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Record W4405283129 · doi:10.3390/insects15120983

Gene Silencing via Ingestion of Double-Stranded RNA in Wireworm of Agriotes Species

2024· article· en· W4405283129 on OpenAlexafffund
Jyoti Joshi, Robert Coffin, Gefu Wang‐Pruski

Bibliographic record

VenueInsects · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsDalhousie University
FundersGenome AtlanticMitacs
KeywordsBiologyRNA interferenceRNA silencingGene knockdownGeneGene silencingRNACell biologyGenetics

Abstract

fetched live from OpenAlex

Wireworms are the most destructive soil insect pests affecting horticultural crops. The damage often renders them unsuitable for commercial purposes, resulting in substantial economic losses. RNA interference (RNAi) has been broadly used to inhibit gene functions to control insect populations. It employs double-stranded RNA (dsRNA) to knockdown essential genes in target organisms, rendering them incapable of development or survival. Although it is a robust approach, the primary challenges are identifying effective target genes and delivering their dsRNA into wireworms. Thus, the present study established a liquid ingestion methodology that efficiently delivers dsRNA into wireworms. We then investigated the effects of four target genes on wireworm mortality. The highest mortality rate reached 50% when the gene encoding vacuolar ATPase subunit A was targeted. Its transcript content in the fed wireworms was also significantly reduced. The mortality rates of the other three target genes of vacuolar ATPase subunit E, beta-actin, and chitin synthase 1 were 28%, 33%, and 35%, respectively. This is the first report demonstrating an efficient feeding methodology and the silencing of target genes in wireworms. Our findings indicate that RNAi is an effective alternative method for controlling the wireworm pest, and can be used to develop field treatment strategies.

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 score0.009
Threshold uncertainty score0.372

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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