Biology subjects

lu, y.

Publications and source records attributed to lu, y..

3 recordsLinked to original sources

bioRxiv2024

Human DDIT4L intron retention contributes to cognitive impairment and amyloid plaque formation.

Cognitive impairment and amyloid plaques are the most important clinical and neuropathological feature for dementia, especially in Alzheimers disease (AD). However, the etiology of dementia is complicated. The present study reveals that an aberrant splicing of DDIT4L, the isoform DDIT4L intron retention (DIR), occurs in AD patients. Homozygous DIR-knock-in (KI) mice showed DIR expression in hippocampal neurons, marked cognitive impairment, augmented A{beta} deposition and enhanced Tau phosphorylation. The DIR colocalized with thioflavin S-positive plaques and gelsolin in AD patients. The DIR induced A{beta} deposition and cognitive impairment by interacting with gelsolin. Moreover, DIR interacted with GluA1, the subunit of the AMPA receptor, contributing to synaptic deficiency and cognitive impairment. Furthermore, an anti-DIR monoclonal antibody (mAb) alleviated cognitive impairment and reduced A{beta} deposition and Tau phosphorylation. Thus, DIR contributes to cognitive impairment and amyloid plaques, and could be a potential therapeutic target for dementia.

neuroscience↗

bioRxiv2023

Force sensing GPR133 is essential for normal balance and modulates vestibular hair cell membrane excitability via Gi signaling and CNGA3 coupling

The maintenance of normal balance sensing is a fundamental prerequisite for virtually every activity of daily life. As one set most important balance information collectors, vestibular hair cells convert mechanical stimuli from head movement into electrical signals through a mechanoelectrical transduction (MET) process. The molecular mechanism underlying equilibrioception and MET in vestibular hair cells is not well understood but is generally believed to be mediated by ion channels. However, whether these procedures are also mediated by other receptors, such as GPCRs, which are known to govern light, odorant and taste sensing, is not known. Here, by screening the expression of force-sensitive adhesion GPCRs in vestibular hair cells and phenotype profiling in animal models, we identified that a seven-transmembrane receptor, GPR133, was able to sense force in utricle hair cells and was required for maintenance of normal equilibrioception. Notably, GPR133 converted mechanical stimuli into changes in intracellular cAMP levels through Gi engagement and then modulated plasma membrane excitability and mediated MET by coupling to CNGA3 activity changes in approximately 30% of GPR133-expressing utricle hair cells. GPR133-mediated MET and its coupling with CNGA3 were recapitulated by an in vitro reconstitution system. Further chemical labeling, mass spectrometry and cryo-EM analysis provided potential structural information on force-induced GPR133 activation and Gi3 engagement. Collectively, our findings reveal the essential role of GPR133 in the maintenance of normal equilibrioception and suggest that GPCR family members can participate in the MET process in utricle hair cells through modulation of intracellular second messenger levels and ion channel coupling.

physiology↗

bioRxiv2020

Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network

Single-cell RNA sequencing (scRNA-seq) technologies promise to characterize the transcriptome of genes at cellular resolution, which shed light on unfolding cell heterogeneity and diversity. Fast-growing scRNA-seq profiles require efficient clustering algorithms to identify the same type of cells. Although many methods have been developed for cell clustering, existing clustering methods are limited to extract the representations from expression data of individual cells, while ignoring the high-order structural relations between cells. Here, we proposed GraphSCC, a robust graph artificial intelligence model to cluster single cells by accounting for structural relations between cells. The representation learned from the graph convolutional network, together with another representation output from a denoising autoencoder network, are optimized by a dual self-supervised module for better cell clustering. The experimental results indicate that GraphSCC model outperforms state-of-the-art methods in terms of various evaluation metrics on both simulated and real datasets. Further visualizations show that GraphSCC provides representations for better intra-cluster compactness and inter-cluster separability.

bioinformatics↗