Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle's feed efficiency

Guan, Le Luo; Nkrumah, J. D.; Basarab, J. A.; Moore, S. S.

doi:10.1111/j.1574-6968.2008.01343.x

Cited by 238 publications

(207 citation statements)

References 22 publications

(30 reference statements)

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“…Such effects are supported by numerous studies that have shown different animal species, even those that are closely related, possess host-specific microbiomes [41,42,83,[121][122][123] and by the finding that strains of a single microbial species that are isolated from different host species are genetically distinct and more adept for survival in their host of origin [10]. Diet is of critical importance to many, if not all microbiomes.…”

Section: Host Influences-the Tertiary Evolutionary Pressuresmentioning

confidence: 54%

Towards an Evolutionary Model of Animal-Associated Microbiomes

Yeoman

Chia

Yıldırım

et al. 2011

Entropy

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Second-generation sequencing technologies have granted us greater access to the diversity and genetics of microbial communities that naturally reside endo-and ecto-symbiotically with animal hosts. Substantial research has emerged describing the diversity and broader trends that exist within and between host species and their associated microbial ecosystems, yet the application of these data to our evolutionary understanding of microbiomes appears fragmented. For the most part biological perspectives are based on limited observations of oversimplified communities, while mathematical and/or computational modeling of these concepts often lack biological precedence. In recognition of this disconnect, both fields have attempted to incorporate ecological theories, although their applicability is currently a subject of debate because most ecological theories were developed based on observations of macro-organisms and their ecosystems. For the OPEN ACCESSEntropy 2011, 13 571 purposes of this review, we attempt to transcend the biological, ecological and computational realms, drawing on extensive literature, to forge a useful framework that can, at a minimum be built upon, but ideally will shape the hypotheses of each field as they move forward. In evaluating the top-down selection pressures that are exerted on a microbiome we find cause to warrant reconsideration of the much-maligned theory of multi-level selection and reason that complexity must be underscored by modularity.

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Section: Host Influences-the Tertiary Evolutionary Pressuresmentioning

confidence: 54%

Towards an Evolutionary Model of Animal-Associated Microbiomes

Yeoman

Chia

Yıldırım

et al. 2011

Entropy

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“…Low-RFI animals (Nkrumah et al 2006;Hegarty et al 2007). Comparisons of ruminal microbiomes between low-and high-RFI animals using a variety of methods have shown differences in bacterial and archaeal community profiles correlated with RFI, although these associations are often influenced by the energy content of the diet (Guan et al 2008;Zhou et al 2010;Carberry et al 2012;Hernandez-Sanabria et al 2012). Methanogen-related differences observed in these studies included a specific high-RFI-related PCR-DGGE band associated with Methanobrevibacter smithii PS (Zhou et al 2010), an elevated abundance of Methanosphaera stadtmanae, and Methanobrevibacter sp.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2014

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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 CH 4 mitigation at the levels of microbiota composition and transcriptional regulation.

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“…These methods using a non-targeted approach are able to pick up differences and similarities in the community of different microbial groups in the rumen associated with host ruminant species, diet and feed efficiency (Larue et al, 2005;Sadet et al, 2007;Guan et al, 2008 and references above; Suen et al, 2011) but they do not provide direct sequence information. In addition, the number of bands or peaks detected by these techniques is several orders of magnitude lower than the estimated diversity in the rumen (Hess et al, 2011;Kim et al, 2011).…”

Section: Rumen Microbial Diversitymentioning

confidence: 99%

Rumen microbial (meta)genomics and its application to ruminant production

Morgavi

Kelly

Janssen

et al. 2013

Animal

194

151

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Meat and milk produced by ruminants are important agricultural products and are major sources of protein for humans. Ruminant production is of considerable economic value and underpins food security in many regions of the world. However, the sector faces major challenges because of diminishing natural resources and ensuing increases in production costs, and also because of the increased awareness of the environmental impact of farming ruminants. The digestion of feed and the production of enteric methane are key functions that could be manipulated by having a thorough understanding of the rumen microbiome. Advances in DNA sequencing technologies and bioinformatics are transforming our understanding of complex microbial ecosystems, including the gastrointestinal tract of mammals. The application of these techniques to the rumen ecosystem has allowed the study of the microbial diversity under different dietary and production conditions. Furthermore, the sequencing of genomes from several cultured rumen bacterial and archaeal species is providing detailed information about their physiology. More recently, metagenomics, mainly aimed at understanding the enzymatic machinery involved in the degradation of plant structural polysaccharides, is starting to produce new insights by allowing access to the total community and sidestepping the limitations imposed by cultivation. These advances highlight the promise of these approaches for characterising the rumen microbial community structure and linking this with the functions of the rumen microbiota. Initial results using high-throughput culture-independent technologies have also shown that the rumen microbiome is far more complex and diverse than the human caecum. Therefore, cataloguing its genes will require a considerable sequencing and bioinformatic effort. Nevertheless, the construction of a rumen microbial gene catalogue through metagenomics and genomic sequencing of key populations is an attainable goal. A rumen microbial gene catalogue is necessary to understand the function of the microbiome and its interaction with the host animal and feeds, and it will provide a basis for integrative microbiome–host models and inform strategies promoting less-polluting, more robust and efficient ruminants.

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Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle's feed efficiency

Cited by 238 publications

References 22 publications

Towards an Evolutionary Model of Animal-Associated Microbiomes

Towards an Evolutionary Model of Animal-Associated Microbiomes

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

Rumen microbial (meta)genomics and its application to ruminant production

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