Quantification of Macronutrients Intake in a Thermogenetic Neuronal Screen using Drosophila Larvae

Gonçalo M. Poças, Pedro M. Domingos, Christen K. Mirth

Research output: Contribution to journalArticleOtherpeer-review

Abstract

Foraging and feeding behaviors allow animals to access sources of energy and nutrients essential for their development, health, and fitness. Investigating the neuronal regulation of these behaviors is essential for the understanding of the physiological and molecular mechanisms underlying nutritional homeostasis. The use of genetically tractable animal models such as worms, flies, and fish greatly facilitates these types of studies. In the last decade, the fruit fly Drosophila melanogaster has been used as a powerful animal model by neurobiologists investigating the neuronal control of feeding and foraging behaviors. While undoubtedly valuable, most studies examine adult flies. Here, we describe a protocol that takes advantage of the simpler larval nervous system to investigate neuronal substrates controlling feeding behaviors when larvae are exposed to diets differing in their protein and carbohydrates content. Our methods are based on a quantitative colorimetric no-choice feeding assay, performed in the context of a neuronal thermogenetic-activation screen. As a read-out, the amount of food eaten by larvae over a 1 h interval was used when exposed to one of the three dye-labeled diets that differ in their protein to carbohydrates (P:C) ratios. The efficacy of this protocol is demonstrated in the context of a neurogenetic screen in larval Drosophila, by identifying candidate neuronal populations regulating the amount of food eaten in diets of different macronutrient quality. We were also able to classify and group the genotypes tested into phenotypic classes. Besides a brief review of the currently available methods in the literature, the advantages and limitations of these methods are discussed and, also, some suggestions are provided about how this protocol might be adapted to other specific experiments.

Original languageEnglish
Article numbere61323
Number of pages21
JournalJournal of Visualized Experiments
Volume2020
Issue number160
DOIs
Publication statusPublished - 11 Jun 2020

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