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Yan Wu

Publications and source records attributed to Yan Wu.

2 recordsLinked to original sources

Ribosome dynamics at the conserved PGP motif governs 2A peptide-bond-skipping efficiency.

Viral 2A oligopeptides drive an unusual ribosome recoding event in which peptide-bond formation fails at a conserved PG↓P motif, producing two discrete proteins without canonical termination. Despite decades of study, the molecular basis of 2A-mediated peptide-bond skipping remains poorly understood. Here, we combine quantitative 2A reporters with high-resolution ribosome profiling to interrogate ribosome dynamics at the core 2A sequences. We identify a pausing event at the terminal proline codon of the PGP motif that functions as a kinetic decision point: ribosome dwell time at this site inversely correlates with skipping efficiency. Increasing nascent chain flexibility by inserting linkers immediately upstream of the 2A sequence reduces ribosome occupancy at the terminal proline codon and enhances peptide-bond skipping. Strikingly, amino acid repeats positioned distally upstream also modulate 2A activity, indicating long-range coupling between nascent chain properties outside of the ribosome and the peptidyl transferase center inside the ribosome. In particular, hydrophobic residues potently suppress skipping, an effect that can be rescued by extending flexible segments within the peptide exit tunnel. Together, our findings support a model in which nascent chain features-beyond the core 2A motif-dynamically tune ribosomal recoding efficiency through co-translational feedback into the catalytic center.

Ribosomes

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics