Back

Neural mechanism of acute stress management by trace aminergic signalling in the lateral habenula

Yang, S. H.; Yang, E.; Lee, J.; Kim, J. Y.; Yoo, H.; Park, H. S.; Jung, J. T.; Lee, D.; Chun, S.; Lee, H. W.; Kim, H.

2022-06-14 neuroscience
10.1101/2022.02.02.478114 bioRxiv
Show abstract

Stress management is a prerequisite for the survival of vertebrates because chronic stress may cause depression and is known to negatively modulate the dopaminergic reward system1. Enhanced excitability of neurons in the lateral habenula (LHb) induced by chronic stress is essential for silencing dopaminergic neurons in the ventral tegmental area (VTA) via GABAergic neurons in the rostromedial tegmental nucleus (RMTg)2-10. However, the effect of acute stress on the LHb-RMTg-VTA pathway is unknown11-14. Here, we show that both aromatic L-amino acid decarboxylase (AADC)-expressing neurons (D-neurons)15 in the LHb and dopaminergic neurons in the VTA are activated by acute stress, whereas GABAergic neurons in the RMTg are not. Selective stimulation of LHb D-neurons and AADC knockdown of these neurons reverse-regulate the RMTg-VTA pathway. Circuit tracing and electrophysiology data demonstrate that trace aminergic signalling by LHb D-neurons directly suppresses RMTg GABAergic neurons. Furthermore, local activation of trace amine-associated receptor 1 (TAAR1; a trace amine receptor) in the RMTg is sufficient to rescue the despair-like behaviour produced by the loss of AADC expression. Our results identify a novel efferent pathway from the LHb to the RMTg whereby trace aminergic signalling allows the brain to manage acute stress by preventing VTA dopaminergic neuron hypoactivity. The TAAR1-mediated trace aminergic signalling in the LHb-RMTg pathway may hold promise as a therapeutic target for stress-mediated psychological diseases.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.