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Metagenomic Analysis of Chicken Gut Microbiota for Improving Metabolism and Health of Chickens - A Review

  • Choi, Ki Young (Department of Systems Biotechnology, Chung-Ang University) ;
  • Lee, Tae Kwon (Department of Environmental Engineering, Yonsei University) ;
  • Sul, Woo Jun (Department of Systems Biotechnology, Chung-Ang University)
  • 투고 : 2015.01.09
  • 심사 : 2015.03.31
  • 발행 : 2015.09.01

초록

Chicken is a major food source for humans, hence it is important to understand the mechanisms involved in nutrient absorption in chicken. In the gastrointestinal tract (GIT), the microbiota plays a central role in enhancing nutrient absorption and strengthening the immune system, thereby affecting both growth and health of chicken. There is little information on the diversity and functions of chicken GIT microbiota, its impact on the host, and the interactions between the microbiota and host. Here, we review the recent metagenomic strategies to analyze the chicken GIT microbiota composition and its functions related to improving metabolism and health. We summarize methodology of metagenomics in order to obtain bacterial taxonomy and functional inferences of the GIT microbiota and suggest a set of indicator genes for monitoring and manipulating the microbiota to promote host health in future.

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참고문헌

  1. Andersson, A. F., M. Lindberg, H. Jakobsson, F. Backhed, P. Nyren, and L. Engstrand. 2008. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS One. 3(7):e2836. https://doi.org/10.1371/journal.pone.0002836
  2. Aydin, R., M. W. Pariza, and M. E. Cook. 2001. Olive oil prevents the adverse effects of dietary conjugated linoleic acid on chick hatchability and egg quality. J. Nutr. 131:800-806.
  3. Baurhoo, B., L. Phillip, and C. A. Ruiz-Feria. 2007. Effects of purified lignin and mannan oligosaccharides on intestinal integrity and microbial populations in the ceca and litter of broiler chickens. Poult. Sci. 86:1070-1078. https://doi.org/10.1093/ps/86.6.1070
  4. Beckmann, L., O. Simon, and W. Vahjen. 2006. Isolation and identification of mixed linked beta -glucan degrading bacteria in the intestine of broiler chickens and partial characterization of respective 1,3-1,4-beta-glucanase activities. J. Basic Microbiol. 46:175-185. https://doi.org/10.1002/jobm.200510107
  5. Brisbin, J. T., J. Gong, S. Sharif. 2008. Interactions between commensal bacteria and the gut-associated immune system of the chicken. Anim. Health Res. Rev. 9:101-110. https://doi.org/10.1017/S146625230800145X
  6. Caporaso, J. G., J. Kuczynski, J. Stombaugh, K. Bittinger, F. D. Bushman, E. K. Costello, N. Fierer, A. G. Pena, J. K. Goodrich, and J. I. Gordon et al. 2010. QIIME allows analysis of highthroughput community sequencing data. Nat. Methods 7:335-336. https://doi.org/10.1038/nmeth.f.303
  7. Case, R. J., Y. Boucher, I. Dahllof, C. Holmstrom, W. F. Doolittle, and S. Kjelleberg. 2007. Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology studies. Appl. Environ. Microbiol. 73:278-288. https://doi.org/10.1128/AEM.01177-06
  8. Chichlowski, M., W. J. Croom, F. W. Edens, B. W. McBride, R. Qiu, C. C. Chiang, L. R. Daniel, G. B. Havenstein, and M. D. Koci. 2007. Microarchitecture and spatial relationship between bacteria and ileal, cecal, and colonic epithelium in chicks fed a direct-fed microbial, PrimaLac, and salinomycin. Poult. Sci. 86:1121-1132. https://doi.org/10.1093/ps/86.6.1121
  9. Claesson, M. J., Q. Wang, O. O'Sullivan, R. Greene-Diniz, J. R. Cole, R. P. Ross, and P. W. O'Toole. 2010. Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucl. Acids Res. 38:e200. https://doi.org/10.1093/nar/gkq873
  10. Clemente, J. C., L. K. Ursell, L. W. Parfrey, and R. Knight. 2012. The impact of the gut microbiota on human health: An integrative view. Cell 148:1258-1270. https://doi.org/10.1016/j.cell.2012.01.035
  11. Cole, J. R., Q. Wang, J. A. Fish, B. Chai, D. M. McGarrell, Y. Sun, C. T. Brown, A. Porras-Alfaro, C. R. Kuske, and J. M. Tiedje. 2014. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucl. Acids Res. 42:D633-642. https://doi.org/10.1093/nar/gkt1244
  12. Colwell, R. K. 2009. Biodiversity: concepts, patterns, and measurement. In: The Princeton Guide to Ecology (Ed. S. A. Levin). Princeton University Press, Princeton, NJ, USA. PP. 257-263.
