A Systematic Meta-Analysis of Transcriptional Profiling Studies Characterizing the Effect of Antidepressant Treatments on the Prefrontal Cortex in Animal Models – UROP Symposium

A Systematic Meta-Analysis of Transcriptional Profiling Studies Characterizing the Effect of Antidepressant Treatments on the Prefrontal Cortex in Animal Models

Sophia Espinoza

Research Mentor(s): Megan Hagenauer
Department or Program: Michigan Neuroscience Institute
Authors: Sophia Espinoza1, Eva Geoghegan2, Megan Hagenauer3, Phi T. Nguyen2, Rene Hen2, Stan Watson3, Huda Akil3
Session: Session 1: 12:00pm-12:50pm
Poster: 21

Abstract

Major Depressive Disorder affects between 5 and 17 percent of all people and can cause significant burden (Bains et al., 2023). Major Depressive Disorder is treated with a variety of pharmaceutical and non-pharmaceutical treatments for which the mechanisms aren’t well understood. To understand the effects of treatments on the brain, some researchers use transcriptional profiling studies using technologies such as microarray and RNA-Seq to measure the expression of thousands of genes. We conducted a systematic meta-analysis of transcriptional profiling studies to examine the converging effects of different antidepressant treatments on the prefrontal cortex. Datasets were collected through the Gemma database, using search terms such as “antidepress*”, “SSRI”, and a full list of names for traditional and non-traditional antidepressant treatments including treatments involving ketamine and MDMA. After filtering these results down to only the datasets including cerebral cortex samples, we were left with 145 datasets. We will further screen the datasets using the inclusion criteria so that the datasets include rat/mouse models, are looking specifically at the effects of depression treatment, and are examining the prefrontal cortex. We will use R programming to extract the effect sizes and their variance for each gene in each dataset, and then run a meta-analysis for each gene across datasets. The anticipated results of this meta-analysis are that we identify which genes are upregulated and downregulated in a wide variety of antidepressant treatments. Our results will provide better insight into how antidepressant treatment affects the prefrontal cortex and hopefully provide us with a better understanding of how this can be related to humans through the examination of animal models.

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