16S rRNA 基因序列分析的选择影响了乳制品加工环境中高度多变的表面微生物群的特征描述。

IF 5 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2024-10-21 DOI:10.1128/msystems.00620-24
Sarah E Daly, Jingzhang Feng, Devin Daeschel, Jasna Kovac, Abigail B Snyder
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引用次数: 0

摘要

准确了解从食品加工环境表面收集的微生物群对食品质量和安全非常重要。本研究评估了从乳制品加工环境表面采集的微生物群中提取的 16S rRNA 基因序列分析的八个不同生物信息学工作流程所产生的分类组成、α 和β多样性值的差异。我们发现,从环境表面采集的微生物群在密度(0-9.09 log10 CFU/cm2)和香农α多样性(0.01-3.40)方面差异很大。因此,根据所使用的序列分析方法,低丰度菌属(即相对丰度低于 1%)的特征和已鉴定菌属的数量(114-173 个)差异很大。包括李斯特菌在内的一些低丰度属在扩增子序列变异法(ASV)和操作分类单元法(OTU)之间存在差异。与稀释法相比,以对数比率为中心的转换使α和β多样性值增大。此外,与 OTU 方法相比,ASV 方法也提高了阿尔法和贝塔多样性值(P < 0.05)。因此,对于稀疏、不均匀、低密度的数据集,OTU 法和稀释法更适合表面微生物群的分类和生态特征描述。有人建议使用与培养无关的 16S rRNA 扩增子测序来描述这种表面微生物群,作为加强环境监测的一种工具。然而,对于最合适的生物信息学分析方法还没有达成共识,无法准确捕捉食品加工环境中表面细菌的不同水平和类型。在此,我们量化了不同生物信息学分析对从纽约州三家培养乳制品厂采集的 16S rRNA 扩增子序列的结果和解释的影响。这项研究为研究乳制品加工环境中的环境微生物群选择合适的 16S rRNA 分析程序提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The choice of 16S rRNA gene sequence analysis impacted characterization of highly variable surface microbiota in dairy processing environments.

Accurate knowledge of the microbiota collected from surfaces in food processing environments is important for food quality and safety. This study assessed discrepancies in taxonomic composition and alpha and beta diversity values generated from eight different bioinformatic workflows for the analysis of 16S rRNA gene sequences extracted from the microbiota collected from surfaces in dairy processing environments. We found that the microbiota collected from environmental surfaces varied widely in density (0-9.09 log10 CFU/cm2) and Shannon alpha diversity (0.01-3.40). Consequently, depending on the sequence analysis method used, characterization of low-abundance genera (i.e., below 1% relative abundance) and the number of genera identified (114-173 genera) varied considerably. Some low-abundance genera, including Listeria, varied between the amplicon sequence variant (ASV) and operational taxonomic unit (OTU) methods. Centered log-ratio transformation inflated alpha and beta diversity values compared to rarefaction. Furthermore, the ASV method also inflated alpha and beta diversity values compared to the OTU method (P < 0.05). Therefore, for sparse, uneven, low-density data sets, the OTU method and rarefaction are better for taxonomic and ecological characterization of surface microbiota.IMPORTANCECulture-dependent environmental monitoring programs are used by the food industry to identify foodborne pathogens and spoilage biota on surfaces in food processing environments. The use of culture-independent 16S rRNA amplicon sequencing to characterize this surface microbiota has been proposed as a tool to enhance environmental monitoring. However, there is no consensus on the most suitable bioinformatic analyses to accurately capture the diverse levels and types of bacteria on surfaces in food processing environments. Here, we quantify the impact of different bioinformatic analyses on the results and interpretation of 16S rRNA amplicon sequences collected from three cultured dairy facilities in New York State. This study provides guidance for the selection of appropriate 16S rRNA analysis procedures for studying environmental microbiota in dairy processing environments.

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来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
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