  13. Cruaud, R., A. Vigneron, C. Lucchetti-Miganeh, P. E. Ciron, A. Godfroy, and M. A. Cambon-Bonavita. 2014. Influence of DNA extraction method, 16S rRNA targeted hypervariable regions, and sample origin on microbial diversity detected by 454 pyrosequencing in marine chemosynthetic ecosystems. Appl. Environ. Microbiol. 80:4626-4639. https://doi.org/10.1128/AEM.00592-14
  14. Darling, A. E., G. Jospin, E. Lowe, F. A. Matsen 4th, H. M. Bik, and J. A. Eisen. 2014. PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ. 2:e243. https://doi.org/10.7717/peerj.243
  15. DeSantis, T. Z., P. Hugenholtz, N. Larsen, M. Rojas, E. L. Brodie, K. Keller, T. Huber, D. Dalevi, P. Hu, and G. L. Andersen. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72:5069-5072. https://doi.org/10.1128/AEM.03006-05
  16. Dethlefsen, L., S. Huse, M. L. Sogin, and D. A. Relman. 2008. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol. 6(11):e280. https://doi.org/10.1371/journal.pbio.0060280
  17. Dunkley, K. D., C. S. Dunkley, N. L. Njongmeta, T. R. Callaway, M. E. Hume, L. F. Kubena, D. J. Nisbet, and S. C. Ricke. 2007. Comparison of in vitro fermentation and molecular microbial profiles of high-fiber feed substrates incubated with chicken cecal inocula. Poult. Sci. 86:801-810. https://doi.org/10.1093/ps/86.5.801
  18. Edgar, R. C., B. J. Haas, J. C. Clemente, C. Quince, and R. Knight. 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194-2200. https://doi.org/10.1093/bioinformatics/btr381
  19. Elnifro, E. M., A. M. Ashshi, R. J. Cooper, and P. E. Klapper. 2000. Multiplex PCR: optimization and application in diagnostic virology. Clin. Microbiol. Rev. 13:559-570. https://doi.org/10.1128/CMR.13.4.559-570.2000
  20. Eren, A. M., L. Maignien, W. J. Sul, L. G. Murphy, S. L. Grim, H. G. Morrison, and M. L. Sogin. 2014. Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4:1111-1119.
  21. Fernandez, F., M. Hinton, and B. Van Gils. 2002. Dietary mannanoligosaccharides and their effect on chicken caecal microflora in relation to Salmonella Enteritidis colonization. Avian Pathol. 31:49-58. https://doi.org/10.1080/03079450120106000
  22. Fish, J. A., B. Chai, Q. Wang, Y. Sun, C. T. Brown, J. M. Tiedje, and J. R. Cole. 2013. FunGene: the functional gene pipeline and repository. Front Microbiol. 4:291.
