Expression of Interleukin-8 Promotes Neutrophil Infiltration and Genetic Instability in Mutatect Tumors
Bibliographic record
Abstract
Neutrophils represent a potential source of genotoxic reactive oxygen and nitrogen species in the tumor microenvironment. Using Mutatect cell lines, which can form subcutaneous tumors in syngeneic C57BL/6 mice, we have previously established that the number of spontaneously infiltrating neutrophils correlates with the number of mutations at the hypoxanthine phosphoribosyltransferase (Hprt) locus. We now describe the properties of four lines that express different levels of the neutrophil chemokine, interleukin-8 (IL-8), from a tetracycline (TET)-responsive promoter. In a series involving 45 animals, IL-8-expressing lines produced tumors with a higher neutrophil content than the control line. Analysis of the 45 tumors revealed that the neutrophil level again strongly correlated with hprt mutant frequency (MF) (P<.0001, r=0.88). Administration of TET was effective in lowering the neutrophil content of low IL-8-expressing tumors, but not high IL-8-expressing tumors. Although the IL-8 transgene was stable in all lines in vitro, high IL-8-expressing lines completely lost the transgene in vivo whereas low IL-8-expressing lines showed no evidence of transgene instability. These results provide further evidence, based on the study of an endogenous gene (hprt) and an IL-8 transgene, that neutrophils may contribute to genetic instability in tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".