Signatures of competition and strain structure within the major blood‐stage antigen of <i>Plasmodium falciparum</i> in a local community in Ghana
Bibliographic record
Abstract
Abstract The concept of niche partitioning has received considerable theoretical attention at the interface of ecology and evolution of infectious diseases. Strain theory postulates that pathogen populations can be structured into distinct nonoverlapping strains by frequency‐dependent selection in response to intraspecific competition for host immune space. The malaria parasite Plasmodium falciparum presents an opportunity to investigate this phenomenon in nature, under conditions of high recombination rate and extensive antigenic diversity. The parasite's major blood‐stage antigen, PfEMP1, is encoded by the hyperdiverse var genes. With a dataset that includes thousands of var DBLα sequence types sampled from asymptomatic cases within an area of high endemicity in Ghana, we address how var diversity is distributed within isolates and compare this to the distribution of microsatellite allelic diversity within isolates to test whether antigenic and neutral regions of the genome are structured differently. With respect to var DBLα sequence types, we find that on average isolates exhibit significantly lower overlap than expected randomly, but that there also exists frequent pairs of isolates that are highly related. Furthermore, the linkage network of var DBLα sequence types reveals a pattern of nonrandom modularity unique to these antigenic genes, and we find that modules of highly linked DBLα types are not explainable by neutral forces related to var recombination constraints, microsatellite diversity, sampling location, host age, or multiplicity of infection. These findings of reduced overlap and modularity among the var antigenic genes are consistent with a role for immune selection as proposed by strain theory. Identifying the evolutionary and ecological dynamics that are responsible for the nonrandom structure in P. falciparum antigenic diversity is important for designing effective intervention in endemic areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".