Abstract
Residual feed intake (RFI) is a widely used indicator of feed efficiency in cattle; however, its relationship with the fecal microbiota of Hanwoo, a Korean native cattle breed, has not yet been explored. This study aimed to examine the fecal microbiota composition of Hanwoo steers exhibiting divergent RFI at the fattening stage. Sixty-three Hanwoo steers were raised under the same dietary and environmental conditions and fed a total mixed ration. A 78-day feeding trial was conducted (from 19 to 21 months of age), during which growth performance was evaluated. Steers were ranked by RFI, and those with RFI values < − 0.6 or > 0.6 were selected to represent two extreme RFI phenotypes: low RFI (L-RFI; efficient; −0.96 ± 0.14, n = 6) and high RFI (H-RFI; inefficient; 0.96 ± 0.48, n = 5). Fecal samples were collected from both L-RFI and H-RFI steers at the end of the feeding trial for metataxonomic analysis using 16S rRNA amplicon sequencing and the QIIME2 pipeline. Average daily gain (ADG) and body weight were similar between the two groups, but dry matter intake (DMI) and RFI values were significantly higher in H-RFI steers. Bacterial alpha- or beta-diversity did not differ significantly between the two RFI groups. Linear Discriminant Analysis (LDA) coupled with Effect Size measurements (LEfSe) revealed that the phylum Verrucomicrobiota and its representative genus Akkermansia were the most abundant in the L-RFI group (LDA score > 2, P < 0.05). In contrast, the genera Acetitomaculum and Kandleria were the most abundant in the H-RFI group (LDA score > 2, P < 0.05). Functional analysis based on PICRUSt2 predictions revealed that H-RFI steers had higher abundances of genes associated with carbohydrate utilization and amino acid biosynthesis compared to L-RFI steers (LDA score > 2, P < 0.05). The findings of this study demonstrate that feed efficiency is associated with the fecal microbial composition and functional features in Hanwoo steers, highlighting the importance of microbial characteristics in nutrient utilization and production efficiency.
Data availability
The raw 16 S rRNA amplicon reads are available in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1348594.
References
Herd, R. M. & Arthur, P. F. Physiological basis for residual feed intake. J. Anim. Sci. 87, E64–71. https://doi.org/10.2527/jas.2008-1345 (2009).
Koch, R. M., Swiger, L. A., Chambers, D. & Gregory, K. E. Efficiency of feed use in beef cattle. J. Anim. Sci. 22, 486–494 (1963).
Berry, D. P. & Crowley, J. J. Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle. J. Anim. Sci. 91, 1594–1613. https://doi.org/10.2527/jas.2012-5862 (2013).
Manzanilla-Pech, C. I. V., Stephansen, R. B., Difford, G. F., Lovendahl, P. & Lassen, J. Selecting for feed efficient cows will help to reduce methane gas emissions. Front. Genet. 13, 885932. https://doi.org/10.3389/fgene.2022.885932 (2022).
Freetly, H. C. et al. Digestive tract microbiota of beef cattle that differed in feed efficiency. J. Anim. Sci. 98. https://doi.org/10.1093/jas/skaa008 (2020).
Kim, M., Morrison, M. & Yu, Z. Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol. Ecol. 76, 49–63. https://doi.org/10.1111/j.1574-6941.2010.01029.x (2011).
Kim, M. & Wells, J. E. A Meta-analysis of bacterial diversity in the feces of cattle. Curr. Microbiol. 72, 145–151. https://doi.org/10.1007/s00284-015-0931-6 (2016).
Henderson, G. et al. Rumen microbial community composition varies with diet and host, but a core Microbiome is found across a wide geographical range. Sci. Rep. 5 https://doi.org/10.1038/srep14567 (2015).
Kim, M. et al. Investigation of bacterial diversity in the feces of cattle fed different diets. J. Anim. Sci. 92, 683–694. https://doi.org/10.2527/jas.2013-6841 (2014).
