1887

Journal of Medical Microbiology logo

Volume 72, Issue 6

Development and evaluation of Microbe Finder (MiFi): a novel diagnostic platform for pathogen detection from metagenomic data Open Access

Abstract

With expanding demand for diagnostics, newer methodologies are needed for faster, user-friendly and multiplexed pathogen detection. Metagenome-based diagnostics offer potential solutions to address these needs as sequencing technologies have become affordable. However, the diagnostic utility of sequencing technologies is currently limited since analysis of the large amounts of data generated, are either computationally expensive or carry lower sensitivity and specificity for pathogen detection.

There is a need for novel, user friendly, and computationally inexpensive platforms for metagenome sequence analysis for diagnostic applications.

In this study, we report the use of MiFi (Microbe Finder), a computationally inexpensive algorithm with a user-friendly online interface, for accurate, rapid and multiplexed pathogen detection from metagenome sequence data. Detection is accomplished based on identification of signature genomic sequence segments of the target pathogen in metagenome sequence data. In this study we used bovine respiratory disease (BRD) complex as a model.

Using MiFi, multiple target bacteria and a DNA virus were successfully detected in a multiplex format from metagenome sequences acquired from bovine lung tissue. Overall, 51 clinical samples were assessed and MiFi showed 100 % analytical specificity and varying levels of analytical sensitivity (62.5 %–100 %) when compared with other traditional pathogen detection techniques, such as PCR. Consistent detection of bacteria was possible from lung samples artificially spiked with 10–10 c.f.u. of .

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): bovine respiratory disease complex , MiFi® , Nanopore sequencing and pathogen detection
Funding
This study was supported by the:
  • Oklahoma State University
    • Principle Award Recipient: AkhileshRamachandran
© 2023 The Authors
  • 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/jmm/10.1099/jmm.0.001720
2023-06-22
2024-05-02
Loading full text...

Full text loading...

/deliver/fulltext/jmm/72/6/jmm001720.html?itemId=/content/journal/jmm/10.1099/jmm.0.001720&mimeType=html&fmt=ahah

