Fluctuations of Intracellular Iron Modulate Elastin Production
Bibliographic record
Abstract
Production of insoluble elastin, the major component of elastic fibers, can be modulated by numerous intrinsic and exogenous factors. Because patients with hemolytic disorders characterized with fluctuations in iron concentration demonstrate defective elastic fibers, we speculated that iron might also modulate elastogenesis. In the present report we demonstrate that treatment of cultured human skin fibroblasts with low concentration of iron 2-20 microm (ferric ammonium citrate) induced a significant increase in the synthesis of tropoelastin and deposition of insoluble elastin. Northern blot and real-time reverse transcription-PCR analysis revealed that treatment with 20 microm iron led to an increase of approximately 3-fold in elastin mRNA levels. Because treatment with an intracellular iron chelator, desferrioxamine, caused a significant decrease in elastin mRNA level and consequent inhibition of elastin deposition, we conclude that iron facilitates elastin gene expression. Our experimental evidence also demonstrates the existence of an opposite effect, in which higher, but not cytotoxic concentrations of iron (100-400 microm) induced the production of intracellular reactive oxygen species that coincided with a significant decrease in elastin message stability and the disappearance of iron-dependent stimulatory effect on elastogenesis. This stimulatory elastogenic effect was reversed, however, in cultures simultaneously treated with high iron concentration (200 microm) and the intracellular hydroxyl radical scavenger, dimethylthiourea. Thus, presented data, for the first time, demonstrate the existence of two opposite iron-dependent mechanisms that may affect the steady state of elastin message. We speculate that extreme fluctuations in intracellular iron levels result in impaired elastic fiber production as observed in hemolytic diseases.
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".