1887

Microbiology Society logo

Volume 167, Issue 7

PacBio sequencing revealed variation in the microbiota diversity, species richness and composition between milk collected from healthy and mastitis cows Free

Abstract

Mastitis is the economically most important disease of dairy cows. This study used PacBio single-molecule real-time sequencing technology to sequence the full-length 16S rRNAs from 27 milk samples (18 from mastitis and nine from healthy cows; the cows were at different stages of lactation). We observed that healthy or late stage milk microbiota had significantly higher microbial diversity and richness. The community composition of the microbiota of different groups also varied greatly. The healthy cow milk microbiota was predominantly comprised of , , and , while the milk from mastitis cows was predominantly comprised of . The prevalence of and in the milk samples was confirmed by digital droplets PCR. Differences in the milk microbiota diversity and composition could suggest an important role for some these microbes in protecting the host from mastitis while others associated with mastitis. The results of our research serve as useful references for designing strategies to prevent and treat mastitis.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): bovine mastitis , digital droplets PCR , l6S rRNA , milk microbiota and PacBio single-molecule real-time sequencing
Funding
This study was supported by the:
  • Inner Mongolia Autonomous Region (CN) (Award Grant CARS-36)
    • Principle Award Recipient: HepingZhang
© 2021 The Authors
Loading

Article metrics loading...

/content/journal/micro/10.1099/mic.0.000968
2021-07-22
2024-04-30
Loading full text...

Full text loading...

/deliver/fulltext/micro/167/7/mic000968.html?itemId=/content/journal/micro/10.1099/mic.0.000968&mimeType=html&fmt=ahah

