Global genomic approaches to the iron-regulated proteome.
Bibliographic record
Abstract
Iron interacts with cells to regulate the proteome through complex effects on gene expression. In simple organisms such as bacteria and yeast, intra- and extra-cellular iron influences gene expression through defined signal transduction pathways. In higher organisms, effects are probably mediated at the transcriptional level through secondary effects of reactive oxygen species, while post-transcriptional effects operate through well-defined pathways involving iron-regulatory proteins. To investigate the impact of iron levels on gene expression and the proteome, approaches such as differential display and subtractive hybridization have the advantage of surveying the entire genome. However, they are technically demanding and have given way to microarray techniques. To date, numerous microarray experiments with various organisms have not yielded any definitive picture of the role of iron. Common themes throughout such studies are that both iron excess and iron depletion influence expression of proteins related to energy metabolism, cell proliferation, matrix structure, and the metabolism of iron itself. That no consistent set of genes is involved from one study to the next probably results both from the uncertainties inherent in the technique and the biological variability of the systems under study. We briefly describe two types of iron-dependent microarray experiments from our laboratory to examine major cellular targets of iron toxicity. Using Affymetrix oligonucleotide arrays with cardiac cells, we found several hundred genes whose mRNA levels were affected by iron, including an increase in several genes responding to oxidative stress and a decrease in several kinases and phosphatases. In a simpler experiment using a human liver cell line with a limited cDNA array, we targeted 13 genes affected by iron chelation. Metabolic pathway analysis shows links of 5 of these through phorbol ester responsiveness, and additional links through prostaglandin E2. We conclude that definitive understanding of the complex iron-regulated proteome requires global gene approaches and rigorous interlaboratory standardization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".