Molecular Parasitology
Paul Ulrich, Georgia State University
Of the approximately 1000 different proteins that populate eukaryotic mitochondria, ~50% have no known function. Molecular Parasitology is a course-based intensively engages undergraduates by investigating roles of uncharacterized, mitochondrial proteins in trypanosomatid parasites. Students utilize basic bioinformatics (subcellular localization, conserved domain prediction, BLAST, secondary structure) to predict protein function followed by construction and transfection of GFP-fusion constructs into cell lines to validate their predictions. The CURE is designed to encourage independent problem-solving, science identity, and support career success.

A Microbial-Traits CURE for terrestrial ecosystems (mT-CURE)
Hinsby Cadillo-Quiroz, Arizona State University at the Tempe Campus
Microbial traits are defined as phenotypic properties whose expression determines the tempo and mode of microbial activity. Growth rate, pH range, Q10, substrate affinity and nutrient-use efficiency, are traits whose variation across microbes can lead to differential contribution to ecosystem processes. The combination and distribution of traits among organisms can generate functional guilds in soils; hence a quantitative understanding of trait presence, trait magnitude and trait spectrum of variation can be a powerful component for function-based modeling of the contribution of different microbes to ecosystem level processes like carbon degradation or generation of methane gas in soils. In this "Microbial-Traits CURE for terrestrial ecosystems (mT-CURE)" students will participate for two semesters, develop hypothesis on the variability of traits and its organismal and/or environmental sources. During first semester evaluations of traits will be completed and hypothesis addressed using previous ecosystem-specific microbial culture collections derived from previous CUREs. In second semester, an effort to predict traits from microbes directly from genomic information will be evaluated against trait values collected on the first semester. The goal is to use available and accessible bioinformatic approaches to test their predictive capacity for validation and possible future expansion into metagenomic-assembled genomes.