Varied microbial community assembly and specialization patterns driven by early life microbiome perturbation and modulation in young ruminants
Bibliographic record
Abstract
Abstract Perturbations and modulations during early life are vital to affect gut microbiome assembly and establishment. In this study, we assessed how microbial communities shifted during calf diarrhea and with probiotic yeast supplementation (Saccharomyces cerevisiae var. boulardii, SCB) and determined the key bacterial taxa contributing to the microbial assembly shifts using a total of 393 fecal samples collected from 84 preweaned calves during an 8-week trial. Our results revealed that the microbial assembly patterns differed between healthy and diarrheic calves at 6- and 8-week of the trial, with healthy calves being stochastic-driven and diarrheic calves being deterministic-driven. The two-state Markov model revealed that SCB supplementation had a higher possibility to shift microbial assembly from deterministic- to stochastic-driven in diarrheic calves. Furthermore, a total of 23 and 21 genera were specific ecotypes to assembly patterns in SCB-responsive (SCB-fed calves did not exhibit diarrhea) and nonresponsive (SCB-fed calves occurred diarrhea) calves, respectively. Among these ecotypes, the area under a receiver operating characteristic curve revealed that Blautia and Ruminococcaceae UCG 014, two unidentified genera from the Ruminococcaceae family, had the highest predictiveness for microbial assembly patterns in SCB-responsive calves, while Prevotellaceae, Blautia, and Escherichia-Shigella were the most predictive bacterial taxa for microbial assembly patterns in SCB-nonresponsive calves. Our study suggests that microbiome perturbations and probiotic yeast supplementation serving as deterministic factors influenced assembly patterns during early life with critical genera being predictive for assembly patterns, which sheds light on mechanisms of microbial community establishment in the gut of neonatal calves during early life.
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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 | 0.000 | 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 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".