Department of Archaeology, Cultural Heritage and Museology, Zhejiang University, Hangzhou 310028, China.
Department of Archaeology, Cultural Heritage and Museology, Zhejiang University, Hangzhou 310028, China.
Sci Total Environ. 2023 Jul 20;883:163694. doi: 10.1016/j.scitotenv.2023.163694. Epub 2023 Apr 25.
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.
土壤中的丝残留物形成了独特的小生境，称为“丝球”。在这里，我们提出了一个假设，即丝球微生物群具有很大的潜力作为解开具有重要考古和保护价值的古代丝绸纺织品降解的生物标志物。为了验证我们的假设，在这项研究中，我们通过室内土壤微宇宙模型和室外环境，使用扩增子测序对 16S 和 ITS 基因进行了研究，监测了丝降解过程中微生物群落组成的动态变化。使用 Welch 两样本 t 检验、PCoA、负二项式广义对数线性模型和聚类等方法评估了微生物群落的差异。通过差异重叠曲线（DOC）模型、中性模型和空模型比较了丝球和体土微生物群之间群落组装机制的差异。还应用了一种成熟的机器学习算法——随机森林，对丝降解的潜在生物标志物进行筛选。结果说明了丝在微生物降解过程中的生态和微生物变化。绝大多数定殖于丝球微生物群的微生物与体土中的微生物有很大的差异。某些微生物菌群可以作为丝降解的指示物，这将为在野外进行考古丝残留物的鉴定提供新的视角。总之，这项研究通过微生物群落的动态变化为通过考古丝残留物的鉴定提供了一个新的视角。
