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Record W4399035692 · doi:10.1016/j.isci.2024.110131

An HIV-1 CRISPR-Cas9 membrane trafficking screen reveals a role for PICALM intersecting endolysosomes and immunity

2024· article· en· W4399035692 on OpenAlexafffund
Paola Guizar, Ana Luiza Abdalla, Anne Monette, Kristin Davis, Ramon Edwin Caballero, Meijuan Niu, Xinyun Liu, Oluwaseun Ajibola, Thomas T. Murooka, Liang Chen, Andrew J. Mouland

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of ManitobaCentre Hospitalier de l’Université de MontréalMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health ResearchFonds de recherche du QuébecCanadian Blood ServicesMcGill University
KeywordsCRISPRHuman immunodeficiency virus (HIV)ImmunityBiologyComputational biologyChemistryVirologyImmune systemImmunologyGeneticsGene

Abstract

fetched live from OpenAlex

<h2>Summary</h2> HIV-1 hijacks host proteins involved in membrane trafficking, endocytosis, and autophagy that are critical for virus replication. Molecular details are lacking but are essential to inform on the development of alternative antiviral strategies. Despite their potential as clinical targets, only a few membrane trafficking proteins have been functionally characterized in HIV-1 replication. To further elucidate roles in HIV-1 replication, we performed a CRISPR-Cas9 screen on 140 membrane trafficking proteins. We identified phosphatidylinositol-binding clathrin assembly protein (PICALM) that influences not only infection dynamics but also CD4<sup>+</sup> SupT1 biology. The knockout (KO) of PICALM inhibited viral entry. In CD4<sup>+</sup> SupT1 T cells, KO cells exhibited defects in intracellular trafficking and increased abundance of intracellular Gag and significant alterations in autophagy, immune checkpoint PD-1 levels, and differentiation markers. Thus, PICALM modulates a variety of pathways that ultimately affect HIV-1 replication, underscoring the potential of PICALM as a future target to control HIV-1.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.403

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)0.0000.000

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 head0.018
GPT teacher head0.313
Teacher spread0.295 · 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 designSimulation or modeling
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

Citations6
Published2024
Admission routes2
Has abstractyes

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