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Record W2781556955 · doi:10.1016/j.ebiom.2018.01.004

HIV Activates the Tyrosine Kinase Hck to Secrete ADAM Protease-Containing Extracellular Vesicles

2018· article· en· W2781556955 on OpenAlexfundno aff
Jung‐Hyun Lee, Christian Ostalecki, Zhe Zhao, Tapio Kesti, Heiko Bruns, B. Simon, Thomas Harrer, Kalle Saksela, Andreas S. Baur

Bibliographic record

VenueEBioMedicine · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
FundersBundesministerium für Bildung und ForschungFriedrich-Alexander-Universität Erlangen-NürnbergChildhood Cancer CanadaDeutsche ForschungsgemeinschaftComprehensive Cancer Center, City of Hope
KeywordsExtracellular vesiclesSecretionExtracellularTyrosine kinaseCell biologyVirologyProteaseVesicleHuman immunodeficiency virus (HIV)ChemistryTyrosineBiologyMedicineBiochemistrySignal transductionEnzymeMembrane

Abstract

fetched live from OpenAlex

HIV-Nef activates the myeloid cell-typical tyrosine kinase Hck, but its molecular role in the viral life cycle is not entirely understood. We found that HIV plasma extracellular vesicles (HIV pEV) containing/10 proteases and Nef also harbor Hck, and analyzed its role in the context of HIV pEV secretion. Myeloid cells required Hck for the vesicle-associated release of ADAM17. This could be induced by the introduction of Nef and implied that HIV targeted Hck for vesicle-associated ADAM17 secretion from a myeloid compartment. The other contents of HIV-pEV, however, including miRNA and effector protein profiles, as well as the presence of haptoglobin suggested hepatocytes as a possible cellular source. HIV liver tissue analysis supported this assumption, revealing induction of Hck translation, evidence for ADAM protease activation and HIV infection. Our findings suggest that HIV targets Hck to induce pro-inflammatory vesicles release and identifies hepatocytes as a possible host cell compartment.

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)0.000
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 score0.168
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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

Citations33
Published2018
Admission routes1
Has abstractyes

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