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Synonyms:
   Euryarchaeota 

Broader Terms:
   Archaea 
   Archaebacteria 
   Euryarchaeota 

More Specific:
   Archaeoglobi 
   Euryotes 
   Halobacteria 
   Methanobacteria 
   Methanococci 
   Methanomicrobia 
   Methanopyri 
   Thermococci 
   Thermoplasmata 
   environmental samples 
   unclassified 
 
 
Latest Articles on Euryarchaeota Woese et al. 1990 from uBioRSS
Molecular characterization of the thioredoxin system from Methanosarcinaac... - Latest Issue of European Journal of Biochemistry
Genomes in turmoil: quantification of genome dynamics in prokaryote superge... - BMC Biology


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61.  Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study.LinkIT
Saborío-Montero A, Gutiérrez-Rivas M, García-Rodríguez A, Atxaerandio R, Goiri I, López de Maturana E, Jiménez-Montero JA, Alenda R, González-Recio O
Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie J. Anim. Breed. Genet. Structural equation models to disentangle the biological relationship between microbiota and complex traits: Methane production in dairy cattle as a case of study. 36-48 10.1111/jbg.12444 The advent of metagenomics in animal breeding poses the challenge of statistically modelling the relationship between the microbiome, the host genetics and relevant complex traits. A set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed here to jointly analyse the host-metagenome-phenotype relationship. A non-recursive bivariate model was set as benchmark to compare the recursive model. The relative abundance of rumen microbes (RA), methane concentration (CH4 ) and the host genetics was used as a case of study. Data were from 337 Holstein cows from 12 herds in the north and north-west of Spain. Microbial composition from each cow was obtained from whole metagenome sequencing of ruminal content samples using a MinION device from Oxford Nanopore Technologies. Methane concentration was measured with Guardian® NG infrared gas monitor from Edinburgh Sensors during cow's visits to the milking automated system. A quarterly average from the methane eructation peaks for each cow was computed and used as phenotype for CH4 . Heritability of CH4 was estimated at 0.12 ± 0.01 in both the recursive and bivariate models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH4 ranged from -0.76 to 0.65 in the non-recursive bivariate model and from -0.68 to 0.69 in the recursive model. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the seven genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. These results suggest that rumen's whole metagenome recursively regulates methane emissions in dairy cows and that both CH4 and the microbiota compositions are partially controlled by the host genotype. © 2019 Blackwell Verlag GmbH. Saborío-Montero Alejandro A https://orcid.org/0000-0002-9840-0058 Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Madrid, Spain. Universitat Politècnica de València, Valencia, Spain. Gutiérrez-Rivas Mónica M https://orcid.org/0000-0001-9752-7498 Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Madrid, Spain. García-Rodríguez Aser A https://orcid.org/0000-0001-5519-6766 Departamento de Producción Animal, NEIKER-Tecnalia, Vitoria-Gasteiz, Spain. Atxaerandio Raquel R https://orcid.org/0000-0002-8906-8516 Departamento de Producción Animal, NEIKER-Tecnalia, Vitoria-Gasteiz, Spain. Goiri Idoia I https://orcid.org/0000-0002-6522-0085 Departamento de Producción Animal, NEIKER-Tecnalia, Vitoria-Gasteiz, Spain. López de Maturana Evangelina E https://orcid.org/0000-0001-9425-3911 Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Madrid, Spain. Jiménez-Montero José Antonio JA Departamento técnico de Confederación de Asociaciones de Frisona Española (CONAFE), Madrid, Spain. Alenda Rafael R Departamento de Producción Agraria, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain. González-Recio Oscar O https://orcid.org/0000-0002-9106-4063 Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Madrid, Spain. Departamento de Producción Agraria, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain. eng RTA2015-00022-CO3 (METALGEN) national plan of rese national plan of research, development and innovation 2013-2020 RTA2015-00022-CO3 National Plan of Research, Development and Innovation 2013-2020 Journal Article 2019 10 15 Germany J Anim Breed Genet 100955807 0931-2668 IM genetic correlation heritability metagenome methane emissions recursive model structural equation models 2019 07 02 2019 09 17 2019 09 18 2019 10 17 6 0 2019 10 17 6 0 2019 10 17 6 0 ppublish 31617268 10.1111/jbg.12444 REFERENCES, 2020</i></font><br><font color=#008000>http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=0<br></font></span><br>62.  <a href=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=0 class=title>Salicibibacter halophilus sp. nov., a moderately 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