Comparative analysis of gut microbiota of Chinese Kunming dog, German Shepherd dog, and Belgian Malinois dog
Bibliographic record
Abstract
Importance: The composition of the gut microbiota is essential for a dog's health and its adaptation to the environment.Different bacteria can produce the same essential metabolites beneficial to health owing to bacterial functional redundancy in microbial communities.Objective: This study examined the gut bacterial communities of dogs from different breeds, all kept under identical domestication conditions.Methods: Noninvasive sampling and 16S rRNA high-throughput sequencing were used to compare the composition and function of the gut microbiota of three dog breeds: the Chinese Kunming dog (CKD), German Shepherd dog (GSD), and Belgian Malinois dog (BMD). Results:The gut microbiota of the three dog breeds consisted of 257 species across 146 genera, 60 families, 35 orders, 15 classes, and 10 phyla.The dominant bacterial phyla across the three breeds were Firmicutes (57.44%),Fusobacteriota (28.86%), and Bacteroidota (7.63%), while the dominant bacterial genera across the three breeds were Peptostreptococcus (21.08%),Fusobacterium (18.50%),Lactobacillus (12.37%), and Cetobacter (10.29%).Further analysis revealed significant differences in the intestinal flora of the three breeds at the phylum and genus levels.The intestinal flora of BMD was significantly richer than that of CKD and GSD.The functional prediction and Kyoto Encyclopedia of Genes and Genomes analysis showed that the primary functions of the gut microbiota in these breeds were similar, with significant enrichment in various metabolic pathways, including carbohydrate and amino acid metabolism, secondary metabolite biosynthesis, and microbial metabolism in different environments.The intestinal flora of these breeds also played a crucial role in genetic information processing, including transcription, translation, replication, and material transport.Conclusions and Relevance: These results provide novel insights into the intestinal flora of intervention dogs and suggest novel methods to improve their health status, which help increase microbial diversity and normalize metabolite production in diseased dogs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".