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Diet shifts provoke complex and variable changes in the metabolic networks of the ruminal microbiome

Overview of attention for article published in Microbiome, June 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

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1 blog
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14 X users

Citations

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28 Dimensions

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58 Mendeley
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Title
Diet shifts provoke complex and variable changes in the metabolic networks of the ruminal microbiome
Published in
Microbiome, June 2017
DOI 10.1186/s40168-017-0274-6
Pubmed ID
Authors

Sara M. Wolff, Melinda J. Ellison, Yue Hao, Rebecca R. Cockrum, Kathy J. Austin, Michael Baraboo, Katherine Burch, Hyuk Jin Lee, Taylor Maurer, Rocky Patil, Andrea Ravelo, Tasia M. Taxis, Huan Truong, William R. Lamberson, Kristi M. Cammack, Gavin C. Conant

Abstract

Grazing mammals rely on their ruminal microbial symbionts to convert plant structural biomass into metabolites they can assimilate. To explore how this complex metabolic system adapts to the host animal's diet, we inferred a microbiome-level metabolic network from shotgun metagenomic data. Using comparative genomics, we then linked this microbial network to that of the host animal using a set of interface metabolites likely to be transferred to the host. When the host sheep were fed a grain-based diet, the induced microbial metabolic network showed several critical differences from those seen on the evolved forage-based diet. Grain-based (e.g., concentrate) diets tend to be dominated by a smaller set of reactions that employ metabolites that are nearer in network space to the host's metabolism. In addition, these reactions are more central in the network and employ substrates with shorter carbon backbones. Despite this apparent lower complexity, the concentrate-associated metabolic networks are actually more dissimilar from each other than are those of forage-fed animals. Because both groups of animals were initially fed on a forage diet, we propose that the diet switch drove the appearance of a number of different microbial networks, including a degenerate network characterized by an inefficient use of dietary nutrients. We used network simulations to show that such disparate networks are not an unexpected result of a diet shift. We argue that network approaches, particularly those that link the microbial network with that of the host, illuminate aspects of the structure of the microbiome not seen from a strictly taxonomic perspective. In particular, different diets induce predictable and significant differences in the enzymes used by the microbiome. Nonetheless, there are clearly a number of microbiomes of differing structure that show similar functional properties. Changes such as a diet shift uncover more of this type of diversity.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 17%
Researcher 8 14%
Student > Master 7 12%
Student > Bachelor 5 9%
Student > Doctoral Student 3 5%
Other 11 19%
Unknown 14 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 26%
Biochemistry, Genetics and Molecular Biology 10 17%
Medicine and Dentistry 3 5%
Immunology and Microbiology 3 5%
Veterinary Science and Veterinary Medicine 3 5%
Other 9 16%
Unknown 15 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 July 2017.
All research outputs
#2,173,304
of 23,498,099 outputs
Outputs from Microbiome
#860
of 1,511 outputs
Outputs of similar age
#42,971
of 318,445 outputs
Outputs of similar age from Microbiome
#33
of 41 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,511 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.0. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 318,445 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.