Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome

Shi, Weibing; Moon, Christina D.; Leahy, Sinead C.; Kang, Dongwan; Froula, Jeff; Kittelmann, Sandra; Fan, Christina; Deutsch, Samuel; Gagic, Dragana; Seedorf, Henning; Kelly, William J.; Atua, Renee; Sang, Carrie; Soni, Priya; Li, Dong; Pinares-Patiño, Cesar S.; McEwan, John C.; Janssen, Peter H.; Chen, Feng; Visel, Axel; Wang, Zhong; Attwood, Graeme T.; Rubin, Edward M.

doi:10.1101/gr.168245.113

Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome

Journal Article · Fri Jun 06 00:00:00 EDT 2014 · Genome Research

DOI:https://doi.org/10.1101/gr.168245.113· OSTI ID:1625629

^[1]; Moon, Christina D. ^[2]; Leahy, Sinead C. ^[2]; Kang, Dongwan ^[3]; Froula, Jeff ^[3]; Kittelmann, Sandra ^[2]; Fan, Christina ^[3]; Deutsch, Samuel ^[3]; Gagic, Dragana ^[2]; Seedorf, Henning ^[2]; Kelly, William J. ^[2]; Atua, Renee ^[2]; Sang, Carrie ^[2]; Soni, Priya ^[2]; Li, Dong ^[2]; Pinares-Patiño, Cesar S. ^[2]; McEwan, John C. ^[2]; Janssen, Peter H. ^[2]; Chen, Feng ^[3]; Visel, Axel ^[4] more »

USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Genomics Division; DOE/OSTI
AgResearch Limited, Palmerston (New Zealand). Grasslands Research Centre
USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Genomics Division
USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Genomics Division; Univ. of California, Merced, CA (United States). School of Natural Sciences

Ruminant livestock represent the single largest anthropogenic source of the potent greenhouse gas methane, which is generated by methanogenic archaea residing in ruminant digestive tracts. While differences between individual animals of the same breed in the amount of methane produced have been observed, the basis for this variation remains to be elucidated. To explore the mechanistic basis of this methane production, we measured methane yields from 22 sheep, which revealed that methane yields are a reproducible, quantitative trait. Deep metagenomic and metatranscriptomic sequencing demonstrated a similar abundance of methanogens and methanogenesis pathway genes in high and low methane emitters. However, transcription of methanogenesis pathway genes was substantially increased in sheep with high methane yields. These results identify a discrete set of rumen methanogens whose methanogenesis pathway transcription profiles correlate with methane yields and provide new targets for CH4 mitigation at the levels of microbiota composition and transcriptional regulation.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of California, Oakland, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1625629

Journal Information:: Genome Research, Journal Name: Genome Research Journal Issue: 9 Vol. 24; ISSN 1088-9051

