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Modeling Depression and Antidepressant Treatment Accurately in Drosophila Melanogaster

CSEF · 2023 Behavioral & Social Sciences Honorable_mention Award

Deng Ashley · 12

Overview

Over half of 37 million antidepressant users experience side effects that influence long-term antidepressant efficacy. However, their biological causes are still mostly unknown. By using animal models, these causes can be more effectively explored. In particular, drosophila melanogaster, or the fruit fly, is lauded for its utility in studying human depression. Despite this, simulation of depression and antidepressant treatment in drosophila is minimally studied compared to its counterpart in mice. To close this gap, this research explored two methods of drosophila simulation: using food deprivation and sucrose treatment to mimic depression and antidepressant treatment, respectively. High/low locomotor activity was examined as a standard for non-depressed/depressed behavior. This study used the Drosophila Activity Monitoring System (DAMS) to observe locomotor activity of 4 red-eyed wild-type adult flies through photobeam breaks. Flies were monitored through 3 stages: 1) baseline; 2) food deprivation; and 3) sucrose treatment (standard food mixed with 5% sucrose solution). All activity was analyzed using the DAMSystem3 Software in 3 categories: Rest (Rt), Position (Pn), and Counts Total (CT), with a Bin Time of 5 minutes. Experimental results showed that food deprivation was not a successful simulation of depression; it increased locomotor activity rather than decreased. Similarly, while sucrose treatment succeeded in increasing activity, its immediate efficacy compared to slow-acting antidepressants disputes its validity in mimicking antidepressant response. Results also yielded uncertainty regarding locomotor activity as a behavioral standard. Future studies should explore sucrose through the GAL-4/UAS system. If further developed, antidepressant drosophila studies could advance beyond basic testing into studying biological pathways.

Source coverage

This record comes from a published award list, not a complete project archive. Its abstract comes from CSEF's public project showcase as archived by the Internet Archive before judging (https://web.archive.org/web/20230401224130/https://ca-csef.zfairs.com/showcase/ShowcaseInfo?f=838e60b7-ea75-46e8-865c-fde4864244b3); the version presented may differ.

Awards (1)

  • Category Award: HM

Competition history

  • CSEF 2023 Behavioral & Social Sciences · Entry S0420

Resources

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