Microbial patterns in rumen are associated with gain of weight in beef cattle

de Freitas, Anderson Santos; de David, Diego Bitencourt; Takagaki, Beatriz Midori; Roesch, Luiz Fernando Würdig

doi:10.1007/s10482-020-01440-3

Original Paper
Published: 24 June 2020

Microbial patterns in rumen are associated with gain of weight in beef cattle

Antonie van Leeuwenhoek volume 113, pages 1299–1312 (2020)Cite this article

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Abstract

Ruminal microorganisms play a pivotal role in cattle nutrition. The discovery of the main microbes or of a microbial community responsible for enhancing the gain of weight in beef cattle might be used in therapeutic approaches to increase animal performance and cause less environmental damages. Here, we examined the differences in bacterial and fungal composition of rumen samples of Braford heifers raised in natural grassland of the Pampa Biome in Brazil. We aimed to detect microbial patterns in the rumen that could be correlated with the gain of weight. We hypothesized that microorganisms important to digestion process are increased in animals with a higher gain of weight. The gain of weight of seventeen healthy animals was monitored for 60 days. Ruminal samples were obtained and the 16S and ITS1 genes were amplified and sequenced to identify the closest microbial relatives within the microbial communities. A predictive model based on microbes responsible for the gain of weight was build and further tested using the entire dataset., The main differential abundant microbes between groups included the bacterial taxa RFN20, Prevotella, Anaeroplasma and RF16 and the fungal taxa Aureobasidium, Cryptococcus, Sarocladium, Pleosporales and Tremellales. The predictive model detected some of these taxa associated with animals with the high gain of weight group, most of them being organisms that have been correlated to the production of substances that improve the ruminal digestion process. These findings provide new insights about cattle nutrition and suggest the use of these microbes to improve beef cattle breeding.

