Dynamics of microbial community composition during degradation of silks in burial environment.

The silk residues in the soil formed the unique niche, termed "silksphere." Here, we proposed a hypothesis that silksphere microbiota have great potential as a biomarker for unraveling the degradation of the ancient silk textiles with great archaeological and conservation values. To test our hypothesis, in this study, we monitored the dynamics of microbial community composition during silk degradation via both indoor soil microcosmos model and outdoor environment with amplicon sequencing against 16S and ITS gene. Microbial community divergence was evaluated with Welch two sample t-test, PCoA, negative binomial generalized log-linear model and clustering, etc. Community assembly mechanisms differences between silksphere and bulk soil microbiota were compared with dissimilarity-overlap curve (DOC) model, Neutral model and Null model. A well-established machine learning algorithm, random forest, was also applied to the screening of potential biomarkers of silk degradation. The results illustrated the ecological and microbial variability during the microbial degradation of silk. Vast majority of microbes populating the silksphere microbiota strongly diverged from those in bulk soil. Certain microbial flora can serve as an indicator of silk degradation, which would lead to a novel perspective to perform identification of archaeological silk residues in the field. To sum up, this study provides a new perspective to perform the identification of archaeological silk residue through the dynamics of microbial communities.