1887

Abstract

Antimicrobial resistance is a major threat to human and animal health. There is an urgent need to ensure that antimicrobials are used appropriately to limit the emergence and impact of resistance. In the human and veterinary healthcare setting, traditional culture and antimicrobial sensitivity testing typically requires 48–72 h to identify appropriate antibiotics for treatment. In the meantime, broad-spectrum antimicrobials are often used, which may be ineffective or impact non-target commensal bacteria. Here, we present a rapid, culture-free, diagnostics pipeline, involving metagenomic nanopore sequencing directly from clinical urine and skin samples of dogs. We have planned this pipeline to be versatile and easily implementable in a clinical setting, with the potential for future adaptation to different sample types and animals. Using our approach, we can identify the bacterial pathogen present within 5 h, in some cases detecting species which are difficult to culture. For urine samples, we can predict antibiotic sensitivity with up to 95 % accuracy. Skin swabs usually have lower bacterial abundance and higher host DNA, confounding antibiotic sensitivity prediction; an additional host depletion step will likely be required during the processing of these, and other types of samples with high levels of host cell contamination. In summary, our pipeline represents an important step towards the design of individually tailored veterinary treatment plans on the same day as presentation, facilitating the effective use of antibiotics and promoting better antimicrobial stewardship.

Funding
This study was supported by the:
  • Dogs Trust
    • Principle Award Recipient: J.Ross Fitzgerald
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.001066
2023-07-20
2024-05-02
Loading full text...

Full text loading...

/deliver/fulltext/mgen/9/7/mgen001066.html?itemId=/content/journal/mgen/10.1099/mgen.0.001066&mimeType=html&fmt=ahah

