Antibiotic-mediated gut dysbiosis alters innate-like T cell frequency and phenotype in tumor-bearing mice
Bibliographic record
Abstract
Abstract Innate-like T cells bind to non-peptide antigens in a TCR-dependent manner and are not MHC-restricted. This subpopulation of T cells consists of mucosal associated invariant T cells (MAIT), natural killer T cells (NKT), and gamma delta (γδ) T cells. Innate-like T cells can recognize microbially-derived antigens and interact with gut commensals to help maintain intestinal homeostasis. Additionally, they are implicated in tumour immunity as producers of cytokines and cytotoxic molecules. Thus, innate-like T cells may serve as an intermediary between the gut microbiota and tumor development. Using syngeneic mouse tumour models and flow cytometric analysis, we aim to evaluate the relationships between gut microbiota modulation, tumour growth, checkpoint inhibitor response, and innate-like T cell phenotype. Gut dysbiosis was induced using broad spectrum oral antibiotics two weeks prior to subcutaneous injection of MC38 colon adenocarcinoma cells. Mice with palpable tumours were treated twice with anti-PD-L1 during the experiment. Compared to vehicle control, tumor size reduction in response to anti-PD-L1 was not significantly altered with antibiotic treatment. The following observations were noted in antibiotic-treated mice. The frequency of γδ T cells was decreased in the tumour, blood, and spleen. MAIT, NKT, and γδ T cells in the spleen had lower expression of tissue localization-associated markers, CD103 and CXCR6. MAIT cell activation state, characterized by CD25 and CD69 expression, was also elevated in the spleen. These findings suggest that changes to the immune landscape of tumor-bearing mice due to antibiotic-driven dysbiosis is characterized by alterations in innate-like T cell abundance and phenotype.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".