Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China.
Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong 510515, China; National Anti-Drug Laboratory Guangdong Regional Center, Guangzhou, China.
Forensic Sci Int Genet. 2024 May;70:103020. doi: 10.1016/j.fsigen.2024.103020. Epub 2024 Jan 24.
The microbiome of saliva stains deposited at crime scenes and in everyday settings is valuable for forensic investigations and environmental ecology. However, the dynamics and applications of microbial communities in these saliva stains have not been fully explored. In this study, we analyzed saliva samples that were exposed to indoor conditions for up to 1 year and to different carriers (cotton, sterile absorbent cotton swab, woolen, dacron) in both indoor and outdoor environments for 1 month using high-throughput sequencing. The analysis of microbial composition and Mfuzz clustering showed that the salivary flora, specifically Streptococcus (cluster7), which was associated with microbial contamination, remained stable over short periods of time. However, prolonged exposure led to significant differences due to the invasion of environmental bacteria such as Pseudomonas and Achromobacter. The growth and colonization of environmental flora were promoted by humidity. The neutral model predictions indicated that the assembly of salivary microbial communities in outdoor environments was significantly influenced by stochastic processes, with environmental characteristics having a greater impact on community change compared to surface characteristics. By incorporating data from previous studies on fecal and vaginal secretion microbiology, we developed RF and XGBoost classification models that achieved high accuracy (>98 %) and AUC (>0.8). Additionally, a RF regression model was created to determine the time since deposition (TsD) of the stains. Time inference models yielded a mean absolute error (MAE) of 7.1 days for stains exposed for 1 year and 14.2 h for stains exposed for 14 days. These findings enhance our understanding of the changes in the microbiome of saliva stains over time, in different environments, and on different surfaces. They also have potential applications in assessing potential microbial contamination, identifying body fluids, and inferring the time of deposition.
唾液斑在犯罪现场和日常环境中的微生物组对于法医调查和环境生态学具有重要价值。然而,这些唾液斑中微生物群落的动态和应用尚未得到充分探索。在这项研究中,我们使用高通量测序分析了在室内条件下暴露长达 1 年的唾液样本,以及在室内和室外环境中使用不同载体(棉花、无菌棉签、羊毛、涤纶)暴露 1 个月的唾液样本。微生物组成分析和 Mfuzz 聚类分析表明,唾液菌群,特别是与微生物污染相关的链球菌(cluster7),在短时间内保持稳定。然而,长时间暴露会因环境细菌如假单胞菌和不动杆菌的入侵而导致显著差异。湿度促进了环境菌群的生长和定植。中性模型预测表明,户外环境中唾液微生物群落的组装受到随机过程的显著影响,与表面特征相比,环境特征对群落变化的影响更大。通过整合先前关于粪便和阴道分泌物微生物组学的研究数据,我们开发了 RF 和 XGBoost 分类模型,这些模型实现了>98%的高准确率和>0.8 的 AUC。此外,还创建了一个 RF 回归模型来确定污渍的沉积时间(TsD)。时间推断模型对暴露 1 年的污渍的平均绝对误差(MAE)为 7.1 天,对暴露 14 天的污渍的 MAE 为 14.2 小时。这些发现增强了我们对不同环境和不同表面的唾液斑中微生物组随时间变化的理解。它们还具有评估潜在微生物污染、识别体液和推断沉积时间的潜在应用。