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Record W4304149056 · doi:10.1139/cjps-2022-0019

Genome-wide identification and expression analysis of the polyamine oxidase gene family in soybean

2022· article· en· W4304149056 on OpenAlexvenueno aff
Kuanwei Yu, Na Chen, Xunchao Zhao, Kezhen Zhao, Yuhang Zhan, Ning Xia, Xue Zhao, Yingpeng Han

Bibliographic record

VenueCanadian Journal of Plant Science · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPolyamine Metabolism and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPolyamine oxidaseGene familyAbiotic stressBiologyGeneAbiotic componentGeneticsPolyamineGene expressionGenomeBiochemistrySpermineEnzyme

Abstract

fetched live from OpenAlex

Polyamine oxidases (PAOs) are flavin adenine dinucleotide-dependent enzymes that are involved in polyamine catabolism and play an essential role in growth and developmental processes as well as the response to abiotic stresses. Although the PAO gene families have been intensively studied in many plants, the soybean ( Glycine max (L.) Merr.) PAO gene family has not been systematically identified. Here, we identified six PAO genes in the soybean genome and named them GmPAO1– GmPAO6. The phylogenetic analysis revealed that plant PAO proteins are divided into four classes. GmPAO1 and GmPAO4 belong to class I; GmPAO2, GmPAO5, and GmPAO6 belong to class IV. Similar to most dicotyledonous plants, soybeans do not contain class II. Interestingly, we identified an additional SWIRM-domain PAO gene GmPAO3, which exists between classes III and IV . GmPAO3 had a different gene structure and expression. To determine the individual roles of GmPAOs, we analyzed their expression levels in various tissues and under abiotic stress. Each GmPAO gene can respond in a specific tissue under specific abiotic stress. The data can help to clarify the role of GmPAOs in abiotic stress responses in soybean and provide a breeding basis for enhancing soybean tolerance to abiotic stresses.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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