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Linking perturbations to temporal changes in diversity, stability, and compositions of neonatal calf gut microbiota: prediction of diarrhea

Abstract

Perturbations in early life gut microbiota can have long-term impacts on host health. In this study, we investigated antimicrobial-induced temporal changes in diversity, stability, and compositions of gut microbiota in neonatal veal calves, with the objective of identifying microbial markers that predict diarrhea. A total of 220 samples from 63 calves in first 8 weeks of life were used in this study. The results suggest that increase in diversity and stability of gut microbiota over time was a feature of “healthy” (non-diarrheic) calves during early life. Therapeutic antimicrobials delayed the temporal development of diversity and taxa–function robustness (a measure of microbial stability). In addition, predicted genes associated with beta lactam and cationic antimicrobial peptide resistance were more abundant in gut microbiota of calves treated with therapeutic antimicrobials. Random forest machine learning algorithm revealed that Trueperella, Streptococcus, Dorea, uncultured Lachnospiraceae, Ruminococcus 2, and Erysipelatoclostridium may be key microbial markers that can differentiate “healthy” and “unhealthy” (diarrheic) gut microbiota, as they predicted early life diarrhea with an accuracy of 84.3%. Our findings suggest that diarrhea in veal calves may be predicted by the shift in early life gut microbiota, which may provide an opportunity for early intervention (e.g., prebiotics or probiotics) to improve calf health with reduced usage of antimicrobials.

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Fig. 1: Temporal changes of Shannon index in gut microbiota.
Fig. 2: Temporal changes of Bray–Curtis distance between two successive days of age at sampling in gut microbiota.
Fig. 3: Temporal changes of attenuation and buffering values in gut microbiota.
Fig. 4: Prediction of health status based on fecal microbial markers.
Fig. 5: The ranks of bacterial genera between “healthy” and “unhealthy” gut microbiota estimated using multinomial regression.

References

  1. 1.

    Mathew AG, Cissell R, Liamthong S. Antibiotic resistance in bacteria associated with food animals: a United States perspective of livestock production. Foodborne Pathog Dis. 2007;4:115–33.

    CAS  PubMed  Google Scholar 

  2. 2.

    Bauer E, Williams BA, Smidt H, Verstegen MW, Mosenthin R. Influence of the gastrointestinal microbiota on development of the immune system in young animals. Curr Issues Intest Microbiol. 2006;7:35–52.

    CAS  PubMed  Google Scholar 

  3. 3.

    Hulbert LE, Moisá SJ. Stress, immunity, and the management of calves. J Dairy Sci. 2016;99:3199–216.

    CAS  PubMed  Google Scholar 

  4. 4.

    Gensollen T, Iyer SS, Kasper DL, Blumberg RS. How colonization by microbiota in early life shapes the immune system. Science. 2016;352:539–44.

    CAS  Article  Google Scholar 

  5. 5.

    Kerr CA, Grice DM, Tran CD, Bauer DC, Li D, Hendry P, et al. Early life events influence whole-of-life metabolic health via gut microflora and gut permeability. Crit Rev Microbiol. 2015;41:326–40.

    CAS  PubMed  Google Scholar 

  6. 6.

    Zeissig S, Blumberg RS. Life at the beginning: perturbation of the microbiota by antibiotics in early life and its role in health and disease. Nat Immunol. 2014;5:307–10.

    Google Scholar 

  7. 7.

    Cox LM, Blaser MJ. Antibiotics in early life and obesity. Nat Rev Endocrinol. 2015;11:182–90.

    PubMed  Google Scholar 

  8. 8.

    Oikonomou G, Teixeira AG, Foditsch C, Bicalho ML, Machado VS, Bicalho RC. Fecal microbial diversity in pre-weaned dairy calves as described by pyrosequencing of metagenomic 16S rDNA. Associations of Faecalibacterium species with health and growth. PloS ONE. 2013;8:e63157.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Oultram J, Phipps E, Teixeira AG, Foditsch C, Bicalho ML, Machado VS, et al. Effects of antibiotics (oxytetracycline, florfenicol or tulathromycin) on neonatal calves’ faecal microbial diversity. Vet Rec. 2015;177:598.