Publisher:: Cold Spring Harbor Laboratory PressCopyright Statement

Country of Publication:: United States

Language:: English

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Evaluation of 16S rRNA Gene Primer Pairs for Monitoring Microbial Community Structures Showed High Reproducibility within and Low Comparability between Datasets Generated with Multiple Archaeal and Bacterial Primer Pairs Fischer, Martin A.; Güllert, Simon; Neulinger, Sven C. Frontiers in Microbiology, Vol. 7 https://doi.org/10.3389/fmicb.2016.01297	journal	August 2016
Diurnal Dynamics of Gaseous and Dissolved Metabolites and Microbiota Composition in the Bovine Rumen van Lingen, Henk J.; Edwards, Joan E.; Vaidya, Jueeli D. Frontiers in Microbiology, Vol. 8 https://doi.org/10.3389/fmicb.2017.00425	journal	March 2017
Enhancing the Resolution of Rumen Microbial Classification from Metatranscriptomic Data Using Kraken and Mothur Neves, Andre L. A.; Li, Fuyong; Ghoshal, Bibaswan Frontiers in Microbiology, Vol. 8 https://doi.org/10.3389/fmicb.2017.02445	journal	December 2017
Identification, Comparison, and Validation of Robust Rumen Microbial Biomarkers for Methane Emissions Using Diverse Bos Taurus Breeds and Basal Diets Auffret, Marc D.; Stewart, Robert; Dewhurst, Richard J. Frontiers in Microbiology, Vol. 8 https://doi.org/10.3389/fmicb.2017.02642	journal	January 2018
Comparative Analysis of the Microbiota Between Sheep Rumen and Rabbit Cecum Provides New Insight Into Their Differential Methane Production Mi, Lan; Yang, Bin; Hu, Xialu Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.00575	journal	March 2018
Evaluating Established Methods for Rumen 16S rRNA Amplicon Sequencing With Mock Microbial Populations McGovern, Emily; Waters, Sinéad M.; Blackshields, Gordon Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.01365	journal	June 2018
The Planktonic Core Microbiome and Core Functions in the Cattle Rumen by Next Generation Sequencing Wirth, Roland; Kádár, Gyula; Kakuk, Balázs Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.02285	journal	September 2018
Shifts of Hydrogen Metabolism From Methanogenesis to Propionate Production in Response to Replacement of Forage Fiber With Non-forage Fiber Sources in Diets in vitro Wang, Kun; Nan, Xuemei; Chu, Kangkang Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.02764	journal	November 2018
Applications of the Soil, Plant and Rumen Microbiomes in Pastoral Agriculture Attwood, Graeme T.; Wakelin, Steve A.; Leahy, Sinead C. Frontiers in Nutrition, Vol. 6 https://doi.org/10.3389/fnut.2019.00107	journal	July 2019
Climate Clever Clovers: New Paradigm to Reduce the Environmental Footprint of Ruminants by Breeding Low Methanogenic Forages Utilizing Haplotype Variation Kaur, Parwinder; Appels, Rudi; Bayer, Philipp E. Frontiers in Plant Science, Vol. 8 https://doi.org/10.3389/fpls.2017.01463	journal	September 2017
Rumen and Fecal Microbial Community Structure of Holstein and Jersey Dairy Cows as Affected by Breed, Diet, and Residual Feed Intake Noel, Samantha J.; Olijhoek, Dana W.; Mclean, Farran Animals, Vol. 9, Issue 8 https://doi.org/10.3390/ani9080498	journal	July 2019
Additional file 3 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants Xie, Fei; Jin, Wei; Si, Huazhe figshare https://doi.org/10.6084/m9.figshare.14775423.v1	dataset	January 2021
Additional file 4 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants Xie, Fei; Jin, Wei; Si, Huazhe figshare https://doi.org/10.6084/m9.figshare.14775429.v1	dataset	January 2021
Additional file 5 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants Xie, Fei; Jin, Wei; Si, Huazhe figshare https://doi.org/10.6084/m9.figshare.14775432	dataset	January 2021
Additional file 6 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants Xie, Fei; Jin, Wei; Si, Huazhe figshare https://doi.org/10.6084/m9.figshare.14775438	dataset	January 2021
Additional file 7 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants Xie, Fei; Jin, Wei; Si, Huazhe figshare https://doi.org/10.6084/m9.figshare.14775441	dataset	January 2021
