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Record W2886750678 · doi:10.1038/s42003-018-0121-8

Structural basis for promotion of duodenal iron absorption by enteric ferric reductase with ascorbate

2018· article· en· W2886750678 on OpenAlexaff
Menega Ganasen, Hiromi Togashi, Hanae Takeda, Honami Asakura, Takehiko Tosha, Keitaro Yamashita, Kunio Hirata, Yuko Nariai, Takeshi Urano, Xiaojing Yuan, Iqbal Hamza, A. Grant Mauk, Yoshitsugu Shiro, Hiroshi Sugimoto, Hitomi Sawai

Bibliographic record

VenueCommunications Biology · 2018
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of British Columbia
FundersAdvanced Science InstituteNational Institute of Diabetes and Digestive and Kidney DiseasesRIKENJapan Society for the Promotion of ScienceFumi Yamamura Memorial Foundation for Female Natural Scientists
KeywordsFerricEnteric coatedChemistryReductasePromotion (chess)Absorption (acoustics)BiochemistryGastroenterologyInternal medicineMedicineEnzymeMaterials scienceInorganic chemistryPolitical science

Abstract

fetched live from OpenAlex

Abstract Dietary iron absorption is regulated by duodenal cytochrome b (Dcytb), an integral membrane protein that catalyzes reduction of nonheme Fe 3+ by electron transfer from ascorbate across the membrane. This step is essential to enable iron uptake by the divalent metal transporter. Here we report the crystallographic structures of human Dcytb and its complex with ascorbate and Zn 2+ . Each monomer of the homodimeric protein possesses cytoplasmic and apical heme groups, as well as cytoplasmic and apical ascorbate-binding sites located adjacent to each heme. Zn 2+ coordinates to two hydroxyl groups of the apical ascorbate and to a histidine residue. Biochemical analysis indicates that Fe 3+ competes with Zn 2+ for this binding site. These results provide a structural basis for the mechanism by which Fe 3+ uptake is promoted by reducing agents and should facilitate structure-based development of improved agents for absorption of orally administered 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.262

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 studies0.0000.001
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 head0.038
GPT teacher head0.327
Teacher spread0.290 · 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

Citations51
Published2018
Admission routes1
Has abstractyes

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