16S rRNA基因在微生物生态学中的应用

16S rRNA(Small subunit ribosomal RNA)基因是对原核微生物进行系统进化分类研究时最常用的分子标志物(Biomarker),广泛应用于微生物生态学研究中。近些年来随着高通量测序技术及数据分析方法等的不断进步,大量基于16S rRNA基因的研究使得微生物生态学得到了快速发展,然而使用16S rRNA基因作为分子标志物时也存在诸多问题,比如水平基因转移、多拷贝的异质性、基因扩增效率的差异、数据分析方法的选择等,这些问题影响了微生物群落组成和多样性分析时的准确性。对当前使用16S rRNA基因分析微生物群落组成和多样性的进展情况做一总结,重点讨论当前存在的主要问题以及各种分析方法的发展,尤其是与高通量测序技术有关的实验和数据处理问题。

The applications of the 16S rRNA gene in microbial ecology: current situation and problems

The 16S rRNA (small subunit ribosomal RNA) gene is a universal marker for phylogenetic reconstructions to approximate the tree of life owing to its presence in all prokaryotes and its high conservation. Sequencing of 16S rRNA genes amplified directly from environmental samples is commonly used to study microbial community composition and diversity. Great advances in pyrosequencing technology and bioinformatics in recent years enable us to obtain sequence data from large-scale environmental samples efficiently and cost-effectively. However, some critical problems need to be addressed when the 16S rRNA gene is used for microbial diversity studies, such as horizontal gene transfer (HGT), intragenomic heterogeneity, PCR amplification efficiency, and sequencing data analysis. In this review, we summarize the state-of-the-art applications of 16S rRNA gene as a biomarker for microbial ecology studies, and introduce current pyrosequencing techniques and bioinformatics for large-scale data analysis. This review focuses on four aspects. (i) We introduce the structure and properties of the 16S rRNA gene, e.g. the primary and secondary structure, HGT and heterogeneities of 16S rRNA genes. Based on current available microbial genomes, multi-copy and intragenomic heterogeneities of 16S rRNA genes are recognized. These phenomena may seriously bias the estimations of microbial diversity in environmental samples. Some online tools and databases used for analysis of the 16S rRNA gene sequencing data are also introduced. These tools are used to predict horizontal gene transfer, secondary structure, and to align and classify 16S rRNA gene sequences. (ii) We introduce some 16S rRNA-based techniques commonly used in microbial ecology studies, such as fingerprinting profiling, hybridization, microarray, and high throughput pyrosequencing methods. We compare the advantages and limitations of various methods and recommend how to use them properly based on a specific target. Different methods have different resolutions and detection limitations. Low-resolution profiling methods potentially miss some important information and make it difficult to detail the phylogenetic composition of an environmental sample. Pyrosequencing technique is highly recommended in the future for microbial ecology study. Several sequencing platforms, e.g. Roche 454, Ion Torrent and MiSeq, are compared. (iii) We evaluate the biases that may be introduced during sample preparation and PCR procedures, e.g. DNA extraction, primer selection, PCR optimization, PCR product purification, and data analysis. Amplicon sequencing method suffers from a high level of sequencing and amplification artifacts. It is important to select OTU (operational taxonomic units) classification and chimera removing algorithms. In this case, the Uchime and Uparse are recommended for microbial amplicon pyrosequencing reads. (iv) We introduce some bioinformatics tools for pyrosequencing data analysis, such as chimera check and diversity index calculation. The most popular pipelines for pyrosequencing data analysis include RDP, QIIME and Mothur. In order to link ecological questions with microbial composition data, the methods of ecological statistics must be employed to build the relationships of microbial datasets with environmental variables. Here, we introduce some multiple statistical methods, e.g. PCA and UniFrac analysis. Based on these analyses, microbial data based on 16S rRNA sequencing are linked to the environmental variables, and fundamental ecological questions are addressed. Finally, we recommend researchers to consider these problems systematically when using 16S rRNA-based techniques in microbial ecology study.