Improved GA-ANN based optimized bio-deinking strategy for laccase produced from Trametes versicolor GGRK18, and its gene co-expression network analysis

Abstract

Recycling of waste paper through chemical deinking process led the generation of toxic agents and caused severe issues. Therefore, an alternative deinking process of recycled waste paper is necessity to sustain the eco-friendly environment. This study reports the production of laccase from Trametes versicolor GGRK18 using a genetic algorithm-artificial neural network (GA-ANN) tool, resulting in increased 21.5-fold i.e., 42.06 ± 1.1–906.17 ± 46.76 U/mL laccase activity. The biochemical studies revealed that laccase showed optimum activity at 60 ℃ and pH 4.0, retaining more than 90 % residual activity . Interestingly, the metal ion K+ influenced the laccase activity by 4830.91 ± 129.3 U/mL, and obtained laccase showed an apparent K <inf>m</inf> value of 0.5 µM and V <inf>max</inf> of 1666.67 µmol/mL/min. Furthermore, the deinking efficiency was improved by 48 % and 29.3 % for photocopier and newspaper, while the brightness increased by 34.6 % and 10.4 %, respectively, compared to the control values. The tearing index was significantly improved with 18.2 % and burst factor efficiently decreased by 21.1 % of deinked pulp. Furthermore, our study employs Weighted Correlation Network Analysis (WGCNA)-an R package designed to elucidate gene interactions– using next-generation sequencing data. The resulting network revealed over 70000 interactions among approximately 8917 unique genes, which clustered into 11 distinct modules of co-expressed genes; importantly, laccase genes exhibited co-expression patterns associated with various metabolic processes and oxidative stress pathways. In addition, this study also gives a combinatory strategy for waste paper recycling using laccase-mediated paper deinking and its mechanistic understanding of co-expressed genes in T. versicolor. © 2025 Elsevier Ltd.