References

  1. Rasmussen DB, Fogsgaard K, Rontved CM, Klaas IC, Herskin MS. Changes in thermal nociceptive responses in dairy cows following experimentally induced Escherichia coli mastitis. Acta Veterinaria Scandinavica 2011; 53:32 [View Article]
     [Google Scholar]
  2. Ruegg PL, Pantoja J, C F. Understanding and using somatic cell counts to improve milk quality. Irish J Agric Food Res 2013; 53:101–117
     [Google Scholar]
  3. Metzger SA, Hernandez LL, Skarlupka JH, Walker TM, Suen G et al. A cohort study of the milk microbiota of healthy and inflamed bovine mammary glands from dryoff through 150 days in milk. Front Vet Sci 2018; 5:247 [View Article] [PubMed]
     [Google Scholar]
  4. Espeche MC, Pellegrino M, Frola I, Larriestra A, Bogni C et al. Lactic acid bacteria from raw milk as potentially beneficial strains to prevent bovine mastitis. Anaerobe 2012; 18:103–109 [View Article]
     [Google Scholar]
  5. Pantoja JCF, Hulland C, Ruegg PL. Somatic cell count status across the dry period as a risk factor for the development of clinical mastitis in the subsequent lactation. J Dairy Sci 2009; 92:139–148 [View Article] [PubMed]
     [Google Scholar]
  6. Gonalves JL, Kamphuis C, Vernooij H, Araújo JP, Santos MVD. Pathogen effects on milk yield and composition in chronic subclinical mastitis in dairy cows. Vet J 2020; 262:105473
     [Google Scholar]
  7. Halasa T, Huijps K, Osteras O, Hogeveen H. Economic effects of bovine mastitis and mastitis management: A review. Vet Q 2007; 29:18–31 [View Article]
     [Google Scholar]
  8. Haltia L, Honkanen-Buzalski T, Spiridonova I, Olkonen A, Myllys V. A study of bovine mastitis, milking procedures and management practices on 25 Estonian dairy herds. Acta Vet Scand 2006; 48:22 [View Article] [PubMed]
     [Google Scholar]
  9. Reyher KK, Haine D, Dohoo IR, Revie CW. Examining the effect of intramammary infections with minor mastitis pathogens on the acquisition of new intramammary infections with major mastitis pathogens-a systematic review and meta-analysis. J Dairy Sci 2012; 95:6483–6502 [View Article] [PubMed]
     [Google Scholar]
  10. Cheng J, Qu W, Barkema HW, Nobrega DB, Gao J et al. Antimicrobial resistance profiles of 5 common bovine mastitis pathogens in large chinese dairy herds. J Dairy Sci 2019; 102:2416–2426 [View Article] [PubMed]
     [Google Scholar]
  11. Taponen S, Salmikivi L, Simojoki H, Koskinen MT, Pyorala S. Real-time polymerase chain reaction-based identification of bacteria in milk samples from bovine clinical mastitis with no growth in conventional culturing. J Dairy Sci 2009; 92:2610–2617 [View Article] [PubMed]
     [Google Scholar]
  12. Dohoo IR, Smith J, Andersen S, Kelton DF, Godden S et al. Diagnosing intramammary infections: Evaluation of definitions based on a single milk sample. J Dairy Sci 2011; 94:250–261 [View Article]
     [Google Scholar]
  13. Liao Y-C, Lin S-H, Lin H-H. Completing bacterial genome assemblies: strategy and performance comparisons. Sci Rep 2015; 5:8747 [View Article] [PubMed]
     [Google Scholar]
  14. Hui W, Hou Q, Cao C, Xu H, Zhen Y et al. Identification of microbial profile of Koji using single molecule, real-time sequencing technology. J Food Sci 2017; 82:1193–1199 [View Article] [PubMed]
     [Google Scholar]
  15. Hou Q, Xu H, Zheng Y, Xi X, Kwok L-Y et al. Evaluation of bacterial contamination in raw milk, ultra-high temperature milk and infant formula using single molecule, real-time sequencing technology. J Dairy Sci 2015; 98:8464–8472 [View Article] [PubMed]
     [Google Scholar]
  16. Nakano K, Shiroma A, Tamotsu H, Ohki S, Shimoji M et al. First complete genome sequence of the skin-improving Lactobacillus curvatus strain fba2, isolated from fermented vegetables, determined by Pacbio single-molecule real-time technology. Genome Announc 2016; 4:e008884–e008816 [View Article]
     [Google Scholar]
  17. Mosher JJ, Bowman B, Bernberg EL, Shevchenko O, Kan J et al. Improved performance of the PacSio SMRT technology for 16S rDNA sequencing. J Microbiol Methods 2014; 104:59–60 [View Article] [PubMed]
     [Google Scholar]