Additional file 8 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants Xie, Fei; Jin, Wei; Si, Huazhe figshare https://doi.org/10.6084/m9.figshare.14775444	dataset	January 2021
Additional file 10 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293647	dataset	January 2022
Additional file 11 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293650.v1	image	January 2022
Additional file 12 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293656	image	January 2022
Additional file 13 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293659	image	January 2022
Additional file 14 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293662.v1	dataset	January 2022
Additional file 2 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293668	dataset	January 2022
Additional file 3 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293674	dataset	January 2022
Additional file 4 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293677.v1	dataset	January 2022
Additional file 5 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293680	image	January 2022
Additional file 6 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293686	image	January 2022
Additional file 7 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293689.v1	image	January 2022
Additional file 8 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293692.v1	dataset	January 2022
Additional file 9 of Exploring AI-2-mediated interspecies communications within rumen microbial communities Liu, Xiaozhen; Liu, Qinmeng; Sun, Sihuai figshare https://doi.org/10.6084/m9.figshare.21293695	image	January 2022
Additional file 1 of Effects of melatonin on rumen microorganisms and methane production in dairy cow: results from in vitro and in vivo studies Fu, Yao; Yao, Songyang; Wang, Tiankun figshare https://doi.org/10.6084/m9.figshare.24054628	dataset	January 2023
Additional file 2 of Effects of melatonin on rumen microorganisms and methane production in dairy cow: results from in vitro and in vivo studies Fu, Yao; Yao, Songyang; Wang, Tiankun figshare https://doi.org/10.6084/m9.figshare.24054631	dataset	January 2023
Additional file 1 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host Hess, Melanie K.; Hodgkinson, Hannah E.; Hess, Andrew S. figshare https://doi.org/10.6084/m9.figshare.24159502.v1	dataset	January 2023
Additional file 2 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host Hess, Melanie K.; Hodgkinson, Hannah E.; Hess, Andrew S. figshare https://doi.org/10.6084/m9.figshare.24159505.v1	dataset	January 2023
Additional file 3 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host Hess, Melanie K.; Hodgkinson, Hannah E.; Hess, Andrew S. figshare https://doi.org/10.6084/m9.figshare.24159508	dataset	January 2023
Additional file 4 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host Hess, Melanie K.; Hodgkinson, Hannah E.; Hess, Andrew S. figshare https://doi.org/10.6084/m9.figshare.24159511.v1	dataset	January 2023
Additional file 6 of Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host Hess, Melanie K.; Hodgkinson, Hannah E.; Hess, Andrew S. figshare https://doi.org/10.6084/m9.figshare.24159517.v1	dataset	January 2023
Additional file 1 of A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments Wu, Yingjian; Gao, Na; Sun, Chuqing figshare https://doi.org/10.6084/m9.figshare.25547037	dataset	January 2024
Additional file 1 of Analysis of differences in the rumen microbiome and metabolic function in prepartum dairy cows with different body condition scores Du, Dewei; Wang, Yanzhe; Gao, Yongji figshare https://doi.org/10.6084/m9.figshare.26094313.v1	dataset	January 2024
SUPERSEDED - Assembly of hundreds of microbial genomes from the cow rumen reveals novel microbial species encoding enzymes with roles in carbohydrate metabolism Watson, Mick The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh https://doi.org/10.7488/ds/2099	dataset	January 2017
