Mammary microbiome of lactating organic dairy cows varies by time, tissue site, and infection status
Autoři:
Tucker Andrews aff001; Deborah A. Neher aff001; Thomas R. Weicht aff001; John W. Barlow aff002
Působiště autorů:
Department of Plant and Soil Science, University of Vermont, Burlington, Vermont, United States of America
aff001; Department of Animal and Veterinary Sciences, University of Vermont, Burlington, Vermont, United States of America
aff002
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225001
Souhrn
Infections of the cow udder leading to mastitis and reducing milk quality are a critical challenge facing all dairy farmers. Mastitis may be linked to the ecological disruption of an endogenous mammary microbial community, suggesting an ecosystems approach to management and prevention of this disease. The teat end skin represents a first point of host contact with mastitis pathogens and may offer an opportunity for microbially mediated resistance to infection, yet we know little about the microbial community of teat end skin or its potential interaction with the microbial community of intramammary milk of organic dairy cattle. High-throughput sequencing of marker genes for bacterial and fungal communities was used to characterize the skin and milk microbiome of cows with both a healthy and infected gland (i.e., udder quarter) and to assess the sharing of microbial DNA between these tissue habitat sites. The mammary microbiome varied among cows, through time, and between skin and milk. Microbiomes of milk from healthy and infected quarters reflected a diverse group of microbial DNA sequences, though milk had far fewer operational taxonomic units (OTUs) than skin. Milk microbiomes of infected quarters were generally more variable than healthy quarters and were frequently dominated by a single OTU; teat end skin microbiomes were relatively similar between healthy and infected quarters. Commonly occurring genera that were shared between skin and milk of infected glands included Staphylococcus spp. bacteria and Debaryomyces spp. fungi. Commonly occurring genera that were shared between skin and milk of healthy glands included bacteria SMB53 (Clostridiaceae) and Penicillium spp. fungi. Results support an ecological interpretation of the mammary gland and the notion that mastitis can be described as a dysbiosis, an imbalance of the healthy mammary gland microbiome.
Klíčová slova:
Acinetobacter infections – Bovine mastitis – Mastitis – Microbiome – Milk – Skin infections – Staphylococcal infection – Staphylococcus
Zdroje
1. Pereira ABD. Assessing the research and education needs of the organic dairy industry in the northeastern United States. J Dairy Sci. 2013;96:7340–8. doi: 10.3168/jds.2013-6690 24054299.
2. Ruegg PL. Management of mastitis on organic and conventional dairy farms. J Anim Sci. 2009;87(13 Suppl):43–55. Epub 2008/09/30. doi: 10.2527/jas.2008-1217 18820158.
3. Metzger SA, Hernandez LL, Skarlupka JH, Suen G, Walker TM, Ruegg PL. Influence of sampling technique and bedding type on the milk microbiota: Results of a pilot study. J Dairy Sci. 2018;101(7):6346–56. Epub 2018/04/24. doi: 10.3168/jds.2017-14212 29680645.
4. Hogan JS, Smith KL, Hoblet KH, Todhunter DA, Schoenberger PS, Hueston WD, et al. Bacterial counts in bedding materials used on nine commercial dairies. J Dairy Sci. 1989;72(1):250–8. doi: 10.3168/jds.s0022-0302(89)79103-7 2925950
5. Rowbotham RF, Ruegg PL. Bacterial counts on teat skin and in new sand, recycled sand, and recycled manure solids used as bedding in freestalls. J Dairy Sci. 2016;99(8):6594–608. doi: 10.3168/jds.2015-10674 WOS:000381642500059. 27265163
6. Rainard P. Mammary microbiota of dairy ruminants: fact or fiction? Vet Res. 2017;48(1):25. doi: 10.1186/s13567-017-0429-2 28412972
7. Keeney KM, Yurist-Doutsch S, Arrieta M-C, Finlay BB. Effects of antibiotics on human microbiota and subsequent disease. Annu Rev Microbiol. 2014;68(1):217–35. doi: 10.1146/annurev-micro-091313-103456 24995874.
