Holobiont–holobiont interactions across host–ectoparasite systems
Bibliographic record
Abstract
Holobionts - hosts together with their resident microorganisms - provide a framework for studying life as a network of interdependent partners. Within host-ectoparasite holobionts, the dialogue between the two microbiomes offers powerful clues to ecological balance, disease dynamics and evolution. Because each holobiont is structurally and functionally compartmentalised, microbes exchanged at the interface can elicit highly local, niche-specific effects that ripple through the system. This review synthesises evidence for microbiota-to-microbiota interactions in four models: Varroa mite-honeybee, tick-vertebrate, bat fly-bat and mosquito-vertebrate pairs. In all cases, microbes move passively during feeding or contact, then colonise, replicate and modulate physiology and immunity, exerting a longer-lasting influence than transient biochemical cues. We further introduce the idea of indirect modulation, whereby abiotic or biotic factors act on a recipient holobiont through the intermediary of transferred microbes, underscoring the adaptive plasticity of holobiont networks. Bidirectional cross-talk forms self-reinforcing feedback loops that can redefine a microbe as pathogen, symbiont or immunomodulator, and tune its virulence according to context. These mechanisms shape disease transmission, resistance traits and the overall health of both partners. A deeper grasp of such cross-holobiont dynamics will pave the way for microbiota-based vaccines, targeted microbiome engineering and other innovative tools for human, veterinary and environmental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".