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Analysis of 16S rRNA gene sequencing data for the taxonomic characterization of the vaginal and the fecal microbial communities in Hanwoo

  • Choi, Soyoung (Animal Genomics and Bioinformatics Division, National Institute of Animal Science) ;
  • Cha, Jihye (Animal Genomics and Bioinformatics Division, National Institute of Animal Science) ;
  • Song, Minji (Animal Genomics and Bioinformatics Division, National Institute of Animal Science) ;
  • Son, JuHwan (Animal Genomics and Bioinformatics Division, National Institute of Animal Science) ;
  • Park, Mi-Rim (Animal Genomics and Bioinformatics Division, National Institute of Animal Science) ;
  • Lim, Yeong-jo (Animal Genomics and Bioinformatics Division, National Institute of Animal Science) ;
  • Kim, Tae-Hun (Animal Genomics and Bioinformatics Division, National Institute of Animal Science) ;
  • Lee, Kyung-Tai (Animal Genomics and Bioinformatics Division, National Institute of Animal Science) ;
  • Park, Woncheoul (Animal Genomics and Bioinformatics Division, National Institute of Animal Science)
  • Received : 2022.01.25
  • Accepted : 2022.07.09
  • Published : 2022.11.01

Abstract

Objective: The study of Hanwoo (Korean native cattle) has mainly been focused on meat quality and productivity. Recently the field of microbiome research has increased dramatically. However, the information on the microbiome in Hanwoo is still insufficient, especially relationship between vagina and feces. Therefore, the purpose of this study is to examine the microbial community characteristics by analyzing the 16S rRNA sequencing data of Hanwoo vagina and feces, as well as to confirm the difference and correlation between vaginal and fecal microorganisms. As a result, the goal is to investigate if fecal microbiome can be used to predict vaginal microbiome. Methods: A total of 31 clinically healthy Hanwoo that delivered healthy calves more than once in Cheongju, South Korea were enrolled in this study. During the breeding season, we collected vaginal and fecal samples and sequenced the microbial 16S rRNA genes V3-V4 hypervariable regions from microbial DNA of samples. Results: The results revealed that the phylum-level microorganisms with the largest relative distribution were Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria in the vagina, and Firmicutes, Bacteroidetes, and Spirochaetes in the feces, respectively. In the analysis of alpha, beta diversity, and effect size measurements (LefSe), the results showed significant differences between the vaginal and fecal samples. We also identified the function of these differentially abundant microorganisms by functional annotation analyses. But there is no significant correlation between vaginal and fecal microbiome. Conclusion: There is a significant difference between vaginal and fecal microbiome, but no significant correlation. Therefore, it is difficult to interrelate vaginal microbiome as fecal microbiome in Hanwoo. In a further study, it will be necessary to identify the genetic relationship of the entire microorganism between vagina and feces through the whole metagenome sequencing analysis and meta-transcriptome analysis to figure out their relationship.

Keywords

Acknowledgement

This study was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ0148262020)" Rural Development Administration, Republic of Korea.

