Palabras clave
eficiencia alimenticia
microorganismos
metagenómica
sostenibilidad
Holobiability
feed efficiency
microorganisms
metagenomics
sustainability
Resumen
La generación de dispositivos capaces de secuenciar ADN a partir de muestras de matrices compuestas de distintos microorganismos ha marcado un hito en la clasificación taxonómica de estos. En el ámbito de la producción animal de rumiantes, el estudio genómico del microbioma del rumen (metagenómica) y su relación con el genotipo de los hospedadores ha despertado el interés de los investigadores. Esto se debe a las potenciales oportunidades de seleccionar rumiantes que puedan transmitir a su descendencia características que generen microbiomas más sostenibles para los propósitos de los sistemas productivos, tales como mejorar la eficiencia alimenticia y productividad mientras se reducen las emisiones de metano (CH4). El objetivo de esta revisión fue exponer una selección de documentación científica relacionada con el análisis del microbioma y su relevancia como potencial estrategia para la selección de rumiantes en los programas de mejora genética. Se ha concluido que la integración de tecnologías de análisis metagenómico en la investigación del microbioma del rumen ofrece nuevas perspectivas para la mejora genética en la producción de rumiantes. Estas tecnologías promueven sistemas productivos más eficientes y sostenibles, con menor impacto ambiental.
Citas
Berg, G., D. Rybakova, D. Fischer, T. Cernava, M.C. Champomier-Vergès, T. Charles, X. Chen, L. Cocolin, K. Eversole, G. Herrero-Corral, M. Kazou,L.Kinkel, L. Lange, N. Lima, A. Loy, J. A. Macklin, E. Maguin, T. Mauchline, R. McClure, B. Mitter, M. Ryan, I. Sarand, H. Smidt, B. Schelkle, H. Roume, G. S. Kiran, J. Selvin, R. Soares-Correa-de Souza, L. van Overbeek, B. K. Singh, M. Wagner, A. Walsh, A. Sessitsch y M. Schloter. 2020. Microbiome definition re-visited: old concepts and new challenges. Microbiome, 8:103.doi: 10.1186/s40168-020-00875-0
Boggio, G. M., H. F. Monteiro, F. S. Lima, C. C. Figueiredo, R. S. Bisinotto, J. E. P. Santos, B. Mion, F. S. Schenkel, E. S. Ribeiro, K. A. Weigel y F. Peñagaricano. 2023. Host and rumen microbiome contributions to feed efficiency traits in Holstein cows. Journal of Dairy Science. doi:10.3168/JDS.2023-23869.
Brandt, C., E. Bongcam-Rudloff y B. Müller. 2020. Abundance Tracking by Long-Read Nanopore Sequencing of Complex Microbial Communities in Samples from 20 Different Biogas/Wastewater Plants. Applied Science, 10:7518. doi:10.3390/app10217518.
Chakravorty, S., D. Helb, M. Burday y N. Connell. 2007. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. Journal of Microbiological Methods, 69 (2):330–339. doi:10.1016/j.mimet.2007.02.005.A.
Chaucheyras-Durand, F. y F. Ossa. 2014. The rumen microbiome: Composition, abundance, diversity, and new investigative tools. The Proffesional Animal Scientist, 30 (1):1–12. doi:10.15232/S1080-7446(15)30076-0.
Clarridge, J. E. 2004. Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clinical Microbiology Reviews, 17 (4):840–862. doi:10.1128/CMR.17.4.840.
de Haas, Y., P. C. Garnsworthy, B. Kuhla, E. Negussie, M. Pszczola, E. Wall y J. Lassen. 2016. Genetic control of greenhouse gas emissions. Advances in Animal Bioscience, 7 (2):196–199. doi:10.1017/s2040470016000121.
de Haas, Y., M. Pszczola, H. Soyeurt, E. Wall y J. Lassen. 2017. Invited review: Phenotypes to genetically reduce greenhouse gas emissions in dairying. Journal of Dairy Science, 100 (2): 855-870. doi:10.3168/jds.2016-11246.
de Haas, Y., J. Windig, M. Calus, J. Dijkstra, M. de Haan, A. Bannink y R. Veerkamp. 2011. Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection. Journal of Dairy Science, 94 (12):6122–6134. doi:10.3168/jds.2011-4439.
