Characterization and expression profiling of glutathione S-transferases in the diamondback moth, Plutella xylostella (L.)
Bibliographic record
Abstract
BACKGROUND: Glutathione S-transferases (GSTs) are multifunctional detoxification enzymes that play important roles in insects. The completion of several insect genome projects has enabled the identification and characterization of GST genes over recent years. This study presents a genome-wide investigation of the diamondback moth (DBM), Plutella xylostella, a species in which the GSTs are of special importance because this pest is highly resistant to many insecticides. RESULTS: A total of 22 putative cytosolic GSTs were identified from a published P. xylostella genome and grouped into 6 subclasses (with two unclassified). Delta, Epsilon and Omega GSTs were numerically superior with 5 genes for each of the subclasses. The resulting phylogenetic tree showed that the P. xylostella GSTs were all clustered into Lepidoptera-specific branches. Intron sites and phases as well as GSH binding sites were strongly conserved within each of the subclasses in the GSTs of P. xylostella. Transcriptome-, RNA-seq- and qRT-PCR-based analyses showed that the GST genes were developmental stage- and strain-specifically expressed. Most of the highly expressed genes in insecticide resistant strains were also predominantly expressed in the Malpighian tubules, midgut or epidermis. CONCLUSIONS: To date, this is the most comprehensive study on genome-wide identification, characterization and expression profiling of the GST family in P. xylostella. The diversified features and expression patterns of the GSTs are inferred to be associated with the capacity of this species to develop resistance to a wide range of pesticides and biological toxins. Our findings provide a base for functional research on specific GST genes, a better understanding of the evolution of insecticide resistance, and strategies for more sustainable management of the pest.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".