    CAS  PubMed  Google Scholar 

  10. 10.

    Beisner BE, Haydon DT, Cuddington K. Alternative stable states in ecology. Front Ecol Environ. 2003;1:376–82.

    Google Scholar 

  11. 11.

    McEachran AD, Blackwell BR, Hanson JD, Wooten KJ, Mayer GD, Cox SB, et al. Antibiotics, bacteria, and antibiotic resistance genes: aerial transport from cattle feed yards via particulate matter. Environ Health Persp. 2015;123:337–43.

    Google Scholar 

  12. 12.

    Cho Y, Yoon K-J. An overview of calf diarrhea-infectious etiology, diagnosis, and intervention. J Vet Sci. 2014;15:1–17.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Knights D, Parfrey LW, Zaneveld J, Lozupone C, Knight R. Human-associated microbial signatures: examining their predictive value. Cell Host Microbe. 2011;10:292–6.

    CAS  PubMed  Google Scholar 

  14. 14.

    Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60.

    CAS  PubMed  Google Scholar 

  15. 15.

    Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500:541–6.

    PubMed  Google Scholar 

  16. 16.

    Teng F, Yang F, Huang S, Bo C, Xu ZZ, Amir A, et al. Prediction of early childhood caries via spatial-temporal variations of oral microbiota. Cell Host Microbe. 2015;18:296–306.

    CAS  PubMed  Google Scholar 

  17. 17.

    Villot C, Ma T, Renaud D, Hosseini-Ghaffari M, Gibson DJ, Skidmore A, et al. Saccharomyces cerevisiae boulardii CNCM I-1079 affects health, growth and fecal microbiota in milk-fed veal calves. J Dairy Sci. 2019;102:7011–25.

    CAS  PubMed  Google Scholar 

  18. 18.

    Lesmeister K, Heinrichs A. Effects of corn processing on growth characteristics, rumen development, and rumen parameters in neonatal dairy calves. J Dairy Sci. 2004;87:3439–50.

    CAS  PubMed  Google Scholar 

  19. 19.

    Buts JP, De Keyser N. Effects of Saccharomyces boulardii on intestinal mucosa. Dig Dis Sci. 2006;51:1485–92.

    PubMed  Google Scholar 

  20. 20.

    Yu Z, Morrison M. Comparisons of different hypervariable regions of rrs genes for use in fingerprinting of microbial communities by PCR-denaturing gradient gel electrophoresis. Appl Environ Micro. 2004;70:4800–6.

    CAS  Google Scholar 

  21. 21.

    Kroes I, Lepp PW, Relman DA. Bacterial diversity within the human subgingival crevice. Proc Natl Acad Sci USA. 1999;96:14547–52.

    CAS  PubMed  Google Scholar 

  22. 22.

    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME2. Nat Biotechnol. 2019;37:852–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:226.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Heberle H, Meirelles GV, da Silva FR, Telles GP, Minghim R. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinform. 2015;16:169.

    Google Scholar 

  26. 26.

    Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2: an improved and extensible approach for metagenome inference. BioRxiv. 2019:672295.

  27. 27.

    Bokulich NA, Dillon MR, Zhang Y, Rideout JR, Bolyen E, Li H, et al. q2-longitudinal: longitudinal and paired-sample analyses of microbiome data. mSystems. 2018;3:e00219–18.

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Eng A, Borenstein E. Taxa-function robustness in microbial communities. Microbiome. 2018;6:45.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Urie NJ, Lombard JE, Shivley CB, Kopral CA, Adams AE, Earleywine TJ, et al. Preweaned heifer management on US dairy operations: part V. Factors associated with morbidity and mortality in preweaned dairy heifer calves. J Dairy Sci. 2018;101:9229–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Cawley GC, Talbot NL. On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res. 2010;11:2079–107.

    Google Scholar 

  31. 31.

    Vandeputte D, Kathagen G, D’hoe K, Vieira-Silva S, Valles-Colomer M, Sabino J, et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature. 2017;551:507–11.

    CAS  PubMed  Google Scholar 

  32. 32.

    Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. Tensorflow: a system for large-scale machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16); USENIX Association, Savannah, GA, 2016. p. 265–83.

  33. 33.

    Morton JT, Marotz C, Washburne A, JSilverman J, Zaramela LS, Edlund A, et al. Establishing microbial composition measurement standards with reference frames. Nat Commun. 2019;10:2719.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289–300.

    Google Scholar 

  35. 35.

    Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486:222–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO. Development of the human infant intestinal microbiota. PLoS Biol. 2007;5:e177.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Klein-Jöbstl D, Schornsteiner E, Mann E, Wagner M, Drillich M, Schmitz-Esser S. Pyrosequencing reveals diverse fecal microbiota in Simmental calves during early development. Front Microbiol. 2014;5:622.

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Dill-McFarland KA, Breaker JD, Suen G. Microbial succession in the gastrointestinal tract of dairy cows from 2 weeks to first lactation. Sci Rep. 2017;7:40864.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Panda S, El khader I, Casellas F, Vivancos JL, Cors MG, Santiago A, et al. Short-term effect of antibiotics on human gut microbiota. PLoS ONE. 2014;9:e95476.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Jakobsson HE, Jernberg C, Andersson AF, Sjölund-Karlsson M, Jansson JK, Engstrand L. Short-term antibiotic treatment has differing long-term impacts on the human throat and gut microbiome. PloS ONE. 2010;5:e9836.

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Pérez-Cobas AE, Gosalbes MJ, Friedrichs A, Knecht H, Artacho A, Eismann K, et al. Gut microbiota disturbance during antibiotic therapy: a multi-omic approach. Gut. 2013;62:1591–601.

    PubMed  Google Scholar 

  42. 42.

    Sommer F, Anderson JM, Bharti R, Raes J, Rosenstiel P. The resilience of the intestinal microbiota influences health and disease. Nat Rev Microbiol. 2017;15:630–8.

    CAS  PubMed  Google Scholar 

  43. 43.

    Saraf MK, Piccolo BD, Bowlin AK, Mercer KE, LeRoith T, Chintapalli SV, et al. Formula diet driven microbiota shifts tryptophan metabolism from serotonin to tryptamine in neonatal porcine colon. Microbiome. 2017;5:77.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Jost T, Lacroix C, Braegger CP, Rochat F, Chassard C. Vertical mother–neonate transfer of maternal gut bacteria via breastfeeding. Environ Microbiol. 2013;16:2891–904.

    PubMed  Google Scholar 

  45. 45.

    Sagheddu V, Patrone V, Miragoli F, Morelli L. Abundance and diversity of hydrogenotrophic microorganisms in the infant gut before the weaning period assessed by denaturing gradient gel electrophoresis and quantitative PCR. Front Nutr. 2017;4:29.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Minamoto Y, Dhanani N, Markel ME, Steiner JM, Suchodolski JS. Prevalence of Clostridium perfringens, Clostridium perfringens enterotoxin and dysbiosis in fecal samples of dogs with diarrhea. Vet Microbiol. 2014;174:463–73.

    CAS  PubMed  Google Scholar 

  47. 47.

    AlShawaqfeh MK, Wajid B, Minamoto Y, Markel M, Lidbury JA, Steiner JM, et al. A dysbiosis index to assess microbial changes in fecal samples of dogs with chronic inflammatory enteropathy. FEMS Microbiol Ecol. 2017;93:fix136.

    Google Scholar 

  48. 48.

    Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol. 2008;6:121–31.

    CAS  PubMed  Google Scholar 

  49. 49.

    Furusawa Y, Obata Y, Fukuda S, Endo TA, Nakato G, Takahashi D, et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature. 2013;504:446–50.

    CAS  PubMed  Google Scholar 

  50. 50.

    Ruemmele FM, Schwartz S, Seidman EG, Dionne S, Levy E, Lentze MJ. Butyrate induced Caco-2 cell apoptosis is mediated via the mitochondrial pathway. Gut. 2003;52:94–100.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Bartels CJM, Holzhauer M, Jorritsma R, Swart WAJM, Lam TJGM. Prevalence, prediction and risk factors of enteropathogens in normal and non-normal faeces of young Dutch dairy calves. Prev Vet Med. 2010;93:162–9.

