Maillard, F. | bioRxiv Science

bioRxiv2024

Necromass chemistry interacts with soil mineral and microbial properties to determine fungal carbon and nitrogen persistence in soils

Despite the importance of mineral-associated organic matter (MAOM) in long-term soil carbon (C) and nitrogen (N) persistence, and the significant contribution of fungal necromass to this pool, the factors controlling the formation of fungal-derived MAOM remain unclear. This study investigated how fungal necromass chemistry, specifically melanin, interacts with soil mineral properties and microbial communities to influence MAOM formation and persistence. We cultured the fungus Hyaloscypha bicolor to produce {superscript 1}3C- and {superscript 1}{square}N-labeled necromass with varying melanin content (high or low) and incubated it in both live and sterile soils collected from six Indiana forests that differed in their clay and iron oxide (FeOx) content. After 38 days, we found that seven times more fungal-derived N was incorporated into MAOM than fungal-derived C, with fungal N comprising 20% of the MAOM-N pool. Low melanin necromass formed more MAOM-C than high melanin necromass, although site-level differences in overall MAOM formation were substantial. Soil clay and FeOx content were strong predictors of MAOM formation, explaining [~]60% and [~]68% of the variation in MAOM-C and MAOM-N, respectively. However, microbial communities significantly influenced MAOM formation, with MAOM-C formation enhanced and MAOM-N formation reduced in sterile soils. Furthermore, the relative abundance of fungal saprotrophs was negatively correlated, and bacterial richness was positively correlated with MAOM formation, and these relationships were influenced by necromass melanin content. This study reveals that microbial communities and soil properties interactively mediate the incorporation of fungal necromass C and N into MAOM, with microbes differentially influencing C and N incorporation, and these processes being further modulated by necromass melanization.

ecology↗

bioRxiv2023

Bacterial community characterization by deep learning aided image analysis in soil chips

Soil microbes play an important role in governing global processes such as carbon cycling, but it is challenging to study them embedded in their natural environment and at the single cell level due to the opaque nature of the soil. Nonetheless, progress has been achieved in recent years towards visualizing microbial activities and organo-mineral interaction at the pore scale, especially thanks to the development of microfluidic soil chips creating transparent soil model habitats. Image-based analyses come with new challenges as manual counting of bacteria in thousands of digital images taken from the soil chips is excessively time-consuming, while simple thresholding cannot be applied due to the background of soil minerals and debris. Here, we adopt the well-developed deep learning algorithm Mask-RCNN to quantitatively analyse the bacterial communities in soil samples from different locations in the world. This work demonstrates analysis of bacterial abundance from three contrasting locations (Greenland, Sweden and Kenya) using deep learning in microfluidic soil chips in order to characterize population and community dynamics. We additionally quantified cell- and colony morphology including cell size, shape and the cell aggregation level via calculation of the distance to the nearest neighbor. This approach allows for the first time an automated visual investigation of soil bacterial communities, and a crude biodiversity measure based on phenotypic cell morphology, which could become a valuable complement to molecular studies.

ecology↗