Aggregated DsRed-Tagged Cx43 and Over-Expressed Cx43 are Targeted to Lysosomes in Human Breast Cancer Cells
Bibliographic record
Abstract
To investigate if either wild-type or aggregated Cx43 is abnormally targeted to lysosomes in human breast tumor cells, we examined the fate of DsRed-tagged Cx43 and over-expressed Cx43 in communication-deficient HBL-100 and MDA-MB-231 cells. DsRed-tagged Cx43 was assembled into gap junctions in control normal rat kidney cells that express endogenous Cx43 but not in Cx43-negative HBL-100 cells. However, when HBL-100 cells were engineered to coexpress wild-type Cx43 a population of DsRed-tagged Cx43 was rescued and assembled into gap junctions. Co-expression of wild-type Cx26 failed to rescue the assembly of DsRed-tagged Cx43 into gap junctions. Immunolocalization studies revealed that DsRed-tagged Cx43 was aggregated and partially localized to lysosomes. Interestingly, when human MDA-MB-231 breast tumor cells over-expressed wild-type Cx43, Cx43 protein primarily localized to lysosomes. Together, these studies provide evidence for Cx43 being targeted to lysosomes as a result of misfolding and aggregation, while in other cases, the delivery of wild-type Cx43 to lysosomes appears to be due to defects innate to the breast tumor cell type.
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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 | 0.001 |
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".