Exploring the link between ruminal methane production and physiological changes in Japanese Black cattle during fattening

exploring-the-link-between-ruminal-methane-production-and-physiological-changes-in-japanese-black-cattle-during-fattening
Exploring the link between ruminal methane production and physiological changes in Japanese Black cattle during fattening
  • Stocker, T. Climate Change 2013: the Physical Science Basis: Working Group I Contribution To the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2014).

  • Adopted, I. Climate Change 2014 Synthesis Report (Geneva, Szwitzerland, 2014).

  • Pörtner, H. O. et al. Climate Change 2022: Impacts, Adaptation and Vulnerability (IPCC Sixth Assessment Report, 2022).

  • Van Nevel, C. & Demeyer, D. Control of rumen methanogenesis. Environ. Monit. Assess. 42 (1), 73–97 (1996).

    Google Scholar 

  • Kumar, S. et al. New aspects and strategies for methane mitigation from ruminants. Appl. Microbiol. Biotechnol. 98(1), 31–44 (2014).

    Google Scholar 

  • Uemoto, Y. et al. Development of prediction equation for methane-related traits in beef cattle under high concentrate diets. Anim. Sci. J. 91(1), e13341 (2020).

    Google Scholar 

  • Oikawa, K. et al. Variation among individual beef cattle in methane-to‐carbon dioxide ratio measured under on‐farm conditions using the sniffer method. Anim. Sci. J. 95(1), e13916 (2024).

    Google Scholar 

  • Wang, K., Xiong, B. & Zhao, X. Could propionate formation be used to reduce enteric methane emission in ruminants? Sci. Total Environ. 855, 158867 (2023).

    Google Scholar 

  • Lee, H. et al. Assessing the impact of three feeding stages on rumen bacterial community and physiological characteristics of Japanese black cattle. Sci. Rep. 14(1), 4923 (2024).

    Google Scholar 

  • Khairunisa, B. H. et al. Evolving Understanding of rumen methanogen ecophysiology. Front. Microbiol. 14, 1296008 (2023).

    Google Scholar 

  • Janssen, P. H. & Kirs, M. Structure of the archaeal community of the rumen. Appl. Environ. Microbiol. 74(12), 3619–3625 (2008).

    Google Scholar 

  • Jia, Y., Shi, Y. & Qiao, H. Bacterial community and diversity in the rumen of 11 Mongolian cattle as revealed by 16S rRNA amplicon sequencing. Sci. Rep. 14(1), 1546 (2024).

    Google Scholar 

  • Belanche, A. et al. Enhancing rumen microbial diversity and its impact on energy and protein metabolism in forage-fed goats. Front. Vet. Sci. 10, 1272835 (2023).

    Google Scholar 

  • Goopy, J. P. et al. Low-methane yield sheep have smaller rumens and shorter rumen retention time. Br. J. Nutr. 111(4), 578–585 (2014).

    Google Scholar 

  • Miura, H. et al. Identification of the core rumen bacterial taxa and their population dynamics during the fattening period in Japanese black cattle. Anim. Sci. J. 92(1), e13601 (2021).

    Google Scholar 

  • Morotomi, M., Nagai, F. & Watanabe, Y. Description of Christensenella Minuta gen. Nov., sp. Nov., isolated from human faeces, which forms a distinct branch in the order Clostridiales, and proposal of christensenellaceae fam. Nov. Int. J. Syst. Evol. Microbiol. 62(1), 144–149 (2012).

    Google Scholar 

  • Ramayo-Caldas, Y. et al. Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows. J. Anim. Breed. Genet. 137(1), 49–59 (2020).

    Google Scholar 

  • Mi, L. et al. Comparative analysis of the microbiota between sheep rumen and rabbit cecum provides new insight into their differential methane production. Front. Microbiol. 9, 575 (2018).

    Google Scholar 

  • 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(6), 1270 (2023).

    Google Scholar 

  • Himelbloom, B. H. & Canale-Parola, E. Clostridium methylpentosum sp. nov.: a ring-shaped intestinal bacterium that ferments only methylpentoses and Pentoses. Arch. Microbiol. 151, 287–293 (1989).

    Google Scholar 

  • Smith, P. E. et al. Differences in the composition of the rumen microbiota of finishing beef cattle divergently ranked for residual methane emissions. Front. Microbiol. 13, 855565 (2022).

    Google Scholar 

  • Wallace, R. J. et al. The rumen microbial metagenome associated with high methane production in cattle. BMC Genom. 16, 1–14 (2015).

    Google Scholar 

  • Liu, C. et al. Role of age-related shifts in rumen bacteria and methanogens in methane production in cattle. Front. Microbiol. 8, 1563 (2017).

    Google Scholar 

  • Lopes, L. D. et al. Exploring the sheep rumen Microbiome for carbohydrate-active enzymes. Antonie Van Leeuwenhoek. 108, 15–30 (2015).

    Google Scholar 

  • Danielsson, R. et al. Methane production in dairy cows correlates with rumen methanogenic and bacterial community structure. Front. Microbiol. 8, 226 (2017).

