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....
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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. |
doi_str_mv | 10.1038/s41598-018-36673-w |
format | Article |
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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.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-018-36673-w</identifier><identifier>PMID: 30626904</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>45/23 ; 45/43 ; 631/208/1348 ; 631/326/2565/2142 ; Accuracy ; Animal Feed ; Animal populations ; Animals ; Biodegradation ; Bovidae ; Cattle ; Cellulose ; Efficiency ; Euryarchaeota - growth & development ; Fatty acids ; Feed conversion ; Feed efficiency ; Feeds ; Firmicutes - growth & development ; Gastrointestinal Microbiome - genetics ; Gastrointestinal Microbiome - physiology ; Humanities and Social Sciences ; Information processing ; Livestock ; Livestock production ; Metagenome ; Methanobrevibacter - growth & development ; Microbiota ; multidisciplinary ; Prevotella - growth & development ; Relative abundance ; Rumen ; Rumen - microbiology ; Science ; Science (multidisciplinary) ; Sustainable production</subject><ispartof>Scientific reports, 2019-01, Vol.9 (1), p.11-11, Article 11</ispartof><rights>The Author(s) 2019</rights><rights>This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c577t-f30974761846a2a4213e238e9aba1c9ccfeea0ce0caa6073cee5b06f9b0c5293</citedby><cites>FETCH-LOGICAL-c577t-f30974761846a2a4213e238e9aba1c9ccfeea0ce0caa6073cee5b06f9b0c5293</cites><orcidid>0000-0001-6804-2002 ; 0000-0002-9106-4063 ; 0000-0002-7653-6632 ; 0000-0003-4277-5237</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327033/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327033/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,41096,42165,51551,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30626904$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Delgado, Beatriz</creatorcontrib><creatorcontrib>Bach, Alex</creatorcontrib><creatorcontrib>Guasch, Isabel</creatorcontrib><creatorcontrib>González, Carmen</creatorcontrib><creatorcontrib>Elcoso, Guillermo</creatorcontrib><creatorcontrib>Pryce, Jennie E.</creatorcontrib><creatorcontrib>Gonzalez-Recio, Oscar</creatorcontrib><title>Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><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.</description><subject>45/23</subject><subject>45/43</subject><subject>631/208/1348</subject><subject>631/326/2565/2142</subject><subject>Accuracy</subject><subject>Animal Feed</subject><subject>Animal populations</subject><subject>Animals</subject><subject>Biodegradation</subject><subject>Bovidae</subject><subject>Cattle</subject><subject>Cellulose</subject><subject>Efficiency</subject><subject>Euryarchaeota - growth & development</subject><subject>Fatty acids</subject><subject>Feed conversion</subject><subject>Feed efficiency</subject><subject>Feeds</subject><subject>Firmicutes - growth & development</subject><subject>Gastrointestinal Microbiome - genetics</subject><subject>Gastrointestinal Microbiome - physiology</subject><subject>Humanities and Social Sciences</subject><subject>Information processing</subject><subject>Livestock</subject><subject>Livestock production</subject><subject>Metagenome</subject><subject>Methanobrevibacter - growth & development</subject><subject>Microbiota</subject><subject>multidisciplinary</subject><subject>Prevotella - growth & development</subject><subject>Relative abundance</subject><subject>Rumen</subject><subject>Rumen - microbiology</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Sustainable production</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9UctuFDEQtBCIRCE_kAOyxIXLgF_jGV-QUERCpEhcInG0er09GwePvdgzWe3f493NCw74YrerusrtIuSMs0-cyf5zUbw1fcN430itO9lsXpFjwVTbCCnE6xfnI3Jayh2rqxVGcfOWHEmmhTZMHZP08zYFpHkeMdIRJ1hhTCPSgr9njM7HFYUQ0qZQF6AUP2z3V3FJ1xmX3k27ckBcUhwG73zt2e5hHyf4hTTgPYZSK-pgmgK-I28GCAVPH_YTcnPx7eb8e3P94_Lq_Ot149qum5pBMtOpTvNeaRCgBJcoZI8GFsCdca5aAnPIHIBmnXSI7YLpwSyYq1PKE_LlILueFyMuHcYpQ7Dr7EfIW5vA27-R6G_tKt1bLUXHpKwCHx8Ecqo_USY7-uIwBIiY5mIF74zkWrU7rw__UO_SnGOdrrJ0q5UxmlWWOLBcTqVkHJ4ew5ndJWoPidqaqN0naje16f3LMZ5aHvOrBHkglArFFeZn7__I_gEe9q98</recordid><startdate>20190109</startdate><enddate>20190109</enddate><creator>Delgado, Beatriz</creator><creator>Bach, Alex</creator><creator>Guasch, Isabel</creator><creator>González, Carmen</creator><creator>Elcoso, Guillermo</creator><creator>Pryce, Jennie E.