Proteomic Analysis and Functional Characterization of P4-ATPase Phospholipid Flippases from Murine Tissues
Bibliographic record
Abstract
P4-ATPases are a subfamily of P-type ATPases that flip phospholipids across membranes to generate lipid asymmetry, a property vital to many cellular processes. Mutations in several P4-ATPases have been linked to severe neurodegenerative and metabolic disorders. Most P4-ATPases associate with one of three accessory subunit isoforms known as CDC50A (TMEM30A), CDC50B (TMEM30B), and CDC50C (TMEM30C). To identify P4-ATPases that associate with CDC50A, in vivo, and determine their tissue distribution, we isolated P4-ATPases-CDC50A complexes from retina, brain, liver, testes, and kidney on a CDC50A immunoaffinity column and identified and quantified P4-ATPases from their tryptic peptides by mass spectrometry. Of the 12 P4-ATPase that associate with CDC50 subunits, 10 P4-ATPases were detected. Four P4-ATPases (ATP8A1, ATP11A, ATP11B, ATP11C) were present in all five tissues. ATP10D was found in low amounts in liver, brain, testes, and kidney, and ATP8A2 was present in significant amounts in retina, brain, and testes. ATP8B1 was detected only in liver, ATP8B3 and ATP10A only in testes, and ATP8B2 primarily in brain. We also show that ATP11A, ATP11B and ATP11C, like ATP8A1 and ATP8A2, selectively flip phosphatidylserine and phosphatidylethanolamine across membranes. These studies provide new insight into the tissue distribution, relative abundance, subunit interactions and substrate specificity of P4-ATPase-CDC50A complexes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".