MétaCan
Menu
Back to cohort
Record W4212800364 · doi:10.3390/biom12020330

The Halophyte Dehydrin Sequence Landscape

2022· article· en· W4212800364 on OpenAlexaff
Siwar Ghanmi, Steffen P. Graether, Moez Hanin

Bibliographic record

VenueBiomolecules · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHalophytePhylogenetic treeBiologyIn silicoBotanyComputational biologyEvolutionary biologyGeneticsGeneEcologySalinity

Abstract

fetched live from OpenAlex

Dehydrins (DHNs) belong to the LEA (late embryogenesis abundant) family group II, that comprise four conserved motifs (the Y-, S-, F-, and K-segments) and are known to play a multifunctional role in plant stress tolerance. Based on the presence and order of these segments, dehydrins are divided into six subclasses: YnSKn, FnSKn, YnKn, SKn, Kn, and KnS. DHNs are rarely studied in halophytes, and their contribution to the mechanisms developed by these plants to survive in extreme conditions remains unknown. In this work, we carried out multiple genomic analyses of the conservation of halophytic DHN sequences to discover new segments, and examine their architectures, while comparing them with their orthologs in glycophytic plants. We performed an in silico analysis on 86 DHN sequences from 10 halophytic genomes. The phylogenetic tree showed that there are different distributions of the architectures among the different species, and that FSKn is the only architecture present in every plant studied. It was found that K-, F-, Y-, and S-segments are highly conserved in halophytes and glycophytes with a few modifications, mainly involving charged amino acids. Finally, expression data collected for three halophytic species (Puccinillia tenuiflora, Eutrema salsugenium, and Hordeum marinum) revealed that many DHNs are upregulated by salt stress, and the intensity of this upregulation depends on the DHN architecture.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.668

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 studies0.0010.000
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 head0.018
GPT teacher head0.212
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 designNot applicable
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

Citations17
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBiomoleculesSame topicPlant Stress Responses and ToleranceFrench-language works237,207