Bonner Mitchell, Barrera Patiño Claudia P, Borsatto Andrew Ramos, Soares Jennifer M, Blanco Kate C, Bagnato Vanderlei S
Biomedical Engineering, Texas A&M University, 400 Bizzell St, College Station, TX 77843, USA.
Sao Carlos Institute of Physics, University of Sao Paulo, IFSC-USP, Sao Carlos 13566-590, SP, Brazil.
Antibiotics (Basel). 2025 Aug 15;14(8):831. doi: 10.3390/antibiotics14080831.
BACKGROUND/OBJECTIVES: The progression of antibiotic resistance is increasingly recognized as a dynamic and time-dependent phenomenon, challenging conventional diagnostics that define resistance as a binary trait.
Biomolecules have fingerprints in Fourier-transform infrared spectroscopy (FTIR). The targeting of specific molecular groups, combined with principal component analysis (PCA) and machine learning algorithms (ML), enables the identification of bacteria resistant to antibiotics.
In this work, we investigate how effective classification depends on the use of different numbers of principal components, spectral regions, and defined resistance thresholds. Additionally, we explore how the time-dependent behavior of certain spectral regions (different biomolecules) may demonstrate behaviors that, independently, do not capture a complete picture of resistance development. FTIR spectra were obtained from exposed to azithromycin, trimethoprim/sulfamethoxazole, and oxacillin at sequential time points during resistance induction. Combining spectral windows substantially improved model performance, with accuracy reaching up to 96%, depending on the antibiotic and number of components. Early resistance patterns were detected as soon as 24 h post-exposure, and the inclusion of all three biochemical windows outperformed single-window models. Each spectral region contributed distinctively, reflecting biochemical remodeling associated with specific resistance mechanisms.
These results indicate that antibiotic resistance should be viewed as a temporally adaptive trajectory rather than a static state. FTIR-based biochemical profiling, when integrated with ML, enables projection of phenotypic transitions and supports real-time therapeutic decision-making. This strategy represents a shift toward adaptive antimicrobial management, with the potential to personalize interventions based on dynamic resistance monitoring through spectral biomarkers.
背景/目的：抗生素耐药性的发展越来越被认为是一种动态的、时间依赖性的现象，这对将耐药性定义为二元特征的传统诊断方法提出了挑战。
生物分子在傅里叶变换红外光谱（FTIR）中有指纹图谱。对特定分子基团进行靶向分析，并结合主成分分析（PCA）和机器学习算法（ML），能够识别对抗生素耐药的细菌。
在这项工作中，我们研究了有效分类如何取决于主成分数量、光谱区域的使用以及定义的耐药阈值。此外，我们还探讨了某些光谱区域（不同生物分子）的时间依赖性行为如何可能表现出单独无法完整反映耐药性发展情况的行为。在耐药诱导过程中的连续时间点，从暴露于阿奇霉素、甲氧苄啶/磺胺甲恶唑和苯唑西林的细菌中获取FTIR光谱。结合光谱窗口显著提高了模型性能，根据抗生素和成分数量的不同，准确率高达96%。在暴露后24小时即可检测到早期耐药模式，包含所有三个生化窗口的模型优于单窗口模型。每个光谱区域都有独特的贡献，反映了与特定耐药机制相关的生化重塑。
这些结果表明，抗生素耐药性应被视为一种随时间变化的适应性轨迹，而非静态状态。基于FTIR的生化分析与ML相结合，能够预测表型转变并支持实时治疗决策。这种策略代表了向适应性抗菌管理的转变，有可能通过光谱生物标志物基于动态耐药监测实现个性化干预。