  23. Finn, R. D., J. Clements, and S. R. Eddy. 2011. HMMER web server: interactive sequence similarity searching. Nucl. Acids Res. 39:W29-37 https://doi.org/10.1093/nar/gkr367
  24. Ganner, A. and G. Schatzmayr. 2012. Capability of yeast derivatives to adhere enteropathogenic bacteria and to modulate cells of the innate immune system. Appl. Microbiol. Biotechnol. 95:289-297. https://doi.org/10.1007/s00253-012-4140-y
  25. Gilles, A., E. Meglecz, N. Pech, S. Ferreira, T. Malausa, and J. F. Martin. 2011. Accuracy and quality assessment of 454 GSFLX Titanium pyrosequencing. BMC Genomics 12:245. https://doi.org/10.1186/1471-2164-12-245
  26. Haas, B. J., D. Gevers, A. M. Earl, M. Feldgarden, D. V. Ward, G. Giannoukos, D. Ciulla, D. Tabbaa, S. K. Highlander, E. Sodergren, B. Methe, T. Z. DeSantis; Human Microbiome Consortium, J. F. Petrosino, R. Knight, and B. W. Birren. 2011. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21:494-504. https://doi.org/10.1101/gr.112730.110
  27. Hang J., V. Desai, N. Zavajevski, Y. Yang, X. Lin, R. V. Satya, L. J. Martinez, J. M. Blaylock, R. G. Jarman, S. J. Thomas, and R. A. Kuschner. 2014. 16S rRNA gene pyrosequencing of reference and clinical samples and investigation of the temperature stability of microbiome profiles. Microbiome 2:31 https://doi.org/10.1186/2049-2618-2-31
  28. He, Z., T. J. Gentry, C. W. Schadt, L. Wu, J. Liebich, S. C. Chong, Z. Huang, W. Wu, B. Gu, P. Jardine, C. Criddle, and J. Zhou. 2007. GeoChip: A comprehensive microarray for investigating biogeochemical, ecological and environmental processes. ISME J. 1:67-77. https://doi.org/10.1038/ismej.2007.2
  29. Howe, A. C., J. K. Jansson, S. A. Malfatti, S. G. Tringe, J. M. Tiedje, and C. T. Brown. 2014. Tackling soil diversity with the assembly of large, complex metagenomes. Proc. Natl. Acad. Sci. USA 111:4904-4909. https://doi.org/10.1073/pnas.1402564111
  30. Hughes, J. B., J. J. Hellmann, T. H. Ricketts, and B. J. Bohannan. 2011. Counting the uncountable: statistical approaches to estimating microbial diversity. Appl. Environ. Microbiol. 67:4399-4406.
  31. Huse, S. M., J. A. Huber, H. G. Morrison, M. L. Sogin, and D. M. Welch. 2007. Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol. 8:R143. https://doi.org/10.1186/gb-2007-8-7-r143
  32. Jin, L. Z., Y. W. Ho, N. Abdullah, and S. Jalaludin. 1998. Growth performance, intestinal microbial populations, and serum cholesterol of broilers fed diets containing Lactobacillus cultures. Poult. Sci. 77:1259-1265. https://doi.org/10.1093/ps/77.9.1259
  33. Johnson, D. R., T. K. Lee, J. Park, K. Fenner, and D. E. Helbling. 2014. The functional and taxonomic richness of wastewater treatment plant microbial communities are associated with each other and with ambient nitrogen and carbon availability. Environ. Microbiol. doi: 10.1111/1462-2920.12429.
