Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, 100193, Beijing, PR China.
Technology Center of Xiamen Entry-exit Inspection and Quarantine Bureau, Xiamen, Fujian, 361026, PR China.
BMC Genomics. 2022 Jan 7;23(1):36. doi: 10.1186/s12864-021-08263-0.
Bioassessment and biomonitoring of meat products are aimed at identifying and quantifying adulterants and contaminants, such as meat from unexpected sources and microbes. Several methods for determining the biological composition of mixed samples have been used, including metabarcoding, metagenomics and mitochondrial metagenomics. In this study, we aimed to develop a method based on next-generation DNA sequencing to estimate samples that might contain meat from 15 mammalian and avian species that are commonly related to meat bioassessment and biomonitoring.
In this project, we found the meat composition from 15 species could not be identified with the metabarcoding approach because of the lack of universal primers or insufficient discrimination power. Consequently, we developed and evaluated a meat mitochondrial metagenomics (3MG) method. The 3MG method has four steps: (1) extraction of sequencing reads from mitochondrial genomes (mitogenomes); (2) assembly of mitogenomes; (3) mapping of mitochondrial reads to the assembled mitogenomes; and (4) biomass estimation based on the number of uniquely mapped reads. The method was implemented in a python script called 3MG. The analysis of simulated datasets showed that the method can determine contaminant composition at a proportion of 2% and the relative error was < 5%. To evaluate the performance of 3MG, we constructed and analysed mixed samples derived from 15 animal species in equal mass. Then, we constructed and analysed mixed samples derived from two animal species (pork and chicken) in different ratios. DNAs were extracted and used in constructing 21 libraries for next-generation sequencing. The analysis of the 15 species mix with the method showed the successful identification of 12 of the 15 (80%) animal species tested. The analysis of the mixed samples of the two species revealed correlation coefficients of 0.98 for pork and 0.98 for chicken between the number of uniquely mapped reads and the mass proportion.
To the best of our knowledge, this study is the first to demonstrate the potential of the non-targeted 3MG method as a tool for accurately estimating biomass in meat mix samples. The method has potential broad applications in meat product safety.
对肉类产品进行生物评估和生物监测的目的是识别和量化掺假剂和污染物，如来自意外来源的肉类和微生物。已经使用了几种方法来确定混合样品的生物组成，包括代谢组学、宏基因组学和线粒体宏基因组学。在这项研究中，我们旨在开发一种基于下一代 DNA 测序的方法，以估计可能含有来自 15 种哺乳动物和禽类的肉类的样品，这些物种通常与肉类生物评估和生物监测有关。
在本项目中，我们发现由于缺乏通用引物或识别能力不足，代谢组学方法无法识别 15 种物种的肉类组成。因此，我们开发并评估了一种肉类线粒体宏基因组学（3MG）方法。3MG 方法有四个步骤：（1）从线粒体基因组（线粒体基因组）中提取测序reads；（2）组装线粒体基因组；（3）将线粒体reads 映射到组装的线粒体基因组；（4）基于唯一映射reads 的数量进行生物量估计。该方法在一个名为 3MG 的 python 脚本中实现。对模拟数据集的分析表明，该方法可以在 2%的比例下确定污染物的组成，相对误差<5%。为了评估 3MG 的性能，我们构建并分析了由 15 种动物物种等质量混合的样品。然后，我们构建并分析了由两种动物物种（猪肉和鸡肉）以不同比例混合的样品。提取 DNA 并用于构建 21 个用于下一代测序的文库。用该方法对 15 种物种混合样品的分析表明，成功识别了测试的 15 种动物物种中的 12 种（80%）。两种物种混合样品的分析显示，猪肉和鸡肉的唯一映射reads 数量与质量比例之间的相关系数分别为 0.98 和 0.98。
据我们所知，这项研究首次证明了非靶向 3MG 方法作为一种准确估计肉类混合样品生物量的工具的潜力。该方法在肉类产品安全方面具有广泛的应用潜力。
