The evolution of resistance to antibiotics provides a timely and relevant topic for teaching undergraduate students evolutionary biology. Here, we present a module incorporating modified sequencing data from eight antibiotic resistant pathogen outbreaks in hospital settings with bioinformatics and phylogenetic analyses. This module uses whole genome sequencing data from hospital outbreaks investigated by the Centers for Disease Control and Prevention to provide examples of antibiotic resistance spread. Students work in groups to analyze outbreak data to identify the bacterial species and antibiotic resistance genes, to infer a phylogenetic tree examining relatedness among isolates, and to determine a possible source of the outbreak. Students then compile their results in individual reports and provide recommendations for preventing the further spread of antibiotic resistant organisms. In addition to providing genomic outbreak data, we include a teaching concepts guide discussing three integral components of the module: how evolutionary biology concepts of natural selection and competition impact antibiotic resistance; outbreak investigation information to aid in phylogenetic analysis and creation of recommendations; and instructions for the bioinformatics protocol. Completion of this module provides students an opportunity to think critically about the evolution of resistance, practice bioinformatics techniques, and relate evolutionary biology to current events.

抗生素耐药性的进化为本科生进化生物学教学提供了一个及时且相关的主题。在这里，我们提出了一个模块，该模块结合了医院环境中八次抗生素耐药病原体爆发的修改测序数据以及生物信息学和系统发育分析。该模块使用疾病控制和预防中心调查的医院疫情的全基因组测序数据来提供抗生素耐药性传播的例子。学生分组分析疫情数据，以确定细菌种类和抗生素抗性基因，推断出检查分离株之间相关性的系统发育树，并确定疫情可能的来源。然后，学生们将他们的结果汇编成个人报告，并提供防止抗生素抗性生物体进一步传播的建议。除了提供基因组爆发数据外，我们还包括一个教学概念指南，讨论该模块的三个组成部分：自然选择和竞争的进化生物学概念如何影响抗生素耐药性；疫情调查信息，以帮助进行系统发育分析和提出建议；以及生物信息学协议的说明。完成本模块为学生提供了批判性思考耐药性进化、实践生物信息学技术并将进化生物学与时事联系起来的机会。