The Development of Microbiota and Metabolome in Small Intestine of Sika Deer (Cervus nippon) from Birth to Weaning
Bibliographic record
Abstract
The dense and diverse community of microorganisms inhibiting the gastrointestinal tract of ruminant animals plays critical roles in the metabolism and absorption of nutrients, and gut associated immune function. Understanding microbial colonization in the small intestine of new born ruminants is a vital first step towards manipulating gut function through interventions during early life to produce long-term positive effects on host productivity and health. Yet the knowledge of microbiota colonization and its induced metabolites of small intestine during early life is still limited. In the present study, we examined the microbiota and metabolome in the jejunum and ileum of neonatal sika deer (Cervus nippon) from birth to weaning at days 1, 42 and 70. The microbial data showed that diversity and richness were increased with age, but a highly individual variation was observed at day 1. Principal coordinate analysis revealed significant differences in microbial community composition across three time points in the jejunum and ileum. The abundance of Halomonas spp., Lactobacillus spp., Escherichia-Shigella and Bacteroides spp. tended to be decreased, while the proportion of Intestinibacter spp., Cellulosilyticum spp., Turicibacter spp., Clostridium sensu stricto 1 and Romboutsia spp. was significantly increased with age. For metabolome, metabolites separated from each other across the three time points in both jejunum and ileum. Moreover, the amounts of methionine, threonine, and putrescine were increased, while the amounts of myristic acid and pentadecanoic acid were decreased with age, respectively. The present study demonstrated that microbiota colonization and the metabolome becomes more developed in the small intestine with age. This may shed new light on the microbiota-metabolome-immune interaction during development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".