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Record W4390110941 · doi:10.1002/pld3.556

Differential expansion and retention patterns of LRR‐RLK genes across plant evolution

2023· article· en· W4390110941 on OpenAlexafffund
Zachary Kileeg, Aparna Haldar, Hasna Khan, Arooj Qamar, G. Adam Mott

Bibliographic record

VenuePlant Direct · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubfamilyBiologyArabidopsisLineage (genetic)Adaptation (eye)Gene familyEvolutionary biologyGeneGeneticsPhylogeneticsComputational biologyGenomeMutantNeuroscience

Abstract

fetched live from OpenAlex

To maximize overall fitness, plants must accurately respond to a host of growth, developmental, and environmental signals throughout their life. Many of these internal and external signals are perceived by the leucine-rich repeat receptor-like kinases, which play roles in regulating growth, development, and immunity. This largest family of receptor kinases in plants can be divided into subfamilies based on the conservation of the kinase domain, which demonstrates that shared evolutionary history often indicates shared molecular function. Here we investigate the evolutionary history of this family across the evolution of 112 plant species. We identify lineage-specific expansions of the malectin-domain containing subfamily LRR subfamily I primarily in the Brassicales and bryophytes. Most other plant lineages instead show a large expansion in LRR subfamily XII, which in Arabidopsis is known to contain key receptors in pathogen perception. This striking asymmetric expansion may reveal a dichotomy in the evolutionary history and adaptation strategies employed by plants. A greater understanding of the evolutionary pressures and adaptation strategies acting on members of this receptor family offers a way to improve functional predictions for orphan receptors and simplify the identification of novel stress-related receptors.

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 score0.889
Threshold uncertainty score0.143

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 head0.022
GPT teacher head0.223
Teacher spread0.200 · 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

Citations12
Published2023
Admission routes2
Has abstractyes

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