Application of metagenomic shotgun sequencing to detect vector-borne pathogens in clinical blood samples


Autoři: Prakhar Vijayvargiya aff001;  Patricio R. Jeraldo aff002;  Matthew J. Thoendel aff001;  Kerryl E. Greenwood-Quaintance aff004;  Zerelda Esquer Garrigos aff001;  M. Rizwan Sohail aff001;  Nicholas Chia aff002;  Bobbi S. Pritt aff004;  Robin Patel aff001
Působiště autorů: Division of Infectious Diseases, Mayo Clinic, Rochester, Minnesota, United States of America aff001;  Department of Surgery, Mayo Clinic, Rochester, Minnesota, United States of America aff002;  Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America aff003;  Division of Clinical Microbiology, Mayo Clinic, Rochester, Minnesota, United States of America aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222915

Souhrn

Background

Vector-borne pathogens are a significant public health concern worldwide. Infections with these pathogens, some of which are emerging, are likely under-recognized due to the lack of widely-available laboratory tests. There is an urgent need for further advancement in diagnostic modalities to detect new and known vector-borne pathogens. We evaluated the utility of metagenomic shotgun sequencing (MGS) as a pathogen agnostic approach for detecting vector-borne pathogens from human blood samples.

Methods

Residual whole blood samples from patients with known infection with Babesia microti, Borrelia hermsii, Plasmodium falciparum, Mansonella perstans, Anaplasma phagocytophilum or Ehrlichia chaffeensis were studied. Samples underwent DNA extraction, removal of human DNA, whole genome amplification, and paired-end library preparation, followed by sequencing on Illumina HiSeq 2500. Bioinformatic analysis was performed using the Livermore Metagenomics Analysis Toolkit (LMAT), Metagenomic Phylogenetic Analysis (MetaPhlAn2), Genomic Origin Through Taxonomic CHAllenge (GOTTCHA) and Kraken 2.

Results

Eight samples were included in the study (2 samples each for P. falciparum and A. phagocytophilum). An average of 27.5 million read pairs was generated per sample (range, 18.3–38.8 million) prior to removal of human reads. At least one of the analytic tools was able to detect four of six organisms at the genus level, and the organism present in five of eight specimens at the species level. Mansonella and Ehrlichia species were not detected by any of the tools; however, mitochondrial cytochrome c oxidase subunit I amino acid sequence analysis suggested the presence of M. perstans genetic material.

Conclusions

MGS is a promising tool with the potential to evolve as a non-hypothesis driven diagnostic test to detect vector-borne pathogens, including protozoa and helminths.

Klíčová slova:

Disease vectors – Eukaryota – Genome analysis – Pathogens – Plasmodium – Sequence analysis – Sequence databases – Babesia


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PLOS One


2019 Číslo 10

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