Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, N-1432 Ås, Norway.
TINE SA, P.O. Box 7, Kalbakken, N-0902 Oslo, Norway.
Int J Food Microbiol. 2024 Jun 16;418:110706. doi: 10.1016/j.ijfoodmicro.2024.110706. Epub 2024 Apr 15.
The metaproteomics field has recently gained more and more interest as a valuable tool for studying both the taxonomy and function of microbiomes, including those used in food fermentations. One crucial step in the metaproteomics pipeline is selecting a database to obtain high-quality taxonomical and functional information from microbial communities. One of the best strategies described for building protein databases is using sample-specific or study-specific protein databases obtained from metagenomic sequencing. While this is true for high-diversity microbiomes (such as gut and soil), there is still a lack of validation for different database construction strategies in low-diversity microbiomes, such as those found in fermented dairy products where starter cultures containing few species are used. In this study, we assessed the performance of various database construction strategies applied to metaproteomics on two low-diversity microbiomes obtained from cheese production using commercial starter cultures and analyzed by LC-MS/MS. Substantial differences were detected between the strategies, and the best performance in terms of the number of peptides and proteins identified from the spectra was achieved by metagenomic-derived databases. However, extensive databases constructed from a high number of available online genomes obtained a similar taxonomical and functional annotation of the metaproteome compared to the metagenomic-derived databases. Our results indicate that, in the case of low-diversity dairy microbiomes, the use of publically available genomes to construct protein databases can be considered as an alternative to metagenome-derived databases.
宏蛋白质组学领域最近越来越受到关注，成为研究微生物组的分类学和功能的有价值的工具，包括用于食品发酵的微生物组。宏蛋白质组学分析流程中的一个关键步骤是选择数据库，以便从微生物群落中获得高质量的分类学和功能信息。用于构建蛋白质数据库的最佳策略之一是使用来自宏基因组测序的特定于样本或特定于研究的蛋白质数据库。虽然这种方法适用于高多样性的微生物组（如肠道和土壤），但在低多样性的微生物组（如发酵乳制品中使用少数物种的起始培养物）中，不同数据库构建策略的验证仍然缺乏。在这项研究中，我们评估了应用于使用商业起始培养物从奶酪生产中获得的两个低多样性微生物组的各种数据库构建策略在 LC-MS/MS 分析的宏蛋白质组学中的性能。在不同策略之间检测到了显著的差异，并且在从光谱中鉴定的肽和蛋白质数量方面，基于宏基因组的数据库表现最佳。然而，与基于宏基因组的数据库相比，从大量可用的在线基因组构建的广泛数据库对宏蛋白质组进行了类似的分类学和功能注释。我们的结果表明，在低多样性乳制品微生物组的情况下，使用公开可用的基因组来构建蛋白质数据库可以被认为是替代基于宏基因组的数据库的一种方法。
