Design and Characterization of a Multistage Peptide-Based Vaccine Platform to Target <i>Mycobacterium tuberculosis</i> Infection
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The complex immunopathology of Mycobacterium tuberculosis ( Mtb ) is one of the main challenges in developing a novel vaccine against this pathogen, particularly regarding eliciting protection against both active and latent stages. Multistage vaccines, which contain antigens expressed in both phases, represent a promising strategy for addressing this issue, as testified by the tuberculosis vaccine clinical pipeline. Given this approach, we designed and characterized a multistage peptide-based vaccine platform containing CD4+ and CD8+ T cell epitopes previously validated for inducing a relevant T cell response against Mtb . After preliminary screening, CFP10 (32–39), GlfT2 (4–12), HBHA (185–194), and PPE15 (1–15) were selected as promising candidates, and we proved that the PM1 pool of these peptides triggered a T cell response in Mtb -sensitized human peripheral blood mononuclear cells (PBMCs). Taking advantage of the use of thiol-maleimide chemoselective ligation, we synthesized a multiepitope conjugate ( Ac-CGHP ). Our results showed a structure–activity relationship between the conjugation and a higher tendency to fold and assume an ordered secondary structure. Moreover, the palmitoylated conjugate ( Pal-CGHP ) comprising the same peptide antigens was associated with an enhanced cellular uptake in human and murine antigen-presenting cells and a better immunogenicity profile. Immunization study, conducted in BALB/c mice, showed that Pal-CGHP induced a significantly higher T cell proliferation and production of IFNγ and TNFα over PM1 formulated in the Sigma Adjuvant System.
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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".