with machine learning (ML) for the sensitive 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 without preliminary purification or isolation the created fragments were captured by functional SERS substrates. Based on subsequent SERS measurements, a database was created for the training and validation of ML. Method validation was performed using separately measured spectra, which did not overlap with the database used for ML training. To check the efficiency of recognising the target fragment, control experiments involved bacterial plasmids containing different resistance genes, the use of inappropriate enzymes, or the absence of plasmid.
SERS-ML allowed express detection of bacterial plasmids containing a characteristic gene fragment up to the 10 concentration of the initial plasmid, despite the complex composition of the biological sample, including the presence of interfering plasmids. Our approach offers a promising alternative to existing methods for monitoring antibiotic-resistant bacteria, characterized by its simplicity, low detection limit, and the potential for rapid and straightforward analysis.
抗生素耐药性是一个严重的医学问题，在常用的β-内酰胺类抗生素中尤为明显。迫切需要快速、准确的早期检测方法，这突显了它们在促进及时干预、遏制抗生素耐药性传播和改善患者预后方面的作用。
本研究介绍了表面增强拉曼光谱（SERS）与机器学习（ML）的结合在敏感检测负责抗生素耐药性出现和传播的特征基因片段中的应用。为了使检测过程更接近实际情况，我们使用细菌质粒作为起始生物对象，其中包含或不包含特征基因片段（比例高达 1:10），编码β-内酰胺类抗生素耐药性。质粒经过酶消化，在没有初步纯化或分离的情况下，将产生的片段捕获到功能化的 SERS 基底上。基于随后的 SERS 测量，创建了一个用于 ML 训练和验证的数据库。方法验证使用了未与用于 ML 训练的数据库重叠的单独测量的光谱。为了检查识别目标片段的效率，控制实验涉及含有不同耐药基因的细菌质粒、使用不合适的酶或不存在质粒的情况。
SERS-ML 允许在复杂的生物样本组成（包括存在干扰质粒的情况下）下，对含有特征基因片段的细菌质粒进行快速检测，浓度低至初始质粒的 10 倍。我们的方法为监测抗生素耐药菌提供了一种有前途的替代方法，其特点是简单、检测限低，并且具有快速、直接分析的潜力。
