Finding the right fit: evaluation of short-read and long-read sequencing approaches to maximize the utility of clinical microbiome data Open Access

Abstract

A long-standing challenge in human microbiome research is achieving the taxonomic and functional resolution needed to generate testable hypotheses about the gut microbiota’s impact on health and disease. With a growing number of live microbial interventions in clinical development, this challenge is renewed by a need to understand the pharmacokinetics and pharmacodynamics of therapeutic candidates. While short-read sequencing of the bacterial 16S rRNA gene has been the standard for microbiota profiling, recent improvements in the fidelity of long-read sequencing underscores the need for a re-evaluation of the value of distinct microbiome-sequencing approaches. We leveraged samples from participants enrolled in a phase 1b clinical trial of a novel live biotherapeutic product to perform a comparative analysis of short-read and long-read amplicon and metagenomic sequencing approaches to assess their utility for generating clinical microbiome data. Across all methods, overall community taxonomic profiles were comparable and relationships between samples were conserved. Comparison of ubiquitous short-read 16S rRNA amplicon profiling to long-read profiling of the 16S-ITS-23S rRNA amplicon showed that only the latter provided strain-level community resolution and insight into novel taxa. All methods identified an active ingredient strain in treated study participants, though detection confidence was higher for long-read methods. Read coverage from both metagenomic methods provided evidence of active-ingredient strain replication in some treated participants. Compared to short-read metagenomics, approximately twice the proportion of long reads were assigned functional annotations. Finally, compositionally similar bacterial metagenome-assembled genomes (MAGs) were recovered from short-read and long-read metagenomic methods, although a greater number and more complete MAGs were recovered from long reads. Despite higher costs, both amplicon and metagenomic long-read approaches yielded added microbiome data value in the form of higher confidence taxonomic and functional resolution and improved recovery of microbial genomes compared to traditional short-read methodologies.

Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000794
2022-03-18
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/mgen/8/3/mgen000794.html?itemId=/content/journal/mgen/10.1099/mgen.0.000794&mimeType=html&fmt=ahah

