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Record W2122377860 · doi:10.1038/srep12922

The human papillomavirus (HPV) E7 protein antagonises an Imiquimod-induced inflammatory pathway in primary human keratinocytes

2015· article· en· W2122377860 on OpenAlexfundno aff
Kathryn H. Richards, Christopher W. Wasson, Oliver Watherston, Rosella Doble, G. Eric Blair, Miriam Wittmann, Andrew Macdonald

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
FundersMedical Research CouncilQueen's University BelfastQueen's UniversityCancer Research UK
KeywordsImiquimodTLR7Immune systemInnate immune systemImmunologyBiologyMediatorCancer researchCell biologyToll-like receptor

Abstract

fetched live from OpenAlex

High-risk human papillomaviruses (HPV) are the etiological pathogen of cervical and a number of ano-genital cancers. How HPVs overcome the significant barriers of the skin immune system has been the topic of intensive research. The E6 and E7 oncoproteins have emerged as key players in the deregulation of host innate immune pathways that are required for the recruitment of effector cells of the immune response. Here we demonstrate that E7, and to a lesser extend E6, strongly reduce NFκB activation in response to the inflammatory mediator imiquimod. Moreover, we establish that undifferentiated keratinocytes do not express the putative receptor for imiquimod, TLR7, and as such are stimulated by imiquimod through a novel pathway. Inhibition of imiquimod induced cytokine production required residues in the CR1 and CR3 regions of E7 and resulted in reduced nuclear translocation and acetylation of the p65 sub-unit of NFκB. The results provide further evidence for a TLR7-independent role of imiquimod in the epithelial immune response and reinforce the ability of the HPV oncoproteins to disrupt the innate immune response, which may have important consequences for establishment of a chronic infection.

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)0.004
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 score0.286
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.070
GPT teacher head0.347
Teacher spread0.277 · 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

Citations43
Published2015
Admission routes1
Has abstractyes

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