Center for Human Identification, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX 76107, USA; Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd', Fort Worth, TX 76107, USA.
Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, 3500 Camp Bowie Blvd', Fort Worth, TX 76107, USA.
Forensic Sci Int Genet. 2019 Jan;38:130-139. doi: 10.1016/j.fsigen.2018.10.003. Epub 2018 Oct 5.
From the perspective of forensics genetics, the human microbiome is a rich, relatively untapped resource for human identity testing. Since it varies within and among people, and perhaps temporally, the potential forensic applications of the use of the microbiome can exceed that of human identification. However, the same inherent variability in microbial distributions may pose a substantial barrier to forming predictions on an individual as the source of the microbial sample unless stable signatures of the microbiome are identified and targeted. One of the more commonly adopted strategies for microbial human identification relies on quantifying which taxa are present and their respective abundance levels. It remains an open question if such microbial signatures are more individualizing than estimates of the degree of genetic relatedness between microbial samples. This study attempts to address this question by contrasting two prediction strategies. The first approach uses phylogenetic distance to predict the host individual; thus it operates under the premise that microbes within individuals are more closely related than microbes between/among individuals. The second approach uses population genetic measures of diversity at clade-specific markers, serving as a fine-grained assessment of microbial composition and quantification. Both assessments were performed using targeted sequencing of 286 markers from 22 microbial taxa sampled in 51 individuals across three body sites measured in triplicate. Nearest neighbor and reverse nearest neighbor classifiers were constructed based on the pooled data and yielded 71% and 78% accuracy, respectively, when diversity was considered, and performed significantly worse when a phylogenetic distance was used (54% and 63% accuracy, respectively). However, empirical estimates of classification accuracy were 100% when conditioned on a maximum nearest neighbor distance when diversity was used, while identification based on a phylogenetic distance failed to reach saturation. These findings suggest that microbial strain composition is more individualizing than that of a phylogeny, perhaps indicating that microbial composition may be more individualizing than recent common ancestry. One inference that may be drawn from these findings is that host-environment interactions may maintain the targeted microbial profile and that this maintenance may not necessarily be repopulated by intra-individual microbial strains.
从法医学遗传学的角度来看,人类微生物组是一个丰富的、相对未被开发的资源,可用于进行人类身份测试。由于它在人与人之间以及可能在时间上存在差异,因此使用微生物组进行潜在的法医应用可能会超过人类识别。然而,微生物分布的固有变异性可能会对形成关于个体作为微生物样本来源的预测构成实质性障碍,除非确定并针对微生物组的稳定特征进行预测。一种更常用的微生物人类识别策略是量化存在哪些分类群及其各自的丰度水平。微生物特征是否比微生物样本之间遗传相关性的估计更具个体特异性,这仍然是一个悬而未决的问题。本研究试图通过对比两种预测策略来解决这个问题。第一种方法使用系统发育距离来预测宿主个体;因此,它的前提是个体内部的微生物比个体之间的微生物更密切相关。第二种方法使用特定于类群的标记的种群遗传多样性措施,作为微生物组成和量化的精细评估。这两种评估都是使用靶向测序在三个部位的 51 名个体中采集的 22 个微生物分类群中的 286 个标记进行的,每个部位重复测量了三次。基于 pooled 数据构建了最近邻和反向最近邻分类器,在考虑多样性时,分别产生了 71%和 78%的准确性,而使用系统发育距离时则表现明显更差(分别为 54%和 63%的准确性)。然而,当考虑最大最近邻距离时,使用多样性的分类准确性的经验估计值为 100%,而基于系统发育距离的识别则未达到饱和。这些发现表明,微生物菌株组成比系统发育更具个体特异性,这可能表明微生物组成可能比最近的共同祖先更具个体特异性。从这些发现中可以得出一个推断,即宿主-环境相互作用可能维持目标微生物谱,而这种维持不一定是通过个体内部的微生物菌株重新定植来实现的。