  34. Jumpertz, R., D. S. Le, P. J. Turnbaugh, C. Trinidad, C. Bogardus, J. I. Gordon, and J. Krakoff. 2011. Energy-balance studies reveal associations between gut microbes, caloric load, and nutrient absorption in humans. Am. J. Clin. Nutr. 94:58-65. https://doi.org/10.3945/ajcn.110.010132
  35. Kaakoush, N. O., N. Sodhi, J. W. Chenu, J. M. Cox, S. M. Riordan, and H. M. Mitchell. 2014. The interplay between Campylobacter and Helicobacter species and other gastrointestinal microbiota of commercial broiler chickens. Gut Pathog. 6:18. https://doi.org/10.1186/1757-4749-6-18
  36. Kunin, V., A. Engelbrektson, H. Ochman, and P. Hugenholtz. 2010. Wrinkles in the rare biosphere: Pyrosequencing errors can lead to artificial inflation of diversity estimates. Environ. Microbiol. 12:118-123. https://doi.org/10.1111/j.1462-2920.2009.02051.x
  37. Latshaw, J. D. and L. Zhao. 2011. Dietary protein effects on hen performance and nitrogen excretion. Poult. Sci. 90:99-106. https://doi.org/10.3382/ps.2010-01035
  38. Lei, F., Y. Yin, Y. Wang, B. Deng, H. D. Yu, L. Li, C. Xiang, S. Wang, B. Zhu, and X. Wang. 2012. Higher-level production of volatile fatty acids in vitro by chicken gut microbiotas than by human gut microbiotas as determined by functional analyses. Appl. Environ. Microbiol. 78:5763-5772. https://doi.org/10.1128/AEM.00327-12
  39. Lauber, C. L., N. Zhou, J. I. Gordon, R. Knight, and N. Fierer. 2010. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol. Lett. 307(1):80-86. https://doi.org/10.1111/j.1574-6968.2010.01965.x
  40. Lei, F., Y. Yin, Y. Wang, B. Deng, H. D. Yu, L. Li, C. Xiang, S. Wang, B. Zhu, and X. Wang. 2012. Higher-level production of volatile fatty acids in vitro by chicken gut microbiotas than by human gut microbiotas as determined by functional analyses. Appl. Environ. Microbiol. 78:5763-5772. https://doi.org/10.1128/AEM.00327-12
  41. Looft, T., T. A. Johnson, H. K. Allen, D. O. Bayles, D. P. Alt, R. D. Stedtfeld, W. J. Sul, T. M. Stedtfeld, B. Chai, J. R. Cole, S. A. Hashsham, J. M. Tiedje, and T. B. Stanton. 2012. In-feed antibiotic effects on the swine intestinal microbiome. Proc. Natl. Acad. Sci. USA 109:1691-1696. https://doi.org/10.1073/pnas.1120238109
  42. Lozupone, C. A. and R. Knight. 2005. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71:8228-8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005
  43. Lozupone, C. A. and R. Knight. 2007. Global patterns in bacterial diversity. Proc. Natl. Acad. Sci. USA 104:11436-11440. https://doi.org/10.1073/pnas.0611525104
  44. Lozupone, C. A., J. I. Stombaugh, J. I. Gordon, J. K. Jansson, and R. Knight. 2012. Diversity, stability and resilience of the human gut microbiota. Nature 489:220-230. https://doi.org/10.1038/nature11550
  45. Luo, C., D. Tsementzi, N. Kyrpides, T. Read, and K. T. Konstantinidis. 2012. Direct comparisons of Illumina vs. Roche 454 sequencing technologies on the same microbial community DNA sample. PLoS One. 7(2):e30087.
  46. Martiny, J. B. H., J. A. Eisen, K. Penn, S. D. Allison, and M. C. Horner-Devine. 2011. Drivers of bacterial beta-diversity depend on spatial scale. Proc. Natl. Acad. Sci. USA 108:7850-7854. https://doi.org/10.1073/pnas.1016308108
  47. McCubbin, D. R., B. J. Apelberg, S. Roe, and F. Divita. 2002. Livestock ammonia management and particulate-related health benefits. Environ. Sci. Technol. 36:1141-1146. https://doi.org/10.1021/es010705g
  48. Medinger, R., V. Nolte, R. V. Pandey, S. Jost, B. Ottenwalder, C. Schlotterer, and J. Boenigk. 2010. Diversity in a hidden world: potential and limitation of next-generation sequencing for surveys of molecular diversity of eukaryotic microorganisms. Mol. Ecol. 1:32-40.
