Genetic diversity in the Plasmodium falciparum next-generation blood stage vaccine candidate antigen PfCyRPA in Senegal
Bibliographic record
Abstract
Abstract The Plasmodium falciparum cysteine-rich protective antigen (PfCyRPA) is a promising target as a next-generation blood-stage malaria vaccine and together with PCRCR complex members, the reticulocyte binding-like homologous protein 5 (PfRh5) and the Rh5-interacting protein (PfRipr), are currently being evaluated in clinical trials. PfCyRPA is essential for merozoite invasion and appears to be highly conserved within the P. falciparum parasite populations. Here, we used a targeted deep amplicon next-generation sequencing approach to assess the breadth of PfCyRPA genetic diversity in 95 P. falciparum clinical isolates from Kédougou, an area with a high seasonal malaria transmission in Senegal. Our data show the dominant prevalence of PfCyRPA wild type reference allele, while we also identify a total of 15 single nucleotide polymorphisms (SNPs). Of these, only five have previously been reported, while the majority of the SNPs were present as singletons within our sampled population. Moreover, the variant read frequency of the identified SNPs varied from 2.6 to 100%, while the majority of the SNPs were present at frequencies greater than 25% in polygenomic samples. We also applied a structure-based modelling approach to thread these SNPs onto PfCyRPA crystal structures and showed that these polymorphisms have different predicted functional impacts on the interactions with binding partner PfRH5 or neutralizing antibodies. Our prediction revealed that the majority of these SNPs have minor effects on PfCyRPA antibodies, while others alter its structure, stability, or interaction with PfRH5. Altogether, our present findings reveal conserved PfCyRPA epitopes which will inform downstream investigations on next-generation structure-guided malaria vaccine design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".