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References

Akin DE, Borneman WS (1990) Role of rumen fungi in fiber degradation. J Dairy Sci 73:3023–3032. https://doi.org/10.3168/jds.S0022-0302(90)78989-8
CAS Article PubMed Google Scholar
Brown DR, Bradbury JM, Johansson K-E (2015) Anaeroplasma. In: Whitman WB, Rainey F, Kämpfer P et al (eds) Bergey’s manual of systematics of archaea and bacteria. John Wiley & Sons Ltd, Chichester, pp 1–5
Google Scholar
Caporaso JG, Lauber CL, Walters WA et al (2011) Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci 108:4516–4522. https://doi.org/10.1073/pnas.1000080107
Article PubMed Google Scholar
Carberry CA, Kenny DA, Han S et al (2012) Effect of phenotypic residual feed intake and dietary forage content on the rumen microbial community of beef cattle. Appl Environ Microbiol 78:4949–4958. https://doi.org/10.1128/AEM.07759-11
CAS Article PubMed PubMed Central Google Scholar
Chiquette J, Allison MJ, Rasmussen MA (2008) Prevotella bryantii 25A used as a probiotic in early-lactation dairy cows: effect on ruminal fermentation characteristics, milk production, and milk composition. J Dairy Sci 91:3536–3543. https://doi.org/10.3168/jds.2007-0849
CAS Article PubMed Google Scholar
Cunha CS, Marcondes MI, Veloso CM et al (2019) Compositional and structural dynamics of the ruminal microbiota in dairy heifers and its relationship to methane production. J Sci Food Agric 99:210–218. https://doi.org/10.1002/jsfa.9162
CAS Article PubMed Google Scholar
de Jesus RB, Omori WP, Lemos EGDM, de Souza JAM (2015) Bacterial diversity in bovine rumen by metagenomic 16S rDNA sequencing and scanning electron microscopy. Acta Sci Anim Sci 37:251. https://doi.org/10.4025/actascianimsci.v37i3.26535
CAS Article Google Scholar
Dixon P (2003) VEGAN, a package of R functions for community ecology. J Veg Sci 14:927–930
Article Google Scholar
Easley JF, McCall JT, Davis GK, Shirley RL (1965) Analytical methods for feeds and tissues. Nutrition Laboratory, Deptartment of Animal Science, University of Florida, Gainesville, 81 pp
Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB (2013) ANOVA-Like Differential Expression (ALDEx) Analysis for mixed population RNASeq. PLoS ONE 8: e67019. https://doi.org/10.1371/journal.pone.0067019
CAS Article PubMed PubMed Central Google Scholar
Findley K, Rodriguez-Carres M, Metin B et al (2009) Phylogeny and phenotypic characterization of pathogenic Cryptococcus species and closely related saprobic taxa in the Tremellales. Eukaryot Cell 8:353–361. https://doi.org/10.1128/EC.00373-08
CAS Article PubMed PubMed Central Google Scholar
Fliegerova K, Kaerger K, Kirk P, Voigt K (2015) Rumen fungi. In: Puniya AK, Singh R, Kamra DN (eds) Rumen microbiology: from evolution to revolution. Springer, New Delhi, pp 97–112
Chapter Google Scholar
Gardes M, Bruns TD (1993) ITS primers with enhanced specificity for basidiomycetes—application to the identification of mycorrhizae and rusts. Mol Ecol 2:113–118. https://doi.org/10.1111/j.1365-294X.1993.tb00005.x
CAS Article PubMed Google Scholar
Giraldo A, Gené J, Sutton DA et al (2015) Phylogeny of Sarocladium (Hypocreales). Pers Mol Phylogeny Evol Fungi 34:10–24. https://doi.org/10.3767/003158515X685364
CAS Article Google Scholar
Gloor G ANOVA-Like differential expression tool for high throughput sequencing data. 19
Gloor GB, Reid G (2016) Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can J Microbiol 62:692–703. https://doi.org/10.1139/cjm-2015-0821
CAS Article PubMed Google Scholar
Gloor GB, Macklaim JM, Fernandes AD (2016) Displaying variation in large datasets: plotting a visual summary of effect sizes. J Comput Gr Stat 25:971–979. https://doi.org/10.1080/10618600.2015.1131161
Article Google Scholar
Good IJ (1953) The population frequencies of species and the estimation of population parameters. Biometrika 40:237. https://doi.org/10.2307/2333344
Article Google Scholar
Gostinčar C, Grube M, Gunde-Cimerman N (2011) Evolution of fungal pathogens in domestic environments? Fungal Biol 115:1008–1018. https://doi.org/10.1016/j.funbio.2011.03.004
Article PubMed Google Scholar
Gostinčar C, Ohm RA, Kogej T et al (2014) Genome sequencing of four Aureobasidium pullulans varieties: biotechnological potential, stress tolerance, and description of new species. BMC Genom 15:549. https://doi.org/10.1186/1471-2164-15-549
CAS Article Google Scholar
Greiner T, Bäckhed F (2011) Effects of the gut microbiota on obesity and glucose homeostasis. Trends Endocrinol Metab 22:117–123. https://doi.org/10.1016/j.tem.2011.01.002
CAS Article PubMed Google Scholar
Guimarães RA, Lobo VLDS, Côrtes MVCB et al (2017) Characterization of Sarocladium oryzae and its reduction potential of rice leaf blast. Pesquisa Agropecuária Trop 47:41–52. https://doi.org/10.1590/1983-40632016v4742738
Article Google Scholar
Gupta VK, Chaudhari NM, Iskepalli S, Dutta C (2015) Divergences in gene repertoire among the reference Prevotella genomes derived from distinct body sites of human. BMC Genom 16:153. https://doi.org/10.1186/s12864-015-1350-6
