Dataset Information

Detection of pathogenic bacteria in the blood from sepsis patients using 16S rRNA gene amplicon sequencing analysis.

ABSTRACT: 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.

SUBMITTER: Watanabe N

PROVIDER: S-EPMC6093674 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Detection of pathogenic bacteria in the blood from sepsis patients using 16S rRNA gene amplicon sequencing analysis.

Watanabe Nobuo N Kryukov Kirill K Nakagawa So S Takeuchi Junko S JS Takeshita Meiko M Kirimura Yukiko Y Mitsuhashi Satomi S Ishihara Toru T Aoki Hiromichi H Inokuchi Sadaki S Imanishi Tadashi T Inoue Shigeaki S

PloS one 20180815 8

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 ...[more]

PMID: 30110400

Similar Datasets

Project description:The human impact on natural habitats is increasing the complexity of human-wildlife interactions and leading to the emergence of infectious diseases worldwide. Highly successful synanthropic wildlife species, such as rodents, will undoubtedly play an increasingly important role in transmitting zoonotic diseases. We investigated the potential for recent developments in 16S rRNA amplicon sequencing to facilitate the multiplexing of the large numbers of samples needed to improve our understanding of the risk of zoonotic disease transmission posed by urban rodents in West Africa. In addition to listing pathogenic bacteria in wild populations, as in other high-throughput sequencing (HTS) studies, our approach can estimate essential parameters for studies of zoonotic risk, such as prevalence and patterns of coinfection within individual hosts. However, the estimation of these parameters requires cleaning of the raw data to mitigate the biases generated by HTS methods. We present here an extensive review of these biases and of their consequences, and we propose a comprehensive trimming strategy for managing these biases. We demonstrated the application of this strategy using 711 commensal rodents, including 208 Mus musculus domesticus, 189 Rattus rattus, 93 Mastomys natalensis, and 221 Mastomys erythroleucus, collected from 24 villages in Senegal. Seven major genera of pathogenic bacteria were detected in their spleens: Borrelia, Bartonella, Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus, and Orientia. Mycoplasma, Ehrlichia, Rickettsia, Streptobacillus, and Orientia have never before been detected in West African rodents. Bacterial prevalence ranged from 0% to 90% of individuals per site, depending on the bacterial taxon, rodent species, and site considered, and 26% of rodents displayed coinfection. The 16S rRNA amplicon sequencing strategy presented here has the advantage over other molecular surveillance tools of dealing with a large spectrum of bacterial pathogens without requiring assumptions about their presence in the samples. This approach is therefore particularly suitable to continuous pathogen surveillance in the context of disease-monitoring programs. IMPORTANCE Several recent public health crises have shown that the surveillance of zoonotic agents in wildlife is important to prevent pandemic risks. High-throughput sequencing (HTS) technologies are potentially useful for this surveillance, but rigorous experimental processes are required for the use of these effective tools in such epidemiological contexts. In particular, HTS introduces biases into the raw data set that might lead to incorrect interpretations. We describe here a procedure for cleaning data before estimating reliable biological parameters, such as positivity, prevalence, and coinfection, using 16S rRNA amplicon sequencing on an Illumina MiSeq platform. This procedure, applied to 711 rodents collected in West Africa, detected several zoonotic bacterial species, including some at high prevalence, despite their never before having been reported for West Africa. In the future, this approach could be adapted for the monitoring of other microbes such as protists, fungi, and even viruses.

Dataset Information

Detection of pathogenic bacteria in the blood from sepsis patients using 16S rRNA gene amplicon sequencing analysis.

Publications

Detection of pathogenic bacteria in the blood from sepsis patients using 16S rRNA gene amplicon sequencing analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets