bioRxivNeurosciencePreprint
Effects of chronic exposure to fluoxetine, eicosapentaenoic acid, and lipopolysaccharide on behavior and hippocampal transcriptome in the rat model of prolonged chronic unpredictable stress
Konstantin A. DeminiD, Tatiana O. KolesnikovaiD, David S. GalstyaniD, Nataliya A. Krotova, Nikita P. IlyiniD, Ksenia A. DerzhavinaiD, Maria V. Seredinskaya, Yuriy M. Kositsyn, Dmitry V. Sorokin, Maria O. Nerush, Abubakar-Askhab S. Khaybaev, Sofia A. Pushkareva, Elena V. PeterseniD, Murilo S. de Abreu, Alexey Masharsky, Allan V. KalueffiD
Abstract
Animal models are widely used to study stress-induced affective disorders and associated with them neuroinflammation and other neuroimmune processes. Here, we examined rat behavioral and hippocampal transcriptomic responses to prolonged chronic unpredictable stress (PCUS), as well as following a 4-week treatment with a classical antidepressant fluoxetine, an anti-inflammatory agent eicosapentaenoic acid (EPA), a pro-inflammatory agent lipopolysaccharide (LPS) and their combinations. Overall, PCUS evoked an anxiety-like behavioral phenotype in rats (corrected by chronic fluoxetine alone or combined with other drugs), EPA was anxiolytic and LPS promoted anxiety in this model. PCUS evoked pronounced transcriptomic responses in rat hippocampi, including >200 differentially expressed genes. While pharmacological manipulations did not affect hippocampal gene expression markedly, Gpr6, Drd2 and Adora2a were downregulated in stressed rats treated with fluoxetine+EPA, suggesting G protein-coupled receptor 6, dopamine D2 receptor and adenosine A2A receptor as potential evolutionarily conserved targets in chronic stress. Overall, these findings support the validity of rat PCUS paradigm as a useful tool to study stress-related affective pathologies and calls for further research probing how various conventional and novel drugs modulate behavioral and neurotranscriptomic biomarkers of chronic stress.
- organism: rat
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- Citations
- 1
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