Delgado, B., A. Bach, I. Guasch, C. González, G. Elcoso, J.E. Pryce y O. González-Recio. 2018. Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Scientific Reports, 9 (1):1–13. doi:10.1038/s41598-018-36673-w.
Editorial. 2023. Method of the Year 2022: long-read sequencing. Nature Methods, 20 (1):1–1. doi:10.1038/s41592-022-01759-x.
Elizondo-Salazar, J. A. y C. R. Monge-Rojas. 2020. Fistulación en bovinos y uso de la técnica de degradabilidad ruminal para análisis de alimentos. Nutrición Animal Tropical, 14 (1):209–229.
Fitzsimons, C., D. A. Kenny, M. H. Deighton, A. G. Fahey y M. McGee. 2013. Methane emissions, body composition, and rumen fermentation traits of beef heifers differing in residual feed intake1. Journal of Animal Science, 91 (12):5789–5800. doi:10.2527/jas.2013-6956.
Giordano, F., L. Aigrain, M. A. Quail, P. Coupland, J. K. Bonfield, R. M. Davies, G. Tischler, D. K. Jackson, T. M. Keane, J. Li, J. X. Yue, G. Liti, R. Durbin y Z. Ning. 2017. De novo yeast genome assemblies from MinION, PacBio y MiSeq platforms. ScientificReports, 7 (1):1–10. doi:10.1038/s41598-017-03996-z.
González-Recio, O., M. Martinez-Alvaro, F. Tiezzi, A. Saborio-Montero, C. Maltecca y R. Roehe. 2023a. Invited review: Novel methods and perspectives for modulating the rumen microbiome through selective breeding as a means to improve complex traits: Implications for methane emissions in cattle. Livestock Science, 269:105171. doi:10.1016/J.LIVSCI.2023.105171.
González-Recio, O., A. Saborío-Montero, A. López-García, B. Delgado y C. Ovilo. 2019. Improving phenotypic prediction in dairy cattle breeding using the metagenome. Burleigh Dodds Science Publishing.
González-Recio, O., N. Scrobota, J. López-Paredes, A. Saborío-Montero, A. Fernández, E. López de Maturana, B. Villanueva, I. Goiri, R. Atxaerandio y A. García-Rodríguez. 2023b. Review: Diving into the cow hologenome to reduce methane emissions and increase sustainability. Animal, 17:100780. doi:10.1016/J.ANIMAL.2023.100780.
González-Recio, O., I. Zubiria, A. García-Rodríguez, A. Hurtado y R. Atxaerandio. 2017. Short communication: Signs of host genetic regulation in the microbiome composition in 2 dairy breeds: Holstein and Brown Swiss.. Journal of Dairy Science, 101 (3):1–8. doi:10.3168/jds.2017-13179.
Handelsman, J., M. R. Rondon, S. F. Brady, J. Clardy y R. M. Goodman. 1998. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chemistry and Biology, 5 (10):R245–R249. doi:10.1016/S1074-5521(98)90108-9.
Henderson, G., F. Cox, S. Kittelmann, V. H. Miri, M. Zethof, S. J. Noel, G. C. Waghorn y P. H. Janssen. 2013. Effect of DNA extraction methods and sampling techniques on the apparent structure of cow and sheep rumen microbial communities. PLoS One, 8. doi:10.1371/JOURNAL.PONE.0074787.
Hurley, A., N. Lopez-Villalobos, S. McParland, E. Lewis, E. Kennedy, J. Burke y D. Berry. 2018. Characteristics of feed efficiency within and across lactation in dairy cows and the effect of genetic selection. Journal of Dairy Science, 101 (2). doi:10.3168/jds.2017-12841.
Janda, J. M. y S. L. Abbott. 2007. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: Pluses, perils, and pitfalls. Journal of Clinical Microbiology, 45 (9):2761–2764. doi:10.1128/JCM.01228-07.
Johnson, K. A. y D. E. Johnson. 1995. Methane emissions from cattle. Journal of Animal Science, 73:2483–2492.
King, E. E., R. P. Smith, B. St-Pierre y A. D. G. Wright. 2011. Differences in the rumen methanogen populations of lactating Jersey and Holstein dairy cows under the same diet regimen.. Appliedand Environmental Microbiology, 77:5682–7. doi:10.1128/AEM.05130-11.