  18. 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; 6:1–26
     [Google Scholar]
  19. Wenger AM, Peluso P, Rowell WJ, Chang PC, 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]
  20. Vijayakumar M, Park JH, Ki KS, Lim DH, Kim SB et al. The effect of lactation number, stage, length, and milking frequency on milk yield in Korean Holstein dairy cows using automatic milking system. Asian-Australas J Anim Sci 2017; 30:1093–1098 [View Article] [PubMed]
     [Google Scholar]
  21. Metzger SA, Hernandez LL, Skarlupka JH, Suen G, Walker TM et al. Influence of sampling technique and bedding type on the milk microbiota: results of a pilot study. J Dairy Sci 2018; 101:6346–6356 [View Article] [PubMed]
     [Google Scholar]
  22. Francisco Martinez-Blanch J, Sanchez G, Garay E, Aznar R. Detection and quantification of viable Bacillus cereus in food by RT-QPCR. Eur Food Res Tech 2011; 232:951–955
     [Google Scholar]
  23. Ma C, Sun Z, Zeng B, Huang S, Zhao J et al. Cow-to-mouse fecal transplantations suggest intestinal microbiome as one cause of mastitis. Microbiome 2018; 6:200 [View Article] [PubMed]
     [Google Scholar]
  24. Cremonesi P, Cortimiglia C, Picozzi C, Minozzi G, Malvisi M et al. Development of a droplet digital polymerase chain reaction for rapid and simultaneous identification of common food borne pathogens in soft cheese. Front Microbiol 2016; 7:1725 [View Article] [PubMed]
     [Google Scholar]
  25. Mosher JJ, Bernberg EL, Shevchenko O, Kan J, Kaplan LA. Efficacy of a 3rd generation high-throughput sequencing platform for analyses of 16S rRNA genes from environmental samples. J Microbiol Methods 2013; 95:175–181 [View Article] [PubMed]
     [Google Scholar]
  26. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 2010; 7:335–336 [View Article] [PubMed]
     [Google Scholar]
  27. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010; 26:2460–2461 [View Article] [PubMed]
     [Google Scholar]
  28. Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward D et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res 2011; 21:494–504 [View Article]
     [Google Scholar]
  29. Cole JR, Chai B, Farris RJ, Wang Q, Kulam-Syed-Mohideen AS et al. The Ribosomal Database Project (RDP-II): Introducing Myrdp space and quality controlled public data. Nucleic Acids Research 2007; 35:D172–D169
     [Google Scholar]
  30. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 2006; 72:5069–5072 [View Article] [PubMed]
     [Google Scholar]
  31. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T et al. The SILVA Ribosomal RNA Gene Database Project: Improved data processing and web-based tools. Nucleic Acids Res 2013; 41:D590–6 [View Article]
     [Google Scholar]
  32. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 2009; 26:1641–1650 [View Article] [PubMed]
     [Google Scholar]
  33. Anderson MJ, Walsh DC. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing?. Ecological Monographs 2013; 83:557–574 [View Article]
     [Google Scholar]
  34. Nilsson RH, Veldre V, Hartmann M, Unterseher M, Amend A et al. An open source software package for automated extraction of ITS1 and ITS2 from fungal ITS sequences for use in high-throughput community assays and molecular ecology. Fungal Ecology 2010; 3:284–287 [View Article]
     [Google Scholar]
  35. Ginestet C. Ggplot2: Elegant graphics for data analysis. J R Stat Soc Ser A Stat Soc 2011; 174:245–246 [View Article]
     [Google Scholar]
  36. Paster BJ, Dewhirst FE, Olsen I, Fraser GJ. Phylogeny of Bacteroides, Prevotella, and Porphyromonas spp. and related bacteria. J Bacteriol 1994; 176:725–732 [View Article] [PubMed]
     [Google Scholar]
  37. Murphy SC, Martin NH, Barbano DM, Wiedmann M. Influence of raw milk quality on processed dairy products: how do raw milk quality test results relate to product quality and yield. J Dairy Sci 2016; 99:10128–10149 [View Article] [PubMed]
     [Google Scholar]
  38. Fromm H, Boor KJ. Characterization of pasteurized fluid milk shelf-life attributes. J Food Sci 2004; 69:M207–M214 [View Article]
     [Google Scholar]
