<i>In vivo</i> antigen expression regulates CD4 T cell differentiation and vaccine efficacy against <i>Mycobacterium tuberculosis</i> infection
Bibliographic record
Abstract
Abstract New vaccines are urgently needed against Mycobacterium tuberculosis (Mtb), which kills more than 1.4 million people each year. CD4 T cell differentiation is a key determinant of protective immunity against Mtb, but it is not fully understood how host-pathogen interactions shape individual antigen-specific T cell populations and their protective capacity. Here, we investigated the immunodominant Mtb antigen, MPT70, which is upregulated in response to IFN-γ or nutrient/oxygen deprivation of in vitro infected macrophages. Using a murine aerosol infection model, we compared the in vivo expression kinetics of MPT70 to a constitutively expressed antigen, ESAT-6, and analysed their corresponding CD4 T cell phenotype and vaccine-protection. For wild-type Mtb, we found that in vivo expression of MPT70 was delayed compared to ESAT-6. This delayed expression was associated with induction of less differentiated MPT70-specific CD4 T cells but, compared to ESAT-6, also reduced protection after vaccination. In contrast, infection with an MPT70-overexpressing Mtb strain promoted highly differentiated KLRG1 + CX3CR1 + CD4 T cells with limited lung-homing capacity. Importantly, this differentiated phenotype could be prevented by vaccination and, against the overexpressing strain, vaccination with MPT70 conferred similar protection as ESAT-6. Together our data indicate that high in vivo antigen expression drives T cells towards terminal differentiation and that targeted vaccination with adjuvanted protein can counteract this phenomenon by maintaining T cells in a protective less-differentiated state. These observations shed new light on host-pathogen interactions and provide guidance on how future Mtb vaccines can be designed to tip the immune-balance in favor of the host. Importance Tuberculosis, caused by Mtb, constitutes a global health crisis of massive proportions and the impact of the current COVID-19 pandemic is expected to cause a rise in tuberculosis-related deaths. Improved vaccines are therefore needed more than ever, but a lack of knowledge on protective immunity hampers their development. The present study shows that constitutively expressed antigens with high availability drive highly differentiated CD4 T cells with diminished protective capacity, which could be a survival strategy by Mtb to evade T cell immunity against key antigens. We demonstrate that immunisation with such antigens can counteract this phenomenon by maintaining antigen-specific T cells in a state of low differentiation. Future vaccine strategies should therefore explore combinations of multiple highly expressed antigens and we suggest that T cell differentiation could be used as a readily measurable parameter to identify these in both preclinical and clinical studies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".