Altering the Gut Microbiome of Cattle: Considerations of Host-Microbiome Interactions for Persistent Microbiome Manipulation Clemmons, Brooke A.; Voy, Brynn H.; Myer, Phillip R. Microbial Ecology, Vol. 77, Issue 2 https://doi.org/10.1007/s00248-018-1234-9	journal	July 2018
Comparative in vitro study of caecal microbial activity in brown hares and domestic rabbits which were offered the same diet Miśta, Dorota; Króliczewska, B.; Pecka-Kiełb, E. Mammal Research, Vol. 63, Issue 3 https://doi.org/10.1007/s13364-018-0357-1	journal	February 2018
Cow responses and evolution of the rumen bacterial and methanogen community following a complete rumen content transfer De Mulder, T.; Vandaele, L.; Peiren, N. The Journal of Agricultural Science, Vol. 156, Issue 8 https://doi.org/10.1017/s0021859618001053	journal	October 2018
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Review: Fifty years of research on rumen methanogenesis: lessons learned and future challenges for mitigation Beauchemin, K. A.; Ungerfeld, E. M.; Eckard, R. J. animal, Vol. 14, Issue S1 https://doi.org/10.1017/s1751731119003100	journal	February 2020
Culture- and metagenomics-enabled analyses of the Methanosphaera genus reveals their monophyletic origin and differentiation according to genome size Hoedt, Emily C.; Parks, Donovan H.; Volmer, James G. The ISME Journal, Vol. 12, Issue 12 https://doi.org/10.1038/s41396-018-0225-7	journal	August 2018
Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen Stewart, Robert D.; Auffret, Marc D.; Warr, Amanda Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-03317-6	journal	February 2018
Supplementation of Pelleted Hazel (Corylus avellana) Leaves Decreases Methane and Urinary Nitrogen Emissions by Sheep at Unchanged Forage Intake Wang, Shaopu; Terranova, Melissa; Kreuzer, Michael Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-23572-3	journal	April 2018
Methylotrophic methanogens everywhere — physiology and ecology of novel players in global methane cycling Söllinger, Andrea; Urich, Tim Biochemical Society Transactions, Vol. 47, Issue 6 https://doi.org/10.1042/bst20180565	journal	December 2019
Effects of livestock manure properties and temperature on the methanogen community composition and methane production during storage Guo, Guang; Chen, Yongxing; Tian, Fang Environmental Technology, Vol. 41, Issue 2 https://doi.org/10.1080/09593330.2018.1491640	journal	August 2018
SpaRC: scalable sequence clustering using Apache Spark Shi, Lizhen; Meng, Xiandong; Tseng, Elizabeth Bioinformatics, Vol. 35, Issue 5 https://doi.org/10.1093/bioinformatics/bty733	journal	August 2018
Metatranscriptomics reveals mycoviral populations in the ovine rumen Hitch, Thomas C. A.; Edwards, Joan E.; Gilbert, Rosalind A. FEMS Microbiology Letters, Vol. 366, Issue 13 https://doi.org/10.1093/femsle/fnz161	journal	July 2019
Poor feed efficiency in sheep is associated with several structural abnormalities in the community metabolic network of their ruminal microbes1 Patil, Rocky D.; Ellison, Melinda J.; Wolff, Sara M. Journal of Animal Science, Vol. 96, Issue 6 https://doi.org/10.1093/jas/sky096	journal	May 2018
Genetic parameters of plasma and ruminal volatile fatty acids in sheep fed alfalfa pellets and genetic correlations with enteric methane emissions1 Jonker, Arjan; Hickey, Sharon M.; McEwan, John C. Journal of Animal Science, Vol. 97, Issue 7 https://doi.org/10.1093/jas/skz162	journal	June 2019
Changes in rumen microbiota of cows in response to dietary supplementation with nitrate, linseed and saponin alone or in combination Popova, Milka; Guyader, Jessie; Silberberg, Mathieu bioRxiv https://doi.org/10.1101/383067	posted_content	January 2018
Deconvolute individual genomes from metagenome sequences through read clustering Li, Kexue; Wang, Lili; Shi, Lizhen https://doi.org/10.1101/620666	posted_content	April 2019
A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling Hess, Melanie K.; Rowe, Suzanne J.; Van Stijn, Tracey C. https://doi.org/10.1101/694133	posted_content	July 2019
