Senior Biological Research

Addressing Antibiotic Resistance

Students use primary literature to identify a research question and hypothesis that they used to choose a local soil sample to perform experiments about antibiotic resistance. The data generated from these experiments were reviewed and entered into the antimicrobial resistance database maintained by Tufts University and used for further experiments with Caltech.

The World Health Organization recognizes antibiotic resistance as one of the top global public health threats. Goal 2 of the current National Action Plan recommends strengthening screening for antibiotic resistance. Students contribute to this goal by using environmental DNA extraction and PCR methodology to screen local soil samples for antibiotic resistance genes.

Generating Data for and Presenting at the California Institute of Techology

The Senior Biological Research class recently presented data at Caltech that was empirically-generated at Poly for Caltech’s Karthikeyan lab. The data involved screening bacterial DNA samples from Caltech for antibiotic resistance genes (ARG) using protocols that students had to optimize, followed by using this data to test the prediction accuracy of an ARG prediction model. The design, optimization and data generation took place over the entire academic year culminating with data presentation and a question and answer session to the lab group at Caltech. The PRI thanks the Karthikeyan lab for this collaborative project.

Research Skills Progression:

• Level 1: Use primary literature to identify environmental variable(s) that correlates with a given well-studied ARG, and use this to inform the choice of soil in the greater Los Angeles area to screen for the given ARG. 

Data submission to researchers at Tufts to review and enter in their antibiotic resistance database.

• Level 2: Use primary literature to identify any ARG of the student's choice, optimize detection methods, including self-design of PCR primers, and then identify and screen a soil sample for the ARG using correlating environmental variables similar to level 1.

Data submission to researchers at Tufts to review and enter in their antibiotic resistance database.

• Level 3: Use optimized protocols from Level 2 to screen bacterial DNA samples from Caltech to empirically verify the presence of ARGs relative to AI model predictions for the given bacterial genome sequence, and thereby validate prediction accuracy of the model.