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Abstract

Using a previously described metagenomics dataset of 27 billion reads, we reconstructed over 50 000 metagenome-assembled genomes (MAGs) of organisms resident in the porcine gut, 46.5 % of which were classified as >70 % complete with a <10 % contamination rate, and 24.4 % were nearly complete genomes. Here, we describe the generation and analysis of those MAGs using time-series samples. The gut microbial communities of piglets appear to follow a highly structured developmental programme in the weeks following weaning, and this development is robust to treatments including an intramuscular antibiotic treatment and two probiotic treatments. The high resolution we obtained allowed us to identify specific taxonomic ‘signatures’ that characterize the gut microbial development immediately after weaning. Additionally, we characterized the carbohydrate repertoire of the organisms resident in the porcine gut. We tracked the abundance shifts of 294 carbohydrate active enzymes, and identified the species and higher-level taxonomic groups carrying each of these enzymes in their MAGs. This knowledge can contribute to the design of probiotics and prebiotic interventions as a means to modify the piglet gut microbiome.

Funding
This study was supported by the:
  • Australian Research Council (Award LP150100912)
    • Principle Award Recipient: DanielaGaio
  • Australian Research Council (Award LP150100912)
    • Principle Award Recipient: ToniA Chapman
  • Australian Research Council (Award LP150100912)
    • Principle Award Recipient: AaronE Darling
  • Australian Research Council (Award LP150100912)
    • Principle Award Recipient: StevenP Djordjevic
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2021-08-09
2024-04-23
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References

  1. Brown CT, Hug LA, Thomas BC, Sharon I, Castelle CJ et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 2015; 523:208–211 [View Article] [PubMed]
    [Google Scholar]
  2. Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ et al. A new view of the tree of life. Nat Microbiol 2016; 1:16048 [View Article] [PubMed]
    [Google Scholar]
  3. 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]
  4. Mande SS, Mohammed MH, Ghosh TS. Classification of metagenomic sequences: methods and challenges. Brief Bioinform 2012; 13:669–681 [View Article] [PubMed]
    [Google Scholar]
  5. Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci USA 2011; 108:4554–4561 [View Article] [PubMed]
    [Google Scholar]
  6. Barnes EM, Carter EL, Lewis JD. Predicting microbiome function across space is confounded by strain-level differences and functional redundancy across taxa. Front Microbiol 2020; 11:101 [View Article] [PubMed]
    [Google Scholar]
  7. Perna NT, Plunkett G, Burland V, Mau B, Glasner JD et al. Genome sequence of enterohaemorrhagic Escherichia coli O157:H7. Nature 2001; 409:529–533 [View Article] [PubMed]
    [Google Scholar]
  8. Mandel MJ, Wollenberg MS, Stabb EV, Visick KL, Ruby EG. A single regulatory gene is sufficient to alter bacterial host range. Nature 2009; 458:215–218 [View Article] [PubMed]
    [Google Scholar]
  9. Valguarnera E, Wardenburg JB. Good gone bad: one toxin away from disease for Bacteroides fragilis. J Mol Biol 2020; 432:765–785 [View Article] [PubMed]
    [Google Scholar]
  10. Bowers RM, Clum A, Tice H, Lim J, Singh K et al. Impact of library preparation protocols and template quantity on the metagenomic reconstruction of a mock microbial community. BMC Genomics 2015; 16:856 [View Article] [PubMed]
    [Google Scholar]
  11. Sanders JG, Nurk S, Salido RA, Minich J, Xu ZZ et al. Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads. Genome Biol 2019; 20:226 [View Article] [PubMed]
    [Google Scholar]
  12. Sczyrba A, Hofmann P, Belmann P, Koslicki D, Janssen S et al. Critical assessment of metagenome interpretation – benchmark of metagenomics software. Nat Methods 2017; 14:1063–1071 [View Article] [PubMed]
    [Google Scholar]
