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Record W2913971350 · doi:10.7554/elife.43230

Optical manipulation of sphingolipid biosynthesis using photoswitchable ceramides

2019· article· en· W2913971350 on OpenAlexfundno aff
Matthijs Kol, Benjamin M. Williams, Henry Toombs‐Ruane, Henri G. Franquelim, Sergei M. Korneev, Christian Schroeer, Petra Schwille, Dirk Trauner, Joost C. M. Holthuis, James A. Frank

Bibliographic record

VenueeLife · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsSphingolipidCeramideSphingomyelinCell biologyMetabolic pathwayATP synthaseBiochemistryCeramide synthaseBiologyChemistryBiophysicsMetabolismEnzymeApoptosisMembrane

Abstract

fetched live from OpenAlex

Ceramides are central intermediates of sphingolipid metabolism that also function as potent messengers in stress signaling and apoptosis. Progress in understanding how ceramides execute their biological roles is hampered by a lack of methods to manipulate their cellular levels and metabolic fate with appropriate spatiotemporal precision. Here, we report on clickable, azobenzene-containing ceramides, caCers, as photoswitchable metabolic substrates to exert optical control over sphingolipid production in cells. Combining atomic force microscopy on model bilayers with metabolic tracing studies in cells, we demonstrate that light-induced alterations in the lateral packing of caCers lead to marked differences in their metabolic conversion by sphingomyelin synthase and glucosylceramide synthase. These changes in metabolic rates are instant and reversible over several cycles of photoswitching. Our findings disclose new opportunities to probe the causal roles of ceramides and their metabolic derivatives in a wide array of sphingolipid-dependent cellular processes with the spatiotemporal precision of light.

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 score0.005
Threshold uncertainty score0.433

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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

Citations36
Published2019
Admission routes1
Has abstractyes

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