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Record W1983624151 · doi:10.1039/c4mt00315b

Iron-dependent turnover of IRP-1/c-aconitase in kidney cells

2015· article· en· W1983624151 on OpenAlexafffund
Ying Liu, Douglas M. Templeton

Bibliographic record

VenueMetallomics · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon Transport and Channel Regulation
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAconitaseChemistryKidneyCell biologyProtein turnoverGlutamate receptorBiochemistryBiologyProtein biosynthesisMitochondrionEndocrinologyReceptor

Abstract

fetched live from OpenAlex

The kidney plays an important role in iron homeostasis and actively reabsorbs citrate. The bifunctional iron-regulatory protein IRP-1 potentially regulates iron trafficking and participates in citrate metabolism as a cytosolic (c-) aconitase. We investigated the role of cellular iron status in determining the expression and dynamics of IRP-1 in two renal cell types, with the aim of identifying a role of the protein in cellular ROS levels, citrate metabolism and glutamate production. The effects of iron supplementation and chelation on IRP-1 protein and mRNA levels and protein turnover were compared in cultured primary rat mesangial cells and a porcine renal tubule cell line (LLC-PK1). Levels of ROS were measured in both cell types, and c-aconitase activity, glutamate, and glutathione were measured in LLC-PK1 cells, with and without IRP-1 silencing and in glutamine-supplemented or nominally glutamine-free medium. Iron supplementation decreased IRP-1 levels (e.g., approx. 40% in mesangial cells treated with 10 μg ml(-1) iron for 16 h) and increased ubiquitinated IRP-1 levels in both cells types, with iron chelation having the opposite effect. Although iron increased ROS levels (three-fold with 20 μg ml(-1) iron in mesangial cells and more modestly by about 30% with 50 μg ml(-1) in LLC-PK1 cells, both after 24 h), protein degradation was not ROS-dependent. In LLC-PK1 cells, 10 μg ml(-1) iron (24 h) increased both aconitase activity (30%) and secreted glutamate levels (65%). Silencing did not remove the glutamate response to iron but decreased the c-aconitase activity of the residual protein independent of iron loading (37% and 46% of control levels, without and with iron treatment, respectively). However, in glutamine-free medium, glutamate was still increased by iron, even in IRP-1-silenced cells, and did not correspond to c-aconitase. Silencing decreased the amount of ferritin measured in response to iron loading, decreased the affect of iron on total glutathione by 48%, and increased the response of ROS to iron loading by 38%. We conclude that iron increases turnover of IRP-1 in kidney cells, while increasing aconitase activity, suggesting that the apoprotein (aconitase-inactive) form is not exclusively responsible for turnover. Iron increases glutamate levels in tubule epithelial cells, but this appears to be independent of c-aconitase activity or the availability of extracellular glutamine. IRP-1 protein levels are not regulated by ROS, but IRP-1-dependent ferritin expression may decrease ROS and increase total glutathione levels, suggesting that ferritin levels are more important than citrate metabolism in protecting renal cells against iron.

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 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 score0.011
Threshold uncertainty score0.343

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)0.0000.000

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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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.

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

Citations7
Published2015
Admission routes2
Has abstractyes

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