Detection of pathogenic bacteria in the blood from sepsis patients using 16S rRNA gene amplicon sequencing analysis.
Nobuo WatanabeKirill KryukovSo NakagawaJunko S TakeuchiMeiko TakeshitaYukiko KirimuraSatomi MitsuhashiToru IshiharaHiromichi AokiSadaki InokuchiTadashi ImanishiShigeaki InouePublished in: PloS one (2018)
Prompt identification of causative pathogenic bacteria is imperative for the treatment of patients suffering from infectious diseases, including sepsis and pneumonia. However, current culture-based methodologies have several drawbacks including their limitation of use to culturable bacterial species. To circumvent these problems, we attempted to detect bacterial DNA in blood using next-generation DNA sequencing (NGS) technology. We conducted metagenomic and 16S ribosomal RNA (rRNA) gene amplicon sequencing of DNA extracted from bacteria-spiked blood using an Ion Personal Genome Machine. NGS data was analyzed using our in-house pipeline Genome Search Toolkit and database GenomeSync. The metagenomic sequencing analysis successfully detected three gram-positive and three gram-negative bacteria spiked in the blood, which was associated with a significant portion of non-bacterial reads, even though human blood cells were separated by low-speed centrifugation prior to DNA extraction. Sequencing analysis of seven variable regions of the 16S rRNA gene amplicon also successfully detected all six bacteria spiked in the blood. The methodology using 16S rRNA gene amplicon analysis was verified using DNA from the blood of six patients with sepsis and four healthy volunteers with potential pathogenic bacteria in the blood being identified at the species level. These findings suggest that our system will be a potential platform for practical diagnosis in the future.
Keyphrases
- circulating tumor
- genome wide
- intensive care unit
- single cell
- cell free
- single molecule
- copy number
- acute kidney injury
- endothelial cells
- emergency department
- newly diagnosed
- genome wide identification
- septic shock
- machine learning
- ejection fraction
- signaling pathway
- dna methylation
- climate change
- current status
- risk assessment
- cell death
- artificial intelligence
- extracorporeal membrane oxygenation
- prognostic factors
- wastewater treatment
- drug induced
- patient reported