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Record W825179612 · doi:10.1038/srep11975

Analyzing Arabidopsis thaliana root proteome provides insights into the molecular bases of enantioselective imazethapyr toxicity

2015· article· en· W825179612 on OpenAlexaff
Haifeng Qian, Haiping Lu, Haiyan Ding, Michel Lavoie, Yali Li, Weiping Liu, Zhengwei Fu

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsUniversité Laval
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsProteomeArabidopsis thalianaToxicityBiochemistryCatabolismAmino acidRhizosphereMetabolismBiologyEnzymeChemistryBacteriaGeneGenetics

Abstract

fetched live from OpenAlex

Imazethapyr (IM) is a widely used chiral herbicide that inhibits the synthesis of branched-chain amino acids (BCAAs). IM is thought to exert its toxic effects on amino acid synthesis mainly through inhibition of acetolactate synthase activity, but little is known about the potential effects of IM on other key biochemical pathways. Here, we exposed the model plant Arabidospsis thaliana to trace S- and R-IM enantiomer concentrations and examined IM toxicity effects on the root proteome using iTRAQ. Conventional analyses of root carbohydrates, organic acids, and enzyme activities were also performed. We discovered several previously unknown key biochemical pathways targeted by IM in Arabidospsis. 1,322 and 987 proteins were differentially expressed in response to R- and S-IM treatments, respectively. Bioinformatics and physiological analyses suggested that IM reduced the BCAA tissue content not only by strongly suppressing BCAA synthesis but also by increasing BCAA catabolism. IM also affected sugar and starch metabolism, changed the composition of root cell walls, increased citrate production and exudation, and affected the microbial community structure of the rhizosphere. The present study shed new light on the multiple toxicity mechanisms of a selective herbicide on a model plant.

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)0.001
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 score0.141
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.229
Teacher spread0.207 · 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

Citations68
Published2015
Admission routes1
Has abstractyes

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