An mRNA vaccine induces antimycobacterial immunity by activating DNA damage repair and autophagy
Bibliographic record
Abstract
Effective vaccines are urgently needed for the control of tuberculosis (TB). Here, we report that a mRNA TB vaccine is highly effective and exhibits both prophylactic and therapeutic activities in the zebrafish model of TB. Adult zebrafish immunized with the mRNA vaccine survived significantly longer than the DNA vaccine after Mycobacterium marinum challenge, and post-infection treatment with the mRNA vaccine drastically reduced the bacterial burden. The mRNA vaccine activated multiple DNA break repair systems that are essential for the normal development and function of adaptive immunity, but not the canonical DNA damage responses that promote cell death, demonstrating a profound connection between DNA damage repair and the activation of immune responses under physiological processes of immunization. Remarkably, the mRNA vaccine induced autophagy in granulomas, coinciding with bacterial killing and cell survival. Collectively, these findings demonstrate that the mRNA vaccine elicits potent innate and adaptive immunity, conferring effective host protection against mycobacterial challenge.Funding: This work was supported by the National Key Research and Development Program of China (2021YFC2301503, 2018YFD0500900); National Natural Science Foundation of China (82272353); Joint Funds of the National Natural Science Foundation of China (U23A20237); Shanghai 2020 "Science and Technology Innovation Action Plan" Technological Innovation Fund (20Z11900500).Declaration of Interest: The authors declare no competing interest.Ethical Approval: All zebrafish were raised under standard conditions in compliance with laboratory animal guidelines for the ethical review of animal welfare (GB/T 35823-2018). All zebrafish experiments were approved by the local animal care committees at Fudan University.
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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".