About Us: HCS Colloquium

Apr 8, 2016, 11:30am - 12:45pm
244 Kottman Hall (Columbus) video-linked to 121 Fisher Auditorium (Wooster)

Presenter: Michael Dzakovich

Advisor: Dr. David Francis

SAC members: Dr. Joshua Blakeslee, Dr. Leah McHale, Dr. Steven J. Schwartz

Proposal Type:  PhD Proposal

Title of Presentation: Leveraging Natural Variation in Carotenoid Biosynthetic Genes and Implications for Human Health

Abstract: Carotenoids and apocarotenoids are posited to exhibit disease-preventative properties partly through their accumulation in the liver. Recent evidence suggests tomato carotenoids protect against liver diseases by altering gene expression. Carotenoid biosynthesis in tomatoes is modified by allelic variation in carotenoid isomerase (tangerine) and the fruit-specific beta-cyclase (Beta). Preliminary data indicates that tangerine alleles differentially affect carotenoid biosynthesis. My objectives are: 1) Determine how carotenoid pathway intermediates and apocarotenoids are affected by natural variation in genes; 2) Ascertain how other pathways are affected by allelic variation in tangerine using untargeted metabolomics; 3) Distinguish how red and tangerine tomatoes alter the transcriptome of mouse liver tissue. I hypothesize that allelic variation in tangerine will modulate fruit metabolism due to metabolic crosstalk. To test this hypothesis, I will quantify fruit carotenoids using high performance liquid chromatography (HPLC). Apocarotenoids will be analyzed using liquid chromatography tandem mass spectrometry (LC-MS/MS). To ascertain how other pathways are affected by tangerine alleles, I will use liquid chromatography time-of-flight mass spectrometry (LC-QTOF-MS). Statistical analysis, clustering techniques, and searchable libraries of metabolites will be used to identify differences. Furthermore, I hypothesize that highly bioavailable carotenoids unique to tangerine tomatoes will differentially modulate liver gene expression in mice compared to those fed red tomato-supplemented diets. Gene expression will be quantified using RNA-seq and these data will be integrated with metabolome data generated by a collaborator. These studies will result in characterized germplasm and novel information that can guide future pre-clinical and clinical studies related to functional foods and disease prevention.