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Animal Microbiome

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Effect of the macroalgae Asparagopsis taxiformis on methane production and rumen microbiome assemblage

  • Breanna Michell Roque
  • Charles Garrett Brooke
  • Joshua Ladau
  • Tamsen Polley
  • Lyndsey Jean Marsh
  • Negeen Najafi
  • Pramod Pandey
  • Latika Singh
  • Joan King Salwen
  • Emiley Eloe-Fadrosh
  • Ermias Kebreab
  • Matthias HessEmail author
Research article

Abstract

Background

Recent studies using batch-fermentation suggest that the red macroalgae Asparagopsis taxiformis has the potential to reduce methane (CH4) production from beef cattle by up to ~ 99% when added to Rhodes grass hay; a common feed in the Australian beef industry. These experiments have shown significant reductions in CH4 without compromising other fermentation parameters (i.e. volatile fatty acid production) with A. taxiformis organic matter (OM) inclusion rates of up to 5%. In the study presented here, A. taxiformis was evaluated for its ability to reduce methane production from dairy cattle fed a mixed ration widely utilized in California, the largest milk producing state in the US.

Results

Fermentation in a semi-continuous in-vitro rumen system suggests that A. taxiformis can reduce methane production from enteric fermentation in dairy cattle by 95% when added at a 5% OM inclusion rate without any obvious negative impacts on volatile fatty acid production. High-throughput 16S ribosomal RNA (rRNA) gene amplicon sequencing showed that seaweed amendment effects rumen microbiome consistent with the Anna Karenina hypothesis, with increased β-diversity, over time scales of approximately 3 days. The relative abundance of methanogens in the fermentation vessels amended with A. taxiformis decreased significantly compared to control vessels, but this reduction in methanogen abundance was only significant when averaged over the course of the experiment. Alternatively, significant reductions of CH4 in the A. taxiformis amended vessels was measured in the early stages of the experiment. This suggests that A. taxiformis has an immediate effect on the metabolic functionality of rumen methanogens whereas its impact on microbiome assemblage, specifically methanogen abundance, is delayed.

Conclusions

The methane reducing effect of A. taxiformis during rumen fermentation makes this macroalgae a promising candidate as a biotic methane mitigation strategy for dairy cattle. But its effect in-vivo (i.e. in dairy cattle) remains to be investigated in animal trials. Furthermore, to obtain a holistic understanding of the biochemistry responsible for the significant reduction of methane, gene expression profiles of the rumen microbiome and the host animal are warranted.

Keywords

16S rRNA community profiling Asparagopsis taxiformis Feed supplementation Greenhouse gas mitigation In-vitro rumen fermentation Macroalgae Rumen microbiome 

Abbreviations

16S rRNA

16 Svedberg ribosomal ribonucleic acid

AMOVA

Analysis of molecular variance

bp

Base pair

C

Celsius

CH4

Methane

Co

Company

CO2

Carbon dioxide

DM

Dry matter

DNA

Deoxyribonucleic acid

FID

Flame ionization detector

g

Gram

GC

Gas chromatography

hrs

Hours

IACUC

Institution of Animal Care and Use Committee

ml

Milliliters

OM

Organic matter

OTU

Operational taxonomic unit

PCoA

Principal coordinate analysis

PCR

Polymerase chain reaction

PVC

Poly vinyl chloride

SBR

Super basic ration

SD

Standard deviation

TDN

Total digestible nutrients

TGP

Total gas production

VFA

Volatile fatty acid

Notes

Acknowledgements

The authors would like to thank Kyra Smart, Susan Parkyn and Ania Kossakowski for their assistance in maintaining the artificial rumen system. Authors also express their appreciation to Dr. DePeters and Doug Gisi for providing access to fistulated animals.

Funding

This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231, by ELM Innovations, by the Hellman Foundation, U.S. Department of Agriculture Contract Number: 2017–67007-25944, and the College of Agricultural and Environmental Sciences at UC Davis.

