Belk Aeriel, Xu Zhenjiang Zech, Carter David O, Lynne Aaron, Bucheli Sibyl, Knight Rob, Metcalf Jessica L
Department of Animal Sciences, Colorado State University, Fort Collins, CO 80525, USA.
Department of Pediatrics, University of California, La Jolla, San Diego, CA 92093, USA.
Genes (Basel). 2018 Feb 16;9(2):104. doi: 10.3390/genes9020104.
Death investigations often include an effort to establish the postmortem interval (PMI) in cases in which the time of death is uncertain. The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment. Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types (gravesoil, skin of the torso, skin of the head), gene markers (16S ribosomal RNA (rRNA), 18S rRNA, internal transcribed spacer regions (ITS)), and taxonomic levels (sequence variants, species, genus, etc.). We also tested whether particular suites of indicator microbes were informative across different datasets. Generally, results indicate that the most accurate models for predicting PMI were built using gravesoil and skin data using the 16S rRNA genetic marker at the taxonomic level of phyla. Additionally, several phyla consistently contributed highly to model accuracy and may be candidate indicators of PMI.
在死亡时间不确定的案件中，死因调查通常包括确定死后间隔时间（PMI）的工作。死后间隔时间有助于识别死者，并验证证人陈述和嫌疑人的不在场证明。最近的研究表明，微生物提供了一个准确的时钟，从死亡时开始计时，并依赖于通常栖息在尸体及其周围环境中的微生物群落的生态变化。在此，我们通过测试基于不同样本类型（墓土、躯干皮肤、头部皮肤）、基因标记（16S核糖体RNA（rRNA）、18S rRNA、内转录间隔区（ITS））和分类水平（序列变体、物种、属等）构建的模型，探索如何构建最强大的随机森林回归模型来预测PMI。我们还测试了特定的指示微生物组合在不同数据集中是否具有信息价值。一般来说，结果表明，预测PMI最准确的模型是使用墓土和皮肤数据，在门的分类水平上使用16S rRNA基因标记构建的。此外，有几个门始终对模型准确性有很大贡献，可能是PMI的候选指示物。
