Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing

https://doi.org/10.1016/j.bbrc.2015.12.083Get rights and content

Highlights

  • Human microbiome has emerged as a major player in regulating health and disease.

  • Accurate identification of microbes constituting the microbiome is major challenge.

  • We report comparative study of whole genome shotgun (WGS) and amplicon sequencing.

  • WGS enhances detection of bacterial species with high accuracy.

  • WGS increased detection of microbial diversity and prediction of putative genes.

Abstract

The human microbiome has emerged as a major player in regulating human health and disease. Translational studies of the microbiome have the potential to indicate clinical applications such as fecal transplants and probiotics. However, one major issue is accurate identification of microbes constituting the microbiota. Studies of the microbiome have frequently utilized sequencing of the conserved 16S ribosomal RNA (rRNA) gene. We present a comparative study of an alternative approach using whole genome shotgun sequencing (WGS). In the present study, we analyzed the human fecal microbiome compiling a total of 194.1 × 106 reads from a single sample using multiple sequencing methods and platforms. Specifically, after establishing the reproducibility of our methods with extensive multiplexing, we compared: 1) The 16S rRNA amplicon versus the WGS method, 2) the Illumina HiSeq versus MiSeq platforms, 3) the analysis of reads versus de novo assembled contigs, and 4) the effect of shorter versus longer reads. Our study demonstrates that whole genome shotgun sequencing has multiple advantages compared with the 16S amplicon method including enhanced detection of bacterial species, increased detection of diversity and increased prediction of genes. In addition, increased length, either due to longer reads or the assembly of contigs, improved the accuracy of species detection.

Introduction

The human microbiome is important in maintaining health, whereas dysbiosis has been associated with various diseases (e.g., inflammatory bowel disease and coronary artery disease) and conditions (e.g. obesity) [1], [2]. These observations suggest that modulation of the fecal microbiome could become an important therapeutic modality for some diseases. For example, fecal transplants have been shown to alleviate diarrhea caused by Clostridium difficile infection and temporarily improve insulin sensitivity [3], [4]. However, a major concern when administering fecal transplants, or even probiotics, is the long-term biological effects of the inoculum on the recipient microbiota. It is essential to precisely identify and enumerate the bacterial species in the inoculum as well as in the recipient microbiome in order to understand the complex interactions among the microbes [5], [6]. The gut microbiome, which has been the most extensively studied of the human microbiomes, is highly diverse and has been shown to include thousands of different bacterial species [7], [8]. The diverse community of bacteria is composed of a small number of abundant species plus a large number of rare or low abundance species [9]. The differential functions of the abundant and rare species remain poorly understood. Thus, to effectively understand the ecology of the fecal microbiome, it is imperative to analyze both the rare and the abundant microbes.

The number of studies investigating the microbiome has exploded since the technological advances in high-throughput sequencing that facilitate culture- and cloning-independent analysis [10]. These technical advances have been paradigm shifting since the majority (>90%) of microbial species cannot be readily cultured using current laboratory culture techniques [11], [12], [13]. The most common sequencing approach to analyze the microbiome, which has been used to compile most of the data collated by the Human Microbiome Project (HMP), is amplicon analysis of the 16S ribosomal RNA (rRNA) gene [14], [15]. In this method, a 16S rRNA region is amplified by PCR with primers that recognize highly conserved regions of the gene and sequenced [16]. The limitations of this method are that the annotation is based on putative association of the 16S rRNA gene with a taxa defined as an operational taxonomic unit (OTU). In general, OTUs are analyzed at the phyla or genera level, and can be less precise at the species level. In addition, specific genes are not directly sequenced, but rather predicted based on the OTUs. Due to horizontal gene transfer and the existence of numerous bacterial strains [17], [18], [19], the lack of direct gene identification potentially limits understanding of a microbiome.

An alternative approach to the 16S rRNA amplicon sequencing method is whole genome shotgun sequencing (WGS) in which random fragments of genome are sequenced. The major advantages of the WGS method are that the taxa can be more accurately defined at the species level. Another important consideration is that the 16S and WGS methods commonly utilize different databases for classification of taxa. However, WGS is more expensive and requires more extensive data analysis [10], [20], [21], [22]. In addition, to identify and understand the bacterial genes in a taxa, it may be necessary to sequence a genome with high coverage [20].

In the present study, we analyzed a single human fecal microbiome with a total of 194.1 × 106 reads using multiple methods and platforms. The high number of reads supported an analysis with multiple experimental methods. Specifically, we established the reproducibility of our methods with extensive multiplexing. Also, we investigated four factors of microbiome analysis. We compared: 1) The 16S rRNA amplicon versus the WGS method, 2) the Illumina HiSeq versus MiSeq platforms, 3) the analysis of reads versus de novo assembled contigs, and 4) the effect of shorter versus longer reads. Our results demonstrate important advantages of the WGS sequencing method.

Section snippets

Subject recruitment and sample collection

Informed consent was obtained from the subject. An adult subject provided self-collected stool. The study was approved by the Institutional Review Board of the University of Illinois at Chicago (Protocol # 2014-0528), and the experimental methods were carried out in accordance with the approved guidelines.

Fecal metagenomic DNA isolation

A fresh voided stool specimen was processed for total DNA isolation. Approximately 100 mg of stool was transferred to an Eppendorf safe lock tube and processed with a PowerSoil DNA isolation

Analysis of amplicon (16S rRNA) versus whole genome shotgun sequencing

In this study, we performed extremely deep sequencing of a fecal sample using different sequencing methods (16S and WGS metagenomic sequencing), sequencing platforms (HiSeq and MiSeq), analysis strategies (reads and de novo assembled contigs) and read length (100, 150 and 300 bp) to rigorously determine the optimal methods for microbiome analysis (Fig. 1). A freshly voided sample was processed and high quality (greater than 5 kb) metagenomic DNA was isolated (Fig. S2a,b). To evaluate

Discussion

In this study we performed extremely deep sequencing (194.1 × 106 reads) of a single sample using multiple approaches to evaluate parameters that can affect sequencing results and analytical interpretation to determine optimal methods. First, we compared the results of 16S versus WGS sequencing. The 16S amplicon approach has been the most commonly employed method to analyze bacterial microbiomes and has several important advantages: 1) it is cost effective, 2) data analysis can be performed by

Author contributions

RR, AR and DLP: designed study; RR: prepared libraries and sequencing, AR and RR performed data analysis, AM: performed bioinformatics pipe line analysis; HSM: assisted with sample collection; RR, AR and DLP wrote the manuscript.

Competing financial interests

The authors have declared that no conflict of interest exists. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

This work was supported in part by RO1 HL081663 and RO1 AI053878 to DLP.

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    These authors contributed equally and considered as co-first authors.

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