References

  1. Stephen AM, Cummings JH. The microbial contribution to human faecal mass. J Med Microbiol 1980; 13:45–56 [View Article] [PubMed]
    [Google Scholar]
  2. Gehrig JL, Venkatesh S, Chang H-W, Hibberd MC, Kung VL et al. Effects of microbiota-directed foods in gnotobiotic animals and undernourished children. Science 2019; 365:eaau4732 [View Article] [PubMed]
    [Google Scholar]
  3. Bauer H, Horowitz RE, Levenson SM, Popper H. The response of the lymphatic tissue to the microbial flora. Studies on germfree mice. Am J Pathol 1963; 42:471–483 [PubMed]
    [Google Scholar]
  4. Olszak T, An D, Zeissig S, Vera MP, Richter J et al. Microbial exposure during early life has persistent effects on natural killer T cell function. Science 2012; 336:489–493 [View Article] [PubMed]
    [Google Scholar]
  5. Fukuda S, Toh H, Hase K, Oshima K, Nakanishi Y et al. Bifidobacteria can protect from enteropathogenic infection through production of acetate. Nature 2011; 469:543–547 [View Article] [PubMed]
    [Google Scholar]
  6. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG et al. Human gut microbiome viewed across age and geography. Nature 2012; 486:222–227 [View Article] [PubMed]
    [Google Scholar]
  7. Roediger WEW. Role of anaerobic bacteria in the metabolic welfare of the colonic mucosa in man. Gut 1980; 21:793–798 [View Article] [PubMed]
    [Google Scholar]
  8. Neuman H, Debelius JW, Knight R, Koren O. Microbial endocrinology: the interplay between the microbiota and the endocrine system. FEMS Microbiol Rev 2015; 39:509–521 [View Article] [PubMed]
    [Google Scholar]
  9. Lynch SV, Pedersen O. The human intestinal microbiome in health and disease. N Engl J Med 2016; 375:2369–2379 [View Article] [PubMed]
    [Google Scholar]
  10. FDA, CBER Early clinical trials with live biotherapeutic products: chemistry, manufacturing, and control information; guidance for industry; 2012 http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guida
  11. Rouanet A, Bolca S, Bru A, Claes I, Cvejic H et al. Live biotherapeutic products, a road map for safety assessment. Front Med (Lausanne) 2020; 7:237 [View Article] [PubMed]
    [Google Scholar]
  12. Sela DA, Chapman J, Adeuya A, Kim JH, Chen F et al. The genome sequence of Bifidobacterium longum subsp. infantis reveals adaptations for milk utilization within the infant microbiome. Proc Natl Acad Sci U S A 2008; 105:18964–18969 [View Article] [PubMed]
    [Google Scholar]
  13. Geva-Zatorsky N, Sefik E, Kua L, Pasman L, Tan TG et al. Mining the human gut microbiota for immunomodulatory organisms. Cell 2017; 168:928–943 [View Article] [PubMed]
    [Google Scholar]
  14. Woese CR, Fox GE. Phylogenetic structure of the prokaryotic domain: the primary kingdoms (archaebacteria/eubacteria/urkaryote/16S ribosomal RNA/molecular phylogeny). Proc Natl Acad Sci USA 1977; 74:5088–5090
    [Google Scholar]
  15. Hamady M, Knight R. Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome Res 2009; 19:1141–1152 [View Article] [PubMed]
    [Google Scholar]
  16. Johnson JS, Spakowicz DJ, Hong B-Y, Petersen LM, Demkowicz P et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun 2019; 10:5029 [View Article] [PubMed]
    [Google Scholar]
  17. Janda JM, Abbott SL. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J Clin Microbiol 2007; 45:2761–2764 [View Article] [PubMed]
    [Google Scholar]
  18. Wenger AM, Peluso P, Rowell WJ, Chang P-C, Hall RJ et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat Biotechnol 2019; 37:1155–1162 [View Article] [PubMed]
    [Google Scholar]
  19. Earl JP, Adappa ND, Krol J, Bhat AS, Balashov S et al. Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes. Microbiome 2018; 6:190 [View Article] [PubMed]
    [Google Scholar]
  20. Graf J, Ledala N, Caimano MJ, Jackson E, Gratalo D et al. High-resolution differentiation of enteric bacteria in premature infant fecal microbiomes using a novel rRNA amplicon. mBio 2021; 12:1–18 [View Article] [PubMed]
    [Google Scholar]
  21. Gilbert JA, Blaser MJ, Caporaso JG, Jansson JK, Lynch SV et al. Current understanding of the human microbiome. Nat Med 2018; 24:392–400 [View Article] [PubMed]
    [Google Scholar]
  22. Ranjan R, Rani A, Metwally A, McGee HS, Perkins DL. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem Biophys Res Commun 2016; 469:967–977 [View Article] [PubMed]
    [Google Scholar]
  23. Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Gohl DM et al. Evaluating the Information Content of Shallow Shotgun Metagenomics. mSystems 2018; 3:e00069-18 [View Article] [PubMed]
    [Google Scholar]
  24. Kyrpides NC, Hugenholtz P, Eisen JA, Woyke T, Gö ker MM et al. Community page genomic encyclopedia of bacteria and archaea: sequencing a myriad of type strains. Comparative Evolution and Molecular Biology 2014; 1:
    [Google Scholar]
  25. Nayfach S, Shi ZJ, Seshadri R, Pollard KS, Kyrpides NC. New insights from uncultivated genomes of the global human gut microbiome. Nature 2019; 568:505–510 [View Article] [PubMed]
    [Google Scholar]
  26. Almeida A, Mitchell AL, Boland M, Forster SC, Gloor GB et al. A new genomic blueprint of the human gut microbiota. Nature 2019; 568:499–504 [View Article] [PubMed]
    [Google Scholar]
  27. Ghurye JS, Cepeda-Espinoza V, Pop M. Metagenomic assembly: overview, challenges and applications. Yale J Biol Med 2016; 89:353–362 [PubMed]
    [Google Scholar]
  28. Bickhart DM, Watson M, Koren S, Panke-Buisse K, Cersosimo LM et al. Assignment of virus and antimicrobial resistance genes to microbial hosts in a complex microbial community by combined long-read assembly and proximity ligation. Genome Biol 2019; 20:153 [View Article] [PubMed]
    [Google Scholar]
  29. Lang D, Zhang S, Ren P, Liang F, Sun Z et al. Comparison of the two up-to-date sequencing technologies for genome assembly: HiFi reads of Pacific Biosciences Sequel II system and ultralong reads of Oxford Nanopore. Gigascience 2020; 9:giaa123 [View Article] [PubMed]
    [Google Scholar]