Lederberg, B. J. y A. T. McCray. 2001. ’ Ome Sweet ’ Omics-- A Genealogical Treasury of Words. Scientist, 15 (7):8.
Lima, F. S., G. Oikonomou, S. F. Lima, M. L. S. Bicalho, E. K. Ganda, J. C. de O. Filho, G. Lorenzo, P. Trojacanec y R. C. Bicalhoa. 2015. Prepartum and postpartum rumen fluid microbiomes: characterization and correlation with production traits in dairy cows.. Appliedand Environmental Microbiology, 81 (4):1327–37. doi:10.1128/AEM.03138-14.
López-García, A., C. Pineda-Quiroga, R. Atxaerandio, A. Pérez, I. Hernández, A. García-Rodríguez y O. González-Recio. 2018. Comparison of Mothur and QIIME for the Analysis of Rumen Microbiota Composition Based on 16S rRNA Amplicon Sequences. Frontiers in Microbiology, 9. doi:10.3389/FMICB.2018.03010.
López-García, A., A. Saborío-Montero, M. Gutiérrez-Rivas, R. Atxaerandio, I. Goiri, A. García-Rodríguez, J. A. Jiménez-Montero, C. González, J. Tamames, F. Puente-Sánchez, M. Serrano, R. Carrasco, C. Óvilo y O. González-Recio. 2022. Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle. GigaScience, 11:1–14. doi:10.1093/gigascience/giab088.
López-Paredes, J., I. Goiri, R. Atxaerandio, A. García-Rodríguez, E. Ugarte, J. A. Jiménez-Montero, R. Alenda y O. González-Recio. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection: 1. Genetic parameters of direct methane using noninvasive methods and proxies of methane. Journal of Dairy Science, 103 (8):7199–7209. doi:10.3168/JDS.2019-17597.
López-Paredes, J., A. Saborío-Montero, N. Charfeddine, J. Jiménez-Montero y O. González-Recio. 2021. Dry matter intake, methane emissions and microbiome profiles as new traits for feed efficiency. Interbull Bulletin,56:111–120.
Malmuthuge, N. y L. L. Guan. 2016. Gut microbiome and omics: a new definition to ruminant production and health. Animals Frontiers, 6 (2): 8. doi:10.2527/af.2016-0017.
Manzanilla-Pech, C. I. V., R. B. Stephansen, G. F. Difford, P. Løvendahl y J. Lassen. 2022. Selecting for Feed Efficient Cows Will Help to Reduce Methane Gas Emissions. Frontiers Genetics, 13:1. doi:10.3389/FGENE.2022.885932.
Martínez-Álvaro, M., M. D. Auffret, R. D. Stewart, R. J. Dewhurst, C. A. Duthie, J. A. Rooke, R. J. Wallace, B. Shih, T. C. Freeman, M. Watson y R. Roehe. 2020. Identification of Complex Rumen Microbiome Interaction Within Diverse Functional Niches as Mechanisms Affecting the Variation of Methane Emissions in Bovine. Frontiers in Microbiology, 11:659. doi:10.3389/fmicb.2020.00659.
Martínez-Álvaro, M., J. Mattock, M. Auffret, Z. Weng, C. A. Duthie, R. J. Dewhurst, M. A. Cleveland, M. Watson y R. Roehe. 2022. Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions. Microbiome, 10 (1): 166. doi:10.1186/S40168-022-01352-6.
Monteiro, H. F., Z. Zhou, M. S. Gomes, P. M. G. Peixoto, E. C. R. Bonsaglia, I. F. Canisso, B. C. Weimer y F. S. Lima. 2022. Rumen and lower gut microbiomes relationship with feed efficiency and production traits throughout the lactation of Holstein dairy cows. Scientific Reports, 12:4904. doi:10.1038/s41598-022-08761-5.
Negussie, E., Y. de Haas, F. Dehareng, R. J. Dewhurst, J. Dijkstra, N. Gengler, D. P. Morgavi, H. Soyeurt, S. van Gastelen, T. Yan y F. Biscarini. 2017. Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions.. Journal of Dairy Science, 100 (4):2433–2453. doi:10.3168/jds.2016-12030.
