Lancaster Environment Centre, Lancaster University , Lancaster LA1 4YQ, United Kingdom.
School of Pharmacy and Biomedical Sciences, University of Central Lancashire , Preston PR1 2HE, United Kingdom.
Anal Chem. 2017 Sep 19;89(18):9814-9821. doi: 10.1021/acs.analchem.7b01765. Epub 2017 Aug 28.
Overusage of antibiotics leads to the widespread induction of antibiotic-resistance genes (ARGs). Developing an approach to allow real-time monitoring and fast prediction of ARGs dynamics in clinical or environmental samples has become an urgent matter. Vibrational spectroscopy is potentially an ideal technique toward the characterization of the microbial composition of microbiota as it is nondestructive, high-throughput, and label-free. Herein, we employed attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectroscopy and developed a spectrochemical tool to quantify the static and dynamic composition of kanamycin resistance in artificial microbiota to evaluate microbial antibiotic resistance. Second-order differentiation was introduced in identifying the spectral biomarkers, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was used for the multivariate analysis of the entire spectral features employed. The calculated results of the mathematical dispersion model coupled with PCA-LDA showed high similarity to the designed microbiota structure, with no significant difference (P > 0.05) in the static treatments. Moreover, our model successfully predicted the dynamics of kanamycin resistance within artificial microbiota under kanamycin pressures. This work lends new insights into the potential role of spectrochemical analyses in investigating the existence and trends of antibiotic resistance in microbiota.
抗生素的过度使用导致了抗生素耐药基因（ARGs）的广泛诱导。开发一种能够实时监测和快速预测临床或环境样本中 ARGs 动态的方法已经成为当务之急。振动光谱学是一种很有前途的技术，可以用于研究微生物群落的微生物组成，因为它是非破坏性的、高通量的和无标记的。在此，我们采用衰减全反射傅里叶变换红外（ATR-FT-IR）光谱，并开发了一种光谱化学工具来定量人工微生物群落中卡那霉素耐药的静态和动态组成，以评估微生物的抗生素耐药性。二阶导数用于识别光谱生物标志物，主成分分析（PCA）和线性判别分析（LDA）用于对整个光谱特征进行多元分析。计算得到的数学离散模型与 PCA-LDA 的结果与设计的微生物群落结构高度相似，静态处理之间没有显著差异（P>0.05）。此外，我们的模型成功预测了卡那霉素压力下人工微生物群落中卡那霉素耐药性的动态变化。这项工作为光谱化学分析在研究微生物群中抗生素耐药性的存在和趋势方面的潜在作用提供了新的见解。
