Division of Medical Bioinformatics, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Siriraj Long-Read Lab (Si-LoL), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Sci Rep. 2023 Mar 20;13(1):4540. doi: 10.1038/s41598-023-31626-4.
The market for the application of probiotics as a livestock health improvement supplement has increased in recent years. However, most of the available products are quality-controlled using low-resolution techniques and un-curated databases, resulting in misidentification and incorrect product labels. In this work, we deployed two workflows and compared results obtained by full-length 16S rRNA genes (16S) and metagenomic (Meta) data to investigate their reliability for the microbial composition of both liquid and solid forms of animal probiotic products using Oxford Nanopore long-read-only (without short-read). Our result revealed that 16S amplicon data permits to detect the bacterial microbiota even with the low abundance in the samples. Moreover, the 16S approach has the potential to provide species-level resolution for prokaryotes but not for assessing yeast communities. Whereas, Meta data has more power to recover of high-quality metagenome-assembled genomes that enables detailed exploration of both bacterial and yeast populations, as well as antimicrobial resistance genes, and functional genes in the population. Our findings clearly demonstrate that implementing these workflows with long-read-only monitoring could be applied to assessing the quality and safety of probiotic products for animals and evaluating the quality of probiotic products on the market. This would benefit the sustained growth of the livestock probiotic industry.
近年来，益生菌作为牲畜健康改善补充剂的应用市场有所增加。然而，大多数可用的产品都是使用低分辨率技术和未经过审核的数据库进行质量控制的，这导致了错误的鉴定和不正确的产品标签。在这项工作中，我们部署了两个工作流程，并比较了全长 16S rRNA 基因（16S）和宏基因组（Meta）数据的结果，以调查它们在使用 Oxford Nanopore 长读长（无短读）的情况下，对液体和固体形式的动物益生菌产品微生物组成的可靠性。我们的结果表明，16S 扩增子数据即使在样本中丰度较低的情况下也可以检测到细菌微生物群。此外，16S 方法具有提供原核生物种水平分辨率的潜力，但不适合评估酵母群落。而 Meta 数据具有更高的能力来恢复高质量的宏基因组组装基因组，从而能够详细探索细菌和酵母种群以及种群中的抗生素耐药基因和功能基因。我们的研究结果清楚地表明，使用长读长监测来实施这些工作流程可以应用于评估动物益生菌产品的质量和安全性，并评估市场上益生菌产品的质量。这将有利于牲畜益生菌产业的持续增长。
