2Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia
* To whom correspondence should be addressed.
Received: July 2, 2024; Revised: July 23, 2024; Accepted: September 2, 2024
Synthetic peptides have a wide range of clinical effects. Of particular interest are peptides based on adrenocorticotropic hormone (ACTH) both as already used and as potential drugs for preventing consequences of cerebral ischemia. However, it is necessary to study influence of the peptide on the brain cells under normal physiological conditions, including understanding the risks of their use. Here, we used high-throughput RNA sequencing (RNA-Seq) to identify differentially expressed genes (DEGs) in the brain frontal cortex of rat receiving intraperitoneal administration of ACTH-like peptides ACTH(4-7)PGP (Semax) and ACTH(6-9)PGP, or saline. We identified 258 and 228 DEGs, respectively, with the fold change > 1.5 and Padj < 0.05 at 22.5 h after the first administration of Semax and ACTH(6-9)PGP. Metabolic pathways, characterizing both common and specific effects of the peptides on the transcriptome were identified. Both peptides predominantly caused decrease in expression of the genes associated with the immune system. At the same time, when comparing the effects of ACTH(6-9)PGP relative to Semax, DEGs were identified that characterized the main differences in the effects of the peptides. These genes were mostly downregulated and associated with neurosignaling systems and regulation of ion channels, thus characterizing differences in the effects of the peptides. Our data show how differences in the structure of ACTH derivatives are associated with the changes in the brain cell transcriptome following exposure to these related peptides. Furthermore, our results demonstrate that when studying influence of regulatory peptides on transcriptome under pathological conditions, it is necessary to take into account their actions under normal physiological conditions.
KEY WORDS: ACTH-like peptides, RNA-Seq, transcriptome, gene expression, gene networksDOI: 10.1134/S0006297924090104
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