CAS Article Google Scholar
Hamady M, Walker JJ, Harris JK et al (2008) Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Nat Methods 5:235–237. https://doi.org/10.1038/nmeth.1184
CAS Article PubMed PubMed Central Google Scholar
Henderson G, Ganesh S, Jonker A et al (2015) Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci Rep. https://doi.org/10.1038/srep14567
Article PubMed PubMed Central Google Scholar
Hou YM, Zhang X, Zhang NN et al (2019) Genera Acremonium and Sarocladium cause brown spot on bagged apple fruit in China. Plant Dis 103:1889–1901. https://doi.org/10.1094/PDIS-10-18-1794-RE
CAS Article PubMed Google Scholar
Indugu N, Vecchiarelli B, Baker LD et al (2017) Comparison of rumen bacterial communities in dairy herds of different production. BMC Microbiol. https://doi.org/10.1186/s12866-017-1098-z
Article PubMed PubMed Central Google Scholar
Ishaq SL, AlZahal O, Walker N, McBride B (2017) An investigation into rumen fungal and protozoal diversity in three rumen fractions, during high-fiber or grain-induced sub-acute ruminal acidosis conditions, with or without active dry yeast supplementation. Front Microbiol. https://doi.org/10.3389/fmicb.2017.01943
Article PubMed PubMed Central Google Scholar
Jami E, Mizrahi I (2012) Composition and similarity of bovine rumen microbiota across individual animals. PLoS ONE 7:e33306. https://doi.org/10.1371/journal.pone.0033306
CAS Article PubMed PubMed Central Google Scholar
Jami E, White BA, Mizrahi I (2014) Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS ONE 9:e85423. https://doi.org/10.1371/journal.pone.0085423
CAS Article PubMed PubMed Central Google Scholar
Kirk PL (1950) Kjeldahl method for total nitrogen. Anal Chem 22:354–358
CAS Article Google Scholar
Kobayashi CCBA, Souza LKH, Fernandes ODFL et al (2005) Characterization of Cryptococcus neoformans isolated from urban environmental sources in Goiânia, Goiás State, Brazil. Revista do Instituto de Medicina Tropical de São Paulo 47:203–207. https://doi.org/10.1590/S0036-46652005000400005
Article PubMed Google Scholar
Lancaster RJ (1949) Estimation of digestibility of grazed pasture from fæces nitrogen. Nature 163:330–331. https://doi.org/10.1038/163330b0
CAS Article PubMed Google Scholar
Lemos LN, Fulthorpe RR, Triplett EW, Roesch LFW (2011) Rethinking microbial diversity analysis in the high throughput sequencing era. J Microbiol Methods 86:42–51. https://doi.org/10.1016/j.mimet.2011.03.014
CAS Article PubMed Google Scholar
Linn J, Hutjens M, Shaver R et al (2018) The ruminant digestive system. In: University of Minnesota Extension. https://extension.umn.edu/dairy-nutrition/ruminant-digestive-system#large-intestine-1000463. Accessed 23 May 2019
Liu XB, Guo ZK, Huang GX (2017) Sarocladium brachiariae sp. nov., an endophytic fungus isolated from Brachiaria brizantha. Mycosphere 8:827–834. https://doi.org/10.5943/mycosphere/8/7/2
Article Google Scholar
Malmuthuge N, Guan LL (2017) Understanding host-microbial interactions in rumen: searching the best opportunity for microbiota manipulation. J Anim Sci Biotechnol. https://doi.org/10.1186/s40104-016-0135-3
Article PubMed PubMed Central Google Scholar
Malmuthuge N, Griebel PJ, Guan LL (2014) Taxonomic identification of commensal bacteria associated with the mucosa and digesta throughout the gastrointestinal tracts of preweaned calves. Appl Environ Microbiol 80:2021–2028. https://doi.org/10.1128/AEM.03864-13
CAS Article PubMed PubMed Central Google Scholar
Mccann JC, Wickersham TA, Loor JJ (2014) High-throughput methods redefine the rumen microbiome and its relationship with nutrition and metabolism. Bioinform Biol Insights. https://doi.org/10.4137/BBI.S15389
Article PubMed PubMed Central Google Scholar
McMahon P, Purwantara A (2016) Vascular streak dieback (Ceratobasidium theobromae): history and biology. In: Bailey BA, Meinhardt LW (eds) Cacao Diseases: a history of old enemies and new encounters. Springer, Cham, pp 307–335
Chapter Google Scholar
Mosquera-Espinosa AT, Bayman P, Prado GA et al (2013) The double life of Ceratobasidium: orchid mycorrhizal fungi and their potential for biocontrol of Rhizoctonia solani sheath blight of rice. Mycologia 105:141–150. https://doi.org/10.3852/12-079
Article PubMed Google Scholar
Myer PR, Kim M, Freetly HC, Smith TPL (2016) Evaluation of 16S rRNA amplicon sequencing using two next-generation sequencing technologies for phylogenetic analysis of the rumen bacterial community in steers. J Microbiol Methods 127:132–140. https://doi.org/10.1016/j.mimet.2016.06.004
CAS Article PubMed Google Scholar
Nathani NM, Kothari RK, Patel AK, Joshi CG (2015) Functional characterization reveals novel putative coding sequences in Prevotella ruminicola genome extracted from rumen metagenomic studies. J Mol Microbiol Biotechnol 25:292–299. https://doi.org/10.1159/000437265
CAS Article PubMed Google Scholar
