PSXII-23 iTRAQ-based proteomic analysis reveals key proteins affecting feed efficiency in Holstein heifers limit-fed with low or high concentrate diets.
Bibliographic record
Abstract
Limit-feeding high concentrate diets was proposed as an effective method to improve feed efficiency in heifers’ raising, while the physiological mechanism behind was still unclear. The objective of this study was to evaluate the effects of limit-feeding different levels of concentrate diets on liver proteomics by iTRAQ in Holstein heifers. Twelve half-sib Holstein heifers (8 to 10 months and 253 ± 29 kg of BW) were assigned into two groups and fed diets containing different levels of concentrate (20% and 80%, namely C20 and C80, respectively) for 28 days. The quantity of diets provided to high concentrate groups was restricted so that there was a similar intake of metabolizable energy to low concentrate fed groups. At the end of experiment, liver biopsies were obtained and iTRAQ-8Plex were performed. Data were analyzed using the PROC MIXED procedure of SAS. The C80 group had higher (P < 0.01) feed efficiency (average daily gain/dry matter intake) than C20 group. In total, 25,633 peptides corresponding to 3,499 proteins were detected from liver proteomic analysis. Comparison of expression patterns between C20 and C80 revealed 60 differentially expressed proteins (DEPs) (fold change > 1.2; P < 0.05), of which 28 were up-regulated and 32 were down-regulated in the C20 group. Functional annotation suggested that three DEPs (GOT2, GLUD1, PSPH) involved in amino acid metabolism, four DEPs (ECHS1, UGT1A6, UGDH, ALDOB) involved in carbohydrate metabolism, six DEPs (ACOX1, ACSS3, ACAT1, HMGCS2, CYP2E1, CYP1A1) involved in lipid metabolism, and three DEPs (EPHX1, GSTP1, CBR1) involved in cell redox homeostasis were related to the energy metabolism in our study. Nine DEPs were analysed using parallel reaction monitoring to confirm the reliability of the iTRAQ analysis. Our results demonstrated that amino acids, carbohydrate, and lipid metabolism and oxidative reduction pathways in the liver were all involved energy utilization in high or low concentrate limit-fed heifers. Key Words:
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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 | 0.000 |
| 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".