Dissection of the epoxyjanthitrem pathway in Epichloë sp. LpTG-3 strain AR37 by CRISPR gene editing
Bibliographic record
Abstract
Epichloë festucae var. lolii and Epichloë sp. Lp TG-3 are filamentous fungal endophytes of perennial ryegrass ( Lolium perenne ) that have a substantial impact on New Zealand’s agricultural economy by conferring biotic advantages to the host grass. Overall, Epichloë endophytes contribute NZ$200 million to the economy annually, with strain AR37 estimated to contribute NZ$3.6 billion to the New Zealand economy over a 20-year period. This strain produces secondary metabolites, including epoxyjanthitrems, which are a class of indole diterpenes, associated with the observed effects of AR37 on livestock and insect pests. Until very recently, AR37 was intractable to genetic modification but this has changed with the application of CRISPR-Cas9 based gene editing techniques. In this paper, gene inactivation by CRISPR-Cas9 was used to deconvolute the genetic basis for epoxyjanthitrem biosynthesis, including creating an AR37 strain that has been edited to remove the biosynthesis of all indole diterpenes. We show that gene editing of Epichloë can be achieved without off-target events or introduction of foreign DNA (footprint-less) through an AMA1-based plasmid that simultaneously expresses the CRISPR-Cas9 system and selectable marker. Genetic modification events in these transformants were investigated through genome sequencing and in planta chemistry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".