Genetic variations in the <i>SPP1</i> promoter affect gene expression and the level of osteopontin secretion into bovine milk
Bibliographic record
Abstract
Osteopontin (OPN) is now recognized as an important cytokine and extracellular integrin-binding protein at the crossroads of inflammation and homeostasis. In a previous study, we found that OPN gene (SPP1) polymorphisms are associated with milk performance traits and somatic cell score (SCS), a parameter used to estimate the genetic value of udder health in dairy cattle. In this study, we assessed whether the genetic variations had an impact on SPP1 promoter activity, immune response and the level of OPN secreted into milk. The influence of DNA polymorphisms on the promoter activity of SPP1 was confirmed in vitro. To measure the impact of the genetic variations on OPN secretion into milk, we measured OPN levels in both plasma and milk throughout lactation. Cows were grouped by the OPN haplotypes associated with a high (H2 × H3) or low (H1 × H4) SCS. For both H2 × H3 and H1 × H4, the OPN level in plasma remained low throughout lactation, although the concentration in the milk of H1 × H4 cows increased more in late lactation. Moreover, the macrophages of H1 × H4 cows expressed a lower SPP1 and proinflammatory IL6 in response to infection. Regarding the immune cell response, cows with the genetic potential to secrete higher OPN levels during late lactation had macrophages expressing fewer proinflammatory cytokines, a situation that might explain the genetic association with low somatic cells. Although OPN's favorable roles during late lactation remain to be elucidated, the tissue remodeling properties associated with OPN may be beneficial for reducing the incidence of infection during the transition period in lactating cows.
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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".