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Record W2101945227 · doi:10.1111/tpj.12520

<i>Lotus japonicus <scp>SUNERGOS</scp>1</i> encodes a predicted subunit A of a <scp>DNA</scp> topoisomerase <scp>VI</scp> that is required for nodule differentiation and accommodation of rhizobial infection

2014· article· en· W2101945227 on OpenAlexafffund
Hwi Joong Yoon, Md Shakhawat Hossain, Mark Held, Hong‐Wei Hou, Marilyn Kehl, Alexandre Tromas, Shusei Sato, Satoshi Tabata, Stig Uggerhøj Andersen, Jens Stougaard, Loretta Ross, Krzysztof Szczygłowski

Bibliographic record

VenueThe Plant Journal · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsAgriculture and Agri-Food CanadaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaDanmarks Grundforskningsfond
KeywordsProtein subunitLotus japonicusMolecular biologyChemistryBiologyBiochemistryGeneMutant

Abstract

fetched live from OpenAlex

A symbiotic mutant of Lotus japonicus, called sunergos1-1 (suner1-1), originated from a har1-1 suppressor screen. suner1-1 supports epidermal infection by Mesorhizobium loti and initiates cell divisions for organogenesis of nodule primordia. However, these processes appear to be temporarily stalled early during symbiotic interaction, leading to a low nodule number phenotype. This defect is ephemeral and near wild-type nodule numbers are reached by suner1-1 at a later point after infection. Using an approach that combined map-based cloning and next-generation sequencing we have identified the causative mutation and show that the suner1-1 phenotype is determined by a weak recessive allele, with the corresponding wild-type SUNER1 locus encoding a predicted subunit A of a DNA topoisomerase VI. Our data suggest that at least one function of SUNER1 during symbiosis is to participate in endoreduplication, which is an essential step during normal differentiation of functional, nitrogen-fixing nodules.

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)0.001
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 score0.299
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.211
Teacher spread0.189 · 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

Citations32
Published2014
Admission routes2
Has abstractyes

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