Localization of Mitochondrial 60-kD Heat Shock Chaperonin Protein (Hsp60) in Pituitary Growth Hormone Secretory Granules and Pancreatic Zymogen Granules
Bibliographic record
Abstract
We used quantitative immunogold electron microscopy and biochemical analysis to evaluate the subcellular distribution of Hsp60 in rat tissues. Western blot analysis, employing both monoclonal and polyclonal antibodies raised against mammalian Hsp60, shows that only a single 60-kD protein is reactive with the antibodies in brain, heart, kidney, liver, pancreas, pituitary, spleen, skeletal muscle, and adrenal gland. Immunogold labeling of tissues embedded in the acrylic resin LR Gold shows strong labeling of mitochondria in all tissues. However, in the anterior pitutary and in pancreatic acinar cells, Hsp60 also localizes in secretory granules. The labeled granules in the pituitary and pancreas were determined to be growth hormone granules and zymogen granules, respectively, using antibodies to growth hormone and carboxypeptidase A. Immunogold labeling of Hsp60 in all compartments was prevented by preadsorption of the antibodies with recombinant Hsp60. Biochemically purified zymogen granules free of mitochondrial contamination are shown by Western blot analysis to contain Hsp60, confirming the morphological localization results in pancreatic acinar cells. In kidney distal tubule cells, low Hsp60 reactivity is associated with infoldings of the basal plasma membrane. In comparison, the plasma membrane in kidney proximal tubule cells and in other tissues examined showed only background labeling. These findings raise interesting questions concerning translocation mechanisms and the cellular roles of Hsp60.
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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 | 0.000 |
| 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".