Reservoirs of Antibiotic Resistance--Bioinformatics

随着抗生素耐药性的出现威胁到我们治疗致病性细菌感染的能力，成功控制传染病必须考虑到每一个潜在的耐药性来源。抗生素的选择性作用不仅作用于被治疗的感染，而且作用于与抗生素接触的所有生物体。跨物种基因转移的许多机制表明，共生生物中耐药基因的频率可以作为病原菌耐药出现的标记或预测因子。收集共生和致病生物中耐药基因频率的数据对于分析这些频率之间的关系以及随后开发预测抗生素耐药性问题出现的工具是必要的。因此，我们提出了一个三步过程来解决共生生物中抗生素耐药基因的频率可以用来预测病原菌群体中随后出现的抗生素耐药的假设1)实施生物信息学数据库来描述共生生物中的耐药基因型。2)实施抗生素耐药性水库（ROAR）网络参与者的研究项目，充分利用收集到的数据，具体解决环境中共生生物中耐药基因的频率可以作为病原菌生物耐药紧急情况的预测因子的假设；3)开发一个数学模型，该模型将作为预测医学上重要细菌物种耐药性出现的工具，使用目标2收集的数据分析来测试和完善模型。这些新的生物信息学工具及其促进的研究活动将应用于人类疾病的抗生素预防和治疗管理、畜牧业和园艺以及转基因生物的管理。因此，应用于这一问题的ROAR网络的持续增长将加快基础和应用研究的步伐，并更好地为遏制抗生素耐药性的持续努力提供信息。

With the emergence of antibiotic resistance threatening our ability to treat pathogenic bacterial infection, successful control of infectious diseases must consider every potential source of resistance. The selective forces antibiotics exert act not only on the treated infection, but also on all organisms with which the drugs come into contact. Numerous mechanisms of cross-species gene transfer suggest that the frequency of resistance genes in commensal organisms may act as a marker or predictor of the emergence of resistance in pathogenic bacteria. Collection of data on resistance gene frequencies in commensal and pathogenic organisms is necessary for the analysis of the relationship between these frequencies, and the subsequent development of a tool for predicting the emergence of antibiotic resistance problems. We therefore propose a 3 step process to address the hypothesis that the frequency of antibiotic resistance genes in commensal organisms can be used to predict the subsequent emergence of antibiotic resistance in pathogenic bacterial populations 1) Implementation of a bioinformatics database to profile resistance genotypes in commensal organisms. 2) Implementation of research projects by Reservoirs of Antibiotic Resistance (ROAR) network participants to fully utilize the collected data in specifically addressing the hypothesis that the frequency of resistance genes in commensal organisms found in the environment can act as a predictor for the emergency of antibiotic resistance in pathogenic bacterial organisms; 3) Development of a mathematical model that will serve as a tool for predicting the emergence of resistance in medically important bacterial species, using analyses of the data gathered in Aim 2 to test and refine the model. These new bioinformatics tools and the research activities that they facilitate will have applications in the management of antibiotic prophylaxis and therapy in human disease, animal husbandry and horticulture and in management of genetically modified organisms. The continued growth of the ROAR network, applied to this question, will thus accelerate the pace of basic and applied research, and better inform the continuing efforts to contain antibiotic resistance.