Microorganisms are potential markers for identifying body fluids (venous and menstrual blood, semen, saliva, and vaginal secretion) and skin tissue in forensic genetics. Existing published studies have mainly focused on investigating microbial DNA by 16 S rRNA gene sequencing or metagenome shotgun sequencing. We rarely find microbial RNA level investigations on common forensic body fluid/tissue. Therefore, the use of metatranscriptomics to characterize common forensic body fluids/tissue has not been explored in detail, and the potential application of metatranscriptomics in forensic science remains unknown. Here, we performed 30 metatranscriptome analyses on six types of common forensic sample from healthy volunteers by massively parallel sequencing. After quality control and host RNA filtering, a total of 345,300 unigenes were assembled from clean reads. Four kingdoms, 137 phyla, 267 classes, 488 orders, 985 families, 2052 genera, and 4690 species were annotated across all samples. Alpha- and beta-diversity and differential analysis were also performed. As a result, the saliva and skin groups demonstrated high alpha diversity (Simpson index), while the venous blood group exhibited the lowest diversity despite a high Chao1 index. Specifically, we discussed potential microorganism contamination and the “core microbiome,” which may be of special interest to forensic researchers. In addition, we implemented and evaluated artificial neural network (ANN), random forest (RF), and support vector machine (SVM) models for forensic body fluid/tissue identification (BFID) using genus- and species-level metatranscriptome profiles. The ANN and RF prediction models discriminated six forensic body fluids/tissue, demonstrating that the microbial RNA-based method could be applied to BFID. Unlike metagenomic research, metatranscriptomic analysis can provide information about active microbial communities; thus, it may have greater potential to become a powerful tool in forensic science for microbial-based individual identification. This study represents the first attempt to explore the application potential of metatranscriptome profiles in forensic science. Our findings help deepen our understanding of the microorganism community structure at the RNA level and are beneficial for other forensic applications of metatranscriptomics.

微生物是法医遗传学中识别体液（静脉血和经血、精液、唾液和阴道分泌物）和皮肤组织的潜在标记。现有已发表的研究主要集中在通过 16 S rRNA基因测序或宏基因组鸟枪法测序来研究微生物 DNA。我们很少发现对普通法医体液/组织进行微生物RNA水平的研究。因此，利用宏转录组学来表征常见的法医体液/组织尚未得到详细探索，宏转录组学在法医学中的潜在应用仍然未知。在这里，我们通过大规模并行测序对来自健康志愿者的六种常见法医样本进行了 30 项宏转录组分析。经过质量控制和宿主 RNA 过滤，总共 345,300 个 unigenes 从干净的读数中组装而成。所有样本均注释了四个界、137 个门、267 个纲、488 个目、985 个科、2052 个属和 4690 个物种。还进行了α-和β-多样性和差异分析。结果，唾液和皮肤组表现出较高的 α 多样性（辛普森指数），而静脉血型尽管 Chao1 指数较高，但表现出最低的多样性。具体来说，我们讨论了潜在的微生物污染和“核心微生物组”，这可能是法医研究人员特别感兴趣的。此外，我们还使用属和种级别的宏转录组图谱实施并评估了用于法医体液/组织识别 (BFID) 的人工神经网络 (ANN)、随机森林 (RF) 和支持向量机(SVM) 模型。ANN 和 RF 预测模型区分了六种法医体液/组织，证明基于微生物 RNA 的方法可以应用于 BFID。与宏基因组研究不同，宏转录组分析可以提供有关活跃微生物群落的信息；因此，它可能有更大的潜力成为法医学中基于微生物的个体识别的强大工具。这项研究是探索元转录组谱在法医学中应用潜力的首次尝试。我们的研究结果有助于加深我们对 RNA 水平上微生物群落结构的理解，并且有利于宏转录组学的其他法医应用。