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

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study design
PMID PubMed identifier for scientific articles.
DOI Digital object identifier for electronic documents.
Scarsella E, Stefanon B, Cintio M, Licastro D, Sgorlon S, Dal Monego S, Sandri M
PloS one
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.

Experiment 1


Curated date: 2023/11/07

Curator: Davvve

Revision editor(s): Davvve


Location of subjects
Host species Species from which microbiome was sampled. Contact us to have more species added.
Canis lupus familiaris
Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology
Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces
Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology
diet Dietary,Diets,diet

Lab analysis

Sequencing type
16S variable region One or more hypervariable region(s) of the bacterial 16S gene
Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).
relative abundances
Statistical test
Significance threshold p-value or FDR threshold used for differential abundance testing (if any)
MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?
Matched on Factors on which subjects have been matched on in a case-control study
diet, sex