8. Lederberg J. Infectious history. Science. 2000;288(5464):287–93. doi: 10.1126/science.288.5464.287 10777411
9. van Baarlen P, Kleerebezem M, Wells JM. Omics approaches to study host–microbiota interactions. Curr Opin Microbiol. 2013;16(3):270–7. doi: 10.1016/j.mib.2013.07.001 23891019
10. Oikonomou G, Machado VS, Santisteban C, Schukken YH, Bicalho RC. Microbial diversity of bovine mastitic milk as described by pyrosequencing of metagenomic 16s rDNA. PLoS One. 2012;7(10):e47671. doi: 10.1371/journal.pone.0047671 23082192
11. Oikonomou G, Bicalho ML, Meira E, Rossi RE, Foditsch C, Machado VS, et al. Microbiota of cow’s milk; distinguishing healthy, sub-clinically and clinically diseased quarters. PLoS One. 2014;9(1):e85904. doi: 10.1371/journal.pone.0085904 24465777
12. Falentin H, Rault L, Nicolas A, Bouchard DS, Lassalas J, Lamberton P, et al. Bovine teat microbiome analysis revealed reduced alpha diversity and significant changes in taxonomic profiles in quarters with a history of mastitis. Front Microbiol. 2016;7(480). doi: 10.3389/fmicb.2016.00480 27242672
13. Kuehn JS, Gorden PJ, Munro D, Rong R, Dong Q, Plummer PJ, et al. Bacterial community profiling of milk samples as a means to understand culture-negative bovine clinical mastitis. PLoS One. 2013;8(4):e61959. doi: 10.1371/journal.pone.0061959 23634219
14. Bhatt VD, Ahir VB, Koringa PG, Jakhesara SJ, Rank DN, Nauriyal DS, et al. Milk microbiome signatures of subclinical mastitis-affected cattle analysed by shotgun sequencing. J Appl Microbiol. 2012;112(4):639–50. doi: 10.1111/j.1365-2672.2012.05244.x WOS:000301432200003. 22277077
15. Kuang Y, Tani K, Synnott AJ, Ohshima K, Higuchi H, Nagahata H, et al. Characterization of bacterial population of raw milk from bovine mastitis by culture-independent PCR-DGGE method. Biochem Eng J. 2009;45(1):76–81. doi: 10.1016/j.bej.2009.02.010
16. Vanderhaeghen W, Piepers S, Leroy F, Van Coillie E, Haesebrouck F, De Vliegher S. Identification, typing, ecology and epidemiology of coagulase negative staphylococci associated with ruminants. Vet J. 2015;203(1):44–51. doi: 10.1016/j.tvjl.2014.11.001 25467994
17. Woodward WD, Ward ACS, Fox LK, Corbeil LB. Teat skin normal flora and colonization with mastitis pathogen inhibitors. Vet Microbiol. 1988;17(4):357–65. doi: 10.1016/0378-1135(88)90049-1 3188374
18. Grice EA, Segre JA. The skin microbiome. Nat Rev Microbiol. 2011;9(4):244–53. Epub 2011/03/17. doi: 10.1038/nrmicro2537 21407241; PubMed Central PMCID: PMC3535073.
19. Gill JJ, Sabour PM, Gong J, Yu H, Leslie KE, Griffiths MW. Characterization of bacterial populations recovered from the teat canals of lactating dairy and beef cattle by 16S rRNA gene sequence analysis. FEMS Microbiol Ecol. 2006;56(3):471–81. doi: 10.1111/j.1574-6941.2006.00091.x 16689878
20. Derakhshani H, Fehr KB, Sepehri S, Francoz D, De Buck J, Barkema HW, et al. Microbiota of the bovine udder: Contributing factors and potential implications for udder health and mastitis susceptibility. J Dairy Sci. 2018;101(12):10605–25. doi: 10.3168/jds.2018-14860 30292553
21. Adkins PRF, Middleton JR, Fox LK, Pighetti G, Petersson-Wolfe C, National Mastitis Council USA. Laboratory handbook on bovine mastitis: National Mastitis Council; 2017.
22. Friman M, Hiitio H, Niemi M, Holopainen J, Pyorala S, Simojoki H. The effect of a cannula milk sampling technique on the microbiological diagnosis of bovine mastitis. Vet J. 2017;226:57–61. Epub 2017/09/16. doi: 10.1016/j.tvjl.2017.07.003 28911843.
23. Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75(15):5111–20. Epub 2009/06/09. doi: 10.1128/AEM.00335-09 19502440; PubMed Central PMCID: PMC2725504.
