ReviewInternational journal of molecular sciences2026
Intron Retention as a Homeostatic State Variable for Drug Response and Recovery: Lessons from Depression for Broader Applications.
Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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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