Li, F., Hitch, T. C. A., Chen, Y., Creevey, C. J. & Guan, L. L. Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen Microbiome and its associations with feed efficiency in beef cattle. Microbiome 7, 6. https://doi.org/10.1186/s40168-019-0618-5 (2019).
Lee, S. et al. Effects of different feeding systems on ruminal fermentation, digestibility, methane emissions, and microbiota of Hanwoo steers. J. Anim. Sci. Technol. 65, 1270–1289. https://doi.org/10.5187/jast.2023.e82 (2023).
Jami, E., Israel, A., Kotser, A. & Mizrahi, I. Exploring the bovine rumen bacterial community from birth to adulthood. Isme J. 7, 1069–1079. https://doi.org/10.1038/ismej.2013.2 (2013).
Kim, M. Invited Review - Assessment of the Gastrointestinal microbiota using 16S ribosomal RNA gene amplicon sequencing in ruminant nutrition. Anim. Biosci. 36, 364–373. https://doi.org/10.5713/ab.22.0382 (2023).
Li, F. et al. Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome 7, 92. https://doi.org/10.1186/s40168-019-0699-1 (2019).
Myer, P. R., Smith, T. P., Wells, J. E., Kuehn, L. A. & Freetly, H. C. Rumen Microbiome from steers differing in feed efficiency. PLoS One. 10, e0129174. https://doi.org/10.1371/journal.pone.0129174 (2015).
Paz, H. A. et al. Rumen bacterial community structure impacts feed efficiency in beef cattle. J. Anim. Sci. 96, 1045–1058. https://doi.org/10.1093/jas/skx081 (2018).
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. 5, 16116. https://doi.org/10.1038/srep16116 (2015).
Wells, J. E., Kim, M., Bono, J. L., Kuehn, L. A. & Benson, A. K. Meat science and muscle biology symposium: Escherichia coli O157:H7, diet, and fecal Microbiome in beef cattle. J. Anim. Sci. 92, 1345–1355. https://doi.org/10.2527/jas.2013-7282 (2014).
Hu, X. et al. Gastrointestinal biogeography of luminal microbiota and Short-Chain fatty acids in Sika deer (Cervus nippon). Appl. Environ. Microbiol. 88, e0049922. https://doi.org/10.1128/aem.00499-22 (2022).
Lourenco, J. M. et al. Fecal microbiome differences in angus steers with differing feed efficiencies during the feedlot-finishing phase. Microorganisms 10, (2022). https://doi.org/10.3390/microorganisms10061128
Welch, C. B. et al. Evaluation of the fecal bacterial communities of Angus steers with divergent feed efficiencies across the lifespan from weaning to slaughter. Front. Vet. Sci. 8, 597405. https://doi.org/10.3389/fvets.2021.597405 (2021).
Ortiz-Chura, A. et al. Rumen microbiota associated with feed efficiency in beef cattle are highly influenced by diet composition. Anim. Nutr. 21, 378–389. https://doi.org/10.1016/j.aninu.2024.11.027 (2025).
Nugrahaeningtyas, E., Lee, J. S., Lee, D. J., Kim, J. K. & Park, K. H. Impacts of guidelines transition on greenhouse gas inventory in the livestock sector: a study case of Korea. J. Anim. Sci. Technol. 67, 453–467. https://doi.org/10.5187/jast.2024.e7 (2025).
Arthur, P. F. et al. Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. J. Anim. Sci. 79, 2805–2811. https://doi.org/10.2527/2001.79112805x (2001).
Welch, C. B. et al. The impact of feed efficiency selection on the ruminal, cecal, and fecal microbiomes of Angus steers from a commercial feedlot. J. Anim. Sci. 98 https://doi.org/10.1093/jas/skaa230 (2020).
Gonzalez, D., Morales-Olavarria, M., Vidal-Veuthey, B. & Cardenas, J. P. Insights into early evolutionary adaptations of the Akkermansia genus to the vertebrate gut. Front. Microbiol. 14, 1238580. https://doi.org/10.3389/fmicb.2023.1238580 (2023).