References

  1. Antimicrobial Resistance C Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 2022; 399:629–655
    [Google Scholar]
  2. Nuttall T. Choosing the best antimicrobial for the job. Vet Rec 2013; 172:12–13 [View Article] [PubMed]
    [Google Scholar]
  3. Samrot AV, Sean TC, Bhavya KS, Sahithya CS, Chan-Drasekaran S et al. Leptospiral infection, pathogenesis and its diagnosis-a review. Pathogens 2021; 10:145 [View Article] [PubMed]
    [Google Scholar]
  4. Riedel S, Halls J, Dutta S, Toraskar N, Lemon J et al. Clinical evaluation of the acuitas® AMR gene panel for rapid detection of bacteria and genotypic antibiotic resistance determinants. Diagn Microbiol Infect Dis 2021; 100:115383 [View Article] [PubMed]
    [Google Scholar]
  5. Simner PJ, Musser KA, Mitchell K, Wise MG, Lewis S et al. Multicenter evaluation of the acuitas AMR gene panel for detection of an extended panel of antimicrobial resistance genes among bacterial isolates. J Clin Microbiol 2022; 60:e0209821 [View Article] [PubMed]
    [Google Scholar]
  6. Baldan R, Cliff PR, Burns S, Medina A, Smith GC et al. evelopment and evaluation of a nanopore 16S rRNA gene sequencing service for same day targeted treatment of bacterial respiratory infection in the intensive care unit. J Infect 2021; 83:167–174 [View Article] [PubMed]
    [Google Scholar]
  7. Fu Y, Chen Q, Xiong M, Zhao J, Shen S et al. Clinical performance of nanopore targeted sequencing for diagnosing infectious diseases. Microbiol Spectr 2022; 10:e0027022 [View Article] [PubMed]
    [Google Scholar]
  8. Komiya S, Matsuo Y, Nakagawa S, Morimoto Y, Kryukov K et al. MinION, a portable long-read sequencer, enables rapid vaginal microbiota analysis in a clinical setting. BMC Med Genomics 2022; 15:68 [View Article] [PubMed]
    [Google Scholar]
  9. Matsuo Y. Full-length 16S rRNA gene analysis using long-read nanopore sequencing for rapid identification of bacteria from clinical specimens. In Arakawa K. eds Nanopore Sequencing: Methods and Protocols New York, NY: Springer US; 2023 pp 193–213
    [Google Scholar]
  10. Matsuo Y, Komiya S, Yasumizu Y, Yasuoka Y, Mizushima K et al. Full-length 16S rRNA gene amplicon analysis of human gut microbiota using MinION. BMC Microbiol 2021; 21:35 [View Article] [PubMed]
    [Google Scholar]
  11. Sheka D, Alabi N, Gordon PMK. Oxford nanopore sequencing in clinical microbiology and infection diagnostics. Brief Bioinform 2021; 22:bbaa403 [View Article] [PubMed]
    [Google Scholar]
  12. Zhang LL, Zhang C, Peng JP. Application of nanopore sequencing technology in the clinical diagnosis of infectious diseases. Biomed Environ Sci 2022; 35:381–392 [View Article] [PubMed]
    [Google Scholar]
  13. Avershina E, Frye SA, Ali J, Taxt AM, Ahmad R. Ultrafast and cost-effective pathogen identification and resistance gene detection in a clinical setting using nanopore flongle sequencing. Front Microbiol 2022; 13:822402 [View Article] [PubMed]
    [Google Scholar]
  14. Taxt AM, Avershina E, Frye SA, Naseer U, Ahmad R. Rapid identification of pathogens, antibiotic resistance genes and plasmids in blood cultures by nanopore sequencing. Sci Rep 2020; 10:7622 [View Article] [PubMed]
    [Google Scholar]
  15. Street TL, Barker L, Sanderson ND, Kavanagh J, Hoosdally S et al. Optimizing DNA extraction methods for nanopore sequencing of Neisseria gonorrhoeae directly from urine samples. J Clin Microbiol 2020; 58:e01822-19 [View Article] [PubMed]
    [Google Scholar]
  16. Sanderson ND, Swann J, Barker L, Kavanagh J, Hoosdally S et al. High precision Neisseria gonorrhoeae variant and antimicrobial resistance calling from metagenomic Nanopore sequencing. Genome Res 2020; 30:1354–1363 [View Article] [PubMed]
    [Google Scholar]
  17. Charalampous T, Kay GL, Richardson H, Aydin A, Baldan R et al. Nanopore metagenomics enables rapid clinical diagnosis of bacterial lower respiratory infection. Nat Biotechnol 2019; 37:783–792 [View Article] [PubMed]
