Impact of Plasmodium falciparum infection on DNA methylation of circulating immune cells
Bibliographic record
Abstract
The regulation of immune cell responses to infection is a complex process that involves various molecular mechanisms, including epigenetic regulation. DNA methylation has been shown to play central roles in regulating gene expression and modulating cell response during infection. However, the nature and extent to which DNA methylation is involved in the host immune response in human malaria remains largely unknown. Here, we present a longitudinal study investigating the temporal dynamics of genome-wide in vivo DNA methylation profiles using 189 MethylationEPIC 850 K profiles from 66 children in Burkina Faso, West Africa, sampled three times: before infection, during symptomatic parasitemia, and after malaria treatment. The results revealed major changes in the DNA methylation profiles of children in response to both Plasmodium falciparum infection and malaria treatment, with widespread hypomethylation of CpGs upon infection (82% of 6.8 K differentially methylated regions). We document a remarkable reversal of CpG methylation profiles upon treatment to pre-infection states. These changes implicate divergence in core immune processes, including the regulation of lymphocyte, neutrophil, and myeloid leukocyte function. Integrative DNA methylation-mRNA analysis of a top differentially methylated region overlapping the pro-inflammatory gene TNF implicates DNA methylation of TNF cis regulatory elements in the molecular mechanisms of TNF regulation in human malaria. Our results highlight a central role of epigenetic regulation in mounting the host immune response to P. falciparum infection and in response to malaria treatment.
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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 | 0.000 | 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".