Using Wood Rot Phenotypes to Illuminate the “Gray” Among Decomposer Fungi

Abstract

Wood-decomposing fungi use distinct strategies to deconstruct wood that can significantly vary carbon release rates and fates. White and brown rot-type fungi attack lignin as a prerequisite to access carbohydrates (white rot) or selectively remove carbohydrates (brown rot). Soft rot fungi use less well-studied mechanisms to deconstruct wood (e.g., cavitation and erosion). These fungi often co-exist in nature, creating a balance in carbon turnover that could presumably “tip” in a changing climate. There is no simple genetic marker, however, to distinguish fungi by rot types, and traditional black and white distinctions (brown and white, in this case) cannot explain a spectrum of “gray” carbon loss possibilities. In this study, we tested 39 wood-degrading fungal strains along this spectrum of rot types. We tracked wood mass loss and chemical changes in aspen blocks in early- to mid-decay stages, including three signatures of fungal nutritional mode measured from wood rather than from fungus: dilute alkali solubility, water-soluble monosaccharides, and lignin loss (%) relative to density loss (%) (L/D). Results were then plotted relative to rot types and correlated with gene counts, combining new data with past results in some cases. Results yielded a novel distinction in soluble monosaccharide patterns for brown rot fungi, and reliable distinctions between white and brown rot fungi, although soft rot fungi were not as clearly distinguished as suggested in past studies. Gene contents (carbohydrate-active enzymes and peroxidases) also clearly distinguished brown and white rot fungi, but did not offer reliable correlation with lignin vs. carbohydrate selectivity. These results support the use of wood residue chemistry to link fungal genes (with known or unknown function) with emergent patterns of decomposition. Wood signatures, particularly L/D, not only confirm the rot type of dominant fungi, but they offer a more nuanced, continuous variable to which we can correlate genomic, transcriptomic, and secretomic evidence rather than limit it to functional categories as distinct “bins.”