    [Google Scholar]
  18. Yang L, Haidar G, Zia H, Nettles R, Qin S et al. Metagenomic identification of severe pneumonia pathogens with rapid Nanopore sequencing in mechanically-ventilated patients. medRxiv 201919002774 [View Article]
    [Google Scholar]
  19. Schmidt K, Mwaigwisya S, Crossman LC, Doumith M, Munroe D et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J Antimicrob Chemother 2017; 72:104–114 [View Article] [PubMed]
    [Google Scholar]
  20. Whittle E, Yonkus JA, Jeraldo P, Alva-Ruiz R, Nelson H et al. Optimizing Nanopore sequencing for rapid detection of microbial species and antimicrobial resistance in patients at risk of surgical site infections. mSphere 2022; 7:e0096421 [View Article] [PubMed]
    [Google Scholar]
  21. Janes VA, Matamoros S, Munk P, Clausen P, Koekkoek SM et al. Metagenomic DNA sequencing for semi-quantitative pathogen detection from urine: a prospective, laboratory-based, proof-of-concept study. Lancet Microbe 2022; 3:e588–e597 [View Article] [PubMed]
    [Google Scholar]
  22. Irwin AD, Coin LJM, Harris PNA, Cotta MO, Bauer MJ et al. Optimising treatment outcomes for children and adults through rapid genome sequencing of sepsis pathogens. A study protocol for a prospective, multi-centre trial (DIRECT). Front Cell Infect Microbiol 2021; 11:667680 [View Article] [PubMed]
    [Google Scholar]
  23. Lycett SJ, Hughes J, McHugh MP, da Silva Filipe A, Dewar R et al. Epidemic waves of COVID-19 in Scotland: a genomic perspective on the impact of the introduction and relaxation of lockdown on SARS-CoV-2. medRxiv 2021 [View Article]
    [Google Scholar]
  24. Charalampous T, Alcolea-Medina A, Snell LB, Alder C, Tan M et al. Routine respiratory metagenomics service for intensive care unit patients. medRxiv 2023 [View Article]
    [Google Scholar]
  25. Břinda K, Callendrello A, Ma KC, MacFadden DR, Charalampous T et al. Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing. Nat Microbiol 2020; 5:455–464 [View Article] [PubMed]
    [Google Scholar]
  26. Steinig E, Pitt M, Aglua I, Suttie A, Greenhill A et al. Genomic neighbor typing for bacterial outbreak surveillance. bioRxiv 2022 [View Article]
    [Google Scholar]
  27. Jeck WR, Iafrate AJ, Nardi V. Nanopore flongle sequencing as a rapid, single-specimen clinical test for fusion detection. J Mol Diagn 2021; 23:630–636 [View Article] [PubMed]
    [Google Scholar]
  28. Moss EL, Maghini DG, Bhatt AS. Complete, closed bacterial genomes from microbiomes using nanopore sequencing. Nat Biotechnol 2020; 38:701–707 [View Article] [PubMed]
    [Google Scholar]
  29. Huggins LG, Colella V, Atapattu U, Koehler AV, Traub RJ. Nanopore sequencing using the full-length 16S rRNA gene for detection of blood-borne bacteria in dogs reveals a novel species of hemotropic mycoplasma. Microbiol Spectr 2022; 10:e0308822 [View Article] [PubMed]
    [Google Scholar]
  30. Bokma J, Vereecke N, Pas ML, Chantillon L, Vahl M et al. Evaluation of Nanopore sequencing as a diagnostic tool for the rapid identification of mycoplasma bovis from Individual and pooled respiratory tract samples. J Clin Microbiol 2021; 59:e0111021 [View Article] [PubMed]
    [Google Scholar]
  31. Kamathewatta KI, Bushell RN, Young ND, Stevenson MA, Billman-Jacobe H et al. Exploration of antibiotic resistance risks in a veterinary teaching hospital with Oxford Nanopore long read sequencing. PLoS One 2019; 14:e0217600 [View Article] [PubMed]
    [Google Scholar]
  32. Lanszki Z, Tóth GE, Schütz É, Zeghbib S, Rusvai M et al. Complete genomic sequencing of canine distemper virus with nanopore technology during an epizootic event. Sci Rep 2022; 12:4116 [View Article] [PubMed]
    [Google Scholar]
  33. Brown E, Freimanis G, Shaw AE, Horton DL, Gubbins S et al. Characterising foot-and-mouth disease virus in clinical samples using nanopore sequencing. Front Vet Sci 2021; 8:656256 [View Article] [PubMed]
