Use of milk proteins as biomarkers of changes in the rumen metaproteome of Holstein cows fed low-fiber, high-starch diets
Bibliographic record
Abstract
Dietary levels of undegraded neutral detergent fiber (uNDF240) and rumen fermentable starch (RFS) can impact the rumen microbiome and milk composition. The objective of the study is to investigate the use of milk proteins as biomarkers of rumen microbial activity through a comparative evaluation of the rumen microbial and milk protein profiles produced by Holstein cows fed diets with varying contents of physically effective uNDF240 (peuNDF240) and RFS. Eight ruminally cannulated lactating Holstein cows were included in a larger study as part of a 4 × 4 Latin square design with 4 28-d periods to assess 4 diets varying in peuNDF240 and RFS content. For this experiment, cows received one of 2 dietary treatments: 1) low peuNDF240, high RFS (LNHR) diet or 2) high peuNDF240, low RFS (HNLR) diet. Within each period, rumen fluid samples were collected from each cow on d 26 (1400 h) and d 27 (0600 h and 1000 h), and milk samples were collected from each cow on d 25 (2030 h), d 26 (0430 h, 1230 h, and 2030 h), and d 27 (0430 h and 1230 h). Microbial proteins were isolated from each rumen fluid sample. For milk samples, milk proteins were fractionated, and the whey fraction was subsequently isolated. Isolated proteins within each rumen fluid or milk sample were isobarically labeled and analyzed by LC-MS/MS. Product ion spectra acquired from rumen fluid samples were searched using SEQUEST against 71 composite databases. In contrast, product ion spectra acquired from milk samples were searched against the Bos taurus database. Data were analyzed using the PROC MIXED procedure in SAS 9.4 to assess the impact of diet and time of sampling. To increase stringency, the false discovery rate-adjusted P -value ( P FDR ) was also calculated to account for multiple comparisons. Using the mixed procedure, a total of 129 rumen microbial proteins were quantified across 24 searched microbial species. Of these, the abundance of 14 proteins across 9 microbial species was impacted due to diet and diet × time interaction, including 7 proteins associated with energetics pathways. Among the 159 quantified milk proteins, the abundance of 21 proteins was impacted due to the diet and diet × time interaction. The abundance of 19 of these milk proteins was impacted due to diet × time interactions. Of these, 16 proteins had the disparity across diets at the 0430 h sampling time, including proteins involved in host defense, nutrient synthesis, and transportation, suggesting that biological shifts resulting from diet-induced rumen changes are not diurnally uniform across milkings. The concentration of lipoprotein lipase (LPL) was statistically higher in the milk from the cows fed with the LNHR diet, which was numerically confirmed with an ELISA. Further, as determined by ELISA, the LPL concentration was significantly higher in the milk from the cows fed with the LNHR diet at 0430 h sampling point, suggesting that LPL concentration may indicate dietary carbohydrate-induced ruminal changes. The results of this study suggest that diet-induced rumen changes can be reflected in milk in a diurnal pattern, further highlighting the need to consider sampling time points for using milk proteins as a representative biomarker of rumen microbial activity.
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 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 | 0.001 | 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".