Comparing DNA isolation and sequencing strategies for 16S rRNA gene amplicon analysis in biofilm containing environments

IF 1.7 4区生物学 Q4 BIOCHEMICAL RESEARCH METHODS

Journal of microbiological methods Pub Date : 2024-03-16 DOI:10.1016/j.mimet.2024.106921

Ilgaz Cakin , Barbara Morrissey , Matthew Gordon , Paul P.J. Gaffney , Lucio Marcello , Kenneth Macgregor , Mark A. Taggart

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引用次数: 0

Abstract

Bacteria are primarily responsible for biological water treatment processes in constructed wetland systems. Gravel in constructed wetlands serves as an essential substrate onto which complex bacterial biofilms may successfully grow and evolve. To fully understand the bacterial community in these systems it is crucial to properly isolate biofilms and process DNA from such substrates. This study looked at how best to isolate bacterial biofilms from gravel substrates in terms of bacterial richness. It considered factors including the duration of agitation during extraction, extraction temperature, and enzyme usage. Further, the 16S taxonomy data subsequently produced from Illumina MiSeq reads (using the SILVA 132 ribosomal RNA (rRNA) database on the DADA2 pipeline) were compared with the 16S data produced from Oxford Nanopore Technologies (ONT) MinION reads (using the NCBI 16S database on the EPI2ME pipeline). Finally, performance was tested by comparing the taxonomy data generated from the Illumina MiSeq and ONT MinION reads using the same (SILVA 132) database. We found no significant differences in the effective number of species observed when using different bacterial biofilm detachment techniques. However, enzyme treatment enhanced the total concentration of DNA. In terms of wetland community profiles, relative abundance differences within each sample type were clearer at the genus level. For genus-level taxonomic classification, MinION sequencing with the EPI2ME pipeline (NCBI database) produced bacterial abundance information that was poorly correlated with that from the Illumina MiSeq and DADA2 pipelines (SILVA132 database). When using the same database for each sequencing technology (SILVA132), the correlation between relative abundances at genus-level improved from negligible to moderate. This study provides detailed information of value to researchers working on constructed wetlands regarding efficient biofilm detachment techniques for DNA isolation and 16 s metabarcoding platforms for sequencing and data analysis.

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比较在含有生物膜的环境中进行 16S rRNA 基因扩增片段分析的 DNA 分离和测序策略。

在人工湿地系统中，细菌是水处理过程中的主要生物。人工湿地中的砾石是复杂细菌生物膜成功生长和进化的重要基质。要全面了解这些系统中的细菌群落，关键是要正确分离生物膜并处理来自此类基质的 DNA。本研究探讨了如何从砾石基质中分离细菌生物膜的最佳细菌丰富度。研究考虑的因素包括提取过程中的搅拌时间、提取温度和酶的使用。此外，Illumina MiSeq 读数（在 DADA2 管道上使用 SILVA 132 核糖体 RNA (rRNA) 数据库）随后产生的 16S 分类数据与牛津纳米孔技术公司 (ONT) MinION 读数（在 EPI2ME 管道上使用 NCBI 16S 数据库）产生的 16S 数据进行了比较。最后，通过比较 Illumina MiSeq 和 ONT MinION 读取（使用相同的 (SILVA 132) 数据库）生成的分类数据，对性能进行了测试。我们发现，在使用不同的细菌生物膜剥离技术时，观察到的有效物种数量没有明显差异。不过，酶处理提高了 DNA 的总浓度。就湿地群落概况而言，每种样本类型内的相对丰度差异在属一级更为明显。在属一级的分类中，使用 EPI2ME 管道（NCBI 数据库）进行 MinION 测序产生的细菌丰度信息与来自 Illumina MiSeq 和 DADA2 管道（SILVA132 数据库）的信息相关性较差。当每种测序技术使用相同的数据库（SILVA132）时，属级相对丰度之间的相关性从可忽略不计提高到中等水平。这项研究为研究人员提供了关于高效生物膜分离技术（用于 DNA 分离）和 16 s 元条码平台（用于测序和数据分析）的详细信息，这些信息对研究人造湿地很有价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of microbiological methods 生物-生化研究方法

CiteScore

4.30

自引率

4.50%

发文量

151

审稿时长

29 days

期刊介绍： The Journal of Microbiological Methods publishes scholarly and original articles, notes and review articles. These articles must include novel and/or state-of-the-art methods, or significant improvements to existing methods. Novel and innovative applications of current methods that are validated and useful will also be published. JMM strives for scholarship, innovation and excellence. This demands scientific rigour, the best available methods and technologies, correctly replicated experiments/tests, the inclusion of proper controls, calibrations, and the correct statistical analysis. The presentation of the data must support the interpretation of the method/approach. All aspects of microbiology are covered, except virology. These include agricultural microbiology, applied and environmental microbiology, bioassays, bioinformatics, biotechnology, biochemical microbiology, clinical microbiology, diagnostics, food monitoring and quality control microbiology, microbial genetics and genomics, geomicrobiology, microbiome methods regardless of habitat, high through-put sequencing methods and analysis, microbial pathogenesis and host responses, metabolomics, metagenomics, metaproteomics, microbial ecology and diversity, microbial physiology, microbial ultra-structure, microscopic and imaging methods, molecular microbiology, mycology, novel mathematical microbiology and modelling, parasitology, plant-microbe interactions, protein markers/profiles, proteomics, pyrosequencing, public health microbiology, radioisotopes applied to microbiology, robotics applied to microbiological methods,rumen microbiology, microbiological methods for space missions and extreme environments, sampling methods and samplers, soil and sediment microbiology, transcriptomics, veterinary microbiology, sero-diagnostics and typing/identification.