References

  1. Fuller CW, Middendorf LR, Benner SA, Church GM, Harris T et al. The challenges of sequencing by synthesis. Nat Biotechnol 2009; 27:1013–1023 [View Article] [PubMed]
     [Google Scholar]
  2. Bennett S. Solexa Ltd. Pharmacogenomics 2004; 5:433–438 [View Article] [PubMed]
     [Google Scholar]
  3. Rothberg JM, Hinz W, Rearick TM, Schultz J, Mileski W et al. An integrated semiconductor device enabling non-optical genome sequencing. Nature 2011; 475:348–352 [View Article] [PubMed]
     [Google Scholar]
  4. Bustillo J, Fife K, Merriman B, Rothberg J. Development of the ion torrent CMOS chip for DNA sequencing. In 2013 IEEE International Electron Devices Meeting 2013 p 8.1.1-8.1.4
     [Google Scholar]
  5. Clarke J, Wu H-C, Jayasinghe L, Patel A, Reid S et al. Continuous base identification for single-molecule nanopore DNA sequencing. Nat Nanotechnol 2009; 4:265–270 [View Article] [PubMed]
     [Google Scholar]
  6. Ambardar S, Gupta R, Trakroo D, Lal R, Vakhlu J. High throughput sequencing: an overview of sequencing chemistry. Indian J Microbiol 2016; 56:394–404 [View Article] [PubMed]
     [Google Scholar]
  7. McCarthy A. Third generation DNA sequencing: pacific biosciences’ single molecule real time technology. Chem Biol 2010; 17:675–676 [View Article] [PubMed]
     [Google Scholar]
  8. Espindola AS, Cardwell KF. Microbe Finder (MiFi®): Implementation of an Interactive Pathogen Detection Tool in Metagenomic Sequence Data. Plants 2021; 10:250 [View Article] [PubMed]
     [Google Scholar]
  9. Espindola AS, Schneider W, Cardwell KF, Carrillo Y, Hoyt PR et al. Inferring the presence of aflatoxin-producing Aspergillus flavus strains using RNA sequencing and electronic probes as a transcriptomic screening tool. PLoS One 2018; 13:e0198575 [View Article] [PubMed]
     [Google Scholar]
  10. Espindola A, Schneider W, Hoyt PR, Marek SM, Garzon C. A new approach for detecting fungal and oomycete plant pathogens in next generation sequencing metagenome data utilising electronic probes. Int J Data Min Bioinform 2015; 12:115–128 [View Article] [PubMed]
     [Google Scholar]
  11. Espindola AS, Sempertegui-Bayas D, Bravo-Padilla DF, Freire-Zapata V, Ochoa-Corona F et al. TASPERT: Target-Specific Reverse Transcript pools to improve HTS plant virus diagnostics. Viruses 2021; 13:1223 [View Article] [PubMed]
     [Google Scholar]
  12. Espindola AS, Cardwell K, Martin FN, Hoyt PR, Marek SM et al. A step towards validation of high-throughput sequencing for the identification of plant pathogenic oomycetes. Phytopathology 2022; 112:1859–1866 [View Article] [PubMed]
     [Google Scholar]
  13. 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]
  14. Thoendel MJ, Jeraldo PR, Greenwood-Quaintance KE, Yao JZ, Chia N et al. Identification of prosthetic joint infection pathogens using a shotgun metagenomics approach. Clin Infect Dis 2018; 67:1333–1338 [View Article] [PubMed]
     [Google Scholar]
  15. Vijayvargiya P, Jeraldo PR, Thoendel MJ, Greenwood-Quaintance KE, Esquer Garrigos Z et al. Application of metagenomic shotgun sequencing to detect vector-borne pathogens in clinical blood samples. PLoS One 2019; 14:e0222915 [View Article] [PubMed]
     [Google Scholar]
  16. Gyarmati P, Kjellander C, Aust C, Song Y, Öhrmalm L et al. Metagenomic analysis of bloodstream infections in patients with acute leukemia and therapy-induced neutropenia. Sci Rep 2016; 6:23532 [View Article] [PubMed]
     [Google Scholar]
  17. Afshinnekoo E, Chou C, Alexander N, Ahsanuddin S, Schuetz AN et al. Precision metagenomics: rapid metagenomic analyses for infectious disease diagnostics and public health surveillance. J Biomol Tech 2017; 28:40–45 [View Article]
     [Google Scholar]
  18. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
     [Google Scholar]
  19. Kim D, Song L, Breitwieser FP, Salzberg SL. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 2016; 26:1721–1729 [View Article]
     [Google Scholar]
  20. Stobbe AH, Daniels J, Espindola AS, Verma R, Melcher U et al. E-probe Diagnostic Nucleic acid Analysis (EDNA): a theoretical approach for handling of next generation sequencing data for diagnostics. J Microbiol Methods 2013; 94:356–366 [View Article] [PubMed]
     [Google Scholar]
  21. Brodersen BW. Bovine respiratory syncytial virus. Vet Clin North Am Food Anim Pract 2010; 26:323–333 [View Article] [PubMed]
     [Google Scholar]
  22. Wolfger B, Timsit E, White BJ, Orsel K. A systematic review of bovine respiratory disease diagnosis focused on diagnostic confirmation, early detection, and prediction of unfavorable outcomes in feedlot cattle. Vet Clin North Am Food Anim Pract 2015; 31:351–365 [View Article] [PubMed]
     [Google Scholar]
  23. Frank GH. Bacteria as etiologic agents in bovine respiratory disease. In Bovine Respiratory Disease: A Symposium College Station: Texas A&M University Press; 1984 pp 347–362
     [Google Scholar]
  24. Rice JA, Carrasco-Medina L, Hodgins DC, Shewen PE. Mannheimia haemolytica and bovine respiratory disease. Anim Health Res Rev 2007; 8:117–128 [View Article] [PubMed]
     [Google Scholar]
  25. Vijaya Satya R, Zavaljevski N, Kumar K, Reifman J. A high-throughput pipeline for designing microarray-based pathogen diagnostic assays. BMC Bioinformatics 2008; 9:185 [View Article] [PubMed]