  39. Derakhshani H, Fehr KB, Sepehri S, Francoz D, De Buck J et al. Invited review: microbiota of the bovine udder: contributing factors and potential implications for udder health and mastitis susceptibility. J Dairy Sci 2018; 101:10605–10625 [View Article] [PubMed]
     [Google Scholar]
  40. Hernandez-Raquet G, Hossain MA, Papadimitriou K, Even S, Loir YL. Milk microbiota: What are we exactly talking about. Front microbiol 2020; 11:60
     [Google Scholar]
  41. Braem G, De Vliegher S, Verbist B, Heyndrickx M, Leroy F et al. Culture-independent exploration of the teat apex microbiota of dairy cows reveals a wide bacterial species diversity. Vet Microbiol 2012; 157:383–390 [View Article] [PubMed]
     [Google Scholar]
  42. Kuehn JS, Gorden PJ, Munro D, Rong R, Dong Q et al. Bacterial community profiling of milk samples as a means to understand culture-negative bovine clinical mastitis. PloS One 2013; 8:e61959 [View Article]
     [Google Scholar]
  43. Derakhshani H, Plaizier JC, De Buck J, Barkema HW, Khafipour E. Composition and co-occurrence patterns of the microbiota of different niches of the bovine mammary gland: Potential associations with mastitis susceptibility, udder inflammation, and teat-end hyperkeratosis. Anim Microbiome 20201–7
     [Google Scholar]
  44. Nakamura T, Kawase H, Kimura K, Watanabe Y, Ohtani M et al. Concentrations of sialyloligosaccharides in bovine colostrum and milk during the prepartum and early lactation. J Dairy Sci 2003; 86:1315–1320 [View Article] [PubMed]
     [Google Scholar]
  45. Tao N, DePeters EJ, German JB, Grimm R, Lebrilla CB. Variations in bovine milk oligosaccharides during early and middle lactation stages analyzed by high-performance liquid chromatography-chip/mass spectrometry. J Dairy Sci 2009; 92:2991–3001 [View Article] [PubMed]
     [Google Scholar]
  46. Kuang Y, Tani K, Synnott AJ, Ohshima K, Higuchi H et al. Characterization of bacterial population of raw milk from bovine mastitis by culture-independent PCR–DGGE method. Biochem Eng J 2009; 45:76–81 [View Article]
     [Google Scholar]
  47. Falentin H, Rault L, Nicolas A, Bouchard DS, Lassalas J 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 [View Article] [PubMed]
     [Google Scholar]
  48. Hoque MN, Istiaq A, Clement RA, Sultana M, Hossain MA. Metagenomic deep sequencing reveals association of microbiome signature with functional biases in bovine mastitis. Sci Rep 2019; 9:13536 [View Article] [PubMed]
     [Google Scholar]
  49. Taponen S, Mcguinness D, Hiiti H, Simojoki H, Pyrl S. Bovine milk microbiome: a more complex issue than expected. Vet Res 2019; 50:44 [View Article]
     [Google Scholar]
  50. Li N, Wang Y, You C, Ren J, Chen W et al. Variation in raw milk microbiota throughout 12 months and the impact of weather conditions. Scientific Reports 2018; 8:1–10
     [Google Scholar]
  51. Bouchard DS, Bianca S, Taous S, Lucie R, Pierre G et al. Lactic acid bacteria isolated from bovine mammary microbiota: potential allies against bovine mastitis. PLoS ONE 2015; 10:e0144831 [View Article]
     [Google Scholar]
  52. Yu J, Ren Y, Xi X, Huang W, Zhang H. A novel Lactobacilli-based teat disinfectant for improving bacterial communities in the milks of cow teats with subclinical mastitis. Front Microbiol 2017; 8:1782 [View Article] [PubMed]
     [Google Scholar]
  53. Pascal R. Mammary microbiota of dairy ruminants: Fact or fiction?. Vet Res 2017; 48:25 [View Article] [PubMed]
     [Google Scholar]
  54. Rodrigues M, Lima SF, Higgins CH, Canniatti-Brazaca SG, Bicalho RC. The Lactococcus genus as a potential emerging mastitis pathogen group: a report on an outbreak investigation. J Dairy Sci 2016; 99:9864–9874 [View Article] [PubMed]
     [Google Scholar]
  55. Dortet L, Legrand P, Soussy C-J, Cattoir V. Bacterial identification, clinical significance, and antimicrobial susceptibilities of Acinetobacter ursingii and Acinetobacter schindleri, two frequently misidentified opportunistic pathogens. J Clin Microbiol 2006; 44:4471–4478 [View Article] [PubMed]
     [Google Scholar]
