Bioinformatics Pilot Program Abstract: Agnes J. Ayme-Southgate
Differential gene expression during task progressions in the honey bee
Dr. Agnes J. Ayme-Southgate, Department of Biology, College of Charleston
Initially one surprising finding of genome projects was that the number of genes detected was often similar irrespective to perceived species complexity. One process explaining this apparent discrepancy is the availability of RNA alternative splicing to generate multiple protein isoforms from a single gene. Understanding regulation of alternative splicing has therefore become an important question in molecular genetics, evolution, and cell/developmental biology. The proposed research leverages the behavioral and physiological plasticity of the honeybee (Apis mellifera) model system during the transition from nurse to forager behaviors in response to age and various environmental cues. We will investigate how alternative splicing contributes to remodeling of the flight muscle system leading to increased flight capacity in the older foragers. More generally this model system can contribute to our understanding of muscle plasticity and remodeling in response to exercise, age, injury, and diseases.In Aim 1, we will obtain RNAseq data for three conditions, young worker nurse and precocious foragers at 8 and 15 days. Bioinformatics analyses will be carried out to generate a list of differentially expressed transcript variantsin pairwise comparison of these three conditions. From the list of annotated genes, we will determine the genes for myofibrillar proteins and splicing factors that undergo either a qualitative and/or quantitative switch in isoform composition during the nurse-to-forager transition. These candidate genes will be selected for further analysis. In Aim 2, we will review the annotated transcripts available from the Apis mellifera reference genome for the candidate genesusing Bioinformatics tools. We will alsoconfirm novel splice variantsby end-point RT-PCR and cDNA sequencing. We will validate their quantitative pattern of differential isoform expression as a factor of behavior or flight experience using qRT-PCR analysis.