Biosciences Division, Argonne National Laboratory, Lemont, IL, United States; Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, United States; Department of Surgery, University of Chicago, Chicago, IL, United States.
Biosciences Division, Argonne National Laboratory, Lemont, IL, United States.
Forensic Sci Int. 2020 Aug;313:110351. doi: 10.1016/j.forsciint.2020.110351. Epub 2020 May 30.
When mapped to the environments we interact with on a daily basis, the 36 million microbial cells per hour that humans emit leave a trail of evidence that can be leveraged for forensic analysis. We employed 16S rRNA amplicon sequencing to map unique microbial sequence variants between human skin and building surfaces in three experimental conditions: over time during controlled and uncontrolled incidental interactions with a door handle, and during multiple mock burglaries in ten real residences. We demonstrate that humans (n = 30) leave behind microbial signatures that can be used to track interaction with various surfaces within a building, but the likelihood of accurately detecting the specific burglar for a given home was between 20-25%. Also, the human microbiome contains rare microbial taxa that can be combined to create a unique microbial profile, which when compared to 600 other individuals can improve our ability to link an individual 'burglar' to a residence. In total, 5512 discriminating, non-singleton unique exact sequence variants (uESVs) were identified as unique to an individual, with a minimum of 1 and a maximum of 568, suggesting some people maintain a greater degree of unique taxa compared to our population of 600. Approximate 60-77% of the unique exact sequence variants originated from the hands of participants, and these microbial discriminators spanned 36 phyla but were dominated by the Proteobacteria (34%). A fitted regression generated to determine whether an intruder's uESVs found on door handles in an office decayed over time in the presence or absence of office workers, found no significant shift in proportion of uESVs over time irrespective of the presence of office workers. While it was possible to detect the correct burglars' microbiota as having contributed to the invaded space, the predictions were very weak in comparison to accepted forensic standards. This suggests that at this time 16S rRNA amplicon sequencing of the built environment microbiota cannot be used as a reliable trace evidence standard for criminal investigations.
当将这些数据映射到我们日常生活中所接触的环境时，人类每小时排放的 3600 万微生物细胞会留下一系列证据，这些证据可以用于法医分析。我们采用 16S rRNA 扩增子测序技术，在三种实验条件下（受控和非受控偶然接触门把手时随时间推移的情况，以及在十个真实住宅中进行的多次模拟入室盗窃的情况），绘制人类皮肤与建筑表面之间独特微生物序列变体图谱。结果表明，人类（n=30）会留下微生物特征，可以用于追踪与建筑物内各种表面的接触情况，但准确检测特定住宅中特定入侵者的可能性在 20-25%之间。此外，人类微生物组中含有稀有微生物类群，可以将其组合创建独特的微生物图谱，将其与 600 名其他个体进行比较，可以提高我们将特定“入侵者”与住宅联系起来的能力。总共确定了 5512 个具有区分度的、非单倍体的独特精确序列变异（uESV），这些变异是个体特有的，最少为 1 个，最多为 568 个，这表明有些人比我们 600 人的人群更能保持独特的分类群。大约 60-77%的独特精确序列变异来自参与者的手部，这些微生物分类器跨越 36 个门，但以 Proteobacteria（34%）为主。拟合回归模型来确定办公室中入侵者的 uESV 是否会随着时间的推移在有或没有办公人员的情况下在门把手处衰减，发现无论办公人员是否存在，uESV 的比例在时间上没有明显变化。虽然有可能检测到正确的入侵者的微生物群落在入侵空间中作出了贡献，但与公认的法医标准相比，这些预测非常微弱。这表明，目前情况下，16S rRNA 扩增子测序的建筑环境微生物群不能作为可靠的犯罪调查痕迹证据标准。
