Metagenomic Approach for Identification of the Pathogens Associated with Diarrhea in Stool Specimens

Abstract

The potential to rapidly capture the entire microbial community structure and/or gene content makes metagenomic sequencing an attractive tool for pathogen identification and the detection of resistance/virulence genes in clinical settings. Here, we assessed the consistency between PCR from a diagnostic laboratory, quantitative PCR (qPCR) from a research laboratory, 16S rRNA gene sequencing, and metagenomic shotgun sequencing (MSS) for Clostridium difficile identification in diarrhea stool samples. Twenty-two C. difficile-positive diarrhea samples identified by PCR and qPCR and five C. difficile-negative diarrhea controls were studied. C. difficile was detected in 90.9% of C. difficile-positive samples using 16S rRNA gene sequencing, and C. difficile was detected in 86.3% of C. difficile-positive samples using MSS. CFU inferred from qPCR analysis were positively correlated with the relative abundance of C. difficile from 16S rRNA gene sequencing (r(2) = -0.60) and MSS (r(2) = -0.55). C. difficile was codetected with Clostridium perfringens, norovirus, sapovirus, parechovirus, and anellovirus in 3.7% to 27.3% of the samples. A high load of Candida spp. was found in a symptomatic control sample in which no causative agents for diarrhea were identified in routine clinical testing. Beta-lactamase and tetracycline resistance genes were the most prevalent (25.9%) antibiotic resistance genes in these samples. In summary, the proof-of-concept study demonstrated that next-generation sequencing (NGS) in pathogen detection is moderately correlated with laboratory testing and is advantageous in detecting pathogens without a priori knowledge.

FIG 1

Correlation of qPCR with metagenomic sequencing in detection of C. difficile in the diarrhea samples. CFU derived from qPCR were positively correlated with the relative abundances of C. difficile detected by 16S rRNA gene sequencing (A) and MSS (B).

FIG 2

Microbial community profile of the diarrhea samples revealed by MSS. The distribution of C. difficile and the taxa whose relative abundances are higher than that of C. difficile are illustrated by heatmap. Each row represents a taxon, and each column represents a sample. The samples are in the same order as Table 1. Relative abundances with log10 transformation are used in the heatmap.

FIG 3

Prevalence of antibiotic resistance genes. The prevalence of antibiotic resistance genes is illustrated by a bar plot. The antibiotic categories are listed on the left side of the bar plot.