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Record W2073502661 · doi:10.1042/bst0300735

Iron targeting to mitochondria in erythroid cells

2002· article· en· W2073502661 on OpenAlexaff
Prem Ponka, Alex D. Sheftel, Andrew Zhang

Bibliographic record

VenueBiochemical Society Transactions · 2002
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsFerrochelataseEndosomeMitochondrionCell biologyTransferrinTransferrin receptorHemeEndocytosisProtoporphyrin IXChemistryWortmanninBiochemistryBiologyKinaseReceptorEnzyme

Abstract

fetched live from OpenAlex

Immature erythroid cells have an exceptionally high capacity to synthesize haem that is, at least in part, the result of the unique control of iron metabolism in these cells. In erythroid cells the vast majority of Fe released from endosomes must cross both the outer and the inner mitochondrial membranes to reach ferrochelatase, which inserts Fe into protoporphyrin IX. Based on the fact that Fe is specifically targeted into erythroid mitochondria, we have proposed that a transient mitochondria-endosome interaction is involved in Fe transfer to ferrochelatase [Ponka (1997) Blood 89, 1-25]. In this study, we examined whether the inhibition of endosome mobility within erythroid cells would decrease the rate of (59)Fe incorporation into haem. We found that, in reticulocytes, the myosin light-chain kinase inhibitor, wortmannin, and the calmodulin antagonist, W-7, caused significant inhibition of (59)Fe incorporation from (59)Fe-transferrin-labelled endosomes into haem. These results, together with confocal microscopy studies using transferrin and mitochondria labelled by distinct fluorescent markers, suggest that, in erythroid cells, endosome mobility, and perhaps their contact with mitochondria, plays an important role in a highly efficient utilization of iron for haem synthesis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.222
Teacher spread0.213 · 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 teacher head, not a consensus.

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

Citations24
Published2002
Admission routes1
Has abstractyes

Explore more

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