    Google Scholar 

  • Yang, C. et al. Consequences of inhibiting methanogenesis on the biohydrogenation of fatty acids in bovine ruminal digesta. Anim. Feed Sci. Technol. 254, 114189 (2019).

    Google Scholar 

  • Kjeldsen, M. H. et al. Gas exchange, rumen hydrogen sinks, and nutrient digestibility and metabolism in lactating dairy cows fed 3-nitrooxypropanol and cracked rapeseed. J. Dairy. Sci. 107(4), 2047–2065 (2024).

    Google Scholar 

  • Huws, S. A. et al. As yet uncultured bacteria phylogenetically classified as Prevotella, lachnospiraceae incertae sedis and unclassified Bacteroidales, clostridiales and Ruminococcaceae May play a predominant role in ruminal biohydrogenation. Environ. Microbiol. 13(6), 1500–1512 (2011).

    Google Scholar 

  • Uematsu, H. & Hoshino, E. Degradation of arginine and other amino acids by Eubacterium nodatum ATCC 33099. Microb. Ecol. Health Dis. 9(6), 305–311 (1996).

    Google Scholar 

  • Kim, M. et al. Effect of residual methane emission on physiological characteristics and carcass performance in Japanese black cattle. Anim. Sci. J. 95(1), e13954 (2024).

    Google Scholar 

  • Smith, P. E. et al. Effect of divergence in residual methane emissions on feed intake and efficiency, growth and carcass performance, and indices of rumen fermentation and methane emissions in finishing beef cattle. J. Anim. Sci. 99(11), skab275 (2021).

    Google Scholar 

  • Betancur-Murillo, C. L., Aguilar-Marín, S. B. & Jovel, J. Prevotella: A key player in ruminal metabolism. Microorganisms 11(1), 1 (2022).

    Google Scholar 

  • Kittelmann, S. et al. Two different bacterial community types are linked with the low-methane emission trait in sheep. PloS One. 9(7), e103171 (2014).

    Google Scholar 

  • Shi, W. et al. Methane yield phenotypes linked to differential gene expression in the sheep rumen Microbiome. Genome Res. 24(9), 1517–1525 (2014).

    Google Scholar 

  • Pitta, D. et al. Differences in methanogenesis pathways and microbial diversity in the rumen of low- and high-methane-yield phenotype dairy cows. J. Dairy. Sci. 103, 159–159 (2020).

    Google Scholar 

  • Hoedt, E. C. et al. Culture-and metagenomics-enabled analyses of the methanosphaera genus reveals their monophyletic origin and differentiation according to genome size. ISME J. 12(12), 2942–2953 (2018).

    Google Scholar 

  • Fricke, W. F. et al. The genome sequence of methanosphaera stadtmanae reveals why this human intestinal archaeon is restricted to methanol and H2 for methane formation and ATP synthesis. J. Bacteriol. 188(2), 642–658 (2006).

    Google Scholar 

  • Pitta, D. et al. Temporal changes in total and metabolically active ruminal methanogens in dairy cows supplemented with 3-nitrooxypropanol. J. Dairy. Sci. 104(8), 8721–8735 (2021).

    Google Scholar 

  • 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(1), 14567 (2015).

    Google Scholar 

  • Feldewert, C., Lang, K. & Brune, A. The hydrogen threshold of obligately methyl-reducing methanogens. FEMS Microbiol. Lett. 367(17), fnaa137 (2020).

    Google Scholar 

  • Wu, H. H. et al. The pathway for coenzyme M biosynthesis in bacteria. Proc. Natl. Acad. Sci. 119(36), e2207190119 (2022).

    Google Scholar 

  • Milligan, L. Carbon dioxide fixing pathways of glutamic acid synthesis in the rumen. Can. J. Biochem. 48(4), 463–468 (1970).

    Google Scholar 

  • Hino, T. & Russell, J. B. Effect of reducing-equivalent disposal and NADH/NAD on deamination of amino acids by intact rumen microorganisms and their cell extracts. Appl. Environ. Microbiol. 50(6), 1368–1374 (1985).

    Google Scholar 

  • Asanuma, N., Iwamoto, M. & Hino, T. The production of formate, a substrate for methanogenesis, from compounds related with the glyoxylate cycle by mixed ruminal microbes. Nihon Chikusan Gakkaiho. 70(2), 67–73 (1999).

    Google Scholar 

  • Li, Y. et al. Revealing the developmental characterization of rumen Microbiome and its host in newly received cattle during receiving period contributes to formulating precise nutritional strategies. Microbiome 11(1), 238 (2023).

    Google Scholar 

  • Couchet, M. et al. Ornithine transcarbamylase–From structure to metabolism: an update. Front. Physiol. 12, 748249 (2021).

    Google Scholar 

  • Castillo, A. R. et al. A review of efficiency of nitrogen utilisation in lactating dairy cows and its relationship with environmental pollution. J. Anim. Feed Sci. 9(1), 1–32 (2000).

    Google Scholar 

  • Dewhurst, R. J. & Newbold, J. R. Effect of ammonia concentration on rumen microbial protein production in vitro. Br. J. Nutr. 127(6), 847–849 (2022).

