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Record W2470759386 · doi:10.1073/pnas.1605015113

Bacterial lipids activate, synergize, and inhibit a developmental switch in choanoflagellates

2016· article· en· W2470759386 on OpenAlexfundno aff
Arielle Woznica, Alexandra M. Cantley, Christine Beemelmanns, Elizaveta Freinkman, Jon Clardy, Nicole King

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsnot available
FundersNational Center for Complementary and Integrative HealthNational Cancer InstituteNational Institutes of HealthDeutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der WissenschaftenNational Institute of General Medical SciencesCanadian Institute for Advanced ResearchHoward Hughes Medical Institute
KeywordsMulticellular organismBiologyRosette (schizont appearance)BacteriaCell biologyGeneticsCellImmunology

Abstract

fetched live from OpenAlex

In choanoflagellates, the closest living relatives of animals, multicellular rosette development is regulated by environmental bacteria. The simplicity of this evolutionarily relevant interaction provides an opportunity to identify the molecules and regulatory logic underpinning bacterial regulation of development. We find that the rosette-inducing bacterium Algoriphagus machipongonensis produces three structurally divergent classes of bioactive lipids that, together, activate, enhance, and inhibit rosette development in the choanoflagellate Salpingoeca rosetta. One class of molecules, the lysophosphatidylethanolamines (LPEs), elicits no response on its own but synergizes with activating sulfonolipid rosette-inducing factors (RIFs) to recapitulate the full bioactivity of live Algoriphagus. LPEs, although ubiquitous in bacteria and eukaryotes, have not previously been implicated in the regulation of a host-microbe interaction. This study reveals that multiple bacterially produced lipids converge to activate, enhance, and inhibit multicellular development in a choanoflagellate.

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 score0.094
Threshold uncertainty score0.136

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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations160
Published2016
Admission routes1
Has abstractyes

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