University of Connecticut, Department of Molecular and Cell Biology, Storrs, Connecticut, USA
UConn Health, Department of Pediatrics, Farmington, Connecticut, USA.
mBio. 2021 Feb 16;12(1):e03656-20. doi: 10.1128/mBio.03656-20.
Identifying and tracking microbial strains as microbiomes evolve are major challenges in the field of microbiome research. We utilized a new sequencing kit that combines DNA extraction with PCR amplification of a large region of the rRNA operon and downstream bioinformatic data analysis. Longitudinal microbiome samples of coadmitted twins from two different neonatal intensive care units (NICUs) were analyzed using an ∼2,500-base amplicon that spans the 16S and 23S rRNA genes and mapped to a new, custom 16S-23S rRNA database. Amplicon sequence variants (ASVs) inferred using DADA2 provided sufficient resolution for the differentiation of rRNA variants from closely related but not previously sequenced , , and strains, among the first bacteria colonizing the gut of these infants after admission to the NICU. Distinct ASV groups (fingerprints) were monitored between coadmitted twins over time, demonstrating the potential to track the source and spread of both commensals and pathogens. The high-resolution taxonomy obtained from long amplicon sequencing enables the tracking of strains temporally and spatially as microbiomes are established in infants in the hospital environment. Achieving strain-level resolution is a major obstacle for source tracking and temporal studies of microbiomes. In this study, we describe a novel deep-sequencing approach that provides species- and strain-level resolution of the neonatal microbiome. Using , , and as examples, we could monitor their temporal dynamics after antibiotic treatment and in pairs of twins. The strain-level resolution, combined with the greater sequencing depth and decreased cost per read of PacBio Sequel 2, enables this advantageous source- and strain-tracking analysis method to be implemented widely across more complex microbiomes.
鉴定和跟踪微生物菌株,因为微生物组在不断进化,这是微生物组研究领域的主要挑战。我们利用了一种新的测序试剂盒,该试剂盒将 DNA 提取与 rRNA 操纵子大片段的 PCR 扩增以及下游生物信息数据分析相结合。使用跨越 16S 和 23S rRNA 基因并映射到新的定制 16S-23S rRNA 数据库的约 2500 个碱基长的扩增子分析了来自两个不同新生儿重症监护病房(NICU)的同时入院双胞胎的纵向微生物组样本。使用 DADA2 推断的扩增子序列变体(ASV)为区分 rRNA 变体提供了足够的分辨率,这些变体来自密切相关但尚未测序的 、 和 菌株,这些菌株是这些婴儿入院后第一批定植于肠道的细菌。随着时间的推移,在同时入院的双胞胎之间监测到不同的 ASV 组(指纹),这表明有可能追踪共生菌和病原体的来源和传播。从长扩增子测序获得的高分辨率分类有助于在医院环境中对婴儿的微生物组进行时空跟踪菌株。实现菌株水平分辨率是源追踪和微生物组时间研究的主要障碍。在这项研究中,我们描述了一种新的深度测序方法,该方法可提供新生儿微生物组的种和菌株水平分辨率。使用 、 和 作为示例,我们可以监测它们在抗生素治疗后的时间动态以及双胞胎中的情况。与 PacBio Sequel 2 相比,菌株水平分辨率、更大的测序深度和每读取成本的降低,使这种有利的源和菌株跟踪分析方法能够在更复杂的微生物组中广泛实施。