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:
https://doi.org/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
Zdroje
1. Vector-borne diseases 2017 [cited 2019 January 7, 2019]. Available from: https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases.
2. Illnesses on the rise [updated May 1, 2018; cited 2019 January 7, 2019]. Available from: https://www.cdc.gov/vitalsigns/vector-borne/index.html.
3. Parize P, Muth E, Richaud C, Gratigny M, Pilmis B, Lamamy A, et al. Untargeted next-generation sequencing-based first-line diagnosis of infection in immunocompromised adults: A multicentre, blinded, prospective study. Clin Microbiol Infect. 2017;23(8):574 e1– e6. Epub 2017/02/14. doi: 10.1016/j.cmi.2017.02.006 28192237.
4. Ivy MI, Thoendel MJ, Jeraldo PR, Greenwood-Quaintance KE, Hanssen AD, Abdel MP, et al. Direct detection and identification of prosthetic joint infection pathogens in synovial fluid by metagenomic shotgun sequencing. J Clin Microbiol. 2018;56(9). Epub 2018/06/01. doi: 10.1128/JCM.00402-18 29848568; PubMed Central PMCID: PMC6113468.
5. Gyarmati P, Kjellander C, Aust C, Song Y, Ohrmalm L, Giske CG. Metagenomic analysis of bloodstream infections in patients with acute leukemia and therapy-induced neutropenia. Sci Rep. 2016;6:23532. doi: 10.1038/srep23532 26996149; PubMed Central PMCID: PMC4800731.
6. Lim YW, Evangelista JS 3rd, Schmieder R, Bailey B, Haynes M, Furlan M, et al. Clinical insights from metagenomic analysis of sputum samples from patients with cystic fibrosis. J Clin Microbiol. 2014;52(2):425–37. Epub 2014/01/31. doi: 10.1128/JCM.02204-13 24478471; PubMed Central PMCID: PMC3911355.
7. Schmidt K, Mwaigwisya S, Crossman LC, Doumith M, Munroe D, Pires C, et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J Antimicrob Chemother. 2017;72(1):104–14. Epub 2016/09/27. doi: 10.1093/jac/dkw397 27667325.
8. Thoendel M, Jeraldo P, Greenwood-Quaintance KE, Yao J, Chia N, Hanssen AD, et al. Identification of prosthetic joint infection pathogens using a shotgun metagenomics approach. Clin Infect Dis. 2018. Epub 2018/04/13. doi: 10.1093/cid/ciy303 29648630.
9. Babady NE, Sloan LM, Rosenblatt JE, Pritt BS. Detection of Plasmodium knowlesi by real-time polymerase chain reaction. Am J Trop Med Hyg. 2009;81(3):516–8. Epub 2009/08/27. 19706924.
10. Pritt BS, Sloan LM, Johnson DK, Munderloh UG, Paskewitz SM, McElroy KM, et al. Emergence of a new pathogenic Ehrlichia species, Wisconsin and Minnesota, 2009. N Engl J Med. 2011;365(5):422–9. Epub 2011/08/05. doi: 10.1056/NEJMoa1010493 21812671; PubMed Central PMCID: PMC3319926.
11. Didion JP, Martin M, Collins FS. Atropos: specific, sensitive, and speedy trimming of sequencing reads. PeerJ. 2017;5:e3720. Epub 2017/09/07. doi: 10.7717/peerj.3720 28875074; PubMed Central PMCID: PMC5581536.
12. Chu J, Sadeghi S, Raymond A, Jackman SD, Nip KM, Mar R, et al. BioBloom tools: Fast, accurate and memory-efficient host species sequence screening using bloom filters. Bioinformatics. 2014;30(23):3402–4. Epub 2014/08/22. doi: 10.1093/bioinformatics/btu558 25143290; PubMed Central PMCID: PMC4816029.
13. Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. Epub 2016/10/27. doi: 10.7717/peerj.2584 27781170; PubMed Central PMCID: PMC5075697.
