A curated genome-scale metabolic model of Bordetella pertussis metabolism

The Gram-negative bacterium Bordetella pertussis is the causative agent of whooping cough, a serious respiratory infection causing hundreds of thousands of deaths annually worldwide. There are effective vaccines, but their production requires growing large quantities of B. pertussis. Unfortunately, B. pertussis has relatively slow growth in culture, with low biomass yields and variable growth characteristics. B. pertussis also requires a relatively expensive growth medium. We present a new, curated flux balance analysis-based model of B. pertussis metabolism. We enhance the model with an experimentally-determined biomass objective function, and we perform extensive manual curation. We test the model’s predictions with a genome-wide screen for essential genes using a transposon-directed insertional sequencing (TraDIS) approach. We test its predictions of growth for different carbon sources in the medium. The model predicts essentiality with an accuracy of 83% and correctly predicts improvements in growth under increased glutamate:fumarate ratios. We provide the model in SBML format, along with gene essentiality predictions.

革兰氏阴性菌百日咳博德特菌（Bordetella pertussis）是百日咳的致病原，该疾病为严重呼吸道感染，每年在全球造成数十万人死亡。当前已有成熟的百日咳疫苗，但疫苗生产需要大量培养百日咳博德特菌。遗憾的是，该菌在体外培养时生长速度相对缓慢，生物量得率较低且生长特性波动较大，同时其培养所需的培养基成本相对高昂。本研究构建了一款经人工精修的、基于通量平衡分析（flux balance analysis, FBA）的百日咳博德特菌代谢模型，通过实验测定的生物量目标函数对模型进行优化，并开展了大量的人工手动精修工作。本研究利用转座子定向插入测序（transposon-directed insertional sequencing, TraDIS）技术开展全基因组必需基因筛选实验，以验证模型的基因必需性预测结果；同时针对培养基中不同碳源对菌株生长的影响，验证模型的生长预测能力。结果显示，该模型对基因必需性的预测准确率达83%，并可正确预测当培养基中谷氨酸与延胡索酸盐比例升高时菌株生长的改善效果。本研究以系统生物学标记语言（Systems Biology Markup Language, SBML）格式提供该模型，同时附带基因必需性预测结果。