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bioRxiv · 10.1101/399188

Parvalbumin loss following chronic sub-anesthetic NMDA antagonist treatment is age-dependent in the hippocampus: Implications for modeling NMDA hypofunction

Honeycutt, J. A.·Chrobak, J. J.
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Abstract

A marked decrease in parvalbumin (PV), a calcium-binding protein specific to a subset of GABAergic neurons, is a consistent finding in postmortem schizophrenic brain tissue. This reduction is selective to PV and is regionally specific: occurring primarily in the prefrontal cortex and hippocampus (HPC) of patients. Rodent models of NMDA receptor hypofunction utilizing NMDA antagonist treatments - e.g. ketamine (KET) - show schizophrenia-like cognitive and behavioral impairments with parallel changes in PV. While decreased PV is considered a hallmark of neuropathology in schizophrenia, previous work elucidating the effects of KET administration on PV are contradictory, with findings suggesting decreased, increased, or no change in PV expression. Upon close examination of the procedures used across studies, there are two primary inconsistencies, including: 1) the age of animals used; and 2) the timeline of post-treatment tissue collection. To better understand whether these key differences impact observed PV changes, the present study investigated the impact of age and time of sacrifice on chronic KET-induced PV changes in the neocortex and HPC. Our findings suggest an effect of age, but not sacrifice timeline, on PV cell count following 14 days of sub-anesthetic KET treatment. We provide evidence that 1-month-old rats exhibit significant KET-induced HPC PV decreases, while adult rats show a modest increase in HPC PV following chronic KET. Taken together, we propose that PV is a dynamic marker, and that changes in cell counts - and their interpretation - following NDMA antagonist treatment should be considered in the context of age.

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Honeycutt, J. A., Chrobak, J. J.. 2018-08-23. Parvalbumin loss following chronic sub-anesthetic NMDA antagonist treatment is age-dependent in the hippocampus: Implications for modeling NMDA hypofunction. https://doi.org/10.1101/399188

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