Defining the subcellular distribution and metabolic channeling of phosphatidylinositol
Bibliographic record
Abstract
Abstract Phosphatidylinositol (PtdIns) is an essential structural component of eukaryotic membranes that also serves as the common precursor for polyphosphoinositide (PPIn) lipids. Despite the recognized importance of PPIn species for signal transduction and membrane homeostasis, there is still a limited understanding of how the dynamic regulation of PtdIns synthesis and transport contributes to the turnover of PPIn pools. To address these shortcomings, we capitalized on the substrate selectivity of a bacterial enzyme, PtdIns-specific PLC, to establish a molecular toolbox for investigations of PtdIns distribution and availability within intact cells. In addition to its presence within the ER, our results reveal low steady-state levels of PtdIns within the plasma membrane (PM) and endosomes as well as a relative enrichment of PtdIns within the cytosolic leaflets of the Golgi complex, peroxisomes, and outer mitochondrial membranes. Kinetic studies also demonstrate the requirement for sustained PtdIns supply from the ER for the maintenance of monophosphorylated PPIn species within the PM, Golgi complex, and endosomal compartments. Summary Pemberton et al. characterize a molecular toolbox for the visualization and manipulation of phosphatidylinositol (PtdIns) within intact cells. Results using these approaches define the steady-state distribution of PtdIns across subcellular membrane compartments as well as provide new insights into the relationship between PtdIns availability and polyphosphoinositide turnover.
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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 | 0.001 |
| 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".