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Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle
The current research was carried out to determine the associations between the rumen microbiota and traits related with feed efficiency in a Holstein cattle population (n = 30) using whole metagenome sequencing. Improving feed efficiency (FE) is important for a more sustainable livestock production....
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327033/ https://www.ncbi.nlm.nih.gov/pubmed/30626904 http://dx.doi.org/10.1038/s41598-018-36673-w |
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author | Delgado, Beatriz Bach, Alex Guasch, Isabel González, Carmen Elcoso, Guillermo Pryce, Jennie E. Gonzalez-Recio, Oscar |
author_facet | Delgado, Beatriz Bach, Alex Guasch, Isabel González, Carmen Elcoso, Guillermo Pryce, Jennie E. Gonzalez-Recio, Oscar |
author_sort | Delgado, Beatriz |
collection | PubMed |
description | The current research was carried out to determine the associations between the rumen microbiota and traits related with feed efficiency in a Holstein cattle population (n = 30) using whole metagenome sequencing. Improving feed efficiency (FE) is important for a more sustainable livestock production. The variability for the efficiency of feed utilization in ruminants is partially controlled by the gastrointestinal microbiota. Modulating the microbiota composition can promote a more sustainable and efficient livestock. This study revealed that most efficient cows had larger relative abundance of Bacteroidetes (P = 0.041) and Prevotella (P = 0.003), while lower, but non-significant (P = 0.119), relative abundance of Firmicutes. Methanobacteria (P = 0.004) and Methanobrevibacter (P = 0.003) were also less abundant in the high-efficiency cows. A de novo metagenome assembly was carried out using de Bruijn graphs in MEGAHIT resulting in 496,375 contigs. An agnostic pre-selection of microbial contigs allowed high classification accuracy for FE and intake levels using hierarchical classification. These microbial contigs were also able to predict FE and intake levels with accuracy of 0.19 and 0.39, respectively, in an independent population (n = 31). Nonetheless, a larger potential accuracy up to 0.69 was foreseen in this study for datasets that allowed a larger statistical power. Enrichment analyses showed that genes within these contigs were mainly involved in fatty acids and cellulose degradation pathways. The findings indicated that there are differences between the microbiota compositions of high and low-efficiency animals both at the taxonomical and gene levels. These differences are even more evident in terms of intake levels. Some of these differences remain even between populations under different diets and environments, and can provide information on the feed utilization performance without information on the individual intake level. |
format | Online Article Text |
id | pubmed-6327033 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63270332019-01-11 Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle Delgado, Beatriz Bach, Alex Guasch, Isabel González, Carmen Elcoso, Guillermo Pryce, Jennie E. Gonzalez-Recio, Oscar Sci Rep Article The current research was carried out to determine the associations between the rumen microbiota and traits related with feed efficiency in a Holstein cattle population (n = 30) using whole metagenome sequencing. Improving feed efficiency (FE) is important for a more sustainable livestock production. The variability for the efficiency of feed utilization in ruminants is partially controlled by the gastrointestinal microbiota. Modulating the microbiota composition can promote a more sustainable and efficient livestock. This study revealed that most efficient cows had larger relative abundance of Bacteroidetes (P = 0.041) and Prevotella (P = 0.003), while lower, but non-significant (P = 0.119), relative abundance of Firmicutes. Methanobacteria (P = 0.004) and Methanobrevibacter (P = 0.003) were also less abundant in the high-efficiency cows. A de novo metagenome assembly was carried out using de Bruijn graphs in MEGAHIT resulting in 496,375 contigs. An agnostic pre-selection of microbial contigs allowed high classification accuracy for FE and intake levels using hierarchical classification. These microbial contigs were also able to predict FE and intake levels with accuracy of 0.19 and 0.39, respectively, in an independent population (n = 31). Nonetheless, a larger potential accuracy up to 0.69 was foreseen in this study for datasets that allowed a larger statistical power. Enrichment analyses showed that genes within these contigs were mainly involved in fatty acids and cellulose degradation pathways. The findings indicated that there are differences between the microbiota compositions of high and low-efficiency animals both at the taxonomical and gene levels. These differences are even more evident in terms of intake levels. Some of these differences remain even between populations under different diets and environments, and can provide information on the feed utilization performance without information on the individual intake level. Nature Publishing Group UK 2019-01-09 /pmc/articles/PMC6327033/ /pubmed/30626904 http://dx.doi.org/10.1038/s41598-018-36673-w Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Delgado, Beatriz Bach, Alex Guasch, Isabel González, Carmen Elcoso, Guillermo Pryce, Jennie E. Gonzalez-Recio, Oscar Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle |
title | Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle |
title_full | Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle |
title_fullStr | Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle |
title_full_unstemmed | Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle |
title_short | Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle |
title_sort | whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327033/ https://www.ncbi.nlm.nih.gov/pubmed/30626904 http://dx.doi.org/10.1038/s41598-018-36673-w |
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