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Record W2165359856 · doi:10.1093/nar/gkm182

Flow cytometric analysis of DNA binding and cleavage by cell surface-displayed homing endonucleases

2007· article· en· W2165359856 on OpenAlexfundno aff
Petra Volná, Jordan Jarjour, Sarah K. Baxter, Steve R. Roffler, Raymond J. Monnat, Barry Stoddard, Andrew M. Scharenberg

Bibliographic record

VenueNucleic Acids Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthSeattle Children's Research InstituteCancer Research Institute
KeywordsBiologyHoming (biology)Cleavage (geology)DNAHoming endonucleaseMolecular biologyCellEndonucleaseCell biologyGenetics

Abstract

fetched live from OpenAlex

LAGLIDADG homing endonucleases (LHEs) cleave 18-24 bp DNA sequences and are promising enzymes for applications requiring sequence-specific DNA cleavage amongst genome-sized DNA backgrounds. Here, we report a method for cell surface display of LHEs, which facilitates analysis of their DNA binding and cleavage properties by flow cytometry. Cells expressing surface LHEs can be stained with fluorescently conjugated double-stranded oligonucleotides (dsOligos) containing their respective target sequences. The signal is absolutely sequence specific and undetectable with dsOligos carrying single base-pair substitutions. LHE-dsOligo interactions facilitate rapid enrichment and viable recovery of rare LHE expressing cells by both fluorescence-activated cell sorting (FACS) and magnetic cell sorting (MACS). Additionally, dsOligos conjugated with unique fluorophores at opposite termini can be tethered to the cell surface and used to detect DNA cleavage. Recapitulation of DNA binding and cleavage by surface-displayed LHEs provides a high-throughput approach to library screening that should facilitate rapid identification and analysis of enzymes with novel sequence specificities.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.018
GPT teacher head0.345
Teacher spread0.328 · 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

Citations22
Published2007
Admission routes1
Has abstractyes

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