  13. Alneberg J, Bjarnason BS, Schirmer M, de Bruijn I, Quick J et al. Concoct: Clustering contigs on coverage and composition. arXiv 20131312.4038
    [Google Scholar]
  14. Imelfort M, Parks D, Woodcroft BJ, Dennis P, Hugenholtz P et al. GroopM: an automated tool for the recovery of population genomes from related metagenomes. PeerJ 2014; 2:e603 [View Article] [PubMed]
    [Google Scholar]
  15. Cleary B, Brito IL, Huang K, Gevers D, Shea T et al. Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning. Nat Biotechnol 2015; 33:1053–1060 [View Article] [PubMed]
    [Google Scholar]
  16. Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol 2013; 31:533–538 [View Article] [PubMed]
    [Google Scholar]
  17. Garza DR, Dutilh BE. From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems. Cell Mol Life Sci 2015; 72:4287–4308 [View Article] [PubMed]
    [Google Scholar]
  18. Liu M, Darling A. Metagenomic chromosome conformation capture (3C): techniques, applications, and challenges. F1000Res 2015; 4:1377 [View Article] [PubMed]
    [Google Scholar]
  19. Faith JJ, Guruge JL, Charbonneau M, Subramanian S, Seedorf H et al. The long-term stability of the human gut microbiota. Science 2013; 341:1237439 [View Article] [PubMed]
    [Google Scholar]
  20. Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res 2017; 27:626–638 [View Article] [PubMed]
    [Google Scholar]
  21. Gaio D, DeMaere MZ, Anantanawat K, Eamens GJ, Liu M et al. Community composition and development of the post-weaning piglet gut microbiome. bioRxiv 2020; 211326:
    [Google Scholar]
  22. Gaio D, To J, Liu M, Monahan L, Anantanawat K et al. Hackflex: Low cost Illumina sequencing library construction for high sample counts. bioRxiv 2019; 779215:
    [Google Scholar]
  23. Andrews S. FastQC: a Quality Control Tool for High Throughput Sequence Data 2010
  24. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016; 32:3047–3048 [View Article] [PubMed]
    [Google Scholar]
  25. 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]
  26. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv 20131303.3997
    [Google Scholar]
  27. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009; 25:2078–2079 [View Article] [PubMed]
    [Google Scholar]
  28. 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]
  29. 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]
  30. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014; 15:R46 [View Article] [PubMed]
    [Google Scholar]
  31. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
    [Google Scholar]
  32. 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]
  33. Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 2017; 11:2864–2868 [View Article] [PubMed]
    [Google Scholar]
  34. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 2013; 8:e61217 [View Article] [PubMed]
    [Google Scholar]
  35. Wirbel J, Zych K, Essex M, Karcher N, Kartal E et al. Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox. Genome Biol 2021; 22:93 [View Article] [PubMed]
    [Google Scholar]
  36. Wirbel J, Zych K, Essex M, Karcher N, Kartal E et al. SIAMCAT: user-friendly and versatile machine learning workflows for statistically rigorous microbiome analyses. bioRxiv 2020931808
    [Google Scholar]
  37. 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]
  38. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012; 28:3150–3152 [View Article] [PubMed]
    [Google Scholar]
  39. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2015; 12:59 [View Article] [PubMed]
    [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. Li H. seqtk: Toolkit for Processing Sequences in FASTA/Q Formats 2012 https://github.com/lh3/seqtk
  42. Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V et al. The Carbohydrate-Active enZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res 2009; 37:D233–D238 [View Article] [PubMed]
    [Google Scholar]
  43. Zhang H, Yohe T, Huang L, Entwistle S, Wu P et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 2018; 46:W95–W101 [View Article] [PubMed]
    [Google Scholar]
  44. Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 2011; 39:W29–W37 [View Article] [PubMed]
