Department of Chemistry and Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States.
Division of Environmental Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongsangbuk-do, 37673, Republic of Korea.
Biosens Bioelectron. 2023 May 1;227:115144. doi: 10.1016/j.bios.2023.115144. Epub 2023 Feb 11.
Numerous bacteria can cause water- and foodborne diseases and are often found in bacterial mixtures, making their detection challenging. Specific bioreceptors or selective growth media are necessary for most bacterial detection methods. In this work, we collectively used five quorum sensing-based peptides identified from bacterial biofilms to identify 10 different bacterial species (Bacillus subtilis, Campylobacter jejuni, Enterococcus faecium, Escherichia coli, Legionella pneumophila, Listeria monocytogenes, Pseudomonas aeruginosa, Salmonella Typhimurium, Staphylococcus aureus, Vibrio parahaemolyticus) and their mixtures in water and milk. Four different machine learning classification methods were used: k-nearest neighbors (k-NN), decision tree (DT), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). Peptides were crosslinked to submicron particles, and peptide-bacteria interactions on paper microfluidic chips caused the particle aggregation. A wireless, pocket fluorescence microscope (interfaced with a smartphone) counted such particle aggregations. XGBoost showed the best accuracy of 83.75% in identifying bacterial species from water samples using 320 different datasets and 91.67% from milk samples using 140 different datasets (5 peptide features per dataset). Each peptide's contribution to correct classification was evaluated. The results were concentration-dependent, allowing the identification of a dominant species from bacterial mixtures. Using XGBoost and the previous milk database, we tested 14 blind samples of various bacterial mixtures in milk samples, with an accuracy of 81.55% to predict the dominant species. The entire process could be completed within a half hour. The demonstrated system can provide a handheld, low-cost, easy-to-operate tool for potential hygiene spot-checks, public health, or personal healthcare.
许多细菌可导致食源性和水源性疾病，且通常存在于细菌混合物中，这使得它们的检测具有挑战性。大多数细菌检测方法都需要特定的生物受体或选择性生长培养基。在这项工作中，我们共同使用了从细菌生物膜中鉴定出的五种群体感应肽来识别 10 种不同的细菌物种（枯草芽孢杆菌、空肠弯曲菌、屎肠球菌、大肠杆菌、嗜肺军团菌、单核细胞增生李斯特菌、铜绿假单胞菌、鼠伤寒沙门氏菌、金黄色葡萄球菌、副溶血性弧菌）及其在水和牛奶中的混合物。我们使用了四种不同的机器学习分类方法：k-最近邻 (k-NN)、决策树 (DT)、支持向量机 (SVM) 和极端梯度提升 (XGBoost)。肽被交联到亚微米颗粒上，并且肽-细菌在纸微流控芯片上的相互作用导致颗粒聚集。无线、口袋荧光显微镜（与智能手机接口）计数这种颗粒聚集。XGBoost 在使用 320 个不同数据集从水样中识别细菌物种方面表现出最佳的准确性，准确率为 83.75%，在使用 140 个不同数据集从牛奶样本中识别细菌物种方面的准确率为 91.67%（每个数据集有 5 个肽特征）。评估了每个肽对正确分类的贡献。结果是浓度依赖性的，允许从细菌混合物中识别出优势物种。使用 XGBoost 和之前的牛奶数据库，我们在牛奶样本中测试了 14 个不同细菌混合物的盲样，准确率为 81.55%，可以预测优势物种。整个过程可以在半小时内完成。所展示的系统可以为潜在的卫生抽查、公共卫生或个人医疗保健提供一种手持式、低成本、易于操作的工具。
