Expanding Tiny Earth to genomics: a bioinformatics approach for an undergraduate class to characterize antagonistic strains. Issue 1 (23rd January 2020)

• Record Type: Journal Article
• Authors: Basalla, Joseph; Harris, Ryan; Burgess, Elizabeth; Zeedyk, Nicholas; Wildschutte, Hans
• Is Part Of: FEMS microbiology letters. Volume 367:Issue 1(2020)
• Publication Date: 2020-01-23
• Subjects: Pseudomonas -- antibiotic discovery -- transposon mutagenesis -- biosynthetic gene cluster -- bioinformatics -- citizen science -- Tiny Earth; Microbiology -- Periodicals; 579
• DOI: 10.1093/femsle/fnaa018
• Languages: English
• ISSNs: 0378-1097

Abstract

ABSTRACT: The evolution of multidrug resistant pathogens and the diminishing supply of effective antibiotics are global crisis. Tiny Earth (TE) is undergraduate curriculum that encourage students to pursue science careers by engagement in authentic drug discovery research. Through the TE program, students identify environmental strains that inhibit other bacteria. Although these isolates may produce antibiotics based on the antagonistic phenotype, understanding the activity in regard to genome content remains elusive. Previously, we developed a transposon mutagenesis module for use with TE to identify genes involved in antibiotic production. Here, we extend this approach to a second semester undergraduate course to understand the origin of antagonism and genome diversity. Using a bioinformatics strategy, we identified gene clusters involved in activity, and with annotated genomes in hand, students were able to characterize strain diversity. Genomes were analyzed using different computational tools, including average nucleotide identity for species identification and whole genome comparisons. Because the focus of TE involves the evolution of drug resistance, predicted products in strains were identified and verified using a drug susceptibility assay. An application of this curriculum by TE members would assist in efforts with antibiotic discovery. Abstract : Students use a bioinformatics approach to characterize antagonistic strains.