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Updated: May 5, 2026

Animal Models of Depression - Chronic Despair Model CDM
Published on: September 23, 2021
Intron Retention as a Homeostatic State Variable for Drug Response and Recovery: Lessons from Depression for Broader
Norihiro Okada1, Kenshiro Oshima1, Akiko Maruko1
1School of Pharmacy, Kitasato University, Tokyo 108-8641, Japan.
None:
Clinically robust molecular biomarkers for depression have remained elusive, despite extensive transcriptomic research. This gap is consequential: depression is prevalent and heterogeneous, yet objective measures to quantify burden, stratify patients, and track recovery remain limited. Here, we review evidence that intron retention (IR) can serve as a homeostatic state variable-and therefore a sensitive biomarker-reporting stress adaptation and recovery at an upstream regulatory layer, often preceding or outperforming differential gene expression (DEG) readouts. Mechanistically, IR enables bidirectional fine-tuning of effective gene output: increased IR (IncIR) can throttle output under overload, whereas decreased IR (DecIR) releases this brake to restore gene output. Because these shifts are reversible and treatment-responsive, IR signatures can function not only as disease markers but also as pharmacodynamic metrics for blood-based monitoring of drug response and recovery. To evaluate the clinical utility of IR, we use depression as a proof of concept and focus on two interventions: (i) the Kampo formula hangekobokuto (HKT), which is associated with IR normalization consistent with reduced peripheral inflammatory load; and (ii) ketamine, where IR patterns measured before ketamine treatment in non-responders are linked to stronger innate-immune/antiviral activity, suggesting a higher inflammatory load that may limit treatment benefit. Finally, we discuss transdiagnostic extensions beyond depression, using early cognitive decline (mild cognitive impairment, MCI) as a stringent, biologically distal test case for blood-based IR/DI readouts and motivating independent cohort replication and longitudinal validation.
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