Heat Shock Alters the Proteomic Profile of Equine Mesenchymal Stem Cells
Bibliographic record
Abstract
The aim of this research was to determine the impact of heat stress on cell differentiation in an equine mesenchymal stem cell model (EMSC) through the application of heat stress to primary EMSCs as they progressed through the cell specialization process. A proteomic analysis was performed using mass spectrometry to compare relative protein abundances among the proteomes of three cell types: progenitor EMSCs and differentiated osteoblasts and adipocytes, maintained at 37 °C and 42 °C during the process of cell differentiation. A cell-type and temperature-specific response to heat stress was observed, and many of the specific differentially expressed proteins were involved in cell-signaling pathways such as Notch and Wnt signaling, which are known to regulate cellular development. Furthermore, cytoskeletal proteins profilin, DSTN, SPECC1, and DAAM2 showed increased protein levels in osteoblasts differentiated at 42 °C as compared with 37 °C, and these cells, while they appeared to accumulate calcium, did not organize into a whorl agglomerate as is typically seen at physiological temperatures. This altered proteome composition observed suggests that heat stress could have long-term impacts on cellular development. We propose that this in vitro stem cell culture model of cell differentiation is useful for investigating molecular mechanisms that impact cell development in response to stressors.
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 | 0.000 | 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".