Jeong Seong-Geun, Lee Youjin, Jeong Hye-Seon, Park Seong Jun, Yeom Jinki, Choi Chang-Hyung, Kim Byung-Gee
Bio-MAX Institute, Seoul National University, Seoul 08826, Republic of Korea.
Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea.
ACS Sens. 2025 Mar 28;10(3):1880-1888. doi: 10.1021/acssensors.4c03003. Epub 2025 Feb 27.
Probiotic metabolites are gaining attention as potential antibiotic candidates against antibiotic-resistant bacteria. The disk diffusion test, by measuring bacterial aggregate responses, faces challenges in accurately evaluating antimicrobial efficacy when these responses to different probiotic strains are indistinguishable at a macroscopic level. Here, this study presents an analytical method for accurately evaluating antimicrobial activity by analyzing bacterial cell proliferation suppression at a microscopic level. This assay can be used in a coculture system, designed to continuously expose pathogenic bacteria growing on the bottom surface of the culture plate to probiotic metabolites, selectively released from porous capsules positioned above. Bacterial proliferation is optically monitored in real-time and tracked via a computer vision algorithm. Specifically, bacterial proliferation is quantified as their doubling time, calculated using a proposed stochastic kinetic model. This method identifies the most potent antimicrobial strains by determining which probiotic candidates most effectively extend the bacterial doubling time. In comparative experiments using the same strains, this proposed method demonstrated clear distinctions in the antimicrobial efficacy of each strain, unlike the disk diffusion test. Therefore, this approach provides a reliable solution for identifying superior probiotic strains, with potential for widespread use in discovering new antimicrobial agents.
益生菌代谢产物作为对抗抗生素耐药菌的潜在抗生素候选物正受到关注。纸片扩散法通过测量细菌聚集反应来评估抗菌效果，但当不同益生菌菌株的反应在宏观水平上难以区分时，在准确评估抗菌效力方面面临挑战。在此，本研究提出了一种通过在微观水平分析细菌细胞增殖抑制来准确评估抗菌活性的分析方法。该测定法可用于共培养系统，该系统旨在使生长在培养板底部表面的病原菌持续暴露于从位于上方的多孔胶囊中选择性释放的益生菌代谢产物。通过计算机视觉算法实时光学监测并跟踪细菌增殖。具体而言，细菌增殖以其倍增时间进行量化，使用所提出的随机动力学模型进行计算。该方法通过确定哪些益生菌候选物最有效地延长细菌倍增时间来识别最有效的抗菌菌株。在使用相同菌株的比较实验中，与纸片扩散法不同，该方法显示出各菌株抗菌效力的明显差异。因此，该方法为鉴定优良益生菌菌株提供了可靠的解决方案，具有在发现新型抗菌剂方面广泛应用的潜力。
