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Record W4407058584 · doi:10.1016/j.omtn.2025.102472

Enhanced HDR-mediated correction of heterozygous COL7A1 mutations for recessive dystrophic epidermolysis bullosa

2025· article· en· W4407058584 on OpenAlexaff
John Hunt, Alex du Rand, Daniel J. Verdon, Leah Clemance, Evert Jan Loef, Chloe Malhi, Ben Buttle, Yale S. Michaels, Jonathan A. Garlick, P. Rod Dunbar, Diana Purvis, Vaughan Feisst, Hilary M. Sheppard

Bibliographic record

VenueMolecular Therapy — Nucleic Acids · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare ManitobaUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersDirectorate for Biological SciencesUniversity of AucklandMaurice Wilkins Centre for Molecular BiodiscoveryAuckland Medical Research Foundation
KeywordsEpidermolysis bullosa dystrophicaGeneticsMutationEpidermolysis bullosaCompound heterozygosityDermatologyBiologyMedicineGene

Abstract

fetched live from OpenAlex

mutations not previously targeted by existing gene therapies. Editing was achieved using Cas9-nuclease ribonucleoproteins with short single-stranded DNA donor templates, and outcomes were assessed with an Oxford Nanopore Technology sequencing analysis pipeline. We demonstrate precise genomic HDR rates of up to 75% of alleles in primary RDEB keratinocytes and 32% in fibroblasts. This approach restored collagen VII expression in up to 80% of keratinocytes within a bulk-edited population and resulted in correct collagen VII deposition in a 3D skin model. Additionally, at one locus we show that a dual Cas9-nickase strategy is less effective than Cas9-nuclease and prone to large on-target deletions. Our results demonstrate a significant advancement in the efficiency and consistency of HDR editing, potentially paving the way for more effective personalized gene therapies.

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 score0.077
Threshold uncertainty score0.877

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 head0.009
GPT teacher head0.278
Teacher spread0.270 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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