Cellular distribution and motion of essential magnetosome proteins expressed in mammalian cells
Bibliographic record
Abstract
Abstract Magnetosomes are organelle-like structures within magnetotactic bacteria that store iron biominerals in membrane-bound vesicles. In the bacteria, formation of these structures is highly regulated by approximately 30 genes which are conserved throughout different species. To compartmentalize iron in mammalian cells for magnetic resonance imaging using gene-based contrast, we are introducing key magnetosome proteins. We have previously expressed essential magnetosome genes mamI and mamL as fluorescent fusion proteins in the human melanoma cell line MDA-MB-435 and confirmed their co-localization and interaction. Here we investigate the expression of magnetosome genes mamB and mamE in MDA-MB-435 cells, using confocal fluorescence microscopy to observe expression patterns and to analyze particle mobility. Custom software was developed to characterize fluorescent particle trajectories. In mammalian cells, essential magnetosome proteins displayed different diffusive behaviours. However, all magnetosome proteins travelled at similar velocities when undergoing directed motion, suggesting that MamL, MamL+MamI, MamB, and MamE interact with similar mammalian mobile elements. These results confirm that localization and interaction of essential magnetosome proteins is tenable in the mammalian intracellular compartment.
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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".