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Liang, Y.-X.

Publications and source records attributed to Liang, Y.-X..

2 recordsLinked to original sources

Astrocytic μ-δ opioid receptor heterodimers mediate the antidepressant effects of ketamine's metabolite

A deeper understanding of the targets and mechanisms of fast-acting antidepressants, exemplified by ketamine, remains indispensable for better therapeutic strategies and understanding depression. Beyond the canonical neuron-centric NMDAR inhibition hypothesis, brain opioid system and glia-mediated processes are increasingly implicated in ketamines antidepressant efficacy, yet their precise contributions remain poorly understood. Here, we demonstrate that one major metabolite of ketamine, (2R,6R)-hydroxynorketamine (HNK), selectively targets -{delta} opioid receptor heterodimers (-{delta}-ORs) on astrocytes. By promoting the formation and/or stabilization of -{delta}-ORs, HNK engages Gs-coupled signaling, elevates intracellular cAMP, phosphorylates CREB (p-CREB) levels and Ca{superscript 2} dynamics in astrocytes, and consequently restores key astrocytic proteins and functions in depression models. Disrupting -{delta}-OR assembly or Gs signaling abolishes HNK-mediated antidepressant responses both in vitro and in vivo. Collectively, astrocytic opioid receptor heterodimers are critical to antidepressant responses and HNK may serve as a prototype compound for targeting astrocyte dysfunction across a wide range of brain disorders.

neuroscience↗

Canonical self-supervised pretraining paradigm constrains the capacity of genomic language models on regulatory decoding

Recent studies suggest that genomic language models (gLMs) could help decode genomic regulatory code. Here, we systematically evaluated 11 representative gLMs across multiple regulatory genomics applications and found that current gLMs offer limited advantages over the random baseline. Further analysis revealed a systematic misalignment between the canonical sequence-only self-supervised pretraining paradigm and the context-specific dynamic nature of gene regulation, highlighting the need for function-oriented pretraining strategies that explicitly incorporate biochemical and regulatory priors.

bioinformatics↗