Lysergic acid diethylamide induces increased signalling entropy in rats’ prefrontal cortex

• Published in: Journal of neurochemistry 2022-07, Vol.162 (1), p.9-23
• Main Authors: Savino, Aurora, Nichols, Charles D.
• Format: Article
• Language: English
• Subjects: Entropy; Gene expression; LSD; Lysergic acid diethylamide; Lysergide; Medical imaging; Mental disorders; Network analysis; Network topologies; Neuroimaging; Neuroplasticity; Original; Prefrontal cortex; Psychedelic drugs; Signaling; Substance use; Topology
• ISSN: 0022-3042; 1471-4159
• DOI: 10.1111/jnc.15534

Psychedelic drugs are gaining attention from the scientific community as potential new compounds for the treatment of psychiatric diseases such as mood and substance use disorders. The 5‐HT2A receptor has been identified as the main molecular target, and early studies pointed to an effect on the expression of neuroplasticity genes. Analysing RNA‐seq data from the prefrontal cortex of rats chronically treated with lysergic acid diethylamide (LSD), we describe the psychedelic‐induced rewiring of gene co‐expression networks, which become less centralised but more complex, with an overall increase in signalling entropy typical of highly plastic systems. Intriguingly, signalling entropy mirrors, at the molecular level, the increased brain entropy reported through neuroimaging studies in human, suggesting the underlying mechanisms of higher‐order phenomena. Moreover, from the analysis of network topology, we identify potential transcriptional regulators and propose the involvement of different cell types in psychedelics’ activity. Psychedelics are emerging drugs for the therapy of psychiatric conditions, but their molecular mechanisms of action have not been yet fully elucidated. Analysing RNA‐seq data of rats chronically treated with lysergic acid diethylamide (LSD), we show that psychedelics induce the reorganization of gene co‐expression networks in rats’ prefrontal cortex (PFC), with a global increase in signalling entropy and network de‐centralization. Based on gene networks’ structure, we identify novel potential molecular regulators of their transcriptional effects (e.g. Tcf4) and suggest increased cell variability and plasticity as being involved in their mechanisms of action.