  49. Mende, D. R., A. S. Waller, S. Sunagawa, A. I. Jarvelin, M. M. Chan, M. Arumugam, J. Raes, and P. Bork. 2012. Assessment of metagenomic assembly using simulated next generation sequencing data. PLoS One. 7(2):e31386. https://doi.org/10.1371/journal.pone.0031386
  50. Metzker, M. L. 2010. Sequencing technologies - the next generation. Nat. Rev. Genet. 11:31-46. https://doi.org/10.1038/nrg2626
  51. Morris, S. C. 2003. Life's Solution: Inevitable Humans in a Lonely Universe. Cambridge University Press, Cambridge, UK.
  52. Newell, D. G., K. T. Elvers, D. Dopfer, I. Hansson, P. Jones, S. James, J. Gittins, N. J. Stern, R. Davies, I. Connerton, D. Pearson, G. Salvat, and V. M. Allen. 2011. Biosecurity-based interventions and strategies to reduce Campylobacter spp. on poultry farms. Appl. Environ. Microbiol. 77:8605-8614. https://doi.org/10.1128/AEM.01090-10
  53. Nielsen, H. B., M. Almeida, A. S. Juncker, S. Rasmussen, J. Li, S. Sunagawa, D. R. Plichta, L. Gautier, A. G. Pedersen, and E. Le Chatelier et al. 2014. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32:822-828. https://doi.org/10.1038/nbt.2939
  54. Oakley, B. B., H. S. Lillehoj, M. H. Kogut, W. K. Kim, J. J. Maurer, A. Pedroso, M. D. Lee, S. R. Collett, T. J. Johnson, and N. A. Cox. 2014. The chicken gastrointestinal microbiome. FEMS Microbiol. Lett. 360:100-112. https://doi.org/10.1111/1574-6968.12608
  55. Pedroso, A. A., A. L. Hurley-Bacon, A. S. Zedek, T. W. Kwan, A. P. O. Jordan, G. Avellaneda, C. L. Hofacre, B. B. Oakley, S. R. Collett, J. J. Maurer, and M. D. Lee. 2013. Can probiotics improve the environmental microbiome and resistome of commercial poultry production? Int. J. Environ. Res. Public Health 10:4534-4559. https://doi.org/10.3390/ijerph10104534
  56. Pereyra, L. P., S. R. Hiibel, M. V. Prieto Riquelme, K. F. Reardon, and A. Pruden. 2010. Detection and quantification of functional genes of cellulose- degrading, fermentative, and sulfate-reducing bacteria and methanogenic archaea. Appl. Environ. Microbiol. 76:2192-2202. https://doi.org/10.1128/AEM.01285-09
  57. Powell, S., K. Forslund, D. Szklarczyk, K. Trachana, A. Roth, J. Huerta-Cepas, T. Gabaldon, T. Rattei, C. Creevey, M. Kuhn, L. J. Jensen, C. von Mering and P. Bork. 2014. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucl. Acids Res. 42:D231-239. https://doi.org/10.1093/nar/gkt1253
  58. Pruesse, E., C. Quast, K. Knittel, B. M. Fuchs, W. Ludwig, J. Peplies, and F. O. Glockner. 2007. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35:7188-7196. https://doi.org/10.1093/nar/gkm864
  59. Purschke, O., B. C. Schmid, M. T. Sykes, P. Poschlod, S. G. Michalski, W. Durka, I. Kuhn, M. Winter, and H. C. Prentice. 2013. Contrasting changes in taxonomic, phylogenetic and functional diversity during a long-term succession: Insights into assembly processes. J. Ecol. 101:857-866. https://doi.org/10.1111/1365-2745.12098
  60. Qu, A., J. M. Brulc, M. K. Wilson, B. F. Law, J. R. Theoret, L. A. Joens, M. E. Konkel, F. Angly, E. A. Dinsdale, R. A. Edwards, K. E. Nelson, and B. A. White. 2008. Comparative metagenomics reveals host specific metavirulomes and horizontal gene transfer elements in the chicken cecum microbiome. PLoS One 3(8):e2945. https://doi.org/10.1371/journal.pone.0002945
