Application of liquid-based colorimetric method for high throughput screening of bioplastic-degrading strains using esterase assay

To alleviate environmental problems caused by using conventional plastics, bioplastics have garnered significant interest as alternatives to petroleum-based plastics. Despite possessing better degradability traits compared to traditional plastics, the degradation of bioplastics still demands a longer duration than initially anticipated. This necessitates the utilization of degradation strains or enzymes to enhance degradation efficiency, ensuring timely degradation. In this study, a novel screening method to identify bioplastic degraders faster was suggested to circumvent the time-consuming and laborious characteristics of solid-based plate assays. This liquid-based colorimetric method confirmed the extracellular esterase activity with p-nitrophenyl esters. It eliminated the needs to prepare plastic emulsion plates at the initial screening system, shortening the time for the overall screening process and providing more quantitative data. p-nitrophenyl hexanoate (C6) was considered the best substrate among the various p-nitrophenyl esters as substrates. The screening was performed in liquid-based 96well plates, resulting in the discovery of a novel strain, Bacillus sp. SH09, with a similarity of 97.4% with Bacillus licheniformis. Furthermore, clear zone assays, degradation investigations, scanning electron microscopy, and gel permeation chromatography were conducted to characterize the biodegradation capabilities of the new strain, the liquid-based approach offered a swift and less labor-intensive option during the initial stages.

Automated summary

To address environmental issues caused by traditional plastics, bioplastics have gained attention as alternatives. Although bioplastics have better degradability, their degradation still takes longer than anticipated. This study proposes a novel screening method to identify bioplastic degraders faster, saving time and providing more quantitative data.