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Record W2560717368 · doi:10.1182/blood.v110.11.705.705

Low Cytosolic Non-Heme Iron Levels in Erythroid Cells Prevent IRP2-Mediated Ferritin Upregulation during Differentiation.

2007· article· en· W2560717368 on OpenAlexaff
Matthias Schranzhofer, Manfred Schifrer, Bruno Galy, Matthias W. Hentze, Prem Ponka, Muellner W. Ernst

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsMcGill University
Fundersnot available
KeywordsFerritinTransferrin receptorHemeBiochemistryChemistryCytosolFerricCell biologyTransferrinBiologyEnzymeInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract Erythroid cells are the major consumers of iron in the human body. Differentiating erythroid cells shuttle the metal with very high efficiency towards mitochondria for the formation of heme. To satisfy their high iron needs, developing red blood cells (RBC) have to sustain high expression of transferrin receptor 1 (TfR) despite increasing cellular iron concentration. Moreover, synthesis of ferritin must not be activated by incoming iron, since this would represent a counterproductive storage during the phase of high iron demand. Recently we have demonstrated that during terminal differentiation primary erythroid cells satisfy their exceptionally high requirements for iron by switching to a mode where the post-transcriptional, iron-dependent regulatory system, formed by iron responsive proteins (IRP1 and IRP2) and iron responsive elements (IREs), seems to sense a low-iron state. This occurs despite a massive net increase of iron import into the cell (Schranzhofer et al., Blood107:4159, 2006). To examine the hypothesis that erythroid cells have low non-heme iron levels in their cytosol, we experimentally increased the cytosolic iron pool by either inhibiting heme biosynthesis or overloading cells with iron. Both block of heme synthesis by either succinylacetone or isonicotinic acid hydrazide (INH) or administration of ferric ammonium citrate, resulted in a clear increase in ferritin levels. This increase was directly proportional to the increase in the cellular concentration of non-heme-iron. Moreover, the effect of INH, the inhibitor of 5-aminolevulinic acid (ALA) synthase, could be reversed by the addition of ALA. Strikingly, increases in ferritin expression upon perturbation of cellular iron homeostasis strongly correlated with the loss of IRE-binding activity of IRP2 but not IRP1, as determined by mobility shift assays. This suggests that IRP2 is the major regulator of ferritin expression in erythroid cells. To further elaborate on this observation, we cultured primary erythroblasts derived from IRP1−/− and IRP2−/− mice (kindly provided by Drs. M. Hentze and B. Galy). In agreement with the published phenotype of microcytic hypochromic anemia, only erythroblasts lacking IRP2 exhibited a reduction in hemoglobinization. Moreover, only IRP2−/− cells showed a significant increase in ferritin expression, whereas developing RBC lacking IRP1 had levels of ferritin protein equal to wild type cells. We conclude that in erythroid cells efficient shuttling of incoming iron towards mitochondria and its prompt use for heme formation is important to keep the cytosol in an iron-deprived state and consequently ferritin protein levels low. This translational repression seems to be mainly achieved by IRP2. Together with the observation that surface expression of TfR was reduced in IRP2−/− erythroblasts during self renewal but not during terminal differentiation, our results suggest that not only down-regulation of TfR, but also up-regulation of ferritin may be a major factor for the anemic phenotype observed in IRP2−/− mice.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.007
GPT teacher head0.228
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2007
Admission routes1
Has abstractyes

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