  61. Schloss, P. D., S. L. Westcott, T. Ryabin, J. R. Hall, M. Hartmann, E. B. Hollister, R. A. Lesniewski, B. B. Oakley, D. H. Parks, C. J. Robinson, J. W. Sahl, B. Stres, G. G. Thallinger, D. J. Van Horn, and C. F. Weber. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75:7537-7541. https://doi.org/10.1128/AEM.01541-09
  62. Sergeant, M. J., C. Constantinidou, T. A. Cogan, M. R. Bedford, C. W. Penn, and M. J. Pallen. 2014. Extensive microbial and functional diversity within the chicken cecal microbiome. PLoS ONE 9(3):e91941. https://doi.org/10.1371/journal.pone.0091941
  63. Skraban, J., S. Dzeroski, B. Zenko, L. Tusar, and M. Rupnik. 2013. Changes of poultry faecal microbiota associated with Clostridium difficile colonisation. Vet. Microbiol. 165:416-424. https://doi.org/10.1016/j.vetmic.2013.04.014
  64. Smith, C. J. and A. M. Osborn. 2009. Advantages and limitations of quantitative PCR (Q-PCR)-based approaches in microbial ecology. FEMS. Microbiol. Ecol. 67:6-20. https://doi.org/10.1111/j.1574-6941.2008.00629.x
  65. Sogin, M. L., H. G. Morrison, J. A. Huber, D. Mark Welch, S. M. Huse, P. R. Neal, J. M. Arrieta, and G. J. Herndl. 2006. Microbial diversity in the deep sea and the underexplored "rare biosphere". Proc. Natl. Acad. Sci. USA 103:12115-12120 https://doi.org/10.1073/pnas.0605127103
  66. Stanley, D., R. J. Hughes, and R. J. Moore. 2014. Microbiota of the chicken gastrointestinal tract: Influence on health, productivity and disease. Appl. Microbiol. Biotechnol. 98:4301-4310. https://doi.org/10.1007/s00253-014-5646-2
  67. Stedtfeld, R. D., S. W. Baushke, D. M. Tourlousse, S. M. Miller, T. M. Stedtfeld, E. Gulari, J. M. Tiedje, and S. A. Hashsham. 2008. Development and experimental validation of a predictive threshold cycle equation for quantification of virulence and marker genes by high-throughput nanoliter-volume PCR on the OpenArray platform. Appl. Environ. Microbiol. 74:3831-3838. https://doi.org/10.1128/AEM.02743-07
  68. Sul, W. J., J. R. Cole, E. D. Jesus, Q. Wang, R. J. Farris, J. A. Fish, and J. M. Tiedje. 2011. Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering. Proc. Natl. Acad. Sci. USA 108:14637-14642. https://doi.org/10.1073/pnas.1111435108
  69. Tang, Y., A. Underwood, A. Gielbert, M. J. Woodward, and L. Petrovska. 2014. Metaproteomics analysis reveals the adaptation process for the chicken gut microbiota. Appl. Environ. Microbiol. 80:478-485. https://doi.org/10.1128/AEM.02472-13
  70. Thomas, T., J. Gilbert, and F. Meyer. 2012. Metagenomics - A guide from sampling to data analysis. Microb. Inform. Exp. 2:3. https://doi.org/10.1186/2042-5783-2-3
  71. Turnbaugh, P. J., M. Hamady, T. Yatsunenko, B. L. Cantarel, A. Duncan, R. E. Ley, M. L. Sogin, W. J. Jones, B. A. Roe, J. P. Affourtit, M. Egholm, B. Henrissat, A. C. Heath, R. Knight, and J. I. Gordon. 2008. A core gut microbiome in obese and lean twins. Nature 457:480-484.
