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RESEARCH ARTICLE (Open Access)

Ruminal microbiota is associated with feed-efficiency phenotype of fattening bulls fed high-concentrate diets

S. Costa-Roura A , D. Villalba https://orcid.org/0000-0001-8919-0450 A D , M. Blanco B C , I. Casasús https://orcid.org/0000-0003-3943-5311 B C , J. Balcells https://orcid.org/0000-0002-2126-7375 A and A. R. Seradj https://orcid.org/0000-0001-8104-4571 A
+ Author Affiliations
- Author Affiliations

A Departament de Ciència Animal, Universitat de Lleida, Avinguda Alcalde Rovira Roure 191, 25198, Lleida, Spain.

B Unidad de Producción y Sanidad Animal, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Avenida Montañana 930, 50059, Zaragoza, Spain.

C Instituto Agroalimentario de Aragón – IA2 (CITA-Universidad de Zaragoza), Zaragoza, Spain.

D Corresponding author. Email: daniel.villalba@udl.cat

Animal Production Science - https://doi.org/10.1071/AN20344
Submitted: 5 June 2020  Accepted: 27 November 2020   Published online: 28 January 2021

Journal Compilation © CSIRO 2021 Open Access CC BY

Abstract

Context: Improving feed efficiency in livestock production is of great importance to reduce feeding costs.

Aims: To examine the relationship between ruminal microbiota and variation in feed efficiency in beef cattle fed concentrate-based diets.

Methods: Residual feed intake of 389 fattening bulls, supplied with corn-based concentrate and forage ad libitum, was used to estimate animals’ feed efficiency. Faeces and ruminal fluid samples, from 48 bulls chosen at random, were collected to estimate their forage intake and to determine their apparent digestibility, ruminal fermentation and microbiota. Those animals with extreme values of feed efficiency (high-efficiency (HE, n = 12) and low-efficiency (LE, n = 13)) were subjected to further comparisons. Alpha biodiversity was calculated on the basis of the normalised sequence data. Beta diversity was approached through performing a canonical correspondence analysis based on log-transformed sequence data. Genera differential abundance was tested with an ANOVA-like differential expression analysis and genera interactions were determined applying the sparse correlations for compositional data technique.

Key results: No differences in dry matter intake were found between the two categories of feed efficiency (P = 0.699); however, HE animals had higher apparent digestibility of dry matter (P = 0.002), organic matter (P = 0.003) and crude protein (P = 0.043). The concentration of volatile fatty acids was unaffected by feed efficiency (P = 0.676) but butyrate proportion increased with time in LE animals (P = 0.047). Ruminal microbiota was different between HE and LE animals (P = 0.022); both α biodiversity and genera network connectance increased with time in LE bulls (P = 0.005 for Shannon index and P = 0.020 for Simpson index), which suggests that LE animals hosted a more robust ruminal microbiota. Certain genera usually related to high energy loss through methane production were found to establish more connections with other genera in LE animals’ rumen than in HE ones. Microbiota function capability suggested that methane metabolism was decreased in HE finishing bulls.

Conclusions: Rumen microbiota was associated with feed efficiency phenotypes in fattening bulls fed concentrate-based diets.

Implications: The possible trade-off between feed efficiency and robustness of ruminal microbiota should be taken into account for the optimisation of cattle production, especially in systems with intrinsic characteristics that may constitute a disturbance to rumen microbial community.

Keywords: apparent digestibility, beef cattle, feed efficiency, rumen microbial community.