  30. Fujimura KE, Sitarik AR, Havstad S, Lin DL, Levan S et al. Neonatal gut microbiota associates with childhood multisensitized atopy and T cell differentiation. Nat Med 2016; 22:1187–1191 [View Article] [PubMed]
    [Google Scholar]
  31. Bolyen E, Rideout JR, Dillon MR, Abnet CC, Al-Ghalith GA. Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2. Vol. 37, Nature Biotechnology Nature Publishing Group; 2019 pp 852–857
    [Google Scholar]
  32. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 2016; 13:581–583 [View Article] [PubMed]
    [Google Scholar]
  33. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 2012; 6:610–618 [View Article] [PubMed]
    [Google Scholar]
  34. Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W et al. SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 2007; 35:7188–7196 [View Article] [PubMed]
    [Google Scholar]
  35. Rodríguez-Pérez H, Ciuffreda L, Flores C. NanoCLUST: a species-level analysis of 16S rRNA nanopore sequencing data. Bioinformatics 2021; 37:1600–1601 [View Article] [PubMed]
    [Google Scholar]
  36. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 2020; 38:685–688 [View Article] [PubMed]
    [Google Scholar]
  37. Baym M, Kryazhimskiy S, Lieberman TD, Chung H, Desai MM et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE 2015; 10:e0128036 [View Article] [PubMed]
    [Google Scholar]
  38. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
    [Google Scholar]
  39. Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci 2017; 3:e104 [View Article]
    [Google Scholar]
  40. Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods 2018; 15:962–968 [View Article] [PubMed]
    [Google Scholar]
  41. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: A new versatile metagenomic assembler. Genome Res 2017; 27:824–834 [View Article] [PubMed]
    [Google Scholar]
  42. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2015; 12:59–60 [View Article] [PubMed]
    [Google Scholar]
  43. Huson DH, Beier S, Flade I, Górska A, El-Hadidi M et al. MEGAN community edition - interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol 2016; 12:e1004957 [View Article] [PubMed]
    [Google Scholar]
  44. Huson DH, Albrecht B, Bağcı C, Bessarab I, Górska A et al. MEGAN-LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs. Biol Direct 2018; 13:6 [View Article] [PubMed]
    [Google Scholar]
  45. Portik DM, Brown CT, Pierce-Ward NT. Evaluation of taxonomic profiling methods for long-read shotgun metagenomic sequencing datasets. Bioinformatics 2022 [View Article] [PubMed]
    [Google Scholar]
  46. Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol 2018; 36:996–1004 [View Article] [PubMed]
    [Google Scholar]
  47. Parks DH, Chuvochina M, Chaumeil PA, Rinke C, Mussig AJ et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol 2020; 38:1079–1086 [View Article] [PubMed]
    [Google Scholar]
  48. Feng X, Cheng H, Portik D, Li H. Metagenome assembly of high-fidelity long reads with hifiasm-meta. arXiv 2021
    [Google Scholar]
  49. Kang DD, Li F, Kirton E, Thomas A, Egan R et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019; 7:e7359 [View Article] [PubMed]
    [Google Scholar]
  50. Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 2018; 3:836–843 [View Article] [PubMed]
    [Google Scholar]
  51. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 2015; 25:1043–1055 [View Article] [PubMed]
    [Google Scholar]
  52. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019; 36:1925–1927 [View Article] [PubMed]
    [Google Scholar]
  53. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 2018; 34:3094–3100 [View Article] [PubMed]
    [Google Scholar]
  54. Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol 2021; 17:e1009442 [View Article] [PubMed]
    [Google Scholar]
  55. Mizrahi-Man O, Davenport ER, Gilad Y. Taxonomic classification of bacterial 16S rRNA genes using short sequencing reads: evaluation of effective study designs. PLoS ONE 2013; 8:e53608 [View Article] [PubMed]
    [Google Scholar]
  56. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 2013; 41:e1 [View Article] [PubMed]
    [Google Scholar]
  57. Carrow HC, Batachari LE, Chu H. Strain diversity in the microbiome: Lessons from Bacteroides fragilis. PLoS Pathog 2020; 16:e1009056 [View Article] [PubMed]
    [Google Scholar]
  58. Fitz-Gibbon S, Tomida S, Chiu B-H, Nguyen L, Du C et al. Propionibacterium acnes strain populations in the human skin microbiome associated with acne. J Invest Dermatol 2013; 133:2152–2160 [View Article] [PubMed]
    [Google Scholar]
  59. de Goffau MC, Lager S, Salter SJ, Wagner J, Kronbichler A et al. Recognizing the reagent microbiome. Nat Microbiol 2018; 3:851–853 [View Article] [PubMed]
    [Google Scholar]
  60. 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]
  61. Nygaard AB, Tunsjø HS, Meisal R, Charnock C. A preliminary study on the potential of Nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Sci Rep 2020; 10:3209 [View Article] [PubMed]
    [Google Scholar]
  62. Kameoka S, Motooka D, Watanabe S, Kubo R, Jung N et al. Benchmark of 16S rRNA gene amplicon sequencing using Japanese gut microbiome data from the V1-V2 and V3-V4 primer sets. BMC Genomics 2021; 22:527 [View Article] [PubMed]
    [Google Scholar]
  63. Maukonen J, Simões C, Saarela M. The currently used commercial DNA-extraction methods give different results of clostridial and actinobacterial populations derived from human fecal samples. FEMS Microbiol Ecol 2012; 79:697–708 [View Article] [PubMed]
    [Google Scholar]
  64. Kralik P, Ricchi M. A basic guide to real time PCR in microbial diagnostics: definitions, parameters, and everything. Front Microbiol 2017; 8:108 [View Article] [PubMed]
    [Google Scholar]
  65. 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]
  66. Sahlin K, Medvedev P. Author Correction: Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis. Nat Commun 2021; 12:992 [View Article] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000794
Loading
/content/journal/mgen/10.1099/mgen.0.000794
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Supplementary material 2

EXCEL

Most cited Most Cited RSS feed