References

  1. de Oliveira MNV, Jewell KA, Freitas FS, et al. Characterizing the microbiota across the gastrointestinal tract of a Brazilian Nelore steer. Vet Microbiol 2013;164:307-14. https://doi.org/10.1016/j.vetmic.2013.02.013
  2. Gomez DE, Arroyo LG, Costa MC, Viel L, Weese JS. Characterization of the fecal bacterial microbiota of healthy and diarrheic dairy calves. J Vet Intern Med 2017;31:928-39. https://doi.org/10.1111/jvim.14695
  3. Fecteau ME, Pitta DW, Vecchiarelli B, et al. Dysbiosis of the Fecal Microbiota in Cattle Infected with Mycobacterium avium subsp. paratuberculosis. PLoS One 2016;11:e0160353. https://doi.org/10.1371/journal.pone.0160353
  4. Sheldon IM, Dobson H. Postpartum uterine health in cattle. Anim Reprod Sci 2004;82:295-306. https://doi.org/10.1016/j.anireprosci.2004.04.006
  5. Wang J, Xu J, Han Q, et al. Changes in the vaginal microbiota associated with primary ovarian failure. BMC Microbiol 2020;20:230. https://doi.org/10.1186/s12866-020-01918-0
  6. Appiah MO, Wang J, Lu W. Microflora in the reproductive tract of cattle: a review. Agriculture 2020;10:232. https://doi.org/10.3390/agriculture10060232
  7. Clemmons BA, Reese ST, Dantas FG, et al. Vaginal and uterine bacterial communities in postpartum lactating cows. Front Microbiol 2017;8:1047. https://doi.org/10.3389/fmicb.2017.01047
  8. Quereda JJ, Barba M, Moce ML, et al. Vaginal microbiota changes during estrous cycle in dairy heifers. Front Vet Sci 2020;7:371. https://doi.org/10.3389/fvets.2020.00371
  9. Wang Y, Ametaj BN, Ambrose DJ, Ganzle MG. Characterisation of the bacterial microbiota of the vagina of dairy cows and isolation of pediocin-producing Pediococcus acidilactici. BMC Microbiol 2013;13:19. https://doi.org/10.1186/1471-2180-13-19
  10. Dobbler P, Mai V, Procianoy RS, et al. The vaginal microbial communities of healthy expectant Brazilian mothers and its correlation with the newborn's gut colonization. World J Microbiol Biotechnol 2019;35:159. https://doi.org/10.1007/s11274-019-2737-3
  11. Amabebe E, Anumba DOC. Female gut and genital tract microbiota-induced crosstalk and differential effects of shortchain fatty acids on immune sequelae. Front Immunol 2020; 11:2184. https://doi.org/10.3389/fimmu.2020.02184
  12. Klein-Jobstl D, Quijada NM, Dzieciol M, et al. Microbiota of newborn calves and their mothers reveals possible transfer routes for newborn calves' gastrointestinal microbiota. PLoS One 2019;14:e0220554. https://doi.org/10.1371/journal.pone.0220554
  13. Laguardia-Nascimento M, Branco KMGR, Gasparini MR, et al. Vaginal microbiome characterization of Nellore cattle using metagenomic analysis. PLoS One 2015;10:e0143294. https://doi.org/10.1371/journal.pone.0143294
  14. Woo JS, Kim KH, Cho ES, et al. Effect of microorganisms collected from uterus of Hanwoo cattle with low conception rate on the development of IVF-derived embryos. Korean J Agric Sci 2015;42:355-9. https://doi.org/10.7744/cnujas.2015.42.4.355
  15. Holman DB, Gzyl KE. A meta-analysis of the bovine gastrointestinal tract microbiota. FEMS Microbiol Ecol 2019;95: fiz072. https://doi.org/10.1093/femsec/fiz072
  16. Kim ET, Lee SJ, Kim TY, et al. Dynamic changes in fecal microbial communities of neonatal dairy calves by aging and diarrhea. Animals 2021;11:1113. https://doi.org/10.3390/ani11041113
  17. Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 2019;37:852-7. https://doi.org/10.1038/s41587-019-0209-9
  18. 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. https://doi.org/10.1038/nmeth.3869
  19. McDonald D, Price MN, Goodrich J, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 2012; 6:610-8. https://doi.org/10.1038/ismej.2011.139
  20. Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods 2013;10:1200-2. https://doi.org/10.1038/nmeth.2658
  21. Douglas GM, Maffei VJ, Zaneveld J, et al. PICRUSt2: An improved and customizable approach for metagenome inference. bioRxiv 2020:672295. https://doi.org/10.1101/672295
  22. Fernandes AD, Reid JNS, Macklaim JM, et al. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2014;2:15. https://doi.org/10.1186/2049-2618-2-15
  23. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102:15545-50. https://doi.org/10.1073/pnas.0506580102
  24. Wang J, Li Z, Ma X, et al. Translocation of vaginal microbiota is involved in impairment and protection of uterine health. Nat Commun 2021;12:4191. https://doi.org/10.1038/s41467-021-24516-8
  25. Ling Z, Kong J, Liu F, et al. Molecular analysis of the diversity of vaginal microbiota associated with bacterial vaginosis. BMC Genomics 2010;11:488. https://doi.org/10.1186/1471-2164-11-488
  26. Freetly HC, Dickey A, Lindholm-Perry AK, et al. Digestive tract microbiota of beef cattle that differed in feed efficiency. Anim Sci J 2020;98:skaa008. https://doi.org/10.1093/jas/skaa008
  27. Ravel J, Gajer P, Abdo Z, et al. Vaginal microbiome of repro - ductive-age women. Proc Natl Acad Sci USA 2011;108:4680-7. https://doi.org/10.1073/pnas.1002611107
  28. Miller EA, Beasley DE, Dunn RR, Archie EA. Lactobacilli dominance and vaginal pH: why is the human vaginal microbiome unique? Front Microbiol 2016;7:1936. https://doi.org/10.3389/fmicb.2016.01936
  29. Ault TB, Clemmons BA, Reese ST, et al. Bacterial taxonomic composition of the postpartum cow uterus and vagina prior to artificial insemination. Anim Sci J 2019;97:4305-13. https://doi.org/10.1093/jas/skz212
  30. Alipour MJ, Jalanka J, Pessa-Morikawa T, et al. The composition of the perinatal intestinal microbiota in cattle. Sci Rep 2018;8:10437. https://doi.org/10.1038/s41598-018-28733-y
  31. Mao S, Zhang M, Liu J, Zhu W. Characterising the bacterial microbiota across the gastrointestinal tracts of dairy cattle: membership and potential function. Sci Rep 2015;5:16116. https://doi.org/10.1038/srep16116
  32. La Reau AJ, Suen G. The Ruminococci: key symbionts of the gut ecosystem. J Microbiol 2018;56:199-208. https://doi.org/10.1007/s12275-018-8024-4
  33. Papale M, Rizzo C, Caruso G, et al. First insights into the microbiology of three Antarctic briny systems of the Northern Victoria Land. Diversity 2021;13:323. https://doi.org/10.3390/d13070323
  34. Mattos-Graner RO, Duncan MJ. Two-component signal transduction systems in oral bacteria. J Oral Microbiol 2017;9: 1400858. https://doi.org/10.1080/20002297.2017.1400858
  35. Thomas L, Cook L. Two-component signal transduction systems in the human pathogen Streptococcus agalactiae. Infect Immun 2020;88:e00931-19. https://doi.org/10.1128/IAI.00931-19
  36. Seo JS, Keum YS, Li QX. Bacterial degradation of aromatic compounds. Int J Environ Res Public Health 2009;6:278-309. https://doi.org/10.3390/ijerph6010278
  37. Kwon M, Seo SS, Kim MK, Lee DO, Lim MC. Compositional and functional differences between microbiota and cervical carcinogenesis as identified by shotgun metagenomic sequencing. Cancers 2019;11:309. https://doi.org/10.3390/cancers11030309
  38. Meale SJ, Li S, Azevedo P, et al. Weaning age influences the severity of gastrointestinal microbiome shifts in dairy calves. Sci Rep 2017;7:198. https://doi.org/10.1038/s41598-017-00223-7