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Differential contributions to the transcriptome of duplicated genes in response to abiotic stresses in natural and synthetic polyploids

2011· article· en· W2039845217 on OpenAlexaff
Shaowei Dong, Keith L. Adams

Bibliographic record

VenueNew Phytologist · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTranscriptomeAbiotic componentBiologyGeneAbiotic stressFight-or-flight responseNatural (archaeology)GeneticsEvolutionary biologyComputational biologyGene expressionEcologyPaleontology

Abstract

fetched live from OpenAlex

Polyploidy has occurred throughout plant evolution and can result in considerable changes to gene expression when it takes place and over evolutionary time. Little is known about the effects of abiotic stress conditions on duplicate gene expression patterns in polyploid plants. We examined the expression patterns of 60 duplicated genes in leaves, roots and cotyledons of allotetraploid Gossypium hirsutum in response to five abiotic stress treatments (heat, cold, drought, high salt and water submersion) using single-strand conformation polymorphism assays, and 20 genes in a synthetic allotetraploid. Over 70% of the genes showed stress-induced changes in the relative expression levels of the duplicates under one or more stress treatments with frequent variability among treatments. Twelve pairs showed opposite changes in expression levels in response to different abiotic stress treatments. Stress-induced expression changes occurred in the synthetic allopolyploid, but there was little correspondence in patterns between the natural and synthetic polyploids. Our results indicate that abiotic stress conditions can have considerable effects on duplicate gene expression in a polyploid, with the effects varying by gene, stress and organ type. Differential expression in response to environmental stresses may be a factor in the preservation of some duplicated genes in polyploids.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.985

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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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 designObservational
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

Citations102
Published2011
Admission routes1
Has abstractyes

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