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Record W4310088510 · doi:10.1126/sciadv.adc9785

High-temperature adaptation of an <i>OsNRT2.3</i> allele is thermoregulated by small RNAs

2022· article· en· W4310088510 on OpenAlexfundno aff
Yong Zhang, Hisae Tateishi‐Karimata, Tamaki Endoh, Qiongli Jin, Kexin Li, Xiaoru Fan, Yingjun Ma, Limin Gao, Haiyan Lü, Art E. Cho, Xuefeng Yao, Chunming Liu, Naoki Sugimoto, Shiwei Guo, Xiangdong Fu, Qirong Shen, Guohua Xu, Luís Herrera‐Estrella, Xiaorong Fan

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsnot available
FundersInstitute of Genetics and Developmental Biology, Chinese Academy of SciencesNational Key Research and Development Program of ChinaGovernment of Jiangsu ProvinceChinese Academy of SciencesJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyInstitute of GeneticsNational Natural Science Foundation of ChinaTexas Tech University
KeywordsAlleleUntranslated regionAgricultureGeneYield (engineering)BiologyGene isoformFood securityMessenger RNAAgronomyGeneticsEcologyMaterials science

Abstract

fetched live from OpenAlex

Climate change negatively affects crop yield, which hinders efforts to reach agricultural sustainability and food security. Here, we show that a previously unidentified allele of the nitrate transporter gene OsNRT2.3 is required to maintain high yield and high nitrogen use efficiency under high temperatures. We demonstrate that this tolerance to high temperatures in rice accessions harboring the HTNE-2 (high temperature resistant and nitrogen efficient-2) alleles from enhanced translation of the OsNRT2.3b mRNA isoform and the decreased abundance of a unique small RNA (sNRT2.3-1) derived from the 5′ untranslated region of OsNRT2.3 . sNRT2.3-1 binds to the OsNRT2.3a mRNA in a temperature-dependent manner. Our findings reveal that allelic variation in the 5′ untranslated region of OsNRT2.3 leads to an increase in OsNRT2.3b protein levels and higher yield during high-temperature stress. Our results also provide a breeding strategy to produce rice varieties with higher grain yield and lower N fertilizer input suitable for a sustainable agriculture that is resilient against climate change.

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 score0.460
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.206
Teacher spread0.194 · 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

Citations22
Published2022
Admission routes1
Has abstractyes

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