Back

Repurposing cell growth-regulating compounds identifies kenpaullone which ameliorates pathologic pain via normalization of inhibitory neurotransmission

Yeo, M.; Chen, Y.; Jiang, C.; Chen, G.; Lioudino, M.; Zeng, Q.; Wang, Z.; Busciglio, J.; Ji, R.-R.; Liedtke, W.

2019-09-12 neuroscience
10.1101/767681 bioRxiv
Show abstract

Inhibitory GABA-ergic neurotransmission is fundamental for the adult vertebrate central nervous system and requires low chloride ion concentration in neurons. This basic ionic-homeostatic mechanism relies on expression and function of KCC2, a neuroprotective ionic transporter that extrudes neuronal chloride. Importantly, no other transporter can rescue KCC2 deficit, and attenuated expression of KCC2 is strongly associated with circuit malfunction in chronic pain, epilepsy, neuro-degeneration, neuro-trauma, and other neuro-psychiatric illnesses. To isolate Kcc2 gene expression-enhancing compounds, we screened 1057 cell growth-regulating compounds in cultured primary cortical neurons. We identified kenpaullone (KP), which enhanced Kcc2/KCC2 expression and function in cultured rodent and human neurons by inhibiting GSK3{beta}. KP effectively reduced pathologic pain in preclinical mouse models of nerve constriction injury and bone cancer. In nerve-injury pain, KP restored Kcc2 expression and GABA-evoked chloride reversal potential in the spinal cord dorsal horn. Delta-catenin, a phosphorylation-target of GSK3{beta} in neurons, activated the Kcc2 promoter via Kaiso transcription factor. Validating this new pathway in-vivo, transient spinal over-expression of delta-catenin mimicked KP analgesia. With relevance for pathologic pain, our discoveries of a newly repurposed compound and a novel genetically-encoded mechanism that each enhance Kcc2 gene expression enable us to re-normalize disrupted inhibitory neurotransmission through genetic re-programming.

Matching journals

The top 2 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.