Induction of tissue- and stressor-specific kinomic responses in chickens exposed to hot and cold stresses
Bibliographic record
Abstract
Defining cellular responses at the level of global cellular kinase (kinome) activity is a powerful approach to deciphering complex biology and identifying biomarkers. Here we report on the development of a chicken-specific peptide array and its application to characterizing kinome responses within the breast (pectoralis major) and thigh (iliotibialis) muscles of poultry subject to temperature stress to mimic conditions experienced by birds during commercial transport. Breast and thigh muscles exhibited unique kinome profiles, highlighting the distinct nature of these tissues. Against these distinct backgrounds, tissue- and temperature-specific kinome responses were observed. In breast, both cold and hot stresses activated calcium-dependent metabolic adaptations. Also within breast, but specific to cold stress, was the activation of ErbB signaling as well as dynamic patterns of phosphorylation of AMPK, a key regulatory enzyme of metabolism. In thigh, cold stress induced responses suggestive of the occurrence of tissue damage, including activation of innate immune signaling pathways and tissue repair pathways (TGF-β). In contrast, heat stress in thigh activated pathways associated with protein and fat metabolism through adipocytokine and mammalian target of rapamycin (mTOR) signaling. Defining the responses of these tissues to these stresses through conventional markers of pH, glycolytic potential, and meat quality offered a similar conclusion of the tissue- and stressor-specific responses, validating the kinome results. Collectively, the results of this study highlight the unique cellular responses of breast and thigh tissues to heat and cold stresses and may offer insight into the unique susceptibilities, as well as functional consequences, of these tissues to thermal stress.
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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".