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Record W3033123873 · doi:10.1016/j.omtn.2020.05.030

CRISPR-Cas13a Inhibits HIV-1 Infection

2020· article· en· W3033123873 on OpenAlexafffund
Lijuan Yin, Fei Zhao, Hong Sun, Zhen Wang, Yu Huang, Weijun Zhu, Fengwen Xu, Shan Mei, Xiaoman Liu, Di Zhang, Wei Liang, Shan Cen, Siqi Hu, Chen Liang, Fei Guo

Bibliographic record

VenueMolecular Therapy — Nucleic Acids · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill UniversityJewish General Hospital
FundersPeking Union Medical CollegeCanadian Institutes of Health ResearchChinese Academy of Medical SciencesNational Natural Science Foundation of ChinaChinese Academy of Meteorological SciencesMinistry of Science and Technology of the People's Republic of China
KeywordsCRISPRBiologyRNAEffectorPlasmidTrans-activating crRNAVirologyDNAGeneCas9GeneticsCell biology

Abstract

fetched live from OpenAlex

CRISPR-Cas provides bacteria and archaea with immunity against invading phages and foreign plasmid DNA and has been successfully adapted for gene editing in a variety of species. The class 2 type VI CRISPR-Cas effector Cas13a targets and cleaves RNA, providing protection against RNA phages. Here we report the repurposing of CRISPR-Cas13a to inhibit human immunodeficiency virus type 1 (HIV-1) infection through targeting HIV-1 RNA and diminishing viral gene expression. We observed strong inhibition of HIV-1 infection by CRISPR-Cas13a in human cells. We showed that CRISPR-Cas13a not only diminishes the level of newly synthesized viral RNA, either from the transfected plasmid DNA or from the viral DNA, which is integrated into cellular DNA, but it also targets and destroys the viral RNA that enters cells within viral capsid, leading to strong inhibition of HIV-1 infection. Together, our results suggest that CRISPR-Cas13a provides a potential novel tool to treat viral diseases in humans.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.271
Teacher spread0.260 · 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

Citations76
Published2020
Admission routes2
Has abstractyes

Explore more

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