Ultralow Background Membrane Editors for Spatiotemporal Control of Phosphatidic Acid Metabolism and Signaling
Bibliographic record
Abstract
Phosphatidic acid (PA) is a multifunctional lipid with important metabolic and signaling functions, and efforts to dissect its pleiotropy demand strategies for perturbing its levels with spatiotemporal precision. Previous membrane editing approaches for generating local PA pools used light-mediated induced proximity to recruit a PA-synthesizing enzyme, phospholipase D (PLD), from the cytosol to the target organelle membrane. Whereas these optogenetic PLDs exhibited high activity, their residual activity in the dark led to undesired chronic lipid production. Here, we report ultralow background membrane editors for PA wherein light directly controls PLD catalytic activity, as opposed to localization and access to substrates, exploiting a light-oxygen-voltage (LOV) domain-based conformational photoswitch inserted into the PLD sequence and enabling their stable and nonperturbative targeting to multiple organelle membranes. By coupling organelle-targeted LOVPLD activation to lipidomics analysis, we discovered different rates of metabolism for PA and its downstream products depending on the subcellular location of PA production. We also elucidated signaling roles for PA pools on different membranes in conferring local activation of AMP-activated protein kinase signaling. This work illustrates how membrane editors featuring acute, optogenetic conformational switches can provide new insights into organelle-selective lipid metabolic and signaling pathways.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".