Biology subjects

Catalano, J. L.

Publications and source records attributed to Catalano, J. L..

2 recordsLinked to original sources

bioRxiv2021

The foraging gene affects alcohol sensitivity, metabolism and memory in Drosophila

The genetic basis of alcohol use disorder (AUD) is complex. Understanding how natural genetic variation contributes to alcohol phenotypes can help identify mechanisms underlying the genetic contribution of AUD. Recently, a single nucleotide polymorphism in the human foraging (for) gene ortholog, Protein Kinase cGMP-Dependent 1 (PRKG1), was found to be associated with stress-induced risk for alcohol abuse. However, the mechanistic role that PRKG1 plays in AUD is not well understood. We use natural variation in the Drosophila for gene to describe how variation of cGMP-dependent protein kinase (PKG) activity modifies ethanol-induced phenotypes. We found that variation in for affects ethanol-induced increases in locomotion and memory of the appetitive properties of ethanol intoxication. Further, these differences may stem from the ability to metabolize ethanol. Together, this data suggests that natural variation in PKG modulates cue reactivity for alcohol, and thus could influence alcohol cravings by differentially modulating metabolic and behavioral sensitivities to alcohol.

genetics↗

bioRxiv2020

Behavioral features of motivated response to alcohol in Drosophila

Animals avoid predators and find the best food and mates by learning from the consequences of their behavior. However, reinforcers are not always uniquely appetitive or aversive but can have complex properties. Most intoxicating substances fall within this category; provoking aversive sensory and physiological reactions while simultaneously inducing overwhelming appetitive properties. Here we describe the subtle behavioral features associated with continued seeking for alcohol despite aversive consequences. We developed an automated runway apparatus to measure how Drosophila respond to consecutive exposures of a volatilized substance. Behavior within this Behavioral Expression of Ethanol Reinforcement Runway (BEER Run) demonstrated a defined shift from aversive to appetitive responses to volatilized ethanol. Behavioral metrics attained by combining computer vision and machine learning methods, reveal that a subset of 9 classified behaviors and component behavioral features associate with this shift. We propose this combination of 9 behaviors can be used to navigate the complexities of operant learning to reveal motivated goal-seeking behavior.

animal behavior and cognition↗