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

@entity5599 @entity294 @entity3168 @entity803 @entity2718
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 @entity5599 ( @entity5599 ) for @entity2718 identification in @entity294 stool samples. Twenty-two @entity2718 -positive @entity294 samples identified by PCR and qPCR and five @entity2718 -negative @entity294 controls were studied. @entity2718 was detected in 90.9% of @entity2718 -positive samples using 16S rRNA gene sequencing, and @entity2718 was detected in 86.3% of @entity2718 -positive samples using @entity5599 . CFU inferred from qPCR analysis were positively correlated with the relative abundance of @entity2718 from 16S rRNA gene sequencing (r(2) = -0.60) and @entity5599 (r(2) = -0.55). @entity2718 was codetected with @entity3168 , 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 @entity294 were identified in routine clinical testing. Beta-lactamase and @entity803 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.