    [Google Scholar]
  34. Vanmechelen B, Bertelsen MF, Rector A, Van den Oord JJ, Laenen L et al. Identification of a novel species of papillomavirus in giraffe lesions using nanopore sequencing. Vet Microbiol 2017; 201:26–31 [View Article] [PubMed]
    [Google Scholar]
  35. D’Andreano S, Cuscó A, Francino O. Rapid and real-time identification of fungi up to species level with long amplicon nanopore sequencing from clinical samples. Biol Methods Protoc 2021; 6:bpaa026 [View Article] [PubMed]
    [Google Scholar]
  36. Ferrer L, García-Fonticoba R, Pérez D, Viñes J, Fàbregas N et al. Whole genome sequencing and de novo assembly of Staphylococcus pseudintermedius: a pangenome approach to unravelling pathogenesis of canine pyoderma. Vet Dermatol 2021; 32:654–663 [View Article] [PubMed]
    [Google Scholar]
  37. Loeffler A, Lloyd DH. What has changed in canine pyoderma? A narrative review. Vet J 2018; 235:73–82 [View Article] [PubMed]
    [Google Scholar]
  38. Wong C, Epstein SE, Westropp JL. Antimicrobial susceptibility patterns in urinary tract infections in dogs (2010-2013). J Vet Intern Med 2015; 29:1045–1052 [View Article] [PubMed]
    [Google Scholar]
  39. White SD. Systemic treatment of bacterial skin infections of dogs and cats. Vet Dermatol 1996; 7:133–143 [View Article] [PubMed]
    [Google Scholar]
  40. Paradis M, Lemay S, Scott DW, Miller WH, Wellington J et al. Efficacy of enrofloxacin in the treatment of canine bacterial pyoderma. Vet Dermatol 1990; 1:123–127 [View Article] [PubMed]
    [Google Scholar]
  41. Byron JK. Urinary tract infection. Vet Clin North Am Small Anim Pract 2019; 49:211–221 [View Article] [PubMed]
    [Google Scholar]
  42. Norris CR, Williams BJ, Ling GV, Franti CE et al. Recurrent and persistent urinary tract infections in dogs: 383 cases (1969-1995). J Am Anim Hosp Assoc 2000; 36:484–492 [View Article] [PubMed]
    [Google Scholar]
  43. Reddy BS, Kumari KN, Rao VV. Efficacy of enrofloxacin in the treatment of recurrent pyoderma in dogs. J Adv Vet Anim Res 2014; 4:108–112
    [Google Scholar]
  44. Bannoehr J, Ben Zakour NL, Waller AS, Guardabassi L, Thoday KL et al. Population genetic structure of the Staphylococcus intermedius group: insights into agr diversification and the emergence of methicillin-resistant strains. J Bacteriol 2007; 189:8685–8692 [View Article] [PubMed]
    [Google Scholar]
  45. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
    [Google Scholar]
  46. McIntyre KM, Setzkorn C, Hepworth PJ, Morand S, Morse AP et al. A quantitative prioritisation of human and domestic animal pathogens in Europe. PLoS One 2014; 9:e103529 [View Article] [PubMed]
    [Google Scholar]
  47. Nicholls SM, Quick JC, Tang S, Loman NJ. Ultra-deep, long-read nanopore sequencing of mock microbial community standards. Gigascience 2019; 8:giz043 [View Article] [PubMed]
    [Google Scholar]
  48. De Coster W, D’Hert S, Schultz DT, Cruts M, Van Broeckhoven C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 2018; 34:2666–2669 [View Article] [PubMed]
    [Google Scholar]
  49. Wick RR. Porechop: adapter trimmer for oxford nanopore reads; 2017 https://github.com/rrwick/Porechop
  50. Kolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol 2019; 37:540–546 [View Article] [PubMed]
    [Google Scholar]
  51. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article] [PubMed]
    [Google Scholar]
  52. Seemann T. ABRicate; 2022 https://github.com/tseemann/abricate
  53. Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J et al. AMRFinderPlus and the reference gene catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep 2021; 11:12728 [View Article] [PubMed]
    [Google Scholar]
  54. Hall MB. Rasusa: randomly subsample sequencing reads to a specified coverage. J Open Source Softw 2022; 7:3941 [View Article]
    [Google Scholar]
