Avian Malaria Parasites Modulate Gut Microbiome Assembly in Canaries
Bibliographic record
Abstract
Rodent and human malaria parasites cause dysbiosis in the host gut microbiome, but whether Plasmodium species affecting birds cause dysbiosis in their hosts is currently unknown. Here we used a model of avian malaria infection to test whether parasite infection modulates the bird microbiome. To this aim, bird fecal microbiomes were characterized at different time points after infection of canaries with the avian malaria parasite Plasmodium homocircumflexum. Avian malaria caused no significant changes in the alpha and beta diversity of the microbiome in infected birds. In contrast, we discovered changes in the composition and abundance of several taxa. Co-occurrence networks were used to characterize the assembly of the microbiome and trajectories of microbiome structural states progression were found to be different between infected and uninfected birds. Prediction of functional profiles in bacterial communities using PICRUSt2 showed infection by P. homocircumflexum to be associated with the presence of specific degradation and biosynthesis metabolic pathways, which were not found in healthy birds. Some of the metabolic pathways with decreased abundance in the infected group had significant increase in the later stage of infection. The results showed that avian malaria parasites affect bacterial community assembly in the host gut microbiome. Microbiome modulation by malaria parasites could have deleterious consequences for the host bird. Knowing the intricacies of bird-malaria-microbiota interactions may prove helpful in determining key microbial players and informing interventions to improve animal health.
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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".