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Record W3202915119 · doi:10.1111/tra.12820

<scp>EAP45</scp> association with budding <scp>HIV</scp>‐1: Kinetics and domain requirements

2021· article· en· W3202915119 on OpenAlexfundno aff
Bo Meng, Pedro P. Vallejo Ramirez, Katharina M. Scherer, Ezra Bruggeman, Julia C. Kenyon, Clemens F. Kaminski, Andrew Lever

Bibliographic record

VenueTraffic · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilMicrobiology SocietyMedical Research CouncilWellcome TrustMedImmuneMedical Research Council Canada
KeywordsESCRTCytokinesisBiologyCell biologySeptinBuddingJurkat cellsFunction (biology)Viral entryTSG101Cell divisionCellViral replicationEndosomeVirologyBiochemistryIntracellularT cellGeneticsVirusMicrovesiclesGene

Abstract

fetched live from OpenAlex

A number of viruses including HIV use the ESCRT system to bud from the infected cell. We have previously confirmed biochemically that ESCRT-II is involved in this process in HIV-1 and have defined the molecular domains that are important for this. Here, using SNAP-tag fluorescent labelling and both fixed and live cell imaging we show that the ESCRT-II component EAP45 colocalises with the HIV protein Gag at the plasma membrane in a temporal and quantitative manner, similar to that previously shown for ALIX and Gag. We show evidence that a proportion of EAP45 may be packaged within virions, and we confirm the importance of the N terminus of EAP45 and specifically the H0 domain in this process. By contrast, the Glue domain of EAP45 is more critical for recruitment during cytokinesis, emphasising that viruses have ways of recruiting cellular components that may be distinct from those used by some cellular processes. This raises the prospect of selective interference with the pathway to inhibit viral function while leaving cellular functions relatively unperturbed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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