Host–symbiont–pathogen interactions in blood-feeding parasites: nutrition, immune cross-talk and gene exchange
Bibliographic record
Abstract
Animals are common hosts of mutualistic, commensal and pathogenic microorganisms. Blood-feeding parasites feed on a diet that is nutritionally unbalanced and thus often rely on symbionts to supplement essential nutrients. However, they are also of medical importance as they can be infected by pathogens such as bacteria, protists or viruses that take advantage of the blood-feeding nutritional strategy for own transmission. Since blood-feeding evolved multiple times independently in diverse animals, it showcases a gradient of host-microbe interactions. While some parasitic lineages are possibly asymbiotic and manage to supplement their diet from other food sources, other lineages are either loosely associated with extracellular gut symbionts or harbour intracellular obligate symbionts that are essential for the host development and reproduction. What is perhaps even more diverse are the pathogenic lineages that infect blood-feeding parasites. This microbial diversity not only puts the host into a complicated situation - distinguishing between microorganisms that can greatly decrease or increase its fitness - but also increases opportunity for horizontal gene transfer to occur in this environment. In this review, I first introduce this diversity of mutualistic and pathogenic microorganisms associated with blood-feeding animals and then focus on patterns in their interactions, particularly nutrition, immune cross-talk and gene exchange.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".