Belzer, C. & de Vos, W. M. Microbes inside–from diversity to function: the case of Akkermansia. Isme J. 6, 1449–1458. https://doi.org/10.1038/ismej.2012.6 (2012).
de Vos, W. M. Microbe profile: Akkermansia muciniphila: a conserved intestinal symbiont that acts as the gatekeeper of our mucosa. Microbiol. (Reading). 163, 646–648. https://doi.org/10.1099/mic.0.000444 (2017).
Greening, R. C. & Leedle, J. A. Enrichment and isolation of acetitomaculum ruminis, gen. nov., sp. nov.: acetogenic bacteria from the bovine rumen. Arch. Microbiol. 151, 399–406. https://doi.org/10.1007/BF00416597 (1989).
Kumar, S. et al. Sharpea and Kandleria are lactic acid producing rumen bacteria that do not change their fermentation products when co-cultured with a methanogen. Anaerobe 54, 31–38. https://doi.org/10.1016/j.anaerobe.2018.07.008 (2018).
Fregulia, P., Neves, A. L. A., Dias, R. J. P. & Campos, M. M. A review of rumen parameters in bovines with divergent feed efficiencies: what do these parameters tell Us about improving animal productivity and sustainability? Livest. Sci. 254, 104761 (2021).
Hegarty, R. S., Goopy, J. P., Herd, R. M. & McCorkell, B. Cattle selected for lower residual feed intake have reduced daily methane production. J. Anim. Sci. 85, 1479–1486. https://doi.org/10.2527/jas.2006-236 (2007).
Nkrumah, J. D. et al. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84, 145–153. https://doi.org/10.2527/2006.841145x (2006).
AOAC International. Official Methods of Analysis of AOAC International 21st edn (AOAC International, 2019).
Van Soest, P. J., Robertson, J. B. & Lewis, B. A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy. Sci. 74, 3583–3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2 (1991).
National Academies of Sciences, Engineering, and Medicine. Nutrient Requirements of Beef Cattle: Eighth Revised Edition (National Academies, 2016).
Yu, Z. T. & Morrison, M. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques 36, 808–812. https://doi.org/10.2144/04365st04 (2004).
Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 Km salinity gradient of the Baltic sea. Isme J. 5, 1571–1579. https://doi.org/10.1038/ismej.2011.41 (2011).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible Microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Callahan, B. J. et al. DADA2: High-resolution sample inference from illumina amplicon data. Nat. Methods. 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–596. https://doi.org/10.1093/nar/gks1219 (2013).
Chong, J., Liu, P., Zhou, G. & Xia, J. Using Microbiomeanalyst for comprehensive statistical, functional, and meta-analysis of Microbiome data. Nat. Protoc. 15, 799–821. https://doi.org/10.1038/s41596-019-0264-1 (2020).
Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688. https://doi.org/10.1038/s41587-020-0548-6 (2020).
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. https://doi.org/10.1093/nar/28.1.27 (2000).
Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Lawrence Erlbaum Associates, 1988).
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2008).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12 (2011).
Funding
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2025-23963320). Additional support was also partly provided by IPET and KosFarm through the Smart Farm Innovation Technology Development Program, funded by MAFRA, MSIT, and RDA (RS-2025-02307074). This work was also supported by the “Regional Innovation System & Education (RISE)” through the Gwangju RISE Center, funded by the Ministry of Education (MOE) and the Gwangju Metropolitan Government, Republic of Korea (2025-RISE-05-011).
Author information
Authors and Affiliations
Contributions
C.P. and M.K. designed and conceptualized the study. C.P., M.S.K., S.J., and S.M. performed the experimental work and laboratory analyses. C.P., Z.Y., S.H.K., and M.K. conducted the data analysis and drafted the manuscript. C.P., Z.Y., M.K. contributed to manuscript preparation and revision. All authors have read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Park, C., Kim, MS., Yu, Z. et al. Effect of divergent residual feed intake on the fecal microbiota in fattening Hanwoo steers. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39485-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-39485-5