  55. Ackerman AL, Anger JT, Khalique MU, Ackerman JE, Tang J et al. Optimization of DNA extraction from human urinary samples for mycobiome community profiling. PLoS One 2019; 14:e0210306 [View Article] [PubMed]
    [Google Scholar]
  56. Bialasiewicz S, Duarte TPS, Nguyen SH, Sukumaran V, Stewart A et al. Rapid diagnosis of Capnocytophaga canimorsus septic shock in an immunocompetent individual using real-time Nanopore sequencing: a case report. BMC Infect Dis 2019; 19:660 [View Article] [PubMed]
    [Google Scholar]
  57. Yang L, Haidar G, Zia H, Nettles R, Qin S et al. Metagenomic identification of severe pneumonia pathogens in mechanically-ventilated patients: a feasibility and clinical validity study. Respir Res 2019; 20:265 [View Article] [PubMed]
    [Google Scholar]
  58. Ferreira FA, Helmersen K, Visnovska T, Jørgensen SB, Aamot HV. Rapid nanopore-based DNA sequencing protocol of antibiotic-resistant bacteria for use in surveillance and outbreak investigation. Microb Genom 2021; 7:000557 [View Article] [PubMed]
    [Google Scholar]
  59. Dalla-Costa LM, Morello LG, Conte D, Pereira LA, Palmeiro JK et al. Comparison of DNA extraction methods used to detect bacterial and yeast DNA from spiked whole blood by real-time PCR. J Microbiol Methods 2017; 140:61–66 [View Article] [PubMed]
    [Google Scholar]
  60. Kim D, Hofstaedter CE, Zhao C, Mattei L, Tanes C et al. Optimizing methods and dodging pitfalls in microbiome research. Microbiome 2017; 5:52 [View Article] [PubMed]
    [Google Scholar]
  61. Lucena-Aguilar G, Sánchez-López AM, Barberán-Aceituno C, Carrillo-Ávila JA, López-Guerrero JA et al. DNA source selection for downstream applications based on DNA quality indicators analysis. Biopreserv Biobank 2016; 14:264–270 [View Article] [PubMed]
    [Google Scholar]
  62. Helmersen K, Aamot HV. DNA extraction of microbial DNA directly from infected tissue: an optimized protocol for use in nanopore sequencing. Sci Rep 2020; 10:2985 [View Article] [PubMed]
    [Google Scholar]
  63. Wang C-X, Huang Z, Fang W, Zhang Z, Fang X et al. Preliminary assessment of nanopore-based metagenomic sequencing for the diagnosis of prosthetic joint infection. Int J Infect Dis 2020; 97:54–59 [View Article] [PubMed]
    [Google Scholar]
  64. Israeli O, Guedj-Dana Y, Shifman O, Lazar S, Cohen-Gihon I et al. Rapid Amplicon Nanopore Sequencing (RANS) for the differential diagnosis of monkeypox virus and other vesicle-forming pathogens. Viruses 2022; 14:1817 [View Article] [PubMed]
    [Google Scholar]
  65. Chan WS, Au CH, Lam HY, Wang CLN, Ho D-Y et al. Evaluation on the use of Nanopore sequencing for direct characterization of coronaviruses from respiratory specimens, and a study on emerging missense mutations in partial RdRP gene of SARS-CoV-2. Virol J 2020; 17:183 [View Article] [PubMed]
    [Google Scholar]
  66. Mosbruger TL, Dinou A, Duke JL, Ferriola D, Mehler H et al. Utilizing nanopore sequencing technology for the rapid and comprehensive characterization of eleven HLA loci; addressing the need for deceased donor expedited HLA typing. Hum Immunol 2020; 81:413–422 [View Article] [PubMed]
    [Google Scholar]
  67. De Santis D, Truong L, Martinez P, D’Orsogna L. Rapid high-resolution HLA genotyping by MinION Oxford nanopore sequencing for deceased donor organ allocation. HLA 2020; 96:141–162 [View Article] [PubMed]
    [Google Scholar]
  68. de Siqueira GMV, Pereira-Dos-Santos FM, Silva-Rocha R, Guazzaroni M-E. Nanopore sequencing provides rapid and reliable insight into microbial profiles of intensive care units. Front Public Health 2021; 9:710985 [View Article] [PubMed]
    [Google Scholar]
  69. Grädel C, Terrazos Miani MA, Barbani MT, Leib SL, Suter-Riniker F et al. Rapid and cost-efficient enterovirus genotyping from clinical samples using flongle flow cells. Genes 2019; 10:659 [View Article] [PubMed]
    [Google Scholar]
  70. Leggett RM, Alcon-Giner C, Heavens D, Caim S, Brook TC et al. Rapid MinION profiling of preterm microbiota and antimicrobial-resistant pathogens. Nat Microbiol 2020; 5:430–442 [View Article] [PubMed]