24. Emerson JB, Keady PB, Brewer TE, Clements N, Morgan EE, Awerbuch J, et al. Impacts of flood damage on airborne bacteria and fungi in homes after the 2013 Colorado front range flood. Environ Sci Technol. 2015;49(5):2675–84. doi: 10.1021/es503845j 25643125
25. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature methods. 2013;10(10):996–8. Epub 2013/08/21. doi: 10.1038/nmeth.2604 23955772.
26. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1. Epub 2010/08/17. doi: 10.1093/bioinformatics/btq461 20709691.
27. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6. doi: 10.1038/ismej.2011.139 22134646
28. Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, et al. Towards a unified paradigm for sequence-based identification of fungi. 2013;22(21):5271–7. doi: 10.1111/mec.12481 24112409
29. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R package version 2.5–2 ed: https://CRAN.R-project.org/package=vegan; 2018.
30. McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10(4). doi: 10.1371/journal.pcbi.1003531 WOS:000336507500019. 24699258
31. Anderson MJ. Distance-based tests for homogeneity of multivariate dispersions. Biometrics. 2006;62(1):245–53. doi: 10.1111/j.1541-0420.2005.00440.x 16542252
32. Culhane AC, Perrière G, Higgins DG. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics. 2003;4(1):59. doi: 10.1186/1471-2105-4-59 14633289
33. De Cáceres M, Legendre P, Moretti M. Improving indicator species analysis by combining groups of sites. Oikos. 2010;119(10):1674–84. doi: 10.1111/j.1600-0706.2010.18334.x
34. Csardi G NT. The igraph software package for complex network research. InterJournal 2006;Complex Systems:1695.
35. Shade A, Handelsman J. Beyond the Venn diagram: the hunt for a core microbiome. Environ Microbiol. 2012;14(1):4–12. doi: 10.1111/j.1462-2920.2011.02585.x 22004523
36. Murtagh F, Legendre P. Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion? J Classif. 2014;31:274–95.
37. Hiitio H, Simojoki H, Kalmus P, Holopainen J, Pyorala S, Taponen S. The effect of sampling technique on PCR-based bacteriological results of bovine milk samples. J Dairy Sci. 2016;99(8):6532–41. Epub 2016/05/23. doi: 10.3168/jds.2015-10811 27209134.
38. Ganda EK, Bisinotto RS, Lima SF, Kronauer K, Decter DH, Oikonomou G, et al. Longitudinal metagenomic profiling of bovine milk to assess the impact of intramammary treatment using a third-generation cephalosporin. Sci Rep. 2016;6:37565. doi: 10.1038/srep37565 27874095
39. Bonsaglia ECR, Gomes MS, Canisso IF, Zhou Z, Lima SF, Rall VLM, et al. Milk microbiome and bacterial load following dry cow therapy without antibiotics in dairy cows with healthy mammary gland. Sci Rep. 2017;7(1):8067. doi: 10.1038/s41598-017-08790-5 28808353
40. Braem G, De Vliegher S, Verbist B, Heyndrickx M, Leroy F, De Vuyst L. Culture-independent exploration of the teat apex microbiota of dairy cows reveals a wide bacterial species diversity. Vet Microbiol. 2012;157(3–4):383–90. Epub 2012/01/24. doi: 10.1016/j.vetmic.2011.12.031 22266158.
41. Verdier-Metz I, Gagne G, Bornes S, Monsallier F, Veisseire P, Delbès-Paus C, et al. Cow teat skin, a potential source of diverse microbial populations for cheese production. Appl Environ Microbiol. 2012;78(2):326–33. doi: 10.1128/AEM.06229-11 22081572
42. Doyle CJ, Gleeson D, O'Toole PW, Cotter PD. Impacts of seasonal housing and teat preparation on raw milk microbiota: a high-throughput sequencing study. Appl Environ Microbiol. 2017;83(2). Epub 2016/11/07. doi: 10.1128/aem.02694-16 27815277; PubMed Central PMCID: PMC5203630.
43. Detilleux JC. Neutrophils in the war against Staphylococcus aureus: predator-prey models to the rescue. J Dairy Sci. 2004;87(11):3716–24. Epub 2004/10/16. doi: 10.3168/jds.S0022-0302(04)73510-9 15483155.
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