PLoS BiologyNeurosciencePeer-reviewed journal article
Neuron-glia interaction through Serotonin-BDNF-NGFR axis enables regenerative neurogenesis in Alzheimer’s model of adult zebrafish brain
Prabesh BhattaraiiD, Mehmet İlyas CoşacakiD, Violeta Mashkaryan, Sevgican DemiriD, Stanislava Dimitrova Popova, Nambirajan GovindarajaniD, Kerstin Brandt, Yixin ZhangiD, Weipang Chang, Konstantinos AmpatzisiD, Çağhan KızıliD
Abstract
It was recently suggested that supplying the brain with new neurons could counteract Alzheimers disease. This provocative idea requires further testing in experimental models where the molecular basis of disease-induced neuronal regeneration could be investigated. We previously found that zebrafish stimulates neural stem cell (NSC) plasticity and neurogenesis in Alzheimers disease and could help to understand the mechanisms to be harnessed for develop new neurons in diseased mammalian brains. Here, by performing single-cell transcriptomics, we found that Amyloid toxicity-induced Interleukin-4 induces NSC proliferation and neurogenesis by suppressing the tryptophan metabolism and reducing the production of Serotonin. NSC proliferation was suppressed by Serotonin via downregulation of BDNF-expression in Serotonin-responsive periventricular neurons. BDNF enhances NSC plasticity and neurogenesis via NGFRA/NFkB signaling in zebrafish but not in rodents. Collectively, our results suggest a complex neuron-glia interaction that regulates regenerative neurogenesis after Alzheimers disease conditions in zebrafish.\n\nKey findings- Amyloid-induced Interleukin-4 suppresses Serotonin (5-HT) production in adult zebrafish brain\n- 5-HT affects htr1-expresing neurons and suppresses bdnf expression\n- BDNF enhances plasticity in neural stem cells via NGFRA/NFkB signaling\n- BDNF/NGFRA signaling is a neuro-regenerative mechanism in zebrafish but not in mammals.
- organism: zebrafish
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 119
- FWCI
- 7.18
- Field percentile
- top 2%
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