Kang Miseon, Park Jin Hwa, Kim Hyun Jung
Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365 Republic of Korea; Department of Food Biotechnology, University of Science and Technology, Daejeon 34113 Republic of Korea.
Food Safety and Distribution Research Group, Korea Food Research Institute, Wanju 55365 Republic of Korea.
Food Res Int. 2025 Feb;202:115698. doi: 10.1016/j.foodres.2025.115698. Epub 2025 Jan 6.
Bacillus cereus group (BCG) includes closely related bacterial species with different phenotypic characteristics, such as pathogenic potential, enzymatic capacity, and thermotypes. Psychrotolerant BCG (pBCG) strains can grow and produce toxins at low temperatures, creating concerns for the food industry. However, many current routine food diagnosis methods do not consider pBCG, and predictive modeling, which is an essential tool for food safety and public health, has not been developed using pBCG, but rather using mesophilic strains (mBCG). Given the limited information on predictive modeling and accurate strain identification of pBCG, we developed a predictive model for fried rice, a known causative food for BCG foodborne disease, using pBCG food isolates and whole-genome sequencing for accurate taxonomic classification. In predictive modeling, four pBCG food isolates selected through phenotypic screening grew at temperatures above 5 °C, whereas mBCG reference strains did not grow below 13 °C. The primary and secondary models for pBCG (covering 5-37 °C) and mBCG (covering 13-37 °C) fit well, with R > 0.98. By validating the dynamic model under three time-varying temperature profiles, we observed root mean square error values < 0.42 log CFU/g and acceptable simulation zone values > 82%. The four pBCG isolates used in the predictive model were identified using whole-genome sequencing as B. cereus sensu stricto, B. toyonensis, and B. mycoides, which carried enterotoxin genes. Psychrotolerant signatures of the 16S rRNA and cspA were detected in the BCG9 isolate. The predictive model and genomic characterization of pBCG strains can be applied to manage and control pBCG, ensuring the food quality and safety of fried rice products in the cold chains.
蜡样芽孢杆菌群（BCG）包括具有不同表型特征的密切相关细菌物种，如致病潜力、酶活性和耐热类型。耐冷蜡样芽孢杆菌群（pBCG）菌株可在低温下生长并产生毒素，这给食品行业带来了担忧。然而，目前许多常规食品诊断方法并未考虑pBCG，而预测模型作为食品安全和公共卫生的重要工具，尚未使用pBCG开发，而是使用嗜温菌株（mBCG）。鉴于关于pBCG预测模型和准确菌株鉴定的信息有限，我们使用pBCG食品分离株并通过全基因组测序进行准确的分类学分类，开发了一种针对炒饭（已知的BCG食源性疾病致病食品）的预测模型。在预测模型中，通过表型筛选选择的4株pBCG食品分离株在5℃以上的温度下生长，而mBCG参考菌株在13℃以下不生长。pBCG（覆盖5 - 37℃）和mBCG（覆盖13 - 37℃）的一级和二级模型拟合良好，R>0.98。通过在三种随时间变化的温度曲线下验证动态模型，我们观察到均方根误差值<0.42 log CFU/g，可接受模拟区值>82%。预测模型中使用的4株pBCG分离株通过全基因组测序鉴定为蜡样芽孢杆菌狭义种、东洋芽孢杆菌和蕈状芽孢杆菌，它们携带肠毒素基因。在BCG9分离株中检测到16S rRNA和cspA的耐冷特征。pBCG菌株的预测模型和基因组特征可用于管理和控制pBCG，确保冷链中炒饭产品的食品质量和安全。
