Evidence map›Paper›PMID 28595639›Full record

ArticleMicrobiome2017

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

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 and 6 more

Abstract read
In one paragraph

Article in Microbiome, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed.

  1. Article
  2. Review
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  13. Plants (Basel, Switzerland) · 2024
    Article
  14. Article
  15. International journal of molecular sciences · 2023
    Article
  16. Article
  17. Article
  18. Rumen Microbiome Reveals the Differential Response of COAnimals : an open access journal from MDPI · 2022
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Sara M WolffDivision of Animal Sciences, University of Missouri-Columbia, Columbia, MO, USA.
Melinda J EllisonDepartment of Animal and Veterinary Science, University of Idaho, Moscow, ID, USA.
Yue HaoInformatics Institute, University of Missouri-Columbia, Columbia, MO, USA.
Rebecca R CockrumDepartment of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
Kathy J AustinDepartment of Animal Science, University of Wyoming, Laramie, WY, USA.
Michael BarabooDepartment of Computer Science, Truman State University, Kirksville, MO, USA.
Katherine BurchDepartment of Psychology, Truman State University, Kirksville, MO, USA.
Hyuk Jin LeeDivision of Biological Sciences, University of Missouri-Columbia, Columbia, MO, USA.
Taylor MaurerDepartment of Biology, Kenyon College, Gambier, Ohio, USA.
Rocky PatilDivision of Animal Sciences, University of Missouri-Columbia, Columbia, MO, USA.
Andrea RaveloDivision of Biological Sciences, University of Missouri-Columbia, Columbia, MO, USA.
Tasia M TaxisNational Animal Disease Center, ARS, USDA, Ames, IA, USA.
Huan TruongInformatics Institute, University of Missouri-Columbia, Columbia, MO, USA.
William R LambersonDivision of Animal Sciences, University of Missouri-Columbia, Columbia, MO, USA.
Kristi M CammackDepartment of Animal Sciences, South Dakota State University, Brookings, SD, USA.
Gavin C ConantDivision of Animal Sciences, University of Missouri-Columbia, Columbia, MO, USA. gconant@ncsu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGrazing 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.

resultsUsing 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.

conclusionWe 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.

Indexed as

DietMetabolic Networks and PathwaysMetagenomicsAnimal FeedAnimalsDigestionEdible GrainFeeding BehaviorGastrointestinal MicrobiomeRumenSheepmetabolic networkmetagenomicsvertebrate microbiome

Identifiers

PMID28595639
PMCPMC5465553

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.