Biology subjects

Zhu, Y.-H.

Publications and source records attributed to Zhu, Y.-H..

5 recordsLinked to original sources

bioRxiv2025

Spatial control of secretory vesicle targeting by the Ync13-Rga7-Rng10 complex during cytokinesis

Cytokinesis requires precise coordination of contractile-ring constriction, vesicle trafficking and fusion to the plasma membrane, and extracellular matrix assembly/remodeling at the cleavage furrow to ensure faithful cell division and maintain cell integrity. These processes and proteins involved are broadly conserved across eukaryotes, yet molecular mechanisms controlling the spatiotemporal pathways of membrane trafficking remain poorly understood. Here, using fission yeast genetics, microscopy, and in vitro binding assays, we identify a conserved module including the Munc13 protein Ync13, F-BAR protein Rga7, and coiled-coil protein Rng10 to be critical for precise and selective vesicle targeting during cytokinesis. The module specifically recruit the TRAPP-II but not exocyst complex to tether vesicles containing the glucan synthases Bgs4 and Ags1 along the cleavage furrow. Ync13 subsequently interacts with the SM protein Sec1 for vesicle fusion. Mutations in this pathway disrupt septum integrity and lead to cell lysis. Our work provides key insights into how membrane trafficking is tightly controlled to maintain cell integrity during cytokinesis.

cell biology↗

bioRxiv2025

MKFGO: Integrating Multi-Source Knowledge Fusion with Pre-Trained Language Model for High-Accuracy Protein Function Prediction

Accurately identifying protein functions is essential to understand life mechanisms and thus advance drug discovery. Although biochemical experiments are the gold standard for determining protein functions, they are often time-consuming and labor-intensive. Here, we proposed a novel composite deep-learning method, MKFGO, to infer Gene Ontology (GO) attributes through integrating five complementary pipelines built on multi-source biological data. MKFGO was rigorously benchmarked on 1522 non-redundant proteins, demonstrating superior performance over 11 state-of-the-art function prediction methods. Comprehensive data analyses revealed that the major advantage of MKFGO lies in its two deep-learning components, HFRGO and PLMGO, which derive handcraft features and protein large language model (PLM)-based features, respectively, from protein sequences in different biological views, with effective knowledge fusion at the decision-level. HFRGO leverages an LSTM-attention network embedded with handcraft features, in which the triplet loss-based guilt-by-association strategy is designed to enhance the correlation between feature similarity and function similarity. PLMGO employs the PLM to capture feature embeddings with discriminative functional patterns from sequences. Meanwhile, another three components provide complementary insights for further improving prediction accuracy, driven by protein-protein interaction, GO term probability, and protein-coding gene sequence, respectively. The source codes and models of MKFGO are freely available at https://github.com/yiheng-zhu/MKFGO.

bioinformatics↗

bioRxiv2024

Septins function in exocytosis via physical interactions with the exocyst complex in fission yeast cytokinesis

Septins can function as scaffolds for protein recruitment, membrane-bound diffusion barriers, or membrane curvature sensors. Septins are important for cytokinesis, but their exact roles are still obscure. In fission yeast, four septins (Spn1 to Spn4) accumulate at the rim of the division plane as rings. The octameric exocyst complex, which tethers exocytic vesicles to the plasma membrane, exhibits a similar localization and is essential for plasma membrane deposition during cytokinesis. Without septins, the exocyst spreads across the division plane but absent from the rim during septum formation. These results suggest that septins and the exocyst physically interact for proper localization and function. Indeed, we predicted six pairs of interactions between septin and exocyst subunits by AlphaFold, most of them are confirmed by co-immunoprecipitation and yeast two-hybrid assays. Exocyst mislocalization results in mistargeting of secretory vesicles and their cargos, which leads to cell-separation delay in septin mutants. Our results indicate that septins guide the targeting of exocyst complex on the plasma membrane for vesicle tethering during cytokinesis through physical interactions.

cell biology↗

bioRxiv2023

A CLIC1 network coordinates matrix stiffness and the Warburg effect to promote tumor growth in pancreatic cancer

BACKGROUND & AIMSPDAC is characterized by significant matrix stiffening and reprogrammed glucose metabolism, particularly the Warburg effect. However, it is not clear the connection between matrix stiffness and the Warburg effect and the mechanisms of action in tumor progression. METHODSThe relationship between matrix stiffness and the Warburg effect was investigated from clinical, cellular, and bioinformatical perspectives. The ChIP and luciferase reporter gene assays were used to clarify the regulation mechanism of matrix stiffness on the expression of CLIC1. The expression profile and clinical significance of CLIC1 were determined in GEO datasets and a TMA. Loss-of-function and gain-of-function technics were used to determine the in vitro and in vivo functions of CLIC1. GSEA and western blotting revealed the underlying molecular mechanisms. RESULTSPDAC matrix stiffness is closely associated with the Warburg effect, and CLIC1 is a key molecule connecting tumor matrix stiffness and the Warburg effect. Increased CLIC1 expression induced by matrix stiffness correlates with poor prognosis in PDAC. CLIC1 acts as a promoter of glycolytic metabolism and facilitates tumor growth in a glycolysis-dependent manner. Mechanistically, CLIC1 inhibits the hydroxylation of HIF1 via ROS, which then increases the stability of HIF1. Collectively, PDAC cells can sense extracellular matrix stiffness and upregulate the expression of CLIC1, which facilitates the Warburg effect through ROS/HIF1 signaling, thereby supporting tumor growth. CONCLUSIONSIn the context of tumor therapy, targeted approaches can be considered from the perspectives of both extracellular matrix stiffness and tumor metabolism, of which CLIC1 is one of the targets.

cancer biology↗

bioRxiv2021

TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for High-Accuracy Gene Function Annotations

Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. We proposed a new method (TripletGO) to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profiling, genetic sequence alignment, protein sequence alignment and naive probability, respectively. TripletGO was tested on a large set of 5,754 genes from 8 species (human, mouse, arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2,433 proteins with available expression data from the CAFA3 experiment and achieved function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet-network based profiling method with the feature space mapping technique which can accurately recognize function patterns from transcript expressions. Meanwhile, the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results. The standalone package and an online server of TripletGO are freely available at https://zhanglab.ccmb.med.umich.edu/TripletGO/.

bioinformatics↗