Disrupted presynaptic nectin1-based neuronal adhesion in the entorhinal-hippocampal circuit contributes to early-life stress-induced memory deficits.
Chen WuQian GongXue XuPing FangChi WangJing-Ying YuXing-Xing WangSan-Hua FangWen-Juan ChenHui-Fang LouYu-Hui LiuLiang WangYi-Jun LiuWei ChenXiao-Dong WangPublished in: Translational psychiatry (2022)
The cell adhesion molecule nectin3 and its presynaptic partner nectin1 have been linked to early-life stress-related cognitive disorders, but how the nectin1-nectin3 system contributes to stress-induced neuronal, circuit, and cognitive abnormalities remains to be studied. Here we show that in neonatally stressed male mice, temporal order and spatial working memories, which require the medial entorhinal cortex (MEC)-CA1 pathway, as well as the structural integrity of CA1 pyramidal neurons were markedly impaired in adulthood. These cognitive and structural abnormalities in stressed mice were associated with decreased nectin levels in entorhinal and hippocampal subregions, especially reduced nectin1 level in the MEC and nectin3 level in the CA1. Postnatal suppression of nectin1 but not nectin3 level in the MEC impaired spatial memory, whereas conditional inactivation of nectin1 from MEC excitatory neurons reproduced the adverse effects of early-life stress on MEC-dependent memories and neuronal plasticity in CA1. Our data suggest that early-life stress disrupts presynaptic nectin1-mediated interneuronal adhesion in the MEC-CA1 pathway, which may in turn contribute to stress-induced synaptic and cognitive deficits.
Keyphrases
- early life
- stress induced
- cell adhesion
- type diabetes
- cerebral ischemia
- depressive symptoms
- spinal cord
- escherichia coli
- staphylococcus aureus
- preterm infants
- metabolic syndrome
- emergency department
- spinal cord injury
- subarachnoid hemorrhage
- machine learning
- pseudomonas aeruginosa
- electronic health record
- fluorescent probe
- hepatitis c virus
- functional connectivity
- heat stress
- human immunodeficiency virus
- brain injury
- deep learning
- data analysis
- candida albicans