Understanding the traits underlying vaccine-driven virulence evolution in malaria parasites
Bibliographic record
Abstract
BACKGROUND: Vaccine-driven evolution can erode the beneficial effects of vaccination and is a concern, especially for newly introduced vaccines. While obvious candidates for vaccine-driven evolution are the precise parasite antigens that are the targets of vaccine-induced immunity, traits underlying parasite virulence may also evolve. Previous experimental work in rodent malaria demonstrated that evolution in vaccinated hosts resulted in increased parasite virulence, as measured by anemia (minimum red blood cell density). However, no genetic changes were detected at vaccine target sites, leaving the underlying traits or their interactions with host responses unclear. Using a hierarchical Bayesian framework, we fitted a mathematical model of within-host malaria infection dynamics to experimental time series data from infections in mice inoculated with parasites that had evolved in either vaccinated mice or sham-vaccinated (control) mice. We compared parameter estimates across treatments to understand which parasite traits could plausibly explain differences in infection dynamics and virulence. RESULTS: Vaccine-evolved parasites elicited lower targeted immune killing and anemia-driven erythropoiesis, differences that were observed at the level of treatment means and when accounting for individual-level variation. We validated our model by calculating early-infection parasite multiplication rates, finding no differences across treatments (either experimental or simulated)-differences that would be expected if the vaccine target antigen (AMA-1) had evolved. CONCLUSIONS: Our results emphasize the complexity of virulence, showing that parasite modulation of host responses can influence disease severity. We also highlight the important role for evolution of parasite traits beyond target antigens in response to vaccination.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".