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Record W3150329395 · doi:10.3389/fpls.2021.627129

Proteomic Responses to Alkali Stress in Oats and the Alleviatory Effects of Exogenous Spermine Application

2021· article· en· W3150329395 on OpenAlexfundno aff
Jianhui Bai, Ke Jin, Wei Qin, Yuqing Wang, Qiang Yin

Bibliographic record

VenueFrontiers in Plant Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPolyamine Metabolism and Applications
Canadian institutionsnot available
FundersChinese Academy of Agricultural SciencesAgriculture and Agri-Food CanadaNational Natural Science Foundation of China
KeywordsSpermineJasmonic acidChemistryAbscisic acidBiochemistryFood scienceBotanyBiologySalicylic acidEnzyme

Abstract

fetched live from OpenAlex

Alkali stress limits plant growth and yield more strongly than salt stress and can lead to the appearance of yellow leaves; however, the reasons remain unclear. In this study, we found that (1) the down-regulation of coproporphyrinogen III oxidase, protoporphyrinogen oxidase, and Pheophorbide a oxygenase in oats under alkali stress contributes to the appearance of yellow leaves (as assessed by proteome and western blot analyses). (2) Some oat proteins that are involved in the antioxidant system, root growth, and jasmonic acid (JA) and indole-3-acetic acid (IAA) synthesis are up-regulated in response to alkalinity and help increase alkali tolerance. (3) We added exogenous spermine to oat plants to improve their alkali tolerance, which resulted in higher chlorophyll contents and plant dry weights than in plants subjected to alkaline stress alone. This was due to up-regulation of chitinase and proteins related to chloroplast structure, root growth, and the antioxidant system. Spermine addition increased sucrose utilization efficiency, and promoted carbohydrate export from leaves to roots to increase energy storage in roots. Spermine addition also increased the IAA and JA contents required for root growth.

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 score0.059
Threshold uncertainty score0.187

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 head0.004
GPT teacher head0.225
Teacher spread0.221 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

Explore more

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