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Record W2941755333 · doi:10.1073/pnas.1900867116

Genome-wide CRISPR screen for Zika virus resistance in human neural cells

2019· article· en· W2941755333 on OpenAlexaff
Yun Li, Julien Muffat, Attya Omer, Heather R. Keys, Tenzin Lungjangwa, Irene Bosch, Mehreen Ali Khan, Maria Virgilio, Lee Gehrke, David M. Sabatini, Rudolf Jaenisch

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthHoward Hughes Medical Institute
KeywordsCRISPRBiologyZika virusArbovirusVirologyGenomeNeural stem cellSindbis virusVirusGeneticsCell biologyGeneRNAStem cell

Abstract

fetched live from OpenAlex

Zika virus (ZIKV) is a neurotropic and neurovirulent arbovirus that has severe detrimental impact on the developing human fetal brain. To date, little is known about the factors required for ZIKV infection of human neural cells. We identified ZIKV host genes in human pluripotent stem cell (hPSC)-derived neural progenitors (NPs) using a genome-wide CRISPR-Cas9 knockout screen. Mutations of host factors involved in heparan sulfation, endocytosis, endoplasmic reticulum processing, Golgi function, and interferon activity conferred resistance to infection with the Uganda strain of ZIKV and a more recent North American isolate. Host genes essential for ZIKV replication identified in human NPs also provided a low level of protection against ZIKV in isogenic human astrocytes. Our findings provide insights into host-dependent mechanisms for ZIKV infection in the highly vulnerable human NP cells and identify molecular targets for potential therapeutic intervention.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.223

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 science0.0000.000
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 head0.030
GPT teacher head0.317
Teacher spread0.287 · 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

Citations125
Published2019
Admission routes1
Has abstractyes

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