Nkrumah, J. D., E. K. Okine, G. W. Mathison, K. Schmid, C. Li, J. A. Basarab, M. A. Price, Z. Wang y S. S. Moore. 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 (1):145–53.
Oulas, A., C. Pavloudi, P. Polymenakou, G. A. Pavlopoulos, N. Papanikolaou, G. Kotoulas, C. Arvanitidis y I. Iliopoulos. 2015. Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies.. Bioinformatics Biological Insights, 9:75–88. doi:10.4137/BBI.S12462.
Paz, H. A., C. L. Anderson, M. J. Muller, P. J. Kononoff y S. C. Fernando. 2016. Rumen bacterial community composition in holstein and jersey cows is different under same dietary condition and is not affected by sampling method. Frontiers in Microbiology, 7:1206. doi:10.3389/FMICB.2016.01206/BIBTEX.
Pickering, N. K., V. H. Oddy, J. Basarab, K. Cammack, B. Hayes, R. S. Hegarty, J. Lassen, J. C. Mcewan, S. Miller, C. S. Pinares-Patiño y Y. De Haas. 2015. Animal board invited review: genetic possibilities to reduce enteric methane emissions from ruminants. Animal, 9 (9):1431–1440. doi:10.1017/S1751731115000968.
Pitta, D., N. Indugu, K. Narayan y M. Hennessy. 2022. Symposium review: Understanding the role of the rumen microbiome in enteric methane mitigation and productivity in dairy cows. Journal of Dairy Science, 105 (10):8569–8585. doi:10.3168/JDS.2021-21466.
Poretsky, R., L. M. Rodriguez-R, C. Luo, D. Tsementzi y K. T. Konstantinidis. 2014. Strengths and limitations of 16S rRNA gene amplicon sequencing in revealing temporal microbial community dynamics. PLoS One, 9 (4): e93827. doi:10.1371/journal.pone.0093827.
Ranjan, R., A. Rani, A. Metwally, H. S. Mcgee y D. L. Perkins. 2017. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochemical and Biophysical Research Communications, 469 (4): 667-977. doi:10.1016/j.bbrc.2015.12.083.
Rodicio, M. D. R. y M. D. C. Mendoza. 2004. Identificación bacteriana mediante secuenciación del ARNr 16S: fundamento, metodología y aplicaciones en microbiología clínica. Enfermedades Infecciosas y Microbiología Clinica, 22 (4):238–245. doi:10.1157/13059055.
Roehe, R., R. J. Dewhurst, C. A. Duthie, J. A. Rooke, N. McKain, D. W. Ross, J. J. Hyslop, A. Waterhouse, T. C. Freeman, M. Watson y R. J. Wallace. 2016. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genetics, 12 (2): 1–20. doi:10.1371/journal.pgen.1005846.
Ross, E. M., P. J. Moate, L. C. Marett, B. G. Cocks y B. J. Hayes. 2013. Metagenomic Predictions: From Microbiome to Complex Health and Environmental Phenotypes in Humans and Cattle. PLoS One, 8 (9):1–8. doi:10.1371/journal.pone.0073056.
Saborío-Montero, A., M. Gutiérrez-Rivas, A. García-Rodríguez, R. Atxaerandio, I. Goiri, E. López de Maturana, J.A. Jiménez-Montero, R. Alenda y O. González-Recio. 2020. Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study. Journal of Animal Breeding and Genetics, 137 (1): 36-48. doi:10.1111/jbg.12444.
Saborío-Montero, A., M. Gutiérrez-Rivas, I. Goiri, R. Atxaerandio, A. García-Rodriguez, J. López-Paredes, J. A. Jiménez-Montero y O. González-Recio. 2022. Rumen eukaryotes are the main phenotypic risk factors for larger methane emissions in dairy cattle.. Livestock Science, 263:105023. doi:10.1016/J.LIVSCI.2022.105023.
Saborío-Montero, A., M. Gutiérrez-Rivas, A. López-García, A. García-Rodríguez, R. Atxaerandio, I. Goiri, J. A. Jiménez-Montero y O. González-Recio. 2021a. Holobiont effect accounts for more methane emission variance than the additive and microbiome effects on dairy cattle. Livestock Science, 250:104538. doi:10.1016/j.livsci.2021.104538.
