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Record W2069583221 · doi:10.1128/jvi.01094-14

Human T-Cell Leukemia Virus Type 3 (HTLV-3) and HTLV-4 Antisense-Transcript-Encoded Proteins Interact and Transactivate Jun Family-Dependent Transcription via Their Atypical bZIP Motif

2014· article· en· W2069583221 on OpenAlexaff
Émilie Larocque, Charlotte André‐Arpin, Malgorzata Borowiak, Guy Lemay, William M. Switzer, Madeleine Duc Dodon, Jean-Michel Mesnard, Benoı̂t Barbeau

Bibliographic record

VenueJournal of Virology · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsTransactivationBiologybZIP domainLeucine zipperTranscription factorHEK 293 cellsMolecular biologyTranscription (linguistics)Human T-lymphotropic virus 1TelomeraseJUNBGeneGeneticsLeukemiaT-cell leukemia

Abstract

fetched live from OpenAlex

Human T-cell leukemia virus types 3 and 4 (HTLV-3 and HTLV-4) are recently isolated retroviruses. We have previously characterized HTLV-3- and HTLV-4-encoded antisense genes, termed APH-3 and APH-4, respectively, which, in contrast to HBZ, the HTLV-1 homologue, do not contain a typical bZIP domain (M. Larocque É Halin, S. Landry, S. J. Marriott, W. M. Switzer, and B. Barbeau, J. Virol. 85:12673-12685, 2011, doi:10.1128/JVI.05296-11). As HBZ differentially modulates the transactivation potential of various Jun family members, the effect of APH-3 and APH-4 on JunD-, c-Jun-, and JunB-mediated transcriptional activation was investigated. We first showed that APH-3 and APH-4 upregulated the transactivation potential of all tested Jun family members. Using an human telomerase catalytic subunit (hTERT) promoter construct, our results also highlighted that, unlike HBZ, which solely modulates hTERT expression via JunD, both APH-3 and APH-4 acted positively on the transactivation of the hTERT promoter mediated by tested Jun factors. Coimmunoprecipitation experiments demonstrated that these Jun proteins interacted with APH-3 and APH-4. Although no activation domain was identified for APH proteins, the activation domain of c-Jun was very important in the observed upregulation of its activation potential. We further showed that APH-3 and APH-4 required their putative bZIP-like domains and corresponding leucine residues for interaction and modulation of the transactivation potential of Jun factors. Our results demonstrate that HTLV-encoded antisense proteins behave differently, and that the bZIP-like domains of both APH-3 and APH-4 have retained their interaction potential for Jun members. These studies are important in assessing the differences between HBZ and other antisense proteins, which might further contribute to determining the role of HBZ in HTLV-1-associated diseases. IMPORTANCE HBZ, the antisense transcript-encoded protein from HTLV-1, is now well recognized as a potential factor for adult T-cell leukemia/lymphoma development. In order to better appreciate the mechanism of action of HBZ, comparison to antisense proteins from other HTLV viruses is important. Little is known in relation to the seemingly nonpathogenic HTLV-3 and HTLV-4 viruses, and studies of their antisense proteins are limited to our previously reported study (M. Larocque É Halin, S. Landry, S. J. Marriott, W. M. Switzer, and B. Barbeau, J. Virol. 85:12673-12685, 2011, doi:10.1128/JVI.05296-11). Here, we demonstrate that Jun transcription factors are differently affected by APH-3 and APH-4 compared to HBZ. These intriguing findings suggest that these proteins act differently on viral replication but also on cellular gene expression, and that highlighting their differences of action might lead to important information allowing us to understand the link between HTLV-1 HBZ and ATL in infected individuals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.219
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2014
Admission routes1
Has abstractyes

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