Evidence map›Paper›PMID 32804973›Full record

ArticlePloS one2020

Learning machine approach reveals microbial signatures of diet and sex in dog.

Elisa Scarsella, Bruno Stefanon, Michela Cintio, Danilo Licastro, Sandy Sgorlon, Simeone Dal Monego, Misa Sandri

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
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

21 citing papers in PubMed, 31 citations in OpenAlex.

  1. Article
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  4. Evaluation of extended-spectrumFrontiers in microbiology · 2025
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  10. Factors Affecting Gut Microbiota of Puppies from Birth to Weaning.Animals : an open access journal from MDPI · 2023
    Review
  11. Review
  12. Article
  13. Article
  14. Phenotypic correlates of the working dog microbiome.NPJ biofilms and microbiomes · 2022
    Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Review
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 at 2 institutions in 1 country.

Elisa ScarsellaDepartment of AgroFood, Environmental and Animal Sciences, University of Udine, Udine, Italy.ORCID 0000-0002-1463-4608
Bruno StefanonDepartment of AgroFood, Environmental and Animal Sciences, University of Udine, Udine, Italy.ORCID 0000-0002-7414-5830
Michela CintioDepartment of AgroFood, Environmental and Animal Sciences, University of Udine, Udine, Italy.
Danilo LicastroARGO Open Lab Platform for Genome sequencing, AREA Science Park, Padriciano, Trieste, Italy.
Sandy SgorlonDepartment of AgroFood, Environmental and Animal Sciences, University of Udine, Udine, Italy.
Simeone Dal MonegoARGO Open Lab Platform for Genome sequencing, AREA Science Park, Padriciano, Trieste, Italy.
Misa SandriDepartment of AgroFood, Environmental and Animal Sciences, University of Udine, Udine, Italy.
University of Udine · ITAREA Science Park · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The characterization of the microbial population of many niches of the organism, as the gastrointestinal tract, is now possible thanks to the use of high-throughput DNA sequencing technique. Several studies in the companion animals field already investigated faecal microbiome in healthy or affected subjects, although the methodologies used in the different laboratories and the limited number of animals recruited in each experiment does not allow a straight comparison among published results. In the present study, we report data collected from several in house researches carried out in healthy dogs, with the aim to seek for a variability of microbial taxa in the faeces, caused by factors such as diet and sex. The database contains 340 samples from 132 dogs, collected serially during dietary intervention studies. The procedure of samples collection, storage, DNA extraction and sequencing, bioinformatic and statistical analysis followed a standardized pipeline. Microbial profiles of faecal samples have been analyzed applying dimensional reduction discriminant analysis followed by random forest analysis to the relative abundances of genera in the feces as variables. The results supported the responsiveness of microbiota at a genera taxonomic level to dietary factor and allowed to cluster dogs according this factor with high accuracy. Also sex factor clustered dogs, with castrated males and spayed females forming a separated group in comparison to intact dogs, strengthening the hypothesis of a bidirectional interaction between microbiota and endocrine status of the host. The findings of the present analysis are promising for a better comprehension of the mechanisms that regulate the connection of the microorganisms living the gastrointestinal tract with the diet and the host. This preliminary study deserves further investigation for the identification of the factors affecting faecal microbiome in dogs.

Indexed as

Machine LearningMicrobiotaAlgorithmsAnimalsDietDiscriminant AnalysisDogsFecesFemaleMalePhylogenyPrincipal Component Analysis

Identifiers

PMID32804973
PMCPMC7431105
OpenAlexW3049727055

What OpenQuestion holds

Textmetadata
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.