  72. Videnska, P., F. Sisak, H. Havlickova, M. Faldynova, and I. Rychlik. 2013. Influence of Salmonella enterica serovar Enteritidis infection on the composition of chicken cecal microbiota. BMC Vet. Res. 9:140. https://doi.org/10.1186/1746-6148-9-140
  73. Wang, Q., J. F. Quensen, J. A. Fish, T. K. Lee, Y. Sun, J. M. Tiedje, and J. R. Cole. 2013. Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool. mBio. 4:e00592-13.
  74. Xin, H., R. S. Gates, A. R. Green, F. M. Mitloehner, P. A. Moore Jr., and C. M. Wathes. 2011. Environmental impacts and sustainability of egg production systems. Poult. Sci. 90:263-277. https://doi.org/10.3382/ps.2010-00877
  75. Xu, Z. R., C. H. Hu, M. S. Xia, X. A. Zhan, and M. Q. Wang. 2003. Effects of dietary fructooligosaccharide on digestive enzyme activities, intestinal microflora and morphology of male broilers. Poult. Sci. 82:1030-1036. https://doi.org/10.1093/ps/82.6.1030
  76. Yeoman, C. J., N. Chia, P. Jeraldo, M. Sipos, N. D. Goldenfeld, and B. A. White. 2012. The microbiome of the chicken gastrointestinal tract. Anim. Health Res. Rev. 13:89-99. https://doi.org/10.1017/S1466252312000138
  77. Zhou, H., J. Gong, J. T. Brisbin, H. Yu, B. Sanei, P. Sabour, and S. Sharif. 2007. Appropriate chicken sample size for identifying the composition of broiler intestinal microbiota affected by dietary antibiotics, using the polymerase chain reactiondenaturing gradient gel electrophoresis technique. Poult. Sci. 86:2541-2549. https://doi.org/10.3382/ps.2007-00267

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  6. Characterization of the Culturable Subpopulations of Lactobacillus in the Chicken Intestinal Tract as a Resource for Probiotic Development vol.8, pp.1664-302X, 2017, https://doi.org/10.3389/fmicb.2017.01389
  7. Cecal microbiome divergence of broiler chickens by sex and body weight vol.55, pp.12, 2017, https://doi.org/10.1007/s12275-017-7202-0
  8. Effect of direct-fed microbials on culturable gut microbiotas in broiler chickens: a meta-analysis of controlled trials vol.31, pp.11, 2018, https://doi.org/10.5713/ajas.18.0009
  9. Regulation of CD4+CD8−CD25+ and CD4+CD8+CD25+ T cells by gut microbiota in chicken vol.8, pp.1, 2018, https://doi.org/10.1038/s41598-018-26763-0
  10. Chicken Gut Microbiome and Human Health: Past Scenarios, Current Perspectives, and Futuristic Applications pp.0973-7715, 2019, https://doi.org/10.1007/s12088-019-00785-2
  11. Insights into Broilers' Gut Microbiota Fed with Phosphorus, Calcium, and Phytase Supplemented Diets vol.7, pp.None, 2016, https://doi.org/10.3389/fmicb.2016.02033
  12. L-Glutamine Supplementation Alleviates Constipation during Late Gestation of Mini Sows by Modifying the Microbiota Composition in Feces vol.2017, pp.None, 2017, https://doi.org/10.1155/2017/4862861
  13. Insight Into Dynamics of Gut Microbial Community of Broilers Fed With Fructooligosaccharides Supplemented Low Calcium and Phosphorus Diets vol.6, pp.None, 2015, https://doi.org/10.3389/fvets.2019.00095
  14. Functioning of the Intestinal Ecosystem: From New Technologies in Microbial Research to Practical Poultry Feeding - A Review vol.19, pp.2, 2019, https://doi.org/10.2478/aoas-2019-0007
  15. The Role of Housing Environment and Dietary Protein Source on the Gut Microbiota of Chicken vol.9, pp.12, 2015, https://doi.org/10.3390/ani9121085
  16. Enfoque metagenómico para la caracterización del microbioma de aves corral. Revisión vol.21, pp.2, 2015, https://doi.org/10.15446/rev.colomb.biote.v21n2.78390
