Improving real-time fast-scan cyclic voltammetry serotonin detection to understand differentantidepressant and genetic effects in Drosophila melanogaster

Depression is a common mental illness. However, current treatment options are extremely variable from person to person. The main neurochemical target of interest for common selective serotonin reuptake inhibitors (SSRIs) is the serotonin transporter (SERT) on serotonin neurons. SSRIs bind to SERT and cause extracellular serotonin concentrations to increase. However, it is not clear if all antidepressants share this mechanism of action. Further, it is not understood how different genetic polymorphisms to the gene that encodes SERT affect its structure and function, which impacts antidepressant activity. Currently, it is difficult to study depression because of a lack of biological models and accurate analytical techniques that measure real-time serotonin changes. Fast-scan cyclic voltammetry (FSCV) is a common electrochemical technique that measures neurotransmitters in brain tissue with rapid temporal resolution, however it possesses unique issues with serotonin because it polymerizes onto electrodes and ruins accurate measurements. Also, Drosophila melanogaster, the fruit fly, has not been previously investigated to explore serotonin changes with antidepressants. Thus, this dissertation aims to improve fast-scan cyclic voltammetry (FSCV) detection of serotonin to understand different antidepressant and genetic effects in Drosophila brain tissue.
In this thesis, Chapter 1 introduces the current literature surrounding depression, current treatments, real-time electrochemical serotonin measurement techniques, and Drosophila melanogaster. Chapter 2 explores different FSCV waveforms to understand electrode fouling to serotonin and its major metabolite, 5- hydroxindoleacetic acid, to improve real-time serotonin detection. Chapter 3 uses these new techniques and compares serotonin concentration and reuptake changes with common SSRIs: fluoxetine, escitalopram, paroxetine, and citalopram, to understand their individual mechanisms of action. Chapter 4 explores changes in serotonin with ketamine compared to SSRIs to determine differences in their serotonin mechanisms, as well as downstream effects with feeding and locomotion behaviors. Finally, Chapter 5 covers initial data from different genetic mutations to SERT compared to data collected in Chapters 3-4. Two Drosophila SERT mutant lines were used with specific point mutations or partial gene knock-outs, and FSCV and optogenetics will be used in the future to compare serotonin release and reuptake changes for SSRIs and micro-dose ketamine therapies.
Overall, my dissertation improves analytical detection of serotonin in brain tissue with FSCV, and applies these techniques to understand biological differences between several antidepressant drugs. We show that serotonin release and reuptake changes are unique for different antidepressants and cause dose-dependent behavior changes. This work benefits others in analytical chemistry and neurochemistry who will use these techniques and models to explore new antidepressant therapies or other neurotransmitters, like dopamine and glutamate, to help design and implement successful treatments for those who suffer from depression.