Membrane Physical Properties Regulate the Rate and Acyl Chain Specificity of One of the Steps of the Phosphatidylinositol Cycle
Bibliographic record
Abstract
The plethora of biological functions regulated by phosphatidylinositol (PI) and its phosphorylated forms makes this lipid class one of the most important in cell physiology. The major metabolic pathway for the synthesis of this lipid class is the PI‐cycle. One of the steps of the PI‐cycle is the ATP‐dependent phosphorylation of diacylglycerol (DAG) to produce phosphatidic acid (PA), a reaction catalyzed by the epsilon isoform of diacylglycerol kinases (DGKɛ). The enzyme when assayed with mixed‐micelles exhibits a high degree of acyl chain specificity for its DAG substrate, with 1‐stearoyl‐2‐arachidonoyl glycerol (SAG) being the preferred substrate. These acyl chains are the same as those found in the lipid intermediates of the PI‐cycle, which was proposed to be in part due to the action of DGKɛ. Opposite to other metabolic cycles, steps of the PI‐cycle spans two different locations (membranes) within a cell, the plasma membrane (PM) and endoplasmic reticulum (ER), the same membranes where DGKɛ has been reported to be found. However, it is currently unknown whether DGKɛ catalyze its reaction within the PI‐cycle in the PM and/or ER. Since DGKɛ lacks a putative regulatory domain and these two membranes have several different physical and chemical properties, we hypothesized that either DGKɛ is constitutively active or it is regulated by the properties of the membrane it's bound to. To address this question we use an assay with purified DGKɛ and liposomes. This strategy allowed us to systematically vary the membrane physical and chemical properties while avoiding complications coming from other levels of regulation. In the present work we show that the rate of DGKɛ ‐ catalyzed reaction and its substrate acyl chain specificity are regulated by membrane physical properties. In a locally flat membrane (in relation to the size of the enzyme and its membrane binding platform) the enzyme works at very low basal levels without discriminating the nature of the substrate acyl chains. In these membrane structures the majority of biological negatively charged phospholipids showed an inhibitory effect on the enzyme. However, its product (PA), which is also negatively charged, exhibited the contrary effect leading to an enhancement in enzyme activity and acyl chain specificity. This result suggested a positive feedback mechanism, contrary to what is currently believed. Likewise, the presence of highly curved membranes, specifically those membrane structures bearing negative Gaussian (saddle‐point like) curvature greatly enhanced the rate of reaction as well as the enzyme substrate acyl chain specificity, which was also dependent on the degree of curvature. To sum up, our results indicate that DGKɛ is not likely to catalyze its reaction within the PI‐cycle in stable regions of either the ER or the PM, but rather at the junction between these two organelles where highly curved membrane structures have been reported and/or at PA‐enriched domains. Support or Funding Information This work was supported by the Canadian Natural Sciences and Engineering Research Council grant RGPIN‐2018‐05585. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".