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Record W2614974054 · doi:10.1038/ncomms15245

Zinc is a potent and specific inhibitor of IFN-λ3 signalling

2017· article· en· W2614974054 on OpenAlexaff
Scott Read, Kate O'Connor, Vijayaprakash Suppiah, Chantelle Ahlenstiel, Stephanie Obeid, Kristina M. Cook, Anthony L. Cunningham, Mark W. Douglas, Philip J. Hogg, David Booth, Jacob George, Golo Ahlenstiel

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilCancer Institute NSWUniversity of LeedsNational Health and Medical Research CouncilWestmead Millennium Institute for Medical Research
KeywordsHepatitis C virusInterferonImmunologyIn vivoLiver diseaseViral replicationInflammationBiologyFibrosisVirologyVirusMedicineInternal medicineGenetics

Abstract

fetched live from OpenAlex

Lambda interferons (IFNL, IFN-λ) are pro-inflammatory cytokines important in acute and chronic viral infection. Single-nucleotide polymorphisms rs12979860 and rs8099917 within the IFNL gene locus predict hepatitis C virus (HCV) clearance, as well as inflammation and fibrosis progression in viral and non-viral liver disease. The underlying mechanism, however, is not defined. Here we show that the rs12979860 CC genotype correlates with increased hepatic metallothionein expression through increased systemic zinc levels. Zinc interferes with IFN-λ3 binding to IFNL receptor 1 (IFNLR1), resulting in decreased antiviral activity and increased viral replication (HCV, influenza) in vitro. HCV patients with high zinc levels have low hepatocyte antiviral and inflammatory gene expression and high viral loads, confirming the inhibitory role of zinc in vivo. We provide the first evidence that zinc can act as a potent and specific inhibitor of IFN-λ3 signalling and highlight its potential as a target of therapeutic intervention for IFN-λ3-mediated chronic disease.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.404

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 science0.0010.001
Research integrity0.0000.001
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 head0.083
GPT teacher head0.394
Teacher spread0.311 · 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 designNot applicable
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

Citations56
Published2017
Admission routes1
Has abstractyes

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