Metatranscriptomic Profiling Reveals Linkages between the Active Rumen Microbiome and Feed Efficiency in Beef Cattle Li, Fuyong; Guan, Le Luo Applied and Environmental Microbiology, Vol. 83, Issue 9 https://doi.org/10.1128/aem.00061-17	journal	February 2017
Assessment of Rumen Microbiota from a Large Dairy Cattle Cohort Reveals the Pan and Core Bacteriomes Contributing to Varied Phenotypes Xue, Mingyuan; Sun, Huizeng; Wu, Xuehui Applied and Environmental Microbiology, Vol. 84, Issue 19 https://doi.org/10.1128/aem.00970-18	journal	July 2018
Buccal Swabbing as a Noninvasive Method To Determine Bacterial, Archaeal, and Eukaryotic Microbial Community Structures in the Rumen Kittelmann, Sandra; Kirk, Michelle R.; Jonker, Arjan Applied and Environmental Microbiology, Vol. 81, Issue 21 https://doi.org/10.1128/aem.02385-15	journal	August 2015
Changes in the Rumen Microbiota of Cows in Response to Dietary Supplementation with Nitrate, Linseed, and Saponin Alone or in Combination Popova, Milka; Guyader, Jessie; Silberberg, Mathieu Applied and Environmental Microbiology, Vol. 85, Issue 4 https://doi.org/10.1128/aem.02657-18	journal	November 2018
Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies Noecker, Cecilia; Chiu, Hsuan-Chao; McNally, Colin P. mSystems, Vol. 4, Issue 6 https://doi.org/10.1128/msystems.00579-19	journal	December 2019
The structure of microbial populations in Nelore GIT reveals inter-dependency of methanogens in feces and rumen Andrade, Bruno G. N.; Bressani, Flavia A.; Cuadrat, Rafael R. C. Journal of Animal Science and Biotechnology, Vol. 11, Issue 1 https://doi.org/10.1186/s40104-019-0422-x	journal	February 2020
Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation Kamke, Janine; Kittelmann, Sandra; Soni, Priya Microbiome, Vol. 4, Issue 1 https://doi.org/10.1186/s40168-016-0201-2	journal	October 2016
Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle Li, Fuyong; Hitch, Thomas C. A.; Chen, Yanhong Microbiome, Vol. 7, Issue 1 https://doi.org/10.1186/s40168-019-0618-5	journal	January 2019
Can rumen bacteria communicate to each other? Won, Mi-Young; Oyama, Linda B.; Courtney, Stephen J. Microbiome, Vol. 8, Issue 1 https://doi.org/10.1186/s40168-020-00796-y	journal	February 2020
Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance Xue, Ming-Yuan; Sun, Hui-Zeng; Wu, Xue-Hui Microbiome, Vol. 8, Issue 1 https://doi.org/10.1186/s40168-020-00819-8	journal	May 2020
Occurrence and expression of genes encoding methyl-compound production in rumen bacteria Kelly, William J.; Leahy, Sinead C.; Kamke, Janine Animal Microbiome, Vol. 1, Issue 1 https://doi.org/10.1186/s42523-019-0016-0	journal	November 2019
Methane Production in Dairy Cows Correlates with Rumen Methanogenic and Bacterial Community Structure Danielsson, Rebecca; Dicksved, Johan; Sun, Li Frontiers in Microbiology, Vol. 8 https://doi.org/10.3389/fmicb.2017.00226	journal	February 2017
Effectiveness of Interventions to Modulate the Rumen Microbiota Composition and Function in Pre-ruminant and Ruminant Lambs Saro, Cristina; Hohenester, Ulli M.; Bernard, Mickael Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.01273	journal	June 2018
Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future Huws, Sharon A.; Creevey, Christopher J.; Oyama, Linda B. Frontiers in Microbiology, Vol. 9 https://doi.org/10.3389/fmicb.2018.02161	journal	September 2018
Gut Microbiota of Wild and Captive Alpine Musk Deer (Moschus chrysogaster) Sun, Yewen; Sun, Yujiao; Shi, Zhihui Frontiers in Microbiology, Vol. 10 https://doi.org/10.3389/fmicb.2019.03156	journal	January 2020
Supplementation of Pelleted Hazel (Corylus avellana) Leaves Decreases Methane and Urinary Nitrogen Emissions by Sheep at Unchanged Forage Intake S., Wang,; Melissa, Terranova,; Michael, Kreuzer, ETH Zurich https://doi.org/10.3929/ethz-b-000254685	text	January 2018

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