Developing Magnetic Resonance Reporter Gene Imaging: Essential Magnetosome Proteins Interact in Mammalian Cells
Bibliographic record
Abstract
To detect cellular activities deep within the body using magnetic resonance (MR) platforms, magnetosomes are the ideal model of genetically-encoded nanoparticles. These organelle-like structures produced by magnetotactic bacteria (MTB) store iron biominerals in membrane-bound vesicles and are highly regulated by approximately 30 genes. To translate this technology into a gene-based iron contrast agent in mammalian cells [1], [2], we are introducing essential membrane-associated magnetosome (mam) genes mamI, mamL, mamB, and mamE into the human melanoma cell line MDA-MB-435 using fluorescent fusion protein vectors. While the expression of enhanced green fluorescent protein (EGFP)-MamI alone resulted in a net-like fluorescence pattern, individual expression of red fluorescent Tomato-MamL, Tomato-MamB, or Tomato-MamE all resulted in a mobile, punctate fluorescence pattern. Coexpression of MamL+I resulted in co-localization of both proteins in a mobile, punctate pattern. Here we report the transient expression of three magnetosome proteins (MamL+I+B or MamL+I+E) in mammalian cells, which results in the co-localization and interaction of all three proteins in a mobile, punctate pattern. These results further support interactions between essential magnetosome proteins in the mammalian intracellular compartment and the co-localization required to form a rudimentary magnetosome-like particle.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".