References

Arthur JPF, Herd RM (2008) Residual feed intake in beef cattle. Revista Brasileira de Zootecnia 37, 269–279.
Residual feed intake in beef cattle.Crossref | GoogleScholarGoogle Scholar |

Arthur PF, Archer JA, Johnston DJ, Herd RM, Richardson EC, Parnell PF (2001) Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. Journal of Animal Science 79, 2805–2811.
Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle.Crossref | GoogleScholarGoogle Scholar | 11768108PubMed |

Bergman EN (1990) Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiological Reviews 70, 567–590.
Energy contributions of volatile fatty acids from the gastrointestinal tract in various species.Crossref | GoogleScholarGoogle Scholar | 2181501PubMed |

Brulc JM, Antonopoulos DA, Berg Miller ME, Wilson MK, Yannarell AC, Dinsdale EA, Edwards RE, Frank ED, Emerson JB, Wacklin P, Coutinho PM, Henrissat B, Nelson KE, White BA (2009) Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proceedings of the National Academy of Sciences of the United States of America 106, 1948–1953.
Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases.Crossref | GoogleScholarGoogle Scholar | 19181843PubMed |

Calle ML (2019) Statistical analysis of metagenomics data. Genomics & Informatics 17, e6
Statistical analysis of metagenomics data.Crossref | GoogleScholarGoogle Scholar |

Costa-Roura S, Balcells J, de la Fuente G, Mora-Gil J, Llanes N, Villalba D (2020) Nutrient utilization efficiency, ruminal fermentation and microbial community in Holstein bulls fed concentrate-based diets with different forage source. Animal Feed Science and Technology 269, 114662
Nutrient utilization efficiency, ruminal fermentation and microbial community in Holstein bulls fed concentrate-based diets with different forage source.Crossref | GoogleScholarGoogle Scholar |

de Assis Lage CF, Gesteira Coelho S, Diniz Neto H do C, Rocha Malacco VM, Pacheco Rodrigues JP, Sacramento JP, Samarini Machado F, Ribeiro Pereira LG, Ribeiro Tomich T, Magalhães Campos M (2019) Relationship between feed efficiency indexes and performance, body measurements, digestibility, energy partitioning, and nitrogen partitioning in pre-weaning dairy heifers. PLoS One 14, e0223368
Relationship between feed efficiency indexes and performance, body measurements, digestibility, energy partitioning, and nitrogen partitioning in pre-weaning dairy heifers.Crossref | GoogleScholarGoogle Scholar | 31600254PubMed |

Delgado B, Bach A, Guasch I, González C, Elcoso G, Pryce JE, Gonzalez-Recio O (2019) Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Scientific Reports 9, 11
Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle.Crossref | GoogleScholarGoogle Scholar | 30626904PubMed |

Dunne JA, Williams RJ, Martinez ND (2002) Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecology Letters 5, 558–567.
Network structure and biodiversity loss in food webs: robustness increases with connectance.Crossref | GoogleScholarGoogle Scholar |

Elolimy A, Alharthi A, Zeineldin M, Parys C, Loor JJ (2020) Residual feed intake divergence during the preweaning period is associated with unique hindgut microbiome and metabolome profiles in neonatal Holstein heifer calves. Journal of Animal Science and Biotechnology 11, 13
Residual feed intake divergence during the preweaning period is associated with unique hindgut microbiome and metabolome profiles in neonatal Holstein heifer calves.Crossref | GoogleScholarGoogle Scholar | 31988748PubMed |

Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB (2013) ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-seq. PLoS One 8, e67019
ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-seq.Crossref | GoogleScholarGoogle Scholar | 23843979PubMed |

Friedman J, Alm EJ (2012) Inferring correlation networks from genomic survey data. PLoS Computational Biology 8, e1002687
Inferring correlation networks from genomic survey data.Crossref | GoogleScholarGoogle Scholar | 23028285PubMed |

Ghanbari Maman L, Palizban F, Fallah Atanaki F, Elmi Ghiasi N, Ariaeenejad S, Ghaffari MR, Hosseini Salekdeh G, Kavousi K (2020) Co-abundance analysis reveals hidden players associated with high methane yield phenotype in sheep rumen microbiome. Scientific Reports 10, 4995
Co-abundance analysis reveals hidden players associated with high methane yield phenotype in sheep rumen microbiome.Crossref | GoogleScholarGoogle Scholar | 32193482PubMed |

