Electronic Theses and Dissertations

Date of Award

1-1-2025

Document Type

Dissertation

Degree Name

Ph.D. in Biological Science

First Advisor

Gregg W. Roman

Second Advisor

Nicole Ashpole

Third Advisor

Bradley Jones

School

University of Mississippi

Relational Format

dissertation/thesis

Abstract

Circadian rhythms profoundly influence learning, memory, and sleep, yet the underlying neural mechanisms remain incompletely understood. This study investigates the role of Dorsal Anterior Lateral (DAL) neurons, serotonin (5-HT), and G(o) signaling in the circadian modulation of olfactory learning, short-term olfactory memory (STM), and sleep in Drosophila melanogaster. We hypothesize that DAL neurons transmit time-of-day (TOD) information to the mushroom bodies (MBs) via rhythmic 5-HT release, acting on 5HT1A receptors within the α/β posterior neurons. These receptors, potentially coupled to heterotrimeric G(i) proteins, may regulate learning efficiency by modulating Rutabaga adenylyl cyclase activity according to TOD.

Our findings demonstrate that serotonergic DAL neurons are essential for maintaining circadian learning rhythms in a one-trial STM paradigm. Proper serotonin reuptake, facilitated by SERT expression within DAL neurons, is required for efficient learning. We further show that 5HT1A, but not 5HT1B, receptors are necessary for sustaining learning rhythms, supporting our hypothesis of 5HT1A-mediated TOD-dependent modulation. Additionally, G(o) signaling within MB subsets, particularly α/β and γ neurons, is required for olfactory learning, while disruption within the α/β posterior neurons impacts sleep architecture, revealing distinct yet overlapping roles of these pathways.

These results highlight the complexity of serotonergic modulation and G(o) signaling in coordinating learning and sleep. Our study supports a model in which DAL neurons regulate circadian learning rhythms through rhythmic 5-HT release and 5HT1A receptor activation. Further research is needed to delineate how these systems interact with broader neural networks to integrate circadian signals with cognitive and sleep processes.

Available for download on Thursday, November 18, 2027

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