Taxonomic differences of gut microbiomes drive cellulolytic enzymatic potential within hind-gut fermenting mammals
Bibliographic record
Abstract
Host diet influences the diversity and metabolic activities of the gut microbiome. Previous studies have shown that the gut microbiome provides a wide array of enzymes that enable processing of diverse dietary components. Because the primary diet of the porcupine, Erethizon dorsatum, is lignified plant material, we reasoned that the porcupine microbiome would be replete with enzymes required to degrade lignocellulose. Here, we report on the bacterial composition in the porcupine microbiome using 16S rRNA sequencing and bioinformatics analysis. We extended this analysis to the microbiomes of 20 additional mammals located in Shubenacadie Wildlife Park (Nova Scotia, Canada), enabling the comparison of bacterial diversity amongst three mammalian taxonomic orders (Rodentia, Carnivora, and Artiodactyla). 16S rRNA sequencing was validated using metagenomic shotgun sequencing on selected herbivores (porcupine, beaver) and carnivores (coyote, Arctic wolf). In the microbiome, functionality is more conserved than bacterial composition, thus we mined microbiome data sets to identify conserved microbial functions across species in each order. We measured the relative gene abundances for cellobiose phosphorylase, endoglucanase, and beta-glucosidase to evaluate the cellulose-degrading potential of select mammals. The porcupine and beaver had higher proportions of genes encoding cellulose-degrading enzymes than the Artic wolf and coyote. These findings provide further evidence that gut microbiome diversity and metabolic capacity are influenced by host diet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".