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ZHAI, J.

Publications and source records attributed to ZHAI, J..

2 recordsLinked to original sources

A coupled fluorescence assay for high-throughput screening of polyurethane-degrading enzymes

The global accumulation of plastic waste, particularly persistent polymers like polyurethane (PU), demands urgent solutions. Enzymatic depolymerization offers a viable strategy for PU waste valorization. However, progress has been hindered by the lack of reliable high-throughput screening (HTS) assays capable of precise and quantitative evaluation of enzymatic activity. Current enzyme screening assays face significant limitations due to poor substrate relevance and inadequate quantification methods. Here, we present a novel HTS assay featuring a chemically defined, synthetic poly (ethylene adipate)-based PU substrate that enables unambiguous structure-activity analysis and direct quantification of degradation products (adipic acid and ethylene glycol). Coupled with this substrate is a highly sensitive fluorescence-based detection cascade, in which released ethylene glycol is stoichiometrically converted to resorufin via a two-enzyme system (glycerol dehydrogenase and diaphorase). This assay overcomes key limitations of commercial substrates (e.g., Impranil DLN) by providing quantitative, real-time monitoring of PU hydrolysis with molecular precision. Validation with known PU-degrading enzymes demonstrated that the assay can sensitively distinguish differences in enzymatic activity with high reproducibility and quantitative accuracy. Our platform enables rapid, cost-effective screening of enzyme libraries and engineered variants, significantly advancing enzyme discovery and optimization efforts. By bridging the gap between laboratory research and industrial application, this HTS assay accelerates the development of sustainable PU recycling solutions, offering a critical tool against plastic pollution. Graphical Abstract Legend O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/663332v4_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@17ba5c0org.highwire.dtl.DTLVardef@bef89aorg.highwire.dtl.DTLVardef@1b3770eorg.highwire.dtl.DTLVardef@14d5ef5_HPS_FORMAT_FIGEXP M_FIG (A) Workflow of the enzymatic degradation assay. Enzymes are incubated with chemically defined PEA-PU films. The resulting hydrolysate is collected and analysed using a fluorescence-based assay in microplate format (Ex/Em = 535/588 nm). (B) Fluorescence-based detection mechanism. Assay principle based on ethylene glycol (EG), one of the major degradation products. EG is converted through a two-step enzymatic cascade involving glycerol dehydrogenase (GldA) and diaphorase, leading to the generation of fluorescent resorufin from resazurin. C_FIG

synthetic biology↗

GenMasterTable: A user-friendly desktop application for filtering, summarising, and visualising large-scale annotated genetic variants

BackgroundThe rapid expansion of next-generation sequencing (NGS) technologies has generated vast amounts of genomic data, creating a growing demand for secure, scalable, and accessible tools to support variant interpretation. However, many existing solutions are command-line based, rely on cloud or server infrastructures that may pose data privacy risks, lack flexibility in supporting both VCF and CSV formats, or struggle to handle the scale and complexity of modern genomic datasets. There is a clear need for a user-friendly, locally operated application capable of efficiently processing annotated variant data for large-scale cohort level analysis. ResultsWe introduce GenMasterTable, a free, secure, and cross-platform desktop application designed to simplify variant analysis through an intuitive graphical user interface (GUI). As the first tool to enable comprehensive cohort-level analysis from both VCF and CSV files, GenMasterTable provides advanced functionality for merging, filtering, summarizing, and visualizing large-scale annotated datasets. Tailored for users without programming expertise, it enables rapid and accurate exploration of genetic variants, making it a practical solution for both research and clinical settings. ConclusionGenMasterTable addresses critical limitations in current variant analysis workflows by combining usability, data security, and scalability. Its support for multiple input formats and locally executed operations empowers clinicians, geneticists, and researchers to perform comprehensive variant analysis efficiently without the need for programming expertise.

bioinformatics↗