Saborío-Montero, A., A. López-García, M. Gutiérrez-Rivas, R. Atxaerandio, I. Goiri, A. García-Rodriguez, J. A. Jiménez-Montero, C. González, J. Tamames, F. Puente-Sánchez, L. Varona, M. Serrano, C. Ovilo y O. González-Recio. 2021b. A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding. Journal of Dairy Science, 104 (7):8135–8151. doi:10.3168/jds.2020-20005.
Stewart, R. D., M. D. Auffret, A. Warr, A. W. Walker, R. Roehe y M. Watson. 2019. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nature Biotechnology, 37 (8):953–961. doi:10.1038/s41587-019-0202-3.
Tapio, I., K. J. Shingfield, A. Bonin, D. Fischer, A. R. Bayat, J. Vilkki, P. Taberlet, T. J. Snelling y R. J. Wallace. 2016. Oral Samples as Non-Invasive Proxies for Assessing the Composition of the Rumen Microbial Community. PLoS One, 11 (3): 1–15. doi:10.1371/journal.pone.0151220.
Tapio, I., T. J. Snelling, F. Strozzi y R. J. Wallace. 2017. The ruminal microbiome associated with methane emissions from ruminant livestock. Journal of Animal Scienceand Biotechnology, 8:1–11. doi:10.1186/s40104-017-0141-0.
Thomas, T., J. Gilbert y F. Meyer. 2012. Metagenomics - a guide from sampling to data analysis. Microbial Informatics and Experimentation, 2:1–12. doi:10.1186/2042-5783-2-3.
Ursell, L. K., J. L. Metcalf, L. Wegener Parfrey, y R. Knight. 2013. Definig the Human Microbiome. Nutrition Reviews, 70 (1):1–12. doi:10.1111/j.1753-4887.2012.00493.x.Defining.
Wallace, R. J., G. Sasson, P. C. Garnsworthy, I. Tapio, E. Gregson, P. Bani, P. Huhtanen, A. R. Bayat, F. Strozzi, F. Biscarini, T. J. Snelling, N. Saunders, S. L. Potterton, J. Craigon, A. Minuti, E. Trevisi, M. L. Callegari, F. P. Cappelli, E. H. Cabezas-Garcia, J. Vilkki, C. Pinares-Patino, K. O. Fliegerová, J. Mrázek, H. Sechovcová, J. Kopečný, A. Bonin, F. Boyer, P. Taberlet, F. Kokou, E. Halperin, J. L. Williams, K. J. Shingfield y I. Mizrahi. 2019. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Science Advances, 5 (7):1–12. doi:10.1126/sciadv.aav8391.
Wang, Y. y P. Y. Qian. 2009. Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies. PLoS One, 4 (10): e7401. doi:10.1371/journal.pone.0007401.
Weimer, P. J., D. M. Stevenson y D. R. Mertens. 2010. Shifts in bacterial community composition in the rumen of lactating dairy cows under milk fat - depressing conditions. Journal of Dairy Science, 93 (1):265–278. doi:10.3168/jds.2009-2206.
Welkie, D. G., D. M. Stevenson y P. J. Weimer. 2010. ARISA analysis of ruminal bacterial community dynamics in lactating dairy cows during the feeding cycle. Anaerobe, 16 (2):94–100. doi:10.1016/J.ANAEROBE.2009.07.002.
Willes, R. F. 1972. Permanently Installed Digestive Cannulae. Journal of Dairy Science, 55 (8):1188–1190. doi:10.3168/JDS.S0022-0302(72)85646-7.
Woese, C. R., E. Stackebrandt, T. J. Macke y G. E. Fox. 1985. A phylogenetic definition of the major eubacterial taxa. Systematic and Applied Microbiology, 6 (2):143–51. doi:10.1016/S0723-2020(85)80047-3
Yu, Z. y M. Morrison. 2004. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques, 36 (5):808–812. doi:10.2144/04365ST04/ASSET/IMAGES/LARGE/FIGURE2.JPEG.
Yuan, S., D. B. Cohen, J. Ravel, Z. Abdo y L. J. Forney. 2012. Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One, 7 (3): e33865. doi:10.1371/JOURNAL.PONE.0033865.
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