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ (2017) Microbiome datasets are compositional: and this is not optional. Frontiers in Microbiology 8, 2224
Microbiome datasets are compositional: and this is not optional.Crossref | GoogleScholarGoogle Scholar | 29187837PubMed |

Herd RM, Arthur PF (2009) Physiological basis for residual feed intake. Journal of Animal Science 87, E64–E71.
Physiological basis for residual feed intake.Crossref | GoogleScholarGoogle Scholar | 19028857PubMed |

Hernandez-Urdaneta A, Coppock CE, McDowell RE, Gianola D, Smith NE (1976) Changes in forage-concentrate ratio of complete feeds for dairy cows. Journal of Dairy Science 59, 695–707.
Changes in forage-concentrate ratio of complete feeds for dairy cows.Crossref | GoogleScholarGoogle Scholar |

Huntington G (1990) Energy metabolism in the digestive tract and liver of cattle: influence of physiological state and nutrition. Reproduction, Nutrition, Development 30, 35–47.
Energy metabolism in the digestive tract and liver of cattle: influence of physiological state and nutrition.Crossref | GoogleScholarGoogle Scholar | 2184823PubMed |

Kittelmann S, Pinares-Patiño CS, Seedorf H, Kirk MR, Ganesh S, McEwan JC, Janssen PH (2014) Two different bacterial community types are linked with the low-methane emission trait in sheep. PLoS One 9, e103171
Two different bacterial community types are linked with the low-methane emission trait in sheep.Crossref | GoogleScholarGoogle Scholar | 25383707PubMed |

Li F, Zhou M, Ominski K, Guan LL (2016) Does the rumen microbiome play a role in feed efficiency of beef cattle? Journal of Animal Science 94, 44–48.
Does the rumen microbiome play a role in feed efficiency of beef cattle?Crossref | GoogleScholarGoogle Scholar |

McCann KS (2000) The diversity–stability debate. Nature 405, 228–233.
The diversity–stability debate.Crossref | GoogleScholarGoogle Scholar | 10821283PubMed |

McCann JC, Wiley LM, Forbes TD, Rouquette FM, Tedeschi LO (2014) Relationship between the rumen microbiome and residual feed intake-efficiency of Brahman bulls stocked on bermudagrass pastures. PLoS One 9, e91864
Relationship between the rumen microbiome and residual feed intake-efficiency of Brahman bulls stocked on bermudagrass pastures.Crossref | GoogleScholarGoogle Scholar | 24642871PubMed |

Moya A, Ferrer M (2016) Functional redundancy-induced stability of gut microbiota subjected to disturbance. Trends in Microbiology 24, 402–413.
Functional redundancy-induced stability of gut microbiota subjected to disturbance.Crossref | GoogleScholarGoogle Scholar | 26996765PubMed |

Myer PR, Smith TPL, Wells JE, Kuehn LA, Freetly HC (2015) Rumen microbiome from steers differing in feed efficiency. PLoS One 10, e0129174
Rumen microbiome from steers differing in feed efficiency.Crossref | GoogleScholarGoogle Scholar | 26030887PubMed |

Negesse T, Datt C, Kundu SS (2017) Residual feed intake, digestibility of nutrients and efficiency of water utilizations in Murrah buffalo heifers. Journal of Dairy, Veterinary & Animal Research 5, 74–80.
Residual feed intake, digestibility of nutrients and efficiency of water utilizations in Murrah buffalo heifers.Crossref | GoogleScholarGoogle Scholar |

Nkrumah JD, Okine EK, Mathison GW, Schmid K, Li C, Basarab JA, Price MA, Wang Z, Moore SS (2006) Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. Journal of Animal Science 84, 145–153.
Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle.Crossref | GoogleScholarGoogle Scholar | 16361501PubMed |

Owens FN, Hanson CF (1992) External and internal markers for appraising site and extent of digestion in ruminants. Journal of Dairy Science 75, 2605–2617.
External and internal markers for appraising site and extent of digestion in ruminants.Crossref | GoogleScholarGoogle Scholar | 1452861PubMed |