14. Ames SK, Hysom DA, Gardner SN, Lloyd GS, Gokhale MB, Allen JE. Scalable metagenomic taxonomy classification using a reference genome database. Bioinformatics. 2013;29(18):2253–60. Epub 2013/07/06. doi: 10.1093/bioinformatics/btt389 23828782; PubMed Central PMCID: PMC3753567.
15. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12(10):902–3. Epub 2015/09/30. doi: 10.1038/nmeth.3589 26418763.
16. Freitas TA, Li PE, Scholz MB, Chain PS. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res. 2015;43(10):e69. Epub 2015/03/15. doi: 10.1093/nar/gkv180 25765641; PubMed Central PMCID: PMC4446416.
17. Wood DE, Salzberg SL. Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014;15(3):R46. Epub 2014/03/04. doi: 10.1186/gb-2014-15-3-r46 24580807; PubMed Central PMCID: PMC4053813.
18. Breitwieser FP, Salzberg SL. Pavian: Interactive analysis of metagenomics data for microbiomics and pathogen identification. bioRxiv. 2016:084715. doi: 10.1101/084715
19. Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: Rapid and sensitive classification of metagenomic sequences. Genome Res. 2016;26(12):1721–9. Epub 2016/11/18. doi: 10.1101/gr.210641.116 27852649; PubMed Central PMCID: PMC5131823.
20. Gehringer C, Kreidenweiss A, Flamen A, Antony JS, Grobusch MP, Belard S. Molecular evidence of Wolbachia endosymbiosis in Mansonella perstans in Gabon, Central Africa. J Infect Dis. 2014;210(10):1633–8. Epub 2014/06/07. doi: 10.1093/infdis/jiu320 24903665.
21. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60. Epub 2014/11/18. doi: 10.1038/nmeth.3176 25402007.
22. Breitwieser FP, Baker DN, Salzberg SL. KrakenUniq: confident and fast metagenomics classification using unique k-mer counts. Genome Biol. 2018;19(1):198. Epub 2018/11/18. doi: 10.1186/s13059-018-1568-0 30445993; PubMed Central PMCID: PMC6238331.
23. Breitwieser FP, Lu J, Salzberg SL. A review of methods and databases for metagenomic classification and assembly. Brief Bioinform. 2017. Epub 2017/10/14. doi: 10.1093/bib/bbx120 29028872.
24. Thoendel M, Jeraldo PR, Greenwood-Quaintance KE, Yao JZ, Chia N, Hanssen AD, et al. Comparison of microbial DNA enrichment tools for metagenomic whole genome sequencing. J Microbiol Methods. 2016;127:141–5. doi: 10.1016/j.mimet.2016.05.022 27237775.
25. Gould FK, Freeman R, Law D, Moriarty T. Lysis in detection of intracellular organisms. Lancet. 1988;2(8608):461. Epub 1988/08/20. doi: 10.1016/s0140-6736(88)90459-x 2900397.
26. Hsiao LL, Howard RJ, Aikawa M, Taraschi TF. Modification of host cell membrane lipid composition by the intra-erythrocytic human malaria parasite Plasmodium falciparum. Biochem J. 1991;274 (Pt 1):121–32. Epub 1991/02/15. doi: 10.1042/bj2740121 2001227; PubMed Central PMCID: PMC1149929.
27. Blauwkamp TA, Thair S, Rosen MJ, Blair L, Lindner MS, Vilfan ID, et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat Microbiol. 2019;4(4):663–74. Epub 2019/02/12. doi: 10.1038/s41564-018-0349-6 30742071.
28. Hardwick SA, Chen WY, Wong T, Kanakamedala BS, Deveson IW, Ongley SE, et al. Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis. Nat Commun. 2018;9(1):3096. Epub 2018/08/08. doi: 10.1038/s41467-018-05555-0 30082706; PubMed Central PMCID: PMC6078961.
29. Hornung BVH, Zwittink RD, Kuijper EJ. Issues and current standards of controls in microbiome research. FEMS Microbiol Ecol. 2019;95(5). Epub 2019/04/19. doi: 10.1093/femsec/fiz045 30997495; PubMed Central PMCID: PMC6469980.
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PLOS One
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