Heat-killed bacteria induce genome instability in mouse small intestine, liver and spleen tissues
Bibliographic record
Abstract
Bacterial infection has been associated with several malignancies, yet the exact mechanism of infection-associated carcinogenesis remains obscure. Furthermore, it is still not clear whether oncontransformation requires an active infection process, or merely the presence of inactivated bacteria remnants is enough to cause deleterious effects. Here, we analyzed whether or not consumption of non-pathogenic and pathogenic heat-killed Escherichia coli leads to changes in genome stability in somatic tissues of exposed animals. For one week, mice were given to drink filtered or not-filtered water contaminated with heat-killed non-pathogenic E. coli DH5alpha or heat-killed pathogenic E. coli O157:H7 Sakai. Control animals received tap water. One week after exposure, molecular changes were analyzed in the small intestine, an organ that is in immediate contact with contaminated water. Additionally, we studied the effect in the distant spleen and liver, the organs that are involved in an immune response and detoxification, respectively. Finally, muscles were chosen as neutral tissues that were not supposed to be affected. Intestinal, liver and spleen but not muscle cells responded to all bacterial treatments with an increased level of DNA damage monitored by the induction of gammaH2AX foci. In the intestine, elevated levels of DNA damage were in parallel with an increase in Ku70 and p53 expression. We have also found an elevated level of cellular proliferation in the intestine, liver and spleen but not in muscle tissues of all exposed animals as measured by increase in PCNA levels. Our data suggest that exposure to heat-killed filtered bacteria can trigger substantial molecular responses and cause genomic instability in target and distant organs. Even though bacteria were non-pathogenic and unable to cause infection, their remnants still caused a profound effect on exposed animals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".