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Record W4282049965 · doi:10.1242/dev.200279

The cellular function of ROP GTPase prenylation is important for multicellularity in the moss <i>Physcomitrium patens</i>

2022· article· en· W4282049965 on OpenAlexfundno aff
Liang Bao, Junling Ren, Mary Nguyen, Arkadiusz S. Slusarczyk, Julie M. Thole, Susana Perez Martinez, Jinling Huang, Tomomichi Fujita, Mark Running

Bibliographic record

VenueDevelopment · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsnot available
FundersNational Institute for Basic BiologyUniversity of LouisvilleUniversity of ReginaUniversity of OxfordUniversity of MichiganNational Science Foundation
KeywordsBiologyPrenylationMulticellular organismMossGTPaseCell biologyFunction (biology)BotanyCellGeneticsBiochemistryEnzyme

Abstract

fetched live from OpenAlex

A complete picture of how signaling pathways lead to multicellularity is largely unknown. Previously, we generated mutations in a protein prenylation enzyme, GGB, and showed that it is essential for maintaining multicellularity in the moss Physcomitrium patens. Here, we show that ROP GTPases act as downstream factors that are prenylated by GGB and themselves play an important role in the multicellularity of P. patens. We also show that the loss of multicellularity caused by the suppression of GGB or ROP GTPases is due to uncoordinated cell expansion, defects in cell wall integrity and the disturbance of the directional control of cell plate orientation. Expressing prenylatable ROP in the ggb mutant not only rescues multicellularity in protonemata but also results in development of gametophores. Although the prenylation of ROP is important for multicellularity, a higher threshold of active ROP is required for gametophore development. Thus, our results suggest that ROP activation via prenylation by GGB is a key process at both cell and tissue levels, facilitating the developmental transition from one dimension to two dimensions and to three dimensions in P. patens.

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 score0.791
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.223
Teacher spread0.197 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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