P2X7R antagonists in chronic stress-based depression models: a review

Muecke-Heim, Iven-Alex von; Ries, Clemens; Urbina, Lidia und Deussing, Jan M. (2021): P2X7R antagonists in chronic stress-based depression models: a review. In: European Archives of Psychiatry and Clinical Neuroscience, Bd. 271, Nr. 7: S. 1343-1358

Abstract

Depression affects around 320 million people worldwide. Growing evidence proposes the immune system to be the core interface between psychosocial stress and the neurobiological and behavioural features of depression. Many studies have identified purinergic signalling via the P2X7 receptor (P2X7R) to be of great importance in depression genesis yet only a few have evaluated P2X7R antagonists in chronic stress-based depression models. This review summarizes their findings and analyses their methodology. The four available studies used three to nine weeks of unpredictable, chronic mild stress or unpredictable, chronic stress in male mice or rats. Stress paradigm composition varied moderately, with stimuli being primarily psychophysical rather than psychosocial. Behavioural testing was performed during or after the last week of stress application and resulted in depressive-like behaviours, immune changes (NLRP3 assembly, interleukin-1 beta level increase, microglia activation) and neuroplasticity impairment. During the second half of each stress paradigm, a P2X7R antagonist (Brilliant Blue G, A-438079, A-804598) was applied. Studies differed with regard to antagonist dosage and application timing. Nonetheless, all treatments attenuated the stress-induced neurobiological changes and depressive-like behaviours. The evidence at hand underpins the importance of P2X7R signalling in chronic stress and depression. However, improvements in study planning and reporting are necessary to minimize experimental bias and increase data purview. To achieve this, we propose adherence to the Research Domain Criteria and the STRANGE framework.

• Dokumententyp: Zeitschriftenartikel
• Fakultätsübergreifende Einrichtungen: Graduate School of Systemic Neurosciences (GSN)
• Themengebiete: 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
• ISSN: 0940-1334
• Sprache: Englisch