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Thyroid hormones maintain parvalbumin neuron functions in mouse neocortex

Ren, J.; Markossian, S.; Guyot, R.; Aubert, D.; Li, D.; Cauli, B.; Riet, F.; Wong, J.; Flamant, F.; Richard, S.

2024-07-19 neuroscience
10.1101/2024.07.16.603713 bioRxiv
Show abstract

Parvalbumin-expressing GABAergic interneurons play a key role in maintaining the excitation-inhibition balance in the mammalian neocortex. While postnatal maturation of parvalbumin neurons is highly sensitive to thyroid hormone deficiency, the role of these hormones in mature parvalbumin neurons remains poorly understood. Here, we addressed the possibility that thyroid hormones are also required to maintain the function of mature parvalbumin neurons in the mouse neocortex. To this end, Cre/loxP recombination system was used to express a dominant-negative mutated receptor of thyroid hormones to selectively eliminate thyroid hormone signaling in parvalbumin neurons after the onset of parvalbumin expression. We analyzed the neocortical phenotype of these mice by combining genomics, histology, electrophysiology, sleep recordings and behavioral analysis. In the mutant mice, gene expression analysis revealed a decreased expression of key perineuronal net components. parvalbumin neuron excitability was decreased and mice displayed behavioral hyperactivity and increased susceptibility to epileptic seizures. Additionally, sleep analysis revealed that mutant mice spent more time awake, and had a delayed transition to sleep. Electrophysiological recordings further demonstrated altered gamma oscillations in the somatosensory cortex. The findings of this study underscore that thyroid hormones are essential not only for the differentiation of parvalbumin interneurons, but also for the maintenance of their inhibitory function after the onset of parvalbumin expression. As a consequence, alterations in thyroid hormone signaling, during development or in adulthood, may contribute to the occurrence of neurological and psychiatric disorders involving altered cortical oscillations.

Published in iScience (predicted rank #19) · training set

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