Listeriolysin O Expressed in a Bacterial Vaccine Suppresses CD4+CD25high Regulatory T Cell Function In Vivo
Bibliographic record
Abstract
CD4(+)CD25(high) regulatory T cells (Treg) protect the host from autoimmune diseases but are also obstacles against cancer therapies. An ideal cancer vaccine would stimulate specific cytotoxic responses and reduce/suppress Treg function. In this study, we showed that Escherichia coli expressing listeriolysin O and OVA (E. coli LLO/OVA) demonstrated remarkable levels of protection against OVA-expressing tumor cells. By contrast, E. coli expressing OVA only (E. coli OVA) showed poor protection. High-avidity OVA-specific CTL were induced in E. coli LLO/OVA-vaccinated mice, and CD8(+) depletion--but not NK cell depletion, abolished the antitumor activity of the E. coli LLO/OVA vaccine. Phenotypic analysis of T cells following vaccination with either vaccine revealed preferential generation of CD44(high)CD62L(low) CD8(+) effector memory T cells over CD44(high)CD62L(high) central memory T cells. Unexpectedly, CD4(+) depletion turned E. coli OVA into a vaccine as effective as E. coli LLO/OVA suggesting that a subset of CD4(+) cells suppressed the CD8(+) T cell-mediated antitumor response. Further depletion experiments demonstrated that these suppressive cells consisted of CD4(+)CD25(high) regulatory T cells. We therefore assessed these vaccines for Treg function and found that although CD4(+)CD25(high) expansion and Foxp3 expression within this population was similar in all groups of mice, Treg cells from E. coli LLO/OVA-vaccinated animals were unable to suppress conventional T cells proliferation. These findings provide the first evidence that LLO expression affects Treg cell function and may have important implications for enhancing antitumor vaccination strategies in humans.
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 | 0.001 |
| 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".