Phosphoinositide Metabolism: Biochemistry, Physiology and Genetic Disorders
Bibliographic record
Abstract
Phosphatidylinositol, a glycerophospholipid with a myo-inositol head group, can form seven different phosphoinositides (PItds) by phosphorylation at inositol carbons 3, 4 and/or 5. Over 50 kinases and phosphatases participate in PItd metabolism, creating an interconnected PItd network that allows for precise temporal and spatial regulation of PItd levels. We review paradigms of PItd action, including (1) the establishment of subcellular organelle identity by the acquisition of specific PItd signatures, permitting regulation of key processes of cell biology including trafficking (exocytosis, clathrin-dependent and -independent endocytosis, formation and function of membrane contact sites, cytoskeletal remodeling), (2) signaling through phospholipase C cleavage of phosphatidylinositol 4,5-bisphosphate to inositol 1,4,5-trisphosphate and DAG, and (3) roles of PItds in molecular transport at membrane contact sites. To date, variants in 34 genes of PItd metabolism account for at least 41 distinguishable monogenic conditions. Clinical presentations of these disorders produce a broad and often multisystemic spectrum of effects. The nervous system is often involved, and muscular, immunological, skeletal, renal, ophthalmologic and dermatologic features occur in several conditions. Some syndromes involving PItd metabolism can be distinguished clinically, but most diagnoses currently result from broad molecular diagnostic testing performed for the patient's presenting clinical complaint. Genetic disorders of PItd metabolism are a broad, expanding and challenging category of inborn errors. Challenges include improved documentation of the clinical spectra, development of broad biochemical diagnostic methods for these conditions and better understanding of the PItd networks in different cells and subcellular compartments necessary for the development of disease-specific therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".