Early and gland-specific changes in gene expression in response to LPS-induced mastitis in a single quarter of Holstein cows
Bibliographic record
Abstract
Abstract We previously reported changes in gene expression in mammary tissue from non-inflamed mammary glands adjacent to an inflamed gland challenged with lipopolysaccharide (LPS). We determined if changes in the expression of selected genes in non-inflamed glands would be replicated in RNA isolated from milk fat. Cows were milked twice daily prior to experiment. Per cow, one mammary gland (QLPS) was randomly assigned to receive an intramammary infusion of 50 µg LPS immediately after morning milking on d-0. The ipsilateral (QI) and contralateral (QC) mammary glands adjacent to QLPS remained untreated. Quarter foremilk samples from all mammary glands were collected on d-1 and d-0 for milk composition and isolation of RNA for quantification of selected genes via quantitative polymerase chain reaction. Symptoms of clinical mastitis developed only in QLPS and were apparent within 3 h post-challenge. In QI and QC, lactose percentages were lower at 12 h post-challenge compared to d-1, but milk fat and protein contents were not different. For gene expression, 7 of 13 selected genes were differentially regulated in non-inflamed glands. In QI but not QC, LALBA expression was lower at 12 h post-challenge than on d-1. One gene of interest, LPIN1, was significantly upregulated in QI and QC but downregulated in QLPS at 12 h post-challenge. Five additional immune or stress-related genes were significantly upregulated in QLPS and, to a lesser but significant degree, in QI and QC compared to d-1. Notably, expression of two immune genes (NFKBIA, PTX3) was significantly greater in QI than QC despite QI having a numerically lower somatic cell count. Minor changes in the composition of milk secreted by non-inflamed mammary glands were linked to several immune and stress responses in those glands. Further, individual non-inflamed mammary glands responded differently depending on their position relative to the mastitic gland.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".