  17. Effect of probiotic strains of Bacillus subtilis on the growth parameters of broiler chickens and caecal microbiota vol.222, pp.None, 2020, https://doi.org/10.1051/e3sconf/202022202054
  18. Effects of Antimicrobial peptides on egg production, egg quality and caecal microbiota of hens during the late laying period vol.91, pp.1, 2015, https://doi.org/10.1111/asj.13387
  19. Effects of supplementing freeze‐dried Mitsuokella jalaludinii phytase on the growth performance and gut microbial diversity of broiler chickens vol.104, pp.1, 2015, https://doi.org/10.1111/jpn.13208
  20. Effects of yeast cultures with different fermentation times on the growth performance, caecal microbial community and metabolite profile of broilers vol.104, pp.1, 2015, https://doi.org/10.1111/jpn.13241
  21. Translating ‘big data’: better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry vol.21, pp.1, 2015, https://doi.org/10.1017/s1466252319000124
  22. Respiratory and Gut Microbiota in Commercial Turkey Flocks with Disparate Weight Gain Trajectories Display Differential Compositional Dynamics vol.86, pp.12, 2015, https://doi.org/10.1128/aem.00431-20
  23. Sex differences in growth performance are related to cecal microbiota in chicken vol.150, pp.None, 2015, https://doi.org/10.1016/j.micpath.2020.104710
  24. Effects of alkaline protease on the production performance, egg quality, and cecal microbiota of hens during late laying period vol.92, pp.1, 2015, https://doi.org/10.1111/asj.13658
  25. Ajwain (Trachyspermum copticum) extract in broiler diets: effect on growth performance, carcass components, plasma constituents, immunity and cecum microflora vol.20, pp.1, 2021, https://doi.org/10.1080/1828051x.2021.1926347
  26. Functional Amino Acids in Pigs and Chickens: Implication for Gut Health vol.8, pp.None, 2021, https://doi.org/10.3389/fvets.2021.663727
  27. Effects of Dietary Maltol on Innate Immunity, Gut Health, and Growth Performance of Broiler Chickens Challenged With Eimeria maxima vol.8, pp.None, 2015, https://doi.org/10.3389/fvets.2021.667425
  28. Influenza A virus infection in turkeys induces respiratory and enteric bacterial dysbiosis correlating with cytokine gene expression vol.9, pp.None, 2021, https://doi.org/10.7717/peerj.11806
  29. Supplemental Bacillus subtilis PB6 Improves Growth Performance and Gut Health in Broilers Challenged with Clostridium perfringens vol.2021, pp.None, 2021, https://doi.org/10.1155/2021/2549541
  30. Bacillus subtilis delivery route: effect on growth performance, intestinal morphology, cecal short-chain fatty acid concentration, and cecal microbiota in broiler chickens vol.100, pp.3, 2015, https://doi.org/10.1016/j.psj.2020.10.063
  31. Absence of Circadian Rhythm in Fecal Microbiota of Laying Hens under Common Light vol.11, pp.7, 2015, https://doi.org/10.3390/ani11072065
  32. Comparative analysis of chicken cecal microbial diversity and taxonomic composition in response to dietary variation using 16S rRNA amplicon sequencing vol.48, pp.11, 2015, https://doi.org/10.1007/s11033-021-06712-3
  33. Bacterial communities of the upper respiratory tract of turkeys vol.11, pp.1, 2015, https://doi.org/10.1038/s41598-021-81984-0
  34. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota vol.11, pp.1, 2021, https://doi.org/10.1038/s41598-021-82726-y
  35. Choice of 16S ribosomal RNA primers affects the microbiome analysis in chicken ceca vol.11, pp.1, 2021, https://doi.org/10.1038/s41598-021-91387-w
  36. Clinical Parasitology and Parasitome Maps as Old and New Tools to Improve Clinical Microbiomics vol.10, pp.12, 2021, https://doi.org/10.3390/pathogens10121550