Separating the effects of brown-rot and white-rot fungi on crystalline cellulose using Raman spectroscopy

Abstract Mushroom-forming wood-decay fungi are broadly categorized into white and brown rot. White-rot fungi decompose recalcitrant crystalline cellulose using a large array of hydrolytic and oxidative enzymes. Brown-rot fungi lack many of these enzymes but decompose cellulose via Fenton-generated hydroxyl radicals. To better understand these mechanisms, we developed a Raman spectroscopy-based method to study cellulose decomposition by two white-rot fungi ( Bjerkandera adusta and Trametes versicolor ), a brown-rot fungus ( Fomitopsis pinicola ), and a Stereaceae species of uncertain decay type. Raman spectra of fungi-decomposed cellulose were highly complex, reflecting physical and chemical cellulose modifications and fungal compounds like pigments. To extract signals only from decomposed cellulose and reduce data dimensionality, reference libraries were generated using chemicals that reduce crystallinity (NaOH) or oxidize cellulose (TEMPO). Chemical libraries reduced data complexity and facilitated extraction of cellulose decomposition signals, distinguishing white-rot from brown-rot effects. Wavenumbers related to oxidation better contributed to the separation of the two decomposition types. The reduced datasets also matched the decomposition characteristics of the uncertain decay fungus to those of the brown-rot fungus. The methodology developed here could be used to further characterize plant cell wall biopolymer decomposition in single fungus-single substrate setups and complex soil samples. Importance The degrading activity of saprotrophic fungi plays a crucial role in organic matter decomposition in terrestrial ecosystems, influencing nutrient cycling from plant material. Recently, there has been increasing interest in utilizing spectroscopic techniques for studying organic matter decomposition, as these methods are non-destructive. While Raman spectroscopy has been employed to identify and differentiate chemical compounds, its application to biological samples has been limited due to the complexity of spectral signals, which are challenging to interpret. In this study, we introduce a novel approach to reducing Raman spectral data to elucidate the mechanisms underlying fungal degradation of cellulose. This is achieved by utilizing reduced datasets derived from the spectral analysis of chemically modified cellulose. The dataset can be further expanded to include additional chemical treatments and fungal species, potentially revealing differences in cellulose degradation among various saprotrophs. Moreover, this approach can be adapted for use with other substrates or chemical processes and could be enhanced by integrating omics techniques.