    [Google Scholar]
  45. Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E et al. Nextflow enables reproducible computational workflows. Nat Biotechnol 2017; 35:316–319 [View Article] [PubMed]
    [Google Scholar]
  46. Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods 2018; 15:475–476 [View Article] [PubMed]
    [Google Scholar]
  47. R Core TeamR: a Language and Environment for Statistical Computing 2013
  48. Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 2004; 20:289–290 [View Article] [PubMed]
    [Google Scholar]
  49. Gu Z, Gu L, Eils R, Schlesner M, Brors B. circlize implements and enhances circular visualization in R. Bioinformatics 2014; 30:2811–2812 [View Article] [PubMed]
    [Google Scholar]
  50. Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K et al.Cluster: "Finding Groups in Data”: Cluster Analysis Extended Rousseeuw et al. R Package, version 20
  51. Wilke CO. cowplot: Streamlined Plot Theme and Plot Annotations for “ggplot2.” 2019
  52. Dowle M, Srinivasan A. data.table: Extension of 'data.frame' 2019
  53. Wickham H, François R, Henry L, Müller K. dplyr: a Grammar of Data Manipulation 2019
  54. Millard SP. EnvStats, an R package for environmental statistics. Wiley StatsRef: Statistics Reference Online 2014
    [Google Scholar]
  55. Kassambara A, Mundt F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses 2017
  56. Wickham H. forcats: Tools for Working with Categorical Variables (Factors) 2019
  57. Vu VQ. ggbiplot: a ggplot2 Based Biplot 2011
  58. Kassambara A. ggpubr: “ggplot2” Based Publication Ready Plots 2019
  59. Slowikowski K, Schep A, Hughes S, Lukauskas S, Irisson J-. O et al.ggrepel: Autom Position Non-overlapping Text Labels ‘ggplot2’
  60. Warnes MGR, Bolker B, Bonebakker L, Gentleman R, Huber W et al. Package ‘gplots’: Various R Programming Tools for Plotting Data 2016
    [Google Scholar]
  61. Auguie B. gridExtra: Miscellaneous Functions for “grid” Graphics 2017
  62. Bache SM, Wickham H. magrittr: a Forward-Pipe Operator for R 2014
  63. Bengtsson H, Bravo HC, Gentleman R, Hossjer O, Jaffee H. matrixStats 2020
  64. Lahti L, Shetty S, Blake T, Salojarvi J. Microbiome R Package. Tools for Microbiome Analysis in R 2017
  65. Schauberger P, Walker A. openxlsx: Read, Write and Edit xlsx Files 2019
  66. Kolde R. pheatmap: Pretty Heatmaps 2019
  67. Wickham H, Wickham MH. plyr 2020
  68. Henry L, Wickham H. purrr: Functional Programming Tools 2019
  69. Neuwirth E, Neuwirth ME. RColorBrewer 2011
  70. Wickham H, Hester J, Francois R. readr: Read Rectangular Text Data 2018
  71. Wickham H, Bryan J. readxl: Read Excel Files 2019
  72. Wickham H. reshape 2018
  73. Templ M, Hron K, Filzmoser P. robCompositions: an R-package for Robust Statistical Analysis of Compositional Data 2011
  74. Wickham H, Seidel D. scales: Scale Functions for Visualization 2019
  75. Charif D, Lobry JR. SeqinR 1.0-2: a contributed package to the R project for statistical computing devoted to biological sequences retrieval and analysis. In: Structural Approaches to Sequence Evolution Berlin, Heidelberg: Springer; 2007
    [Google Scholar]
  76. Mahto A. splitstackshape: Stack and Reshape Datasets After Splitting Concatenated Values 2019
  77. Wickham H. stringr: Simple Consistent Wrappers for Common String Operations 2019
  78. Wickham H, Henry L. tidyr: Tidy Messy Data 2019
  79. Wickham H, Averick M, Bryan J, Chang W, McGowan LD et al. Welcome to the tidyverse. J Open Source Softw 2019; 4:1686 [View Article]
    [Google Scholar]
  80. Méric G, Wick RR, Watts SC, Holt KE, Inouye M. Correcting index databases improves metagenomic studies. bioRxiv 2019; 712166:
    [Google Scholar]
  81. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods 2015; 12:902–903 [View Article] [PubMed]
    [Google Scholar]
  82. Xiao L, Estellé J, Kiilerich P, Ramayo-Caldas Y, Xia Z et al. A reference gene catalogue of the pig gut microbiome. Nat Microbiol 2016; 1:16161 [View Article] [PubMed]
    [Google Scholar]
  83. 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]