    [Google Scholar]
  71. Land M, Hauser L, Jun S-R, Nookaew I, Leuze MR et al. Insights from 20 years of bacterial genome sequencing. Funct Integr Genomics 2015; 15:141–161 [View Article] [PubMed]
    [Google Scholar]
  72. Charalampous T, Alcolea-Medina A, Snell LB, Williams TGS, Batra R et al. Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units. Genome Med 2021; 13:182 [View Article] [PubMed]
    [Google Scholar]
  73. Jansson E. Isolation of fastidious mycoplasma from human sources. J Clin Pathol 1971; 24:53–56 [View Article] [PubMed]
    [Google Scholar]
  74. Nakasone I, Kinjo T, Yamane N, Kisanuki K, Shiohira CM. Laboratory-based evaluation of the colorimetric VITEK-2 Compact system for species identification and of the advanced expert system for detection of antimicrobial resistances: VITEK-2 Compact system identification and antimicrobial susceptibility testing. Diagn Microbiol Infect Dis 2007; 58:191–198 [View Article] [PubMed]
    [Google Scholar]
  75. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S et al. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother 2012; 67:2640–2644 [View Article] [PubMed]
    [Google Scholar]
  76. Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res 2020; 48:D517–D525 [View Article] [PubMed]
    [Google Scholar]
  77. Tyson GH, McDermott PF, Li C, Chen Y, Tadesse DA et al. WGS accurately predicts antimicrobial resistance in Escherichia coli. J Antimicrob Chemother 2015; 70:2763–2769 [View Article] [PubMed]
    [Google Scholar]
  78. Holden MTG, Hsu L-Y, Kurt K, Weinert LA, Mather AE et al. A genomic portrait of the emergence, evolution, and global spread of a methicillin-resistant Staphylococcus aureus pandemic. Genome Res 2013; 23:653–664 [View Article] [PubMed]
    [Google Scholar]
  79. Marotz CA, Sanders JG, Zuniga C, Zaramela LS, Knight R et al. Improving saliva shotgun metagenomics by chemical host DNA depletion. Microbiome 2018; 6:42 [View Article] [PubMed]
    [Google Scholar]
  80. Shi Y, Wang G, Lau H-H, Yu J. Metagenomic sequencing for microbial DNA in human samples: emerging technological advances. Int J Mol Sci 2022; 23:2181 [View Article] [PubMed]
    [Google Scholar]
  81. Ye SH, Siddle KJ, Park DJ, Sabeti PC. Benchmarking metagenomics tools for taxonomic classification. Cell 2019; 178:779–794 [View Article] [PubMed]
    [Google Scholar]
  82. Pearman WS, Freed NE, Silander OK. Testing the advantages and disadvantages of short- and long- read eukaryotic metagenomics using simulated reads. BMC Bioinformatics 2020; 21:220 [View Article] [PubMed]
    [Google Scholar]
  83. Govender KN, Eyre DW. Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications. Microb Genom 2022; 8:10 [View Article] [PubMed]
    [Google Scholar]
  84. Dahl LG, Joensen KG, Østerlund MT, Kiil K, Nielsen EM. Prediction of antimicrobial resistance in clinical Campylobacter jejuni isolates from whole-genome sequencing data. Eur J Clin Microbiol Infect Dis 2021; 40:673–682 [View Article] [PubMed]
    [Google Scholar]
  85. Sydenham TV, Overballe-Petersen S, Hasman H, Wexler H, Kemp M et al. Complete hybrid genome assembly of clinical multidrug-resistant Bacteroides fragilis isolates enables comprehensive identification of antimicrobial-resistance genes and plasmids. Microb Genom 2019; 5:11 [View Article] [PubMed]
    [Google Scholar]
  86. Sherry NL, Horan KA, Ballard SA, Gonҫalves da Silva A, Gorrie CL et al. An ISO-certified genomics workflow for identification and surveillance of antimicrobial resistance. Nat Commun 2023; 14:60 [View Article] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.001066
Loading
/content/journal/mgen/10.1099/mgen.0.001066
Loading

Data & Media loading...

Supplements

Supplementary material 1

EXCEL

Supplementary material 2

PDF
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error