    Google Scholar 

  • Stelzer, G. et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform. 54, 1.30.1–1.30.33. https://www.genecards.org/cgi-bin/carddisp.pl?gene=SLC1A1 (2016).

  • Chen, Y. et al. Hepatocyte-specific Gclc deletion leads to rapid onset of steatosis with mitochondrial injury and liver failure. Hepatology 45(5), 1118–1128 (2007).

    Google Scholar 

  • Shi, X. et al. ß-Hydroxybutyrate activates the NF-κB signaling pathway to promote the expression of pro-inflammatory factors in calf hepatocytes. Cell. Physiol. Biochem. 33(4), 920–932 (2014).

    Google Scholar 

  • Haque, M., Cornou, C. & Madsen, J. Estimation of methane emission using the CO2 method from dairy cows fed concentrate with different carbohydrate compositions in automatic milking system. Livest. Sci. 164, 57–66 (2014).

    Google Scholar 

  • Garnsworthy, P. et al. On-farm methane measurements during milking correlate with total methane production by individual dairy cows. J. Dairy Sci. 95(6), 3166–3180 (2012).

    Google Scholar 

  • Blaise, Y. et al. The time after feeding alters methane emission kinetics in Holstein dry cows fed with various restricted diets. Livest. Sci. 217, 99–107 (2018).

    Google Scholar 

  • Oikawa, K. et al. Prediction of methane emissions from fattening cattle using the methane-to‐carbon dioxide ratio. Anim. Sci. J. 94(1), e13828 (2023).

    Google Scholar 

  • Janssen, P. H. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160(1–2), 1–22 (2010).

    Google Scholar 

  • Ungerfeld, E. M. Metabolic hydrogen flows in rumen fermentation: principles and possibilities of interventions. Front. Microbiol. 11, 589 (2020).

    Google Scholar 

  • NARO. Guideline for the Institute of Livestock and Grassland Science (Japan Livestock Industry Association Tokyo, Japan, 2011).

  • Miura, M. et al. Improvement of techniques for liver biopsy in dairy cattle. (1987).

  • NARO. Japanese Feeding Standard for Beef Cattle (Japan Livestock Industry Association Tokyo, Japan, 2009).

  • McLean, J. A. The significance of carbon dioxide and methane measurements in the Estimation of heat production in cattle. Br. J. Nutr. 55, 631–633. https://doi.org/10.1079/BJN19860068 (1986).

    Google Scholar 

  • Terada, F., Abe, H. & Shibata, M. Comparisons of energy utilization between Japanese black and Holstein steers. Asian Australas J. Anim. Sci. 2, 299–300. https://doi.org/10.5713/ajas.1989.299 (1989).

    Google Scholar 

  • Jakobsen, K. & Thorbekt, G. The respiratory quotient inrelation to fat deposition in fattening–growing pigs. Br. J. Nutr. 69(2), 333–343. https://doi.org/10.1079/BJN19930037 (1993).

    Google Scholar 

  • Pertea, M. et al. Transcript-level expression analysis of RNA-seq experiments with HISAT, stringtie and ballgown. Nat. Protoc. 11(9), 1650–1667 (2016).

    Google Scholar 

  • Love, M. I., Huber, W. & Anders, S. Moderated Estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).

    Google Scholar 

  • Illumina, I. 16S Metagenomic sequencing library preparation. Preparing 16S ribosomal RNA gene amplicons for the illumina MiSeq system. 1 28 (2013).

  • Bolyen, E. et al. Reproducible, interactive, scalable and extensible Microbiome data science using QIIME 2. Nat. Biotechnol. 37(8), 852–857 (2019).

    Google Scholar 

  • Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17(1), 10–12 (2011).

    Google Scholar 

  • Callahan, B. J. et al. High-resolution sample inference from illumina amplicon data. Nat. Methods. 13(7), 581–583 (2016).

    Google Scholar 

  • Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).

    Google Scholar 

  • Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41(D1), D590–D596 (2012).

    Google Scholar 

  • Dhariwal, A. et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of Microbiome data. Nucleic Acids Res. 45(W1), W180–W188 (2017).

    Google Scholar 

  • Beals, E. W. Bray-Curtis ordination: an effective strategy for analysis of multivariate ecological data. Adv. Ecol. Res. 1–55. (Elsevier, 1984).

  • Heberle, H. et al. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinform. 16, 1–7 (2015).

    Google Scholar 

  • Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. biotechnol. 38(6), 685–688 (2020).

    Google Scholar 

  • Bates, D. et al. Package ‘lme4’. Convergence,12(2) (2015).

  • Fox, J. et al. Package ‘car’ Vol. 16, 333 (R Foundation for Statistical Computing, 2012).

  • Harrell, F. E. Jr & Harrell, M. F. E. Jr Package ‘hmisc’. CRAN2018 235–236 (2019).

  • Fokkema, M., Zeileis, A. & Fokkema, M. M. Package ‘glmertree’ (2019).

  • Scutari, M. Learning Bayesian networks with the bnlearn R package. arXiv preprint arXiv:0908.3817 (2009).