Evidence map›Paper›PMID 31063485›Full record

ArticlePloS one2019

Using fecal microbiota as biomarkers for predictions of performance in the selective breeding process of pedigree broiler breeders.

Sandra Díaz-Sánchez, Allison R Perrotta, Isaac Rockafellow, Eric J Alm, Ron Okimoto, Rachel Hawken, Irene Hanning

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Article
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  15. Application of Baltic Pine (Plants (Basel, Switzerland) · 2023
    Article
  16. Article
  17. Development and Evaluation of a Commercial Direct-Fed Microbial (ZymosporeAnimals : an open access journal from MDPI · 2022
    Article
  18. Ileal and cecal microbiota response toFrontiers in physiology · 2022
    Article
  19. Effect of DietaryFrontiers in veterinary science · 2022
    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

7 authors.

Sandra Díaz-SánchezDepartment of Food Science, University of Tennessee, Knoxville, TN, United States of America.
Allison R PerrottaDepartment of Civil & Environmental Engineering, MIT, Cambridge, MA, United States of America.
Isaac RockafellowDepartment of Biological Engineering, MIT, Cambridge, MA, United States of America.
Eric J AlmDepartment of Civil & Environmental Engineering, MIT, Cambridge, MA, United States of America.
Ron OkimotoCobb-Vantress, Inc. Research and Development, Genomics and Quantitative Genetics, Siloam Springs, AR, United States of America.
Rachel HawkenCobb-Vantress, Inc. Research and Development, Genomics and Quantitative Genetics, Siloam Springs, AR, United States of America.
Irene HanningThe Graduate School of Genome Sciences and Technology, University of Tennessee, Knoxville, TN, United States of America.ORCID 0000-0003-3886-9564

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Much work has been dedicated to identifying members of the microbial gut community that have potential to augment the growth rate of agricultural animals including chickens. Here, we assessed any correlations between the fecal microbiome, a proxy for the gut microbiome, and feed efficiency or weight gain at the pedigree chicken level, the highest tier of the production process. Because selective breeding is conducted at the pedigree level, our aim was to determine if microbiome profiles could be used to predict feed conversion or weight gain in order to improve selective breeding. Using 16s rRNA amplicon sequencing, we profiled the microbiomes of high and low weight gain (WG) birds and good and poor feed efficient (FE) birds in two pedigree lineages of broiler chickens. We also aimed to understand the dynamics of the microbiome with respect to maturation. A time series experiment was conducted, where fecal samples of chickens were collected at 6 points of the rearing process and the microbiome of these samples profiled. We identified OTUs differences at different taxonomic levels in the fecal community between high and low performing birds within each genetic line, indicating a specificity of the microbial community profiles correlated to performance factors. Using machine-learning methods, we built a classification model that could predict feed conversion performance from the fecal microbial community. With respect to maturation, we found that the fecal microbiome is dynamic in early life but stabilizes after 3 weeks of age independent of lineage. Our results indicate that the fecal microbiome profile can be used to predict feed conversion, but not weight gain in these pedigree lines. From the time series experiments, it appears that these predictions can be evaluated as early as 20 days of age. Our data also indicates that there is a genetic factor for the microbiome profile.

Indexed as

Animal FeedAnimalsBiomarkersChickensFecesMicrobiotaPedigreeRNA, Ribosomal, 16SSelective BreedingWeight GainBiomarkersRNA, Ribosomal, 16S

Identifiers

PMID31063485
PMCPMC6504170

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.