Oral Nanoarmored Live Bacterial Biotherapeutics Bearing Polyphenol-Based Supraparticles Enhance Chemotherapy via Reestablishing Immuno-Oncology-Microbiome Axis
Bibliographic record
Abstract
The immuno-oncology-microbiome (IOM) axis, referring to the gut microbiota-regulated immune interactions on the tumor microenvironment and systemic immunity, is essential for cancer therapies. However, the cytotoxicity of chemotherapeutic agents (Chemos) disrupts the gut microbiota- and gut microbiota-manipulated IOM axis, further diminishing the therapeutic efficacy. Here, we developed oral nanoarmored live bacterial biotherapeutics (supraLBT), to reshape the tumor microenvironment and enhance chemotherapy via reestablishing the IOM axis. The cyto-adhesive polyphenol-based supraparticles, made from green tea polyphenol and food-grade milk protein, attached on microbes ( Escherichia coli Nissle1917, EcN) resisted a range of clinically relevant Chemos via phenolic-mediated noncovalent interactions, enhancing supraLBT survival by 27-fold compared with bare EcN. SupraLBT restored the intestinal microbiota and the disrupted IOM axis, thereby reducing the infiltration of regulatory T cells, increasing the recruitment of cytotoxic CD8 + T cells to the tumor bed, and further inhibiting tumor proliferation and demonstrating enhanced systemic immune responses. Notably, oral supraLBT combined with chemotherapy (doxorubicin) exhibited 2.35-fold greater tumor regression than that of doxorubicin alone, indicating that oral supraLBT can enhance the chemotherapeutic effect. Further investigations revealed that supraLBT reprogrammed the immune tumor microenvironment by upregulating antitumor cytokines and altering the gut microbial composition. Given the intricate interplay between gut microbiota, host immune system, and tumor microenvironment, this work presents a facile and biomaterial-engineered microorganism-based strategy to enhance the synergistic immuno-chemotherapy effects.
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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".