     [Google Scholar]
  26. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M et al. Versatile and open software for comparing large genomes. Genome Biol 2004; 5:R12 [View Article] [PubMed]
     [Google Scholar]
  27. Huang W, Li L, Myers JR, Marth GT. ART: a next-generation sequencing read simulator. Bioinformatics 2012; 28:593–594 [View Article]
     [Google Scholar]
  28. Kishimoto M, Tsuchiaka S, Rahpaya SS, Hasebe A, Otsu K et al. Development of a one-run real-time PCR detection system for pathogens associated with bovine respiratory disease complex. J Vet Med Sci 2017; 79:517–523 [View Article] [PubMed]
     [Google Scholar]
  29. Wick RR, Judd LM, Holt KE. Performance of neural network basecalling tools for Oxford Nanopore sequencing. Genome Biol 2019; 20:129 [View Article] [PubMed]
     [Google Scholar]
  30. Lanfear R, Schalamun M, Kainer D, Wang W, Schwessinger B. MinIONQC: fast and simple quality control for MinION sequencing data. Bioinformatics 2019; 35:523–525 [View Article] [PubMed]
     [Google Scholar]
  31. Fukasawa Y, Ermini L, Wang H, Carty K, Cheung M-S. LongQC: a quality control tool for third generation sequencing long read data. G3 Genes|Genomes|Genetics 2020; 10:1193–1196 [View Article] [PubMed]
     [Google Scholar]
  32. 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]
  33. Miles AA, Misra SS, Irwin JO. The estimation of the bactericidal power of the blood. Epidemiol Infect 1938; 38:732–749 [View Article]
     [Google Scholar]
  34. Hedges AJ. Estimating the precision of serial dilutions and viable bacterial counts. Int J Food Microbiol 2002; 76:207–214 [View Article] [PubMed]
     [Google Scholar]
  35. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015; 31:1674–1676 [View Article] [PubMed]
     [Google Scholar]
  36. Wood D, Kraken LJ. Taxonomic sequence classification system. wood DE, Salzberg SL: Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014; 15: [View Article]
     [Google Scholar]
  37. Leidenfrost RM, Pöther D-C, Jäckel U, Wünschiers R. Benchmarking the MinION: evaluating long reads for microbial profiling. Sci Rep 2020; 10:5125 [View Article] [PubMed]
     [Google Scholar]
  38. Brady A, Salzberg SL. Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models. Nat Methods 2009; 6:673–676 [View Article] [PubMed]
     [Google Scholar]
  39. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J et al. Binning metagenomic contigs by coverage and composition. Nat Methods 2014; 11:1144–1146 [View Article] [PubMed]
     [Google Scholar]
  40. Xu Y, Yang-Turner F, Volk D, Crook D. NanoSPC: a scalable, portable, cloud compatible viral nanopore metagenomic data processing pipeline. Nucleic Acids Res 2020; 48:W366–W371 [View Article]
     [Google Scholar]
  41. Benjamin H, A. A-G, R. S-C, Qiyun Z, M. GD et al. Evaluating the information content of shallow shotgun metagenomics. mSystems 2022; 3:
     [Google Scholar]
  42. Alili R, Belda E, Le P, Wirth T, Zucker J-D et al. Exploring semi-quantitative metagenomic studies using Oxford Nanopore sequencing: A computational and experimental protocol. Genes 2021; 12:1496 [View Article] [PubMed]
     [Google Scholar]
  43. Sabiha B, Haider SA, Jan H, Afridi OK, Ali J. Large scale differences in characterization of gut microbiome between 16S amplicon and shallow shotgun sequencing. J Rehman Med Inst 2019; 5:3–6
     [Google Scholar]
  44. Lugli GA, Ventura M. A breath of fresh air in microbiome science: shallow shotgun metagenomics for A reliable disentangling of microbial ecosystems. Micro Res Rep 2022; 1:8 [View Article]
     [Google Scholar]
  45. Kuiama L, Yifei X, PS T, LS F, Dona F et al. Metagenomic Nanopore sequencing of influenza virus direct from clinical respiratory samples. J Clin Microbiol 2022; 58:e00963–19
     [Google Scholar]
  46. Mahdavinia M, Keshavarzian A, Tobin MC, Landay AL, Schleimer RP. A comprehensive review of the nasal microbiome in chronic rhinosinusitis (CRS). Clin Exp Allergy 2016; 46:21–41 [View Article] [PubMed]
     [Google Scholar]
  47. Greninger AL, Naccache SN, Federman S, Yu G, Mbala P et al. Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis. Genome Med 2015; 7:1–13 [View Article]
     [Google Scholar]
  48. Batovska J, Lynch SE, Rodoni BC, Sawbridge TI, Cogan NOI. Metagenomic arbovirus detection using MinION nanopore sequencing. J Virol Methods 2017; 249:79–84 [View Article] [PubMed]
     [Google Scholar]
  49. Petersen LM, Martin IW, Moschetti WE, Kershaw CM, Tsongalis GJ et al. Third-generation sequencing in the clinical laboratory: exploring the advantages and challenges of Nanopore sequencing. J Clin Microbiol 2019; 58: [View Article]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/jmm/10.1099/jmm.0.001720
Loading
Development and evaluation of Microbe Finder (MiFi)®: a novel in silico diagnostic platform for pathogen detection from metagenomic data
J. Med. Microbiol. 72, 001720 (2023); https://doi.org/10.1099/jmm.0.001720
/content/journal/jmm/10.1099/jmm.0.001720
/content/journal/jmm/10.1099/jmm.0.001720
Loading

Data & Media loading...

Most cited Most Cited RSS feed

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