Membrane curvature allosterically regulates the phosphatidylinositol cycle, controlling its rate and acyl-chain composition of its lipid intermediates
Bibliographic record
Abstract
Signaling events at membranes are often mediated by membrane lipid composition or membrane physical properties. These membrane properties could act either by favoring the membrane binding of downstream effectors or by modulating their activity. Several proteins can sense/generate membrane physical curvature ( i.e. shape). However, the modulation of the activity of enzymes by a membrane's shape has not yet been reported. Here, using a cell-free assay with purified diacylglycerol kinase ɛ (DGKɛ) and liposomes, we studied the activity and acyl-chain specificity of an enzyme of the phosphatidylinositol (PI) cycle, DGKɛ. By systematically varying the model membrane lipid composition and physical properties, we found that DGKɛ has low activity and lacks acyl-chain specificity in locally flat membranes, regardless of the lipid composition. On the other hand, these enzyme properties were greatly enhanced in membrane structures with a negative Gaussian curvature. We also found that this is not a consequence of preferential binding of the enzyme to those structures, but rather is due to a curvature-mediated allosteric regulation of DGKɛ activity and acyl-chain specificity. Moreover, in a fine-tuned interplay between the enzyme and the membrane, DGKɛ favored the formation of structures with greater Gaussian curvature. DGKɛ does not bear a regulatory domain, and these findings reveal the importance of membrane curvature in regulating DGKɛ activity and acyl-chain specificity. Hence, this study highlights that a hierarchic coupling of membrane physical property and lipid composition synergistically regulates membrane signaling events. We propose that this regulatory mechanism of membrane-associated enzyme activity is likely more common than is currently appreciated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".