The regulation of mitochondrial movement within muscle cells
Bibliographic record
Abstract
Mitochondria are dynamic organelles and their movement and morphology are controlled by their interaction with the cytoskeleton. Our purpose was to investigate regulatory events governing mitochondrial movement within muscle cells. Thus, we transfected C2C12 myoblasts with DsRED2‐Mito DNA to visualize mitochondria using realtime imaging. Destabilization of microtubules (MT) using nocodazole reduced organelle velocity and total path length traveled by 72–75%. The amount of time that mitochondria remained stationary increased ~2‐fold when MTs were destabilized. Downregulation of the MT motor protein, Kif5B by siRNA reduced organelle velocity by 33%. Mitochondrial movement was not affected by actin disruption. Thus, the distribution of mitochondria within muscle cells is dependent on a MT‐based transportation system. Using ionomycin to induce an increase in cytoplasmic calcium, we found that organelle velocity was suppressed by 48%, a situation which was reversible upon the chelation of calcium with EGTA. Mitochondrial velocity was also reduced by 22% when cytoplasmic calcium was elevated using thapsigargin, and it returned to basal levels upon the addition of BAPTA‐AM. By understanding mitochondrial movements we can elucidate the underlying basis for organelle interactions, leading to the formation of the mitochondrial reticulum and the distribution of energy in muscle cells. Supported by NSERC.
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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".