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Record W1580305871 · doi:10.1155/2015/495370

Activation of CXCL‐8 Transcription by Hepatitis E Virus ORF‐1 via AP‐1

2015· article· en· W1580305871 on OpenAlexafffund
Zhubing Li, Lu Chen, Qiang Liu

Bibliographic record

VenueMediators of Inflammation · 2015
Typearticle
Languageen
FieldMedicine
TopicHepatitis Viruses Studies and Epidemiology
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchChina Scholarship CouncilSaskatchewan Health Research Foundation
KeywordsVirologyTranscription (linguistics)VirusHepatitis B virusBiology

Abstract

fetched live from OpenAlex

Hepatitis E virus (HEV) is a small nonenveloped single-stranded positive-sense RNA virus and is one of the major causes for acute hepatitis worldwide. CXCL-8 is a small multifunctional proinflammatory chemokine. It was reported recently that HEV infection significantly upregulates CXCL-8 gene expression. In this study, we investigated the mechanism of HEV-induced CXCL-8 transcriptional activation. Using CXCL-8 promoter reporters of different lengths ranging from -1400 to -173, we showed that -173 promoter has the highest promoter activity in the presence of HEV genomic RNA, indicating that the -173 promoter contains sequences responsible for CXCL-8 activation by HEV. Ectopic expression of the ORF-1 protein can upregulate the -173 CXCL-8 promoter activity. In contrast, expression of the ORF-2 protein suppresses the CXCL-8 promoter activity and expression of the ORF-3 protein has no effect on the CXCL-8 promoter activity. We further showed that AP-1 is required for CXCL-8 activation because neither HEV genomic RNA nor the ORF-1 protein can upregulate the -173 CXCL-8 promoter in the absence of the AP-1 binding sequence. Taken together, our results showed that HEV and HEV ORF-1 protein activate the CXCL-8 promoter via AP-1. This novel function of HEV ORF-1 protein should contribute to our understanding of HEV-host interactions and HEV-associated pathogenesis.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.260
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.027
GPT teacher head0.276
Teacher spread0.249 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

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