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Record W4417001059 · doi:10.33584/rps.18.2025.3763

CRISPR-Cas9 gene insertion in Epichloë species

2025· article· W4417001059 on OpenAlexfundno aff
Taryn A. Miller, Debbie Hudson, Nazanin Noorifar, Wade J. Mace, Richard D. Johnson, Linda J. Johnson

Bibliographic record

VenueNZGA Research and Practice Series · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicPlant and fungal interactions
Canadian institutionsnot available
FundersConcordia University
KeywordsGeneInsert (composites)InsertionSecondary metaboliteReporter geneCRISPRCoding region

Abstract

fetched live from OpenAlex

The dairy, meat, and fibre industries in several regions within New Zealand are heavily reliant on selected strains of endophytic fungi, within the genus Epichloë, which confer resistance to a range of insect pests and environmental pressures when in symbiosis with pasture cultivars. Unfortunately, some fungal strains are historically intractable to genetic manipulation, therefore preventing investigation into novel traits. Only recently with the development of CRISPR-Cas systems, a revolutionary gene editing tool, was CRISPR-Cas9 successfully used on one of these intractable strains, Epichloë sp. LpTG-3 strain AR37, to create targeted gene disruptions. This study focused on CRISPR-Cas9 targeted gene insertion capabilities in Epichloë spp. CRISPR-Cas9 was successfully deployed to precisely insert 236 bp of coding sequence from a critical condensation domain of the perA gene, missing in the genetically intractable Epichloë festucae var. lolii strain AR48. CRISPR-Cas9 was also successfully deployed to insert the reporter gene gfp into a precise location within the indole diterpene pathway, a known secondary metabolite pathway in AR37. This research illustrated the ability of CRISPR-Cas9 to repair or insert genes in genetically intractable Epichloë species, with the potential for reconstruction of secondary metabolite pathways for novel compound production and delivery into New Zealand’s pasture-based agricultural system.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.172
GPT teacher head0.417
Teacher spread0.245 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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