This work was funded by the College of Agricultural and Environmental Sciences at the University of California, Davis, the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231, the U.S. Department of Agriculture Contract No. 2017–67007-25944, the Hellman Foundation and by ELM Innovations.

Availability of data and materials

Sequence data generated during this study are available through NCBI’s Sequence Read Archive under the SRA ID SRP152555. Custom-written Java, SQL, and Bash code is available at https://github.com/jladau. All other data is included in this published article and its supplementary information files.

Authors’ contributions

Designed the experiment: BR, CB, EK, JS and MH; Performed the experiments: BR, CB, MH and NN; Generated and analyzed the microbiome data: BR, CB, EE-F, JL, MH and NN. Generated and analyzed GC data: BR, CB, LM, LS, MH, NN, PP; Wrote the paper: BR, CB, EE-F, EK JL, JS, LM, MH and TP. All authors read and approved the final manuscript.

Ethics approval

All animal procedures were performed in accordance with the Institution of Animal Care and Use Committee (IACUC) at University of California, Davis under protocol number 19263.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

42523_2019_4_MOESM1_ESM.xlsx (3.7 mb)
Additional file 1: Table S1. Quality filtering and OTU distribution at each incubation time. Table S2. Diversity indices at each incubation time. Figures S1A., S1B, S1C Rarefaction curves of equilibration, control and A. taxiformis amended vessels respectively. Figure S2. Principle Coordinate Analysis plot. Table S3. OTU table. Table S4. Raw sequence barcodes for archived 16S rRNA gene amplicon data. Table S5. Results of AMOVA and HOMOVA statistical tests. (XLSX 3751 kb)

