Extrapolating the Ligninolytic Ability of White-Rot Fungi during Solid-State Fermentation: A Critical Review
TL;DR
Although substantial empirical knowledge has accumulated regarding the ligninolytic capacities of individual white-rot species, the field still lacks a unifying quantitative framework capable of forecasting enzymatic performance across heterogeneous solid substrates and reactor formats, and it is proposed that hybrid mechanistic–data-driven models represent the most promising avenue for closing this gap.
Abstract
Lignin is the most recalcitrant component of the plant cell wall, and its selective removal underpins numerous biotechnological processes ranging from second-generation biofuel production to animal feed upgrading and industrial effluent treatment. White-rot fungi remain the most efficient natural agents of lignin mineralisation, owing to a suite of extracellular oxidoreductases that include laccase, lignin peroxidase, manganese peroxidase and versatile peroxidase. Solid-state fermentation, a cultivation mode that mimics the natural substrate ecology of wood-decaying basidiomycetes, has become the preferred platform for harnessing this ligninolytic machinery at a scale relevant to industry. Yet a persistent obstacle to translating laboratory findings into predictable, scalable outcomes is the substantial variability in enzyme titres and delignification efficiency observed across fungal species, substrates, process configurations and reactor geometries. This review critically synthesises the literature on the ligninolytic performance of white-rot fungi during solid-state fermentation, with particular emphasis on the methods by which such performance can be extrapolated from small-scale screening experiments to pilot- and industrial-scale operation. The enzymatic basis of lignin depolymerisation is first outlined, followed by an appraisal of interspecific and intraspecific variability in enzyme yield. Substrate and process parameters that govern ligninolytic expression are then examined, before the review turns to the principal extrapolation strategies reported in the literature: statistical and response-surface methodologies, kinetic and mechanistic modelling, data-driven and machine-learning approaches, co-cultivation strategies, and bioreactor engineering approaches to scale-up. Applications enabled by reliable extrapolation, spanning biopulping, ruminant nutrition and bioremediation, are discussed alongside the persistent gaps that limit predictive confidence. The review concludes that although substantial empirical knowledge has accumulated regarding the ligninolytic capacities of individual white-rot species, the field still lacks a unifying quantitative framework capable of forecasting enzymatic performance across heterogeneous solid substrates and reactor formats, and it proposes that hybrid mechanistic–data-driven models represent the most promising avenue for closing this gap.