Noel SJ, Attwood GT, Rakonjac J et al (2017) Seasonal changes in the digesta-adherent rumen bacterial communities of dairy cattle grazing pasture. PLoS ONE 12:e0173819. https://doi.org/10.1371/journal.pone.0173819
CAS Article PubMed PubMed Central Google Scholar
Parish J, Rivera D (2017) Understanding the ruminant animal digestive system. Mississippi State University, p 8
Pourhoseingholi MA, Baghestani AR, Vahedi M (2012) How to control confounding effects by statistical analysis. Gastroenterol Hepatol Bed Bench 5:79–83
PubMed PubMed Central Google Scholar
Pylro VS, Roesch LFW, Morais DK et al (2014a) Data analysis for 16S microbial profiling from different benchtop sequencing platforms. J Microbiol Methods 107:30–37. https://doi.org/10.1016/j.mimet.2014.08.018
CAS Article PubMed Google Scholar
Pylro VS, Roesch LFW, Ortega JM et al (2014b) Brazilian microbiome project: revealing the unexplored microbial diversity—challenges and prospects. Microb Ecol 67:237–241. https://doi.org/10.1007/s00248-013-0302-4
Article PubMed Google Scholar
Refai MK, El-Hariri M, Alarousy R (2017) Cryptococcosis in animals and birds: a review. Eur J Acad Essays 4:202–223
Google Scholar
Roesch LF, Vieira F, Pereira V et al (2009) The Brazilian pampa: a fragile biome. Diversity 1:182–198. https://doi.org/10.3390/d1020182
Article Google Scholar
Rosa FQ, Pagel R, David DB et al (2020) Measurement of nutritional parameters by fecal markers in cattle fed heterogeneous natural grasslands (in press)
Rouse JE (1977) The criollo: Spanish cattle in the Americas. University of Oklahoma Press, Norman
Google Scholar
Shah HN, Chattaway MA, Rajakurana L, Gharbia SE (2015) Prevotella. In: Bergey’s manual of systematics of archaea and bacteria. American Cancer Society, pp 1–25
Shearer CA, Raja HA, Miller AN et al (2009) The molecular phylogeny of freshwater Dothideomycetes. Stud Mycol 64:145–153. https://doi.org/10.3114/sim.2009.64.08
CAS Article PubMed PubMed Central Google Scholar
Shurson GC (2018) Yeast and yeast derivatives in feed additives and ingredients: sources, characteristics, animal responses, and quantification methods. Anim Feed Sci Technol 235:60–76. https://doi.org/10.1016/j.anifeedsci.2017.11.010
CAS Article Google Scholar
Sinsabaugh RL (2010) Phenol oxidase, peroxidase and organic matter dynamics of soil. Soil Biol Biochem 42:391–404. https://doi.org/10.1016/j.soilbio.2009.10.014
CAS Article Google Scholar
Slepecky RA, Starmer WT (2009) Phenotypic plasticity in fungi: a review with observations on Aureobasidium pullulans. Mycologia 101:823–832. https://doi.org/10.3852/08-197
Article PubMed Google Scholar
Smith KE, Garza AL, Butterfield KM et al (2018) Succession of ruminal bacterial species and fermentation characteristics in preweaned Brangus calves1. Transl Anim Sci 2:S48–S52. https://doi.org/10.1093/tas/txy043
CAS Article PubMed PubMed Central Google Scholar
Song J, Jeong JY, Kim M (2017) Diversity census of fungi in the ruminal microbiome: a meta-analysis. J Korea Acad Ind Coop Soc 18:466–472. https://doi.org/10.5762/KAIS.2017.18.12.466
Article Google Scholar
Stewart RD, Auffret MD, Warr A et al (2019) Compendium of 4941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nat Biotechnol 37:953–961. https://doi.org/10.1038/s41587-019-0202-3
CAS Article PubMed PubMed Central Google Scholar
Suetrong S, Schoch CL, Spatafora JW et al (2009) Molecular systematics of the marine Dothideomycetes. Stud Mycol 64:155–173. https://doi.org/10.3114/sim.2009.64.09
CAS Article PubMed PubMed Central Google Scholar
Sun H-Z, Xue M, Guan LL, Liu J (2019) A collection of rumen bacteriome data from 334 mid-lactation dairy cows. Sci Data. https://doi.org/10.1038/sdata.2018.301
Article PubMed PubMed Central Google Scholar
Tapio I, Shingfield KJ, McKain N et al (2016) Oral samples as non-invasive proxies for assessing the composition of the rumen microbial community. PLoS ONE 11:e0151220. https://doi.org/10.1371/journal.pone.0151220
CAS Article PubMed PubMed Central Google Scholar
Turnbaugh PJ, Ridaura VK, Faith JJ et al (2009) The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med 1:6ra14. https://doi.org/10.1126/scitranslmed.3000322
CAS Article PubMed PubMed Central Google Scholar
Walters W, Hyde ER, Berg-Lyons D et al (2016) Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems. https://doi.org/10.1128/mSystems.00009-15
Article PubMed Google Scholar
White TJ, Bruns TD, Lee SB, Taylor JW (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: PCR—protocols and applications—a laboratory manual. Academic Press, pp 315–322
Wirth R, Kádár G, Kakuk B et al (2018) The planktonic core microbiome and core functions in the cattle rumen by next generation sequencing. Front Microbiol. https://doi.org/10.3389/fmicb.2018.02285
Article PubMed PubMed Central Google Scholar
Zhang Y, Schoch CL, Fournier J et al (2009) Multi-locus phylogeny of Pleosporales: a taxonomic, ecological and evolutionary re-evaluation. Stud Mycol 64:85–102. https://doi.org/10.3114/sim.2009.64.04
CAS Article PubMed PubMed Central Google Scholar

