Laboratory Activity to Teach about the Proliferation of Salmonella in Vegetables

Genomics of Environmental Salmonella: Engaging Students in the Microbiology and Bioinformatics of Foodborne Pathogens

We have developed and implemented an undergraduate microbiology course in which students isolate, characterize, and perform whole genome assembly and analysis of Salmonella enterica from stream sediments and poultry litter. In the development of the course and over three semesters, successive teams of undergraduate students collected field samples and performed enrichment and isolation techniques specific for the detection of S. enterica. Eighty-eight strains were confirmed using standard microbiological methods and PCR of the invA gene. The isolates' genomes were Illumina-sequenced by the Center for Food Safety and Applied Nutrition at the FDA and the Virginia state Division of Consolidated Laboratory Services as part of the GenomeTrakr program. Students used GalaxyTrakr and other web- and non-web-based platforms and tools to perform quality control on raw and assembled sequence data, assemble, and annotate genomes, identify antimicrobial resistance and virulence genes, putative plasmids, and other mobile genetic elements. Strains with putative plasmid-borne antimicrobial resistance genes were further sequenced by students in our research lab using the Oxford Nanopore MinIONTM platform. Strains of Salmonella that were isolated include human infectious serotypes such as Typhimurium and Infantis. Over 31 of the isolates possessed antibiotic resistance genes, some of which were located on large, multidrug resistance plasmids. Plasmid pHJ-38, identified in a Typhimurium isolate, is an apparently self-transmissible 183 kb IncA/C2 plasmid that possesses multiple antimicrobial resistance and heavy-metal resistance genes. Plasmid pFHS-02, identified in an Infantis isolate, is an apparently self-transmissible 303 kb IncF1B plasmid that also possesses numerous heavy-metal and antimicrobial resistance genes. Using direct and indirect measures to assess student outcomes, results indicate that course participation contributed to cognitive gains in relevant content knowledge and research skills such as field sampling, molecular techniques, and computational analysis. Furthermore, participants self-reported a deeper interest in scientific research and careers as well as psychosocial outcomes (e.g., sense of belonging and self-efficacy) commonly associated with student success and persistence in STEM. Overall, this course provided a powerful combination of field, wet lab, and computational biology experiences for students, while also providing data potentially useful in pathogen surveillance, epidemiological tracking, and for the further study of environmental reservoirs of S. enterica.

Fitness of Antibiotic-Resistant Bacteria in the Environment: A Laboratory Activity

In this laboratory experiment, we propose an opportunity for students to broaden their understanding of the ecology of antibiotic-resistant and sensitive waterborne bacteria. Antibiotics can be found in rivers or soil as a consequence of agricultural practices or as a result of human use. Concentrations of antibiotics in the environment may range from a few ng to μg L^-1. Such concentrations can affect the selection and fitness of resistant bacteria. In this laboratory activity, students learn how to set up a fitness experiment by using an isogenic pair of antibiotic-resistant and sensitive bacteria in the presence or absence of selective pressure. Microcosms were generated by using filtered river water containing populations of resistant and sensitive bacteria. Competition of both populations was measured in the presence or absence of antibiotics. Students appreciated the use of microcosms for in vitro experiments and the extent to which the fitness of resistant and sensitive bacteria changed in the presence and/or absence of a selective pressure in river water. Student learning was measured by using different types of assessments: multiple-choice, true/false, fill in the blanks, laboratory skills observations, and laboratory reports. After the laboratory activity, the percentage of correct answers significantly rose from ~20% to ~85%. Laboratory skills were also evaluated during the exercises, showing no major issues during the experiment. Students showed proficiency in analyzing the complexity of fitness data by reaching a mean of 5.57 (standard error 0.57) over a maximum score of 7 points.