Thioredoxin-interacting (TXNIP) protein regulates the differentiation of erythroid precursors
Bibliographic record
Abstract
T protein (TXNIP) is involved in various cellular processes includingredox control, metabolism, differentiation, growth and apoptosis. With respect tohematopoiesis, TXNIP has been shown to play roles in natural killer cells, dendritic cells andhematopoietic stem cells. Our study investigates the role of TXNIP in erythropoiesis. Weobserved a rapid and significant increase of TXNIP transcript and protein levels in mouseerythroleukemia (MEL) cells treated with DMSO or HMBA, inducers of erythroiddifferentiation. The upregulation of TXNIP was not abrogated by addition of the antioxidantN-acetylcysteine. The increase of TXNIP expression was confirmed in another model oferythroid differentiation, G1E-ER cells, which undergo differentiation upon activation of theGATA1 transcription factor. In addition, we showed that TXNIP levels are inducedfollowing inhibition of p38 or JNK MAPKs. We also observed an increase in iron uptakeand a decrease in transferrin receptor protein upon TXNIP overexpression, suggesting a rolein iron homeostasis. In vivo, flow cytometry analysis of cells from TXNIP-/mice revealed anew phenotype of impaired terminal erythropoiesis in the spleen, characterized by a partialblock between basophilic and late basophilic/polychromatic erythroblasts. Based on our data,TXNIP emerges as a novel regulator of terminal erythroid differentiation.
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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".