1887

Abstract

The human gut microbiome has been extensively studied, yet the canine gut microbiome is still largely unknown. The availability of high-quality genomes is essential in the fields of veterinary medicine and nutrition to unravel the biological role of key microbial members in the canine gut environment. Our aim was to evaluate nanopore long-read metagenomics and Hi-C (high-throughput chromosome conformation capture) proximity ligation to provide high-quality metagenome-assembled genomes (HQ MAGs) of the canine gut environment. By combining nanopore long-read metagenomics and Hi-C proximity ligation, we retrieved 27 HQ MAGs and 7 medium-quality MAGs of a faecal sample of a healthy dog. Canine MAGs (CanMAGs) improved genome contiguity of representatives from the animal and human MAG catalogues – short-read MAGs from public datasets – for the species they represented: they were more contiguous with complete ribosomal operons and at least 18 canonical tRNAs. Both canine-specific bacterial species and gut generalists inhabit the dog’s gastrointestinal environment. Most of them belonged to , followed by and . We also assembled one and one MAG. CanMAGs harboured antimicrobial-resistance genes (ARGs) and prophages and were linked to plasmids. ARGs conferring resistance to tetracycline were most predominant within CanMAGs, followed by lincosamide and macrolide ones. At the functional level, carbohydrate transport and metabolism was the most variable within the CanMAGs, and mobilome function was abundant in some MAGs. Specifically, we assigned the mobilome functions and the associated mobile genetic elements to the bacterial host. The CanMAGs harboured 50 bacteriophages, providing novel bacterial-host information for eight viral clusters, and Hi-C proximity ligation data linked the six potential plasmids to their bacterial host. Long-read metagenomics and Hi-C proximity ligation are likely to become a comprehensive approach to HQ MAG discovery and assignment of extra-chromosomal elements to their bacterial host. This will provide essential information for studying the canine gut microbiome in veterinary medicine and animal nutrition.

Funding
This study was supported by the:
  • Ministerio de Ciencia, Innovación y Universidades (Award PTQ2018-009961)
    • Principle Award Recipient: NormaFàbregas
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
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2022-03-17
2024-04-23
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References

  1. Pasolli E, Asnicar F, Manara S, Zolfo M, Karcher N et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 2019; 176:649–662 [View Article] [PubMed]
    [Google Scholar]
  2. 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]
  3. Almeida A, Nayfach S, Boland M, Strozzi F, Beracochea M et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol 2021; 39:105–114 [View Article] [PubMed]
    [Google Scholar]
  4. Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol 2017; 35:725–731 [View Article] [PubMed]
    [Google Scholar]
  5. Yuan C, Lei J, Cole J, Sun Y. Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics 2015; 31:i35–i43 [View Article] [PubMed]
    [Google Scholar]
  6. Pilla R, Suchodolski JS. The role of the canine gut microbiome and metabolome in health and gastrointestinal disease. Front Vet Sci 2019; 6:498 [View Article] [PubMed]
    [Google Scholar]
  7. Wernimont SM, Radosevich J, Jackson MI, Ephraim E, Badri DV et al. The effects of nutrition on the gastrointestinal microbiome of cats and dogs: impact on health and disease. Front Microbiol 2020; 11:1266 [View Article] [PubMed]
    [Google Scholar]
  8. Coelho LP, Kultima JR, Costea PI, Fournier C, Pan Y et al. Similarity of the dog and human gut microbiomes in gene content and response to diet. Microbiome 2018; 6:72 [View Article] [PubMed]
    [Google Scholar]
  9. Youngblut ND, de la Cuesta-Zuluaga J, Reischer GH, Dauser S, Schuster N et al. Large-scale metagenome assembly reveals novel animal-associated microbial genomes, biosynthetic gene clusters, and other genetic diversity. mSystems 2020; 5:e01045-20 [View Article]
    [Google Scholar]
  10. Cuscó A, Pérez D, Viñes J, Fàbregas N, Francino O. Long-read metagenomics retrieves complete single-contig bacterial genomes from canine feces. BMC Genomics 2021; 22:330 [View Article] [PubMed]
    [Google Scholar]
  11. Singleton CM, Petriglieri F, Kristensen JM, Kirkegaard RH, Michaelsen TY et al. Connecting structure to function with the recovery of over 1000 high-quality metagenome-assembled genomes from activated sludge using long-read sequencing. Nat Commun 2021; 12:2009 [View Article]
    [Google Scholar]