References

  1. 1.
    Smith PM, Bustamante H, Ahammad H, Clark H, Dong EA, Elsiddig H, Haberl R, Harper J, House M, Jafari O, Masera C, Mbow NH, Ravindranath CW, Rice C, Robledo Abad A, Romanovskaya F, Sperling F, Tubiello F. Agriculture, Forestry and Other Land Use (AFOLU) 2013. In: Climate Change: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, and New York: Cambridge University Press; 2013. https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter11.pdf. Accessed 15 Mar 2018.
  2. 2.
    Myhre G, Shindell D, Bréon F-M, Collins W, Fuglestvedt J, Huang J, Koch D, Lamarque JF, Lee D, Mendoza B, Nakajima T, Robock A, Stephens G, Takemura T, Zhang H. Anthropogenic and Natural Radiative Forcing 2013. In: Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate. Cambridge and New York: Cambridge University Press; 2013. https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter08_FINAL.pdf. Accessed 15 Mar 2018.
  3. 3.
    National Academies of Science Engineering and Medicine (NASEM). Improving characterization of Anthropogenic methane emissions in the United States. Washington: The National Academies Press; 2018. https://www.nap.edu/read/24987. Accessed 15 Mar 2018Google Scholar
  4. 4.
    Henderson C. The influence of extracellular hydrogen on the metabolism of Bacteroides ruminicola, Anaerovibrio lipolytica and Selenomonas ruminantium. Microbiol. 1980;119:485–91.CrossRefGoogle Scholar
  5. 5.
    Czerkawski JW. An introduction to rumen studies. 1st. ed. Oxford Oxfordshire: Pergamon Press; 1986.Google Scholar
  6. 6.
    Beauchemin KA, McGinn SM. Methane emissions from beef cattle: effects of fumaric acid, essential oil, and canola oil. J Anim Sci. 2006;84:1489–96.CrossRefGoogle Scholar
  7. 7.
    Hristov AN, Oh J, Firkins JL, Dijkstra J, Kebreab E, Waghorn G, Makkar HPS, Adesogan A, Yang W, Lee C, Gerber PJ. Special topics - mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. J Anim Sci. 2013;91:5045–69.CrossRefGoogle Scholar
  8. 8.
    Patra A, Park T, Kim M, Yu Z. Rumen methanogens and mitigation of methane emission by anti-methanogenic compounds and substances. J Anim Sci Biotechno. 2017.  https://doi.org/10.1186/s40104-017-0145-9.
  9. 9.
    Gerber PJ, Henderson B, Makkar HPS. Food and Agriculture Organization of the United Nations: Mitigation of greenhouse gas emissions in livestock production: a review of technical options for non-CO2 emissions. Rome: food and agriculture organization of the united nations. 2013. http://www.fao.org/docrep/018/i3288e/i3288e.pdf. Accessed 15 Mar 2018.Google Scholar
  10. 10.
    Machado L, Magnusson M, Paul NA, Kinley R, de Nys R, Tomkins N. Dose-response effects of Asparagopsis taxiformis and Oedogonium sp. on in-vitro fermentation and methane production. J Appl Phycol. 2016;28:1443–52.CrossRefGoogle Scholar
  11. 11.
    Nanri A, Mizoue T, Shimazu T, Ishihara J, Takachi R, Noda M, Iso H, Sasazuki S, Sawada N, Tsugane S. Japan public health center-based prospective study group. Dietary patterns and all-cause, cancer, and cardiovascular disease mortality in Japanese men and women: the Japan public health center-based prospective study. PLoS One. 2017;12:e0174848.CrossRefGoogle Scholar
  12. 12.
    Bansemer MS, Qin JG, Harris JO, Howarth GS, Stone DA. Nutritional requirements and use of macroalgae as ingredients in abalone feed. Rev Aquaculture. 2016;8:121–35.CrossRefGoogle Scholar
  13. 13.
    Elizondo-González R, Quiroz-Guzmán E, Escobedo-Fregoso C, Magallón-Servín P, Peña-Rodríguez A. Use of seaweed Ulva lactuca for water bioremediation and as feed additive for white shrimp Litopenaeus vannamei. PeerJ. 2018;6:e4459.CrossRefGoogle Scholar
  14. 14.
    Abdul QA, Choi RJ, Jung HA, Choi JS. Health benefit of fucosterol from marine algae: a review. J Sci Food Agr. 2016;96:1856–66.CrossRefGoogle Scholar
  15. 15.
    Yang YJ, Nam SJ, Kong G, Kim MK. A case–control study on seaweed consumption and the risk of breast cancer. Brit J Nutr. 2010;103:1345–53.CrossRefGoogle Scholar
  16. 16.
    Corona G, Ji Y, Anegboonlap P, Hotchkiss S, Gill C, Yaqoob P, Spencer JP, Rowland I. Gastrointestinal modifications and bioavailability of brown seaweed phlorotannins and effects on inflammatory markers. Brit J Nutr. 2016;115:1240–53.CrossRefGoogle Scholar
