Peroxisome biogenesis initiated by protein phase separation
Bibliographic record
Abstract
Summary Peroxisomes are organelles that perform beta-oxidation of fatty acids and amino acids. Both rare and prevalent diseases are caused by their disfunction 1 . Among disease-causing mutant genes are those required for protein transport into the peroxisome. The peroxisomal protein import machinery, also shared with chloroplasts, is unique in transporting folded and large, up to 10 nm in diameter, protein complexes into peroxisomes 2 and current models postulate a large pore formed by transmembrane proteins 3 . To date, however, no pore structure has been observed. In the budding yeast Saccharomyces cerevisiae , the minimum transport machinery includes membrane proteins Pex13 and Pex14 and cargo protein-binding transport receptor, Pex5. Here we show that Pex13 undergoes liquid-liquid phase separation (LLPS) with Pex5-cargo. Intrinsically disordered regions (IDR) in Pex13 and Pex5 resemble those found in nuclear pore complex (NPC) proteins. Cargo transport into peroxisomes depends on the number but not patterns of aromatic residues in these IDRs, consistent with their roles as ‘stickers’ in associative polymer models of LLPS 4,5 . Finally, imaging Fluorescence Cross-Correlation Spectroscopy (iFCCS) shows that the transport of cargo correlates with transient focusing of GFP-Pex13/14 on the peroxisome membrane. Pex13 and Pex14 form foci in distinct time-frames, suggesting that they may form channels at different saturating concentrations of Pex5-cargo. Our results suggest a model in which LLPS of Pex5-cargo with Pex13/14 results in transient protein transport channels.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".