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Yu Qiu

Publications and source records attributed to Yu Qiu.

3 recordsLinked to original sources

Mu-opioid receptor desensitization: role of receptor phosphorylation, internalization, and representation.

It is generally accepted that the internalization and desensitization of mu-opioid receptor (MOR) involves receptor phosphorylation and beta-arrestin recruitment. However, a mutant MOR, which is truncated after the amino acid residue Ser363 (MOR363D), was found to undergo phosphorylation-independent internalization and desensitization. As expected, MOR363D, missing the putative agonist-induced phosphorylation sites, did not exhibit detectable agonist-induced phosphorylation. MOR363D underwent slower internalization as reflected in the attenuation of membrane translocation of beta-arrestin 2 when compared with wild type MOR, but the level of receptor being internalized was similar to that of wild type MOR after 4 h of etorphine treatment. Furthermore, MOR363D was observed to desensitize faster than that of wild type MOR upon agonist activation. Surface biotinylation assay demonstrated that the wild type receptors recycled back to membrane after agonist-induced internalization, which contributed to the receptor resensitization and thus partially reversed the receptor desensitization. On the contrary, MOR363D did not recycle after internalization. Hence, MOR desensitization is controlled by the receptor internalization and the recycling of internalized receptor to cell surface in an active state. Taken together, our data indicated that receptor phosphorylation is not absolutely required in the internalization, but receptor phosphorylation and subsequent beta-arrestin recruitment play important roles in the resensitization of internalized receptors.

Analgesics, Opioid↗

6beta-acetoxy nortropane regulated processing of amyloid precursor protein in CHOm1 cells and rat brain.

The effects of the muscarinic receptor agonist 6beta-acetoxy nortropane on amyloid precursor protein (APP) processing were studied in both transfected Chinese hamster ovary cells stably expressing muscarinic M(1) receptors (denoted as CHOm(1) cell line) and in cerebral cortical and hippocampal slices. Exposure of CHOm(1) cells to 6beta-acetoxy nortropane for 1 h significantly increased the secretion of secretory amyloid precursor protein (derived from alpha-secretase cleavage) in a concentration-dependent manner. In the same system, 6beta-acetoxy nortropane reduced the beta-amyloid peptide production. Similar results were obtained in hippocampal and cerebral cortical slices, with 6beta-acetoxy nortropane administration resulting in an increase in secretory amyloid precursor protein and a decrease in beta-amyloid peptide release. The increase of secretory amyloid precursor protein secretion was abolished by preincubation with selective muscarinic M(1) receptor antagonist pirenzepine, but not by preincubation with selective muscarinic M(2) receptor antagonist methoctramine, suggesting that 6beta-acetoxy nortropane promotes secretory amyloid precursor protein release in the brain via muscarinic M(1) receptor activation. These results suggest that 6beta-acetoxy nortropane could exert a beneficial effect on the progress of Alzheimer's disease by promoting amyloid precursor protein processing through alpha-secretase.

Amyloid beta-Peptides↗

Predicting bacterial transcription units using sequence and expression data.

MOTIVATION: A key aspect of elucidating gene regulation in bacterial genomes is identifying the basic units of transcription. We present a method, based on probabilistic language models, that we apply to predict operons, promoters and terminators in the genome of Escherichia coli K-12. Our approach has two key properties: (i) it provides a coherent set of predictions for related regulatory elements of various types and (ii) it takes advantage of both DNA sequence and gene expression data, including expression measurements from inter-genic probes. RESULTS: Our experimental results show that we are able to predict operons and localize promoters and terminators with high accuracy. Moreover, our models that use both sequence and expression data are more accurate than those that use only one of these two data sources.

Algorithms↗