  17. 17.
    Blunt JW, Copp BR, Munro MH, Northcote PT, Prinsep MR. Marine natural products. Nat Prod Rep. 2013;2:144–22.Google Scholar
  18. 18.
    Machado L, Magnusson M, Paul NA, de Nys R, Tomkins N. Effects of marine and freshwater macroalgae on In-Vitro Total gas and methane production. PLoS One. 2014;9:e85289.CrossRefGoogle Scholar
  19. 19.
    Hansen H, Hector B, Feldmann J. A qualitative and quantitative evaluation of the seaweed diet of north Ronaldsay sheep. Anim Feed Sci Tech. 2003;105:21–8.CrossRefGoogle Scholar
  20. 20.
    Marín A, Casas-Valdez M, Carrillo S, Hernández H, Monroy A, Sanginés L, Pérez-Gil F. The marine algae Sargassum spp. (Sargassaceae) as feed for sheep in tropical and subtropical regions. Rev Biol Tropic. 2009;57:1271–81.Google Scholar
  21. 21.
    Dubois B, Tomkins NW, Kinley RD, Bai M, Seymour S, Paul NA, de Nys R. Effect of tropical algae as additives on rumen in-vitro gas production and fermentation characteristics. Am J Plant Sci. 2013;4:34–43.CrossRefGoogle Scholar
  22. 22.
    Wang Y, Xu Z, Bach S, McAllister T. Effects of phlorotannins from Ascophyllum nodosum (brown seaweed) on in-vitro ruminal digestion of mixed forage or barley grain. Anim Feed Sci Tech. 2008;145:375–95.CrossRefGoogle Scholar
  23. 23.
    Gonzalez del Val A, Platas G, Basilio A, Cabello A, Gorrochategui J, Suay I, Vicente F, Portillo E, Jimenez del Rio M, Reina GG, Pelaez F. Screening of antimicrobial activities in red, green and brown macroalgae from gran Canaria (Canary Islands, Spain). Int Microbiol. 2001;4:35–40.PubMedGoogle Scholar
  24. 24.
    Yuan YV, Walsh NA. Antioxidant and antiproliferative activities of extracts from a variety of edible seaweeds. Food Chem Toxicol. 2006;44:1144–50.CrossRefGoogle Scholar
  25. 25.
    Chandini SK, Ganesan P, Bhaskar N. In-vitro antioxidant activities of three selected brown seaweeds of India. Food Chem. 2008;107:707–13.CrossRefGoogle Scholar
  26. 26.
    Kang JY, Khan MNA, Park NH, Cho JY, Lee MC, Fujii H, Hong YK. Antipyretic, analgesic, and anti-inflammatory activities of the seaweed Sargassum fulvellum and Sargassum thunbergii in mice. J Ethnopharmacol. 2008;116:187–90.CrossRefGoogle Scholar
  27. 27.
    Machado L, Magnusson M, Paul NA, Kinley R, de Nys R, Tomkins N. Identification of bioactives from the red seaweed Asparagopsis taxiformis that promote antimethanogenic activity in-vitro. J Appl Phycol. 2016;28:3117–26.CrossRefGoogle Scholar
  28. 28.
    Wood J, Kennedy FS, Wolfe R. Reaction of multihalogenated hydrocarbons with free and bound reduced vitamin B12. Biochemist. 1968;7:1707–13.CrossRefGoogle Scholar
  29. 29.
    Allen KD, Wegener G, White RH. Discovery of multiple modified F430 coenzymes in methanogens and anaerobic methanotrophic archaea suggests possible new roles for F430 in nature. Appl Environl Microb. 2014;80:AEM-02202.Google Scholar
  30. 30.
    Machado L, Tomkins N, Magnusson M, Midgley D, Rocky dN, Rosewarne C. In vitro response of rumen microbiota to the antimethanogenic red macroalga Asparagopsis taxiformis. Microb Ecol. 2018;75:811–8.CrossRefGoogle Scholar
  31. 31.
    Li X, Norman HC, Kinley RD, Laurence M, Wilmot M, Bender H, de Nys R, Tomkins N. Asparagopsis taxiformis decreases enteric methane production from sheep. Anim Prod Sci. 2016;58:681–8.CrossRefGoogle Scholar
  32. 32.
    Cabeza-Luna I, Carro MD, Fernández-Yepes J, Molina-Alcaide E. Effects of modifications to retain protozoa in continuous-culture fermenters on ruminal fermentation, microbial populations, and microbial biomass assessed by two different methods. Anim Feed Sci Tech. 2018;240:117–27.CrossRefGoogle Scholar
  33. 33.
    Holmes DE, Giloteaux L, Orellana R, Williams KH, Robbins MJ, Lovley DR. Methane production from protozoan endosymbionts following stimulation of microbial metabolism within subsurface sediments. Front Microbiol. 2014;5:366.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Belanche A, de la Fuente G, Newbold CJ. Study of methanogen communities associated with different rumen protozoal populations. FEMS Microb Ecol. 2014;90:663–77.CrossRefGoogle Scholar
  35. 35.