</creator><creator>Gonzalez-Recio, Oscar</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-6804-2002</orcidid><orcidid>https://orcid.org/0000-0002-9106-4063</orcidid><orcidid>https://orcid.org/0000-0002-7653-6632</orcidid><orcidid>https://orcid.org/0000-0003-4277-5237</orcidid></search><sort><creationdate>20190109</creationdate><title>Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle</title><author>Delgado, Beatriz ; Bach, Alex ; Guasch, Isabel ; González, Carmen ; Elcoso, Guillermo ; Pryce, Jennie E. ; Gonzalez-Recio, Oscar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c577t-f30974761846a2a4213e238e9aba1c9ccfeea0ce0caa6073cee5b06f9b0c5293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>45/23</topic><topic>45/43</topic><topic>631/208/1348</topic><topic>631/326/2565/2142</topic><topic>Accuracy</topic><topic>Animal Feed</topic><topic>Animal populations</topic><topic>Animals</topic><topic>Biodegradation</topic><topic>Bovidae</topic><topic>Cattle</topic><topic>Cellulose</topic><topic>Efficiency</topic><topic>Euryarchaeota - growth & development</topic><topic>Fatty acids</topic><topic>Feed conversion</topic><topic>Feed efficiency</topic><topic>Feeds</topic><topic>Firmicutes - growth & development</topic><topic>Gastrointestinal Microbiome - genetics</topic><topic>Gastrointestinal Microbiome - physiology</topic><topic>Humanities and Social Sciences</topic><topic>Information processing</topic><topic>Livestock</topic><topic>Livestock production</topic><topic>Metagenome</topic><topic>Methanobrevibacter - growth & development</topic><topic>Microbiota</topic><topic>multidisciplinary</topic><topic>Prevotella - growth & development</topic><topic>Relative abundance</topic><topic>Rumen</topic><topic>Rumen - microbiology</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Sustainable production</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Delgado, Beatriz</creatorcontrib><creatorcontrib>Bach, Alex</creatorcontrib><creatorcontrib>Guasch, Isabel</creatorcontrib><creatorcontrib>González, Carmen</creatorcontrib><creatorcontrib>Elcoso, Guillermo</creatorcontrib><creatorcontrib>Pryce, Jennie E.</creatorcontrib><creatorcontrib>Gonzalez-Recio, Oscar</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Delgado, Beatriz</au><au>Bach, Alex</au><au>Guasch, Isabel</au><au>González, Carmen</au><au>Elcoso, Guillermo</au><au>Pryce, Jennie E.</au><au>Gonzalez-Recio, Oscar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2019-01-09</date><risdate>2019</risdate><volume>9</volume><issue>1</issue><spage>11</spage><epage>11</epage><pages>11-11</pages><artnum>11</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>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.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>30626904</pmid><doi>10.1038/s41598-018-36673-w</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-6804-2002</orcidid><orcidid>https://orcid.org/0000-0002-9106-4063</orcidid><orcidid>https://orcid.org/0000-0002-7653-6632</orcidid><orcidid>https://orcid.org/0000-0003-4277-5237</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 45/23 45/43 631/208/1348 631/326/2565/2142 Accuracy Animal Feed Animal populations Animals Biodegradation Bovidae Cattle Cellulose Efficiency Euryarchaeota - growth & development Fatty acids Feed conversion Feed efficiency Feeds Firmicutes - growth & development Gastrointestinal Microbiome - genetics Gastrointestinal Microbiome - physiology Humanities and Social Sciences Information processing Livestock Livestock production Metagenome Methanobrevibacter - growth & development Microbiota multidisciplinary Prevotella - growth & development Relative abundance Rumen Rumen - microbiology Science Science (multidisciplinary) Sustainable production |
title | Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle |
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