In Situ Measurements of the pH of Mammalian Peroxisomes Using the Fluorescent Protein pHluorin
Bibliographic record
Abstract
Peroxisomes are metabolically active organelles that participate in the oxidation of long-chain fatty acids and in the biosynthesis of bile acids, cholesterol, and ether phospholipids. Even though maintenance of a stable acid-base milieu is essential for proper peroxisomal function, the determination of the peroxisomal pH (pH(p)) remains inconclusive, and little is known about its regulation. To measure the pH of intact peroxisomes in situ, we used the peroxisome-specific carboxyl-terminal targeting sequence, SKL, to deliver a pH-sensitive mutant of the green fluorescent protein (pHluorin-SKL) selectively into peroxisomes. Proper targeting was verified by colocalization with the peroxisomal marker catalase. Peroxisomes were visualized by imaging fluorescence microscopy, and ratiometric measurements were combined with calibration using ionophores or a null-point method to estimate pH(p). The pH(p) was between 6.9 and 7.1, resembling the cytosolic pH. Manipulation of the cytosolic pH in intact cells or after permeabilization of the plasmalemma with streptolysin O revealed that pH(p) changed in parallel, suggesting that the peroxisomal membrane is highly permeable to H(+) (equivalents). We conclude that peroxisomes do not regulate their pH independently, but instead their large H(+) permeability effectively connects them with the buffer reservoir of the cytoplasm and with the homeostatic mechanisms that control cytosolic pH.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".