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Record W4412538787 · doi:10.1101/2025.07.20.665670

A conserved small RNA-generating gene cluster undergoes sequence diversification and contributes to plant immunity

2025· preprint· en· W4412538787 on OpenAlexaff
Feng Li, Yingnan Hou, AmirAli Toghani, Zhixue Wang, Bozeng Tang, Nicola Atkinson, Huijun Li, Qiao Yue, Yan Wang, Jianhua Ma, Jixian Zhai, Wenbo Ma

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogenic Bacteria Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersBiotechnology and Biological Sciences Research CouncilUK Research and Innovation
KeywordsGeneBiologyRNADiversification (marketing strategy)GeneticsSequence (biology)ImmunityGene clusterComputational biologyImmune systemBusiness

Abstract

fetched live from OpenAlex

Abstract Small RNA-mediated gene silencing contributes to plant immunity. The secondary small interfering RNA (siRNA) pathway promotes defense by silencing target genes in invading fungal and oomycete pathogens. Many secondary siRNAs derive from transcripts potentially encoding pentatricopeptide repeat (PPR) proteins. Here, we report that siRNA production is an ancient function of an evolutionarily conserved clade of PPR genes that undergo extensive within-species diversification. In Arabidopsis thaliana , siRNA-source PPR s are physically clustered in one locus on Chromosome 1. These sequences are diversified through gene duplication followed by sequence diversification as well as accumulation of high-impact variations including pseudogenization. This diversity leads to the accumulation of a diverse PPR -siRNA pool, consistent with an engagement in a co-evolutionary arms race with the pathogens. This study defines siRNA-producing PPRs as a family of defense genes and highlights the potential of PPR -siRNA-based engineering for enhancing broad-spectrum disease resistance.

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 score0.467
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.038
GPT teacher head0.208
Teacher spread0.171 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

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