    Newbold CJ, Lassalas B, Jouany JP. The importance of methanogens associated with ciliate protozoa in ruminal methane production in vitro. Lett Appl Microbiol. 1995;21:230–4.CrossRefGoogle Scholar
  36. 36.
    Morgavi DP, Forano E, Martin C, Newbold CJ. Microbial ecosystem and methanogenesis in ruminants. Animal. 2010;4:1024–36.CrossRefGoogle Scholar
  37. 37.
    Wolin MJ, Miller TL, Stewart CS. Microbe-microbe interactions. In: The rumen microbial ecosystem. Dordrecht: Springer; 1997. p. 467–91.CrossRefGoogle Scholar
  38. 38.
    Janssen PH. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim Feed Sci Tech. 2010;160:1–22.CrossRefGoogle Scholar
  39. 39.
    Seymour WM, Campbell DR, Johnson ZB. Relationships between rumen volatile fatty acid concentrations and milk production in dairy cows: a literature study. Anim Feed Sci Tech. 2005;119:155–69.CrossRefGoogle Scholar
  40. 40.
    Zaneveld JR, McMinds R, Thurber RV. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat Microbiol. 2017;2:17121.CrossRefGoogle Scholar
  41. 41.
    Rey-Crespo F, López-Alonso M, Miranda M. The use of seaweed from the Galician coast as a mineral supplement in organic dairy cattle. Animal. 2014;8:580–6.CrossRefGoogle Scholar
  42. 42.
    Czerkawski JW, Breckenridge G. Design and development of a long-term rumen simulation technique (Rusitec). Brit J Nutr. 1977;38:371–84.CrossRefGoogle Scholar
  43. 43.
    Oeztuerk H, Schroeder B, Beyerbach M, Breves G. Influence of living and autoclaved yeasts of Saccharomyces boulardii on in-vitro ruminal microbial metabolism. J Dairy Sci. 2005;88:2594–600.CrossRefGoogle Scholar
  44. 44.
    Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, Gilbert JA, Jansson JK, Caporaso JG, Fuhrman JA, Apprill A. Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. Msystems. 2016;1:e00009–15.CrossRefGoogle Scholar
  45. 45.
    Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CrossRefGoogle Scholar
  46. 46.
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microb. 2009;75:7537–41.CrossRefGoogle Scholar
  47. 47.
    Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microb. 2013;79:5112–20.CrossRefGoogle Scholar
  48. 48.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nuc Acids Res. 2013;41:D590–6.CrossRefGoogle Scholar
  49. 49.
    Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.CrossRefGoogle Scholar
  50. 50.
    DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microb. 2006;72:5069–72.CrossRefGoogle Scholar
  51. 51.
    Chao A. Nonparametric estimation of the number of classes in a population. Scan J Stat. 1984;11:265–70.Google Scholar
  52. 52.
    Good IJ. The population frequencies of species and the estimation of population parameters. Biometrika. 1953;40:237–64.CrossRefGoogle Scholar
  53. 53.
    Shannon CE. A mathematical theory of communication. Bell Sys Tech J. 1948;5:3–55.Google Scholar
  54. 54.
    Yue JC, Clayton MK. A similarity measure based on species proportions. Comm Stat-theory Meth. 2005;34:2123–31.CrossRefGoogle Scholar
  55. 55.
    Bray JR, Curtis JT. An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr. 1957;27:325–49.CrossRefGoogle Scholar
  56. 56.
    Excoffier L, Smouse PE, Quattro JM. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992;131:479–91.PubMedPubMedCentralGoogle Scholar
  57. 57.
    Team RC. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014. URL: https://www.r-project.org/ Google Scholar

Copyright information

© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Breanna Michell Roque
    • 1
  • Charles Garrett Brooke
    • 1
  • Joshua Ladau
    • 2
  • Tamsen Polley
    • 1
  • Lyndsey Jean Marsh
    • 1
  • Negeen Najafi
    • 1
  • Pramod Pandey
    • 3
  • Latika Singh
    • 3
  • Joan King Salwen
    • 4
  • Emiley Eloe-Fadrosh
    • 2
  • Ermias Kebreab
    • 1
  • Matthias Hess
    • 1
    Email author
  1. 1.Department of Animal ScienceUniversity of CaliforniaDavisUSA
  2. 2.Department of Energy Joint Genome InstituteWalnut CreekUSA
  3. 3.Department of Population Health and ReproductionSchool of Veterinary MedicineDavisUSA
  4. 4.Department of Earth System ScienceStanford UniversityStanfordUSA

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