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Record W2160791784 · doi:10.1194/jlr.m032219

Identification of N-acylethanolamines in Dictyostelium discoideum and confirmation of their hydrolysis by fatty acid amide hydrolase

2012· article· en· W2160791784 on OpenAlexaff
Alexander C. Hayes, Jacek Stupak, Jianjun Li, Andrew D. Cox

Bibliographic record

VenueJournal of Lipid Research · 2012
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFatty acid amide hydrolaseDictyostelium discoideumEndocannabinoid systemBiologyBiochemistryAnandamideLipid signalingIn vivoCell biologyEnzymeReceptorCannabinoid receptorGeneGenetics

Abstract

fetched live from OpenAlex

N-acylethanolamines (NAEs) are endogenous lipid-based signaling molecules best known for their role in the endocannabinoid system in mammals, but they are also known to play roles in signaling pathways in plants. The regulation of NAEs in vivo is partly accomplished by the enzyme fatty acid amide hydrolase (FAAH), which hydrolyses NAEs to ethanolamine and their corresponding fatty acid. Inhibition of FAAH has been shown to increase the levels of NAEs in vivo and to produce desirable phenotypes. This has led to the development of pharmaceutical-based therapies for a variety of conditions targeting FAAH. Recently, our group identified a functional FAAH homolog in Dictyostelium discoideum, leading to our hypothesis that D. discoideum also possesses NAEs. In this study, we provide a further characterization of FAAH and identify NAEs in D. discoideum for the first time. We also demonstrate the ability to modulate their levels in vivo through the use of a semispecific FAAH inhibitor and confirm that these NAEs are FAAH substrates through in vitro studies. We believe the demonstration of the in vivo modulation of NAE levels suggests that D. discoideum could be a good simple model organism in which to study NAE-mediated signaling.

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)0.006
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 score0.143
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.348
Teacher spread0.317 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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