MétaCan
Menu
Back to cohort
Record W2106896301 · doi:10.1128/ec.05078-11

Vacuolar Protein Sorting Genes Regulate Mat Formation in Saccharomyces cerevisiae by Flo11p-Dependent and -Independent Mechanisms

2011· article· en· W2106896301 on OpenAlexaff
Neha Sarode, Bethany Miracle, Xin Peng, Owen Ryan, Todd B. Reynolds

Bibliographic record

VenueEukaryotic Cell · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSaccharomyces cerevisiaeBiologyCell biologyESCRTVacuolar protein sortingMutantEndosomeGeneGeneticsIntracellular

Abstract

fetched live from OpenAlex

Saccharomyces cerevisiae generates complex biofilms called mats on low-density (0.3%) agar plates. The mats can be morphologically divided into two regions: (i) hub, the interior region characterized by the presence of wrinkles and channels, and (ii) rim, the smooth periphery. Formation of mats depends on the adhesin Flo11p, which is also required for invasive growth, a phenotype in which the S. cerevisiae yeasts grow as chains of cells that dig into standard-density (2%) agar plates. Although both invasive growth and mat formation depend on Flo11p, mutations that perturb the multivesicular body (MVB) protein sorting pathway inhibit mat formation in a FLO11-independent manner. These mutants, represented by vps27Δ, disrupt mat formation but do not affect invasive growth, FLO11 gene or protein expression, or Flo11p localization. In contrast, an overlapping subset of MVB mutants (represented by ESCRT [endosomal sorting complex required for transport] complex genes such as VPS25) interrupt the Rim101p signal transduction cascade, which is required for FLO11 expression, and thus block both invasive growth and mat formation. In addition, this report shows that mature Flo11p is covalently associated with the cell wall and shed into the extracellular matrix of the growing mat.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.725

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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueEukaryotic CellSame topicFungal and yeast genetics researchFrench-language works237,207