    PubMed  Google Scholar 

  52. 52.

    Zhu Z, Cao M, Zhou X, Li B, Zhang J. Epidemic characterization and molecular genotyping of Shigella flexneri isolated from calves with diarrhea in Northwest China. Antimicrob Resist Infect Control. 2017;6:92.

  53. 53.

    Ma X, Zhang Q, Zheng M, Gao Y, Yuan T, Hale L, et al. Microbial functional traits are sensitive indicators of mild disturbance by lamb grazing. ISME J. 2019;13:1370–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Moya A, Ferrer M. Functional redundancy-induced stability of gut microbiota subjected to disturbance. Trends Microbiol. 2016;24:402–13.

    CAS  PubMed  Google Scholar 

  55. 55.

    Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature. 2012;13:220–30.

    Google Scholar 

  56. 56.

    Mondot S, Kang S, Furet JP, de Cárcer AD, McSweeney C, Morrison M, et al. Highlighting new phylogenetic specificities of Crohn’s disease microbiota. Inflamm Bowel Dis. 2011;17:185–92.

    CAS  PubMed  Google Scholar 

  57. 57.

    Sokol H, Jegou S, McQuitty C, Straub M, Leducq V, Landman C, et al. Specificities of the intestinal microbiota in patients with inflammatory bowel disease and Clostridium difficile infection. Gut Microbes. 2018;9:55–60.

    PubMed  Google Scholar 

  58. 58.

    Song Y, Malmuthuge N, Steele MA, Guan LL. Shift of hindgut microbiota and microbial short chain fatty acids profiles in dairy calves from birth to pre-weaning. FEMS Microbiol Ecol. 2018;93:fix179.

    Google Scholar 

  59. 59.

    Adetoye A, Pinloche E, Adeniyi BA, Ayeni FA. Characterization and anti-salmonella activities of lactic acid bacteria isolated from cattle faeces. BMC Microbiol. 2018;18:96.

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Rzewuska M, Kwiecień E, Chrobak-Chmiel D, Kizerwetter-Świda M, Stefańska I, Gieryńska M. Pathogenicity and virulence of Trueperella pyogenes: a review. Int J Mol Sci. 2019;20:2737.

    CAS  PubMed Central  Google Scholar 

  61. 61.

    Hurst CJ. Opportunistic bacteria associated with mammalian livestock disease. In: The connections between ecology and infectious disease. Cham: Springer; 2018. p. 185–238.

  62. 62.

    Xiong J, Zhu J, Dai W, Dong C, Qiu Q, Li C. Integrating gut microbiota immaturity and disease‐discriminatory taxa to diagnose the initiation and severity of shrimp disease. Environ Microbiol. 2017;19:1490–501.

    PubMed  Google Scholar 

  63. 63.

    Gloor GB, Wu JR, Pawlowsky-Glahn V, Egozcue JJ. It’s all relative: analyzing microbiome data as compositions. Ann Epidemiol. 2016;26:322–9.

    PubMed  Google Scholar 

  64. 64.

    Langille MG. Exploring linkages between taxonomic and functional profiles of the human microbiome. MSystems. 2018;3:e00163–17.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), Lallemand Animal Nutrition, Grober Animal Nutrition, Westgen, BC Dairy Association, Alberta Milk, Sask-Milk, and Dairy Farmers of Manitoba and by the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences and Chinese Scholarship Council Scholarship. We appreciate technical support and guidance from Drs A. Eng and E. Borenstein (University of Washington), F. Chaucheyras-Durand, M. Castex, and A. Aguilar (Lallemand Animal Nutrition), and A. Kerr and H. Copland (Grober Animal Nutrition).

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Ma, T., Villot, C., Renaud, D. et al. Linking perturbations to temporal changes in diversity, stability, and compositions of neonatal calf gut microbiota: prediction of diarrhea. ISME J 14, 2223–2235 (2020). https://doi.org/10.1038/s41396-020-0678-3

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