Effects of Iron and Oxidative Stress on Cx43 Expression and Phosphorylation
Bibliographic record
Abstract
Several studies have shown that increased intracellular reactive oxygen species (ROS) alters the expression of gap junction proteins and influences gap junctional intercellular communication (GJIC). We have previously shown that iron supplements in human breast milk increase lipid peroxidation, generate ROS in cultured enterocytes, and induce apoptosis. In the present study, we demonstrate the effects of oxidative stress and iron supplemented breast milk on the expression and phosphorylation patterns of connexin 43 (Cx43) in CaCo‐2BBE cells. Preliminary semi‐quantitative RT‐PCR data suggests that known inducers of ROS, including hydrogen peroxide and xanthine‐xanthine oxidase (XOX), decrease Cx43 transcript levels. Western blot analysis indicates that, in untreated cells, the dephosphorylated isoform of Cx43 is predominant. Treatment with either hydrogen peroxide or XOX induced formation of phosphorylated isoforms of Cx43, as well as increased overall protein expression of both phosphorylated and nonphosphorylated isoforms. Furthermore, iron treatments also induced expression of phosphorylated Cx43. XOX, hydrogen peroxide and iron treatments also decrease transepithelial electrical resistance across CaCo‐2BBE confluent monolayers. Taken together, these results suggest that ROS and exogenous iron increase Cx43 protein expression and phosphorylation, likely via translational control. Furthermore, there is a correlation between increased Cx43 protein expression and phosphorylation and decreased epithelial membrane integrity.
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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".