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Record W2530925810 · doi:10.1097/qad.0000000000001294

New research on using CRISPR/Cas9 to treat HIV

2016· article· en· W2530925810 on OpenAlexfundaboutno aff
Kristin N. Harper

Bibliographic record

VenueAIDS · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
FundersMcGill University
KeywordsCRISPRCas9Genome editingHuman immunodeficiency virus (HIV)BiologyGuide RNAGeneComputational biologyVirologyGenetics

Abstract

fetched live from OpenAlex

The research community has been captivated by CRISPR/Cas9's many potential health applications; new findings suggest that this system may eventually be used to combat HIV. At the AIDS 2016 meeting in Durban, Kamel Khalili (Chair, Department of Neuroscience, Lewis Katz School of Medicine, Temple University) presented results demonstrating that CRISPR/Cas9 can be used to excise HIV-1 DNA from infected cells in vitro, ex vivo (using patient blood samples), and in vivo (using transgenic mice). ‘These results are significant because they demonstrate that the technology is in place to completely and permanently eradicate HIV DNA from infected cells. This excision represents the ultimate cure for people infected with HIV,’ says Khalili. At the same meeting, Monique Nijhuis (Associate Professor, University Medical Center Utrecht) presented a CRISPR/Cas9 system that prevents HIV from escaping excision by mutating. Nijhuis explains, ‘Two very recent publications demonstrated that HIV can rapidly and consistently escape the inhibitory effect of a single guide RNA-based CRISPR/Cas9 attack. Sequencing of the viral escape variants revealed nucleotide insertions, deletions and substitutions around the Cas9 cleavage site. These observations questioned the feasibility of the CRISPR/Cas9 system-based gene-editing technology as an approach to combat HIV infections. We have shown that the accelerating effect of CRISPR/Cas9 genome-engineering on viral escape can be overcome by combining two strong guide RNAs.’ Chen Liang (Associate Professor, McGill University) says, ‘Both studies represent important progress toward curing HIV/AIDS with the CRISPR gene editing technology. The Khalili group has now shown the possibility of Cas9 editing HIV DNA in multiple different tissues and organs in mice using adeno-associated virus as the delivery vector, which sets up the stage to test CRISPR/Cas9 in clearing HIV DNA in latently infected cells either using humanized mice or nonhuman primate models. The Nijhuis group tackled another barrier by showing that targeting two separate sites in HIV DNA sustainably suppresses HIV replication in cultured T cells, which demonstrates that it is possible to block HIV escape by targeting two or multiple regions in HIV DNA.’ What comes next? Khalili says, ‘Our study was a proof of concept illustrating that the gene editing approach for targeting HIV can eliminate the virus from different organs throughout the body. The next steps are to increase the efficiency of the delivery method and prepare to perform clinical trials.’ Liang notes that future improvements may involve testing different vectors; testing more targets in the HIV genome; and exploring chemically inducible Cas9 systems, which may improve the system's safety profile. Nijhuis lists several other potential barriers to overcome as well, including the risk of modifying the human gene pool, the potential for off-target effects, and a lack of selective advantage among modified cells that would ensure a long-term effect of gene modification. Juan Carlos Izpisua Belmonte (Chair, Gene Expression Laboratory, Salk Institute for Biological Studies) notes, ‘This technique has potential to move to the clinic. In particular, the applications could be applied to patients who have failed classical antiretroviral therapy.’ Acknowledgements Conflicts of interest There are no conflicts of interest.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.258

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 head0.052
GPT teacher head0.425
Teacher spread0.373 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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