  84. Quinn TP, Erb I, Gloor G, Notredame C, Richardson MF et al. A field guide for the compositional analysis of any-omics data. Gigascience 2019; 8:giz107 [View Article] [PubMed]
    [Google Scholar]
  85. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010; 11:R106 [View Article]
    [Google Scholar]
  86. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 2010; 11:R25 [View Article] [PubMed]
    [Google Scholar]
  87. Aitchison J. A Concise Guide to Compositional Data Analysis 2015
    [Google Scholar]
  88. Greenacre M. Variable selection in compositional data analysis using pairwise logratios. Math Geosci 2019; 51:649–682 [View Article]
    [Google Scholar]
  89. Browne PD, Nielsen TK, Kot W, Aggerholm A, Gilbert MTP et al. GC bias affects genomic and metagenomic reconstructions, underrepresenting GC-poor organisms. Gigascience 2020; 9:giaa008 [View Article] [PubMed]
    [Google Scholar]
  90. Tanaka M, Nakayama J. Development of the gut microbiota in infancy and its impact on health in later life. Allergol Int 2017; 66:515–522 [View Article] [PubMed]
    [Google Scholar]
  91. Bian G, Ma S, Zhu Z, Su Y, Zoetendal EG et al. Age, introduction of solid feed and weaning are more important determinants of gut bacterial succession in piglets than breed and nursing mother as revealed by a reciprocal cross-fostering model. Environ Microbiol 2016; 18:1566–1577 [View Article] [PubMed]
    [Google Scholar]
  92. Frese SA, Parker K, Calvert CC, Mills DA. Diet shapes the gut microbiome of pigs during nursing and weaning. Microbiome 2015; 3:28 [View Article] [PubMed]
    [Google Scholar]
  93. Carroll JA, Veum TL, Matteri RL. Endocrine responses to weaning and changes in post-weaning diet in the young pig. Domest Anim Endocrinol 1998; 15:183–194 [View Article] [PubMed]
    [Google Scholar]
  94. Lallès J-P, Boudry G, Favier C, Le Floc’h N, Luron I et al. Gut function and dysfunction in young pigs: physiology. Anim Res 2004; 53:301–316
    [Google Scholar]
  95. Thompson CL, Wang B, Holmes AJ. The immediate environment during postnatal development has long-term impact on gut community structure in pigs. ISME J 2008; 2:739–748 [View Article] [PubMed]
    [Google Scholar]
  96. Kollmann TR, Kampmann B, Mazmanian SK, Marchant A, Levy O. Protecting the newborn and young infant from infectious diseases: lessons from immune ontogeny. Immunity 2017; 46:350–363 [View Article] [PubMed]
    [Google Scholar]
  97. Yu JC, Khodadadi H, Malik A, Davidson B, da Silva Lopes SE. Innate immunity of neonates and infants. Front Immunol 2018; 9:1759 [View Article] [PubMed]
    [Google Scholar]
  98. Aleman FDD, Valenzano DR. Microbiome evolution during host aging. PLoS Pathog 2019; 15:e1007727 [View Article] [PubMed]
    [Google Scholar]
  99. Roselli M, Finamore A, Britti MS, Konstantinov SR, Smidt H et al. The novel porcine Lactobacillus sobrius strain protects intestinal cells from enterotoxigenic Escherichia coli K88 infection and prevents membrane barrier damage. J Nutr 2007; 137:2709–2716 [View Article] [PubMed]
    [Google Scholar]
  100. Kim PI, Jung MY, Chang Y-H, Kim S, Kim S-J et al. Probiotic properties of Lactobacillus and Bifidobacterium strains isolated from porcine gastrointestinal tract. Appl Microbiol Biotechnol 2007; 74:1103–1111 [View Article] [PubMed]
    [Google Scholar]
  101. Stewart RD, Auffret MD, Warr A, Walker AW, Roehe R et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nat Biotechnol 2019; 37:953–961 [View Article] [PubMed]
    [Google Scholar]
  102. El Kaoutari A, Armougom F, Gordon JI, Raoult D, Henrissat B. The abundance and variety of carbohydrate-active enzymes in the human gut microbiota. Nat Rev Microbiol 2013; 11:497–504 [View Article] [PubMed]
    [Google Scholar]
  103. Lairson LL, Henrissat B, Davies GJ, Withers SG. Glycosyltransferases: structures, functions, and mechanisms. Annu Rev Biochem 2008; 77:521–555 [View Article] [PubMed]
    [Google Scholar]
  104. Köhler C-D, Dobrindt U. What defines extraintestinal pathogenic Escherichia coli?. Int J Med Microbiol 2011; 301:642–647 [View Article] [PubMed]
    [Google Scholar]
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