  12. Suzuki Y, Nishijima S, Furuta Y, Yoshimura J, Suda W et al. Long-read metagenomic exploration of extrachromosomal mobile genetic elements in the human gut. Microbiome 2019; 7:119 [View Article] [PubMed]
    [Google Scholar]
  13. Bertrand D, Shaw J, Kalathiyappan M, Ng AHQ, Kumar MS et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat Biotechnol 2019; 37:937–944 [View Article] [PubMed]
    [Google Scholar]
  14. 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]
  15. Yahara K, Suzuki M, Hirabayashi A, Suda W, Hattori M et al. Long-read metagenomics using PromethION uncovers oral bacteriophages and their interaction with host bacteria. Nat Commun 2021; 12:27 [View Article] [PubMed]
    [Google Scholar]
  16. Che Y, Xia Y, Liu L, Li A-D, Yang Y et al. Mobile antibiotic resistome in wastewater treatment plants revealed by Nanopore metagenomic sequencing. Microbiome 2019; 7:44 [View Article] [PubMed]
    [Google Scholar]
  17. Partridge SR, Kwong SM, Firth N, Jensen SO. Mobile genetic elements associated with antimicrobial resistance. Clin Microbiol Rev 2018; 31:e00088-17 [View Article] [PubMed]
    [Google Scholar]
  18. Maghini DG, Moss EL, Vance SE, Bhatt AS. Improved high-molecular-weight DNA extraction, nanopore sequencing and metagenomic assembly from the human gut microbiome. Nat Protoc 2021; 16:458–471 [View Article] [PubMed]
    [Google Scholar]
  19. 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]
  20. Arumugam K, Bağcı C, Bessarab I, Beier S, Buchfink B et al. Annotated bacterial chromosomes from frame-shift-corrected long-read metagenomic data. Microbiome 2019; 7:61 [View Article] [PubMed]
    [Google Scholar]
  21. Burton JN, Liachko I, Dunham MJ, Shendure J. Species-level deconvolution of metagenome assemblies with Hi-C–based contact probability maps. G3 2014; 4:1339–1346 [View Article] [PubMed]
    [Google Scholar]
  22. Beitel CW, Froenicke L, Lang JM, Korf IF, Michelmore RW et al. Strain- and plasmid-level deconvolution of a synthetic metagenome by sequencing proximity ligation products. PeerJ 2014; 2:e415 [View Article] [PubMed]
    [Google Scholar]
  23. 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]
    [Google Scholar]
  24. Bickhart DM, Kolmogorov M, Tseng E, Portik DM, Korobeynikov A et al. Generating lineage-resolved, complete metagenome-assembled genomes from complex microbial communities. Nat Biotechnol 2022 [View Article] [PubMed]
    [Google Scholar]
  25. Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 2017; 27:722–736 [View Article] [PubMed]
    [Google Scholar]
  26. Kolmogorov M, Bickhart DM, Behsaz B, Gurevich A, Rayko M et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods 2020; 17:1103–1110 [View Article] [PubMed]
    [Google Scholar]
  27. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2015; 12:59–60 [View Article] [PubMed]
    [Google Scholar]
  28. 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]
  29. Chaumeil P-A, 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]
  30. Parks DH, Chuvochina M, Chaumeil P-A, 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]
  31. Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 2018; 9:5114 [View Article] [PubMed]
    [Google Scholar]
  32. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res 2017; 45:D566–D573 [View Article] [PubMed]
    [Google Scholar]
  33. Lagesen K, Hallin P, Rødland EA, Staerfeldt H-H, Rognes T et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 2007; 35:3100–3108 [View Article] [PubMed]
    [Google Scholar]
  34. Krawczyk PS, Lipinski L, Dziembowski A. PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res 2018; 46:e35 [View Article] [PubMed]
    [Google Scholar]
  35. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article] [PubMed]
    [Google Scholar]
  36. Chen L, Yang J, Yu J, Yao Z, Sun L et al. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res 2005; 33:D325–D328 [View Article] [PubMed]
    [Google Scholar]
  37. Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 2021; 9:37 [View Article] [PubMed]
    [Google Scholar]
  38. Kieft K, Zhou Z, Anantharaman K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 2020; 8:90 [View Article] [PubMed]
    [Google Scholar]
  39. Bin Jang H, Bolduc B, Zablocki O, Kuhn JH, Roux S et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol 2019; 37:632–639 [View Article] [PubMed]
    [Google Scholar]
  40. Camarillo-Guerrero LF, Almeida A, Rangel-Pineros G, Finn RD, Lawley TD. Massive expansion of human gut bacteriophage diversity. Cell 2021; 184:1098–1109 [View Article] [PubMed]
    [Google Scholar]
  41. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 2018; 34:3094–3100 [View Article] [PubMed]
    [Google Scholar]
