Effect of an intramammary lipopolysaccharide challenge on the hindgut microbial composition and fermentation of dairy cattle experiencing intermittent subacute ruminal acidosis
Bibliographic record
Abstract
Feeding grain-rich diets often results in subacute ruminal acidosis (SARA), a condition associated with ruminal dysbiosis and systemic inflammation. Yet, the effect of SARA on hindgut microbiota, and whether this condition is aggravated by exogenous immune stimuli, is less understood. Therefore, the aims of this study were to determine the effects of an intermittent high-grain SARA model on the hindgut microbial community, and to evaluate whether the effects of SARA on the fecal microbiome and fermentation were further affected by an intramammary lipopolysaccharide (LPS) challenge. A total of 18 early-lactating Simmental cows were divided into 3 groups (n = 6); 2 were fed a SARA-inducing feeding regimen (60% concentrate), 1 was fed a control (CON) diet (40% concentrate). On d 30, 1 SARA group (SARA-LPS) and the CON group (CON-LPS) were intramammarily challenged with a single dose of 50 µg of LPS from Escherichia coli O26:B6, whereas the remaining 6 SARA cows (SARA-PLA) received a placebo. Using a longitudinal randomized controlled design, with grouping according to parity and days in milk), statistical analysis was performed with baseline measurements used as a covariate in a mixed model procedure. The SARA-inducing feeding challenge resulted in decreased fecal pH and increased butyrate as a proportion of total short-chain fatty acids in the feces. On d 30, SARA-challenged cows had decreased fecal diversity as shown by the Shannon and Chao1 indices and a decrease in the relative abundance of Euryarchaeota and cellulolytic genera, and numerical increases in the relative abundance of several Firmicutes associated with starch and secondary fermentation. The LPS challenge did not affect the fecal pH and short-chain fatty acids, but increased the Chao1 richness index in an interaction with the SARA challenge, and affected the relative abundance of Verrucomicrobia (1.13%), Actinobacteria (0.19%), and Spirochaetes (0.002%), suggesting an effect on the microbial ecology of the hindgut during SARA conditions. In conclusion, the SARA-inducing feeding regimen promoted important microbial changes at d 30, including reduced diversity and evenness compared with CON, whereas the external LPS challenge led to changes in the microbial community without affecting fecal fermentation properties.
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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 | 0.000 | 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 | 0.001 |
| 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".