Shahi Shailesh K, Zarei Kasra, Guseva Natalya V, Mangalam Ashutosh K
Department of Pathology, University of Iowa.
Medical Scientist Training Program, University of Iowa.
J Vis Exp. 2019 Oct 15(152). doi: 10.3791/59980.
The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome's role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.
人体肠道中定植着数万亿细菌，这些细菌支持诸如食物代谢、能量获取和免疫系统调节等生理功能。已有研究表明，健康肠道微生物群的扰动在包括多发性硬化症（MS）在内的炎症性疾病的发展中起作用。环境和遗传因素会影响微生物群的组成；因此，识别与疾病表型相关的微生物群落已成为确定微生物群在健康和疾病中作用的第一步。使用16S rRNA宏基因组测序对细菌群落进行分析有助于推进微生物组研究。尽管其应用广泛，但基于16S rRNA的分类分析尚无统一方案。另一个局限性是由于技术困难，如测序读长较短，以及由于反向（R2）读段质量较低，最终分析中仅使用正向（R1）读段，导致分类归属的分辨率较低。需要一种简化的高分辨率方法来表征给定生物样本中的细菌多样性。测序技术的进步使得能够以高分辨率对更长的读段进行测序，这有助于克服其中一些挑战。目前的测序技术与诸如基于R的分区扩增子去噪算法2（DADA2）等公开可用的宏基因组分析流程相结合，有助于在高分辨率下推进微生物分析，因为DADA2可以在属和种水平上对序列进行分类。本文介绍了一种使用16S rRNA基因V3-V4区域的两步扩增进行细菌分析的指南，随后使用免费的分析工具（即DADA2、Phyloseq和METAGENassist）进行分析。相信这个简单而完整的工作流程将成为对进行微生物组分析研究感兴趣的研究人员的一个出色工具。