  42. Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119 [View Article] [PubMed]
    [Google Scholar]
  43. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498–2504 [View Article] [PubMed]
    [Google Scholar]
  44. García-López M, Meier-Kolthoff JP, Tindall BJ, Gronow S, Woyke T et al. Analysis of 1,000 type-strain genomes improves taxonomic classification of Bacteroidetes. Front Microbiol 2019; 10:2083 [View Article] [PubMed]
    [Google Scholar]
  45. Galperin MY, Wolf YI, Makarova KS, Vera Alvarez R, Landsman D et al. COG database update: focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Res 2021; 49:D274–D281 [View Article] [PubMed]
    [Google Scholar]
  46. Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI. Worlds within worlds: evolution of the vertebrate gut microbiota. Nat Rev Microbiol 2008; 6:776–788 [View Article] [PubMed]
    [Google Scholar]
  47. Levin D, Raab N, Pinto Y, Rothschild D, Zanir G et al. Diversity and functional landscapes in the microbiota of animals in the wild. Science 2021; 372:eabb5352 [View Article] [PubMed]
    [Google Scholar]
  48. Grześkowiak Ł, Endo A, Beasley S, Salminen S. Microbiota and probiotics in canine and feline welfare. Anaerobe 2015; 34:14–23 [View Article] [PubMed]
    [Google Scholar]
  49. Santos EO, Thompson F. The family Succinivibrionaceae. In Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F. eds The Prokaryotes Berlin, Heidelberg: Springer; 2014 pp 639–648
    [Google Scholar]
  50. Hernández MAG, Canfora EE, Jocken JWE, Blaak EE. The short-chain fatty acid acetate in body weight control and insulin sensitivity. Nutrients 2019; 11:E1943 [View Article]
    [Google Scholar]
  51. Swanson KS, Dowd SE, Suchodolski JS, Middelbos IS, Vester BM et al. Phylogenetic and gene-centric metagenomics of the canine intestinal microbiome reveals similarities with humans and mice. ISME J 2011; 5:639–649 [View Article] [PubMed]
    [Google Scholar]
  52. Suchodolski JS, Camacho J, Steiner JM. Analysis of bacterial diversity in the canine duodenum, jejunum, ileum, and colon by comparative 16S rRNA gene analysis. FEMS Microbiol Ecol 2008; 66:567–578 [View Article] [PubMed]
    [Google Scholar]
  53. Lyu T, Liu G, Zhang H, Wang L, Zhou S et al. Changes in feeding habits promoted the differentiation of the composition and function of gut microbiotas between domestic dogs (Canis lupus familiaris) and gray wolves (Canis lupus). AMB Express 2018; 8:123 [View Article] [PubMed]
    [Google Scholar]
  54. Kim Y, Leung MHY, Kwok W, Fournié G, Li J et al. Antibiotic resistance gene sharing networks and the effect of dietary nutritional content on the canine and feline gut resistome. Anim Microbiome 2020; 2:4 [View Article] [PubMed]
    [Google Scholar]
  55. Pillai DK, Peterson G, Zurek L. Insights into the diversity of gut microbiota and associated antibiotic resistance genes in healthy dogs. Vet Sci Med 2019; 2:210
    [Google Scholar]
  56. Vandecraen J, Chandler M, Aertsen A, Van Houdt R. The impact of insertion sequences on bacterial genome plasticity and adaptability. Crit Rev Microbiol 2017; 43:709–730 [View Article] [PubMed]
    [Google Scholar]
  57. Díaz-Muñoz SL. Viral coinfection is shaped by host ecology and virus-virus interactions across diverse microbial taxa and environments. Virus Evol 2017; 3:vex011 [View Article] [PubMed]
    [Google Scholar]
  58. Ross A, Ward S, Hyman P. More is better: selecting for broad host range bacteriophages. Front Microbiol 2016; 7:1352 [View Article] [PubMed]
    [Google Scholar]
  59. Marbouty M, Baudry L, Cournac A, Koszul R. Scaffolding bacterial genomes and probing host-virus interactions in gut microbiome by proximity ligation (chromosome capture) assay. Sci Adv 2017; 3:e1602105 [View Article] [PubMed]
    [Google Scholar]
  60. Stalder T, Press MO, Sullivan S, Liachko I, Top EM. Linking the resistome and plasmidome to the microbiome. ISME J 2019; 13:2437–2446 [View Article] [PubMed]
    [Google Scholar]
  61. Marbouty M, Thierry A, Millot GA, Koszul R. MetaHiC phage-bacteria infection network reveals active cycling phages of the healthy human gut. Elife 2021; 10:e60608 [View Article] [PubMed]
    [Google Scholar]
  62. Wick RR, Judd LM, Wyres KL, Holt KE. Recovery of small plasmid sequences via Oxford Nanopore sequencing. Microb Genom 2021; 7:000631 [View Article] [PubMed]
    [Google Scholar]
  63. Sereika M, Kirkegaard RH, Karst SM, Michaelsen TY, Sørensen EA et al. Oxford Nanopore R10.4 long-read sequencing enables near-perfect bacterial genomes from pure cultures and metagenomes without short-read or reference polishing. Biorxiv 2021 [View Article]
    [Google Scholar]
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