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Record W4220758647 · doi:10.1186/s42483-022-00112-6

A virus-derived small RNA targets the rice transcription factor ROC1 to induce disease-like symptom

2022· article· en· W4220758647 on OpenAlexfundno aff
Xiuqin Huang, Feifei Li, Xishan Zhang, Jiahao Chen, Junkai Wang, Wei Jing, Xin Yang, Guohui Zhou, Tong Zhang

Bibliographic record

VenuePhytopathology Research · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsnot available
FundersFok Ying Tung Education FoundationInstitute of Genetics and Developmental Biology, Chinese Academy of SciencesChinese Academy of SciencesPostdoctoral Research Foundation of ChinaInstitute of GeneticsNational Natural Science Foundation of China
KeywordsBiologyVirusVirologyPhenotypeTranscription factorDownregulation and upregulationGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Virus-derived small interfering RNAs (vsiRNAs) in plants can target host transcripts to induce symptom development. Southern rice black-streaked dwarf virus (SRBSDV) is the most damaging rice-infecting virus, which causes severe stunting and poorly developed roots in rice plants, thereby posing a serious threat to rice production. In this study, we show that a vsiRNA (vsiR-S9-18), generated from SRBSDV genome segment 9, targets the transcription factor ROC1 in rice. SRBSDV infection triggered the production of vsiR-S9-18 and resulted in the downregulation of ROC1 in rice plants. Transient expression of vsiR-S9-18 in rice protoplasts and tobacco leaves further demonstrated the specific association of vsiR-S9-18 with ROC1 . Moreover, the ROC1 -knockout rice plants displayed shortened roots, similar to the phenotype of root growth inhibition caused by SRBSDV infection. We propose that vsiR-S9-18 inhibits root elongation via ROC1 , which has been proven to regulate root growth. This finding extends our understanding of the role of vsiRNA in viral disease development and is helpful for the development of new antiviral approaches.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.128
GPT teacher head0.325
Teacher spread0.197 · 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.

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

Citations9
Published2022
Admission routes1
Has abstractyes

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