Perea K, Perz K, Olivo SK, Williams A, Lachman M, Ishaq SL, Thomson J, Yeoman CJ (2017) Feed efficiency phenotypes in lambs involve changes in ruminal, colonic, and small-intestine-located microbiota. Journal of Animal Science 95, 2585–2592.
Feed efficiency phenotypes in lambs involve changes in ruminal, colonic, and small-intestine-located microbiota.Crossref | GoogleScholarGoogle Scholar | 28727071PubMed |

R Core Team (2020) ‘R: a language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna, Austria)

Richardson EC, Herd RM, Arthur PF, Wright J, Xu G, Dibley K, Oddy VH (1996) Possible physiological indicators for net feed conversion efficiency in beef cattle. Proceedings of the Australian Society of Animal Production 21, 103–106.

Savietto D, Berry DP, Friggens NC (2014) Towards an improved estimation of the biological components of residual feed intake in growing cattle. Journal of Animal Science 92, 467–476.
Towards an improved estimation of the biological components of residual feed intake in growing cattle.Crossref | GoogleScholarGoogle Scholar | 24664556PubMed |

Schenkel FS, Miller SP, Wilton JW (2004) Genetic parameters and breed differences for feed efficiency, growth, and body composition traits of young beef bulls. Canadian Journal of Animal Science 84, 177–185.
Genetic parameters and breed differences for feed efficiency, growth, and body composition traits of young beef bulls.Crossref | GoogleScholarGoogle Scholar |

Shabat SKB, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Berg Miller ME, White BA, Shterzer N, Mizrahi I (2016) Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. The ISME Journal 10, 2958–2972.
Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants.Crossref | GoogleScholarGoogle Scholar |

Shi W, Moon CD, Leahy SC, Kang D, Froula J, Kittelmann S, Fan C, Deutsch S, Gagic D, Seedorf H, Kelly WJ, Atua R, Sang C, Soni P, Li D, Pinares-Patiño CS, McEwan JC, Janssen PH, Chen F, Visel A, Wang Z, Attwood GT, Rubin EM (2014) Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome. Genome Research 24, 1517–1525.
Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome.Crossref | GoogleScholarGoogle Scholar | 24907284PubMed |

Tsilimigras MCB, Fodor AA (2016) Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Annals of Epidemiology 26, 330–335.
Compositional data analysis of the microbiome: fundamentals, tools, and challenges.Crossref | GoogleScholarGoogle Scholar |

Ungerfeld EM (2020) Metabolic hydrogen flows in rumen fermentation: principles and possibilities of interventions. Frontiers in Microbiology 11, 589
Metabolic hydrogen flows in rumen fermentation: principles and possibilities of interventions.Crossref | GoogleScholarGoogle Scholar | 32351469PubMed |

Weimer PJ, Stevenson DM, Mantovani HC, Man SLC (2010) Host specificity of the ruminal bacterial community in the dairy cow following near-total exchange of ruminal contents. Journal of Dairy Science 93, 5902–5912.
Host specificity of the ruminal bacterial community in the dairy cow following near-total exchange of ruminal contents.Crossref | GoogleScholarGoogle Scholar | 21094763PubMed |

Woloszynek S, Mell JC, Zhao Z, Simpson G, O’Connor MP, Rosen GL (2019) Exploring thematic structure and predicted functionality of 16S rRNA amplicon data. PLoS One 14, e0219235
Exploring thematic structure and predicted functionality of 16S rRNA amplicon data.Crossref | GoogleScholarGoogle Scholar | 31825995PubMed |

Yuste S, Amanzougarene Z, de Vega A, Fondevila M, Blanco M, Casasús I (2020) Effect of preweaning diet on performance, blood metabolites and rumen fermentation around weaning in calves of two beef breeds. Animal Production Science 60, 1018–1027.
Effect of preweaning diet on performance, blood metabolites and rumen fermentation around weaning in calves of two beef breeds.Crossref | GoogleScholarGoogle Scholar |