Multiple Domains in <scp>PEX16</scp> Mediate Its Trafficking and Recruitment of Peroxisomal Proteins to the <scp>ER</scp>
Bibliographic record
Abstract
Peroxisomes rely on a diverse array of mechanisms to ensure the specific targeting of their protein constituents. Peroxisomal membrane proteins (PMPs), for instance, are targeted by at least two distinct pathways: directly to peroxisomes from their sites of synthesis in the cytosol or indirectly via the endoplasmic reticulum (ER). However, the extent to which each PMP targeting pathway is involved in the maintenance of pre-existing peroxisomes is unclear. Recently, we showed that human PEX16 plays a critical role in the ER-dependent targeting of PMPs by mediating the recruitment of two other PMPs, PEX3 and PMP34, to the ER. Here, we extend these results by carrying out a comprehensive mutational analysis of PEX16 aimed at gaining insights into the molecular targeting signals responsible for its ER-to-peroxisome trafficking and the domain(s) involved in PMP recruitment function at the ER. We also show that the recruitment of PMPs to the ER by PEX16 is conserved in plants. The implications of these results in terms of the function of PEX16 and the role of the ER in peroxisome maintenance in general are discussed.
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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 | 0.000 | 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".