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Funding

Funding was provided by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Grant No. 001) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (Grant No. 434379/2016-6).

Author information

Anderson Santos de Freitas and Diego Bitencourt de David have contributed equally to this work.

Affiliations

Centro Interdisciplinar de Pesquisas em Biotecnologia – CIP-Biotec, Campus São Gabriel, Universidade Federal do Pampa, São Gabriel, Rio Grande do Sul, Brazil
Anderson Santos de Freitas, Beatriz Midori Takagaki & Luiz Fernando Würdig Roesch
Departamento de Diagnóstico e Pesquisa Agropecuária – DDPA, Secretaria Estadual da Agricultura, Pecuária e Desenvolvimento Rural – SEADPR/RS, São Gabriel, Rio Grande do Sul, Brazil
Diego Bitencourt de David

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Anderson Santos de Freitas
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Diego Bitencourt de David
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Beatriz Midori Takagaki
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Luiz Fernando Würdig Roesch
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Contributions

Conceptualization, ASF, DBD, and LFWR; methodology, ASF, DBD and BMT; validation, DBD; formal analysis, ASF and LFWR; investigation, ASF, DBD and BMT; resources, DBD and LFWR; data curation, ASF and LFWR; writing—original draft preparation, ASF and DBD; writing—review and editing, ASF, DBD and LFWR; visualization, BMT; supervision, LFWR; project administration, DBD and LFWR; funding acquisition, DBD and LFWR.

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Correspondence to Luiz Fernando Würdig Roesch.

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All animal management and research procedures were approved by the Animal Welfare Committee of the State Foundation of Agricultural Research (FEPAGRO) registered under the Number 15/14.

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Supplementary Figure S1

Overall comparisons of bacterial and fungal communities respectively at OTU level based on principal coordinates analysis (PCoA), depicting clusters of bacterial communities in 17 samples each from the Higher (red), Lower (blue) and Middle (green) gain of weight groups. Each point represents a microbial community in a sample. Points closer to each other represent similar microbial communities, while points farther from each other represent dissimilar microbial communities. The statistical significance of sample groupings was tested with the Adonis function using distance matrices as primary input. R2 values were 0.140 (p = 0.052) for Bacteria and 0.198 (p = 0.195) for Fungi (TIFF 320 kb)

Supplementary Figure S2

Centered log ratio transformed abundance of all bacterial phyla present in ruminal samples from bovines with Higher ADG (yellow) and Lower ADG (blue). Differences were tested with the ALDEx package. No significant difference between groups was found (effect size < 1, p value > 0.1). Note that negative clr values indicate very low abundance (TIFF 1909 kb)

Supplementary Figure S3

Centered log ratio transformed abundance of all fungal phyla present in ruminal samples from bovines with Higher ADG (yellow) and Lower ADG (blue). Differences were tested with the ALDEx package. No significant difference between groups was found (effect size < 1, p value > 0.1). Note that negative clr values indicate very low abundance (TIFF 844 kb)

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de Freitas, A.S., de David, D.B., Takagaki, B.M. et al. Microbial patterns in rumen are associated with gain of weight in beef cattle. Antonie van Leeuwenhoek 113, 1299–1312 (2020). https://doi.org/10.1007/s10482-020-01440-3

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Received: 31 March 2020
Accepted: 15 June 2020
Published: 24 June 2020
Issue Date: September 2020
DOI: https://doi.org/10.1007/s10482-020-01440-3

Keywords

Bacteria
Fungi
Livestock
Microbiome
Next generation sequencing