ePoster

INFLAMMATION‑LINKED MITOCHONDRIAL DYSFUNCTION IN PTSD SUSCEPTIBILITY

Charlotte Ryeand 4 co-authors

University of Cambridge

FENS Forum 2026 (2026)
Barcelona, Spain
Board PS03-08AM-230

Presentation

Date TBA

Board: PS03-08AM-230

Poster preview

INFLAMMATION‑LINKED MITOCHONDRIAL DYSFUNCTION IN PTSD SUSCEPTIBILITY poster preview

Event Information

Poster Board

PS03-08AM-230

Abstract

Posttraumatic stress disorder (PTSD) is associated not only with severe psychological symptoms but also with elevated rates of physical comorbidities. These comorbidities not only worsen overall health burden, but may also be associated with poorer treatment response. While the evidence linking PTSD to physical morbidity and mortality is strong, the mechanisms are largely unknown. Growing evidence suggests these conditions may be linked to systemic inflammation and impaired mitochondrial function. Understanding how inflammatory and bioenergetic pathways converge in PTSD may provide critical insights into mechanisms of susceptibility and resilience, with implications for biomarker discovery and targeted interventions.
To probe the interaction between inflammation and mitochondrial dysfunction, adult male (n=48) and female (n=48) Lister Hooded rats were exposed to the refined stress‑enhanced fear learning (rSEFL) paradigm, a robust and validated analogue of PTSD‑like behaviour. Animals underwent massed shock exposure followed by fear conditioning in a novel context, with freezing used as the primary index of fear responding. Plasma TNF‑α and lactate were measured to enable evaluation of both pre‑existing and trauma‑induced alterations in inflammatory and bioenergetic states. Western blotting was performed to quantify oxidative phosphorylation subunits, providing molecular insight into mitochondrial function across relevant neural circuits.
Initial evidence indicates that rSEFL-associated increases in TNF‑α correlate with mitochondrial alterations, involving Complex I in the prelimbic cortex and Complex IV/V in the basolateral amygdala. Furthermore, using machine learning algorithms, we propose a novel categorisation of individuals based on behavioural profiles, offering greater biological relevance than current approaches.

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