  56. Li L, Renye JA, Feng L, Zeng Q, Tang Y et al. Characterization of the indigenous microflora in raw and pasteurized buffalo milk during storage at refrigeration temperature by high-throughput sequencing. J Dairy Sci 2016; 99:7016–7024 [View Article] [PubMed]
     [Google Scholar]
  57. Quigley L, O’Sullivan O, Stanton C, Beresford TP, Ross RP et al. The complex microbiota of raw milk. FEMS Microbiol Rev 2013; 37:664–698 [View Article] [PubMed]
     [Google Scholar]
  58. Moroni P, Rossi CS, Pisoni G, Bronzo V, Castiglioni B et al. Relationships between somatic cell count and intramammary infection in buffaloes. J Dairy Sci 2006; 89:998–1003
     [Google Scholar]
  59. Durojaiye O, Gaur S, Alsaffar L. Bacteraemia and breast abscess: unusual extra-intestinal manifestations of Clostridium difficile infection. J Med Microbiol 2011; 60:378–380 [View Article] [PubMed]
     [Google Scholar]
  60. Osman KM, El-Enbaawy M, Ezzeldeen NA, Hussein HMG. Mastitis in dairy buffalo and cattle in Egypt due to Clostridium perfringens: prevalence, incidence, risk factors and costs. Revue Scientifique Et Technique-Office International Des Epizooties 2009; 28:975–986
     [Google Scholar]
  61. Parkinson TJ, Merrall M, Fenwick SG. A case of bovine mastitis caused by Bacillus cereus. N Z Vet J 1999; 47:151–152 [View Article] [PubMed]
     [Google Scholar]
  62. Pang M, Xie X, Bao H, Sun L, He T et al. Insights into the bovine milk microbiota in dairy farms with different incidence rates of subclinical mastitis. Front Microbiol 2018; 9:2379 [View Article] [PubMed]
     [Google Scholar]
  63. Mikkola R, Kolari M, Andersson MA, Helin J, Salkinoja-Salonen MS. Toxic lactonic lipopeptide from food poisoning isolates of bacillus licheniformis. Febs Journal 2010; 267:4068–4074
     [Google Scholar]
  64. Almaw G, Zerihun A, Asfaw Y. Bovine mastitis and its association with selected risk factors in smallholder dairy farms in and around bahir dar, ethiopia. Trop Anim Health Prod 2008; 40:427–432 [View Article]
     [Google Scholar]
  65. Amer S, Gálvez FLA, Fukuda Y, Tada C, Jimenez IL et al. Prevalence and etiology of mastitis in dairy cattle in El Oro Province, Ecuador. J Vet Med Sci 2018; 80:861–868 [View Article]
     [Google Scholar]
  66. Sadashiv SO, Kaliwal BB. Isolation, characterization and antibiotic resistance of Bacillus sps. From bovine mastitis in the region of north Karnataka, India. Int J Curr Microbiol Appl Sci 2014; 03:360–373
     [Google Scholar]
  67. Oikonomou G, Machado VS, Santisteban C, Schukken YH, Bicalho RC. Microbial diversity of bovine mastitis milk as described by pyrosequencing of metagenomic 16s rDNA. PloS One 2012; 7:e47671 [View Article] [PubMed]
     [Google Scholar]
  68. Pyorala S, Jousimies-Somer H, Mero M. Clinical, bacteriological and therapeutic aspects of bovine mastitis caused by aerobic and anaerobic pathogens. Br Vet J 1992; 148:54–62 [View Article] [PubMed]
     [Google Scholar]
  69. Schwaiger K, Wimmer M, Huber-Schlenstedt R, Fehlings K, Hoelzel CS et al. Hot topic: bovine milk samples yielding negative or nonspecific results in bacterial culturing-The possible role of PCR-single strand conformation polymorphism in mastitis diagnosis. J Dairy Sci 2012; 95:98–101
     [Google Scholar]
  70. Yoshida N, Emoto T, Yamashita T, Watanabe H, Hayashi T et al. Bacteroides vulgatus and Bacteroides dorei reduce gut microbial lipopolysaccharide production and inhibit atherosclerosis. Circulation 2018; 138:2486–2498 [View Article] [PubMed]
     [Google Scholar]
  71. Xu H, Huang W, Hou Q, Kwok L, Sun Z et al. The effects of probiotics administration on the milk production, milk components and fecal bacteria microbiota of dairy cows. Science Bulletin 2017; 62:767–774 [View Article]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/micro/10.1099/mic.0.000968
Loading
PacBio sequencing revealed variation in the microbiota diversity, species richness and composition between milk collected from healthy and mastitis cows
Microbiology 167, 000968 (2021); https://doi.org/10.1099/mic.0.000968
/content/journal/micro/10.1099/mic.0.000968
/content/journal/micro/10.1099/mic.0.000968
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error