Rastegari Farzaneh, Driscoll Mark, Riordan Jesse D, Nadeau Joseph H, Johnson Jethro S, Weinstock George M
Department of Computer Science and Engineering, University of Connecticut, 371 Fairfield Way, Storrs, CT 06269, USA.
The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.
Int J Mol Sci. 2025 Jan 29;26(3):1180. doi: 10.3390/ijms26031180.
When conducting sequence-based analysis of microbiome samples, it is important to accurately represent the bacterial communities present. The aim of this study was to compare two commercially available DNA isolation and PCR amplification approaches to determine their impact on the taxonomic composition of microbiome samples following 16S rRNA gene sequencing. A well-established 16S rRNA gene profiling approach, which was widely used in the Human Microbiome Project (HMP), was compared with a novel alkaline degenerative technique that utilizes alkaline cell lysis in combination with a degenerate pool of primers for nucleic acid extraction and PCR amplification. When comparing these different approaches for the microbiome profiling of human and mouse fecal samples, we found that the alkaline-based method was able to detect greater taxonomic diversity. An in silico analysis of predicted primer binding against a curated 16S rRNA gene reference database further suggested that this novel approach had the potential to reduce population bias found with traditional methods, thereby offering opportunities for improved microbial community profiling.
在对微生物组样本进行基于序列的分析时，准确呈现所存在的细菌群落非常重要。本研究的目的是比较两种市售的DNA分离和PCR扩增方法，以确定它们对16S rRNA基因测序后微生物组样本的分类组成的影响。将一种广泛应用于人类微生物组计划（HMP）的成熟的16S rRNA基因分析方法，与一种新型碱性变性技术进行了比较，该技术利用碱性细胞裂解结合简并引物池进行核酸提取和PCR扩增。在比较这些用于人类和小鼠粪便样本微生物组分析的不同方法时，我们发现基于碱性的方法能够检测到更丰富的分类多样性。对针对精心策划的16S rRNA基因参考数据库预测的引物结合进行的计算机模拟分析进一步表明，这种新方法有可能减少传统方法中发现的群体偏差，从而为改进微生物群落分析提供机会。
