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Record W2885552279 · doi:10.1128/jvi.01135-18

HIV-1 Employs Multiple Mechanisms To Resist Cas9/Single Guide RNA Targeting the Viral Primer Binding Site

2018· article· en· W2885552279 on OpenAlexafffund
Zhen Wang, Wenzhou Wang, Ya Cheng Cui, Qinghua Pan, Weijun Zhu, Patrick Gendron, Fei Guo, Shan Cen, Michael Witcher, Chen Liang

Bibliographic record

VenueJournal of Virology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and CancerMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsCas9BiologyCRISPRSubgenomic mRNAGuide RNADNAReverse transcriptaseGenome editingPrimer (cosmetics)VirologyHuman immunodeficiency virus (HIV)GeneticsRNAGeneComputational biologyMolecular biologyChemistry

Abstract

fetched live from OpenAlex

The results of this study demonstrate that the gene-editing complex Cas9/sgRNA can be programmed to target and cleave HIV-1 PBS DNA, and thus, inhibit HIV-1 infection. Given that almost all HIV-1 strains have the same PBS, which is copied from the cellular tRNA 3 Lys during reverse transcription, PBS-targeting sgRNA can be used to inactivate HIV-1 DNA of different strains. We also discovered that HIV-1 uses different mechanisms to resist Cas9/sgRNAs, depending on whether they target the plus or the minus strand of PBS DNA. These findings allow us to predict that a Cas9 variant that uses the CCA sequence as the protospacer adjacent motif (PAM) should more strongly and persistently suppress HIV-1 replication. Together, these data have identified the PBS as the target DNA of Cas9/sgRNA and have predicted how to improve Cas9/sgRNA to achieve more efficient and sustainable suppression of HIV-1 infection, therefore improving the capacity of Cas9/sgRNA in curing HIV-1 infection.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.285
Teacher spread0.276 · 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

Citations32
Published2018
Admission routes2
Has abstractyes

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