Generality of toxins in defensive symbiosis: Ribosome-inactivating proteins and defense against parasitic wasps in Drosophila
Bibliographic record
Abstract
While it has become increasingly clear that multicellular organisms often harbor microbial symbionts that protect their hosts against natural enemies, the mechanistic underpinnings underlying most defensive symbioses are largely unknown. Spiroplasma bacteria are widespread associates of terrestrial arthropods, and include strains that protect diverse Drosophila flies against parasitic wasps and nematodes. Recent work implicated a ribosome-inactivating protein (RIP) encoded by Spiroplasma, and related to Shiga-like toxins in enterohemorrhagic Escherichia coli, in defense against a virulent parasitic nematode in the woodland fly, Drosophila neotestacea. Here we test the generality of RIP-mediated protection by examining whether Spiroplasma RIPs also play a role in wasp protection, in D. melanogaster and D. neotestacea. We find strong evidence for a major role of RIPs, with ribosomal RNA (rRNA) from the larval endoparasitic wasps, Leptopilina heterotoma and Leptopilina boulardi, exhibiting the hallmarks of RIP activity. In Spiroplasma-containing hosts, parasitic wasp ribosomes show abundant site-specific depurination in the α-sarcin/ricin loop of the 28S rRNA, with depurination occurring soon after wasp eggs hatch inside fly larvae. Interestingly, we found that the pupal ectoparasitic wasp, Pachycrepoideus vindemmiae, escapes protection by Spiroplasma, and its ribosomes do not show high levels of depurination. We also show that fly ribosomes show little evidence of targeting by RIPs. Finally, we find that the genome of D. neotestacea's defensive Spiroplasma encodes a diverse repertoire of RIP genes, which are differ in abundance. This work suggests that specificity of defensive symbionts against different natural enemies may be driven by the evolution of toxin repertoires, and that toxin diversity may play a role in shaping host-symbiont-enemy interactions.
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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) | 0.000 | 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".